67191 v2 WORLD BANK – EUROPE AND CENTRAL ASIA REGION – FINANCIAL AND PRIVATE SECTOR DEVELOPMENT DEPARTMENT—CENTRAL EUROPE AND THE BALTICS COUNTRY DEPARTMENT WORLD BANK SECTOR REPORT – NATURAL DISASTERS, CLIMATE CHANGE AND INSURANCE Volume II: Statistical Annex Financial and Fiscal Instruments for Catastrophe Risk Management ADDRESSING LOSSES FROM FLOOD HAZARDS IN CENTRAL EUROPE (POLAND, CZECH REPUBLIC, HUNGARY AND SLOVAKIA) 5. Appendix 5. Appendix 5.1 Input Data 5.1.1 Administrative Divisions Table 5.1: National levels of the administrative divisions with the Nomenclature of Units for Territorial Statistics (NUTS) and Local Administrative Units (LAU) of the European Union / Eurostat. NUTS Level Poland Czech Rep Hungary Slovakia Voivodeship (16) NUTS-2 [wojewodztwo] Province (14) County (20) Province (8) NUTS-3 [kraj] [megye] [kraj] 149 District (379) District (77) District (174) District (79) LAU-1 [powiat] [okres] [kisterseg] [okres] Municipality Municipality Municipality Municipality 150 LAU-2 (almost 3,100) (over 6,200) (over 3,100) (almost 2,900) [gmina] [obec] [kozseg] [obec] a) Czech Republic The territory of the Czech Republic is administratively divided into three principal hierarchical levels, i.e. 14 provinces [kraj], 77 districts [okres], and over 6,200 municipalities [obec]. For the purpose of the Study, the 77 districts are used as the smallest territorial unit with an average area of 1020 sq.km and average population of 135,000 inhabitants. 149 formerly NUTS-4; the Polish districts are usually elementary territorial units containing a single central town and the rural municipalities in its vicinity where the central town may be a separate urban district not belonging to the surrounding rural district (resembling the English districts prior to 1974 reform or the German kreise prior to 1969-77 reform in the West and 1993-94 reform in the East). Unlike the Polish districts, the Slovak and Hungarian ones have the central town included within the district (resembling the Irish counties). The Czech districts are groups of 2-4 such elementary districts including several towns (resembling the English districts after the 1974 reform or the German kreise since 1969-77 western and 2007-11 eastern reform, respectively). In Slovakia, the two biggest cities are subdivided into several districts while in the other three countries the whole city is always in a single district. 150 formerly NUTS-5; in the Czech Republic, Hungary, and Slovakia, the municipalities represent basically a city, a town, or a village, i.e. they resemble the English parishes, French communes, or German orts, etc.. Unlike in those countries, the Polish municipalities are a kind of sub-districts or groups of towns and villages, i.e. they resemble the Scandinavian kommuner, Dutch gemeenten, or in rural areas the English wards or French cantons. Therefore, some of the Polish rural municipalities do not contain their main town, which is considered as a separate urban municipality. 5. Appendix Figure5.1: First order administrative divisions of the Czech Republic – province [kraj] level corresponding to NUTS-3 of the EU. The letters stand for the official signs of the provinces. Table 5.2: First order administrative divisions of the Czech Republic – province [kraj] level corresponding to NUTS-3 of the EU. The main towns are in brackets. A Praha J Kraj VysoÄ?ina (Jihlava) S StÅ™edoÄ?eský kraj Moravskoslezský kraj B Jihomoravský kraj (Brno) K Karlovarský kraj T (Ostrava) JihoÄ?eský kraj (ÄŒeské C L Liberecký kraj U Ústecký kraj BudÄ›jovice) E Pardubický kraj M Olomoucký kraj Z Zlínský kraj H Královéhradecký kraj P Plzeňský kraj 5. Appendix Figure5.2: Second order administrative divisions of the Czech Republic – district [okres] level corresponding to LAU-1 of the EU. The two- letters stand for the official signs of the districts. Table 5.3: Second order administrative divisions of the Czech Republic – district [okres] level corresponding to LAU-1 of the EU. AB Praha BE Beroun JC JiÄ?ín NB Nymburk RO Rokycany BK Blansko JE Jeseník NJ Nový JiÄ?ín SM Semily BM Brno-mÄ›sto JH JindÅ™ichův Hradec OL Olomouc SO Sokolov BN BeneÅ¡ov JI Jihlava OP Opava ST Strakonice Jablonec nad BO Brno-venkov JN OV Ostrava-mÄ›sto SU Å umperk Nisou BR Bruntál KH Kutná Hora PB Příbram SY Svitavy BV BÅ™eclav KI Karviná PE PelhÅ™imov TA Tábor ÄŒeské CB KL Kladno PI Písek TC Tachov BudÄ›jovice CH Cheb KM Kroměříž PJ Plzeň-jih TP Teplice CK ÄŒeský Krumlov KO Kolín PM Plzeň-mÄ›sto TR TÅ™ebíÄ? CL ÄŒeská Lípa KT Klatovy PR PÅ™erov TU Trutnov Uherské CR Chrudim KV Karlovy Vary PS Plzeň-sever UH HradiÅ¡tÄ› CV Chomutov LI Liberec PT Prachatice UL Ústí nad Labem DC DÄ›Ä?ín LN Louny PU Pardubice UO Ústí nad Orlicí DO Domažlice LT Litoměřice PV ProstÄ›jov VS Vsetín FM Frýdek-Místek MB Mladá Boleslav PY Praha-východ VY VyÅ¡kov HB HavlíÄ?kův Brod ME MÄ›lník PZ Praha-západ ZL Zlín HK Hradec Králové MO Most RA Rakovník ZN Znojmo Rychnov nad ŽÄ?ár nad HO Hodonín NA Náchod RK ZR Kněžnou Sázavou 5. Appendix b) Slovakia The territory of Slovakia is administratively divided into three principal hierarchical levels, i.e. 8 provinces [kraj], 79 districts [okres], and almost 2,900 municipalities [obec]. For the purpose of the Study, the 79 districts are used as the smallest territorial unit with an average area of 620 sq.km and an average population of 68,000 inhabitants. Figure5.3: First order administrative divisions of Slovakia – province [kraj] level corresponding to NUTS-3 of the EU. The letters stand for the official signs of the provinces. Table 5.4: First order administrative divisions of Slovakia – province [kraj] level corresponding to NUTS-3 of the EU. The main towns are in brackets. Bratislavský PreÅ¡ovský Trnavský BA KE KoÅ¡ický (KoÅ¡ice) PO TT (Bratislava) (PreÅ¡ov) (Trnava) Banskobystrický TrenÄ?iansky BB NR Nitriansky (Nitra) TN ZA Žilinský (Žilina) (Banská Bystrica) (TrenÄ?ín) Figure 5.4: Second order administrative divisions of Slovakia – district [okres] level corresponding to LAU-1 of the EU. Table 5.5: Second order administrative divisions of Slovakia – district [okres] level corresponding to LAU-1 of the EU. BA Bratislava KK Kežmarok PD Prievidza SL Stará Ľubovňa SpiÅ¡ská Nová BB Banská Bystrica KM Kysucké Nové Mesto PE Partizánske SN Ves BJ Bardejov KN Komárno PK Pezinok SO Sobrance Bánovce nad BN KS KoÅ¡ice – okolie PN PieÅ¡Å¥any SP Stropkov Bebravou BR Brezno LC LuÄ?enec PO PreÅ¡ov SV Snina 5. Appendix BS Banská Å tiavnica LE LevoÄ?a PP Poprad TN TrenÄ?ín BY BytÄ?a LM Liptovský Mikuláš PT Poltár TO TopoľÄ?any TurÄ?ianske CA ÄŒadca LV Levice PU Púchov TR Teplice DK Dolný Kubín MA Malacky RA Revúca TS Tvrdošín DS Dunajská Streda MI Michalovce RK Ružomberok TT Trnava Rimavská DT Detva ML Medzilaborce RS TV TrebiÅ¡ov Sobota GA Galanta MT Martin RV Rožňava VK Veľký Krtíš Vranov nad GL Gelnica MY Myjava SA Å aľa VT Topľou Nové Mesto nad HC Hlohovec NM SB Sabinov ZA Žilina Váhom HE Humenné NO Námestovo SC Senec ZC Žarnovica IL Ilava NR Nitra SE Senica ZH Žiar nad Hronom KA Krupina NZ Nové Zámky SI Skalica ZM Zlaté Moravce KE KoÅ¡ice PB Považská Bystrica SK Svidník ZV Zvolen c) Hungary The territory of Hungary is administratively divided into three principal hierarchical levels, i.e. 20 counties [megye], 174 districts [kisterseg], and over 3,100 municipalities [kozseg]. For the purpose of the Study, the 174 districts are used as the smallest territorial unit with an average area of 530 sq.km and an average population of 58,000 inhabitants. Figure 5.5: First order administrative divisions of Hungary – county [megye] level corresponding to NUTS-3 of the EU. Table 5.6: First order administrative divisions of Hungary – county [megye] level corresponding to NUTS-3 of the EU. The main towns are in brackets. Gy r-Moson-Sopron SzSz Szabolcs-Szatmár- Bp 1 Budapest GyMS 8 15 (Gy r) B Bereg (Nyíregyháza) Hajdú-Bihar Jász-Nagykun-Szolnok Bar 2 Baranya (Pécs) HB 9 JNSz 16 (Debrecen) (Szolnok) Bács-Kiskun BK 3 Hev 10 Heves (Eger) Tol 17 Tolna (Szekszárd) (Kecskemét) Békés Komárom-Esztergom Bek 4 KomE 11 Vas 18 Vas (Szombathely) (Békéscsaba) (Tatabánya) BA Borsod-Abaúj- 5 Nog 12 Nógrád (Salgótarján) Vesz 19 Veszprém (Veszprém) Z Zemplén (Miskolc) Cso 6 Csongrád (Szeged) Pest 13 Pest (Budapest) Zal 20 Zala (Zalaegerszeg) Fejér Fej 7 Som 14 Somogy (Kaposvár) (Székesfehérvár) 5. Appendix Figure 5.6: Second order administrative divisions of Hungary – district [kisterseg] level corresponding to LAU-1 of the EU. Table 5.7: Second order administrative divisions of Hungary – district level corresponding to LAU-1 of the EU. 101 Budapest 602 Hódmez vásárhelyi 1106 Tatabányai 1509 Tiszavasvári 201 Komlói 603 Kisteleki 1107 Oroszlányi 1510 Vásárosnaményi Ibrány-Nagyhalászi 202 Mohácsi 604 Makói 1201 Balassagyarmati 1511 (Ibrány) 203 Sásdi 605 Mórahalmi 1202 Bátonyterenyei 1512 Záhonyi 204 Sellyei 606 Szegedi 1203 Pásztói 1601 Jászberényi 205 Siklósi 607 Szentesi 1204 Rétsági 1602 Karcagi 206 Szigetvári 701 Bicskei 1205 Salgótarjáni 1603 Kunszentmártoni 207 Pécsi 702 Dunaújvárosi 1206 Szécsényi 1604 Szolnoki 208 Pécsváradi 703 Enyingi 1301 Aszódi 1605 Tiszafüredi 209 Szentl rinci 704 Gárdonyi 1302 Ceglédi 1606 Törökszentmiklósi 301 Bajai 705 Móri 1303 Dabasi 1607 Mez túri 302 Bácsalmási 706 Sárbogárdi 1304 Gödöll i 1701 Bonyhádi 303 Kalocsai 707 Székesfehérvári 1305 Monori 1702 Dombóvári 304 Kecskeméti 708 Abai 1306 Nagykátai 1703 Paksi 305 Kisk rösi 709 Adonyi 1307 Ráckevei 1704 Szekszárdi 306 Kiskunfélegyházi 710 Ercsi 1308 Szobi 1705 Tamási 307 Kiskunhalasi 801 Csornai 1309 Váci 1801 Celldömölki 308 Kiskunmajsai 802 Gy ri 1310 Budaörsi 1802 Csepregi 309 Kunszentmiklósi 803 Kapuvári 1311 Dunakeszi 1803 Körmendi 310 Jánoshalmi 804 Mosonmagyaróvári 1312 Gyáli 1804 K szegi Sopron-Fert di 401 Békéscsabai 805 1313 Pilisvörösvári 1805 riszentpéteri (Sopron) 402 Mez kovácsházi 806 Téti 1314 Szentendrei 1806 Sárvári 403 Orosházi 807 Pannonhalmi 1315 Veresegyházi 1807 Szentgotthárdi 404 Sarkadi 901 Balmazújvárosi 1316 Érdi 1808 Szombathelyi 405 Szarvasi 902 Berettyóújfalui 1401 Barcsi 1809 Vasvári 406 Szeghalmi 903 Debreceni 1402 Csurgói 1901 Ajkai 407 Békési 904 Hajdúböszörményi 1403 Fonyódi 1902 Balatonalmádi 408 Gyulai 905 Hajdúszoboszlói 1404 Kaposvári 1903 Balatonfüredi 5. Appendix 501 Miskolci 906 Polgári 1405 Lengyeltói 1904 Pápai 502 Edelényi 907 Püspökladányi 1406 Marcali 1905 Sümegi Derecske- 503 Encsi 908 Létavértesi 1407 Nagyatádi 1906 Tapolcai (Derecske) 504 Kazinbarcikai 909 Hajdúhadházi 1408 Siófoki 1907 Várpalotai 505 Mez kövesdi 1001 Egri 1409 Tabi 1908 Veszprémi 506 Ózdi 1002 Hevesi 1410 Balatonföldvári 1909 Zirci 507 Sárospataki 1003 Füzesabonyi 1411 Kadarkúti 2001 Keszthelyi 508 Sátoraljaújhelyi 1004 Gyöngyösi 1501 Baktalórántházi 2002 Lenti 509 Szerencsi 1005 Hatvani 1502 Csengeri 2003 Letenyei 510 Szikszói 1006 Pétervásári 1503 Fehérgyarmati 2004 Nagykanizsai 511 Tiszaújvárosi 1007 Bélapátfalvai 1504 Kisvárdai 2005 Zalaegerszegi Abaúj-Hegyközi 512 1101 Dorogi 1505 Mátészalkai 2006 Zalaszentgróti (Gönc) Bodrogközi 513 1102 Tatai 1506 Nagykállói 2007 Hévízi (Cigánd) 514 Mez csáti 1103 Esztergomi 1507 Nyírbátori 2008 Pacsai 515 Tokaji 1104 Kisbéri 1508 Nyíregyházi 2009 Zalakarosi 601 Csongrádi 1105 Komáromi 5. Appendix d) Poland The territory of Poland is administratively divided into three principal hierarchical levels, i.e. 16 voivodeships/duchies [wojewodztwo], 379 districts [powiat], and almost 3,100 municipalities [gmina]. For the purpose of the Study, the 379 districts are used as the smallest territorial unit with an average area of 825 sq.km and an average population of 100,000 inhabitants. Figure 5.7: First order administrative divisions of Poland – voivodeship [wojewodztwo] level corresponding to the NUTS-2 of the EU. The letters stand for the official signs of the voivodeships. Table 5.8: First order administrative divisions of Poland – voivodeship [wojewodztwo] level corresponding to the NUTS-2 of the EU. The main towns are in brackets. B PD Podlaskie (BiaÅ‚ystok) N WM WarmiÅ„sko-mazurskie (Olsztyn) Kujawsko-pomorskie (Bydgoszcz / C KP O OP Opolskie (Opole) ToruÅ„) D DS DolnoÅ›lÄ…skie (WrocÅ‚aw) P WP Wielkopolskie (PoznaÅ„) E LD Å?ódzkie (Å?ódź) R PK Podkarpackie (Rzeszów) Lubuskie (Gorzów Wielkopolski / F LB S SL ÅšlÄ…skie (Katowice) Zielona Góra) G PM Pomorskie (GdaÅ„sk) T SW ÅšwiÄ™tokrzyskie (Kielce) K MP MaÅ‚opolskie (Kraków) W MA Mazowieckie (Warszawa) L LU Lubelskie (Lublin) Z ZP Zachodniopomorskie (Szczecin) 5. Appendix Figure 5.8: Second order administrative divisions of Poland – districts [powiat] level corresponding to the LAU-1 of the EU. The letters stand for the official signs of the districts (western part) 5. Appendix Figure 5.9: Second order administrative divisions of Poland – districts [powiat] level corresponding to the LAU-1 of the EU. The letters stand for the official signs of the districts (eastern part) Table 5.9: Second order administrative divisions of Poland – urban districts [powiat grodzki] level corresponding to the LAU-1 of the EU. BI BiaÅ‚ystok GD GdaÅ„sk PO PoznaÅ„ SO Sosnowiec BL Å?om a GS SÅ‚upsk RK Krosno SPI Piekary ÅšlÄ…skie BS SuwaÅ‚ki GSP Sopot RP PrzemyÅ›l SR Rybnik CB Bydgoszcz KN Nowy SÄ…cz RT Tarnobrzeg ST Tychy CG GrudziÄ…dz KR Kraków RZ Rzeszów SW ÅšwiÄ™tochÅ‚owice CT ToruÅ„ KT Tarnów SB Bielsko-BiaÅ‚a SY Bytom BiaÅ‚a CW WÅ‚ocÅ‚awek LB Podlaska SC CzÄ™stochowa SZ Zabrze DJ Jelenia Góra LC CheÅ‚m SD DÄ…browa Górnicza SZO ory DL Legnica LU Lublin SG Gliwice TK Kielce DW WrocÅ‚aw LZ Zamość SH Chorzów WA Warszawa Siemianowice EL Å?ódź NE ElblÄ…g SI ÅšlÄ…skie WO OstroÅ‚Ä™ka EP Piotrków Trybunalski NO Olsztyn SJ Jaworzno WP PÅ‚ock ES Skierniewice OP Opole SJZ JastrzÄ™bie-Zdrój WR Radom 5. Appendix Gorzów FG Wielkopolski PK Kalisz SK Katowice WS Siedlce FZ Zielona Góra PL Leszno SL Ruda ÅšlÄ…ska ZK Koszalin GA Gdynia PN Konin SM MysÅ‚owice ZS Szczecin Table 5.10: Second order administrative divisions of Poland – rural districts [powiat ziemski] level corresponding to the LAU-1 of the EU. The main towns are in brackets. BAU augustowski (Augustów) GSZ sztumski (Sztum) RDE dÄ™bicki (DÄ™bica) BBI bielski (Bielsk Podlaski) GTC tczewski (Tczew) RJA jarosÅ‚awski (JarosÅ‚aw) BGR grajewski (Grajewo) GWE wejherowski (Wejherowo) RJS jasielski (JasÅ‚o) BHA hajnowski (Hajnówka) KBC bocheÅ„ski (Bochnia) RKL kolbuszowski (Kolbuszowa) BIA biaÅ‚ostocki (BiaÅ‚ystok) KBR brzeski (Brzesko) RKR kroÅ›nieÅ„ski (Krosno) BKL kolneÅ„ski (Kolno) KCH chrzanowski (Chrzanów) RLA Å‚aÅ„cucki (Å?aÅ„cut) dÄ…browski (DÄ…browa BLM Å‚om yÅ„ski (Å?om a) KDA RLE le ajski (Le ajsk) Tarnowska) BMN moniecki (MoÅ„ki) KGR gorlicki (Gorlice) RLS leski (Lesko) BSE sejneÅ„ski (Sejny) KLI limanowski (Limanowa) RLU lubaczowski (Lubaczów) BSI siemiatycki (Siemiatycze) KMI miechowski (Miechów) RMI mielecki (Mielec) BSK sokólski (Sokółka) KMY myÅ›lenicki (MyÅ›lenice) RNI ni aÅ„ski (Nisko) BSU suwalski (SuwaÅ‚ki) KNS nowosÄ…decki (Nowy SÄ…cz) RPR przemyski (PrzemyÅ›l) wysokomazowiecki BWM KNT nowotarski (Nowy Targ) RPZ przeworski (Przeworsk) (Wysokie Mazowieckie) ropczycko-sÄ™dziszowski BZA zambrowski (Zambrów) KOL olkuski (Olkusz) RRS (Ropczyce) aleksandrowski CAL KOS oÅ›wiÄ™cimski (OÅ›wiÄ™cim) RSA sanocki (Sanok) (Aleksandrów Kujawski) CBR brodnicki (Brodnica) KPR proszowicki (Proszowice) RSR strzy owski (Strzy ów) CBY bydgoski (Bydgoszcz) KRA krakowski (Kraków) RST stalowowolski (Stalowa Wola) CCH cheÅ‚miÅ„ski (CheÅ‚mno) KSU suski (Sucha Beskidzka) RTA tarnobrzeski (Tarnobrzeg) golubsko-dobrzyÅ„ski CGD KTA tarnowski (Tarnów) RZE rzeszowski (Rzeszów) (Golub-DobrzyÅ„) CGR grudziÄ…dzki (GrudziÄ…dz) KTT tatrzaÅ„ski (Zakopane) SBE bÄ™dziÅ„ski (BÄ™dzin) inowrocÅ‚awski CIN KWA wadowicki (Wadowice) SBI bielski (Bielsko-BiaÅ‚a) (InowrocÅ‚aw) CLI lipnowski (Lipno) KWI wielicki (Wieliczka) SBL bieruÅ„sko-lÄ™dziÅ„ski (BieruÅ„) CMG mogileÅ„ski (Mogilno) LBI bialski (BiaÅ‚a Podlaska) SCI cieszyÅ„ski (Cieszyn) nakielski (NakÅ‚o nad CNA LBL biÅ‚gorajski (BiÅ‚goraj) SCZ czÄ™stochowski (CzÄ™stochowa) NoteciÄ…) CRA radziejowski (Radziejów) LCH cheÅ‚mski (CheÅ‚m) SGL gliwicki (Gliwice) CRY rypiÅ„ski (Rypin) LHR hrubieszowski (Hrubieszów) SKL kÅ‚obucki (KÅ‚obuck) sÄ™poleÅ„ski (SÄ™pólno CSE LJA janowski (Janów Lubelski) SLU lubliniecki (Lubliniec) KrajeÅ„skie) CSW Å›wiecki (Åšwiecie) LKR kraÅ›nicki (KraÅ›nik) SMI mikoÅ‚owski (Mikołów) CTR toruÅ„ski (ToruÅ„) LKS krasnostawski (Krasnystaw) SMY myszkowski (Myszków) CTU tucholski (Tuchola) LLB lubartowski (Lubartów) SPS pszczyÅ„ski (Pszczyna) CWA wÄ…brzeski (WÄ…brzeźno) LLE Å‚Ä™czyÅ„ski (Å?Ä™czna) SRB rybnicki (Rybnik) CWL wÅ‚ocÅ‚awski (WÅ‚ocÅ‚awek) LLU Å‚ukowski (Å?uków) SRC raciborski (Racibórz) CZN niÅ„ski ( nin) LOP opolski (Opole Lubelskie) STA tarnogórski (Tarnowskie Góry) DB waÅ‚brzyski (WaÅ‚brzych) LPA parczewski (Parczew) SWD wodzisÅ‚awski (WodzisÅ‚aw ÅšlÄ…ski) bolesÅ‚awiecki DBL LPU puÅ‚awski (PuÅ‚awy) SZA zawierciaÅ„ski (Zawiercie) (BolesÅ‚awiec) dzier oniowski DDZ LRA radzyÅ„ski (RadzyÅ„ Podlaski) SZY ywiecki ( ywiec) (Dzier oniów) 5. Appendix DGL gÅ‚ogowski (GÅ‚ogów) LRY rycki (Ryki) TBU buski (Busko Zdrój) DGR górowski (Góra) LSW Å›widnicki (Åšwidnik) TJE jÄ™drzejowski (JÄ™drzejów) tomaszowski (Tomaszów DJA jaworski (Jawor) LTM TKA kazimierski (Kazimierza Wielka) Lubelski) jeleniogórski (Jelenia DJE LUB lubelski (Lublin) TKI kielecki (Kielce) Góra) kamiennogórski DKA LWL wÅ‚odawski (WÅ‚odawa) TKN konecki (KoÅ„skie) (Kamienna Góra) DKL kÅ‚odzki (KÅ‚odzko) LZA zamojski (Zamość) TLW wÅ‚oszczowski (WÅ‚oszczowa) DLB lubaÅ„ski (LubaÅ„) NBA bartoszycki (Bartoszyce) TOP opatowski (Opatów) DLE legnicki (Legnica) NBR braniewski (Braniewo) TOS ostrowiecki DLU lubiÅ„ski (Lubin) NDZ dziaÅ‚dowski (DziaÅ‚dowo) TPI piÅ„czowski (PiÅ„czów) DLW lwówecki (Lwówek ÅšlÄ…ski) NEB elblÄ…ski (ElblÄ…g) TSA sandomierski (Sandomierz) DMI milicki (Milicz) NEL eÅ‚cki (EÅ‚k) TSK skar yski (Skar ysko-Kamienna) DOA oÅ‚awski (OÅ‚awa) NGI gi ycki (Gi ycko) TST starachowicki (Starachowice) DOL oleÅ›nicki (OleÅ›nica) NGO goÅ‚dapski (GoÅ‚dap) TSZ staszowski (Staszów) DPL polkowicki (Polkowice) NIL iÅ‚awski (IÅ‚awa) WBR biaÅ‚obrzeski (BiaÅ‚obrzegi) DSR Å›redzki (Åšroda ÅšlÄ…ska) NKE kÄ™trzyÅ„ski (KÄ™trzyn) WCI ciechanowski (Ciechanów) lidzbarski (Lidzbark DST strzeliÅ„ski (Strzelin) NLI WG garwoliÅ„ski (Garwolin) WarmiÅ„ski) DSW Å›widnicki (Åšwidnica) NMR mrÄ…gowski (MrÄ…gowo) WGM grodziski (Grodzisk Mazowiecki) DTR trzebnicki (Trzebnica) NNI nidzicki (Nidzica) WGR grójecki (Grójec) nowomiejski (Nowe Miasto DWL woÅ‚owski (Wołów) NNM WGS gostyniÅ„ski (Gostynin) Lubawskie) DWR wrocÅ‚awski (WrocÅ‚aw) NOE olecki (Olecko) WKZ kozienicki (Kozienice) zÄ…bkowicki (ZÄ…bkowice DZA NOL olsztyÅ„ski (Olsztyn) WL legionowski (Legionowo) ÅšlÄ…skie) DZG zgorzelecki (Zgorzelec) NOS ostródzki (Ostróda) WLI lipski (Lipsko) DZL zÅ‚otoryjski (ZÅ‚otoryja) NPI piski (Pisz) WLS Å‚osicki (Å?osice) EBE beÅ‚chatowski (BeÅ‚chatów) NSZ szczycieÅ„ski (Szczytno) WM miÅ„ski (MiÅ„sk Mazowiecki) EBR brzeziÅ„ski (Brzeziny) NWE wÄ™gorzewski (WÄ™gorzewo) WMA makowski (Maków Mazowiecki) EKU kutnowski (Kutno) OB brzeski (Brzeg) WML mÅ‚awski (MÅ‚awa) nowodworski (Nowy Dwór ELA Å‚aski (Å?ask) OGL gÅ‚ubczycki (GÅ‚ubczyce) WND Mazowiecki) kÄ™dzierzyÅ„sko-kozielski ELC Å‚owicki (Å?owicz) OK WOR ostrowski (Ostrów Mazowiecka) (KÄ™dzierzyn-Koźle) ELE Å‚Ä™czycki (Å?Ä™czyca) OKL kluczborski (Kluczbork) WOS ostroÅ‚Ä™cki (OstroÅ‚Ä™ka) ELW łódzki wschodni (Å?ódź) OKR krapkowicki (Krapkowice) WOT otwocki (Otwock) EOP opoczyÅ„ski (Opoczno) ONA namysÅ‚owski (Namysłów) WPI piaseczyÅ„ski (Piaseczno) EPA pabianicki (Pabianice) ONY nyski (Nysa) WPL pÅ‚ocki (PÅ‚ock) EPD poddÄ™bicki (PoddÄ™bice) OOL oleski (Olesno) WPN pÅ‚oÅ„ski (PÅ‚oÅ„sk) piotrkowski (Piotrków EPI OPO opolski (Opole) WPR pruszkowski (Pruszków) Trybunalski) EPJ pajÄ™czaÅ„ski (PajÄ™czno) OPR prudnicki (Prudnik) WPU puÅ‚tuski (PuÅ‚tusk) radomszczaÅ„ski trzelecki (Strzelce ERA OST WPY przysuski (Przysucha) (Radomsko) Opolskie) rawski (Rawa ERW PCH chodzieski (Chodzie ) WPZ przasnyski (Przasnysz) Mazowiecka) czarnkowsko-trzcianecki ESI sieradzki (Sieradz) PCT WRA radomski (Radom) (Czarnków) skierniewicki ESK PGN gnieźnieÅ„ski (Gniezno) WSC sochaczewski (Sochaczew) (Skierniewice) tomaszowski (Tomaszów grodziski (Grodzisk ETM PGO WSE sierpecki (Sierpc) Mazowiecki) Wielkopolski) wieruszowski EWE PGS gostyÅ„ski (GostyÅ„) WSI siedlecki (Siedlce) (Wieruszów) 5. Appendix EWI wieluÅ„ski (WieluÅ„) PJA jarociÅ„ski (Jarocin) WSK sokoÅ‚owski (Sokołów Podlaski) zduÅ„skowolski (ZduÅ„ska EZD PKA kaliski (Kalisz) WSZ szydÅ‚owiecki (SzydÅ‚owiec) Wola) EZG zgierski (Zgierz) PKE kÄ™piÅ„ski (KÄ™pno) WWE wÄ™growski (WÄ™grów) gorzowski (Gorzów FGW PKL kolski (KoÅ‚o) WWL woÅ‚omiÅ„ski (WoÅ‚omin) Wielkopolski) kroÅ›nieÅ„ski (Krosno FKR PKN koniÅ„ski (Konin) WWY wyszkowski (Wyszków) OdrzaÅ„skie) miÄ™dzyrzecki warszawski zachodni (O arów FMI PKR krotoszyÅ„ski (Krotoszyn) WZ (MiÄ™dzyrzecz) Mazowiecki) FNW nowosolski (Nowa Sól) PKS koÅ›ciaÅ„ski (KoÅ›cian) WZU uromiÅ„ski ( uromin) strzelecko-drezdenecki FSD PLE leszczyÅ„ski (Leszno) WZW zwoleÅ„ski (ZwoleÅ„) (Strzelce KrajeÅ„skie) FSL sÅ‚ubicki (SÅ‚ubice) PMI miÄ™dzychodzki (MiÄ™dzychód) WZY yrardowski ( yrardów) FSU sulÄ™ciÅ„ski (SulÄ™cin) PNT nowotomyski (Nowy TomyÅ›l) ZBI biaÅ‚ogardzki (BiaÅ‚ogard) Å›wiebodziÅ„ski FSW POB obornicki (Oborniki) ZCH choszczeÅ„ski (Choszczno) (Åšwiebodzin) ostrowski (Ostrów FWS wschowski (Wschowa) POS ZDR drawski (Drawsko Pomorskie) Wielkopolski) FZA arski ( ary) POT ostrzeszowski (Ostrzeszów) ZGL goleniowski (Goleniów) FZG agaÅ„ski ( agaÅ„) PP pilski (PiÅ‚a) ZGR gryfiÅ„ski (Gryfino) zielonogórski (Zielona FZI PPL pleszewski (Pleszew) ZGY gryficki (Gryfice) Góra) GBY bytowski (Bytów) PRA rawicki (Rawicz) ZKA kamieÅ„ski (KamieÅ„ Pomorski) GCH chojnicki (Chojnice) PSE Å›remski (Åšrem) ZKL koÅ‚obrzeski (KoÅ‚obrzeg) GCZ czÅ‚uchowski (CzÅ‚uchów) PSL sÅ‚upecki (SÅ‚upca) ZKO koszaliÅ„ski (Koszalin) gdaÅ„ski (Pruszcz Å›redzki (Åšroda GDA PSR ZLO Å‚obeski (Å?obez) GdaÅ„ski) Wielkopolska) GKA kartuski (Kartuzy) PSZ szamotulski (SzamotuÅ‚y) ZMY myÅ›liborski (MyÅ›libórz) GKS koÅ›cierski (KoÅ›cierzyna) PTU turecki (Turek) ZPL policki (Police) GKW kwidzyÅ„ski (Kwidzyn) PWA wÄ…growiecki (WÄ…growiec) ZPY pyrzycki (Pyrzyce) GLE lÄ™borski (LÄ™bork) PWL wolsztyÅ„ski (Wolsztyn) ZSD Å›widwiÅ„ski (Åšwidwin) GMB malborski (Malbork) PWR wrzesiÅ„ski (WrzeÅ›nia) ZSL sÅ‚awieÅ„ski (SÅ‚awno) nowodworski (Nowy Dwór stargardzki (Stargard GND PZ poznaÅ„ski (PoznaÅ„) ZST GdaÅ„ski) SzczeciÅ„ski) GPU pucki (Puck) PZL zÅ‚otowski (ZÅ‚otów) ZSW ÅšwinoujÅ›cie bieszczadzki (Ustrzyki GSL sÅ‚upski (SÅ‚upsk) RBI ZSZ szczecinecki (Szczecinek) Dolne) starogardzki (Starogard GST RBR brzozowski (Brzozów) ZWA waÅ‚ecki (WaÅ‚cz) GdaÅ„ski) 5. Appendix 5.1.2 Average Exchange Rates between National Currencies and Euro Table 5.11: Average Exchange Rates between National Currencies and Euro used in this study Czech Rep [CZK] Slovakia [SKK] Hungary [HUF] Poland [PLN] 25.902 30.230 269.35 3.9953 5.1.3 Calculation of Net Fixed Asset Value Growth Indexes Table 5.12: Net Fixed asset values in the years 2006 and 2007 and the growth indexes calculated 151 152 153 154 Czech Rep. Slovakia Hungary Poland Net Fixed 12 351 504 mil CZK 7 764 617 mil SKK 91 476 bil HUF 1 913 333 mil PLN asset 2006 Net Fixed 13 067 516 mil CZK 8 118 400 mil SKK 97 992 bil HUF 2 061 215 mil PLN asset 2007 Net Fixed Asset Growth 1.0580 1.0456 1.0712 1.0773 Index 5.1.4 Household Equipment Value Calculation Algorithm (1) The average household equipment value (per household) for the Czech Republic is calculated based on insurance data as an average insured value of the equipment per policy in the residential portfolios. (2) Total household equipment value for the Czech Republic is calculated as an average of two estimates obtained by the following two approaches: a. average household equipment value * total household count155 b. Value of AN.1111156 * (average household equipment value / average value of the building157) (3) Total household equipment value for other V–4 countries except for the Czech Republic is calculated as an average of two estimates obtained by the following two approaches: a. Total household equipment value for the Czech Republic multiplied by the population proportion of the respective country and the Czech Republic b. Total household equipment value for the Czech Republic multiplied by the GDP proportion of the respective country and the Czech Republic 151 Data source for the Czech Republic: [30] 152 Data source for Slovakia: [31] 153 Data source for Hungary: [32], p. 147 154 Data source for Poland: [18] 155 See [17], table 9-1. Private households: by status of head of household 156 dwelling asset category, see Paragraph 3.2.3 157 Calculated form the insurance data as an average insured value of the buildings per policy in the residential portfolios 5. Appendix 5.1.5 Detailed Property Structure of the Individual Countries The property split into the property classes as defined in Paragraph 3.2 for each country are shown in the tables 5.13a-e, 5.14a-e, 5.15a-e and 5.16a-e and in the figures 5.10a-f, 5.11a-f, 5.12a-f and 5.13a-f. The property structure of the whole V–4 Group is shown in the tables 5.17a-e and figures 5.14a-f. In addition to it, the overall comparison of the property structure of all countries in the sector / purpose classification is shown in Table 5.19 and figure 5.19. Table 5.18: List of tables and figures in appendix Detailed Property Structure of the Individual Countries: assistant on how to use the following outputs Country Dimension Characteristics e) V–4 a) CZE b) SVK c) HUN d) POL Industry Branch Tab 5.13d 5.14d 5.15d 5.16d 5.17d (chapter 3.2.1) Fig 5.10e 5.11e 5.12e 5.13e 5.14e Asset Category Tab 5.13b 5.14b 5.15b 5.16b 5.17b 1D (chapter 3.2.3) Fig 5.10b 5.11b 5.12b 5.13b 5.14b Institutional Sector Tab 5.13c 5.14c 5.15c 5.16c 5.17c (chapter 3.2.5) Fig 5.10d 5.11d 5.12d 5.13d 5.14d Industry Branch v. Tab 5.13e 5.14e 5.15e 5.16e 5.17e Asset Category Fig 5.10f 5.11f 5.12f 5.13f 5.14f Industry Branch v. Tab 5.13d 5.14d 5.15d 5.16d 5.17d 2D Institutional Sector Fig 5.10e 5.11e 5.12e 5.13e 5.14e Asset Category v. Tab 5.13c 5.14c 5.15c 5.16c 5.17c Institutional Sector Fig 5.10c 5.11c 5.12c 5.13c 5.14c Infrastructure Tab 5.13a 5.14a 5.15a 5.16a 5.17a Spec. or Enterprise 2D Fig 5.10a 5.11a 5.12a 5.13a 5.14a (chapter 3.2.8) a) Detailed Property Structure, Czech Republic Tab. 5.13a: Property structure by sector / purpose classification for the Czech Republic Property Category % [mil EUR] infrastructure-public 194 378 34% enterprise-public 22 770 4% infrastructure- Public 217 148 38% public infrastructure-non-public 17 146 3% 34% enterprise-non-public 332 226 59% TOTAL 566 520 enterprise- public 4% enterprise-non- public infrastructure- 59% non-public 3% Fig. 5.10a: Property structure by sector / purpose classification for the Czech Republic Tab. 5.13b: Property structure by asset categories for the Czech Republic Inventories Household 6% Property eguipment Category code % Dwellings [mil EUR] 5% 22% Dwellings AN.1111 126 075 22% Machinery and Other buildings equipment and structures AN.1112 297 449 53% 14% Machinery and equipment AN.1113 80 166 14% Inventories AN.12 36 281 6% Household eguipment - 26 549 5% TOTAL 566 520 Other buildings and structures 53% Fig. 5.10b: Property structure by asset categories for the Czech Republic Tab. 5.13c: Property structure by institutional sectors and asset categories AN.12 AN.1111-2 AN.1113 household Total institutional invento- public buildings equipment equipment property % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 104 072 6 327 3 274 26 549 140 223 24.8% private 121 280 59 768 28 101 0 209 149 36.9% private public 198 173 14 070 4 905 0 217 148 38.3% TOTAL 423 524 80 166 36 281 26 549 566 520 % 74.8% 14.2% 6.4% 4.7% building equipment households inventories household equip. households 25% 0 50 000 100 000 150 000 200 000 250 000 public Property value [mil. EUR] 38% Fig. 5.10c: Property structure by institutional sectors and asset categories for the Czech Republic Property value [mil. EUR] 0 50 000 100 000 150 000 200 000 A+B C D private 37% E F Fig. 5.10d: Property structure by institutional sectors for the Czech Republic G H Tab. 5.13d: Property structure by institutional sectors and industry branches for the Czech Republic I house- Total J public private Industry branch holds property % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 984 6 592 1 292 8 868 1.6% C (Mining and quarrying) 2 224 3 596 13 5 833 1.0% M D (Manufacturing) 1 485 82 157 3 342 86 984 15.4% public E (Electricity, gas and water N private supply) 23 329 11 618 19 34 966 6.2% households F (Construction) 233 7 391 1 448 9 072 1.6% O G (Wholesale and retail trade; repair) 531 30 494 4 172 35 197 6.2% Fig. 5.10e: Structure of the property for the Czech Republic by industry branches and H (Hotels and restaurant) 511 5 716 812 7 039 1.2% institutional sectors I (Transport, storage and communications) 63 076 13 816 1 601 78 493 13.9% J (Financial intermediation) 443 6 266 63 6 772 1.2% Property value [mil. EUR] K (Real estate, renting, 0 50 000 100 000 150 000 200 000 research) 16 359 35 974 126 484 178 818 31.6% L (Public administration 53 417 1 0 53 419 9.4% A+B M (Education) 27 576 175 62 27 812 4.9% N (Health and social work) 10 731 1 412 635 12 778 2.3% C O (Other community, social and personal services) 16 249 3 940 280 20 469 3.6% D TOTAL 217 148 209 149 140 223 566 520 % 38.3% 36.9% 24.8% E F Tab. 5.13e: Property structure by asset categories and industry branches for the Czech G AN.12 AN.1111-2 AN.1113 household Total invento- H Industry branch buildings equipment equipment property % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 6 142 2 726 0 0 8 868 1.6% C 3 967 1 699 168 0 5 833 1.0% J D 37 533 33 648 15 803 0 86 984 15.4% E 28 541 5 668 757 0 34 966 6.2% K F 4 074 3 210 1 789 0 9 072 1.6% G 16 856 6 266 12 075 0 35 197 6.2% L H 6 087 792 160 0 7 039 1.2% I 66 515 11 492 486 0 78 493 13.9% M building J 5 413 1 044 315 0 6 772 1.2% equipment K 144 896 5 989 1 383 26 549 178 818 31.6% N inventories L 48 528 2 002 2 889 0 53 419 9.4% household equip. M 26 635 1 124 54 0 27 812 4.9% O N 10 057 2 583 138 0 12 778 2.3% O 18 281 1 924 264 0 20 469 3.6% Fig. 5.10f: Structure of the property for the Czech Republic by industry branches and TOTAL 423 524 80 166 36 281 26 549 566 520 asset categories % 74.8% 14.2% 6.4% 4.7% b) Detailed Property Structure, Slovakia Tab. 5.14a: Property structure by sector / purpose classification for Slovakia Property enterprise-non- Category % [mil EUR] public 56% infrastructure-public 96 014 34% enterprise-public 11 797 4% infrastructure- Public 107 812 38% public infrastructure-non-public 16 245 6% 34% enterprise-non-public 162 323 57% TOTAL 286 379 enterprise- public 4% infrastructure- non-public 6% Fig. 5.11a: Property structure by sector / purpose classification for Slovakia Household Tab. 5.14b: Property structure by asset categories for Slovakia Inventories eguipment Property Category Code % 6% 4% [mil EUR] Dwellings 24% Dwellings AN.1111 69 496 24% Machinery and Other buildings equipment and structures AN.1112 141 943 50% 16% Machinery and equipment AN.1113 45 296 16% Inventories AN.12 18 242 6% Household eguipment - 11 403 4% TOTAL 286 379 Other buildings and structures 50% Fig. 5.11b: Property structure by asset categories for Slovakia Tab. 5.14c: Property structure by institutional sectors and asset categories for Slovakia AN.12 AN.1111-2 AN.1113 household Total institutional invento- public buildings equipment equipment property % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 52 673 2 526 1 529 11 403 68 131 23.8% private 70 483 26 122 13 831 0 110 436 38.6% private public 88 282 16 647 2 882 0 107 812 37.6% TOTAL 211 439 45 296 18 242 11 403 286 379 % 73.8% 15.8% 6.4% 4.0% building equipment households inventories household equip. households 24% 0 20 000 40 000 60 000 80 000 100 000 120 000 public Property value [mil. EUR] 38% Fig. 5.11c: Property structure by institutional sectors and asset categories for Slovakia Property value [mil. EUR] 0 20 000 40 000 60 000 80 000 100 000 A+B C D private 38% E F Fig. 5.11d: Property structure by institutional sectors for Slovakia G H Tab. 5.14d: Property structure by institutional sectors and industry branches for Slovakia I house- Total J public private Industry branch holds property % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 728 4 889 966 6 583 2.3% C (Mining and quarrying) 1 378 2 228 8 3 613 1.3% M D (Manufacturing) 783 43 320 1 762 45 865 16.0% public E (Electricity, gas and water N private supply) 30 046 14 962 25 45 033 15.7% households F (Construction) 63 2 007 393 2 464 0.9% O G (Wholesale and retail trade; repair) 244 13 997 1 915 16 156 5.6% Fig. 5.11e: Structure of the property for Slovakia by industry branches and institutional H (Hotels and restaurant) 144 1 614 229 1 987 0.7% sectors I (Transport, storage and communications) 24 088 5 276 611 29 975 10.5% J (Financial intermediation) 189 2 679 27 2 896 1.0% Property value [mil. EUR] K (Real estate, renting, 0 20 000 40 000 60 000 80 000 100 000 research) 8 268 18 182 61 911 88 361 30.9% L (Public administration 30 206 1 0 30 207 10.5% A+B M (Education) 4 834 31 11 4 875 1.7% N (Health and social work) 3 680 484 218 4 381 1.5% C O (Other community, social and personal services) 3 162 767 55 3 983 1.4% D TOTAL 107 812 110 436 68 131 286 379 % 37.6% 38.6% 23.8% E F Tab. 5.14e: Property structure by asset categories and industry branches for Slovakia G AN.12 AN.1111-2 AN.1113 household Total invento- H Industry branch buildings equipment equipment property % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 5 253 1 329 0 0 6 583 2.3% C 3 101 409 104 0 3 613 1.3% J D 20 939 16 598 8 328 0 45 865 16.0% E 35 328 8 741 964 0 45 033 15.7% K F 1 055 925 484 0 2 464 0.9% G 7 519 3 084 5 553 0 16 156 5.6% L H 1 536 406 45 0 1 987 0.7% I 22 447 7 340 187 0 29 975 10.5% M building J 1 994 766 135 0 2 896 1.0% equipment K 74 851 1 408 700 11 403 88 361 30.9% N inventories L 25 895 2 678 1 633 0 30 207 10.5% household equip. M 4 675 191 9 0 4 875 1.7% O N 3 583 751 47 0 4 381 1.5% O 3 262 670 51 0 3 983 1.4% Fig. 5.11f: Structure of the property for Slovakia by industry branches and asset TOTAL 211 439 45 296 18 242 11 403 286 379 categories % 73.8% 15.8% 6.4% 4.0% c) Detailed Property Structure, Hungary Tab. 5.15a: Property structure by sector / purpose classification for Hungary Property Category % infrastructure- [mil EUR] public 26% infrastructure-public 103 405 26% enterprise-public 9 381 2% Public 112 786 28% infrastructure-non-public 20 384 5% enterprise- enterprise-non-public 272 022 67% public TOTAL 405 192 2% infrastructure- non-public 5% enterprise-non- public 67% Fig. 5.12a: Property structure by sector / purpose classification for Hungary Tab. 5.15b: Property structure by asset categories for Hungary Inventories Household Property 7% eguipment Dwellings Category Code % [mil EUR] 4% 25% Dwellings AN.1111 102 360 25% Machinery and Other buildings equipment and structures AN.1112 196 680 49% 15% Machinery and equipment AN.1113 61 885 15% Inventories AN.12 27 272 7% Household eguipment - 16 996 4% TOTAL 405 192 Other buildings and structures 49% Fig. 5.12b: Property structure by asset categories for Hungary Tab. 5.15c: Property structure by institutional sectors and asset categories for Hungary AN.12 AN.1111-2 AN.1113 household Total institutional invento- public buildings equipment equipment property % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 106 157 5 917 3 130 16 996 132 200 32.6% private 92 075 48 837 19 295 0 160 207 39.5% private public 100 808 7 130 4 848 0 112 786 27.8% TOTAL 299 040 61 885 27 272 16 996 405 192 building % 73.8% 15.3% 6.7% 4.2% equipment households inventories household equip. public households 28% 33% 0 50 000 100 000 150 000 200 000 Property value [mil. EUR] Fig. 5.12c: Property structure by institutional sectors and asset categories for Hungary Property value [mil. EUR] 0 25 000 50 000 75 000 100 000 125 000 150 000 A+B C D private 39% E F Fig. 5.12d: Property structure by institutional sectors for Hungary G H Tab. 5.15d: Property structure by institutional sectors and industry branches for Hungary I house- Total J public private Industry branch holds property % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 322 11 162 8 271 19 756 4.9% C (Mining and quarrying) 0 1 376 12 1 388 0.3% M D (Manufacturing) 20 52 293 815 53 128 13.1% public E (Electricity, gas and water N private supply) 76 18 941 12 19 028 4.7% households F (Construction) 203 6 243 426 6 872 1.7% O G (Wholesale and retail trade; repair) 163 20 559 1 106 21 828 5.4% Fig. 5.12e: Structure of the property for Hungary by industry branches and institutional H (Hotels and restaurant) 183 2 644 131 2 959 0.7% sectors I (Transport, storage and communications) 2 330 26 480 274 29 085 7.2% J (Financial intermediation) 0 3 241 103 3 344 0.8% Property value [mil. EUR] K (Real estate, renting, 0 25 000 50 000 75 000 100 000 125 000 150 000 research) 8 490 15 823 112 615 136 928 33.8% L (Public administration 81 505 0 0 81 505 20.1% A+B M (Education) 10 300 28 807 11 135 2.7% N (Health and social work) 5 925 84 278 6 287 1.6% C O (Other community, social and personal services) 3 269 1 332 7 349 11 950 2.9% D TOTAL 112 786 160 207 132 200 405 192 % 27.8% 39.5% 32.6% E F Tab. 5.15e: Property structure by institutional sectors and industry branches for Hungary G AN.12 AN.1111-2 AN.1113 household Total invento- H Industry branch buildings equipment equipment property % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 15 139 4 617 0 0 19 756 4.9% C 875 481 32 0 1 388 0.3% J D 21 239 23 582 8 307 0 53 128 13.1% E 13 835 4 848 346 0 19 028 4.7% K F 3 897 1 702 1 273 0 6 872 1.7% G 8 088 5 098 8 641 0 21 828 5.4% L H 2 481 386 91 0 2 959 0.7% I 17 183 10 860 1 042 0 29 085 7.2% M building J 2 704 640 0 0 3 344 0.8% equipment K 113 732 3 150 3 050 16 996 136 928 33.8% N inventories L 73 576 3 523 4 406 0 81 505 20.1% household equip. M 10 109 1 018 8 0 11 135 2.7% O N 5 054 1 221 12 0 6 287 1.6% O 11 127 760 64 0 11 950 2.9% Fig. 5.12f: Structure of the property for Hungary by industry branches and asset TOTAL 299 040 61 885 27 272 16 996 405 192 categories % 73.8% 15.3% 6.7% 4.2% d) Detailed Property Structure, Poland Tab. 5.16a: Property structure by sector / purpose classification for Poland Property Category % infrastructure- [mil EUR] public 24% infrastructure-public 300 577 24% enterprise-public 161 348 13% Public 461 926 37% infrastructure-non-public 90 601 7% enterprise-non-public 695 655 56% TOTAL 1 248 182 enterprise- public 13% enterprise-non- infrastructure- public non-public 56% 7% Fig. 5.13a: Property structure by sector / purpose classification for Poland Tab. 5.16b: Property structure by asset categories for Poland Inventories Household Property 7% eguipment Category Code % [mil EUR] 5% Dwellings 26% Dwellings AN.1111 319 610 26% Other buildings Machinery and and structures AN.1112 562 836 45% equipment Machinery and 18% equipment AN.1113 220 460 18% Inventories AN.12 84 628 7% Household eguipment - 60 649 5% TOTAL 1 248 182 Other buildings and structures 44% Fig. 5.13b: Property structure by asset categories for Poland Tab. 5.16c: Property structure by institutional sectors and asset categories for Poland AN.12 AN.1111-2 AN.1113 household Total institutional invento- public buildings equipment equipment property % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 256 913 14 448 7 075 60 649 339 084 27.2% private 284 247 110 367 52 559 0 447 172 35.8% private public 341 286 95 645 24 994 0 461 926 37.0% TOTAL 882 446 220 460 84 628 60 649 1 248 182 % 70.7% 17.7% 6.8% 4.9% building equipment households inventories household equip. households 27% public 0 100 000 200 000 300 000 400 000 500 000 Property value [mil. EUR] 37% Fig. 5.13c: Property structure by institutional sectors and asset categories for Poland Property value [mil. EUR] 0 100 000 200 000 300 000 400 000 500 000 A+B C D private 36% E F Fig. 5.13d: Property structure by institutional sectors for Poland G H Tab. 5.16d: Property structure by institutional sectors and industry branches for Poland I house- Total J public private Industry branch holds property % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 5 652 30 890 6 054 42 596 3.4% C (Mining and quarrying) 5 980 8 719 302 15 001 1.2% M D (Manufacturing) 79 338 115 680 4 009 199 026 15.9% public E (Electricity, gas and water N private supply) 54 080 79 176 2 744 135 999 10.9% households F (Construction) 5 985 12 878 2 523 21 386 1.7% O G (Wholesale and retail trade; repair) 2 454 73 821 10 100 86 374 6.9% Fig. 5.13e: Structure of the property for Poland by industry branches and institutional H (Hotels and restaurant) 1 242 7 795 1 107 10 145 0.8% sectors I (Transport, storage and communications) 64 722 23 726 2 749 91 197 7.3% J (Financial intermediation) 4 416 12 950 131 17 497 1.4% Property value [mil. EUR] K (Real estate, renting, 0 100 000 200 000 300 000 400 000 500 000 research) 56 281 70 113 307 162 433 556 34.7% L (Public administration 101 308 314 76 101 698 8.1% A+B M (Education) 35 493 1 715 606 37 814 3.0% N (Health and social work) 17 881 2 256 1 014 21 150 1.7% C O (Other community, social and personal services) 27 094 7 141 508 34 742 2.8% D TOTAL 461 926 447 172 339 084 1 248 182 % 37.0% 35.8% 27.2% E F Tab. 5.16e: Property structure by asset categories and industry branches for Poland G AN.12 AN.1111-2 AN.1113 household Total invento- H Industry branch buildings equipment equipment property % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 37 571 5 025 0 0 42 596 3.4% C 9 831 4 739 431 0 15 001 1.2% J D 84 844 78 043 36 139 0 199 026 15.9% E 99 871 33 217 2 911 0 135 999 10.9% K F 11 214 5 972 4 199 0 21 386 1.7% G 36 829 19 855 29 690 0 86 374 6.9% L H 8 790 1 123 231 0 10 145 0.8% I 52 544 38 082 570 0 91 197 7.3% M building J 11 165 5 515 817 0 17 497 1.4% equipment K 359 436 10 081 3 390 60 649 433 556 34.7% N inventories L 91 255 4 944 5 499 0 101 698 8.1% household equip. M 35 493 2 249 73 0 37 814 3.0% O N 15 374 5 548 228 0 21 150 1.7% O 28 229 6 065 448 0 34 742 2.8% Fig. 5.13f: Structure of the property for Poland by industry branches and asset TOTAL 882 446 220 460 84 628 60 649 1 248 182 categories % 70.7% 17.7% 6.8% 4.9% e) Detailed Property Structure, V-4 Group Tab. 5.17a: Property structure by sector / purpose classification for V-4 countries Property Category % enterprise-non- [mil EUR] public infrastructure- infrastructure-public 694 375 28% 58% public enterprise-public 205 297 8% 28% Public 899 671 36% infrastructure-non-public 144 376 6% enterprise-non-public 1 462 226 58% TOTAL 2 506 273 enterprise- public 8% infrastructure- non-public 6% Fig. 5.14a: Property structure by sector / purpose classification for V-4 countries Tab. 5.17b: Property structure by asset categories for V-4 countries Household Property Inventories eguipment Category Code % [mil EUR] 7% 5% Dwellings 25% Dwellings AN.1111 617 541 25% Machinery and Other buildings equipment and structures AN.1112 1 198 908 48% 16% Machinery and equipment AN.1113 407 806 16% Inventories AN.12 166 422 7% Household eguipment - 115 597 5% TOTAL 2 506 273 Other buildings and structures 47% Fig. 5.14b: Property structure by asset categories for V-4 countries Tab. 5.17c: Property structure by institutional sectors and asset categories for V-4 countries AN.12 AN.1111-2 AN.1113 household Total institutional invento- public buildings equipment equipment property % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 519 815 29 219 15 008 115 597 679 638 27.1% private 568 084 245 094 113 785 0 926 964 37.0% private public 728 549 133 493 37 629 0 899 671 35.9% TOTAL 1 816 449 407 806 166 422 115 597 2 506 273 % 72.5% 16.3% 6.6% 4.6% building equipment households inventories household equip. households public 0 250 000 500 000 750 000 1 000 000 27% 36% Property value [mil. EUR] Fig. 5.14c: Property structure by institutional sectors and asset categories for V-4 countries Property value [mil. EUR] 0 250 000 500 000 750 000 1 000 000 A+B C D E private 37% F Fig. 5.14d: Property structure by institutional sectors for V-4 countries G H Tab. 5.17d: Property structure by institutional sectors and industry branches for V-4 countries I house- Total J public private Industry branch holds property % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 7 686 53 533 16 584 77 803 3.1% C (Mining and quarrying) 9 582 15 919 335 25 836 1.0% M D (Manufacturing) 81 625 293 449 9 929 385 003 15.4% public E (Electricity, gas and water N private supply) 107 530 124 697 2 799 235 026 9.4% households F (Construction) 6 485 28 519 4 789 39 793 1.6% O G (Wholesale and retail trade; repair) 3 391 138 871 17 292 159 555 6.4% Fig. 5.14e: Structure of the property for V-4 countries by industry branches and H (Hotels and restaurant) 2 080 17 770 2 279 22 130 0.9% institutional sectors I (Transport, storage and communications) 154 216 69 299 5 235 228 750 9.1% J (Financial intermediation) 5 048 25 136 325 30 509 1.2% Property value [mil. EUR] K (Real estate, renting, 0 250 000 500 000 750 000 1 000 000 research) 89 398 140 092 608 172 837 662 33.4% L (Public administration 266 437 316 77 266 829 10.6% A+B M (Education) 78 202 1 948 1 486 81 636 3.3% N (Health and social work) 38 217 4 235 2 144 44 596 1.8% C O (Other community, social and personal services) 49 773 13 180 8 192 71 144 2.8% D TOTAL 899 671 926 964 679 638 2 506 273 % 35.9% 37.0% 27.1% E F Tab. 5.17e: Property structure by asset categories and industry branches for V-4 countries G AN.12 AN.1111-2 AN.1113 household Total invento- H Industry branch buildings equipment equipment property % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 64 105 13 698 0 0 77 803 3.1% C 17 774 7 327 735 0 25 836 1.0% J D 164 555 151 870 68 578 0 385 003 15.4% E 177 575 52 473 4 978 0 235 026 9.4% K F 20 239 11 810 7 744 0 39 793 1.6% G 69 292 34 302 55 960 0 159 555 6.4% L H 18 895 2 707 528 0 22 130 0.9% I 158 690 67 774 2 286 0 228 750 9.1% M building J 21 277 7 966 1 267 0 30 509 1.2% equipment K 692 915 20 627 8 523 115 597 837 662 33.4% N inventories L 239 254 13 148 14 427 0 266 829 10.6% household equip. M 76 911 4 582 143 0 81 636 3.3% O N 34 068 10 102 426 0 44 596 1.8% O 60 898 9 419 827 0 71 144 2.8% Fig. 5.14f: Structure of the property for V-4 countries by industry branches and asset TOTAL 1 816 449 407 806 166 422 115 597 2 506 273 categories % 72.5% 16.3% 6.6% 4.6% 5. Appendix Property [mil EUR] 0 200 000 400 000 600 000 800 000 1 000 000 1 200 000 1 400 000 Czech Republic infrastructure-public enterprise-public Slovakia infrastructure-non-public enterprise-non public Hungary Poland Figure 5.15: Comparison of the property structure in the sector / purpose classification158 Table 5.19: Comparison of the property structure in the sector / purpose classification [billion EUR] infrastructure- enterprise- infrastructure- enterprise- Country public TOTAL % public public non-public non public Czech Republic 194 23 217 17 332 567 23% Slovakia 96 12 108 16 162 286 11% Hungary 103 9 113 20 272 405 16% Poland 301 161 462 91 696 1 248 50% V–4 694 205 900 144 1 462 2 506 % 28% 8% 36% 6% 58% 158 See Paragraph Sector / Purpose Re-classification of the Property 5. Appendix 5.1.6 Historical Flood Loss Data a) Czech Republic, 1997 Flood Table 5.20: Physical structure of the losses159 during the July 1997 flood in the Czech Republic (historical prices of the year 1997 in mil CZK and current prices in mil EUR). Total loss* Total loss** Institutional sector % [mil CZK] [mil EUR] households 7 813 524 13% private 33 055 2 217 55% public 19 232 1 290 32% TOTAL 60 100 4 031 * historical prices of the year 1997 in mil CZK ** prices of the end of the year 2007 in mil EUR Table 5.21: Institutional sector split of the losses159 during the July 1997 flood in the Czech Republic (historical prices of the year 1997 in mil CZK and current prices in mil EUR). Total loss* Total loss** Category code % [mil CZK] [mil EUR] AN.1111, Buildings and structures 39 200 2 629 65% AN.1112 Other 20 900 1 402 35% TOTAL 60 100 4 031 * historical prices of the year 1997 in mil CZK ** prices of the end of the year 2007 in mil EUR Other households 35% public 13% 32% private 55% Buildings and structures 65% Figure 5.16: Structure of the July 1997 losses159 Figure 5.17: Institutional sector split of the July by asset categories 1997 losses159 159 Data source: [25]; excluding the ecological losses (see also Paragraph 3.2.7) 5. Appendix Table 5.22: Spatial distribution of the July 1997 losses159 (only losses reported by the district governments; historical prices of the year 1997 in mil CZK). Province District Loss % Ostrava 4 354 17.1% Bruntál 3 276 12.9% Nový JiÄ?ín 1 382 5.4% Moravskoslezský Opava 1 178 4.6% Frýdek-Místek 1 137 4.5% Karviná 862 3.4% Moravskoslezský 12 189 47.8% Olomouc 2 484 9.7% Jeseník 1 983 7.8% Olomoucký PÅ™erov 977 3.8% Å umperk 726 2.8% Olomoucký 6 170 24.2% Vsetín 1 700 6.7% Zlín 1 397 5.5% Zlínský Uherské HradiÅ¡tÄ› 1 036 4.1% Kroměříž 769 3.0% Zlínský 4 902 19.2% Ústí nad Orlicí 464 1.8% Pardubický Svitavy 236 0.9% Pardubice 131 0.5% Pardubický 831 3.3% Jihomoravský Hodonín 406 1.6% Jihomoravský 406 1.6% Královehradecký Trutnov 276 1.1% Královehradecký 276 1.1% VysoÄ?ina ŽÄ?ár nad Sázavou 175 0.7% VysoÄ?ina 175 0.7% other districts 541 2.1% Total 25 490 100% Figure 5.18: Spatial distribution of the July 1997 losses159 (only losses reported by the district governments) 5. Appendix b) Czech Republic, August 2002 Flood Table 5.23: Structure of the losses160 during the August 2002 flood in the Czech Republic (mil CZK, historical prices of the year 2002). Province (NUTS-3) Category code Jiho- StÅ™edo- Jiho- Praha Ä?eský Ä?eský Ústecký Plzeňský moravský TOTAL % Dwellings AN.1111 4 831 1 361 2 875 2 507 239 11 920 22% Other buildings and AN.1112 5 624 6 883 3 693 5 704 2 556 24 513 46% structures Machinery and equipment AN.1113 2 794 1 074 1 422 1 053 94 6 481 12% Inventories AN.12 2 816 1 235 756 609 52 5 477 10% Household equipment - 586 441 1 153 297 62 2 744 5% Others - 1 529 251 464 191 185 2 675 5% TOTAL 18 179 11 245 10 362 10 361 3 188 476 53 811 % 34% 21% 19% 19% 6% 1% Table 5.24: Structure of the losses160 of the August 2002 flood in the Czech Republic (mil EUR, prices of the end of the year 2007 in mil EUR). Province (NUTS-3) Category code Jiho- StÅ™edo- Jiho- Praha Ä?eský Ä?eský Ústecký Plzeňský moravský TOTAL % Dwellings AN.1111 231 65 137 120 11 570 22% Other buildings and AN.1112 269 329 177 273 122 1 172 46% structures Machinery and equipment AN.1113 134 51 68 50 4 310 12% Inventories AN.12 135 59 36 29 2 262 10% Household equipment - 28 21 55 14 3 131 5% Others - 73 12 22 9 9 128 5% TOTAL 869 538 495 495 152 23 2 573 % 34% 21% 19% 19% 6% 1% Figure 5.19: Spatial distribution of the August 2002 losses160 – province loss as percent of the total national loss 160 Data source: [13], aggregated form, excluding the ecological losses, losses on harvest and losses in Prague subway (see also Paragraph 3.2.7); 5. Appendix Household Others equipment 5% Dwellings 5% 22% Inventories 10% Machinery Other and buildings equipment and 12% structures 46% Figure 5.20: Structure of the August 2002 loss160 by asset categories c) Poland, August 1997 Flood Table 5.25: Classification of the Poland 1997 flood losses161 % of losses Total loss* Total loss** % of total Sector / category on of fixed [mil PLN] [mil EUR] losses asset private 2 129 953 18% 22% public 4 896 2 190 40% 51% household 1 389 621 11% 15% agriculture 900 403 7% 9% technical infrastructure 199 89 2% 2% of which buildings and structures 8 435 3 774 70% 89% Fixed asset 9 513 4 256 79% 100% non-fixed asset and other losses 2 587 1 157 21% TOTAL 12 100 5 414 100% * historical prices of the year 1997 in mil PLN ** prices of the end of the year 2007 in mil EUR 161 Data source: Central Statistical Office of Poland, unpublished data 5. Appendix Figure 5.21: Area affected by the Poland 1997 flood. Source of the map: Centrum Edukacji Hydrologiczno Meteorologicznej IMGW, see [50] 5. Appendix 5.1.7 Gauging stations used for the stochastic method Figure 5.22: Map of 145 gauging stations in the Czech Republic used for the stochastic method of LEC calculation 5.1.8 Flood Hazard Zones Table 5.26: Flood Hazard Zones Length Country Czech Rep Slovakia Hungary Poland Length [km] 17,000 10,400 20,000 36,000 a) Czech Republic Flood hazard zones for approximately 17,000 river kilometers were modeled. The return periods include 20, 50, 100, 250, and 500 years. Figure 5.23: Flood hazard zones of the Czech Republic, 50 years return period. 5. Appendix b) Slovakia Flood hazard zones for approximately 10,000 river kilometers were modeled. The return periods include 20, 50, 100, 250, and 500 years. Figure 5.24: Flood hazard zones of Slovakia, 50 years return period. c) Hungary Flood hazard zones for approximately 20,000 river kilometers were modeled. The return periods include 50, 100, 250, and 500 years. Figure 5.25: Flood hazard zones of Hungary, 50 years return period. 5. Appendix d) Poland Flood hazard zones for approximately 36,000 river kilometers were modeled. The return periods include 50, 100, 250, and 500 years. Figure 5.26: Flood hazard zones of Poland, 50 years return period. e) Property Distribution into the Flood Hazard Zones Table 5.27: Property value distribution into the flood hazard zones Outside flood Country Sector 20 50 100 250 500 prone area Czech Total 0.8% 5.7% 7.0% 3.7% 2.3% 80.6% Republic Public 0.6% 5.0% 6.5% 3.4% 2.2% 82.3% Total 2.5% 11.3% 14.7% 20.3% 4.9% 46.3% Slovakia Public 2.1% 11.1% 14.5% 18.3% 4.9% 49.2% Total 0.9% 8.9% 11.9% 8.9% 2.5% 67.0% Hungary Public 0.9% 8.5% 11.5% 8.5% 2.4% 68.1% Total 1.4% 5.6% 12.5% 6.3% 3.5% 70.7% Poland Public 1.3% 5.4% 12.4% 6.2% 3.5% 71.2% Total 1.3% 6.8% 11.4% 7.7% 3.2% 69.6% V–4 Public 1.0% 8.1% 11.1% 8.5% 2.7% 68.7% 5. Appendix Property proportion within country [%] 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Czech Republic Slovakia Hungary Poland V-4 20 50 100 250 500 Outside flood prone area Figure 5.27: Total property value distribution into the flood hazard zones Property proportion within country [%] 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Czech Republic Slovakia Hungary Poland V-4 20 50 100 250 500 Outside flood prone area Figure 5.28: Public property value distribution into the flood hazard zones 5. Appendix 5.1.9 Flood Scenarios Table 5.28: Flood Scenarios counts used in the study Czech Rep. Slovakia Hungary Poland V–4 Total Catchment- 7 5 7 12 31 based Real Flood 2 1 3 Event based Pan-regional 2 2 2 2 8 Cross-border 3 3 Total 11 7 9 15 3 45 Table 5.29: Flood Scenarios used in the study Czech Rep. Slovakia Hungary Poland Labe / Elbe Dunaj / Danube Danube / Duna Vistula Upper Berounka Vah + Nitra Drava Vistula Middle Morava river Hron + Ipel Hernad + Sajo Vistula Lower Dyje Hornad + Slana Tizsa + Bordog Bug Odra / Oder (upper) Tisza + Bodrog Raba San Catchment- Ohre Sio Odra Upper based Vltava / Moldau Koros Odra Lower Warta Notec Nysa+Bobr Baltic West Baltic East Real Flood 1997 Flood - - 1997 Flood Event based 2002 Flood Bohemia Dunaj / Danube Large Duna / Danube Large Odra / Oder Large Pan-regional Moravia Tisza Large Tisza Large Visla / Vistula Large Odra / Oder Dunaj / Danube Duna / Danube Odra / Oder Cross-border Tisza Tisza 5. Appendix Figure 5.29: Catchment-based flood scenarios of the V–4 countries. 5. Appendix a) Czech Republic i. Catchment-based Scenarios Based on the catchments, 7 regional flood scenarios were constructed for the Czech Republic162. Figure 5.30: Catchment-based scenarios of the Czech Republic. ii. Recent Flood Events Based on the recent flood events, 2 regional flood scenarios were constructed for the Czech Republic: the one for the 1997 flood in Moravia and the other for the 2002 flood in Bohemia. Figure 5.31: Scenario of the 1997 flood in the Czech Republic. The intervals correspond to the percentage of property in the districts that were affected by flood. 162 Unlike on the map, the Czech part of the Nysa catchment was not considered in national calculations for the Czech Republic. Neither was considered the Czech part of river Vah in national calculations. 5. Appendix Figure 5.32: Scenario of the 2002 flood in the Czech Republic. The intervals correspond to the percentage of property in the districts that were affected by flood. iii. Pan-regional Scenarios Two pan-regional flood scenarios were constructed for the Czech Republic. They are based on the territories of the two historical countries – Bohemia and Moravia. Their boundaries basically follow, with minor differences, the boundaries of the Czech catchment-based scenarios, the former including four and the later three scenarios. Figure 5.33: Pan-regional scenarios constructed for the Czech Republic. 5. Appendix b) Slovakia i. Catchment-based Scenarios Based on the catchments, 5 regional flood scenarios were constructed for Slovakia163. Figure 5.34: Catchment-based scenarios of Slovakia. ii. Pan-regional Scenarios Two pan-regional flood scenarios were constructed for Slovakia. They are based on the catchments of the tributaries of the two major rivers – Danube and Tisza. Their boundaries follow the boundaries of the Slovak catchment-based scenarios, the former including three and the later two scenarios. Figure 5.35: Pan-regional scenarios constructed for Slovakia. 163 Unlike on the map, the Slovak part of the Upper Vistula (Dunajec) catchment was not considered in national calculations for Slovakia. Neither was considered the Slovak part of river Raba in national calculations. 5. Appendix c) Hungary i. Catchment-based Scenarios Based on the catchments, 7 regional flood scenarios were constructed for Hungary164. Figure 5.36: Catchment-based scenarios of Hungary. ii. Pan-regional Scenarios Two pan-regional flood scenarios were constructed for Hungary. They are based on the catchments of the tributaries of the two major rivers – Danube and Tisza. Their boundaries follow the boundaries of the Hungarian catchment-based scenarios, the former including four and the later three scenarios. Figure 5.37: Pan-regional scenarios constructed for Hungary. 164 Unlike on the map, the Hungarian part of the Ipoly catchment was not considered in national calculations for Hungary. 5. Appendix d) Poland i. Catchment-based Scenarios Based on the catchments, 12 regional flood scenarios were constructed for Poland165. Figure 5.38: Catchment-based scenarios of Poland. ii. Recent Flood Events Based on the recent flood events, 1 regional flood scenario was constructed for Poland: the one for the 1997 flood on upper and central reaches of Vistula, Oder, and Warta rivers. Figure 5.39: Scenario of the 1997 flood in Poland (for the primary data source of the area affected during the 1997 flood delimitation, see Appendix 5.1.6 c and [50]) 165 Unlike on the map, the Polish part of the Elbe/Labe catchment was not considered in national calculations for Poland. 5. Appendix iii. Pan-regional Scenarios Two pan-regional flood scenarios were constructed for Poland. They are based on the catchments of the tributaries of the two major rivers – Vistula and Oder. Their boundaries follow the boundaries of the Polish catchment-based scenarios, the former and the later including five scenarios each. Figure 5.40: Pan-regional scenarios constructed for Poland. e) Cross-border Scenarios Three cross-border scenarios were constructed, including tributaries and catchments of three out of the four major rivers of the V–4 countries: - Oder o including both Polish and Czech parts of the Upper Oder as well as the Lower Oder catchments o excluding the catchments of Warta, Notec, Bobr, and both Polish and Czech parts of the Nysa catchment - Danube o including both Slovak and Hungarian parts of the Danube catchment o excluding166 the catchments of Raba, Sio, Drava, and Ipoly in Hungary; Vah, Nitra, Hron, and Ipel in Slovakia; and Morava167, Dyje, and Vah in the Czech Republic - Tisza o including both Slovak and Hungarian parts of the Tisza, Bodrog, Hernad/Hornad, and Sajo/Slana catchments as well as the Koros/Crisul catchment in Hungary 166 The main tributaries of Danube were excluded as they have a different snow-rain regime in comparison to the glacier regime of Danube, which results in different seasons of the maximum discharge. In addition, the tributaries of the river Danube have much smaller discharges than Danube itself. 167 For simplicity, Slovak part of the Morava catchment was included. In comparison with the Slovak section of the river Danube, the river Morava includes much smaller properties at risk. 5. Appendix Figure 5.41: Cross-border scenarios constructed for the V–4 countries. 5. Appendix 5.1.10 Transportation Network Classes and Relative Value Table 5.30: Transportation network classes168 and relative value of 1 km of the network category used for property distribution of the “networkâ€? property type169 into the LAU-1 units170 and flood hazard zones171 Category Relative value Road network Motorways, speedways 100 European and 1st class roads 50 Other roads 30 Railway network Main lines 60 Other lines 25 5.1.11 CORINE Land Cover Classes Table 5.31: CORINE Land Cover Classes Code Level 1 Level 2 Level 3 1 Artificial surfaces 11 Urban fabric 111 Continuous urban fabric 112 Discontinuous urban fabric 12 Industrial, commercial and transport units 121 Industrial or commercial units 122 Road and rail networks and associated land 123 Port areas 124 Airports 13 Mine, dump and construction sites 131 Mineral extraction sites 132 Dump sites 133 Construction sites 14 Artificial, non-agricultural vegetated areas 141 Green urban areas 142 Sport and leisure facilities 2 Agricultural areas 21 Arable land 211 Non-irrigated arable land 212 Permanently irrigated land 213 Rice fields 22 Permanent crops 221 Vineyards 222 Fruit trees and berry plantations 223 Olive groves 23 Pastures 231 Pastures 24 Heterogeneous agricultural areas 241 Annual crops associated with permanent crops 242 Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural 243 vegetation 244 Agro-forestry areas 3 Forest and semi natural areas 168 For the transportation network data sources see Paragraph 3.4.2 169 For the definition of the network property type see Paragraph 3.2.2 170 See 3.4.2 171 See 3.5 5. Appendix 31 Forests 311 Broad-leaved forest 312 Coniferous forest 313 Mixed forest 32 Scrub and/or herbaceous vegetation associations 321 Natural grasslands 322 Moors and heathland 323 Sclerophyllous vegetation 324 Transitional woodland-shrub 33 Open spaces with little or no vegetation 331 Beaches, dunes, sands 332 Bare rocks 333 Sparsely vegetated areas 334 Burnt areas 335 Glaciers and perpetual snow 4 Wetlands 41 Inland wetlands 411 Inland marshes 412 Peat bogs 42 Maritime wetlands 421 Salt marshes 422 Salines 423 Intertidal flats 5 Water bodies 51 Inland waters 511 Water courses 512 Water bodies 52 Marine waters 521 Coastal lagoons 522 Estuaries 523 Sea and ocean 9 NODATA 99 NODATA 999 NODATA 9 UNCLASSIFIED 99 UNCLASSIFIED LAND SURFACE 990 UNCLASSIFIED LAND SURFACE 995 UNCLASSIFIED WATER BODIES 5. Appendix 5.1.12 Regional Split of Asset Values172 a) Czech Republic i. Industry Branch Figure 5.42: Regional property distribution to NUTS 3 units (provinces) and regional property split into the industry branches for the Czech Republic [mil EUR] Table 5.32: Regional split of asset into industrial branches for the Czech Republic [mil EUR] Province \ Branch AB CDF GHJ E I K L M N O TOTAL % Praha 33 7 789 19 558 3 119 18 964 51 122 15 385 4 799 2 374 6 780 129 923 22.9% StÅ™edoÄ?eský 1 208 16 296 4 485 3 086 9 143 22 156 4 601 2 287 1 230 1 612 66 105 11.7% JihoÄ?eský 1 072 5 315 2 147 5 359 5 304 7 747 3 105 1 635 657 854 33 195 5.9% Plzeňský 763 5 882 1 981 1 217 4 495 8 267 2 952 1 399 726 718 28 400 5.0% Karlovarský 161 2 961 1 330 992 2 156 3 438 990 670 536 460 13 695 2.4% Ústecký 333 9 762 1 970 4 407 5 120 7 520 4 029 1 840 875 1 401 37 258 6.6% Liberecký 185 4 109 1 103 378 2 809 5 717 1 238 1 067 440 912 17 957 3.2% Královéhradecký 585 5 135 1 918 2 534 3 419 8 319 2 966 1 406 720 857 27 859 4.9% Pardubický 629 4 701 1 704 1 466 3 004 7 575 1 699 1 305 484 597 23 164 4.1% VysoÄ?ina 1 125 5 411 1 166 1 833 3 946 6 662 1 453 1 170 484 603 23 854 4.2% Jihomoravský 1 109 9 033 4 515 3 861 7 981 21 518 5 648 3 659 1 387 1 982 60 692 10.7% Olomoucký 734 5 362 1 657 887 3 660 7 712 2 946 1 705 766 861 26 290 4.6% Moravskoslezský 507 14 654 3 619 4 665 5 948 12 383 5 020 3 373 1 479 1 923 53 571 9.4% Zlínský 423 6 245 1 856 1 161 2 545 8 682 1 386 1 498 620 908 25 324 4.5% TOTAL 8 868 102 656 49 008 34 966 78 493 178 818 53 419 27 812 12 778 20 469 567 287 % 1.6% 18.1% 8.6% 6.2% 13.8% 31.5% 9.4% 4.9% 2.3% 3.6% 172 For graphical output of regional split of asset into industrial branches see the Paragraph 4.1.1 5. Appendix ii. Asset Category Figure 5.43: Regional split into asset categories for the Czech Republic [mil EUR] Table 5.33: Regional split into asset categories for the Czech Republic [mil EUR] Household Province \ Category Building Equipment Inventories TOTAL % eqiup. Praha 102 053 12 997 7 283 7 590 129 923 22.9% StÅ™edoÄ?eský 47 329 10 641 4 845 3 289 66 105 11.7% JihoÄ?eský 25 102 5 013 1 930 1 150 33 195 5.9% Plzeňský 20 926 4 332 1 915 1 227 28 400 5.0% Karlovarský 10 281 2 103 801 510 13 695 2.4% Ústecký 27 279 6 303 2 560 1 117 37 258 6.6% Liberecký 13 202 2 729 1 178 849 17 957 3.2% Královéhradecký 20 821 4 045 1 758 1 235 27 859 4.9% Pardubický 17 025 3 486 1 529 1 125 23 164 4.1% VysoÄ?ina 17 372 3 971 1 522 989 23 854 4.2% Jihomoravský 46 155 7 850 3 492 3 195 60 692 10.7% Olomoucký 19 479 3 912 1 753 1 145 26 290 4.6% Moravskoslezský 38 705 9 059 3 968 1 838 53 571 9.4% Zlínský 18 129 4 021 1 886 1 289 25 324 4.5% TOTAL 423 856 80 462 36 420 26 549 567 287 % 74.7% 14.2% 6.4% 4.7% 5. Appendix iii. Institutional Sector Figure 5.44: Regional split of asset into institutional sectors for the Czech Republic [mil EUR] Table 5.34: Regional split of asset into institutional sectors for the Czech Republic [mil EUR] Province \ Sector Public Private Household TOTAL % Praha 50 389 40 417 39 116 129 923 22.9% StÅ™edoÄ?eský 21 204 27 559 17 342 66 105 11.7% JihoÄ?eský 14 800 12 100 6 294 33 195 5.9% Plzeňský 10 955 10 836 6 610 28 400 5.0% Karlovarský 5 661 5 242 2 792 13 695 2.4% Ústecký 16 216 14 876 6 165 37 258 6.6% Liberecký 6 628 6 861 4 467 17 957 3.2% Královéhradecký 11 077 10 229 6 553 27 859 4.9% Pardubický 8 165 9 051 5 948 23 164 4.1% VysoÄ?ina 8 786 9 699 5 368 23 854 4.2% Jihomoravský 23 544 20 528 16 621 60 692 10.7% Olomoucký 10 454 9 695 6 140 26 290 4.6% Moravskoslezský 21 349 22 188 10 034 53 571 9.4% Zlínský 7 934 10 589 6 801 25 324 4.5% TOTAL 217 162 209 872 140 253 567 287 % 38.3% 37.0% 24.7% 5. Appendix b) Slovakia i. Industry Branch Figure 5.45: Regional split of asset into industrial branches for Slovakia [mil EUR] Table 5.35: Regional split of asset into industrial branches for Slovakia [mil EUR] Province \ Industry AB CDF GHJ E I K L M N O TOTAL % Bratislavský kraj 359 9 668 6 828 7 531 5 594 28 105 7 231 816 791 1 261 68 184 23.8% Trnavský kraj 1 155 6 552 1 652 5 947 2 768 7 233 2 616 413 454 290 29 079 10.2% TrenÄ?iansky kraj 604 7 751 1 822 4 534 3 111 9 487 2 482 449 423 354 31 018 10.8% Nitriansky kraj 1 412 4 923 2 207 6 519 3 257 7 348 3 452 580 428 365 30 493 10.6% Žilinský kraj 584 7 021 2 200 5 031 3 464 8 893 3 234 591 557 394 31 971 11.2% Banskobystrický kraj 911 4 989 1 927 4 046 3 697 7 166 3 799 615 592 371 28 113 9.8% PreÅ¡ovský kraj 705 4 470 1 926 3 099 3 154 8 360 3 499 660 574 422 26 868 9.4% KoÅ¡ický kraj 852 6 567 2 477 8 325 4 930 11 768 3 893 751 564 525 40 652 14.2% TOTAL 6 583 51 942 21 039 45 033 29 975 88 361 30 207 4 875 4 381 3 983 286 377 % 2.3% 18.1% 7.3% 15.7% 10.5% 30.9% 10.5% 1.7% 1.5% 1.4% 5. Appendix ii. Asset Category Figure 5.46: Regional split into asset categories for Slovakia [mil EUR] Table 5.36: Regional split into asset categories for Slovakia [mil EUR] Household Province \ Category Building Equipment Inventories eqiup. TOTAL % Bratislavský kraj 51 024 9 166 4 367 3 627 68 184 23.8% Trnavský kraj 21 164 5 085 1 896 933 29 079 10.2% TrenÄ?iansky kraj 22 491 5 196 2 106 1 224 31 018 10.8% Nitriansky kraj 22 528 5 111 1 905 948 30 493 10.6% Žilinský kraj 23 113 5 491 2 220 1 148 31 971 11.2% Banskobystrický kraj 20 784 4 639 1 765 925 28 113 9.8% PreÅ¡ovský kraj 20 027 4 117 1 645 1 079 26 868 9.4% KoÅ¡ický kraj 30 307 6 490 2 337 1 519 40 652 14.2% TOTAL 211 437 45 295 18 242 11 403 286 377 % 73.8% 15.8% 6.4% 4.0% 5. Appendix iii. Institutional Sector Figure 5.47: Regional split of asset into institutional sectors for Slovakia [mil EUR] Table 5.37: Regional split of asset into institutional sectors for Slovakia [mil EUR] Province \ Sector Public Private Household TOTAL % Bratislavský kraj 22 417 24 733 21 034 68 184 23.8% Trnavský kraj 11 047 12 279 5 754 29 079 10.2% TrenÄ?iansky kraj 10 604 13 100 7 314 31 018 10.8% Nitriansky kraj 12 697 11 893 5 903 30 493 10.6% Žilinský kraj 11 904 13 107 6 960 31 971 11.2% Banskobystrický kraj 11 891 10 551 5 672 28 113 9.8% PreÅ¡ovský kraj 10 639 9 773 6 456 26 868 9.4% KoÅ¡ický kraj 16 613 15 001 9 039 40 652 14.2% TOTAL 107 811 110 436 68 131 286 377 % 37.6% 38.6% 23.8% 5. Appendix c) Hungary i. Industry Branch Figure 5.48: Regional split of asset into industrial branches for Hungary [mil EUR] Table 5.38: Regional split of asset into industrial branches for Hungary [mil EUR] Province \ Industry AB CDF GHJ E I K L M N O TOTAL % Budapest 1 245 16 070 12 574 5 437 13 332 64 928 34 649 3 119 1 838 4 940 158 132 40.2% Baranya 1 317 1 247 705 1 278 705 3 139 2 341 442 255 328 11 757 3.0% Bács-Kiskun 1 872 1 882 843 591 776 3 385 2 638 503 285 401 13 175 3.3% Békés 1 143 1 149 515 361 474 2 066 1 610 307 174 245 8 042 2.0% Borsod-Abaúj- Zemplén 951 3 494 917 1 764 934 3 810 3 437 654 366 462 16 789 4.3% Csongrád 1 777 1 786 800 561 736 3 212 2 504 477 270 380 12 504 3.2% Fejér 1 004 4 638 887 681 810 4 553 2 312 432 235 384 15 936 4.1% Gyor-Moson-Sopron 1 054 4 507 959 646 1 126 4 822 2 524 466 261 460 16 825 4.3% Hajdú-Bihar 1 506 2 417 900 527 874 3 642 2 918 616 317 411 14 127 3.6% Heves 483 1 775 466 896 475 1 936 1 746 332 186 235 8 530 2.2% Komárom-Esztergom 604 2 790 533 410 487 2 739 1 391 260 141 231 9 587 2.4% Nógrád 246 903 237 456 241 984 888 169 94 119 4 338 1.1% Pest 313 4 043 3 163 1 368 3 354 16 334 8 717 785 462 1 243 39 782 10.1% Somogy 808 765 433 784 433 1 926 1 437 271 157 201 7 215 1.8% Szabolcs-Szatmár- Bereg 1 207 1 937 721 423 701 2 919 2 339 493 254 329 11 322 2.9% Jász-Nagykun- Szolnok 1 068 1 715 638 374 620 2 584 2 070 437 225 292 10 024 2.5% Tolna 831 787 445 806 445 1 980 1 477 279 161 207 7 416 1.9% Vas 621 2 653 565 380 663 2 838 1 485 274 153 271 9 903 2.5% Veszprém 596 2 753 526 404 481 2 702 1 372 257 139 228 9 458 2.4% Zala 537 2 294 488 329 573 2 454 1 285 237 133 234 8 565 2.2% TOTAL 19 182 59 605 27 314 18 476 28 241 132 953 79 139 10 811 6 105 11 603 393 429 % 4.9% 15.2% 6.9% 4.7% 7.2% 33.8% 20.1% 2.7% 1.6% 2.9% 5. Appendix ii. Asset Category Figure 5.49: Regional split into asset categories for Hungary [mil EUR] Table 5.39: Regional split into asset categories for Hungary [mil EUR] Household Province \ Category Building Equipment Inventories TOTAL % eqiup. Budapest 119 680 20 029 10 363 8 059 158 132 40.2% Baranya 8 875 1 834 658 390 11 757 3.0% Bács-Kiskun 9 771 2 157 827 420 13 175 3.3% Békés 5 964 1 317 505 256 8 042 2.0% Borsod-Abaúj-Zemplén 12 062 3 089 1 165 473 16 789 4.3% Csongrád 9 273 2 047 785 399 12 504 3.2% Fejér 10 894 3 206 1 271 565 15 936 4.1% Gyor-Moson-Sopron 11 633 3 311 1 282 598 16 825 4.3% Hajdú-Bihar 10 361 2 367 948 452 14 127 3.6% Heves 6 129 1 569 592 240 8 530 2.2% Komárom-Esztergom 6 554 1 929 765 340 9 587 2.4% Nógrád 3 117 798 301 122 4 338 1.1% Pest 30 109 5 039 2 607 2 027 39 782 10.1% Somogy 5 446 1 125 404 239 7 215 1.8% Szabolcs-Szatmár-Bereg 8 304 1 897 759 362 11 322 2.9% Jász-Nagykun-Szolnok 7 352 1 679 672 321 10 024 2.5% Tolna 5 599 1 157 415 246 7 416 1.9% Vas 6 847 1 949 755 352 9 903 2.5% Veszprém 6 465 1 903 754 335 9 458 2.4% Zala 5 922 1 686 653 305 8 565 2.2% TOTAL 290 358 60 088 26 480 16 503 393 429 % 73.8% 15.3% 6.7% 4.2% 5. Appendix iii. Institutional Sector Figure 5.50: Regional split of asset into institutional sectors for Hungary [mil EUR] Table 5.40: Regional split of asset into institutional sectors for Hungary [mil EUR] Province Public Private Household TOTAL % Budapest 45 957 53 838 58 337 158 132 40.2% Baranya 3 377 4 931 3 449 11 757 3.0% Bács-Kiskun 3 806 5 416 3 953 13 175 3.3% Békés 2 323 3 306 2 413 8 042 2.0% Borsod-Abaúj-Zemplén 4 872 7 915 4 002 16 789 4.3% Csongrád 3 612 5 140 3 752 12 504 3.2% Fejér 3 427 7 931 4 578 15 936 4.1% Gyor-Moson-Sopron 3 761 8 191 4 874 16 825 4.3% Hajdú-Bihar 4 244 5 843 4 041 14 127 3.6% Heves 2 475 4 021 2 034 8 530 2.2% Komárom-Esztergom 2 062 4 771 2 754 9 587 2.4% Nógrád 1 259 2 045 1 034 4 338 1.1% Pest 11 562 13 545 14 676 39 782 10.1% Somogy 2 072 3 026 2 117 7 215 1.8% Szabolcs-Szatmár-Bereg 3 401 4 683 3 238 11 322 2.9% Jász-Nagykun-Szolnok 3 011 4 146 2 867 10 024 2.5% Tolna 2 130 3 111 2 176 7 416 1.9% Vas 2 213 4 821 2 869 9 903 2.5% Veszprém 2 034 4 707 2 717 9 458 2.4% Zala 1 914 4 169 2 481 8 565 2.2% TOTAL 109 511 155 556 128 362 393 429 % 27.8% 39.5% 32.6% 5. Appendix d) Poland i. Industry Branch Figure 5.51: Regional split of asset into industrial branches for Poland [mil EUR] Table 5.41: Regional split of asset into industrial branches for Poland [mil EUR] Province \ Industry AB CDF GHJ E I K L M N O TOTAL % DolnoÅ›lÄ…skie 3 300 21 513 8 645 13 080 5 585 29 851 9 473 3 102 1 992 2 325 98 866 7.9% Kujawsko-pomorskie 1 822 10 057 4 104 5 815 3 612 17 724 3 108 1 756 1 166 1 844 51 009 4.1% Lubelskie 1 832 7 735 3 562 4 396 4 491 22 975 4 507 2 010 1 061 1 429 53 999 4.3% Lubuskie 980 6 075 2 210 3 472 2 439 9 487 2 865 782 488 1 066 29 865 2.4% Å?ódzkie 2 575 16 976 5 096 9 995 4 142 27 241 4 725 2 451 1 238 2 071 76 510 6.1% MaÅ‚opolskie 3 159 15 474 12 275 8 701 4 533 35 696 8 423 3 837 1 846 2 312 96 258 7.7% Mazowieckie 9 377 39 355 34 636 22 221 26 657 93 981 19 917 6 429 3 004 7 204 262 780 21.1% Opolskie 1 005 8 461 1 983 4 975 2 518 9 257 2 639 811 479 902 33 028 2.6% Podkarpackie 1 787 9 630 3 211 6 102 3 780 19 131 3 905 1 840 1 196 1 623 52 206 4.2% Podlaskie 1 154 3 877 2 123 2 422 3 189 12 461 3 150 1 201 652 810 31 039 2.5% Pomorskie 2 358 10 076 5 869 6 147 5 666 26 724 6 309 2 194 1 054 2 235 68 633 5.5% ÅšlÄ…skie 5 160 42 653 10 786 24 474 6 720 51 247 10 776 3 879 2 835 4 039 162 571 13.0% ÅšwiÄ™tokrzyskie 1 239 7 350 2 412 4 258 2 593 10 206 3 266 1 050 824 603 33 801 2.7% WarmiÅ„sko-mazurskie 1 243 5 028 2 634 2 927 3 603 13 333 3 116 1 170 684 1 040 34 778 2.8% Wielkopolskie 4 039 23 392 10 122 12 181 7 191 36 151 7 230 3 702 1 592 3 566 109 167 8.7% Zachodniopomorskie 1 567 7 760 4 346 4 832 4 477 18 089 8 287 1 600 1 037 1 673 53 668 4.3% TOTAL 42 596 235 413 114 016 135 999 91 197 433 555 101 698 37 814 21 150 34 742 1 248 180 % 3.4% 18.9% 9.1% 10.9% 7.3% 34.7% 8.1% 3.0% 1.7% 2.8% 5. Appendix ii. Asset Category Figure 5.52: Regional split into asset categories for Poland [mil EUR] Table 5.42: Regional split into asset categories for Poland [mil EUR] Household Province \ Category Building Equipment Inventories eqiup. TOTAL % DolnoÅ›lÄ…skie 69 656 18 416 7 066 3 728 98 866 7.9% Kujawsko-pomorskie 35 770 9 175 3 459 2 605 51 009 4.1% Lubelskie 39 433 8 313 2 963 3 290 53 999 4.3% Lubuskie 20 957 5 505 2 022 1 381 29 865 2.4% Å?ódzkie 53 638 13 832 5 166 3 874 76 510 6.1% MaÅ‚opolskie 68 836 15 202 7 016 5 204 96 258 7.7% Mazowieckie 185 724 46 452 18 044 12 560 262 780 21.1% Opolskie 22 577 6 752 2 397 1 302 33 028 2.6% Podkarpackie 37 125 9 008 3 156 2 917 52 206 4.2% Podlaskie 22 632 4 832 1 667 1 908 31 039 2.5% Pomorskie 49 901 10 982 4 073 3 677 68 633 5.5% ÅšlÄ…skie 111 972 31 508 11 878 7 213 162 571 13.0% ÅšwiÄ™tokrzyskie 23 442 6 392 2 385 1 583 33 801 2.7% WarmiÅ„sko-mazurskie 25 214 5 732 1 937 1 895 34 778 2.8% Wielkopolskie 76 067 19 783 8 186 5 131 109 167 8.7% Zachodniopomorskie 39 499 8 576 3 212 2 381 53 668 4.3% TOTAL 882 445 220 459 84 628 60 648 1 248 180 % 70.7% 17.7% 6.8% 4.9% 5. Appendix iii. Institutional Sector Figure 5.53: Regional split of asset into institutional sectors for Poland [mil EUR] Table 5.43: Regional split of asset into institutional sectors for Poland [mil EUR] Province Public Private Household TOTAL % DolnoÅ›lÄ…skie 38 395 39 042 21 429 98 866 7.9% Kujawsko-pomorskie 17 739 18 794 14 476 51 009 4.1% Lubelskie 22 128 14 160 17 711 53 999 4.3% Lubuskie 11 750 10 434 7 682 29 865 2.4% Å?ódzkie 28 942 26 225 21 343 76 510 6.1% MaÅ‚opolskie 32 766 34 488 29 004 96 258 7.7% Mazowieckie 91 187 100 052 71 541 262 780 21.1% Opolskie 16 434 9 362 7 231 33 028 2.6% Podkarpackie 18 771 17 547 15 888 52 206 4.2% Podlaskie 11 454 9 269 10 317 31 039 2.5% Pomorskie 24 143 24 128 20 363 68 633 5.5% ÅšlÄ…skie 61 908 60 328 40 335 162 571 13.0% ÅšwiÄ™tokrzyskie 11 199 13 716 8 886 33 801 2.7% WarmiÅ„sko-mazurskie 13 106 11 297 10 374 34 778 2.8% Wielkopolskie 35 346 44 459 29 362 109 167 8.7% Zachodniopomorskie 26 656 13 870 13 142 53 668 4.3% TOTAL 461 925 447 172 339 084 1 248 180 % 37.0% 35.8% 27.2% 5. Appendix 5.1.13 Population, Income and Weights of the Districts a) Czech Republic Figure 5.54: Spatial distribution of the population – percent share of the districts from the national total (Czech Republic) Figure 5.55: Spatial distribution of the income – percent ratio of the districts to the national average (Czech Republic) 5. Appendix Figure 5.56: Weights of the districts within their respective province for the residential and industrial categories [%] (Czech Republic) Figure 5.57: Weights of the districts within their respective province for the network category [%] (Czech Republic) 5. Appendix Figure 5.58: Spatial distribution of the property – percent share of the districts from the national total [%] (Czech Republic) Table 5.44: Property value, salary, population, weights within province and proportion on the total property of each district (Czech Republic) Total Salary Salary Province Country Province District Populat. property [CZK] index weight weight Praha Praha 129,923 20,800 145.23% 1,212,097 100.00% 22.90% Hlavní mÄ›sto Praha 129,923 20,800 145.23% 1,212,097 100.00% 22.90% BeneÅ¡ov 4,042 13,080 91.33% 92,631 7.03% 0.71% Beroun 4,195 14,082 98.32% 81,307 6.64% 0.74% Kladno 6,415 14,102 98.46% 155,314 12.71% 1.13% Kolín 5,222 13,476 94.09% 93,042 7.27% 0.92% Kutná Hora 3,201 12,454 86.96% 74,585 5.39% 0.56% MÄ›lník 6,538 14,946 104.36% 97,696 8.47% 1.15% StÅ™edoÄ?eský Mladá Boleslav 14,541 16,799 117.30% 120,779 11.77% 2.56% Nymburk 3,942 12,962 90.50% 88,856 6.68% 0.69% Praha-východ 5,910 16,677 116.44% 127,041 12.29% 1.04% Praha-západ 4,844 15,622 109.08% 106,048 9.61% 0.85% Příbram 4,116 12,640 88.26% 110,893 8.13% 0.73% Rakovník 3,137 12,836 89.62% 53,635 3.99% 0.55% StÅ™edoÄ?eský 66,105 14,341 100.13% 1,201,827 100.00% 11.65% ÄŒeské BudÄ›jovice 16,470 15,178 105.98% 184,256 32.86% 2.90% ÄŒeský Krumlov 2,566 13,035 91.01% 61,261 9.38% 0.45% JindÅ™ichův Hradec 3,154 12,124 84.65% 92,693 13.21% 0.56% JihoÄ?eský Písek 2,802 12,526 87.46% 70,310 10.35% 0.49% Prachatice 1,611 12,136 84.74% 51,470 7.34% 0.28% Strakonice 2,878 13,201 92.17% 70,687 10.96% 0.51% Tábor 3,714 13,188 92.08% 102,587 15.90% 0.65% JihoÄ?eský 33,195 13,439 93.83% 633,264 100.00% 5.85% 5. Appendix Domažlice 2,130 13,018 90.90% 59,731 10.07% 0.38% Klatovy 3,644 12,548 87.61% 88,345 14.36% 0.64% Plzeň-mÄ›sto 12,341 15,521 108.37% 180,799 36.35% 2.18% Plzeňský Plzeň-jih 2,554 13,181 92.03% 59,651 10.18% 0.45% Plzeň-sever 2,827 12,812 89.46% 73,061 12.13% 0.50% Rokycany 1,748 13,596 94.93% 46,762 8.24% 0.31% Tachov 3,157 12,699 88.67% 52,725 8.67% 0.56% Plzeňský 28,400 13,759 96.07% 561,074 100.00% 5.01% Cheb 3,260 12,740 88.95% 95,203 30.26% 0.57% Karlovarský Karlovy Vary 6,447 12,986 90.67% 119,165 38.60% 1.14% Sokolov 3,989 13,411 93.64% 93,081 31.14% 0.70% Karlovarský 13,695 13,038 91.04% 307,449 100.00% 2.41% DÄ›Ä?ín 5,027 12,667 88.44% 135,441 15.30% 0.89% Chomutov 6,151 13,409 93.63% 125,743 15.04% 1.08% Litoměřice 6,290 13,103 91.49% 117,159 13.69% 1.11% Ústecký Louny 3,847 12,761 89.10% 86,710 9.87% 0.68% Most 5,942 14,852 103.70% 116,728 15.46% 1.05% Teplice 4,254 13,675 95.48% 129,202 15.76% 0.75% Ústí nad Labem 5,746 13,887 96.96% 120,197 14.89% 1.01% Ústecký 37,258 13,490 94.19% 831,180 100.00% 6.57% ÄŒeská Lípa 3,744 13,681 95.52% 103,254 24.18% 0.66% Jablonec nad Nisou 2,974 13,120 91.61% 89,450 20.09% 0.52% Liberecký Liberec 7,737 13,938 97.32% 166,547 39.74% 1.36% Semily 3,502 12,506 87.32% 74,697 15.99% 0.62% Liberecký 17,957 13,462 93.99% 433,948 100.00% 3.17% Hradec Králové 10,073 14,015 97.86% 161,349 31.14% 1.78% JiÄ?ín 4,367 13,004 90.80% 78,852 14.12% 0.77% Královéhradecký Náchod 4,403 12,115 84.59% 112,507 18.77% 0.78% Rychnov nad 3,253 12,847 89.70% 79,042 13.98% 0.57% Kněžnou Trutnov 5,763 13,252 92.53% 120,462 21.98% 1.02% Královéhradecký 27,859 13,150 91.81% 552,212 100.00% 4.91% Chrudim 3,734 12,169 84.97% 103,860 19.26% 0.66% Pardubice 9,826 13,800 96.36% 163,926 34.47% 1.73% Pardubický Svitavy 4,088 12,264 85.63% 104,756 19.58% 0.72% Ústí nad Orlicí 5,517 12,611 88.05% 138,858 26.69% 0.97% Pardubický 23,164 12,831 89.59% 511,400 100.00% 4.08% PelhÅ™imov 2,911 11,894 83.05% 72,958 13.21% 0.51% HavlíÄ?kův Brod 4,041 12,413 86.67% 95,618 18.07% 0.71% VysoÄ?ina Jihlava 7,760 14,164 98.90% 111,257 23.99% 1.37% TÅ™ebíÄ? 4,658 12,718 88.80% 114,153 22.10% 0.82% ŽÄ?ár nad Sázavou 4,485 12,426 86.76% 119,691 22.64% 0.79% VysoÄ?ina 23,854 12,789 89.30% 513,677 100.00% 4.20% Blansko 4,429 11,719 81.83% 105,663 8.14% 0.78% Brno-mÄ›sto 26,599 15,130 105.64% 368,533 36.64% 4.69% Brno-venkov 7,491 13,501 94.27% 195,644 17.36% 1.32% Jihomoravský BÅ™eclav 5,913 12,370 86.37% 113,171 9.20% 1.04% Hodonín 7,638 12,225 85.36% 157,176 12.63% 1.35% VyÅ¡kov 3,700 12,181 85.05% 87,519 7.01% 0.65% Znojmo 4,922 12,191 85.12% 112,828 9.04% 0.87% Jihomoravský 60,692 13,343 93.16% 1,140,534 100.00% 10.70% 5. Appendix ProstÄ›jov 3,713 12,238 85.45% 109,979 16.44% 0.65% Olomouc 10,702 13,483 94.14% 230,607 37.97% 1.89% Olomoucký PÅ™erov 5,739 12,972 90.57% 135,165 21.41% 1.01% Å umperk 4,659 12,147 84.81% 124,475 18.46% 0.82% Jeseník 1,477 11,276 78.73% 41,565 5.72% 0.26% Olomoucký 26,290 12,760 89.09% 641,791 100.00% 4.63% Bruntál 4,254 11,469 80.08% 98,148 6.46% 0.75% Frýdek-Místek 8,995 13,893 97.00% 210,369 16.77% 1.59% Moravskoslezský Karviná 8,395 14,605 101.98% 275,397 23.07% 1.48% Nový JiÄ?ín 6,183 13,383 93.44% 152,352 11.70% 1.09% Opava 6,051 12,549 87.62% 176,820 12.73% 1.07% Ostrava-mÄ›sto 19,694 15,150 105.78% 336,811 29.27% 3.47% Moravskoslezský 53,571 13,946 97.37% 1,249,897 100.00% 9.44% Zlín 9,647 14,221 99.29% 192,988 35.60% 1.70% Kroměříž 3,421 12,324 86.05% 107,789 17.23% 0.60% Zlínský Uherské HradiÅ¡tÄ› 5,348 12,022 83.94% 144,242 22.49% 0.94% Vsetín 6,908 13,056 91.16% 145,761 24.68% 1.22% Zlínský 25,324 13,051 91.12% 590,780 100.00% 4.46% CZECH REPUBLIC 567,287 14,322 100.00% 10,381,130 100.00% 5. Appendix b) Slovakia Figure 5.59: Spatial distribution of the population – percent share of the districts from the national total (Slovakia) Figure 5.60: Spatial distribution of the income – percent ratio of the districts to the national average (Slovakia) 5. Appendix Figure 5.61: Weights of the districts within their respective province for the residential and industrial categories [%] (Slovakia) Figure 5.62: Weights of the districts within their respective province for the network category [%] (Slovakia) 5. Appendix Figure 5.63: Spatial distribution of the property – percent share of the districts from the national total [%] (Slovakia) Table 5.45: Property value, salary, population, weights within province and proportion on the total property of each district (Slovakia) Total Salary Salary Province Country Province District Population property [SKK] index weight weight Malacky 4,588 21,380 114.12% 66,582 6.73% 1.60% Pezinok 3,197 17,657 94.25% 56,171 4.69% 1.12% Bratislavský Senec 3,328 18,251 97.42% 56,565 4.88% 1.16% Bratislava 57,071 29,270 156.23% 604,927 83.70% 19.93% Bratislavský 68,184 26,974 143.97% 784,245 100.00% 23.81% Trnava 8,151 22,760 121.48% 127,108 28.03% 2.85% Dunajská Streda 5,075 15,649 83.53% 115,088 17.45% 1.77% Galanta 4,394 16,411 87.60% 95,020 15.11% 1.53% Trnavský Hlohovec 2,524 19,764 105.49% 45,319 8.68% 0.88% PieÅ¡Å¥any 3,243 17,968 95.91% 64,060 11.15% 1.13% Senica 3,031 17,694 94.44% 60,797 10.42% 1.06% Skalica 2,662 19,993 106.71% 47,252 9.15% 0.93% Trnavský 29,079 18,607 99.32% 554,644 100.00% 10.15% TrenÄ?ín 6,130 18,747 100.06% 112,868 19.76% 2.14% Bánovce nad Bebravou 1,650 14,934 79.71% 38,126 5.32% 0.58% Ilava 3,086 17,396 92.85% 61,236 9.95% 1.08% Myjava 1,408 17,179 91.69% 28,295 4.54% 0.49% TrenÄ?iansky Nové Mesto nad Váhom 3,433 18,822 100.46% 62,958 11.07% 1.20% Partizánske 1,966 14,320 76.43% 47,385 6.34% 0.69% Považská Bystrica 3,398 18,184 97.06% 64,506 10.96% 1.19% Prievidza 7,267 18,013 96.15% 139,250 23.43% 2.54% Púchov 2,678 20,279 108.24% 45,578 8.63% 0.94% TrenÄ?iansky 31,018 17,837 95.20% 600,202 100.00% 10.83% 5. Appendix Nitra 7,531 18,390 98.16% 163,712 24.70% 2.63% Komárno 4,202 15,708 83.84% 106,958 13.78% 1.47% Levice 5,340 18,011 96.14% 118,522 17.51% 1.86% Nitriansky Nové Zámky 6,037 16,368 87.37% 147,450 19.80% 2.11% Å aľa 2,604 19,237 102.68% 54,123 8.54% 0.91% TopoľÄ?any 3,027 16,343 87.23% 74,055 9.93% 1.06% Zlaté Moravce 1,751 16,285 86.92% 42,996 5.74% 0.61% Nitriansky 30,493 17,223 91.93% 707,816 100.00% 10.65% Žilina 8,241 19,989 106.69% 157,596 25.78% 2.88% BytÄ?a 1,258 15,567 83.09% 30,896 3.94% 0.44% ÄŒadca 3,512 14,480 77.29% 92,709 10.98% 1.23% Dolný Kubín 1,758 17,037 90.94% 39,446 5.50% 0.61% Kysucké Nové Mesto 1,763 19,822 105.80% 34,000 5.51% 0.62% Žilinský Liptovský Mikuláš 3,270 17,040 90.95% 73,362 10.23% 1.14% Martin 4,880 19,131 102.11% 97,522 15.27% 1.70% Námestovo 2,301 15,159 80.91% 58,014 7.20% 0.80% Ružomberok 2,919 18,908 100.92% 59,026 9.13% 1.02% TurÄ?ianske Teplice 627 14,357 76.63% 16,695 1.96% 0.22% Tvrdošín 1,442 15,460 82.52% 35,656 4.51% 0.50% Žilinský 31,971 17,587 93.87% 694,922 100.00% 11.16% Banská Bystrica 5,703 20,340 108.57% 111,084 20.28% 1.99% Banská Å tiavnica 640 14,978 79.95% 16,928 2.28% 0.22% Brezno 2,913 17,814 95.08% 64,796 10.36% 1.02% Detva 1,304 15,660 83.59% 32,982 4.64% 0.46% Krupina 874 15,303 81.68% 22,625 3.11% 0.31% LuÄ?enec 2,885 15,591 83.22% 73,329 10.26% 1.01% Banskobystrický Poltár 798 13,901 74.20% 22,750 2.84% 0.28% Revúca 1,553 15,183 81.04% 40,523 5.52% 0.54% Rimavská Sobota 3,173 15,222 81.25% 82,582 11.29% 1.11% Veľký Krtíš 1,650 14,142 75.48% 46,229 5.87% 0.58% Zvolen 3,187 18,674 99.67% 67,614 11.34% 1.11% Žarnovica 1,147 16,706 89.17% 27,203 4.08% 0.40% Žiar nad Hronom 2,287 18,965 101.23% 47,786 8.14% 0.80% Banskobystrický 28,113 16,969 90.57% 656,431 100.00% 9.82% PreÅ¡ov 6,177 17,367 92.70% 164,686 22.99% 2.16% Bardejov 2,250 13,600 72.59% 76,608 8.38% 0.79% Humenné 2,222 15,957 85.17% 64,484 8.27% 0.78% Kežmarok 2,196 15,415 82.28% 65,944 8.17% 0.77% LevoÄ?a 1,013 14,462 77.19% 32,430 3.77% 0.35% Medzilaborce 356 13,422 71.64% 12,279 1.32% 0.12% PreÅ¡ovský Poprad 4,024 17,849 95.27% 104,377 14.98% 1.41% Sabinov 1,782 14,777 78.87% 55,841 6.63% 0.62% Snina 1,105 13,121 70.03% 39,007 4.11% 0.39% Stará Ľubovňa 1,649 14,783 78.91% 51,633 6.14% 0.58% Stropkov 618 13,725 73.26% 20,863 2.30% 0.22% Svidník 1,027 14,245 76.03% 33,369 3.82% 0.36% Vranov nad Topľou 2,448 14,518 77.49% 78,076 9.11% 0.85% PreÅ¡ovský 26,868 15,558 83.04% 799,597 100.00% 9.38% 5. Appendix KoÅ¡ice-okolie 5,229 16,385 87.46% 111,431 12.86% 1.83% Gelnica 1,386 15,613 83.34% 30,990 3.41% 0.48% Michalovce 5,420 17,267 92.16% 109,610 13.33% 1.89% Rožňava 3,008 16,940 90.42% 61,997 7.40% 1.05% KoÅ¡ický Sobrance 1,076 16,115 86.02% 23,303 2.65% 0.38% SpiÅ¡ská Nová Ves 4,486 16,364 87.34% 95,710 11.03% 1.57% TrebiÅ¡ov 4,789 15,972 85.25% 104,700 11.78% 1.67% KoÅ¡ice 15,259 22,681 121.06% 234,904 37.54% 5.33% KoÅ¡ický 40,652 18,371 98.06% 772,645 100.00% 14.20% SLOVAKIA 286,377 18,735 100.00% 5,570,502 100.00% 5. Appendix c) Hungary Figure 5.64: Spatial distribution of the population – percent share of the districts from the national total (Hungary) Figure 5.65: Spatial distribution of the income – percent ratio of the districts to the national average (Hungary) 5. Appendix Figure 5.66: Weights of the districts within their respective province for the residential and industrial categories [%] (Hungary) Figure 5.67: Weights of the districts within their respective province for the network category [%] (Hungary) 5. Appendix Figure 5.68: Spatial distribution of the property – percent share of the districts from the national total [%] (Hungary) Table 5.46: Property value, salary, population, weights within province and proportion on the total property of each district (Hungary) Total Salary Country property Salary Province Province District [1000 Population property [mil. index weight HUF]173 proportion EUR] Budapest Budapest 158,132 456 84.21% 1,696,128 100.00% 40.19% Budapest 158,132 456 117.99% 1,696,128 100.00% 40.19% Komlói 993 304 84.21% 40,602 8.45% 0.25% Mohácsi 1,245 304 84.21% 50,884 10.59% 0.32% Sásdi 270 228 63.16% 14,731 2.30% 0.07% Sellyei 258 228 63.16% 14,072 2.20% 0.07% Baranya Siklósi 921 304 84.21% 37,632 7.83% 0.23% Szigetvári 497 228 63.16% 27,062 4.22% 0.13% Pécsi 6,787 456 126.32% 184,936 57.72% 1.72% Pécsváradi 314 304 84.21% 12,849 2.67% 0.08% Szentl rinci 472 380 105.26% 15,447 4.02% 0.12% Baranya 11,757 367 94.93% 398,215 100.00% 2.99% 173 Resource of salary 2001 data within LAU-1 units in Hungary: [41], p. 5, chart Egy állandó lakosra jutó szja- alapot képezô jövedelem 2001-ben (ezer Ft); the salary data are available in the form of the discrete intervals only; the salary values listed in Table represent the mean value of the interval to which the LAU-1 unit belongs. 5. Appendix Bajai 1,803 304 84.21% 75,406 13.69% 0.46% Bácsalmási 317 228 63.16% 17,698 2.41% 0.08% Kalocsai 1,298 304 84.21% 54,263 9.85% 0.33% Kecskeméti 5,098 380 105.26% 170,554 38.70% 1.30% Kisk rösi 1,025 228 63.16% 57,150 7.78% 0.26% Bács-Kiskun Kiskunfélegyházi 1,118 304 84.21% 46,741 8.48% 0.28% Kiskunhalasi 1,100 304 84.21% 45,999 8.35% 0.28% Kiskunmajsai 360 228 63.16% 20,082 2.73% 0.09% Kunszentmiklósi 752 304 84.21% 31,430 5.70% 0.19% Jánoshalmi 304 228 63.16% 16,967 2.31% 0.08% Bács-Kiskun 13,175 312 80.81% 536,290 100.00% 3.35% Békéscsabai 2,019 380 105.26% 77,277 25.11% 0.51% Mez kovácsházi 672 228 63.16% 42,843 8.35% 0.17% Orosházi 1,285 304 84.21% 61,477 15.98% 0.33% Sarkadi 383 228 63.16% 24,409 4.76% 0.10% Békés Szarvasi 970 304 84.21% 46,416 12.07% 0.25% Szeghalmi 866 304 84.21% 41,436 10.77% 0.22% Békési 931 304 84.21% 44,520 11.57% 0.24% Gyulai 916 304 84.21% 43,812 11.39% 0.23% Békés 8,042 306 79.17% 382,190 100.00% 2.04% Miskolci 7,582 380 105.26% 271,220 45.16% 1.93% Edelényi 606 228 63.16% 36,155 3.61% 0.15% Encsi 407 228 63.16% 24,241 2.42% 0.10% Kazinbarcikai 1,391 304 84.21% 62,181 8.28% 0.35% Mez kövesdi 977 304 84.21% 43,664 5.82% 0.25% Ózdi 1,212 228 63.16% 72,266 7.22% 0.31% Borsod- Sárospataki 590 304 84.21% 26,385 3.51% 0.15% Abaúj- Sátoraljaújhelyi 534 304 84.21% 23,879 3.18% 0.14% Zemplén Szerencsi 744 228 63.16% 44,337 4.43% 0.19% Szikszói 324 228 63.16% 19,338 1.93% 0.08% Tiszaújvárosi 1,119 456 126.32% 33,348 6.66% 0.28% Abaúj-Hegyközi 254 228 63.16% 15,123 1.51% 0.06% Bodrogközi 398 304 84.21% 17,804 2.37% 0.10% Mez csáti 413 380 105.26% 14,788 2.46% 0.11% Tokaji 239 228 63.16% 14,222 1.42% 0.06% Borsod-Abaúj-Zemplén 16,789 317 82.13% 718,951 100.00% 4.27% Csongrádi 561 304 84.21% 24,206 4.49% 0.14% Hódmez vásárhelyi 1,692 380 105.26% 58,416 13.53% 0.43% Kisteleki 330 228 63.16% 19,012 2.64% 0.08% Csongrád Makói 1,131 304 84.21% 48,828 9.05% 0.29% Mórahalmi 456 228 63.16% 26,265 3.65% 0.12% Szegedi 7,073 456 126.32% 203,508 56.56% 1.80% Szentesi 1,260 380 105.26% 43,516 10.08% 0.32% Csongrád 12,504 387 100.17% 423,751 100.00% 3.18% 5. Appendix Bicskei 1,156 380 105.26% 38,984 7.25% 0.29% Dunaújvárosi 3,069 533 147.65% 73,781 19.26% 0.78% Enyingi 509 304 84.21% 21,442 3.19% 0.13% Gárdonyi 901 456 126.32% 25,321 5.65% 0.23% Móri 1,452 533 147.65% 34,906 9.11% 0.37% Fejér Sárbogárdi 614 304 84.21% 25,878 3.85% 0.16% Székesfehérvári 5,671 533 147.65% 136,343 35.59% 1.44% Abai 857 456 126.32% 24,095 5.38% 0.22% Adonyi 878 456 126.32% 24,678 5.51% 0.22% Ercsi 829 456 126.32% 23,283 5.20% 0.21% Fejér 15,936 476 123.24% 428,711 100.00% 4.05% Csornai 1,071 380 105.26% 34,904 6.37% 0.27% Gy ri 7,685 533 147.65% 178,582 45.68% 1.95% Kapuvári 735 380 105.26% 23,955 4.37% 0.19% Gy r-Moson- Mosonmagyaróvári 2,707 456 126.32% 73,529 16.09% 0.69% Sopron Sopron-Fert di 3,515 456 126.32% 95,470 20.89% 0.89% Téti 591 380 105.26% 19,246 3.51% 0.15% Pannonhalmi 521 380 105.26% 16,981 3.10% 0.13% Gy r-Moson-Sopron 16,825 471 121.80% 442,667 100.00% 4.28% Balmazújvárosi 535 228 63.16% 29,551 3.79% 0.14% Berettyóújfalui 953 228 63.16% 52,672 6.75% 0.24% Debreceni 6,275 380 105.26% 208,061 44.42% 1.59% Hajdúböszörményi 1,426 304 84.21% 59,096 10.09% 0.36% Hajdú-Bihar Hajdúszoboszlói 1,016 380 105.26% 33,681 7.19% 0.26% Polgári 349 304 84.21% 14,463 2.47% 0.09% Püspökladányi 1,236 304 84.21% 51,211 8.75% 0.31% Derecske- Létavértesi 868 304 84.21% 35,974 6.14% 0.22% Hajdúhadházi 1,470 304 84.21% 60,932 10.41% 0.37% Hajdú-Bihar 14,127 326 84.41% 545,641 100.00% 3.59% Egri 2,867 456 126.32% 85,328 33.61% 0.73% Hevesi 1,005 380 105.26% 35,910 11.79% 0.26% Füzesabonyi 540 228 63.16% 32,133 6.33% 0.14% Heves Gyöngyösi 1,727 304 84.21% 77,093 20.24% 0.44% Hatvani 1,791 456 126.32% 53,311 21.00% 0.46% Pétervásári 376 228 63.16% 22,398 4.41% 0.10% Bélapátfalvai 223 228 63.16% 13,287 2.62% 0.06% Heves 8,530 362 93.76% 319,460 100.00% 2.17% Dorogi 1,059 380 105.26% 40,392 11.04% 0.27% Tatai 1,268 456 126.32% 40,314 13.23% 0.32% Esztergomi 1,770 456 126.32% 56,259 18.46% 0.45% Komárom- Kisbéri 553 380 105.26% 21,090 5.77% 0.14% Esztergom Komáromi 1,292 456 126.32% 41,065 13.47% 0.33% Tatabányai 2,784 456 126.32% 88,502 29.04% 0.71% Oroszlányi 862 456 126.32% 27,414 8.99% 0.22% Komárom-Esztergom 9,587 441 114.15% 315,036 100.00% 2.44% Balassagyarmati 856 304 84.21% 42,034 19.73% 0.22% Bátonyterenyei 392 228 63.16% 25,660 9.03% 0.10% Pásztói 679 304 84.21% 33,362 15.66% 0.17% Nógrád Rétsági 656 380 105.26% 25,784 15.13% 0.17% Salgótarjáni 1,354 304 84.21% 66,488 31.21% 0.34% Szécsényi 401 304 84.21% 19,702 9.25% 0.10% Nógrád 4,338 304 78.67% 213,030 100.00% 1.10% 5. Appendix Aszódi 1,028 380 105.26% 35,222 2.58% 0.26% Ceglédi 2,848 304 84.21% 121,992 7.16% 0.72% Dabasi 1,023 304 84.21% 43,802 2.57% 0.26% Gödöll i 3,674 456 126.32% 104,935 9.24% 0.93% Monori 4,386 533 147.65% 107,154 11.02% 1.11% Nagykátai 1,797 304 84.21% 76,993 4.52% 0.46% Ráckevei 3,920 380 105.26% 134,346 9.85% 1.00% Szobi 374 380 105.26% 12,826 0.94% 0.10% Pest Váci 2,425 456 126.32% 69,255 6.10% 0.62% Budaörsi 3,320 533 147.65% 81,116 8.35% 0.84% Dunakeszi 2,991 533 147.65% 73,079 7.52% 0.76% Gyáli 1,312 380 105.26% 44,955 3.30% 0.33% Pilisvörösvári 2,654 533 147.65% 64,854 6.67% 0.67% Szentendrei 3,084 533 147.65% 75,341 7.75% 0.78% Veresegyházi 997 380 105.26% 34,165 2.51% 0.25% Érdi 3,950 533 147.65% 96,515 9.93% 1.00% Pest 39,782 440 113.93% 1,176,550 100.00% 10.11% Barcsi 515 304 84.21% 25,427 7.14% 0.13% Csurgói 273 228 63.16% 17,970 3.79% 0.07% Fonyódi 482 304 84.21% 23,785 6.68% 0.12% Kaposvári 2,567 380 105.26% 101,309 35.57% 0.65% Lengyeltóti 175 228 63.16% 11,483 2.42% 0.04% Somogy Marcali 737 304 84.21% 36,358 10.21% 0.19% Nagyatádi 556 304 84.21% 27,434 7.71% 0.14% Siófoki 964 380 105.26% 38,042 13.36% 0.24% Tabi 281 304 84.21% 13,864 3.89% 0.07% Balatonföldvári 239 304 84.21% 11,803 3.32% 0.06% Kadarkúti 426 304 84.21% 21,021 5.91% 0.11% Somogy 7,215 329 85.24% 328,496 100.00% 1.83% Baktalórántházi 606 228 63.16% 35,371 5.35% 0.15% Csengeri 238 228 63.16% 13,877 2.10% 0.06% Fehérgyarmati 668 228 63.16% 38,956 5.90% 0.17% Kisvárdai 1,236 304 84.21% 54,117 10.92% 0.31% Mátészalkai 1,134 228 63.16% 66,190 10.02% 0.29% Szabolcs- Nagykállói 783 228 63.16% 45,673 6.91% 0.20% Szatmár- Bereg Nyírbátori 768 228 63.16% 44,808 6.78% 0.20% Nyíregyházi 3,250 304 84.21% 142,247 28.70% 0.83% Tiszavasvári 857 304 84.21% 37,531 7.57% 0.22% Vásárosnaményi 536 228 63.16% 31,266 4.73% 0.14% Ibrány-Nagyhalászi 784 228 63.16% 45,768 6.93% 0.20% Záhonyi 463 304 84.21% 20,250 4.09% 0.12% Szabolcs-Szatmár-Bereg 11,322 262 67.67% 576,054 100.00% 2.88% Jászberényi 1,987 304 84.21% 86,895 19.82% 0.51% Karcagi 1,037 304 84.21% 45,328 10.34% 0.26% Jász- Kunszentmártoni 652 228 63.16% 37,990 6.50% 0.17% Nagykun- Szolnoki 4,224 456 126.32% 123,159 42.14% 1.07% Szolnok Tiszafüredi 674 228 63.16% 39,282 6.72% 0.17% Törökszentmiklós 936 304 84.21% 40,917 9.33% 0.24% Mez túri 515 228 63.16% 30,051 5.14% 0.13% Jász-Nagykun-Szolnok 10,024 330 85.43% 403,622 100.00% 2.55% 5. Appendix Bonyhádi 956 380 105.26% 29,324 12.89% 0.24% Dombóvári 894 304 84.21% 34,293 12.06% 0.23% Tolna Paksi 1,929 456 126.32% 49,332 26.01% 0.49% Szekszárdi 2,836 380 105.26% 87,010 38.23% 0.72% Tamási 802 228 63.16% 41,007 10.81% 0.20% Tolna 7,416 359 92.86% 240,966 100.00% 1.89% Celldömölki 845 380 105.26% 25,670 8.53% 0.21% Csepregi 356 380 105.26% 10,799 3.59% 0.09% Körmendi 863 456 126.32% 21,834 8.71% 0.22% K szegi 723 456 126.32% 18,306 7.30% 0.18% Vas riszentpéteri 183 304 84.21% 6,937 1.84% 0.05% Sárvári 729 228 63.16% 36,918 7.36% 0.19% Szentgotthárdi 592 456 126.32% 14,983 5.98% 0.15% Szombathelyi 5,230 533 147.65% 113,252 52.81% 1.33% Vasvári 383 304 84.21% 14,552 3.87% 0.10% Vas 9,903 434 112.35% 263,251 100.00% 2.52% Ajkai 1,309 380 105.26% 57,005 13.84% 0.33% Balatonalmádi 756 456 126.32% 27,457 8.00% 0.19% Balatonfüredi 620 456 126.32% 22,495 6.55% 0.16% Pápai 1,424 380 105.26% 62,007 15.05% 0.36% Veszprém Sümegi 293 304 84.21% 15,939 3.10% 0.07% Tapolcai 829 380 105.26% 36,094 8.76% 0.21% Várpalotai 1,043 456 126.32% 37,868 11.03% 0.27% Veszprémi 2,709 533 147.65% 84,140 28.65% 0.69% Zirci 475 380 105.26% 20,701 5.03% 0.12% Veszprém 9,458 430 111.37% 363,706 100.00% 2.40% Keszthelyi 972 380 105.26% 34,806 11.35% 0.25% Lenti 623 380 105.26% 22,313 7.27% 0.16% Letenyei 388 304 84.21% 17,391 4.54% 0.10% Nagykanizsai 1,870 380 105.26% 66,968 21.83% 0.48% Zala Zalaegerszegi 3,259 456 126.32% 97,266 38.05% 0.83% Zalaszentgróti 509 380 105.26% 18,238 5.95% 0.13% Hévízi 348 380 105.26% 12,473 4.07% 0.09% Pacsai 301 380 105.26% 10,792 3.52% 0.08% Zalakarosi 295 304 84.21% 13,196 3.44% 0.07% Zala 8,565 397 102.79% 293,443 100.00% 2.18% HUNGARY 393,429 386 100.00% 10,066,158 100.00% 5. Appendix d) Poland Figure 5.69: Spatial distribution of the population – percent share of the districts from the national total (Poland) Figure 5.70: Spatial distribution of the income – percent ratio of the districts to the national average (Poland) 5. Appendix Figure 5.71: Weights of the districts within their respective province for the residential and industrial categories [%] (Poland) Figure 5.72: Weights of the districts within their respective province for the residential and industrial categories [%] (Poland) 5. Appendix Figure 5.73: Spatial distribution of the property – percent share of the districts from the national total [%] (Poland) Table 5.47: Property value, salary, population, weights within province and proportion on the total property of each district (Poland) Total Salary Salary Province Country Province District Population property [PLN] index weight weight bolesÅ‚awiecki 2,676 2,324 90.34% 88,557 2.68% 0.21% dzier oniowski 3,046 2,247 87.32% 104,303 3.05% 0.24% gÅ‚ogowski 2,793 2,453 95.32% 87,584 2.79% 0.22% górowski 995 2,102 81.68% 36,418 1.00% 0.08% jaworski 1,477 2,194 85.26% 51,793 1.48% 0.12% jeleniogórski 1,791 2,159 83.90% 63,824 1.79% 0.14% kamiennogórski 1,300 2,173 84.46% 46,004 1.30% 0.10% kÅ‚odzki 4,955 2,308 89.70% 165,146 4.96% 0.40% legnicki 1,510 2,182 84.79% 53,251 1.51% 0.12% lubaÅ„ski 1,220 2,111 82.05% 56,624 1.55% 0.10% lubiÅ„ski 7,199 5,262 204.50% 105,248 7.20% 0.58% lwówecki 1,374 2,214 86.06% 47,732 1.37% 0.11% DolnoÅ›lÄ…skie milicki 823 2,190 85.12% 36,823 1.05% 0.07% (districts) oleÅ›nicki 2,968 2,206 85.73% 103,496 2.97% 0.24% oÅ‚awski 2,391 2,571 99.91% 71,540 2.39% 0.19% polkowicki 2,217 2,780 108.06% 61,331 2.22% 0.18% strzeliÅ„ski 1,364 2,386 92.72% 43,993 1.37% 0.11% Å›redzki 1,580 2,453 95.33% 49,540 1.58% 0.13% Å›widnicki 5,206 2,507 97.44% 159,723 5.21% 0.42% trzebnicki 1,927 2,413 93.77% 78,248 2.46% 0.15% waÅ‚brzyski 6,353 2,684 104.30% 182,104 6.36% 0.51% woÅ‚owski 1,486 2,411 93.69% 47,417 1.49% 0.12% wrocÅ‚awski 3,720 2,698 104.84% 106,080 3.72% 0.30% zÄ…bkowicki 2,065 2,308 89.69% 68,835 2.07% 0.17% zgorzelecki 3,939 3,233 125.64% 93,732 3.94% 0.32% zÅ‚otoryjski 1,455 2,455 95.41% 45,598 1.46% 0.12% Jelenia Góra 2,842 2,549 99.06% 85,782 2.84% 0.23% DolnoÅ›lÄ…skie Legnica 3,108 2,282 88.70% 104,754 3.11% 0.25% (cities) WrocÅ‚aw 25,086 3,049 118.50% 632,930 25.10% 2.01% DolnoÅ›lÄ…skie 98,866 2,671 103.83% 2,878,410 100.00% 7.92% 5. Appendix aleksandrowski 1,206 2,048 79.60% 55,367 2.35% 0.10% brodnicki 1,759 2,199 85.45% 75,204 3.42% 0.14% bydgoski 2,364 2,236 86.91% 99,386 4.60% 0.19% cheÅ‚miÅ„ski 939 2,109 81.96% 51,412 2.24% 0.08% golubsko-dobrzyÅ„ski 1,039 2,165 84.13% 45,111 2.02% 0.08% grudziÄ…dzki 872 2,126 82.64% 38,559 1.70% 0.07% inowrocÅ‚awski 3,940 2,251 87.49% 164,571 7.67% 0.32% lipnowski 1,458 2,074 80.62% 66,063 2.84% 0.12% Kujawsko- mogileÅ„ski 1,080 2,168 84.25% 46,833 2.10% 0.09% pomorskie nakielski 1,940 2,145 83.37% 85,050 3.78% 0.16% (districts) radziejowski 958 2,147 83.43% 41,972 1.87% 0.08% rypiÅ„ski 1,007 2,144 83.34% 44,143 1.96% 0.08% sÄ™poleÅ„ski 893 2,048 79.59% 40,990 1.74% 0.07% Å›wiecki 2,544 2,465 95.80% 97,037 4.95% 0.20% toruÅ„ski 2,055 2,101 81.65% 91,963 4.00% 0.16% tucholski 1,032 2,051 79.71% 47,310 2.01% 0.08% wÄ…brzeski 660 2,193 85.24% 34,763 1.58% 0.05% wÅ‚ocÅ‚awski 1,825 2,011 78.15% 85,303 3.55% 0.15% niÅ„ski 1,530 2,063 80.17% 69,736 2.98% 0.12% Bydgoszcz 10,339 2,691 104.58% 361,222 20.12% 0.83% Kujawsko- GrudziÄ…dz 2,329 2,210 85.90% 99,090 4.53% 0.19% pomorskie (cities) ToruÅ„ 6,051 2,753 107.01% 206,619 11.78% 0.48% WÅ‚ocÅ‚awek 3,189 2,532 98.39% 118,432 6.21% 0.26% Kujawsko-pomorskie 51,009 2,338 90.87% 2,066,136 100.00% 4.09% bialski 2,460 2,042 79.38% 113,511 4.54% 0.20% biÅ‚gorajski 2,504 2,274 88.39% 103,759 4.62% 0.20% cheÅ‚mski 1,838 2,183 84.82% 79,364 3.39% 0.15% hrubieszowski 1,581 2,196 85.33% 67,861 2.92% 0.13% janowski 916 2,091 81.27% 47,650 1.95% 0.07% krasnostawski 1,670 2,299 89.35% 68,460 3.08% 0.13% kraÅ›nicki 2,271 2,160 83.93% 99,099 4.19% 0.18% lubartowski 2,077 2,169 84.30% 90,235 3.84% 0.17% lubelski 3,390 2,240 87.04% 142,662 6.26% 0.27% Lubelskie Å‚Ä™czyÅ„ski 2,215 3,656 142.08% 57,101 4.09% 0.18% (districts) Å‚ukowski 2,437 2,127 82.66% 107,997 4.50% 0.20% opolski 1,459 2,194 85.26% 62,662 2.69% 0.12% parczewski 845 2,196 85.34% 36,265 1.56% 0.07% puÅ‚awski 3,321 2,690 104.53% 116,382 6.13% 0.27% radzyÅ„ski 1,447 2,232 86.74% 61,121 2.67% 0.12% rycki 1,360 2,184 84.88% 58,678 2.51% 0.11% Å›widnicki 1,803 2,345 91.13% 72,472 3.33% 0.14% tomaszowski 1,948 2,099 81.58% 87,484 3.60% 0.16% wÅ‚odawski 905 2,142 83.26% 39,837 1.67% 0.07% zamojski 2,296 1,970 76.55% 109,867 4.24% 0.18% BiaÅ‚a Podlaska 1,454 2,372 92.17% 57,783 2.69% 0.12% Lubelskie CheÅ‚m 1,628 2,263 87.95% 67,782 3.01% 0.13% (cities) Lublin 10,313 2,763 107.38% 351,806 19.05% 0.83% Zamość 1,861 2,643 102.72% 66,375 3.44% 0.15% Lubelskie 53,999 2,356 91.56% 2,166,213 100.00% 4.33% gorzowski 2,170 2,650 103.00% 66,172 7.27% 0.17% kroÅ›nieÅ„ski 1,665 2,390 92.88% 56,297 5.57% 0.13% miÄ™dzyrzecki 1,648 2,286 88.84% 58,279 5.52% 0.13% nowosolski 2,287 2,127 82.67% 86,882 7.66% 0.18% sÅ‚ubicki 1,304 2,265 88.02% 46,551 4.37% 0.10% Lubuskie strzelecko-drezdenecki 1,459 2,354 91.50% 50,072 4.88% 0.12% (districts) sulÄ™ciÅ„ski 855 1,954 75.96% 35,349 2.86% 0.07% Å›wiebodziÅ„ski 1,545 2,226 86.52% 56,094 5.17% 0.12% zielonogórski 2,768 2,475 96.20% 90,389 9.27% 0.22% agaÅ„ski 2,162 2,132 82.87% 81,946 7.24% 0.17% arski 3,083 2,527 98.20% 98,610 10.32% 0.25% wschowski 1,029 2,138 83.11% 38,906 3.45% 0.08% Lubuskie Gorzów Wielkopolski 3,868 2,493 96.88% 125,411 12.95% 0.31% (cities) Zielona Góra 4,023 2,767 107.54% 117,523 13.47% 0.32% Lubuskie 29,865 2,394 93.04% 1,008,481 100.00% 2.39% 5. Appendix beÅ‚chatowski 4,852 3,416 132.75% 112,772 6.33% 0.39% kutnowski 3,057 2,356 91.57% 103,007 3.99% 0.24% Å‚aski 1,264 1,975 76.75% 50,813 1.65% 0.10% Å‚Ä™czycki 1,562 2,347 91.23% 52,849 2.04% 0.13% Å‚owicki 2,284 2,217 86.16% 81,784 2.98% 0.18% łódzki wschodni 1,751 2,126 82.64% 65,368 2.28% 0.14% opoczyÅ„ski 2,278 2,305 89.57% 78,473 2.97% 0.18% pabianicki 3,238 2,159 83.90% 119,110 4.22% 0.26% pajÄ™czaÅ„ski 1,586 2,370 92.09% 53,140 2.07% 0.13% piotrkowski 2,180 1,915 74.43% 90,392 2.84% 0.17% Å?ódzkie poddÄ™bicki 1,191 2,257 87.73% 41,905 1.55% 0.10% (districts) radomszczaÅ„ski 2,903 1,949 75.75% 118,249 3.79% 0.23% rawski 1,364 2,197 85.40% 49,286 1.78% 0.11% sieradzki 3,382 2,231 86.70% 120,351 4.41% 0.27% skierniewicki 1,068 2,249 87.43% 37,701 1.39% 0.09% tomaszowski 3,237 2,131 82.84% 120,584 4.22% 0.26% wieluÅ„ski 1,994 2,028 78.81% 78,076 2.60% 0.16% wieruszowski 1,121 2,112 82.09% 42,149 1.46% 0.09% zduÅ„skowolski 1,863 2,185 84.93% 67,693 2.43% 0.15% zgierski 4,733 2,334 90.71% 161,012 6.17% 0.38% brzeziÅ„ski 555 1,833 71.23% 30,585 0.92% 0.04% Å?ódź 25,596 2,698 104.86% 753,192 33.39% 2.05% Å?ódzkie Piotrków Trybunalski 2,015 2,039 79.24% 78,475 2.63% 0.16% (cities) Skierniewice 1,435 2,328 90.50% 48,932 1.87% 0.11% Å?ódzkie 76,510 2,381 92.56% 2,555,898 100.00% 6.13% bocheÅ„ski 2,929 2,422 94.12% 101,204 2.99% 0.23% brzeski 2,303 2,129 82.74% 90,508 2.35% 0.18% chrzanowski 3,999 2,617 101.72% 127,859 4.09% 0.32% dÄ…browski 1,571 2,246 87.29% 58,529 1.61% 0.13% gorlicki 2,932 2,302 89.46% 106,591 3.00% 0.23% krakowski 7,860 2,653 103.11% 247,903 8.04% 0.63% limanowski 3,220 2,196 85.35% 122,685 3.29% 0.26% miechowski 1,359 2,248 87.37% 50,577 1.39% 0.11% myÅ›lenicki 3,212 2,277 88.49% 118,066 3.28% 0.26% MaÅ‚opolskie nowosÄ…decki 4,509 2,209 85.85% 200,015 5.40% 0.36% (districts) nowotarski 4,557 2,083 80.96% 183,069 4.66% 0.37% olkuski 3,825 2,808 109.13% 113,993 3.91% 0.31% oÅ›wiÄ™cimski 4,618 2,522 98.00% 153,238 4.72% 0.37% proszowicki 1,002 2,261 87.89% 43,424 1.20% 0.08% suski 1,936 2,303 89.51% 82,347 2.32% 0.16% tarnowski 4,912 2,113 82.14% 194,487 5.02% 0.39% tatrzaÅ„ski 1,560 2,346 91.17% 65,168 1.87% 0.12% wadowicki 4,016 2,170 84.32% 154,899 4.11% 0.32% wielicki 3,093 2,412 93.74% 107,305 3.16% 0.25% Kraków 27,080 2,995 116.41% 756,583 27.69% 2.17% MaÅ‚opolskie Nowy SÄ…cz 2,396 2,373 92.24% 84,468 2.45% 0.19% (cities) Tarnów 3,371 2,429 94.41% 116,118 3.45% 0.27% MaÅ‚opolskie 96,258 2,496 97.01% 3,279,036 100.00% 7.71% Mazowieckie biaÅ‚obrzeski 1,156 2,429 94.40% 33,521 0.51% 0.09% (districts) ciechanowski 3,762 2,520 97.93% 90,600 1.42% 0.30% garwoliÅ„ski 4,070 2,321 90.21% 106,423 1.54% 0.33% gostyniÅ„ski 1,686 2,178 84.65% 46,989 0.64% 0.14% grodziski 3,507 3,090 120.07% 79,929 1.54% 0.28% grójecki 4,283 2,691 104.60% 96,578 1.62% 0.34% kozienicki 3,021 2,984 115.99% 61,433 1.14% 0.24% legionowski 4,574 2,793 108.54% 99,390 1.73% 0.37% lipski 1,174 2,272 88.31% 36,362 0.52% 0.09% Å‚osicki 1,189 2,224 86.42% 32,443 0.45% 0.10% makowski 1,630 2,137 83.04% 46,290 0.62% 0.13% miÅ„ski 5,626 2,397 93.16% 142,433 2.13% 0.45% mÅ‚awski 2,444 2,034 79.04% 72,936 0.93% 0.20% nowodworski 4,084 3,257 126.60% 76,088 1.55% 0.33% ostroÅ‚Ä™cki 3,587 2,594 100.80% 83,926 1.36% 0.29% ostrowski 2,811 2,281 88.64% 74,815 1.06% 0.23% otwocki 5,050 2,607 101.32% 117,555 1.91% 0.40% piaseczyÅ„ski 7,536 3,033 117.87% 150,800 2.85% 0.60% 5. Appendix pÅ‚ocki 4,167 2,369 92.06% 106,769 1.58% 0.33% pÅ‚oÅ„ski 3,391 2,356 91.57% 87,363 1.28% 0.27% pruszkowski 8,477 3,490 135.64% 147,414 3.21% 0.68% przasnyski 2,011 2,312 89.85% 52,781 0.76% 0.16% przysuski 1,574 2,197 85.39% 43,468 0.60% 0.13% puÅ‚tuski 1,851 2,202 85.58% 51,011 0.70% 0.15% radomski 4,831 2,009 78.07% 145,947 1.83% 0.39% siedlecki 2,842 2,141 83.20% 80,582 1.08% 0.23% sierpecki 1,947 2,209 85.84% 53,498 0.74% 0.16% sochaczewski 3,923 2,845 110.57% 83,694 1.49% 0.31% sokoÅ‚owski 2,221 2,389 92.86% 56,414 0.84% 0.18% szydÅ‚owiecki 1,370 2,078 80.77% 39,996 0.52% 0.11% warszawski zachodni 5,402 3,180 123.59% 103,108 2.05% 0.43% wÄ™growski 2,423 2,182 84.80% 67,411 0.92% 0.19% woÅ‚omiÅ„ski 8,758 2,566 99.72% 207,155 3.32% 0.70% wyszkowski 2,670 2,251 87.50% 71,968 1.01% 0.21% zwoleÅ„ski 1,174 2,234 86.81% 37,015 0.52% 0.09% uromiÅ„ski 1,151 2,028 78.81% 39,969 0.51% 0.09% yrardowski 3,365 2,726 105.95% 74,917 1.27% 0.27% OstroÅ‚Ä™ka 2,557 2,869 111.49% 54,109 0.97% 0.20% PÅ‚ock 7,408 3,541 137.63% 126,968 2.81% 0.59% Mazowieckie Radom 9,473 2,557 99.37% 224,857 3.59% 0.76% (cities) Siedlce 3,319 2,618 101.75% 76,939 1.26% 0.27% Warszawa 115,286 4,100 159.34% 1,706,624 43.65% 9.24% Mazowieckie 262,780 3,089 120.07% 5,188,488 100.00% 21.05% brzeski 2,839 2,445 95.01% 92,090 8.60% 0.23% gÅ‚ubczycki 1,485 2,364 91.87% 49,818 4.50% 0.12% kÄ™dzierzyÅ„sko-kozielski 3,735 2,924 113.64% 101,291 11.31% 0.30% kluczborski 2,079 2,373 92.23% 69,479 6.30% 0.17% krapkowicki 2,702 3,186 123.82% 67,244 8.18% 0.22% Opolskie namysÅ‚owski 1,315 2,380 92.50% 43,795 3.98% 0.11% (districts) nyski 4,129 2,258 87.75% 145,017 12.50% 0.33% oleski 1,932 2,263 87.95% 67,705 5.85% 0.15% opolski 4,129 2,430 94.46% 134,696 12.50% 0.33% prudnicki 1,665 2,225 86.46% 59,354 5.04% 0.13% strzelecki 2,418 2,401 93.32% 79,851 7.32% 0.19% Opolskie Opole (cities) 4,599 2,877 111.81% 126,748 13.92% 0.37% Opolskie 33,028 2,525 98.15% 1,037,088 100.00% 2.65% bieszczadzki 563 2,306 89.61% 22,106 1.06% 0.05% brzozowski 1,253 2,147 83.44% 65,074 2.90% 0.10% dÄ™bicki 3,397 2,319 90.13% 132,585 6.39% 0.27% jarosÅ‚awski 3,146 2,337 90.82% 121,853 5.92% 0.25% jasielski 2,920 2,303 89.50% 114,765 5.49% 0.23% kolbuszowski 1,466 2,161 84.00% 61,401 2.76% 0.12% kroÅ›nieÅ„ski 2,582 2,119 82.36% 110,270 4.86% 0.21% le ajski 1,800 2,360 91.73% 69,028 3.39% 0.14% lubaczowski 1,093 2,139 83.14% 56,960 2.53% 0.09% Å‚aÅ„cucki 1,811 2,104 81.77% 77,916 3.41% 0.15% Podkarpackie mielecki 3,557 2,415 93.86% 133,314 6.69% 0.28% (districts) ni aÅ„ski 1,574 2,125 82.57% 67,044 2.96% 0.13% przemyski 1,700 2,164 84.12% 71,078 3.20% 0.14% przeworski 1,952 2,248 87.36% 78,591 3.67% 0.16% ropczycko-sÄ™dziszowski 1,743 2,212 85.98% 71,303 3.28% 0.14% rzeszowski 3,953 2,127 82.67% 168,189 7.44% 0.32% sanocki 2,335 2,232 86.75% 94,666 4.39% 0.19% stalowowolski 3,053 2,546 98.96% 108,522 5.74% 0.24% strzy owski 1,139 2,051 79.71% 61,924 2.64% 0.09% tarnobrzeski 1,500 2,532 98.39% 53,643 2.82% 0.12% leski 586 2,463 95.73% 26,535 1.36% 0.05% Krosno 1,121 2,137 83.04% 47,479 2.11% 0.09% Podkarpackie PrzemyÅ›l 1,710 2,315 89.98% 66,867 3.22% 0.14% (cities) Rzeszów 5,030 2,735 106.29% 166,454 9.46% 0.40% Tarnobrzeg 1,224 2,225 86.48% 49,771 2.30% 0.10% Podkarpackie 52,206 2,294 89.16% 2,097,338 100.00% 4.18% 5. Appendix augustowski 1,638 2,972 115.51% 58,867 5.96% 0.13% biaÅ‚ostocki 3,348 2,264 87.99% 138,004 10.64% 0.27% bielski 1,587 2,497 97.05% 59,301 5.04% 0.13% grajewski 1,456 2,735 106.32% 49,663 4.63% 0.12% hajnowski 1,208 2,387 92.76% 47,224 3.84% 0.10% kolneÅ„ski 959 2,273 88.32% 39,387 3.05% 0.08% Podlaskie Å‚om yÅ„ski 1,325 2,424 94.21% 51,013 4.21% 0.11% (districts) moniecki 908 2,274 88.36% 42,668 3.30% 0.07% sejneÅ„ski 452 2,279 88.58% 21,170 1.64% 0.04% siemiatycki 1,126 2,189 85.09% 47,998 3.58% 0.09% sokólski 1,741 2,271 88.26% 71,543 5.53% 0.14% suwalski 825 2,191 85.15% 35,139 2.62% 0.07% wysokomazowiecki 1,525 2,390 92.87% 59,542 4.84% 0.12% zambrowski 1,108 2,315 89.95% 44,681 3.52% 0.09% BiaÅ‚ystok 8,561 2,716 105.56% 294,143 27.20% 0.69% Podlaskie Å?om a 1,565 2,317 90.06% 63,036 4.97% 0.13% (cities) SuwaÅ‚ki 1,706 2,298 89.30% 69,281 5.42% 0.14% Podlaskie 31,039 2,462 95.71% 1,192,660 100.00% 2.49% bytowski 1,703 2,278 88.52% 75,527 2.86% 0.14% chojnicki 2,266 2,124 82.55% 92,174 3.25% 0.18% czÅ‚uchowski 1,495 2,274 88.39% 56,816 2.15% 0.12% gdaÅ„ski 2,693 2,613 101.55% 89,073 3.86% 0.22% kartuski 2,550 2,295 89.20% 112,221 4.28% 0.20% koÅ›cierski 1,686 2,158 83.89% 67,513 2.42% 0.14% kwidzyÅ„ski 2,453 2,611 101.48% 81,172 3.52% 0.20% Pomorskie lÄ™borski 1,762 2,391 92.91% 63,705 2.53% 0.14% (districts) malborski 1,682 2,318 90.09% 62,712 2.41% 0.13% nowodworski 788 2,239 87.01% 35,542 1.32% 0.06% pucki 2,106 2,404 93.42% 75,693 3.02% 0.17% sÅ‚upski 2,370 2,209 85.85% 92,704 3.40% 0.19% starogardzki 3,689 2,596 100.88% 122,794 5.29% 0.30% tczewski 3,397 2,594 100.81% 113,148 4.87% 0.27% wejherowski 5,361 2,493 96.87% 185,831 7.69% 0.43% sztumski 1,074 2,222 86.37% 41,763 1.54% 0.09% GdaÅ„sk 18,317 3,473 134.98% 455,717 26.28% 1.47% Pomorskie Gdynia 9,263 3,198 124.30% 250,242 13.29% 0.74% (cities) SÅ‚upsk 2,705 2,399 93.25% 97,419 3.88% 0.22% Sopot 1,274 3,286 127.73% 39,154 2.14% 0.10% Pomorskie 68,633 2,724 105.88% 2,210,920 100.00% 5.50% bÄ™dziÅ„ski 4,722 2,371 92.13% 151,160 2.90% 0.38% bielski 4,752 2,362 91.79% 152,695 2.92% 0.38% cieszyÅ„ski 5,385 2,387 92.75% 171,231 3.31% 0.43% czÄ™stochowski 3,678 2,086 81.06% 133,823 2.26% 0.29% gliwicki 3,575 2,370 92.10% 114,490 2.20% 0.29% kÅ‚obucki 2,103 1,882 73.15% 84,789 1.29% 0.17% lubliniecki 2,338 2,320 90.18% 76,468 1.44% 0.19% mikoÅ‚owski 3,211 2,657 103.26% 91,706 1.97% 0.26% ÅšlÄ…skie myszkowski 1,994 2,110 82.02% 71,714 1.23% 0.16% (districts) pszczyÅ„ski 3,313 2,389 92.86% 105,230 2.04% 0.27% raciborski 3,646 2,499 97.11% 110,731 2.24% 0.29% rybnicki 2,299 2,359 91.70% 73,931 1.41% 0.18% tarnogórski 4,412 2,432 94.51% 137,684 2.71% 0.35% bieruÅ„sko-lÄ™dziÅ„ski 1,799 2,427 94.33% 56,255 1.11% 0.14% wodzisÅ‚awski 4,354 2,128 82.69% 155,317 2.68% 0.35% zawierciaÅ„ski 4,054 2,495 96.95% 123,316 2.49% 0.32% ywiecki 5,532 2,797 108.72% 150,079 3.40% 0.44% ÅšlÄ…skie Bielsko-BiaÅ‚a 6,439 2,781 108.09% 175,690 3.96% 0.52% (cities) Bytom 6,039 2,480 96.39% 184,765 3.71% 0.48% Chorzów 3,735 2,494 96.92% 113,678 2.30% 0.30% CzÄ™stochowa 8,037 2,517 97.84% 242,300 4.94% 0.64% DÄ…browa Górnicza 5,242 3,089 120.05% 128,795 3.22% 0.42% Gliwice 8,185 3,147 122.29% 197,393 5.03% 0.66% JastrzÄ™bie-Zdrój 5,346 4,318 167.84% 93,939 3.29% 0.43% Jaworzno 4,066 3,230 125.55% 95,520 2.50% 0.33% Katowice 15,334 3,727 144.87% 312,201 9.43% 1.23% MysÅ‚owice 2,392 2,423 94.19% 74,912 1.47% 0.19% 5. Appendix Piekary ÅšlÄ…skie 1,864 2,396 93.11% 59,061 1.15% 0.15% Ruda ÅšlÄ…ska 4,784 2,511 97.58% 144,584 2.94% 0.38% Rybnik 4,923 2,648 102.92% 141,080 3.03% 0.39% Siemianowice ÅšlÄ…skie 2,439 2,585 100.46% 71,621 1.50% 0.20% Sosnowiec 7,406 2,525 98.13% 222,586 4.56% 0.59% ÅšwiÄ™tochÅ‚owice 1,711 2,381 92.54% 54,525 1.05% 0.14% Tychy 4,731 2,767 107.52% 129,776 2.91% 0.38% Zabrze 6,986 2,804 108.98% 189,062 4.30% 0.56% ory 1,742 2,133 82.88% 62,008 1.07% 0.14% ÅšlÄ…skie 162,571 2,651 103.04% 4,654,115 100.00% 13.02% buski 1,832 2,272 88.31% 73,445 5.42% 0.15% jÄ™drzejowski 2,460 2,516 97.79% 89,065 7.28% 0.20% kazimierski 802 2,059 80.04% 35,468 2.37% 0.06% kielecki 4,980 2,270 88.23% 199,847 14.73% 0.40% konecki 2,025 2,207 85.77% 83,590 5.99% 0.16% ÅšwiÄ™to- opatowski 1,509 2,450 95.20% 56,109 4.46% 0.12% krzyskie ostrowiecki 2,837 2,241 87.08% 115,333 8.39% 0.23% (districts) piÅ„czowski 1,216 2,650 103.00% 41,811 3.60% 0.10% sandomierski 2,384 2,678 104.09% 81,074 7.05% 0.19% skar yski 2,226 2,557 99.38% 79,292 6.58% 0.18% starachowicki 2,197 2,131 82.81% 93,954 6.50% 0.18% staszowski 2,060 2,547 98.98% 73,697 6.10% 0.17% wÅ‚oszczowski 1,271 2,466 95.83% 46,963 3.76% 0.10% ÅšwiÄ™to- krzyskie Kielce (cities) 6,003 2,656 103.22% 205,902 17.76% 0.48% ÅšwiÄ™tokrzyskie 33,801 2,414 93.83% 1,275,550 100.00% 2.71% bartoszycki 1,391 2,138 83.11% 60,703 3.99% 0.11% braniewski 1,040 2,243 87.19% 43,267 2.98% 0.08% dziaÅ‚dowski 1,524 2,189 85.06% 65,025 4.37% 0.12% elblÄ…ski 1,260 2,085 81.04% 56,400 3.61% 0.10% eÅ‚cki 2,015 2,205 85.71% 85,307 5.78% 0.16% gi ycki 1,339 2,205 85.70% 56,671 3.84% 0.11% iÅ‚awski 2,005 2,078 80.75% 90,086 5.75% 0.16% kÄ™trzyÅ„ski 1,430 2,031 78.92% 65,753 4.10% 0.11% WarmiÅ„sko- lidzbarski 917 2,008 78.05% 42,618 2.63% 0.07% mazurskie mrÄ…gowski 1,205 2,244 87.23% 50,127 3.46% 0.10% (districts) nidzicki 648 2,078 80.77% 33,729 2.15% 0.05% nowomiejski 914 1,962 76.27% 43,486 2.62% 0.07% olecki 829 2,275 88.42% 34,012 2.38% 0.07% olsztyÅ„ski 2,837 2,310 89.79% 114,643 8.13% 0.23% ostródzki 2,417 2,151 83.60% 104,890 6.93% 0.19% piski 1,473 2,395 93.07% 57,408 4.22% 0.12% szczycieÅ„ski 1,662 2,240 87.07% 69,241 4.76% 0.13% goÅ‚dapski 537 1,871 72.72% 26,818 1.54% 0.04% wÄ™gorzewski 522 2,070 80.46% 23,551 1.50% 0.04% WarmiÅ„sko- ElblÄ…g 3,297 2,429 94.41% 126,710 9.45% 0.26% mazurskie (cities) Olsztyn 5,517 2,931 113.92% 175,710 15.82% 0.44% WarmiÅ„sko-mazurskie 34,778 2,283 88.75% 1,426,155 100.00% 2.79% Wielkopolskie chodzieski 1,422 2,296 89.23% 47,092 1.29% 0.11% (districts) czarnkowsko-trzcianecki 2,661 2,341 90.97% 86,448 2.41% 0.21% gnieźnieÅ„ski 4,225 2,283 88.72% 140,752 3.83% 0.34% gostyÅ„ski 2,291 2,296 89.24% 75,861 2.08% 0.18% grodziski 1,297 1,992 77.42% 49,490 1.18% 0.10% jarociÅ„ski 1,841 1,986 77.17% 70,489 1.67% 0.15% kaliski 2,171 2,049 79.62% 80,569 1.97% 0.17% kÄ™piÅ„ski 1,291 1,763 68.51% 55,670 1.17% 0.10% kolski 3,033 2,609 101.39% 88,404 2.75% 0.24% koniÅ„ski 4,417 2,694 104.69% 124,703 4.01% 0.35% koÅ›ciaÅ„ski 1,894 2,256 87.68% 77,992 2.10% 0.15% krotoszyÅ„ski 2,203 2,172 84.41% 77,144 2.00% 0.18% leszczyÅ„ski 1,698 2,550 99.12% 50,635 1.54% 0.14% miÄ™dzychodzki 1,032 2,154 83.70% 36,448 0.94% 0.08% nowotomyski 2,274 2,398 93.18% 72,119 2.06% 0.18% obornicki 1,738 2,347 91.22% 56,325 1.58% 0.14% ostrowski 4,800 2,303 89.52% 158,485 4.36% 0.38% ostrzeszowski 1,593 2,223 86.41% 54,477 1.45% 0.13% 5. Appendix pilski 4,632 2,568 99.80% 137,169 4.20% 0.37% pleszewski 1,367 2,045 79.46% 62,103 1.51% 0.11% poznaÅ„ski 10,367 2,597 100.92% 303,595 9.41% 0.83% rawicki 1,488 1,898 73.77% 59,596 1.35% 0.12% sÅ‚upecki 1,668 2,165 84.16% 58,561 1.51% 0.13% szamotulski 2,899 2,549 99.05% 86,510 2.63% 0.23% Å›redzki 1,507 2,549 99.05% 54,909 1.67% 0.12% Å›remski 1,711 2,214 86.05% 58,760 1.55% 0.14% turecki 2,518 2,290 89.02% 83,585 2.28% 0.20% wÄ…growiecki 1,912 2,144 83.31% 67,842 1.74% 0.15% wolsztyÅ„ski 1,581 2,181 84.75% 55,125 1.43% 0.13% wrzesiÅ„ski 2,154 2,207 85.78% 74,223 1.95% 0.17% zÅ‚otowski 1,980 2,193 85.23% 68,641 1.80% 0.16% Kalisz 3,435 2,418 93.98% 108,031 3.12% 0.28% Wielkopolskie Konin 2,864 2,718 105.62% 80,140 2.60% 0.23% (cities) Leszno 1,918 2,277 88.48% 64,057 1.74% 0.15% PoznaÅ„ 23,286 3,157 122.69% 560,932 21.13% 1.87% Wielkopolskie 109,167 2,475 96.19% 3,386,882 100.00% 8.75% biaÅ‚ogardzki 1,349 2,195 85.30% 48,261 2.47% 0.11% choszczeÅ„ski 1,209 2,229 86.61% 49,833 2.59% 0.10% drawski 1,591 2,159 83.90% 57,887 2.92% 0.13% goleniowski 2,476 2,450 95.23% 79,354 4.54% 0.20% gryficki 1,703 2,203 85.62% 60,709 3.12% 0.14% gryfiÅ„ski 3,096 2,929 113.83% 83,028 5.68% 0.25% kamieÅ„ski 1,341 2,209 85.87% 47,683 2.46% 0.11% koÅ‚obrzeski 2,278 2,337 90.82% 76,547 4.18% 0.18% Zachodnio- koszaliÅ„ski 1,882 2,296 89.25% 64,366 3.45% 0.15% pomorskie (districts) myÅ›liborski 1,890 2,207 85.76% 67,257 3.46% 0.15% policki 2,567 3,035 117.96% 66,436 4.71% 0.21% pyrzycki 918 2,113 82.10% 39,927 1.97% 0.07% sÅ‚awieÅ„ski 1,551 2,124 82.56% 57,332 2.84% 0.12% stargardzki 3,525 2,323 90.28% 119,192 6.46% 0.28% szczecinecki 2,526 2,569 99.85% 77,234 4.63% 0.20% Å›widwiÅ„ski 1,283 2,076 80.67% 48,555 2.35% 0.10% waÅ‚ecki 1,651 2,383 92.63% 54,406 3.03% 0.13% Å‚obeski 1,034 2,125 82.59% 38,206 1.90% 0.08% Zachodnio- Koszalin 2,962 2,535 98.52% 107,376 6.35% 0.24% pomorskie Szczecin 15,451 2,976 115.65% 407,811 28.33% 1.24% (cities) ÅšwinoujÅ›cie 1,387 2,666 103.62% 40,871 2.54% 0.11% Zachodniopomorskie 53,668 2,531 98.38% 1,692,271 100.00% 4.30% POLAND 1,248,180 2,573 100.00% 38,115,641 100.00% 5.2 Estimated Loss – Country Overviews 5.2.1 Loss Exceedance Extrapolated from Stochastic Method In this paragraph, the loss exceedance curves (LEC) and survival functions calculated by stochastic (probabilistic) method174 for each of V–4 countries and the whole V–4 Group including estimated confidence intervals175 for each LEC are shown. All graphs and tables are shown for the total as well as public property. 174 For description of the stochastic method loss calculation, see Paragraph 3.10 175 For description of the confidence limits estimation, see Paragraph 3.11.3 5. Appendix a) Loss Exceedance in the Czech Republic Table 5.48: LEC of total losses and losses on public property (Czech Republic) [million EUR] RTP [years] 20 50 100 250 500 Probability 5.0% 2.0% 1.0% 0.4% 0.2% LEC – total property 768 2 001 3 405 5 586 7 169 lower limit 307 1 001 2 043 3 352 4 301 upper limit 1 229 3 002 4 767 7 821 10 036 LEC – public property 245 638 1 086 1 781 2 286 lower limit 98 319 651 1 069 1 372 upper limit 392 957 1 520 2 494 3 200 12 000 3 500 LEC LEC Lower confidence limit Lower confidence limit Upper confidence limit 3 000 10 000 Upper confidence limit Loss [mil EUR] Loss [mil EUR] 2 500 8 000 2 000 6 000 1 500 4 000 1 000 2 000 500 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Figure 5.74: LEC and confidence limits for losses Figure 5.75: LEC and confidence limits for losses on total property (Czech Republic) on public property (Czech Republic) 6.0% 12 000 LEC - total Survival function - total Lower limit - total Upper limit - total Lower limit - total LEC - public 5.0% Upper limit - total 10 000 Lower limit - public Loss [mil EUR] Probability [%] Survival function - public Upper limit - public Lower limit - public 4.0% Upper limit - public 8 000 3.0% 6 000 2.0% 4 000 1.0% 2 000 0.0% 0 0 2 000 4 000 6 000 8 000 10 000 0 100 200 300 400 500 Loss [mil EUR] Return Period [years] Figure 5.76: Survival functions and confidence Figure 5.77: Comparison of LEC and confidence limits for losses on total property and public limits for losses on total property and public property (Czech Republic) property (Czech Republic) 5. Appendix b) Loss Exceedance in Slovakia Table 5.49: LEC of total losses and losses on public property (Slovakia) [million EUR] RTP [years] 20 50 100 250 500 Probability 5.0% 2.0% 1.0% 0.4% 0.2% LEC – total property 1 191 3 219 5 452 8 942 11 659 lower limit 476 1 609 3 271 5 365 6 995 upper limit 1 905 4 828 7 633 12 519 16 322 LEC – public property 412 1 114 1 887 3 095 4 036 lower limit 165 557 1 132 1 857 2 421 upper limit 659 1 671 2 642 4 334 5 650 18 000 6 000 LEC LEC 16 000 Lower confidence limit Lower confidence limit Loss [mil EUR] Upper confidence limit 5 000 Upper confidence limit Loss [mil EUR] 14 000 12 000 4 000 10 000 3 000 8 000 6 000 2 000 4 000 1 000 2 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Figure 5.78: LEC and confidence limits for losses Figure 5.79: LEC and confidence limits for losses on total property (Slovakia) on public property (Slovakia) 6.0% 18 000 LEC - total Survival function - total Lower limit - total 16 000 Upper limit - total Loss [mil EUR] Lower limit - total Probability [%] 5.0% LEC - public Upper limit - total Lower limit - public 14 000 Survival function - public Upper limit - public Lower limit - public 4.0% 12 000 Upper limit - public 10 000 3.0% 8 000 2.0% 6 000 4 000 1.0% 2 000 0.0% 0 0 5 000 10 000 15 000 0 100 200 300 400 500 Loss [mil EUR] Return Period [years] Figure 5.80: Survival functions and confidence Figure 5.81: Comparison of LEC and confidence limits for losses on total property and public limits for losses on total property and public property (Slovakia) property (Slovakia) 5. Appendix c) Loss Exceedance in Hungary Table 5.50: LEC of total losses and losses on public property (Hungary) [million EUR] RTP [years] 20 50 100 250 500 Probability 5.0% 2.0% 1.0% 0.4% 0.2% LEC – total property 966 2 485 4 235 6 948 8 865 lower limit 386 1 242 2 541 4 169 5 319 upper limit 1 546 3 727 5 929 9 728 12 411 LEC – public property 248 638 1 087 1 783 2 275 lower limit 99 319 652 1 070 1 365 upper limit 397 957 1 522 2 497 3 185 14 000 LEC 3 500 LEC Lower confidence limit Lower confidence limit Upper confidence limit Upper confidence limit Loss [mil EUR] Loss [mil EUR] 12 000 3 000 10 000 2 500 8 000 2 000 6 000 1 500 4 000 1 000 2 000 500 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Figure 5.82: LEC and confidence limits for losses Figure 5.83: LEC and confidence limits for losses on total property (Hungary) on public property (Hungary) 6.0% Survival function - total 14 000 Lower limit - total LEC - total Probability [%] Upper limit - total Lower limit - total Loss [mil EUR] 12 000 Upper limit - total 5.0% Survival function - public LEC - public Lower limit - public Lower limit - public Upper limit - public Upper limit - public 10 000 4.0% 8 000 3.0% 6 000 2.0% 4 000 1.0% 2 000 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 0 Loss [mil EUR] 0 100 200 300 400 500 Return Period [years] Figure 5.84: Survival functions and confidence Figure 5.85: Comparison of LEC and confidence limits for losses on total property and public limits for losses on total property and public property (Hungary) property (Hungary) 5. Appendix d) Loss Exceedance in Poland Table 5.51: LEC of total losses and losses on public property (Poland) [million EUR] RTP [years] 20 50 100 250 500 Probability 5.0% 2.0% 1.0% 0.4% 0.2% LEC – total property 2 508 5 755 9 954 16 350 19 751 lower limit 1 003 2 877 5 972 9 810 11 851 upper limit 4 013 8 632 13 935 22 890 27 652 LEC – public property 904 2 074 3 586 5 891 7 117 lower limit 361 1 037 2 152 3 535 4 270 upper limit 1 446 3 110 5 021 8 247 9 963 30 000 LEC 12 000 LEC Lower confidence limit Lower confidence limit Upper confidence limit Loss [mil EUR] Loss [mil EUR] Upper confidence limit 25 000 10 000 20 000 8 000 15 000 6 000 10 000 4 000 5 000 2 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Figure 5.86: LEC and confidence limits for losses Figure 5.87: LEC and confidence limits for losses on total property (Poland) on public property (Poland) 6.0% Survival function - total 30 000 LEC - total Lower limit - total Lower limit - total Probability [%] Upper limit - total Loss [mil EUR] Upper limit - total LEC - public 5.0% Survival function - public 25 000 Lower limit - public Lower limit - public Upper limit - public Upper limit - public 4.0% 20 000 3.0% 15 000 2.0% 10 000 1.0% 5 000 0.0% 0 0 2 000 4 000 6 000 8 000 10 000 Loss [mil EUR] 0 100 200 300 400 500 Return Period [years] Figure 5.88: Survival functions and confidence Figure 5.89: Comparison of LEC and confidence limits for losses on total property and public limits for losses on total property and public property (Poland) property (Poland) 5. Appendix e) Loss Exceedance in the V–4 Group Table 5.52: LEC of total losses and losses on public property (V–4 Group) [million EUR] RTP [years] 20 50 100 250 500 Probability 5.0% 2.0% 1.0% 0.4% 0.2% LEC – total property 4 960 10 708 18 680 30 703 35 904 lower limit 1 984 5 354 11 208 18 422 21 542 upper limit 7 937 16 062 26 152 42 984 50 265 LEC – public property 1 646 3 553 6 198 10 187 11 913 lower limit 658 1 776 3 719 6 112 7 148 upper limit 2 633 5 329 8 677 14 262 16 678 Sum of LEC over individual 176 5 433 13 460 23 046 37 827 47 444 countries, total property Sum of LEC over individual 1 809 4 464 7 646 12 551 15 713 countries, public property LEC LEC Loss [mil EUR] 60 000 20 000 Lower confidence limit Lower confidence limit Loss [mil EUR] Upper confidence limit 18 000 Upper confidence limit 50 000 SUM over individual countries Sum over individual countries 16 000 14 000 40 000 12 000 30 000 10 000 8 000 20 000 6 000 10 000 4 000 2 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Figure 5.90: LEC and confidence limits for Figure 5.91: LEC and confidence limits for losses on total property (V–4 Group) losses on public property177 (V–4 Group) Survival function - total LEC - total Loss [mil EUR] 6.0% 60 000 Lower limit - total Lower limit - total Probability [%] Upper limit - total Upper limit - total LEC - public 5.0% Survival function - public 50 000 Lower limit - public Upper limit - public Lower limit - public Upper limit - public 4.0% 40 000 3.0% 30 000 2.0% 20 000 1.0% 10 000 0.0% 0 0 10 000 20 000 30 000 40 000 50 000 60 000 0 100 200 300 400 500 Loss [mil EUR] Return Period [years] Figure 5.92: Survival functions and confidence Figure 5.93: Comparison of LEC and limits for losses on total property and public confidence limits for losses on total property and property (V–4 Group) public property (V–4 Group) 176 see Paragraph 3.11.2 177 The dashed line represents the sum of LEC of individual countries; see Paragraph 3.11.2 5. Appendix 5.2.2 LEC of All Scenarios This appendix contains loss exceedance curves (LEC) as well as survival functions of all scenarios, calculated for both total and public property. For a comparison, also the LEC calculated by the stochastic method are shown on the charts. Table 5.53: List of outputs – LEC of all scenarios LEC – total property LEC – public property Country Tables Figures Tables Figures Czech Republic 5.54 5.94a-d 5.55 5.95a-d Slovakia 5.56 5.96a-d 5.57 5.97a-d Hungary 5.58 5.98a-d 5.59 5.99a-d Poland 5.60 5.100a-d 5.61 5.101a-d V–4 Group 5.62 5.102a-d 5.63 5.103a-d a1) LEC of All Scenarios, Czech Republic, Losses on Total Property 6 000 Tab. 5.54: LEC for all scenarios [mil EUR] Loss [mil EUR] RTP [years] 20 50 100 250 500 Probability of loss exceedance 5.0% 2.0% 1.0% 0.4% 0.2% 5 000 Catchment-based scenarios Labe CZ Labe / Elbe 231 1 566 2 753 4 185 5 010 CZ Berounka 42 233 381 563 678 CZ Morava river 92 1 412 2 346 3 583 4 244 4 000 Morava river CZ Dyje 61 988 1 490 2 119 2 502 CZ Odra (upper) 81 780 1 554 2 562 3 126 CZ Ohre 40 169 368 561 652 CZ Vltava 81 773 1 215 1 771 2 126 3 000 Odra (upper) Scenarios based on real events CZ 2002 175 1 424 2 423 3 603 4 319 Dyje CZ 1997 189 2 538 4 383 6 767 8 089 Pan-regional scenarios 2 000 Vltava CZ Bohemia 441 2 987 5 093 7 613 9 105 CZ Moravia 234 3 180 5 390 8 264 9 872 Stochastic method Berounka 1 000 CZ Stochastic 768 2 001 3 405 5 586 7 169 Ohre 0 0 100 200 300 400 500 Return Period [years] Fig. 5.94a: LEC of the catchment-based scenarios 9 000 12 000 CZ 2002 Loss [mil EUR] CZ Bohemia Loss [mil EUR] 8 000 CZ 1997 CZ Moravia CZ Stochastic 10 000 CZ Stochastic 7 000 6 000 8 000 5 000 6 000 4 000 3 000 4 000 2 000 2 000 1 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Fig. 5.94b: LEC of the scenarios based on real events Fig. 5.94c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability CZ 2002 CZ 1997 4.5% CZ Bohemia CZ Labe / Elbe 4.0% CZ Berounka CZ Morava river CZ Dyje CZ Odra (upper) 3.5% CZ Ohre CZ Vltava CZ Moravia CZ Stochastic 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 Loss [mil EUR] Probabilistic Odra (upper) Morava river Berounka Bohemia CZ 1997 Moravia CZ 2002 Vltava Ohre Labe Dyje Fig. 5.94d: Survival functions of all scenarios a2) LEC of All Scenarios, Czech Republic, Losses on Public Property Tab. 5.55: LEC for all scenarios [mil EUR] 1 800 Loss [mil EUR] RTP [years] 20 50 100 250 500 Probability of loss Labe 5.0% 2.0% 1.0% 0.4% 0.2% 1 600 exceedance Catchment-based scenarios CZ Labe / Elbe 46 448 840 1 312 1 581 1 400 Morava river CZ Berounka 7 64 111 168 205 CZ Morava river 20 415 703 1 085 1 289 1 200 CZ Dyje 13 324 506 734 875 CZ Odra (upper) 23 244 453 746 925 Odra (upper) CZ Ohre 12 61 141 218 255 1 000 CZ Vltava 17 255 417 622 750 Dyje Scenarios based on real events 800 CZ 2002 34 442 799 1 222 1 472 Vltava CZ 1997 45 761 1 301 2 024 2 439 Pan-regional scenarios 600 CZ Bohemia 94 909 1 638 2 511 3 021 CZ Moravia 55 983 1 661 2 565 3 089 400 Stochastic method Ohre CZ Stochastic 228 594 1 010 1 657 2 126 200 Berounka 0 0 100 200 300 400 500 Return Period [years] Fig. 5.95a: LEC of the catchment-based scenarios Loss [mil EUR] 3 000 Loss [mil EUR] 3 500 CZ 2002 CZ Bohemia CZ 1997 CZ Moravia 2 500 CZ Stochastic 3 000 CZ Stochastic 2 500 2 000 2 000 1 500 1 500 1 000 1 000 500 500 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Fig. 5.95b: LEC of the scenarios based on real events Fig. 5.95c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability CZ 2002 CZ 1997 4.5% CZ Bohemia CZ Labe / Elbe 4.0% CZ Berounka CZ Morava river CZ Dyje CZ Odra (upper) 3.5% CZ Ohre CZ Vltava CZ Moravia CZ Stochastic 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil EUR] Probabilistic Odra (upper) Morava river Berounka Moravia Bohemia CZ 1997 CZ 2002 Vltava Ohre Labe Dyje Fig. 5.95d: Survival functions of all scenarios b1) LEC of All Scenarios, Slovakia, Losses on Total Property Tab. 5.56: LEC for all scenarios [mil EUR] Loss [mil EUR] SK Dunaj / Danube RTP [years] 20 50 100 250 500 12 000 Probability of loss SK Vah + Nitra 5.0% 2.0% 1.0% 0.4% 0.2% Dunaj / Danube exceedance SK Hron + Ipel Catchment-based scenarios SK Hornad + Slana SK Dunaj / Danube 139 1 130 2 678 7 808 11 011 10 000 SK Tisza + Bodrog SK Vah + Nitra 547 2 956 4 468 6 682 8 032 SK Hron + Ipel 171 891 1 436 2 102 2 472 Vah + Nitra SK Hornad + Slana 191 1 040 1 576 2 384 2 949 8 000 SK Tisza + Bodrog 65 546 979 1 598 1 981 Pan-regional scenarios SK Dunaj / Danube Large 857 4 977 8 581 16 592 21 515 SK Tisza Large 255 1 586 2 555 3 982 4 930 6 000 Stochastic method SK Stochastic 1 191 3 219 5 452 8 942 11 659 4 000 Hornad + Slana Hron + Ipel 2 000 Tisza + Bodrog 0 0 100 200 300 400 500 Return Period [years] Fig. 5.96a: LEC of the catchment-based scenarios Loss [mil EUR] 25 000 SK Dunaj / Danube Large SK Tisza Large SK Stochastic 20 000 15 000 10 000 5 000 0 0 100 200 300 400 500 Return Period [years] Fig. 5.96c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability SK Dunaj / Danube SK Vah + Nitra 4.5% SK Hron + Ipel SK Hornad + Slana SK Tisza + Bodrog SK Dunaj / Danube Large 4.0% SK Tisza Large SK Stochastic 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 5 000 10 000 15 000 20 000 25 000 Loss [mil EUR] Dunaj / Danube Large Tisza Large Tisza + Bodrog Hornad + Slana Dunaj/Danube Hron + Ipel Probabilistic Vah + Nitra Fig. 5.96d: Survival functions of all scenarios b2) LEC of All Scenarios, Slovakia, Losses on Public Property 4 000 Tab. 5.57: LEC for all scenarios [mil EUR] SK Dunaj / Danube Loss [mil EUR] RTP [years] 20 50 100 250 500 SK Vah + Nitra Probability of loss 3 500 Dunaj / Danube 5.0% 2.0% 1.0% 0.4% 0.2% SK Hron + Ipel exceedance SK Hornad + Slana Catchment-based scenarios 3 000 SK Tisza + Bodrog SK Dunaj / Danube 28 375 922 2 578 3 603 SK Vah + Nitra 161 983 1 502 2 251 2 705 Vah + Nitra SK Hron + Ipel 58 328 545 814 964 2 500 SK Hornad + Slana 68 395 599 919 1 148 SK Tisza + Bodrog 20 214 374 602 742 Pan-regional scenarios 2 000 SK Dunaj / Danube Large 247 1 687 2 969 5 642 7 272 SK Tisza Large 88 608 973 1 521 1 891 Stochastic method 1 500 SK Stochastic 412 1 114 1 887 3 095 4 036 Hornad + Slana 1 000 Hron + Ipel 500 Tisza + Bodrog 0 0 100 200 300 400 500 Return Period [years] Fig. 5.97a: LEC of the catchment-based scenarios Loss [mil EUR] 8 000 SK Dunaj / Danube Large SK Tisza Large 7 000 SK Stochastic 6 000 5 000 4 000 3 000 2 000 1 000 0 0 100 200 300 400 500 Return Period [years] Fig. 5.97c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability SK Dunaj / Danube SK Vah + Nitra 4.5% SK Hron + Ipel SK Hornad + Slana SK Tisza + Bodrog SK Dunaj / Danube Large 4.0% SK Tisza Large SK Stochastic 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 Loss [mil EUR] Dunaj / Danube Large Tisza Large Tisza + Bodrog Hornad + Slana Dunaj/Danube Hron + Ipel Probabilistic Vah + Nitra Fig. 5.97d: Survival functions of all scenarios c1) LEC of All Scenarios, Hungary, Losses on Total Property 10 000 HU Danube / Duna Tab. 5.58: LEC for all scenarios [mil EUR] HU Drava Loss [mil EUR] RTP [years] 20 50 100 250 500 HU Hernad + Sajo Probability of loss 9 000 HU Tizsa + Bodrog Danube / Duna exceedance 5.0% 2.0% 1.0% 0.4% 0.2% HU Raba HU Sio Catchments-based scenarios 8 000 HU Koros HU Danube / Duna 230 2 539 4 510 7 363 9 020 HU Drava 26 302 536 877 1 061 7 000 HU Hernad + Sajo 27 306 545 890 1 062 HU Tizsa + Bodrog 154 1 740 3 094 5 056 6 050 Tisza + Bodrog HU Raba 85 944 1 678 2 741 3 304 6 000 HU Sio 62 718 1 277 2 089 2 562 HU Koros 35 402 715 1 170 1 402 5 000 Pan-regional scenarios HU Danube Large 404 4 503 8 001 13 070 15 947 4 000 HU Tisza Large 216 2 448 4 353 7 116 8 514 Raba Stochastic method 3 000 HU Stochastic 966 2 485 4 235 6 948 8 865 Sio 2 000 Koros 1 000 Hernad+Sajo 0 Drava 0 100 200 300 400 500 Return Period [years] Fig. 5.98c: LEC of the pan-regional scenarios HU Danube Loss [mil EUR] 18 000 Large HU Tisza Large 16 000 14 000 12 000 10 000 8 000 6 000 4 000 2 000 0 0 100 200 300 400 500 Return Period [years] Fig. 3: LEC of the pan-regional scenarios Loss exceedance probability 5.0% HU Danube Large HU Tisza Large 4.5% HU Danube / Duna HU Drava 4.0% HU Hernad + Sajo HU Tizsa + Bodrog HU Raba HU Sio 3.5% HU Koros HU Stochastic 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 Loss [mil EUR] Danube / Duna Tisza + Bodrog Danube / Duna Hernad+Sajo Tisza Large Stochastic Drava Koros Raba Sio Large Fig. 5.98d: Survival functions of all scenarios c2) LEC of All Scenarios, Hungary, Losses on Public Property 2 500 HU Danube / Duna Tab. 5.59: LEC for all scenarios [mil EUR] HU Drava Loss [mil EUR] RTP [years] 20 50 100 250 500 HU Hernad + Sajo Danube / Duna Probability of loss HU Tizsa + Bodrog 5.0% 2.0% 1.0% 0.4% 0.2% exceedance HU Raba Catchments-based scenarios 2 000 HU Sio HU Danube / Duna 58 640 1 136 1 856 2 278 HU Koros HU Drava 7 77 138 225 273 Tisza + Bodrog HU Hernad + Sajo 8 91 161 263 314 HU Tizsa + Bodrog 44 497 889 1 451 1 738 HU Raba 18 201 357 584 703 1 500 HU Sio 14 162 289 473 581 HU Koros 10 110 196 321 385 Pan-regional scenarios HU Danube Large 96 1 081 1 920 3 138 3 835 1 000 HU Tisza Large 61 698 1 246 2 035 2 437 Raba Stochastic method HU Stochastic 248 638 1 087 1 783 2 275 Sio 500 Koros Hernad+Sajo 0 Drava 0 100 200 300 400 500 Return Period [years] Fig. 5.99a: LEC of the catchment-based scenarios Loss [mil EUR] 4 500 HU Danube Large 4 000 HU Tisza Large HU Stochastic 3 500 3 000 2 500 2 000 1 500 1 000 500 0 0 100 200 300 400 500 Return Period [years] Fig. 5.99c: LEC of the pan-regional scenarios Loss exceedance probability 5.0% HU Danube Large HU Tisza Large 4.5% HU Danube / Duna HU Drava 4.0% HU Hernad + Sajo HU Tizsa + Bodrog HU Raba HU Sio 3.5% HU Koros HU Stochastic 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 Loss [mil EUR] Hernad+Sajo Tisza + Bodrog Danube / Duna Danube / Duna Tisza Large Stochastic Drava Large Koros Raba Sio Fig. 5.99d: Survival functions of all scenarios d1) LEC of All Scenarios, Poland, Losses on Total Property Tab. 5.60: LEC for all scenarios [mil EUR] PL Vistula Upper PL Vistula Middle Loss [mil EUR] PL Vistula Lower PL Bug RTP [years] 20 50 100 250 500 20 000 PL San PL Odra Upper Probability of loss PL Odra Lower PL Warta exceedance 5.0% 2.0% 1.0% 0.4% 0.2% PL Notec PL Nysa+Bobr Vistula Middle PL Baltic West PL Baltic East Catchments-based scenarios PL Vistula Upper 221 1 116 3 836 6 998 8 595 PL Vistula Middle 1 387 6 975 10 050 14 158 16 721 15 000 Odra Upper PL Vistula Lower 398 2 004 4 015 6 352 7 636 PL Bug 341 1 718 3 462 5 634 6 876 Warta PL San 26 130 465 847 1 041 PL Odra Upper 330 1 661 5 055 8 966 10 912 PL Odra Lower 181 908 1 434 1 973 2 261 Vistula Upper 10 000 PL Warta 175 883 3 692 7 023 8 806 PL Notec 19 98 450 926 1 231 Vistula Lower PL Nysa+Bobr 13 67 215 384 472 PL Baltic West 21 107 478 925 1 192 Bug PL Baltic East 4 21 149 336 450 Scenarios based on real events 5 000 Odra Lower PL 1997 678 3 416 9 891 17 302 21 015 Pan-regional scenarios PL Vistula Large 2 372 11 944 21 827 33 988 40 869 PL Odra Large 718 3 618 10 848 19 273 23 683 others Stochastic method 0 PL Stochastic 2 508 5 755 9 954 16 350 19 751 0 100 200 300 400 500 Return Period [years] Fig. 5.100a: LEC of the catchment-based scenario Loss [mil EUR] 25 000 45 000 PL 1997 PL Vistula Large Loss [mil EUR] PL Stochastic 40 000 PL Odra Large 20 000 PL Stochastic 35 000 30 000 15 000 25 000 20 000 10 000 15 000 5 000 10 000 5 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Fig. 5.100b: LEC of the scenarios based on real events Fig. 5.100c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability Fig. 2: LEC function of the scenarios based on real events PL 1997 PL Vistula Large 4.5% PL Vistula Upper PL Vistula Middle PL Vistula Lower PL Bug PL San PL Odra Upper 4.0% PL Odra Lower PL Odra Large PL Stochastic PL Warta 3.5% PL Notec PL Nysa+Bobr PL Baltic West PL Baltic East 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 45 000 Loss [mil EUR] Vistula Large Vistula Lower Odra Lower Vistula Upper Odra Upper Vistula Middle Odra Large Stochastic others Warta 1997 Bug Fig. 5.100d: Survival functions of all scenarios d2) LEC of All Scenarios, Poland, Losses on Public Property 8 000 Tab. 5.61: LEC for all scenarios [mil EUR] PL Vistula Upper PL Vistula Middle Loss [mil EUR] PL Vistula Lower PL Bug RTP [years] 20 50 100 250 500 PL San PL Odra Upper Probability of loss 7 000 PL Odra Lower PL Warta 5.0% 2.0% 1.0% 0.4% 0.2% PL Notec PL Nysa+Bobr Vistula Middle exceedance PL Baltic West PL Baltic East Catchments-based scenarios PL Vistula Upper 76 382 1 329 2 432 2 991 6 000 PL Vistula Middle 473 2 382 3 461 4 898 5 790 Odra Upper PL Vistula Lower 137 689 1 390 2 202 2 648 PL Bug 117 590 1 209 1 980 2 420 5 000 Warta PL San 9 48 171 311 382 PL Odra Upper 132 664 2 003 3 540 4 307 4 000 Vistula Upper PL Odra Lower 76 381 619 865 996 PL Warta 60 302 1 271 2 411 3 017 PL Notec 7 35 161 333 444 Vistula Lower 3 000 PL Nysa+Bobr 5 26 84 150 184 PL Baltic West 9 43 201 392 504 Bug PL Baltic East 2 8 56 127 170 2 000 Scenarios based on real events Odra Lower PL 1997 254 1 278 3 704 6 474 7 862 Pan-regional scenarios 1 000 PL Vistula Large 812 4 090 7 561 11 823 14 231 PL Odra Large 279 1 409 4 139 7 300 8 948 others Stochastic method 0 PL Stochastic 904 2 074 3 586 5 891 7 117 0 100 200 300 400 500 Return Period [years] Fig. 5.101a: LEC of the catchment-based scenarios 9 000 16 000 PL 1997 PL Vistula Large Loss [mil EUR] Loss [mil EUR] 8 000 PL Stochastic PL Odra Large 14 000 PL Stochastic 7 000 12 000 6 000 10 000 5 000 8 000 4 000 6 000 3 000 2 000 4 000 1 000 2 000 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Return Period [years] Return Period [years] Fig. 5.101b: LEC of the scenarios based on real events Fig. 5.101c: LEC of the pan-regional scenarios 5.0% Loss exceedance probability Fig. 2: LEC function of the scenarios based on real events PL 1997 PL Vistula Large 4.5% PL Vistula Upper PL Vistula Middle PL Vistula Lower PL Bug PL San PL Odra Upper 4.0% PL Odra Lower PL Odra Large PL Stochastic PL Warta 3.5% PL Notec PL Nysa+Bobr PL Baltic West PL Baltic East 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 Loss [mil EUR] Vistula Large Vistula Lower Odra Lower Vistula Upper Odra Upper Vistula Middle Odra Large Stochastic others Warta Bug 1997 Fig. 5.101d: Survival functions of all scenarios e1) LEC of Cross-border Scenarios, Losses on Total Property, V-4 40 000 V4 Odra / Oder Tab. 5.62a: LEC for all scenarios [mil EUR] V4 Dunaj / Danube Loss [mil EUR] RTP [years] 20 50 100 250 500 Probability of loss V4 Tisza Stochastic 5.0% 2.0% 1.0% 0.4% 0.2% 35 000 exceedance V4 Stochastic Cross-border scenarios V4 Odra / Oder 591 3 350 8 044 13 501 16 298 30 000 V4 Dunaj / Danube 369 3 669 7 187 15 171 20 031 V4 Tisza 471 4 034 6 908 11 098 13 444 Stochastic method 25 000 V4 Stochastic 4 960 10 708 18 680 30 703 35 904 Dunaj / Danube 20 000 Odra / Oder 15 000 Tisza 10 000 5 000 0 0 100 200 300 400 500 Return Period [years] Fig. 5.102a: LEC of the cross-border scenarios 5.0% Loss exceedance probability V4 Odra / Oder 4.5% V4 Dunaj / Danube V4 Tisza 4.0% V4 Stochastic 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 Loss [mil EUR] Dunaj / Danube Odra / Oder Stochastic Tisza Fig. 5.102d: Survival functions of all scenarios e2) LEC of Cross-border Scenarios, Losses on Public Property, V-4 14 000 V4 Odra / Oder Tab. 5.63a: LEC for all scenarios [mil EUR] Loss [mil EUR] RTP [years] 20 50 100 250 500 V4 Dunaj / Danube Probability of loss V4 Tisza exceedance 5.0% 2.0% 1.0% 0.4% 0.2% 12 000 Stochastic V4 Stochastic Cross-border scenarios V4 Odra / Oder 230 1 289 3 075 5 152 6 228 V4 Dunaj / Danube 86 1 015 2 058 4 434 5 881 10 000 V4 Tisza 149 1 306 2 218 3 556 4 327 Stochastic method V4 Stochastic 1 639 3 539 6 173 10 147 11 865 8 000 Dunaj / Danube 6 000 Odra / Oder 4 000 Tisza 2 000 0 0 100 200 300 400 500 Return Period [years] Fig. 5.103a: LEC of the cross-border scenarios 5.0% Loss exceedance probability V4 Odra / Oder 4.5% V4 Dunaj / Danube V4 Tisza 4.0% V4 Stochastic 3.5% 3.0% 2.5% 2.0% 1.5% 1.0% 0.5% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 Loss [mil EUR] Dunaj / Danube Odra / Oder Stochastic Tisza Fig. 5.103d: Survival functions of all scenarios 5. Appendix 5.2.3 Loss Structure Comparison of All Scenarios This appendix contains classification of the losses by the sector / purpose classification (see Paragraph 3.2.9.) of all events for the 250 years return period. Table 5.64: List of outputs – Loss Structure Comparison of All Scenarios Country Table Figure Czech Republic 5.65 5.104 Slovakia 5.66 5.105 Hungary 5.67 5.106 Poland 5.68 5.107 V–4 Group 5.69 5.108 Comparison of the Loss Structure of the Scenarios - Czech Republic Tab. 5.65: Loss structure for the RTP 250 years for the scenarios in the Czech Republic [mil EUR] infrastructure- enterprise- infrastructure- enterprise-non scenario public TOTAL public public non-public public Catchment-based scenarios CZ Labe 1 181 131 1 312 102 2 770 4 185 CZ Berounka 156 12 168 6 389 563 CZ Morava river 977 108 1 085 64 2 434 3 583 CZ Dyje 663 71 734 55 1 330 2 119 CZ Odra (upper) 628 118 746 64 1 751 2 562 CZ Ohre 195 23 218 21 322 561 CZ Vltava 575 46 622 55 1 094 1 771 Scenarios based on real events CZ 2002 1 114 108 1 222 103 2 278 3 603 CZ 1997 1 777 247 2 024 145 4 598 6 767 Pan-regional scenarios CZ Bohemia 2 275 236 2 511 200 4 902 7 613 CZ Moravia 2 268 302 2 569 186 5 564 8 319 Loss [mil EUR] 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 CZ Labe CZ Berounka CZ Morava river CZ Dyje infrastructure-public enterprise-public CZ Odra (upper) infrastructure-non-public enterprise-non public CZ Ohre CZ Vltava CZ 2002 CZ 1997 CZ Bohemia CZ Moravia Fig. 5.104: Loss structure for the Return Period 250 years for the scenarios in the Czech Republic [mil EUR] Comparison of the Loss Structure of the Scenarios - Slovakia Tab. 5.66: Loss structure for the RTP 250 years for the scenarios in Slovakia [mil EUR] infrastructure- enterprise- infrastructure- enterprise-non scenario public TOTAL public public non-public public Catchment-based scenarios SK Dunaj / Danube 2 265 313 2 578 396 4 834 7 808 SK Vah + Nitra 2 013 238 2 251 359 4 072 6 682 SK Hron + Ipel 753 61 814 114 1 174 2 102 SK Hornad + Slana 841 78 919 143 1 323 2 384 SK Tisza + Bodrog 549 53 602 88 908 1 598 Pan-regional scenarios SK Dunaj / Danube Large 5 031 611 5 642 870 10 080 16 592 SK Tisza Large 1 390 130 1 521 231 2 230 3 982 Loss [mil EUR] 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 SK Dunaj / Danube SK Vah + Nitra infrastructure-public SK Hron + Ipel enterprise-public infrastructure-non-public enterprise-non public SK Hornad + Slana SK Tisza + Bodrog SK Dunaj / Danube Large SK Tisza Large Fig. 5.105: Loss structure for the Return Period 250 years for the scenarios in Slovakia [mil EUR] Comparison of the Loss Structure of the Scenarios - Hungary Tab. 5.67: Loss structure for the RTP 250 years for the scenarios in Hungary [mil EUR] infrastructure- enterprise- infrastructure- enterprise-non scenario public TOTAL public public non-public public Catchment-based scenarios HU Danube / Duna 1 696 160 1 856 318 5 190 7 363 HU Drava 210 15 225 80 572 877 HU Hernad + Sajo 249 14 263 102 525 890 HU Tizsa + Bodrog 1 363 89 1 452 307 3 297 5 056 HU Raba 533 51 584 49 2 108 2 741 HU Sio 437 36 473 137 1 479 2 089 HU Koros 302 19 321 48 801 1 170 Pan-regional scenarios HU Danube Large 2 875 262 3 138 584 9 349 13 070 HU Tisza Large 1 914 122 2 036 458 4 622 7 116 Loss [mil EUR] 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 HU Danube / Duna HU Drava HU Hernad + Sajo infrastructure-public HU Tizsa + Bodrog enterprise-public infrastructure-non-public enterprise-non public HU Raba HU Sio HU Koros HU Danube Large HU Tisza Large Fig. 5.106: Loss structure for the Return Period 250 years for the scenarios in Hungary [mil EUR] Comparison of the Loss Structure of the Scenarios - Poland Tab. 5.68: Loss structure for the RTP 250 years for the scenarios in Poland [mil EUR] infrastructure- enterprise- infrastructure- enterprise-non scenario public TOTAL public public non-public public Catchment-based scenarios PL Vistula Upper 1 580 852 2 432 496 4 070 6 998 PL Vistula Middle 3 261 1 637 4 898 948 8 313 14 158 PL Vistula Lower 1 437 766 2 202 450 3 699 6 352 PL Bug 1 336 643 1 980 356 3 298 5 634 PL San 203 108 311 55 480 847 PL Odra Upper 2 231 1 309 3 540 786 4 640 8 966 PL Odra Lower 639 226 865 127 981 1 973 PL Warta 1 624 787 2 411 603 4 009 7 023 PL Notec 231 102 333 71 522 926 PL Nysa+Bobr 104 46 150 36 198 384 PL Baltic West 278 114 392 58 475 925 PL Baltic East 98 28 127 22 187 336 Scenarios based on real events PL 1997 4 098 2 376 6 474 1 377 9 451 17 302 Pan-regional scenarios PL Vistula Large 7 817 4 007 11 823 2 305 19 860 33 988 PL Odra Large 4 829 2 470 7 300 1 622 10 351 19 273 Loss [mil EUR] 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 PL Vistula Upper PL Vistula Middle PL Vistula Lower PL Bug PL San infrastructure-public PL Odra Upper enterprise-public PL Odra Lower infrastructure-non-public enterprise-non public PL Warta PL Notec PL Nysa+Bobr PL Baltic West PL Baltic East PL 1997 PL Vistula Large PL Odra Large Fig. 5.107: Loss structure for the Return Period 250 years for the scenarios in Poland [mil EUR] Comparison of the Loss Structure of the Cross-border Scenarios, V-4 Tab. 5.69: Loss structure for the RTP 250 years for V-4 cross-border scenarios [mil EUR] infrastructure- enterprise- infrastructure- enterprise-non scenario public TOTAL public public non-public public V4 Odra / Oder 3 499 1 653 5 152 977 7 373 13 501 V4 Dunaj / Danube 3 961 473 4 434 714 10 024 15 171 V4 Tisza 3 305 252 3 557 689 6 852 11 098 Loss [mil EUR] 0 2 500 5 000 7 500 10 000 12 500 15 000 17 500 V4 Odra / Oder V4 Dunaj / Danube infrastructure-public enterprise-public V4 Tisza infrastructure-non-public enterprise-non public Fig. 5.108: Loss structure for the Return Period 250 years for V-4 cross-border scenarios [mil EUR] 5. Appendix 5.2.4 Estimation of the Average Regional Loss Distribution In this paragraph, the average regional distribution of the losses calculated by scenario method178 for each of V–4 countries is shown, in particular, proportion of each NUTS-3 unit i.e. province (NUTS-2 in Poland) on the total loss of the country and loss intensity179 in each province during a hypothetical 250-years flood in the whole area180. The detailed results of all scenarios are included in Appendix 5.2.5. Table 5.70: Average Regional Loss Distribution for the Czech Republic Province (NUTS-3) % of total loss Loss intensity [%] Moravskoslezský 15.4% 4.6% Olomoucký 12.8% 7.8% Jihomoravský 12.5% 3.3% StÅ™edoÄ?eský 10.5% 2.5% Zlínský 10.0% 6.3% Královéhradecký 6.9% 4.0% Ústecký 6.9% 3.0% Pardubický 6.1% 4.2% Praha 4.7% 0.6% JihoÄ?eský 4.3% 2.1% Liberecký 3.2% 2.8% Plzeňský 2.9% 1.6% VysoÄ?ina 2.4% 1.6% Karlovarský 1.5% 1.8% Table 5.71: Average Regional Loss Distribution for Slovakia Province (NUTS-3) % of total loss Loss intensity [%] Bratislavský kraj 23% 7.2% Trnavský kraj 14% 10.2% Nitriansky kraj 13% 8.9% TrenÄ?iansky kraj 12% 8.2% KoÅ¡ický kraj 12% 6.2% Žilinský kraj 11% 7.1% Banskobystrický kraj 8% 6.2% PreÅ¡ovský kraj 8% 6.1% 178 For description of the scenario method loss calculation, see Paragraph 3.9 179 proportion of the province loss to the total property within the province 180 For methodology, see the Paragraph 3.11.6 5. Appendix Table 5.72: Average Regional Loss Distribution for Hungary Province (NUTS-3) % of total loss Loss intensity [%] Budapest 14.1% 0.7% Pest 10.2% 1.9% Gyor-Moson-Sopron 8.8% 3.9% Csongrád 8.5% 5.0% Borsod-Abaúj-Zemplén 7.2% 3.2% Vas 5.6% 4.2% Fejér 5.5% 2.6% Jász-Nagykun-Szolnok 4.8% 3.5% Komárom-Esztergom 4.5% 3.5% Baranya 4.0% 2.5% Bács-Kiskun 3.9% 2.2% Veszprém 3.2% 2.5% Szabolcs-Szatmár-Bereg 3.2% 2.1% Tolna 2.8% 2.8% Békés 2.7% 2.5% Hajdú-Bihar 2.7% 1.4% Heves 2.6% 2.2% Zala 2.4% 2.1% Somogy 2.0% 2.1% Nógrád 1.1% 2.0% Table 5.73: Average Regional Loss Distribution for Poland Province (NUTS-2) % of total loss Loss intensity [%] Mazowieckie 36.5% 7.6% DolnoÅ›lÄ…skie 9.4% 5.2% Wielkopolskie 9.3% 4.6% MaÅ‚opolskie 7.7% 4.3% ÅšlÄ…skie 7.6% 2.5% Å?ódzkie 5.9% 4.2% Kujawsko-pomorskie 3.3% 3.5% Lubelskie 3.1% 3.1% Lubuskie 3.0% 5.5% Zachodniopomorskie 2.9% 3.0% Opolskie 2.6% 4.3% Podkarpackie 2.4% 2.5% Pomorskie 2.0% 1.6% ÅšwiÄ™tokrzyskie 1.7% 2.7% WarmiÅ„sko-mazurskie 1.5% 2.3% Podlaskie 1.2% 2.1% 5. Appendix a) Average Regional Loss Distribution for the Czech Republic (250 year flood) Figure 5.109: Average regional loss distribution for the Czech Republic for scenario method (as % of the national loss) Figure 5.110: Average regional loss intensity for the Czech Republic for scenario method (as % of the total property in a province) 5. Appendix b) Average Regional Loss Distribution for Slovakia (250 year flood) Figure 5.111: Average regional loss distribution for Slovakia for scenario method (as % of the national loss) Figure 5.112: Average regional loss intensity for Slovakia for scenario method (as % of the total property in a province) 5. Appendix c) Average Regional Loss Distribution for Hungary (250 year flood) Figure 5.113: Average regional loss distribution for Hungary for scenario method (as % of the national loss) Figure 5.114: Average regional loss intensity for Hungary for scenario method (as % of the total property in a province) 5. Appendix d) Average Regional Loss Distribution for Poland (250 year flood) Figure 5.115: Average regional loss distribution for Poland for scenario method (as % of the national loss) Figure 5.116: Average regional loss intensity for Poland for scenario method (as % of the total property in a province) 5. Appendix 5.2.5 Average Loss Structure of the Countries This appendix contains average loss structure (for methodology see Paragraph 3.11.6) of each country for the 250-years flood return period181. In addition to it, the overall comparison of the average loss structure of all countries in the sector / purpose classification182 is shown in Table 5.74 and figure 5.117. Property [mil EUR] 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Czech Republic Slovakia Hungary Poland V-4 infrastructure-public enterprise-public infrastructure-non-public enterprise-non public Figure 5.117: Comparison of the average loss structure in the sector / purpose classification182 Table 5.74: Comparison of the average loss structure in the sector / purpose classification infrastructure- enterprise- infrastructure- enterprise- Country public public public non-public non public Czech Republic 29% 3% 32% 2% 66% Slovakia 31% 4% 35% 5% 60% Hungary 24% 2% 26% 6% 69% Poland 24% 11% 35% 6% 59% V–4 27% 4% 31% 5% 64% Table 5.75: List of outputs – Average Loss Structure of the Countries Country Tables Figures Czech Republic 5.76a-e 5.118a-f Slovakia 5.77a-e 5.119a-f Hungary 5.78a-e 5.120a-f Poland 5.79a-e 5.121a-f V–4 Group 5.80a-e 5.122a-f 181 For the average loss regional distribution, see Appendix 5.2.4 182 See Paragraph Sector / Purpose Re-classification of the Property a) Average Loss Structure, Czech Republic Tab. 5.76a: Losses on the public property Loss Category [%] infrastructure- infrastructure-public 29% public enterprise-public 3% 29% Public 32% infrastructure-non-public 2% enterprise-non-public 66% TOTAL 100% enterprise- public 3% enterprise-non- public infrastructure- 66% non-public 2% Fig. 5.118a: Loss by sector / purpose Classification Tab. 5.76b: Loss structure by asset categories Household Dwellings Loss Inventories Category code eguipment 19% [%] 9% 5% Dwellings AN.1111 19% Other buildings Machinery and and structures AN.1112 48% equipment Machinery and 20% equipment AN.1113 20% Inventories AN.12 9% Household eguipment - 5% TOTAL 100% Other buildings and structures 47% Fig. 5.118b: Loss structure by asset categories building Tab. 5.76c: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment sector ries [%] household equip. [%] [%] [%] [%] households 15.2% 1.0% 0.6% 4.9% 21.7% private 22.3% 16.5% 7.7% 0.0% 46.4% private public 29.1% 2.1% 0.6% 0.0% 31.9% TOTAL 66.7% 19.6% 8.8% 4.9% 100.0% households households public 22% 32% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% Fig. 5.118c: Loss structure by institutional sectors and physical property types 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% A+B C D private E 46% F Fig. 5.118d: Loss structure by institutional sectors G H I Tab. 5.76d: Loss structure by institutional sectors and industry branches house- J Industry branch public private holds Total loss [%] [%] [%] [%] K A+B (Agriculture, hunting L and forestry) 0.1% 1.2% 0.2% 1.4% C (Mining and quarrying) 0.7% 1.2% 0.0% 1.9% M D (Manufacturing) 0.4% 27.5% 1.0% 28.9% public E (Electricity, gas and water N private supply) 3.7% 1.8% 0.0% 5.5% households F (Construction) 0.0% 1.1% 0.1% 1.3% O G (Wholesale and retail trade; repair) 0.0% 4.6% 0.5% 5.2% Fig. 5.118e: Structure of the loss by industry branches and institutional sectors H (Hotels and restaurant) 0.0% 0.5% 0.0% 0.6% I (Transport, storage and communications) 9.1% 2.1% 0.2% 11.4% J (Financial intermediation) 0.0% 0.5% 0.0% 0.5% K (Real estate, renting, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% research) 2.1% 5.3% 19.6% 27.0% L (Public administration 7.7% 0.0% 0.0% 7.7% A+B M (Education) 4.5% 0.0% 0.0% 4.5% N (Health and social work) 1.6% 0.1% 0.1% 1.7% C O (Other community, social and personal services) 2.0% 0.5% 0.0% 2.4% D TOTAL 31.9% 46.4% 21.7% 100.0% E F Tab. 5.76e: Loss structure by institutional sectors and industry branches G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment ries [%] [%] [%] [%] [%] I A+B 1.0% 0.5% 0.0% 1.4% C 1.2% 0.6% 0.0% 1.9% J D 10.8% 12.3% 5.7% 28.9% E 4.4% 1.0% 0.1% 5.5% K F 0.5% 0.5% 0.3% 1.3% G 2.2% 0.9% 2.0% 5.2% L H 0.5% 0.0% 0.0% 0.6% I 9.5% 1.8% 0.0% 11.4% M building J 0.4% 0.1% 0.0% 0.5% equipment K 21.0% 0.9% 0.2% 4.9% 27.0% N 7.0% 0.2% 0.4% inventories L 7.7% household equip. M 4.4% 0.1% 0.0% 4.5% O N 1.4% 0.3% 0.0% 1.7% O 2.3% 0.2% 0.0% 2.4% Fig. 5.118f: Structure of the loss by industry branches and asset categories TOTAL 66.7% 19.6% 8.8% 4.9% 100.0% b) Average loss structure, Slovakia Tab. 5.77a: Losses on the public property Loss Category [%] infrastructure-public 31% infrastructure- enterprise-public 4% public Public 35% 31% infrastructure-non-public 5% enterprise-non-public 60% TOTAL 100% enterprise- public 4% enterprise-non- public infrastructure- 60% non-public 5% Fig. 5.119a: Loss by sector / purpose Classification Tab. 5.77b: Loss structure by asset categories Inventories Household Loss Dwellings Category code 8% eguipment [%] 21% 4% Dwellings AN.1111 21% Machinery and Other buildings equipment and structures AN.1112 47% 19% Machinery and equipment AN.1113 19% Inventories AN.12 8% Household eguipment - 4% TOTAL 100% Other buildings and structures 48% Fig. 5.119b: Loss structure by asset categories building Tab. 5.77c: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment sector ries [%] household equip. [%] [%] [%] [%] households 16.2% 0.8% 0.5% 4.4% 22.0% private 24.4% 12.3% 6.5% 0.0% 43.2% private public 27.5% 6.3% 1.0% 0.0% 34.8% TOTAL 68.1% 19.4% 8.0% 4.4% 100.0% households households 22% public 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 35% Fig. 5.119c: Loss structure by institutional sectors and physical property types 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% A+B C D private 43% E F Fig. 5.119d: Loss structure by institutional sectors G H I Tab. 5.77d: Loss structure by institutional sectors and industry branches house- J Industry branch public private holds Total loss [%] [%] [%] [%] K A+B (Agriculture, hunting L and forestry) 0.2% 1.6% 0.3% 2.1% C (Mining and quarrying) 0.6% 0.9% 0.0% 1.5% M D (Manufacturing) 0.3% 21.4% 0.8% 22.5% public E (Electricity, gas and water N private supply) 10.2% 5.0% 0.0% 15.2% households F (Construction) 0.0% 0.6% 0.1% 0.7% O G (Wholesale and retail trade; repair) 0.0% 4.9% 0.6% 5.5% Fig. 5.119e: Structure of the loss by industry branches and institutional sectors H (Hotels and restaurant) 0.0% 0.5% 0.0% 0.5% I (Transport, storage and communications) 7.9% 1.7% 0.1% 9.7% J (Financial intermediation) 0.0% 0.8% 0.0% 0.8% K (Real estate, renting, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% research) 2.5% 5.6% 20.1% 28.1% L (Public administration 9.6% 0.0% 0.0% 9.6% A+B M (Education) 1.4% 0.1% 0.0% 1.6% N (Health and social work) 1.1% 0.2% 0.0% 1.3% C O (Other community, social and personal services) 0.9% 0.0% 0.0% 0.9% D TOTAL 34.8% 43.2% 22.0% 100.0% E F Tab. 5.77e: Loss structure by institutional sectors and industry branches G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment ries [%] [%] [%] [%] [%] I A+B 1.6% 0.4% 0.0% 2.1% C 1.3% 0.2% 4.5% 6.0% J D 9.0% 9.0% 0.3% 18.3% E 11.4% 3.5% 0.1% 15.0% K F 0.3% 0.3% 2.1% 2.7% G 2.3% 1.1% 0.0% 3.4% L H 0.4% 0.1% 0.0% 0.5% I 6.9% 2.8% 0.0% 9.7% M building J 0.6% 0.2% 0.2% 1.0% equipment K 23.0% 0.4% 0.6% 4.4% 28.5% N 8.0% 1.0% 0.0% inventories L 9.0% household equip. M 1.4% 0.0% 0.0% 1.4% O N 1.0% 0.2% 0.0% 1.2% O 0.9% 0.2% 0.0% 1.1% Fig. 5.119f: Structure of the loss by industry branches and asset categories TOTAL 68.1% 19.4% 8.0% 4.4% 100.0% c) Average Loss Structure, Hungary Tab. 5.78a: Losses on the public property Loss infrastructure- Category [%] public 24% infrastructure-public 24% enterprise-public 2% Public 26% infrastructure-non-public 6% enterprise- enterprise-non-public 69% public TOTAL 100% 2% infrastructure- non-public enterprise-non- 6% public 68% Fig. 5.120a: Loss by sector / purpose Classification Tab. 5.78b: Loss structure by asset categories Household Inventories eguipment Loss Dwellings Category code 8% 4% [%] 23% Dwellings AN.1111 23% Other buildings Machinery and and structures AN.1112 45% equipment Machinery and 20% equipment AN.1113 20% Inventories AN.12 8% Household eguipment - 4% TOTAL 100% Other buildings and structures 45% Fig. 5.120b: Loss structure by asset categories building Tab. 5.78c: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment sector ries [%] household equip. [%] [%] [%] [%] households 22.6% 1.8% 0.8% 4.4% 29.6% private 22.7% 16.0% 6.0% 0.0% 44.7% private public 22.4% 2.0% 1.3% 0.0% 25.7% TOTAL 67.8% 19.8% 8.1% 4.4% 100.0% households households public 30% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 26% Fig. 5.120c: Loss structure by institutional sectors and physical property types 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% A+B C D private 44% E F Fig. 5.120d: Loss structure by institutional sectors G H I Tab. 5.78d: Loss structure by institutional sectors and industry branches house- J Industry branch public private holds Total loss [%] [%] [%] [%] K A+B (Agriculture, hunting L and forestry) 0.1% 3.9% 2.9% 6.9% C (Mining and quarrying) 0.0% 0.4% 0.0% 0.4% M D (Manufacturing) 0.0% 17.5% 0.2% 17.8% public E (Electricity, gas and water N private supply) 0.0% 5.2% 0.0% 5.2% households F (Construction) 0.0% 1.7% 0.1% 1.8% O G (Wholesale and retail trade; repair) 0.0% 5.0% 0.2% 5.3% Fig. 5.120e: Structure of the loss by industry branches and institutional sectors H (Hotels and restaurant) 0.0% 0.7% 0.0% 0.7% I (Transport, storage and communications) 0.5% 6.1% 0.0% 6.7% J (Financial intermediation) 0.0% 0.6% 0.0% 0.6% K (Real estate, renting, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% research) 1.7% 3.2% 24.2% 29.1% L (Public administration 18.2% 0.0% 0.0% 18.2% A+B M (Education) 2.7% 0.0% 0.2% 2.9% N (Health and social work) 1.6% 0.0% 0.1% 1.7% C O (Other community, social and personal services) 0.7% 0.3% 1.6% 2.6% D TOTAL 25.7% 44.7% 29.6% 100.0% E F Tab. 5.78e: Loss structure by institutional sectors and industry branches G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment ries [%] [%] [%] [%] [%] I A+B 5.0% 1.9% 0.0% 6.9% C 0.2% 0.2% 0.0% 0.4% J D 6.1% 8.6% 3.0% 17.8% E 3.6% 1.6% 0.1% 5.2% K F 0.9% 0.5% 0.4% 1.8% G 1.7% 1.3% 2.3% 5.3% L H 0.6% 0.1% 0.0% 0.7% I 3.6% 2.8% 0.3% 6.7% M building J 0.5% 0.1% 0.0% 0.6% equipment K 23.2% 0.8% 0.8% 4.4% 29.1% N 16.0% 1.0% 1.2% inventories L 18.2% household equip. M 2.6% 0.3% 0.0% 2.9% O N 1.3% 0.4% 0.0% 1.7% O 2.4% 0.2% 0.0% 2.6% Fig. 5.120f: Structure of the loss by industry branches and asset categories TOTAL 67.8% 19.8% 8.1% 4.4% 100.0% d) Average Loss Structure, Poland Tab. 5.79a: Losses on the public property infrastructure- Loss Category public [%] 24% infrastructure-public 24% enterprise-public 11% Public 35% infrastructure-non-public 6% enterprise-non-public 59% TOTAL 100% enterprise- public 11% infrastructure- enterprise-non- non-public public 6% 59% Fig. 5.121a: Loss by sector / purpose Classification Tab. 5.79b: Loss structure by asset categories Household Loss eguipment Category code [%] Inventories 6% Dwellings 7% 26% Dwellings AN.1111 26% Other buildings and structures AN.1112 41% Machinery and equipment AN.1113 19% Inventories AN.12 7% Household eguipment - 6% TOTAL 100% Machinery and equipment Other buildings 19% and structures 42% Fig. 5.121b: Loss structure by asset categories Tab. 5.79c: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment sector ries [%] [%] [%] [%] [%] households 20.8% 1.5% 0.7% 6.2% 29.3% private 21.3% 9.6% 4.7% 0.0% 35.6% private public 25.2% 8.2% 1.8% 0.0% 35.1% TOTAL 67.4% 19.2% 7.2% 6.2% 100.0% building equipment households inventories household equip. households 29% public 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 35% Fig. 5.121c: Loss structure by institutional sectors and physical property types 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% A+B C D private 36% E F Fig. 5.121d: Loss structure by institutional sectors G H I Tab. 5.79d: Loss structure by institutional sectors and industry branches house- J Industry branch public private holds Total loss [%] [%] [%] [%] K A+B (Agriculture, hunting L and forestry) 0.4% 2.6% 0.5% 3.6% C (Mining and quarrying) 0.3% 0.4% 0.0% 0.7% M D (Manufacturing) 3.8% 5.6% 0.2% 9.7% public E (Electricity, gas and water N private supply) 3.4% 5.1% 0.2% 8.7% households F (Construction) 0.5% 0.9% 0.2% 1.6% O G (Wholesale and retail trade; repair) 0.2% 7.9% 1.1% 9.1% Fig. 5.121e: Structure of the loss by industry branches and institutional sectors H (Hotels and restaurant) 0.1% 0.7% 0.1% 0.8% I (Transport, storage and communications) 6.9% 3.5% 0.4% 10.8% J (Financial intermediation) 0.9% 1.9% 0.0% 2.8% K (Real estate, renting, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% research) 5.3% 5.7% 26.4% 37.4% L (Public administration 8.0% 0.0% 0.0% 8.0% A+B M (Education) 2.4% 0.2% 0.1% 2.7% N (Health and social work) 1.2% 0.2% 0.1% 1.4% C O (Other community, social and personal services) 1.9% 0.8% 0.1% 2.8% D TOTAL 35.1% 35.6% 29.3% 100.0% E F Tab. 5.79e: Loss structure by institutional sectors and industry branches G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment ries [%] [%] [%] [%] [%] I A+B 3.1% 0.5% 0.0% 3.6% C 0.4% 0.3% 0.0% 0.7% J D 3.6% 4.2% 1.9% 9.7% E 6.0% 2.5% 0.2% 8.7% K F 0.7% 0.5% 0.4% 1.6% G 3.4% 2.3% 3.5% 9.1% L H 0.7% 0.1% 0.0% 0.8% I 5.6% 5.1% 0.1% 10.8% M building J 1.6% 1.0% 0.1% 2.8% equipment K 29.8% 1.1% 0.4% 6.2% 37.4% N 7.0% 0.5% 0.5% inventories L 8.0% household equip. M 2.5% 0.2% 0.0% 2.7% O N 1.0% 0.4% 0.0% 1.4% O 2.2% 0.6% 0.0% 2.8% Fig. 5.121f: Structure of the loss by industry branches and asset categories TOTAL 67.4% 19.2% 7.2% 6.2% 100.0% e) Average Loss Structure, V-4 Group Tab. 5.80a: Losses on the public property Loss Category [%] infrastructure- public infrastructure-public 27% 27% enterprise-public 4% Public 31% infrastructure-non-public 5% enterprise-non-public 64% TOTAL 100% enterprise- public 4% enterprise-non- infrastructure- public non-public 64% 5% Fig. 5.122a: Loss by sector / purpose Classification Tab. 5.80b: Loss structure by asset categories Household Loss Inventories Dwellings Category code eguipment [%] 8% 22% 5% Dwellings AN.1111 22% Other buildings Machinery and and structures AN.1112 46% equipment Machinery and 20% equipment AN.1113 20% Inventories AN.12 8% Household eguipment - 5% TOTAL 100% Other buildings and structures 45% Fig. 5.122b: Loss structure by asset categories building Tab. 5.80c: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment sector ries [%] household equip. [%] [%] [%] [%] households 18.4% 1.2% 0.6% 4.7% 25.0% private 23.1% 14.3% 6.5% 0.0% 43.8% private public 26.1% 4.0% 1.1% 0.0% 31.1% TOTAL 67.6% 19.5% 8.2% 4.7% 100.0% households public households 31% 25% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% Fig. 5.122c: Loss structure by institutional sectors and physical property types 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% A+B C D private E 44% F Fig. 5.122d: Loss structure by institutional sectors G H I Tab. 5.80d: Loss structure by institutional sectors and industry branches house- J Industry branch public private holds Total loss [%] [%] [%] [%] K A+B (Agriculture, hunting L and forestry) 0.2% 2.3% 1.1% 3.6% C (Mining and quarrying) 0.4% 0.8% 0.0% 1.2% M D (Manufacturing) 0.5% 20.3% 0.6% 21.4% public E (Electricity, gas and water N private supply) 4.7% 4.3% 0.0% 9.0% households F (Construction) 0.1% 1.1% 0.1% 1.3% O G (Wholesale and retail trade; repair) 0.0% 5.1% 0.5% 5.7% Fig. 5.122e: Structure of the loss by industry branches and institutional sectors H (Hotels and restaurant) 0.0% 0.6% 0.0% 0.6% I (Transport, storage and communications) 5.7% 3.4% 0.1% 9.2% J (Financial intermediation) 0.1% 0.7% 0.0% 0.8% K (Real estate, renting, 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% research) 2.4% 4.8% 21.8% 29.0% L (Public administration 11.7% 0.0% 0.0% 11.7% A+B M (Education) 2.7% 0.1% 0.1% 2.9% N (Health and social work) 1.4% 0.1% 0.0% 1.5% C O (Other community, social and personal services) 1.2% 0.3% 0.5% 2.0% D TOTAL 31.1% 43.8% 25.0% 100.0% E F Tab. 5.80e: Loss structure by institutional sectors and industry branches G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment ries [%] [%] [%] [%] [%] I A+B 2.7% 0.9% 0.0% 3.6% C 0.9% 0.3% 1.5% 2.7% J D 8.0% 9.3% 2.7% 20.0% E 6.6% 2.2% 0.1% 8.9% K F 0.6% 0.4% 0.9% 2.0% G 2.2% 1.2% 1.6% 5.0% L H 0.5% 0.1% 0.0% 0.6% I 6.4% 2.8% 0.1% 9.2% M building J 0.6% 0.2% 0.1% 0.9% equipment K 23.2% 0.7% 0.5% 4.7% 29.1% N 10.2% 0.7% 0.5% inventories L 11.5% household equip. M 2.6% 0.2% 0.0% 2.8% O N 1.2% 0.3% 0.0% 1.5% O 1.9% 0.2% 0.0% 2.1% Fig. 5.122f: Structure of the loss by industry branches and asset categories TOTAL 67.6% 19.5% 8.2% 4.7% 100.0% 5. Appendix 5.3 Detailed Loss Calculation Results for each Scenario This appendix contains detailed loss calculation results of each scenario, in particular: - Specification of the area affected by the event - LEC and survival function for total as well as public property - 3-dimensional183 loss classification for the return period of 250 years Table 5.81: List of outputs – Detailed Loss Calculation Results for each Scenario Scenario Tables Figures Czech Republic Catchment-based Scenarios CZ Labe / Elbe 5.82a-g 5.123a-k CZ Berounka 5.83a-g 5.124a-k CZ Morava river 5.84a-g 5.125a-k CZ Dyje 5.85a-g 5.126a-k CZ Odra / Oder (upper) 5.86a-g 5.127a-k CZ Ohre 5.87a-g 5.128a-k CZ Vltava / Moldau 5.88a-g 5.129a-k Recent Major Flood Events-based Scenarios CZ 1997 5.89a-g 5.130a-k CZ 2002 5.90a-g 5.131a-k Large Complex Geographical Units-based Scenarios CZ Bohemia 5.91a-g 5.132a-k CZ Moravia 5.92a-g 5.133a-k Slovakia Catchment-based Scenarios SK Dunaj / Danube 5.93a-g 5.134a-k SK Vah + Nitra 5.94a-g 5.135a-k SK Hron + Ipel 5.95a-g 5.136a-k SK Hornad + Slana 5.96a-g 5.137a-k SK Tisza + Bodrog 5.97a-g 5.138a-k Large Complex Geographical Units-based Scenarios SK Dunaj / Danube Large 5.98a-g 5.139a-k SK Tisza Large 5.99a-g 5.140a-k Hungary Catchment-based Scenarios HU Danube / Duna 5.100a-g 5.141a-k HU Drava 5.101a-g 5.142a-k HU Hernad + Sajo 5.102a-g 5.143a-k HU Tizsa + Bordog 5.103a-g 5.144a-k HU Raba 5.104a-g 5.145a-k HU Sio 5.105a-g 5.146a-k HU Koros 5.106a-g 5.147a-k Large Complex Geographical Units-based Scenarios HU Duna / Danube Large 5.107a-g 5.148a-k HU Tisza Large 5.108a-g 5.149a-k 183 see Paragraph 3.2 5. Appendix Poland Catchment-based Scenarios PL Vistula Upper 5.109a-g 5.150a-k PL Vistula Middle 5.110a-g 5.151a-k PL Vistula Lower 5.111a-g 5.152a-k PL Bug 5.112a-g 5.153a-k PL San 5.113a-g 5.154a-k PL Odra Upper 5.114a-g 5.155a-k PL Odra Lower 5.115a-g 5.156a-k PL Warta 5.116a-g 5.157a-k PL Notec 5.117a-g 5.158a-k PL Nysa+Bobr 5.118a-g 5.159a-k PL Baltic West 5.119a-g 5.160a-k PL Baltic East 5.120a-g 5.161a-k Recent Major Flood Events-based Scenarios PL 1997 5.121a-g 5.162a-k Large Complex Geographical Units-based Scenarios PL Visla / Vistula Large 5.122a-g 5.163a-k PL Odra / Oder Large 5.123a-g 5.164a-k V–4 Group Cross-border Scenarios V4 Odra / Oder 5.124a-g 5.165a-k V4 Dunaj / Danube 5.125a-g 5.166a-k V4 Tisza 5.126a-g 5.167a-k Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Labe / Elbe 6 000 Loss [mil. EUR] Total loss Loss on public property 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.123b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.82a: LEC and survival function for the scenario CZ Labe Fig. 5.123a: LEC function for the scenario CZ Labe Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 231 46 Probability 20 Total loss 50 2.0% 1 566 448 Loss on public property 100 1.0% 2 753 840 5.0% 250 0.4% 4 185 1 312 500 0.2% 5 010 1 581 4.0% Loss structure for the RTP 250: Tab. 5.82b: Regional loss structure for the scenario CZ Labe Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] StÅ™edoÄ?eský 1 204 29% 0.9% Královéhradecký 1 081 26% 1.6% Pardubický 841 20% 2.3% 2.0% Ústecký 699 17% 3.9% Liberecký 305 7% 1.1% Praha 45 1% 0.2% VysoÄ?ina 9 0% 0.0% 1.0% TOTAL 4 185 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 Loss [mil. EUR] Fig. 5.123c: Survival function for the scenario CZ Labe Fig. 5.123d: Regional loss structure for the scenario CZ Labe (% of total loss) Fig. 5.123e: Loss intensity in provinces for the scenario CZ Labe (% of property in the province) Tab. 5.82c: Losses by sector / purpose classification for the scenario CZ Labe infrastructure- Loss Category % public [mil EUR] 28% infrastructure-public 1 181 28% enterprise-public 131 3% Public 1 312 31% infrastructure-non-public 102 2% enterprise-non-public 2 770 66% TOTAL 4 185 enterprise-public 3% enterprise-non- infrastructure- public non-public 67% 2% Fig. 5.123f: Loss by sector / purpose classification the scenario CZ Labe Household Tab. 5.82d: Loss structure by asset categories for the scenario CZ Labe Inventories eguipment Dwellings Loss 5% 19% Category code % 9% [mil EUR] Dwellings AN.1111 812 19% Machinery and Other buildings equipment and structures AN.1112 1 995 48% 19% Machinery and equipment AN.1113 800 19% Inventories AN.12 361 9% Household eguipment - 217 5% TOTAL 4 185 Other buildings and structures 48% Fig. 5.123g: Loss structure by asset categories for the scenario CZ Labe building Tab. 5.82e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 669 36 21 217 943 22.5% private 931 682 316 0 1 929 46.1% private public 1 208 81 23 0 1 312 31.4% TOTAL 2 807 800 361 217 4 185 % 67.1% 19.1% 8.6% 5.2% households households public 23% 31% 0 500 1 000 1 500 2 000 2 500 Loss [mil. EUR] Fig. 5.123h: Loss structure by institutional sectors and asset categories for the scenario CZ Labe Loss [mil. EUR] 0 200 400 600 800 1 000 1 200 1 400 A+B C D E private 46% F Fig. 5.123i: Loss structure by institutional sectors for the scenario CZ Labe G H Tab. 5.82f: Loss structure by institutional sectors and industry branches for the scenario CZ Labe I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 4 48 7 60 1.4% C (Mining and quarrying) 20 33 0 53 1.3% M D (Manufacturing) 13 1 152 38 1 204 28.8% public E (Electricity, gas and water N private supply) 165 81 0 246 5.9% households F (Construction) 0 45 5 50 1.2% O G (Wholesale and retail trade; repair) 1 187 20 208 5.0% Fig. 5.123j: Structure of the loss for the scenario CZ Labe by industry branches and H (Hotels and restaurant) 0 20 1 21 0.5% institutional sectors I (Transport, storage and communications) 395 90 7 493 11.8% J (Financial intermediation) 1 19 0 19 0.5% Loss [mil. EUR] K (Real estate, renting, 0 200 400 600 800 1 000 1 200 1 400 research) 92 233 863 1 188 28.4% L (Public administration 315 0 0 315 7.5% A+B M (Education) 170 0 0 170 4.1% N (Health and social work) 59 5 1 65 1.6% C O (Other community, social and personal services) 77 17 0 93 2.2% D TOTAL 1 312 1 929 943 4 185 % 31.4% 46.1% 22.5% E F Tab. 5.82g: Loss structure by asset categories and industry branches, sc. CZ Labe G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 40 20 0 0 60 1.4% C 35 17 0 0 53 1.3% J D 452 513 1 0 965 23.1% E 197 45 240 0 482 11.5% K F 20 19 3 0 42 1.0% G 89 37 11 0 137 3.3% L H 20 1 82 0 103 2.5% I 413 79 0 0 492 11.8% M building J 17 2 0 0 19 0.5% equipment K 926 39 0 217 1 182 28.2% N 289 9 6 0 304 7.3% inventories L household equip. M 166 4 18 0 188 4.5% O N 54 11 0 0 65 1.6% O 89 5 0 0 93 2.2% Fig. 5.123k: Structure of the loss for the scenario CZ Labe by industry branches and TOTAL 2 807 800 361 217 4 185 asset categories % 67.1% 19.1% 8.6% 5.2% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Berounka 800 Loss [mil. EUR] Total loss 700 Loss on public property 600 500 400 300 200 100 0 Fig. 5.124b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.83a: LEC and survival function for the scenario CZ Berounka Fig. 5.124a: LEC function for the scenario CZ Berounka Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 42 7 Probability 20 Total loss 50 2.0% 233 64 Loss on public property 100 1.0% 381 111 5.0% 250 0.4% 563 168 500 0.2% 678 205 4.0% Loss structure for the RTP 250: Tab. 5.83b: Regional loss structure for the scenario CZ Berounka Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Plzeňský 378 67% 1.3% StÅ™edoÄ?eský 145 26% 0.2% Praha 23 4% 0.0% 2.0% Karlovarský 16 3% 0.1% TOTAL 563 1.0% 0.0% 0 100 200 300 400 500 600 700 800 Loss [mil. EUR] Fig. 5.124c: Survival function for the scenario CZ Berounka Fig. 5.124d: Regional loss structure for the scenario CZ Berounka (% of total loss) Fig. 5.124e: Loss intensity in provinces for the scenario CZ Berounka (% of property in the province) Tab. 5.83c: Losses by sector / purpose classification for the scenario CZ Berounka infrastructure- Category Loss % public [mil EUR] 28% infrastructure-public 156 28% enterprise-public 12 2% Public 168 30% infrastructure-non-public 6 1% enterprise-non-public 389 69% TOTAL 563 enterprise-public 2% infrastructure- non-public enterprise-non- 1% public 69% Fig. 5.124f: Loss by sector / purpose classification the scenario CZ Berounka Tab. 5.83d: Loss structure by asset categories for the scenario CZ Berounka Household Dwellings Loss Inventories Category code % eguipment 20% [mil EUR] 9% 5% Dwellings AN.1111 113 20% Machinery and Other buildings equipment and structures AN.1112 267 47% 18% Machinery and equipment AN.1113 101 18% Inventories AN.12 50 9% Household eguipment - 31 5% TOTAL 563 Other buildings and structures 48% Fig. 5.124g: Loss structure by asset categories for the scenario CZ Berounka building Tab. 5.83e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 95 3 2 31 130 23.2% private 124 93 47 0 264 46.9% private public 161 6 2 0 168 29.9% TOTAL 380 101 50 31 563 % 67.6% 18.0% 8.9% 5.5% households public households 30% 23% 0 50 100 150 200 250 300 Loss [mil. EUR] Fig. 5.124h: Loss structure by institutional sectors and asset categories for the scenario CZ Berounka Loss [mil. EUR] 0 50 100 150 200 A+B C D private E 47% F Fig. 5.124i: Loss structure by institutional sectors for the scenario CZ Berounka G H Tab. 5.83f: Loss structure by institutional sectors and industry branches for the scenario CZ Berounka I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 0 7 0 7 1.3% C (Mining and quarrying) 1 1 0 2 0.3% M D (Manufacturing) 1 174 4 179 31.8% public E (Electricity, gas and water N private supply) 11 5 0 16 2.8% households F (Construction) 0 4 0 5 0.8% O G (Wholesale and retail trade; repair) 0 26 1 27 4.8% Fig. 5.124j: Structure of the loss for the scenario CZ Berounka by industry branches and H (Hotels and restaurant) 0 3 0 3 0.5% institutional sectors I (Transport, storage and communications) 61 12 0 74 13.1% J (Financial intermediation) 0 1 0 1 0.3% Loss [mil. EUR] K (Real estate, renting, 0 50 100 150 200 research) 10 30 124 164 29.2% L (Public administration 49 0 0 49 8.7% A+B M (Education) 21 0 0 21 3.8% N (Health and social work) 7 0 0 7 1.2% C O (Other community, social and personal services) 6 1 0 7 1.3% D TOTAL 168 264 130 563 % 29.9% 46.9% 23.2% E F Tab. 5.83g: Loss structure by asset categories and industry branches, sc. CZ Berounka G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 5 2 0 0 7 1.3% C 1 1 0 0 2 0.3% J D 67 77 36 0 179 31.8% E 13 2 0 0 16 2.8% K F 2 2 1 0 5 0.8% G 12 4 11 0 27 4.8% L H 3 0 0 0 3 0.5% I 64 10 0 0 74 13.1% M building J 1 0 0 0 1 0.3% equipment K 129 4 0 31 164 29.2% N 46 1 2 0 49 8.7% inventories L household equip. M 21 0 0 0 21 3.8% O N 6 1 0 0 7 1.2% O 7 0 0 0 7 1.3% Fig. 5.124k: Structure of the loss for the scenario CZ Berounka by industry branches TOTAL 380 101 50 31 563 and asset categories % 67.6% 18.0% 8.9% 5.5% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Morava river 4 500 Loss [mil. EUR] Total loss 4 000 Loss on public property 3 500 3 000 2 500 2 000 1 500 1 000 500 0 Fig. 5.125b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.84a: LEC and survival function for the scenario CZ Morava Fig. 5.125a: LEC function for the scenario CZ Morava Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 92 20 Probability 20 Total loss 50 2.0% 1 412 415 Loss on public property 100 1.0% 2 346 703 5.0% 250 0.4% 3 583 1 085 500 0.2% 4 244 1 289 4.0% Loss structure for the RTP 250: Tab. 5.84b: Regional loss structure for the scenario CZ Morava Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Olomoucký 1 900 53% 7.2% Zlínský 1 410 39% 5.6% Jihomoravský 206 6% 0.3% 2.0% Pardubický 56 2% 0.2% Moravskoslezský 10 0% 0.0% TOTAL 3 583 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 Loss [mil. EUR] Fig. 5.125c: Survival function for the scenario CZ Morava Fig. 5.125d: Regional loss structure for the scenario CZ Morava (% of total loss) Fig. 5.125e: Loss intensity in provinces for the scenario CZ Morava (% of property in the province) Tab. 5.84c: Losses by sector / purpose classification for the scenario CZ Morava infrastructure- Loss public Category % [mil EUR] 27% infrastructure-public 977 27% enterprise-public 108 3% Public 1 085 30% infrastructure-non-public 64 2% enterprise-non-public 2 434 68% TOTAL 3 583 enterprise-public 3% infrastructure- enterprise-non- non-public public 2% 68% Fig. 5.125f: Loss by sector / purpose classification the scenario CZ Morava Tab. 5.84d: Loss structure by asset categories for the scenario CZ Morava Household Dwellings Loss Inventories Category code % eguipment 19% [mil EUR] 9% 5% Dwellings AN.1111 687 19% Machinery and Other buildings equipment and structures AN.1112 1 667 47% 20% Machinery and equipment AN.1113 713 20% Inventories AN.12 333 9% Household eguipment - 182 5% TOTAL 3 583 Other buildings and structures 47% Fig. 5.125g: Loss structure by asset categories for the scenario CZ Morava building Tab. 5.84e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 569 40 22 182 814 22.7% private 788 608 289 0 1 685 47.0% private public 997 65 22 0 1 085 30.3% TOTAL 2 354 713 333 182 3 583 % 65.7% 19.9% 9.3% 5.1% households public households 30% 23% 0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 Fig. 5.125h: Loss structure by institutional sectors and asset categories for the scenario CZ Morava 0 200 400 600 800 1 000 1 200 A+B C D private E 47% F Fig. 5.125i: Loss structure by institutional sectors for the scenario CZ Morava G H Tab. 5.84f: Loss structure by institutional sectors and industry branches for the scenario CZ Morava I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 5 53 9 67 1.9% C (Mining and quarrying) 4 6 0 10 0.3% M D (Manufacturing) 15 1 055 39 1 110 31.0% public E (Electricity, gas and water N private supply) 80 39 0 119 3.3% households F (Construction) 0 47 7 54 1.5% O G (Wholesale and retail trade; repair) 1 170 20 191 5.3% Fig. 5.125j: Structure of the loss for the scenario CZ Morava by industry branches and H (Hotels and restaurant) 0 13 1 14 0.4% institutional sectors I (Transport, storage and communications) 301 70 7 378 10.6% J (Financial intermediation) 0 9 0 9 0.2% K (Real estate, renting, 0 200 400 600 800 1 000 1 200 research) 82 199 727 1 008 28.1% L (Public administration 272 0 0 272 7.6% A+B M (Education) 186 0 0 186 5.2% N (Health and social work) 64 7 3 73 2.1% C O (Other community, social and personal services) 74 18 0 92 2.6% D TOTAL 1 085 1 685 814 3 583 % 30.3% 47.0% 22.7% E F Tab. 5.84g: Loss structure by asset categories and industry branches, sc. CZ Morava G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 44 23 0 0 67 1.9% C 7 3 0 0 10 0.3% J D 416 472 221 0 1 110 31.0% E 96 22 2 0 119 3.3% K F 21 21 12 0 54 1.5% G 81 36 74 0 191 5.3% L H 13 1 0 0 14 0.4% I 315 63 1 0 378 10.6% M building J 8 1 0 0 9 0.2% equipment K 784 35 7 182 1 008 28.1% N inventories L 246 10 17 0 272 7.6% household equip. M 180 6 0 0 186 5.2% O N 59 15 0 0 73 2.1% O 85 7 0 0 92 2.6% Fig. 5.125k: Structure of the loss for the scenario CZ Morava by industry branches and TOTAL 2 354 713 333 182 3 583 asset categories % 65.7% 19.9% 9.3% 5.1% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Dyje 3 000 Loss [mil. EUR] Total loss Loss on public property 2 500 2 000 1 500 1 000 500 0 Fig. 5.126b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.85a: LEC and survival function for the scenario CZ Dyje Fig. 5.126a: LEC function for the scenario CZ Dyje Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 61 13 Probability 20 Total loss 50 2.0% 988 324 Loss on public property 100 1.0% 1 490 506 5.0% 250 0.4% 2 119 734 500 0.2% 2 502 875 4.0% Loss structure for the RTP 250: Tab. 5.85b: Regional loss structure for the scenario CZ Dyje Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Jihomoravský 1 763 83% 2.9% VysoÄ?ina 271 13% 1.1% Pardubický 48 2% 0.2% 2.0% JihoÄ?eský 17 1% 0.1% Zlínský 16 1% 0.1% Olomoucký 4 0% 0.0% TOTAL 2 119 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 Loss [mil. EUR] Fig. 5.126c: Survival function for the scenario CZ Dyje Fig. 5.126d: Regional loss structure for the scenario CZ Dyje (% of total loss) Fig. 5.126e: Loss intensity in provinces for the scenario CZ Dyje (% of property in the province) Tab. 5.85c: Losses by sector / purpose classification for the scenario CZ Dyje Loss Category % [mil EUR] infrastructure- public infrastructure-public 663 31% 31% enterprise-public 71 3% Public 734 35% infrastructure-non-public 55 3% enterprise-non-public 1 330 63% TOTAL 2 119 enterprise-public 3% enterprise-non- public 63% infrastructure- non-public 3% Fig. 5.126f: Loss by sector / purpose classification the scenario CZ Dyje Tab. 5.85d: Loss structure by asset categories for the scenario CZ Dyje Household Inventories eguipment Loss Dwellings Category code % 7% 6% 22% [mil EUR] Dwellings AN.1111 475 22% Machinery and Other buildings equipment and structures AN.1112 1 028 48% 16% Machinery and equipment AN.1113 337 16% Inventories AN.12 151 7% Household eguipment - 129 6% TOTAL 2 119 Other buildings and structures 49% Fig. 5.126g: Loss structure by asset categories for the scenario CZ Dyje Tab. 5.85e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 395 18 11 129 553 26.1% private 430 275 127 0 832 39.2% private public 678 44 13 0 734 34.6% TOTAL 1 502 337 151 129 2 119 % 70.9% 15.9% 7.1% 6.1% building equipment households inventories household equip. households public 26% 0 100 200 300 400 500 600 700 800 900 35% Fig. 5.126h: Loss structure by institutional sectors and asset categories for the scenario CZ Dyje 0 100 200 300 400 500 600 700 800 A+B C D private 39% E F Fig. 5.126i: Loss structure by institutional sectors for the scenario CZ Dyje G H Tab. 5.85f: Loss structure by institutional sectors and industry branches for the scenario CZ Dyje I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 3 33 5 41 1.9% C (Mining and quarrying) 6 10 0 17 0.8% M D (Manufacturing) 4 388 12 404 19.1% public E (Electricity, gas and water N private supply) 86 42 0 128 6.0% households F (Construction) 0 31 4 36 1.7% O G (Wholesale and retail trade; repair) 1 105 11 117 5.5% Fig. 5.126j: Structure of the loss for the scenario CZ Dyje by industry branches and H (Hotels and restaurant) 0 11 1 12 0.6% institutional sectors I (Transport, storage and communications) 216 49 4 270 12.7% J (Financial intermediation) 0 10 0 10 0.5% K (Real estate, renting, 0 100 200 300 400 500 600 700 800 research) 57 139 514 710 33.5% L (Public administration 173 0 0 173 8.2% A+B M (Education) 113 0 0 113 5.3% N (Health and social work) 33 3 1 37 1.7% C O (Other community, social and personal services) 43 10 0 52 2.5% D TOTAL 734 832 553 2 119 % 34.6% 39.2% 26.1% E F Tab. 5.85g: Loss structure by asset categories and industry branches, sc. CZ Dyje G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 27 14 0 0 41 1.9% C 11 5 0 0 17 0.8% J D 151 172 81 0 404 19.1% E 102 24 2 0 128 6.0% K F 15 14 8 0 36 1.7% G 50 21 46 0 117 5.5% L H 11 1 0 0 12 0.6% I 226 44 0 0 270 12.7% M building J 9 1 0 0 10 0.5% equipment K 553 24 4 129 710 33.5% N inventories L 158 5 10 0 173 8.2% household equip. M 110 3 0 0 113 5.3% O N 30 6 0 0 37 1.7% O 49 3 0 0 52 2.5% Fig. 5.126k: Structure of the loss for the scenario CZ Dyje by industry branches and TOTAL 1 502 337 151 129 2 119 asset categories % 70.9% 15.9% 7.1% 6.1% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Odra 3 500 Loss [mil. EUR] Total loss Loss on public property 3 000 2 500 2 000 1 500 1 000 500 0 Fig. 5.127b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.86a: LEC and survival function for the scenario CZ Odra Fig. 5.127a: LEC function for the scenario CZ Odra Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 81 23 Probability 20 Total loss 50 2.0% 780 244 Loss on public property 100 1.0% 1 554 453 5.0% 250 0.4% 2 562 746 500 0.2% 3 126 925 4.0% Loss structure for the RTP 250: Tab. 5.86b: Regional loss structure for the scenario CZ Odra Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Moravskoslezský 2 425 95% 4.5% Olomoucký 122 5% 0.5% Královéhradecký 15 1% 0.1% 2.0% TOTAL 2 562 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil. EUR] Fig. 5.127c: Survival function for the scenario CZ Odra Fig. 5.127d: Regional loss structure for the scenario CZ Odra (% of total loss) Fig. 5.127e: Loss intensity in provinces for the scenario CZ Odra (% of property in the province) Tab. 5.86c: Losses by sector / purpose classification for the scenario CZ Odra infrastructure- public Loss Category % 25% [mil EUR] infrastructure-public 628 25% enterprise-public 118 5% Public 746 29% infrastructure-non-public 64 3% enterprise-public enterprise-non-public 1 751 68% 5% TOTAL 2 562 infrastructure- non-public enterprise-non- 3% public 67% Fig. 5.127f: Loss by sector / purpose classification the scenario CZ Odra Dwellings Tab. 5.86d: Loss structure by asset categories for the scenario CZ Odra Inventories Household 13% Loss 11% eguipment Category code % [mil EUR] 3% Dwellings AN.1111 345 13% Other buildings Machinery and and structures AN.1112 1 222 48% equipment Machinery and 25% equipment AN.1113 636 25% Inventories AN.12 278 11% Household eguipment - 81 3% TOTAL 2 562 Other buildings and structures 48% Fig. 5.127g: Loss structure by asset categories for the scenario CZ Odra building Tab. 5.86e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 259 26 15 81 380 14.8% private 649 540 246 0 1 436 56.0% private public 659 70 17 0 746 29.1% TOTAL 1 567 636 278 81 2 562 % 61.2% 24.8% 10.8% 3.1% households households public 15% 29% 0 200 400 600 800 1 000 1 200 1 400 1 600 Fig. 5.127h: Loss structure by institutional sectors and asset categories for the scenario CZ Odra 0 200 400 600 800 1 000 1 200 A+B C D private 56% E F Fig. 5.127i: Loss structure by institutional sectors for the scenario CZ Odra G H Tab. 5.86f: Loss structure by institutional sectors and industry branches for the scenario CZ Odra I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 1 13 2 15 0.6% C (Mining and quarrying) 67 108 0 175 6.8% M D (Manufacturing) 15 995 38 1 048 40.9% public E (Electricity, gas and water N private supply) 99 50 0 149 5.8% households F (Construction) 0 20 2 22 0.9% O G (Wholesale and retail trade; repair) 1 99 12 112 4.4% Fig. 5.127j: Structure of the loss for the scenario CZ Odra by industry branches and H (Hotels and restaurant) 0 6 0 6 0.3% institutional sectors I (Transport, storage and communications) 161 37 3 202 7.9% J (Financial intermediation) 0 6 0 6 0.2% K (Real estate, renting, 0 200 400 600 800 1 000 1 200 research) 35 87 321 443 17.3% L (Public administration 172 0 0 172 6.7% A+B M (Education) 111 0 0 111 4.3% N (Health and social work) 38 4 1 44 1.7% C O (Other community, social and personal services) 45 11 0 56 2.2% D TOTAL 746 1 436 380 2 562 % 29.1% 56.0% 14.8% E F Tab. 5.86g: Loss structure by asset categories and industry branches, sc. CZ Odra G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 10 5 0 0 15 0.6% C 111 59 5 0 175 6.8% J D 393 446 209 0 1 048 40.9% E 118 28 3 0 149 5.8% K F 9 8 5 0 22 0.9% G 48 21 43 0 112 4.4% L H 6 0 0 0 6 0.3% I 169 33 0 0 202 7.9% M building J 5 1 0 0 6 0.2% equipment K 345 15 3 81 443 17.3% N inventories L 156 6 10 0 172 6.7% household equip. M 108 4 0 0 111 4.3% O N 35 8 0 0 44 1.7% O 53 4 0 0 56 2.2% Fig. 5.127k: Structure of the loss for the scenario CZ Odra by industry branches and TOTAL 1 567 636 278 81 2 562 asset categories % 61.2% 24.8% 10.8% 3.1% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Ohre 700 Loss [mil. EUR] Total loss Loss on public property 600 500 400 300 200 100 0 Fig. 5.128b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.87a: LEC and survival function for the scenario CZ Ohre Fig. 5.128a: LEC function for the scenario CZ Ohre Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 40 12 Probability 20 Total loss 50 2.0% 169 61 Loss on public property 100 1.0% 368 141 5.0% 250 0.4% 561 218 500 0.2% 652 255 4.0% Loss structure for the RTP 250: Tab. 5.87b: Regional loss structure for the scenario CZ Ohre Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Ústecký 340 61% 0.9% Karlovarský 219 39% 1.6% StÅ™edoÄ?eský 2 0% 0.0% 2.0% Plzeňský 0 0% 0.0% TOTAL 561 1.0% 0.0% 0 100 200 300 400 500 600 700 Loss [mil. EUR] Fig. 5.128c: Survival function for the scenario CZ Ohre Fig. 5.128d: Regional loss structure for the scenario CZ Ohre (% of total loss) Fig. 5.128e: Loss intensity in provinces for the scenario CZ Ohre (% of property in the province) Tab. 5.87c: Losses by sector / purpose classification for the scenario CZ Ohre Loss Category % [mil EUR] infrastructure- infrastructure-public 195 35% public enterprise-public 23 4% 35% Public 218 39% infrastructure-non-public 21 4% enterprise-non-public 322 57% TOTAL 561 enterprise-public 4% enterprise-non- public infrastructure- 57% non-public 4% Fig. 5.128f: Loss by sector / purpose classification the scenario CZ Ohre Inventories Household Dwellings Tab. 5.87d: Loss structure by asset categories for the scenario CZ Ohre 7% eguipment 16% Loss 4% Category code % [mil EUR] Machinery and Dwellings AN.1111 90 16% equipment Other buildings 18% and structures AN.1112 303 54% Machinery and equipment AN.1113 104 18% Inventories AN.12 42 7% Household eguipment - 23 4% TOTAL 561 Other buildings and structures 55% Fig. 5.128g: Loss structure by asset categories for the scenario CZ Ohre building Tab. 5.87e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 70 3 2 23 98 17.5% private 125 84 36 0 245 43.6% private public 198 17 3 0 218 38.9% TOTAL 393 104 42 23 561 % 70.1% 18.5% 7.4% 4.0% households households 18% 0 50 100 150 200 250 300 public 39% Fig. 5.128h: Loss structure by institutional sectors and asset categories for the scenario CZ Ohre 0 20 40 60 80 100 120 140 A+B C private 43% D E F Fig. 5.128i: Loss structure by institutional sectors for the scenario CZ Ohre G H Tab. 5.87f: Loss structure by institutional sectors and industry branches for the scenario CZ Ohre I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 0 3 0 3 0.5% C (Mining and quarrying) 13 22 0 35 6.3% M D (Manufacturing) 1 124 3 128 22.9% public E (Electricity, gas and water N private supply) 35 17 0 52 9.3% households F (Construction) 0 6 0 7 1.2% O G (Wholesale and retail trade; repair) 0 24 2 26 4.6% Fig. 5.128j: Structure of the loss for the scenario CZ Ohre by industry branches and H (Hotels and restaurant) 0 7 1 8 1.4% institutional sectors I (Transport, storage and communications) 61 14 1 76 13.5% J (Financial intermediation) 0 1 0 1 0.1% K (Real estate, renting, 0 20 40 60 80 100 120 140 research) 9 23 91 122 21.8% L (Public administration 50 0 0 50 8.9% A+B M (Education) 25 0 0 25 4.5% N (Health and social work) 11 1 0 12 2.2% C O (Other community, social and personal services) 12 3 0 15 2.7% D TOTAL 218 245 98 561 % 38.9% 43.6% 17.5% E F Tab. 5.87g: Loss structure by asset categories and industry branches, sc. CZ Ohre G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 2 1 0 0 3 0.5% C 23 11 1 0 35 6.3% J D 48 55 25 0 128 22.9% E 42 10 1 0 52 9.3% K F 3 2 1 0 7 1.2% G 11 4 10 0 26 4.6% L H 7 1 0 0 8 1.4% I 64 12 0 0 76 13.5% M building J 1 0 0 0 1 0.1% equipment K 96 3 0 23 122 21.8% N inventories L 47 1 3 0 50 8.9% household equip. M 25 0 0 0 25 4.5% O N 10 2 0 0 12 2.2% O 14 1 0 0 15 2.7% Fig. 5.128k: Structure of the loss for the scenario CZ Ohre by industry branches and TOTAL 393 104 42 23 561 asset categories % 70.1% 18.5% 7.4% 4.0% Detailed Results for Catchment-based Scenario, Czech Republic Scenario: CZ Vltava / Moldau 2 500 Loss [mil. EUR] Total loss Loss on public property 2 000 1 500 1 000 500 0 Fig. 5.129b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.88a: LEC and survival function for the scenario CZ Vltava Fig. 5.129a: LEC function for the scenario CZ Vltava Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 81 17 Probability 20 Total loss 50 2.0% 773 255 Loss on public property 100 1.0% 1 215 417 5.0% 250 0.4% 1 771 622 500 0.2% 2 126 750 4.0% Loss structure for the RTP 250: Tab. 5.88b: Regional loss structure for the scenario CZ Vltava Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Praha 670 38% 0.5% JihoÄ?eský 659 37% 1.0% StÅ™edoÄ?eský 282 16% 0.8% 2.0% VysoÄ?ina 88 5% 0.3% Plzeňský 70 4% 0.2% Ústecký 1 0% 0.0% TOTAL 1 771 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 Loss [mil. EUR] Fig. 5.129c: Survival function for the scenario CZ Vltava Fig. 5.129d: Regional loss structure for the scenario CZ Vltava (% of total loss) Fig. 5.129e: Loss intensity in provinces for the scenario CZ Vltava (% of property in the province) Tab. 5.88c: Losses by sector / purpose classification for the scenario CZ Vltava Loss Category % [mil EUR] infrastructure- infrastructure-public 575 32% public enterprise-public 46 3% 32% Public 622 35% infrastructure-non-public 55 3% enterprise-non-public 1 094 62% TOTAL 1 771 enterprise-public 3% enterprise-non- public 62% infrastructure- non-public 3% Fig. 5.129f: Loss by sector / purpose classification the scenario CZ Vltava Household Tab. 5.88d: Loss structure by asset categories for the scenario CZ Vltava Inventories eguipment Dwellings Loss Category code % 8% 6% 20% [mil EUR] Dwellings AN.1111 361 20% Machinery and Other buildings equipment and structures AN.1112 892 50% 16% Machinery and equipment AN.1113 285 16% Inventories AN.12 134 8% Household eguipment - 98 6% TOTAL 1 771 Other buildings and structures 50% Fig. 5.129g: Loss structure by asset categories for the scenario CZ Vltava Tab. 5.88e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 303 13 8 98 422 23.8% private 375 236 115 0 727 41.1% private public 575 36 11 0 622 35.1% TOTAL 1 253 285 134 98 1 771 % 70.8% 16.1% 7.6% 5.6% building equipment households inventories household equip. households public 24% 0 100 200 300 400 500 600 700 800 35% Fig. 5.129h: Loss structure by institutional sectors and asset categories for the scenario CZ Vltava 0 100 200 300 400 500 600 A+B C D private 41% E F Fig. 5.129i: Loss structure by institutional sectors for the scenario CZ Vltava G H Tab. 5.88f: Loss structure by institutional sectors and industry branches for the scenario CZ Vltava I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 1 21 2 24 1.3% C (Mining and quarrying) 0 0 0 0 0.0% M D (Manufacturing) 3 349 9 361 20.4% public E (Electricity, gas and water N private supply) 92 45 0 137 7.8% households F (Construction) 0 18 2 20 1.1% O G (Wholesale and retail trade; repair) 1 99 10 109 6.2% Fig. 5.129j: Structure of the loss for the scenario CZ Vltava by industry branches and H (Hotels and restaurant) 1 16 2 18 1.0% institutional sectors I (Transport, storage and communications) 198 43 3 245 13.8% J (Financial intermediation) 1 23 0 24 1.4% K (Real estate, renting, 0 100 200 300 400 500 600 research) 40 103 393 536 30.3% L (Public administration 159 0 0 159 9.0% A+B M (Education) 67 0 0 67 3.8% N (Health and social work) 21 1 1 22 1.3% C O (Other community, social and personal services) 38 8 0 46 2.6% D TOTAL 622 727 422 1 771 % 35.1% 41.1% 23.8% E F Tab. 5.88g: Loss structure by asset categories and industry branches, sc. CZ Vltava G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 17 7 0 0 24 1.3% C 0 0 0 0 0 0.0% J D 135 154 72 0 361 20.4% E 110 25 2 0 137 7.8% K F 8 8 4 0 20 1.1% G 47 19 43 0 109 6.2% L H 16 2 0 0 18 1.0% I 207 37 0 0 245 13.8% M building J 19 4 1 0 24 1.4% equipment K 419 16 3 98 536 30.3% N inventories L 146 4 8 0 159 9.0% household equip. M 66 1 0 0 67 3.8% O N 19 4 0 0 22 1.3% O 43 3 0 0 46 2.6% Fig. 5.129k: Structure of the loss for the scenario CZ Vltava by industry branches and TOTAL 1 253 285 134 98 1 771 asset categories % 70.8% 16.1% 7.6% 5.6% Detailed Results for Real Event-based Scenario, Czech Republic Scenario: CZ 1997 9 000 Loss [mil. EUR] Total loss 8 000 Loss on public property 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.131b: Area and percentage of each district affected by the flood in the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.90a: LEC and survival function for the scenario CZ 1997 Fig. 5.131a: LEC function for the scenario CZ 1997 Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 189 45 Probability 20 Total loss 50 2.0% 2 538 761 Loss on public property 100 1.0% 4 383 1 301 5.0% 250 0.4% 6 767 2 024 500 0.2% 8 089 2 439 4.0% Loss structure for the RTP 250: Tab. 5.90b: Regional loss structure for the scenario CZ 1997 Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Moravskoslezský 2 448 36% 4.6% Olomoucký 1 758 26% 6.7% Zlínský 1 587 23% 6.3% 2.0% Jihomoravský 710 10% 1.2% Pardubický 244 4% 1.1% VysoÄ?ina 20 0% 0.1% TOTAL 6 767 1.0% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 Loss [mil. EUR] Fig. 5.131c: Survival function for the scenario CZ 1997 Fig. 5.131d: Regional loss structure for the scenario CZ 1997 (% of total loss) Fig. 5.131e: Loss intensity in provinces for the scenario CZ 1997 (% of property in the province) Tab. 5.90c: Losses by sector / purpose classification for the scenario CZ 1997 infrastructure- public Loss Category % 26% [mil EUR] infrastructure-public 1 777 26% enterprise-public 247 4% Public 2 024 30% infrastructure-non-public 145 2% enterprise-non-public 4 598 68% enterprise-public TOTAL 6 767 4% infrastructure- enterprise-non- non-public public 2% 68% Fig. 5.131f: Loss by sector / purpose classification the scenario CZ 1997 Tab. 5.90d: Loss structure by asset categories for the scenario CZ 1997 Household Dwellings Inventories Loss eguipment 17% Category code % 10% [mil EUR] 4% Dwellings AN.1111 1 173 17% Machinery and Other buildings equipment and structures AN.1112 3 178 47% 22% Machinery and equipment AN.1113 1 455 22% Inventories AN.12 659 10% Household eguipment - 302 4% TOTAL 6 767 Other buildings and structures 47% Fig. 5.131g: Loss structure by asset categories for the scenario CZ 1997 building Tab. 5.90e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 944 72 40 302 1 358 20.1% private 1 572 1 236 576 0 3 385 50.0% private public 1 834 147 42 0 2 024 29.9% TOTAL 4 351 1 455 659 302 6 767 % 64.3% 21.5% 9.7% 4.5% households households public 20% 30% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 Loss [mil. EUR] Fig. 5.131h: Loss structure by institutional sectors and asset categories for the scenario CZ 1997 Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 A+B C D private E 50% F Fig. 5.131i: Loss structure by institutional sectors for the scenario CZ 1997 G H Tab. 5.90f: Loss structure by institutional sectors and industry branches for the scenario CZ 1997 I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 6 73 12 91 1.3% C (Mining and quarrying) 72 117 0 189 2.8% M D (Manufacturing) 32 2 184 82 2 298 34.0% public E (Electricity, gas and water N private supply) 207 102 0 309 4.6% households F (Construction) 0 75 10 86 1.3% O G (Wholesale and retail trade; repair) 2 301 36 339 5.0% Fig. 5.131j: Structure of the loss for the scenario CZ 1997 by industry branches and H (Hotels and restaurant) 0 22 1 24 0.4% institutional sectors I (Transport, storage and communications) 517 121 11 649 9.6% J (Financial intermediation) 0 19 0 19 0.3% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 research) 134 328 1 201 1 662 24.6% L (Public administration 482 0 0 482 7.1% A+B M (Education) 328 0 0 328 4.9% N (Health and social work) 111 11 4 127 1.9% C O (Other community, social and personal services) 132 32 0 164 2.4% D TOTAL 2 024 3 385 1 358 6 767 % 29.9% 50.0% 20.1% E F Tab. 5.90g: Loss structure by asset categories and industry branches, sc. CZ 1997 G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 61 31 0 0 91 1.3% C 121 63 5 0 189 2.8% J D 862 977 458 0 2 298 34.0% E 246 58 5 0 309 4.6% K F 35 33 18 0 86 1.3% G 144 63 132 0 339 5.0% L H 23 1 0 0 24 0.4% I 541 107 1 0 649 9.6% M building J 17 2 0 0 19 0.3% equipment K 1 293 57 11 302 1 662 24.6% N inventories L 437 16 29 0 482 7.1% household equip. M 318 11 0 0 328 4.9% O N 102 25 0 0 127 1.9% O 152 12 0 0 164 2.4% Fig. 5.131k: Structure of the loss for the scenario CZ 1997 by industry branches and TOTAL 4 351 1 455 659 302 6 767 asset categories % 64.3% 21.5% 9.7% 4.5% Detailed Results for Real Event-based Scenario, Czech Republic Scenario: CZ 2002 5 000 Loss [mil. EUR] Total loss 4 500 Loss on public property 4 000 3 500 3 000 2 500 2 000 1 500 1 000 500 0 Fig. 5.130b: Area and percentage of each district affected by the flood in the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.89a: LEC and survival function for the scenario CZ 2002 Fig. 5.130a: LEC function for the scenario CZ 2002 Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 175 34 Probability 20 Total loss 50 2.0% 1 424 442 Loss on public property 100 1.0% 2 423 799 5.0% 250 0.4% 3 603 1 222 500 0.2% 4 319 1 472 4.0% Loss structure for the RTP 250: Tab. 5.89b: Regional loss structure for the scenario CZ 2002 Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Ústecký 950 26% 2.5% Praha 753 21% 0.6% JihoÄ?eský 683 19% 2.1% 2.0% StÅ™edoÄ?eský 625 17% 0.9% Plzeňský 439 12% 1.5% Jihomoravský 111 3% 0.2% VysoÄ?ina 44 1% 0.2% 1.0% TOTAL 3 603 0.0% 0 1 000 2 000 3 000 4 000 5 000 Loss [mil. EUR] Fig. 5.130c: Survival function for the scenario CZ 2002 Fig. 5.130d: Regional loss structure for the scenario CZ 2002 (% of total loss) Fig. 5.130e: Loss intensity in provinces for the scenario CZ 2002 (% of property in the province) Tab. 5.89c: Losses by sector / purpose classification for the scenario CZ 2002 Loss Category % infrastructure- [mil EUR] public infrastructure-public 1 114 31% 31% enterprise-public 108 3% Public 1 222 34% infrastructure-non-public 103 3% enterprise-non-public 2 278 63% TOTAL 3 603 enterprise-public 3% enterprise-non- infrastructure- public non-public 63% 3% Fig. 5.130f: Loss by sector / purpose classification the scenario CZ 2002 Tab. 5.89d: Loss structure by asset categories for the scenario CZ 2002 Inventories Household Dwellings Loss 8% eguipment 18% Category code % [mil EUR] 5% Machinery and Dwellings AN.1111 664 18% equipment Other buildings 18% and structures AN.1112 1 801 50% Machinery and equipment AN.1113 660 18% Inventories AN.12 301 8% Household eguipment - 177 5% TOTAL 3 603 Other buildings and structures 51% Fig. 5.130g: Loss structure by asset categories for the scenario CZ 2002 building Tab. 5.89e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 547 27 17 177 767 21.3% private 795 557 263 0 1 615 44.8% private public 1 124 76 22 0 1 222 33.9% TOTAL 2 465 660 301 177 3 603 % 68.4% 18.3% 8.4% 4.9% households households public 21% 34% 0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 Loss [mil. EUR] Fig. 5.130h: Loss structure by institutional sectors and asset categories for the scenario CZ 2002 Loss [mil. EUR] 0 200 400 600 800 1 000 1 200 A+B C D private E 45% F Fig. 5.130i: Loss structure by institutional sectors for the scenario CZ 2002 G H Tab. 5.89f: Loss structure by institutional sectors and industry branches for the scenario CZ 2002 I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 2 34 4 40 1.1% C (Mining and quarrying) 22 35 0 57 1.6% M D (Manufacturing) 9 909 28 945 26.2% public E (Electricity, gas and water N private supply) 174 85 0 258 7.2% households F (Construction) 0 37 4 41 1.1% O G (Wholesale and retail trade; repair) 1 174 17 192 5.3% Fig. 5.130j: Structure of the loss for the scenario CZ 2002 by industry branches and H (Hotels and restaurant) 1 25 2 28 0.8% institutional sectors I (Transport, storage and communications) 372 82 6 461 12.8% J (Financial intermediation) 1 29 0 30 0.8% Loss [mil. EUR] K (Real estate, renting, 0 200 400 600 800 1 000 1 200 research) 72 186 705 963 26.7% L (Public administration 315 0 0 315 8.7% A+B M (Education) 137 0 0 137 3.8% N (Health and social work) 45 3 1 49 1.4% C O (Other community, social and personal services) 72 16 0 87 2.4% D TOTAL 1 222 1 615 767 3 603 % 33.9% 44.8% 21.3% E F Tab. 5.89g: Loss structure by asset categories and industry branches, sc. CZ 2002 G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 28 12 0 0 40 1.1% C 38 19 1 0 57 1.6% J D 354 403 188 0 945 26.2% E 207 48 3 0 258 7.2% K F 17 16 9 0 41 1.1% G 82 33 76 0 192 5.3% L H 25 2 0 0 28 0.8% I 389 71 1 0 461 12.8% M building J 24 4 1 0 30 0.8% equipment K 752 29 5 177 963 26.7% N inventories L 290 8 16 0 315 8.7% household equip. M 134 3 0 0 137 3.8% O N 42 8 0 0 49 1.4% O 82 5 0 0 87 2.4% Fig. 5.130k: Structure of the loss for the scenario CZ 2002 by industry branches and TOTAL 2 465 660 301 177 3 603 asset categories % 68.4% 18.3% 8.4% 4.9% Detailed Results for Large Complex Geographical Units-based Scenario, Czech Republic Scenario: CZ Bohemia 10 000 Loss [mil. EUR] Total loss 9 000 Loss on public property 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.132b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.91a: LEC and survival function for the scenario CZ Bohemia Fig. 5.132a: LEC function for the scenario CZ Bohemia Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 441 94 Probability 20 Total loss 50 2.0% 2 987 909 Loss on public property 100 1.0% 5 093 1 638 5.0% 250 0.4% 7 613 2 511 500 0.2% 9 105 3 021 4.0% Loss structure for the RTP 250: Tab. 5.91b: Regional loss structure for the scenario CZ Bohemia Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] StÅ™edoÄ?eský 1 668 22% 2.5% Královéhradecký 1 103 14% 4.0% Ústecký 1 101 14% 3.0% 2.0% Pardubický 888 12% 3.8% Praha 753 10% 0.6% JihoÄ?eský 665 9% 2.0% Liberecký 503 7% 2.8% 1.0% Plzeňský 459 6% 1.6% Karlovarský 244 3% 1.8% VysoÄ?ina 228 3% 1.0% TOTAL 7 613 0.0% 0 2 000 4 000 6 000 8 000 10 000 Loss [mil. EUR] Fig. 5.132c: Survival function for the scenario CZ Bohemia Fig. 5.132d: Regional loss structure for the scenario CZ Bohemia (% of total loss) Fig. 5.132e: Loss intensity in provinces for the scenario CZ Bohemia (% of property in the province) Tab. 5.91c: Losses by sector / purpose classification for the scenario CZ Bohemia Loss Category % infrastructure- [mil EUR] public infrastructure-public 2 275 30% 30% enterprise-public 236 3% Public 2 511 33% infrastructure-non-public 200 3% enterprise-non-public 4 902 64% TOTAL 7 613 enterprise-public 3% enterprise-non- public infrastructure- 64% non-public 3% Fig. 5.132f: Loss by sector / purpose classification the scenario CZ Bohemia Tab. 5.91d: Loss structure by asset categories for the scenario CZ Bohemia Inventories Household Dwellings Loss eguipment 19% Category code % 8% [mil EUR] 5% Dwellings AN.1111 1 472 19% Machinery and Other buildings equipment and structures AN.1112 3 721 49% 18% Machinery and equipment AN.1113 1 397 18% Inventories AN.12 629 8% Household eguipment - 393 5% TOTAL 7 613 Other buildings and structures 50% Fig. 5.132g: Loss structure by asset categories for the scenario CZ Bohemia building Tab. 5.91e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 212 63 37 393 1 706 22.4% private 1 669 1 178 548 0 3 396 44.6% private public 2 311 156 43 0 2 511 33.0% TOTAL 5 193 1 397 629 393 7 613 % 68.2% 18.4% 8.3% 5.2% households households public 22% 33% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 Fig. 5.132h: Loss structure by institutional sectors and asset categories for the scenario CZ Bohemia 0 500 1 000 1 500 2 000 2 500 A+B C D private 45% E F Fig. 5.132i: Loss structure by institutional sectors for the scenario CZ Bohemia G H Tab. 5.91f: Loss structure by institutional sectors and industry branches for the scenario CZ Bohemia I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 6 89 12 107 1.4% C (Mining and quarrying) 38 62 0 99 1.3% M D (Manufacturing) 21 1 910 61 1 992 26.2% public E (Electricity, gas and water N private supply) 325 159 0 484 6.4% households F (Construction) 0 82 9 91 1.2% O G (Wholesale and retail trade; repair) 2 360 37 400 5.2% Fig. 5.132j: Structure of the loss for the scenario CZ Bohemia by industry branches and H (Hotels and restaurant) 1 50 4 55 0.7% institutional sectors I (Transport, storage and communications) 775 176 14 965 12.7% J (Financial intermediation) 2 46 0 48 0.6% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 research) 166 421 1 565 2 152 28.3% L (Public administration 608 0 0 608 8.0% A+B M (Education) 308 0 0 309 4.1% N (Health and social work) 109 8 3 120 1.6% C O (Other community, social and personal services) 149 33 0 183 2.4% D TOTAL 2 511 3 396 1 706 7 613 % 33.0% 44.6% 22.4% E F Tab. 5.91g: Loss structure by asset categories and industry branches, sc. CZ Bohemia G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 73 34 0 0 107 1.4% C 65 32 2 0 99 1.3% J D 747 849 396 0 1 992 26.2% E 388 89 6 0 484 6.4% K F 37 34 20 0 91 1.2% G 171 71 157 0 400 5.2% L H 51 4 0 0 55 0.7% I 811 153 1 0 965 12.7% M building J 40 6 2 0 48 0.6% equipment K 1 678 69 12 393 2 152 28.3% N inventories L 559 17 33 0 608 8.0% household equip. M 302 7 0 0 309 4.1% O N 100 20 0 0 120 1.6% O 172 11 0 0 183 2.4% Fig. 5.132k: Structure of the loss for the scenario CZ Bohemia by industry branches and TOTAL 5 193 1 397 629 393 7 613 asset categories % 68.2% 18.4% 8.3% 5.2% Detailed Results for Large Complex Geographical Units-based Scenario, Czech Republic Scenario: CZ Moravia 12 000 Loss [mil. EUR] Total loss Loss on public property 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.133b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.92a: LEC and survival function for the scenario CZ Moravia Fig. 5.133a: LEC function for the scenario CZ Moravia Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 237 57 Probability 20 Total loss 50 2.0% 3 233 993 Loss on public property 100 1.0% 5 451 1 671 5.0% 250 0.4% 8 319 2 569 500 0.2% 9 924 3 090 4.0% Loss structure for the RTP 250: Tab. 5.92b: Regional loss structure for the scenario CZ Moravia Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Moravskoslezský 2 448 29% 4.6% Olomoucký 2 039 25% 7.8% Jihomoravský 1 990 24% 3.3% 2.0% Zlínský 1 587 19% 6.3% VysoÄ?ina 159 2% 0.7% Pardubický 79 1% 0.3% JihoÄ?eský 17 0% 0.1% 1.0% TOTAL 8 319 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 Loss [mil. EUR] Fig. 5.133c: Survival function for the scenario CZ Moravia Fig. 5.133d: Regional loss structure for the scenario CZ Moravia (% of total loss) Fig. 5.133e: Loss intensity in provinces for the scenario CZ Moravia (% of property in the province) Tab. 5.92c: Losses by sector / purpose classification for the scenario CZ Moravia infrastructure- Loss public Category % [mil EUR] 27% infrastructure-public 2 268 27% enterprise-public 302 4% Public 2 569 31% infrastructure-non-public 186 2% enterprise-non-public 5 564 67% TOTAL 8 319 enterprise-public 4% enterprise-non- infrastructure- public non-public 67% 2% Fig. 5.133f: Loss by sector / purpose classification the scenario CZ Moravia Dwellings Tab. 5.92d: Loss structure by asset categories for the scenario CZ Moravia Inventories Household Loss eguipment 18% Category code % 9% [mil EUR] 5% Dwellings AN.1111 1 502 18% Machinery and Other buildings equipment and structures AN.1112 3 932 47% 21% Machinery and equipment AN.1113 1 719 21% Inventories AN.12 776 9% Household eguipment - 390 5% TOTAL 8 319 Other buildings and structures 47% Fig. 5.133g: Loss structure by asset categories for the scenario CZ Moravia building Tab. 5.92e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 216 90 50 390 1 746 21.0% private 1 886 1 445 671 0 4 003 48.1% private public 2 331 184 54 0 2 569 30.9% TOTAL 5 434 1 719 776 390 8 319 % 65.3% 20.7% 9.3% 4.7% households households public 21% 31% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 Fig. 5.133h: Loss structure by institutional sectors and asset categories for the scenario CZ Moravia 0 500 1 000 1 500 2 000 2 500 3 000 A+B C D private E 48% F Fig. 5.133i: Loss structure by institutional sectors for the scenario CZ Moravia G H Tab. 5.92f: Loss structure by institutional sectors and industry branches for the scenario CZ Moravia I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 9 97 17 123 1.5% C (Mining and quarrying) 77 126 0 203 2.4% M D (Manufacturing) 36 2 475 94 2 605 31.3% public E (Electricity, gas and water N private supply) 264 131 0 395 4.7% households F (Construction) 1 101 15 116 1.4% O G (Wholesale and retail trade; repair) 3 378 46 426 5.1% Fig. 5.133j: Structure of the loss for the scenario CZ Moravia by industry branches and H (Hotels and restaurant) 0 31 2 33 0.4% institutional sectors I (Transport, storage and communications) 671 159 15 845 10.2% J (Financial intermediation) 1 25 0 26 0.3% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 175 426 1 552 2 154 25.9% L (Public administration 617 0 0 617 7.4% A+B M (Education) 411 0 0 412 4.9% N (Health and social work) 138 14 6 158 1.9% C O (Other community, social and personal services) 166 40 1 207 2.5% D TOTAL 2 569 4 003 1 746 8 319 % 30.9% 48.1% 21.0% E F Tab. 5.92g: Loss structure by asset categories and industry branches, sc. CZ Moravia G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 81 42 0 0 123 1.5% C 130 68 5 0 203 2.4% J D 978 1 108 519 0 2 605 31.3% E 314 74 6 0 395 4.7% K F 47 44 25 0 116 1.4% G 181 80 165 0 426 5.1% L H 31 2 0 0 33 0.4% I 702 141 1 0 845 10.2% M building J 23 3 0 0 26 0.3% equipment K 1 673 76 14 390 2 154 25.9% N inventories L 557 22 38 0 617 7.4% household equip. M 398 14 0 0 412 4.9% O N 127 31 0 0 158 1.9% O 191 15 1 0 207 2.5% Fig. 5.133k: Structure of the loss for the scenario CZ Moravia by industry branches and TOTAL 5 434 1 719 776 390 8 319 asset categories % 65.3% 20.7% 9.3% 4.7% Detailed Results for Catchment-based Scenario, Slovakia Scenario: SK Dunaj / Danube 12 000 Loss [mil. EUR] Total loss Loss on public property 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.134b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.93a: LEC and survival function for the scenario SK Dunaj / Danube Fig. 5.134a: LEC function for the scenario SK Dunaj / Danube Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 139 28 Probability 20 Total loss 50 2.0% 1 130 375 Loss on public property 100 1.0% 2 678 922 5.0% 250 0.4% 7 808 2 578 500 0.2% 11 011 3 603 4.0% Loss structure for the RTP 250: Tab. 5.93b: Regional loss structure for the scenario SK Dunaj / Danube Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Bratislavský kraj 4 712 60% 6.9% Trnavský kraj 2 491 32% 8.6% Nitriansky kraj 545 7% 1.8% 2.0% TrenÄ?iansky kraj 59 1% 0.2% TOTAL 7 808 1.0% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 Loss [mil. EUR] Fig. 5.134c: Survival function for the scenario SK Dunaj / Danube Fig. 5.134d: Regional loss structure for the scenario SK Dunaj / Danube (% of total loss) Fig. 5.134e: Loss intensity in provinces for the scenario SK Dunaj / Danube (% of property in the province) Tab. 5.93c: Losses by sector / purpose classification for the scenario SK Dunaj / Danube enterprise-non- public Loss infrastructure- Category % 62% [mil EUR] public 29% infrastructure-public 2 265 29% enterprise-public 313 4% Public 2 578 33% infrastructure-non-public 396 5% enterprise-non-public 4 834 62% TOTAL 7 808 enterprise-public 4% infrastructure- non-public 5% Fig. 5.134f: Loss by sector / purpose classification the scenario SK Dunaj / Danube Tab. 5.93d: Loss structure by asset categories for the scenario SK Dunaj / Danube Inventories Household Loss 8% eguipment Category code % Dwellings [mil EUR] 5% 24% Machinery and Dwellings AN.1111 1 885 24% equipment Other buildings 18% and structures AN.1112 3 460 44% Machinery and equipment AN.1113 1 440 18% Inventories AN.12 627 8% Household eguipment - 395 5% TOTAL 7 808 Other buildings and structures 45% Fig. 5.134g: Loss structure by asset categories for the scenario SK Dunaj / Danube Tab. 5.93e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 428 74 50 395 1 946 24.9% private 1 880 906 498 0 3 284 42.1% private public 2 038 460 80 0 2 578 33.0% TOTAL 5 345 1 440 627 395 7 808 % 68.5% 18.4% 8.0% 5.1% building equipment households inventories household equip. public households 33% 25% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil. EUR] Fig. 5.134h: Loss structure by institutional sectors and asset categories for the scenario SK Dunaj / Danube Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 3 000 A+B C D private 42% E F Fig. 5.134i: Loss structure by institutional sectors for the scenario SK Dunaj / Danube G H Tab. 5.93f: Loss structure by institutional sectors and industry branches for the scenario SK Dunaj / Danube I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 14 107 19 140 1.8% C (Mining and quarrying) 38 63 0 102 1.3% M D (Manufacturing) 23 1 490 56 1 570 20.1% public E (Electricity, gas and water N private supply) 746 369 0 1 115 14.3% households F (Construction) 1 53 8 61 0.8% O G (Wholesale and retail trade; repair) 5 438 57 500 6.4% Fig. 5.134j: Structure of the loss for the scenario SK Dunaj / Danube by industry H (Hotels and restaurant) 3 39 4 46 0.6% branches and institutional sectors I (Transport, storage and communications) 532 113 11 657 8.4% J (Financial intermediation) 5 85 0 90 1.2% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 224 498 1 786 2 508 32.1% L (Public administration 735 0 0 735 9.4% A+B M (Education) 91 0 0 91 1.2% N (Health and social work) 77 8 3 88 1.1% C O (Other community, social and personal services) 84 19 1 104 1.3% D TOTAL 2 578 3 284 1 946 7 808 % 33.0% 42.1% 24.9% E F Tab. 5.93g: Loss structure by asset categories and industry branches, sc. SK Dunaj / Danube G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 108 31 2 0 142 1.8% C 87 12 315 0 414 5.3% J D 628 627 27 0 1 282 16.4% E 832 257 13 0 1 101 14.1% K F 24 25 192 0 240 3.1% G 205 103 1 0 309 4.0% L H 36 10 4 0 49 0.6% I 465 188 4 0 657 8.4% M building J 59 27 22 0 107 1.4% equipment K 2 049 43 48 395 2 534 32.5% N inventories L 610 78 0 0 688 8.8% household equip. M 88 3 1 0 92 1.2% O N 71 16 1 0 89 1.1% O 84 19 0 0 103 1.3% Fig. 5.134k: Structure of the loss for the scenario SK Dunaj / Danube by industry TOTAL 5 345 1 440 627 395 7 808 branches and asset categories % 68.5% 18.4% 8.0% 5.1% Detailed Results for Catchment-based Scenario, Slovakia Scenario: SK Vah + Nitra 9 000 Loss [mil. EUR] Total loss 8 000 Loss on public property 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.135b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.94a: LEC and survival function for the scenario SK Vah + Nitra Fig. 5.135a: LEC function for the scenario SK Vah + Nitra Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 547 161 Probability 20 Total loss 50 2.0% 2 956 983 Loss on public property 100 1.0% 4 468 1 502 5.0% 250 0.4% 6 682 2 251 500 0.2% 8 032 2 705 4.0% Loss structure for the RTP 250: Tab. 5.94b: Regional loss structure for the scenario SK Vah + Nitra Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] TrenÄ?iansky kraj 2 479 37% 8.0% Žilinský kraj 2 284 34% 7.1% Nitriansky kraj 1 456 22% 4.8% 2.0% Trnavský kraj 451 7% 1.5% PreÅ¡ovský kraj 8 0% 0.0% Banskobystrický kraj 4 0% 0.0% TOTAL 6 682 1.0% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 Loss [mil. EUR] Fig. 5.135c: Survival function for the scenario SK Vah + Nitra Fig. 5.135d: Regional loss structure for the scenario SK Vah + Nitra (% of total loss) Fig. 5.135e: Loss intensity in provinces for the scenario SK Vah + Nitra (% of property in the province) Tab. 5.94c: Losses by sector / purpose classification for the scenario SK Vah + Nitra enterprise-non- public Loss Category % 61% infrastructure- [mil EUR] public infrastructure-public 2 013 30% 30% enterprise-public 238 4% Public 2 251 34% infrastructure-non-public 359 5% enterprise-non-public 4 072 61% TOTAL 6 682 enterprise-public 4% infrastructure- non-public 5% Fig. 5.135f: Loss by sector / purpose classification the scenario SK Vah + Nitra Tab. 5.94d: Loss structure by asset categories for the scenario SK Vah + Nitra Inventories Household Dwellings Loss 8% eguipment 19% Category code % [mil EUR] 4% Dwellings AN.1111 1 276 19% Machinery and Other buildings equipment and structures AN.1112 3 195 48% 21% Machinery and equipment AN.1113 1 378 21% Inventories AN.12 568 8% Household eguipment - 265 4% TOTAL 6 682 Other buildings and structures 48% Fig. 5.135g: Loss structure by asset categories for the scenario SK Vah + Nitra building Tab. 5.94e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 977 50 34 265 1 327 19.9% private 1 710 921 474 0 3 105 46.5% private public 1 783 407 60 0 2 251 33.7% TOTAL 4 471 1 378 568 265 6 682 % 66.9% 20.6% 8.5% 4.0% households households 20% public 0 500 1 000 1 500 2 000 2 500 3 000 3 500 34% Loss [mil. EUR] Fig. 5.135h: Loss structure by institutional sectors and asset categories for the scenario SK Vah + Nitra Loss [mil. EUR] 0 500 1 000 1 500 2 000 A+B C private D 46% E F Fig. 5.135i: Loss structure by institutional sectors for the scenario SK Vah + Nitra G H Tab. 5.94f: Loss structure by institutional sectors and industry branches for the scenario SK Vah + Nitra I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 13 116 18 147 2.2% C (Mining and quarrying) 53 89 0 142 2.1% M D (Manufacturing) 24 1 709 61 1 793 26.8% public E (Electricity, gas and water N private supply) 701 345 0 1 046 15.7% households F (Construction) 0 41 3 44 0.7% O G (Wholesale and retail trade; repair) 1 290 32 323 4.8% Fig. 5.135j: Structure of the loss for the scenario SK Vah + Nitra by industry branches H (Hotels and restaurant) 1 28 2 31 0.5% and institutional sectors I (Transport, storage and communications) 520 108 8 636 9.5% J (Financial intermediation) 0 35 0 36 0.5% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 research) 146 330 1 201 1 677 25.1% L (Public administration 579 0 0 579 8.7% A+B M (Education) 94 0 0 94 1.4% N (Health and social work) 70 6 1 77 1.1% C O (Other community, social and personal services) 50 9 0 59 0.9% D TOTAL 2 251 3 105 1 327 6 682 % 33.7% 46.5% 19.9% E F Tab. 5.94g: Loss structure by asset categories and industry branches, sc. SK Vah + Nitra G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 117 30 0 0 147 2.2% C 123 16 3 0 142 2.1% J D 718 716 359 0 1 793 26.8% E 785 239 22 0 1 046 15.7% K F 18 17 9 0 44 0.7% G 135 63 125 0 323 4.8% L H 26 5 0 0 31 0.5% I 455 178 2 0 636 9.5% M building J 25 10 1 0 36 0.5% equipment K 1 379 22 11 265 1 677 25.1% N 483 59 36 0 579 8.7% inventories L household equip. M 92 2 0 0 94 1.4% O N 64 13 0 0 77 1.1% O 51 8 0 0 59 0.9% Fig. 5.135k: Structure of the loss for the scenario SK Vah + Nitra by industry branches TOTAL 4 471 1 378 568 265 6 682 and asset categories % 66.9% 20.6% 8.5% 4.0% Detailed Results for Catchment-based Scenario, Slovakia Scenario: SK Hron + Ipel 3 000 Loss [mil. EUR] Total loss Loss on public property 2 500 2 000 1 500 1 000 500 0 Fig. 5.136b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.95a: LEC and survival function for the scenario SK Hron + Ipel Fig. 5.136a: LEC function for the scenario SK Hron + Ipel Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 171 58 Probability 20 Total loss 50 2.0% 891 328 Loss on public property 100 1.0% 1 436 545 5.0% 250 0.4% 2 102 814 500 0.2% 2 472 964 4.0% Loss structure for the RTP 250: Tab. 5.95b: Regional loss structure for the scenario SK Hron + Ipel Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Banskobystrický kraj 1 412 67% 5.0% Nitriansky kraj 690 33% 2.3% TrenÄ?iansky kraj 1 0% 0.0% 2.0% TOTAL 2 102 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 Loss [mil. EUR] Fig. 5.136c: Survival function for the scenario SK Hron + Ipel Fig. 5.136d: Regional loss structure for the scenario SK Hron + Ipel (% of total loss) Fig. 5.136e: Loss intensity in provinces for the scenario SK Hron + Ipel (% of property in the province) Tab. 5.95c: Losses by sector / purpose classification for the scenario SK Hron + Ipel Loss Category % [mil EUR] infrastructure- infrastructure-public 753 36% public enterprise-public 61 3% 36% Public 814 39% infrastructure-non-public 114 5% enterprise-non-public 1 174 56% TOTAL 2 102 enterprise-public 3% enterprise-non- public infrastructure- 56% non-public 5% Fig. 5.136f: Loss by sector / purpose classification the scenario SK Hron + Ipel Tab. 5.95d: Loss structure by asset categories for the scenario SK Hron + Ipel Inventories Household Dwellings Loss 8% eguipment Category code % 18% [mil EUR] 4% Machinery and Dwellings AN.1111 375 18% equipment Other buildings 20% and structures AN.1112 1 068 51% Machinery and equipment AN.1113 417 20% Inventories AN.12 165 8% Household eguipment - 77 4% TOTAL 2 102 Other buildings and structures 50% Fig. 5.136g: Loss structure by asset categories for the scenario SK Hron + Ipel Tab. 5.95e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 288 13 9 77 388 18.4% private 508 259 133 0 900 42.8% private public 648 144 22 0 814 38.7% TOTAL 1 443 417 165 77 2 102 % 68.6% 19.8% 7.9% 3.7% building equipment households inventories household equip. households 18% 0 200 400 600 800 1 000 public Loss [mil. EUR] 39% Fig. 5.136h: Loss structure by institutional sectors and asset categories for the scenario SK Hron + Ipel Loss [mil. EUR] 0 100 200 300 400 500 600 A+B C private D 43% E F Fig. 5.136i: Loss structure by institutional sectors for the scenario SK Hron + Ipel G H Tab. 5.95f: Loss structure by institutional sectors and industry branches for the scenario SK Hron + Ipel I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 6 52 8 65 3.1% C (Mining and quarrying) 8 13 0 21 1.0% M D (Manufacturing) 5 454 15 475 22.6% public E (Electricity, gas and water N private supply) 223 109 0 332 15.8% households F (Construction) 0 10 1 11 0.5% O G (Wholesale and retail trade; repair) 0 97 11 108 5.1% Fig. 5.136j: Structure of the loss for the scenario SK Hron + Ipel by industry branches H (Hotels and restaurant) 0 9 1 10 0.5% and institutional sectors I (Transport, storage and communications) 198 41 3 242 11.5% J (Financial intermediation) 0 14 0 15 0.7% Loss [mil. EUR] K (Real estate, renting, 0 100 200 300 400 500 600 research) 42 96 349 487 23.1% L (Public administration 249 0 0 249 11.9% A+B M (Education) 38 0 0 38 1.8% N (Health and social work) 27 2 1 30 1.4% C O (Other community, social and personal services) 17 3 0 20 1.0% D TOTAL 814 900 388 2 102 % 38.7% 42.8% 18.4% E F Tab. 5.95g: Loss structure by asset categories and industry branches, sc. SK Hron + Ipel G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 52 13 0 0 65 3.1% C 20 2 0 0 21 1.0% J D 190 190 95 0 475 22.6% E 250 75 7 0 332 15.8% K F 5 4 2 0 11 0.5% G 45 21 42 0 108 5.1% L H 8 1 0 0 10 0.5% I 174 68 1 0 242 11.5% M building J 10 4 0 0 15 0.7% equipment K 401 5 3 77 487 23.1% N 208 25 16 0 249 11.9% inventories L household equip. M 37 1 0 0 38 1.8% O N 25 5 0 0 30 1.4% O 17 3 0 0 20 1.0% Fig. 5.136k: Structure of the loss for the scenario SK Hron + Ipel by industry branches TOTAL 1 443 417 165 77 2 102 and asset categories % 68.6% 19.8% 7.9% 3.7% Detailed Results for Catchment-based Scenario, Slovakia Scenario: SK Hornad + Slana 3 500 Loss [mil. EUR] Total loss Loss on public property 3 000 2 500 2 000 1 500 1 000 500 0 Fig. 5.137b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.96a: LEC and survival function for the scenario SK Hornad + Slana Fig. 5.137a: LEC function for the scenario SK Hornad + Slana Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 191 68 Probability 20 Total loss 50 2.0% 1 040 395 Loss on public property 100 1.0% 1 576 599 5.0% 250 0.4% 2 384 919 500 0.2% 2 949 1 148 4.0% Loss structure for the RTP 250: Tab. 5.96b: Regional loss structure for the scenario SK Hornad + Slana Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] KoÅ¡ický kraj 1 622 68% 4.0% PreÅ¡ovský kraj 459 19% 1.7% Banskobystrický kraj 303 13% 1.1% 2.0% TOTAL 2 384 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil. EUR] Fig. 5.137c: Survival function for the scenario SK Hornad + Slana Fig. 5.137d: Regional loss structure for the scenario SK Hornad + Slana (% of total loss) Fig. 5.137e: Loss intensity in provinces for the scenario SK Hornad + Slana (% of property in the province) Tab. 5.96c: Losses by sector / purpose classification for the scenario SK Hornad + Slana Loss infrastructure- Category % public [mil EUR] 35% infrastructure-public 841 35% enterprise-public 78 3% Public 919 39% infrastructure-non-public 143 6% enterprise-non-public 1 323 55% TOTAL 2 384 enterprise-public 3% enterprise-non- infrastructure- public non-public 56% 6% Fig. 5.137f: Loss by sector / purpose classification the scenario SK Hornad + Slana Tab. 5.96d: Loss structure by asset categories for the scenario SK Hornad + Slana Household Inventories Dwellings Loss eguipment Category code % 7% 21% [mil EUR] 4% Machinery and Dwellings AN.1111 497 21% equipment Other buildings 19% and structures AN.1112 1 166 49% Machinery and equipment AN.1113 446 19% Inventories AN.12 170 7% Household eguipment - 104 4% TOTAL 2 384 Other buildings and structures 49% Fig. 5.137g: Loss structure by asset categories for the scenario SK Hornad + Slana Tab. 5.96e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 377 15 11 104 507 21.3% private 558 264 136 0 958 40.2% private public 728 167 24 0 919 38.5% TOTAL 1 664 446 170 104 2 384 % 69.8% 18.7% 7.1% 4.4% building equipment households inventories household equip. households 21% 0 200 400 600 800 1 000 1 200 public Loss [mil. EUR] 39% Fig. 5.137h: Loss structure by institutional sectors and asset categories for the scenario SK Hornad + Slana Loss [mil. EUR] 0 100 200 300 400 500 600 700 A+B C private 40% D E F Fig. 5.137i: Loss structure by institutional sectors for the scenario SK Hornad + Slana G H Tab. 5.96f: Loss structure by institutional sectors and industry branches for the scenario SK Hornad + Slana I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 4 37 5 46 1.9% C (Mining and quarrying) 11 19 0 29 1.2% M D (Manufacturing) 5 442 15 461 19.4% public E (Electricity, gas and water N private supply) 278 136 0 414 17.4% households F (Construction) 0 12 1 13 0.6% O G (Wholesale and retail trade; repair) 0 105 12 117 4.9% Fig. 5.137j: Structure of the loss for the scenario SK Hornad + Slana by industry H (Hotels and restaurant) 0 8 0 9 0.4% branches and institutional sectors I (Transport, storage and communications) 229 48 4 280 11.7% J (Financial intermediation) 0 16 0 17 0.7% Loss [mil. EUR] K (Real estate, renting, 0 100 200 300 400 500 600 700 research) 57 129 469 655 27.5% L (Public administration 244 0 0 244 10.2% A+B M (Education) 42 0 0 42 1.8% N (Health and social work) 28 2 1 31 1.3% C O (Other community, social and personal services) 22 4 0 26 1.1% D TOTAL 919 958 507 2 384 % 38.5% 40.2% 21.3% E F Tab. 5.96g: Loss structure by asset categories and industry branches, sc. SK Hornad + Slana G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 37 9 0 0 46 1.9% C 26 3 0 0 29 1.2% J D 185 184 92 0 461 19.4% E 310 95 9 0 414 17.4% K F 5 5 3 0 13 0.6% G 49 23 45 0 117 4.9% L H 8 1 0 0 9 0.4% I 200 79 1 0 280 11.7% M building J 12 4 0 0 17 0.7% equipment K 539 8 4 104 655 27.5% N inventories L 204 25 15 0 244 10.2% household equip. M 41 1 0 0 42 1.8% O N 26 5 0 0 31 1.3% O 22 4 0 0 26 1.1% Fig. 5.137k: Structure of the loss for the scenario SK Hornad + Slana by industry TOTAL 1 664 446 170 104 2 384 branches and asset categories % 69.8% 18.7% 7.1% 4.4% Detailed Results for Catchment-based Scenario, Slovakia Scenario: SK Tisza + Bodrog 2 500 Loss [mil. EUR] Total loss Loss on public property 2 000 1 500 1 000 500 0 Fig. 5.138b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.97a: LEC and survival function for the scenario SK Tisza + Bodrog Fig. 5.138a: LEC function for the scenario SK Tisza + Bodrog Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 65 20 Probability 20 Total loss 50 2.0% 546 214 Loss on public property 100 1.0% 979 374 5.0% 250 0.4% 1 598 602 500 0.2% 1 981 742 4.0% Loss structure for the RTP 250: Tab. 5.97b: Regional loss structure for the scenario SK Tisza + Bodrog Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] KoÅ¡ický kraj 914 57% 2.2% PreÅ¡ovský kraj 684 43% 2.5% TOTAL 1 598 2.0% 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 Loss [mil. EUR] Fig. 5.138c: Survival function for the scenario SK Tisza + Bodrog Fig. 5.138d: Regional loss structure for the scenario SK Tisza + Bodrog (% of total loss) Fig. 5.138e: Loss intensity in provinces for the scenario SK Tisza + Bodrog (% of property in the province) Tab. 5.97c: Losses by sector / purpose classification for the scenario SK Tisza + Bodrog Loss Category % [mil EUR] infrastructure- public infrastructure-public 549 34% 34% enterprise-public 53 3% Public 602 38% infrastructure-non-public 88 6% enterprise-non-public 908 57% TOTAL 1 598 enterprise-public 3% enterprise-non- public infrastructure- 57% non-public 6% Fig. 5.138f: Loss by sector / purpose classification the scenario SK Tisza + Bodrog Tab. 5.97d: Loss structure by asset categories for the scenario SK Tisza + Bodrog Inventories Household Loss eguipment Category code % 7% Dwellings [mil EUR] 4% 21% Machinery and Dwellings AN.1111 342 21% equipment Other buildings 19% and structures AN.1112 771 48% Machinery and equipment AN.1113 297 19% Inventories AN.12 117 7% Household eguipment - 71 4% TOTAL 1 598 Other buildings and structures 49% Fig. 5.138g: Loss structure by asset categories for the scenario SK Tisza + Bodrog Tab. 5.97e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 259 10 7 71 348 21.8% private 375 180 94 0 648 40.6% private public 479 107 16 0 602 37.7% TOTAL 1 113 297 117 71 1 598 % 69.6% 18.6% 7.3% 4.5% building equipment households inventories household equip. households public 22% 0 100 200 300 400 500 600 700 Loss [mil. EUR] 38% Fig. 5.138h: Loss structure by institutional sectors and asset categories for the scenario SK Tisza + Bodrog Loss [mil. EUR] 0 100 200 300 400 500 A+B C private 40% D E F Fig. 5.138i: Loss structure by institutional sectors for the scenario SK Tisza + Bodrog G H Tab. 5.97f: Loss structure by institutional sectors and industry branches for the scenario SK Tisza + Bodrog I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 2 24 3 30 1.9% C (Mining and quarrying) 7 12 0 18 1.2% M D (Manufacturing) 4 311 10 325 20.3% public E (Electricity, gas and water N private supply) 172 84 0 255 16.0% households F (Construction) 0 8 1 9 0.6% O G (Wholesale and retail trade; repair) 0 70 7 78 4.9% Fig. 5.138j: Structure of the loss for the scenario SK Tisza + Bodrog by industry H (Hotels and restaurant) 0 6 0 7 0.4% branches and institutional sectors I (Transport, storage and communications) 149 31 2 182 11.4% J (Financial intermediation) 0 11 0 11 0.7% Loss [mil. EUR] K (Real estate, renting, 0 100 200 300 400 500 research) 39 88 323 450 28.2% L (Public administration 166 0 0 166 10.4% A+B M (Education) 29 0 0 29 1.8% N (Health and social work) 19 2 0 21 1.3% C O (Other community, social and personal services) 15 3 0 17 1.1% D TOTAL 602 648 348 1 598 % 37.7% 40.6% 21.8% E F Tab. 5.97g: Loss structure by asset categories and industry branches, sc. SK Tisza + Bodrog G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 24 6 0 0 30 1.9% C 16 2 0 0 18 1.2% J D 130 130 65 0 325 20.3% E 192 58 5 0 255 16.0% K F 4 3 2 0 9 0.6% G 33 15 30 0 78 4.9% L H 6 1 0 0 7 0.4% I 130 51 1 0 182 11.4% M building J 8 3 0 0 11 0.7% equipment K 370 6 3 71 450 28.2% N inventories L 139 17 10 0 166 10.4% household equip. M 29 1 0 0 29 1.8% O N 18 3 0 0 21 1.3% O 15 2 0 0 17 1.1% Fig. 5.138k: Structure of the loss for the scenario SK Tisza + Bodrog by industry TOTAL 1 113 297 117 71 1 598 branches and asset categories % 69.6% 18.6% 7.3% 4.5% Detailed Results for Pan Regional Scenario, Slovakia Scenario: SK Dunaj / Danube Large 25 000 Loss [mil. EUR] Total loss Loss on public property 20 000 15 000 10 000 5 000 0 Fig. 5.139b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.98a: LEC and survival function for the scenario SK Dunaj / Danube Large Fig. 5.139a: LEC function for the scenario SK Dunaj / Danube Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 857 247 Probability 20 Total loss 50 2.0% 4 977 1 687 Loss on public property 100 1.0% 8 581 2 969 5.0% 250 0.4% 16 592 5 642 500 0.2% 21 515 7 272 4.0% Loss structure for the RTP 250: Tab. 5.98b: Regional loss structure for the scenario SK Dunaj / Danube Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Bratislavský kraj 4 638 28% 6.8% Trnavský kraj 2 965 18% 10.2% Nitriansky kraj 2 725 16% 8.9% 2.0% TrenÄ?iansky kraj 2 536 15% 8.2% Žilinský kraj 2 288 14% 7.2% Banskobystrický kraj 1 431 9% 5.1% PreÅ¡ovský kraj 8 0% 0.0% 1.0% TOTAL 16 592 0.0% 0 5 000 10 000 15 000 20 000 25 000 Loss [mil. EUR] Fig. 5.139c: Survival function for the scenario SK Dunaj / Danube Large Fig. 5.139d: Regional loss structure for the scenario SK Dunaj / Danube Large (% of Fig. 5.139e: Loss intensity in provinces for the scenario SK Dunaj / Danube Large (% of property total loss) in the province) Tab. 5.98c: Losses by sector / purpose classification for the scenario SK Dunaj / Danube Large infrastructure- Loss Category % public [mil EUR] 30% infrastructure-public 5 031 30% enterprise-public 611 4% Public 5 642 34% infrastructure-non-public 870 5% enterprise-non-public 10 080 61% TOTAL 16 592 enterprise-public 4% enterprise-non- infrastructure- public non-public 61% 5% Fig. 5.139f: Loss by sector / purpose classification the scenario SK Dunaj / Danube Large Tab. 5.98d: Loss structure by asset categories for the scenario SK Dunaj / Danube Large Inventories Household Loss eguipment Dwellings Category code % 8% 21% [mil EUR] 4% Dwellings AN.1111 3 536 21% Machinery and Other buildings equipment and structures AN.1112 7 723 47% 20% Machinery and equipment AN.1113 3 236 20% Inventories AN.12 1 360 8% Household eguipment - 737 4% TOTAL 16 592 Other buildings and structures 47% Fig. 5.139g: Loss structure by asset categories for the scenario SK Dunaj / Danube Large building Tab. 5.98e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 2 693 138 93 737 3 661 22.1% private 4 097 2 086 1 106 0 7 289 43.9% private public 4 469 1 011 162 0 5 642 34.0% TOTAL 11 259 3 236 1 360 737 16 592 % 67.9% 19.5% 8.2% 4.4% households households public 22% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 34% Loss [mil. EUR] Fig. 5.139h: Loss structure by institutional sectors and asset categories for the scenario SK Dunaj / Danube Large Loss [mil. EUR] 0 1 000 2 000 3 000 4 000 5 000 A+B C private D 44% E F Fig. 5.139i: Loss structure by institutional sectors for the scenario SK Dunaj / Danube Large G H Tab. 5.98f: Loss structure by institutional sectors and industry branches for the scenario SK Dunaj / Danube Large I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 32 275 45 352 2.1% C (Mining and quarrying) 99 166 0 265 1.6% M D (Manufacturing) 52 3 654 132 3 838 23.1% public E (Electricity, gas and water N private supply) 1 670 823 0 2 493 15.0% households F (Construction) 1 103 12 116 0.7% O G (Wholesale and retail trade; repair) 7 825 99 931 5.6% Fig. 5.139j: Structure of the loss for the scenario SK Dunaj / Danube Large by industry H (Hotels and restaurant) 4 77 7 87 0.5% branches and institutional sectors I (Transport, storage and communications) 1 250 262 22 1 534 9.2% J (Financial intermediation) 5 134 0 140 0.8% Loss [mil. EUR] K (Real estate, renting, 0 1 000 2 000 3 000 4 000 5 000 research) 412 924 3 336 4 671 28.2% L (Public administration 1 563 0 0 1 563 9.4% A+B M (Education) 223 0 0 223 1.3% N (Health and social work) 174 16 5 195 1.2% C O (Other community, social and personal services) 151 31 1 182 1.1% D TOTAL 5 642 7 289 3 661 16 592 % 34.0% 43.9% 22.1% E F Tab. 5.98g: Loss structure by asset categories and industry branches, sc. SK Dunaj / Danube G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 278 74 2 0 354 2.1% C 230 30 318 0 578 3.5% J D 1 537 1 534 480 0 3 550 21.4% E 1 866 571 42 0 2 479 14.9% K F 46 46 203 0 295 1.8% G 385 187 168 0 740 4.5% L H 70 16 4 0 90 0.5% I 1 095 434 7 0 1 535 9.3% M building J 95 41 22 0 158 0.9% equipment K 3 829 71 61 737 4 697 28.3% N inventories L 1 301 162 52 0 1 516 9.1% household equip. M 217 6 1 0 224 1.3% O N 161 34 1 0 196 1.2% O 152 30 0 0 182 1.1% Fig. 5.139k: Structure of the loss for the scenario SK Dunaj / Danube Large by industry TOTAL 11 259 3 236 1 360 737 16 592 branches and asset categories % 67.9% 19.5% 8.2% 4.4% Detailed Results for Pan Regional Scenario, Slovakia Scenario: SK Tisza Large 6 000 Loss [mil. EUR] Total loss Loss on public property 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.140b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.99a: LEC and survival function for the scenario SK Tisza Large Fig. 5.140a: LEC function for the scenario SK Tisza Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 255 88 Probability 20 Total loss 50 2.0% 1 586 608 Loss on public property 100 1.0% 2 555 973 5.0% 250 0.4% 3 982 1 521 500 0.2% 4 930 1 891 4.0% Loss structure for the RTP 250: Tab. 5.99b: Regional loss structure for the scenario SK Tisza Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] KoÅ¡ický kraj 2 535 64% 7.1% PreÅ¡ovský kraj 1 140 29% 4.8% Banskobystrický kraj 306 8% 1.2% 2.0% TOTAL 3 982 1.0% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 Loss [mil. EUR] Fig. 5.140c: Survival function for the scenario SK Tisza Large Fig. 5.140d: Regional loss structure for the scenario SK Tisza Large (% of total loss) Fig. 5.140e: Loss intensity in provinces for the scenario SK Tisza Large (% of property in the province) Tab. 5.99c: Losses by sector / purpose classification for the scenario SK Tisza Large Loss Category % [mil EUR] infrastructure- infrastructure-public 1 390 35% public enterprise-public 130 3% 35% Public 1 521 38% infrastructure-non-public 231 6% enterprise-non-public 2 230 56% TOTAL 3 982 enterprise-public 3% enterprise-non- infrastructure- public non-public 56% 6% Fig. 5.140f: Loss by sector / purpose classification the scenario SK Tisza Large Inventories Tab. 5.99d: Loss structure by asset categories for the scenario SK Tisza Large Household Loss 7% Dwellings eguipment Category code % 21% [mil EUR] 4% Machinery and equipment Dwellings AN.1111 840 21% 19% Other buildings and structures AN.1112 1 937 49% Machinery and equipment AN.1113 743 19% Inventories AN.12 287 7% Household eguipment - 175 4% TOTAL 3 982 Other buildings and structures 49% Fig. 5.140g: Loss structure by asset categories for the scenario SK Tisza Large Tab. 5.99e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 636 26 18 175 854 21.5% private 934 444 230 0 1 607 40.4% private public 1 207 274 39 0 1 521 38.2% TOTAL 2 777 743 287 175 3 982 % 69.7% 18.7% 7.2% 4.4% building equipment households inventories household equip. households 21% 0 200 400 600 800 1 000 1 200 1 400 1 600 1 800 public Loss [mil. EUR] 38% Fig. 5.140h: Loss structure by institutional sectors and asset categories for the scenario SK Tisza Large Loss [mil. EUR] 0 200 400 600 800 1 000 1 200 A+B C private 41% D E F Fig. 5.140i: Loss structure by institutional sectors for the scenario SK Tisza Large G H Tab. 5.99f: Loss structure by institutional sectors and industry branches for the scenario SK Tisza Large I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 6 61 9 76 1.9% C (Mining and quarrying) 18 30 0 48 1.2% M D (Manufacturing) 9 752 25 786 19.8% public E (Electricity, gas and water N private supply) 449 220 0 670 16.8% households F (Construction) 0 21 2 22 0.6% O G (Wholesale and retail trade; repair) 1 175 19 194 4.9% Fig. 5.140j: Structure of the loss for the scenario SK Tisza Large by industry branches H (Hotels and restaurant) 0 14 1 16 0.4% and institutional sectors I (Transport, storage and communications) 377 78 6 462 11.6% J (Financial intermediation) 1 27 0 27 0.7% Loss [mil. EUR] K (Real estate, renting, 0 200 400 600 800 1 000 1 200 research) 96 217 792 1 105 27.8% L (Public administration 409 0 0 409 10.3% A+B M (Education) 71 0 0 71 1.8% N (Health and social work) 47 4 1 52 1.3% C O (Other community, social and personal services) 36 7 0 43 1.1% D TOTAL 1 521 1 607 854 3 982 % 38.2% 40.4% 21.5% E F Tab. 5.99g: Loss structure by asset categories and industry branches, sc. SK Tisza Large G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 61 15 0 0 76 1.9% C 42 5 1 0 48 1.2% J D 316 314 157 0 786 19.8% E 503 153 14 0 670 16.8% K F 9 9 5 0 22 0.6% G 81 37 76 0 194 4.9% L H 13 2 0 0 16 0.4% I 330 129 2 0 462 11.6% M building J 19 7 1 0 27 0.7% equipment K 909 14 7 175 1 105 27.8% N 342 41 25 0 409 10.3% inventories L household equip. M 70 2 0 0 71 1.8% O N 43 9 0 0 52 1.3% O 37 6 0 0 43 1.1% Fig. 5.140k: Structure of the loss for the scenario SK Tisza Large by industry branches TOTAL 2 777 743 287 175 3 982 and asset categories % 69.7% 18.7% 7.2% 4.4% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Danube / Duna 10 000 Loss [mil. EUR] Total loss 9 000 Loss on public property 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.141b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.100a: LEC and survival function for the scenario HU Danube / Duna Fig. 5.141a: LEC function for the scenario HU Danube / Duna Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 230 58 Probability 20 Total loss 50 2.0% 2 539 640 Loss on public property 100 1.0% 4 510 1 136 5.0% 250 0.4% 7 363 1 856 500 0.2% 9 020 2 278 4.0% Loss structure for the RTP 250: Tab. 5.100b: Regional loss structure for the scenario HU Danube / Duna Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Budapest 2 879 39% 0.7% Pest 1 772 24% 1.6% Komárom-Esztergom 915 12% 3.5% 2.0% Gyor-Moson-Sopron 497 7% 1.1% Bács-Kiskun 490 7% 1.3% Fejér 326 4% 0.7% Tolna 254 3% 1.2% 1.0% Baranya 204 3% 0.6% Veszprém 18 0% 0.1% Nógrád 8 0% 0.1% TOTAL 7 363 0.0% 0 2 000 4 000 6 000 8 000 10 000 Loss [mil. EUR] Fig. 5.141c: Survival function for the scenario HU Danube / Duna Fig. 5.141d: Regional loss structure for the scenario HU Danube / Duna (% of total loss) Fig. 5.141e: Loss intensity in provinces for the scenario HU Danube / Duna (% of property in the province) Tab. 5.100c: Losses by sector / purpose classification for the scenario HU Danube / Duna infrastructure- public Loss 23% Category % [mil EUR] infrastructure-public 1 696 23% enterprise-public 160 2% enterprise-public Public 1 856 25% 2% infrastructure-non-public 318 4% enterprise-non-public 5 190 70% TOTAL 7 363 infrastructure- non-public 4% enterprise-non- public 71% Fig. 5.141f: Loss by sector / purpose classification the scenario HU Danube / Duna Tab. 5.100d: Loss structure by asset categories for the scenario HU Danube / Duna Household Loss Inventories Category code % eguipment [mil EUR] 8% 5% Dwellings 23% Dwellings AN.1111 1 709 23% Other buildings and structures AN.1112 3 295 45% Machinery and Machinery and equipment equipment AN.1113 1 370 19% 19% Inventories AN.12 612 8% Household eguipment - 377 5% TOTAL 7 363 Other buildings and structures 45% Fig. 5.141g: Loss structure by asset categories for the scenario HU Danube / Duna building Tab. 5.100e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 816 114 68 377 2 374 32.2% private 1 568 1 119 446 0 3 133 42.6% private public 1 620 137 98 0 1 856 25.2% TOTAL 5 004 1 370 612 377 7 363 % 68.0% 18.6% 8.3% 5.1% households public households 25% 32% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil. EUR] Fig. 5.141h: Loss structure by institutional sectors and asset categories for the scenario HU Danube / Duna Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 3 000 A+B C private D 43% E F Fig. 5.141i: Loss structure by institutional sectors for the scenario HU Danube / Duna G H Tab. 5.100f: Loss structure by institutional sectors and industry branches for the scenario HU Danube / Duna I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 3 150 110 263 3.6% C (Mining and quarrying) 0 30 0 30 0.4% M D (Manufacturing) 0 1 249 18 1 268 17.2% public E (Electricity, gas and water N private supply) 1 296 0 296 4.0% households F (Construction) 2 106 6 114 1.6% O G (Wholesale and retail trade; repair) 2 405 20 428 5.8% Fig. 5.141j: Structure of the loss for the scenario HU Danube / Duna by industry H (Hotels and restaurant) 2 42 1 45 0.6% branches and institutional sectors I (Transport, storage and communications) 42 495 4 541 7.4% J (Financial intermediation) 0 59 1 60 0.8% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 149 280 2 077 2 507 34.0% L (Public administration 1 359 0 0 1 359 18.5% A+B M (Education) 151 0 11 162 2.2% N (Health and social work) 90 1 3 94 1.3% C O (Other community, social and personal services) 53 21 121 196 2.7% D TOTAL 1 856 3 133 2 374 7 363 % 25.2% 42.6% 32.2% E F Tab. 5.100g: Loss structure by asset categories and industry branches, sc. HU Danube / Duna G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 191 72 0 0 263 3.6% C 17 12 1 0 30 0.4% J D 437 614 216 0 1 268 17.2% E 202 89 6 0 296 4.0% K F 59 32 24 0 114 1.6% G 136 108 185 0 428 5.8% L H 37 7 1 0 45 0.6% I 289 231 22 0 541 7.4% M building J 47 14 0 0 60 0.8% equipment K 1 995 68 67 377 2 507 34.0% N inventories L 1 197 72 90 0 1 359 18.5% household equip. M 144 17 0 0 162 2.2% O N 72 21 0 0 94 1.3% O 181 14 1 0 196 2.7% Fig. 5.141k: Structure of the loss for the scenario HU Danube / Duna by industry TOTAL 5 004 1 370 612 377 7 363 branches and asset categories % 68.0% 18.6% 8.3% 5.1% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Drava 1 200 Loss [mil. EUR] Total loss Loss on public property 1 000 800 600 400 200 0 Fig. 5.142b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.101a: LEC and survival function for the scenario HU Drava Fig. 5.142a: LEC function for the scenario HU Drava Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 26 7 Probability 20 Total loss 50 2.0% 302 77 Loss on public property 100 1.0% 536 138 5.0% 250 0.4% 877 225 500 0.2% 1 061 273 4.0% Loss structure for the RTP 250: Tab. 5.101b: Regional loss structure for the scenario HU Drava Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Baranya 500 57% 1.5% Zala 231 26% 1.0% Somogy 143 16% 0.0% 2.0% Vas 3 0% 0.0% TOTAL 877 1.0% 0.0% 0 200 400 600 800 1 000 1 200 Loss [mil. EUR] Fig. 5.142c: Survival function for the scenario HU Drava Fig. 5.142d: Regional loss structure for the scenario HU Drava (% of total loss) Fig. 5.142e: Loss intensity in provinces for the scenario HU Drava (% of property in the province) Tab. 5.101c: Losses by sector / purpose classification for the scenario HU Drava infrastructure- Loss public Category % [mil EUR] 24% infrastructure-public 210 24% enterprise-public 15 2% Public 225 26% infrastructure-non-public 80 9% enterprise-public enterprise-non-public 572 65% 2% TOTAL 877 infrastructure- enterprise-non- non-public public 9% 65% Fig. 5.142f: Loss by sector / purpose classification the scenario HU Drava Tab. 5.101d: Loss structure by asset categories for the scenario HU Drava Household Loss eguipment Category code % 4% [mil EUR] Inventories 7% Dwellings 24% Dwellings AN.1111 206 24% Other buildings Machinery and and structures AN.1112 400 46% equipment Machinery and 20% equipment AN.1113 172 20% Inventories AN.12 64 7% Household eguipment - 35 4% TOTAL 877 Other buildings and structures 45% Fig. 5.142g: Loss structure by asset categories for the scenario HU Drava building Tab. 5.101e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 192 17 6 35 251 28.6% private 217 138 46 0 401 45.7% private public 197 17 11 0 225 25.6% TOTAL 606 172 64 35 877 % 69.1% 19.6% 7.2% 4.0% households households public 29% 26% 0 50 100 150 200 250 300 350 400 450 Loss [mil. EUR] Fig. 5.142h: Loss structure by institutional sectors and asset categories for the scenario HU Drava Loss [mil. EUR] 0 50 100 150 200 250 A+B C D private 45% E F Fig. 5.142i: Loss structure by institutional sectors for the scenario HU Drava G H Tab. 5.101f: Loss structure by institutional sectors and industry branches for the scenario HU Drava I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 1 48 36 85 9.7% C (Mining and quarrying) 0 3 0 3 0.3% M D (Manufacturing) 0 127 2 128 14.6% public E (Electricity, gas and water N private supply) 0 78 0 78 8.9% households F (Construction) 0 16 1 17 1.9% O G (Wholesale and retail trade; repair) 0 39 2 41 4.7% Fig. 5.142j: Structure of the loss for the scenario HU Drava by industry branches and H (Hotels and restaurant) 0 7 0 8 0.9% institutional sectors I (Transport, storage and communications) 4 51 0 55 6.3% J (Financial intermediation) 0 4 0 4 0.4% Loss [mil. EUR] K (Real estate, renting, 0 50 100 150 200 250 research) 14 26 195 235 26.8% L (Public administration 157 0 0 157 17.9% A+B M (Education) 27 0 2 29 3.3% N (Health and social work) 16 0 0 16 1.9% C O (Other community, social and personal services) 6 2 14 22 2.5% D TOTAL 225 401 251 877 % 25.6% 45.7% 28.6% E F Tab. 5.101g: Loss structure by asset categories and industry branches, sc. HU Drava G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 61 23 0 0 85 9.7% C 2 1 0 0 3 0.3% J D 44 62 22 0 128 14.6% E 53 23 2 0 78 8.9% K F 9 5 4 0 17 1.9% G 13 10 18 0 41 4.7% L H 6 1 0 0 8 0.9% I 30 24 2 0 55 6.3% M building J 3 1 0 0 4 0.4% equipment K 188 6 6 35 235 26.8% N inventories L 139 8 10 0 157 17.9% household equip. M 26 3 0 0 29 3.3% O N 13 4 0 0 16 1.9% O 21 1 0 0 22 2.5% Fig. 5.142k: Structure of the loss for the scenario HU Drava by industry branches and TOTAL 606 172 64 35 877 asset categories % 69.1% 19.6% 7.2% 4.0% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Hernad + Sajo 1 200 Loss [mil. EUR] Total loss Loss on public property 1 000 800 600 400 200 0 Fig. 5.143b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.102a: LEC and survival function for the scenario HU Hernad + Sajo Fig. 5.143a: LEC function for the scenario HU Hernad + Sajo Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 27 8 Probability 20 Total loss 50 2.0% 306 91 Loss on public property 100 1.0% 545 161 5.0% 250 0.4% 890 263 500 0.2% 1 062 314 4.0% Loss structure for the RTP 250: Tab. 5.102b: Regional loss structure for the scenario HU Hernad + Sajo Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Borsod-Abaúj-Zemplén 887 100% 1.9% Heves 3 0% 0.0% TOTAL 890 2.0% 1.0% 0.0% 0 200 400 600 800 1 000 1 200 Loss [mil. EUR] Fig. 5.143c: Survival function for the scenario HU Hernad + Sajo Fig. 5.143d: Regional loss structure for the scenario HU Hernad + Sajo (% of total loss) Fig. 5.143e: Loss intensity in provinces for the scenario HU Hernad + Sajo (% of property in the province) Tab. 5.102c: Losses by sector / purpose classification for the scenario HU Hernad + Sajo infrastructure- Loss public Category % [mil EUR] 28% infrastructure-public 249 28% enterprise-public 14 2% Public 263 30% infrastructure-non-public 102 12% enterprise-non-public 525 59% TOTAL 890 enterprise-public 2% infrastructure- non-public enterprise-non- 12% public 58% Fig. 5.143f: Loss by sector / purpose classification the scenario HU Hernad + Sajo Tab. 5.102d: Loss structure by asset categories for the scenario HU Hernad + Sajo Household Loss Inventories eguipment Dwellings Category code % [mil EUR] 8% 4% 23% Dwellings AN.1111 210 24% Machinery and Other buildings equipment and structures AN.1112 406 46% 20% Machinery and equipment AN.1113 175 20% Inventories AN.12 67 8% Household eguipment - 32 4% TOTAL 890 Other buildings and structures 45% Fig. 5.143g: Loss structure by asset categories for the scenario HU Hernad + Sajo Tab. 5.102e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 169 14 6 32 220 24.7% private 218 141 48 0 407 45.7% private public 230 20 13 0 263 29.6% TOTAL 616 175 67 32 890 % 69.2% 19.7% 7.5% 3.6% building equipment households inventories households household equip. public 25% 30% 0 50 100 150 200 250 300 350 400 450 Fig. 5.143h: Loss structure by institutional sectors and asset categories for the scenario HU Hernad + Sajo 0 50 100 150 200 250 A+B C D private 45% E F Fig. 5.143i: Loss structure by institutional sectors for the scenario HU Hernad + Sajo G H Tab. 5.102f: Loss structure by institutional sectors and industry branches for the scenario HU I Hernad + Sajo house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 1 30 22 53 5.9% C (Mining and quarrying) 0 3 0 3 0.3% M D (Manufacturing) 0 130 2 132 14.8% public E (Electricity, gas and water N private supply) 0 100 0 100 11.2% households F (Construction) 0 18 1 19 2.2% O G (Wholesale and retail trade; repair) 0 40 2 42 4.7% Fig. 5.143j: Structure of the loss for the scenario HU Hernad + Sajo by industry H (Hotels and restaurant) 0 6 0 7 0.8% branches and institutional sectors I (Transport, storage and communications) 4 50 0 54 6.1% J (Financial intermediation) 0 4 0 4 0.5% K (Real estate, renting, 0 50 100 150 200 250 research) 12 23 175 210 23.6% L (Public administration 187 0 0 187 21.0% A+B M (Education) 33 0 2 35 3.9% N (Health and social work) 19 0 1 20 2.2% C O (Other community, social and personal services) 6 3 15 24 2.7% D TOTAL 263 407 220 890 % 29.6% 45.7% 24.7% E F Tab. 5.102g: Loss structure by asset categories and industry branches, sc. HU Hernad + Sajo G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 38 14 0 0 53 5.9% C 2 1 0 0 3 0.3% J D 45 64 22 0 132 14.8% E 68 30 2 0 100 11.2% K F 10 5 4 0 19 2.2% G 13 11 18 0 42 4.7% L H 6 1 0 0 7 0.8% I 29 23 2 0 54 6.1% M building J 3 1 0 0 4 0.5% equipment K 168 5 5 32 210 23.6% N inventories L 165 10 12 0 187 21.0% household equip. M 31 4 0 0 35 3.9% O N 15 4 0 0 20 2.2% O 22 2 0 0 24 2.7% Fig. 5.143k: Structure of the loss for the scenario HU Hernad + Sajo by industry TOTAL 616 175 67 32 890 branches and asset categories % 69.2% 19.7% 7.5% 3.6% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Tizsa + Bodrog 7 000 Loss [mil. EUR] Total loss Loss on public property 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.144b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.103a: LEC and survival function for the scenario HU Tizsa + Bodrog Fig. 5.144a: LEC function for the scenario HU Tizsa + Bodrog Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 154 44 Probability 20 Total loss 50 2.0% 1 740 497 Loss on public property 100 1.0% 3 094 889 5.0% 250 0.4% 5 056 1 451 500 0.2% 6 050 1 738 4.0% Loss structure for the RTP 250: Tab. 5.103b: Regional loss structure for the scenario HU Tizsa + Bodrog Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Csongrád 1 740 34% 5.0% Jász-Nagykun-Szolnok 790 16% 2.9% Szabolcs-Szatmár-Bereg 626 12% 2.0% 2.0% Borsod-Abaúj-Zemplén 572 11% 1.2% Heves 516 10% 2.2% Bács-Kiskun 299 6% 0.8% Pest 279 6% 0.3% 1.0% Békés 115 2% 0.5% Nógrád 95 2% 0.8% Hajdú-Bihar 23 0% 0.1% TOTAL 5 056 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 Loss [mil. EUR] Fig. 5.144c: Survival function for the scenario HU Tizsa + Bodrog Fig. 5.144d: Regional loss structure for the scenario HU Tizsa + Bodrog (% of total loss) Fig. 5.144e: Loss intensity in provinces for the scenario HU Tizsa + Bodrog (% of property in the province) Tab. 5.103c: Losses by sector / purpose classification for the scenario HU Tizsa + Bodrog infrastructure- Loss public Category % [mil EUR] 27% infrastructure-public 1 363 27% enterprise-public 89 2% Public 1 452 29% infrastructure-non-public 307 6% enterprise-non-public 3 297 65% enterprise-public TOTAL 5 056 2% infrastructure- non-public 6% enterprise-non- public 65% Fig. 5.144f: Loss by sector / purpose classification the scenario HU Tizsa + Bodrog Tab. 5.103d: Loss structure by asset categories for the scenario HU Tizsa + Bodrog Household Loss eguipment Category code % Inventories 4% [mil EUR] 8% Dwellings 24% Dwellings AN.1111 1 201 1043% Other buildings and structures AN.1112 2 321 2015% Machinery and Machinery and equipment equipment AN.1113 957 831% 19% Inventories AN.12 379 329% Household eguipment - 198 172% TOTAL 5 056 Other buildings and structures 45% Fig. 5.144g: Loss structure by asset categories for the scenario HU Tizsa + Bodrog building Tab. 5.103e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 106 107 36 198 1 447 28.6% private 1 149 736 271 0 2 156 42.7% private public 1 267 113 72 0 1 452 28.7% TOTAL 3 522 957 379 198 5 056 % 69.7% 18.9% 7.5% 3.9% households public households 29% 29% 0 500 1 000 1 500 2 000 2 500 Fig. 5.144h: Loss structure by institutional sectors and asset categories for the scenario HU Tizsa + Bodrog 0 200 400 600 800 1 000 1 200 1 400 A+B C D private 42% E F Fig. 5.144i: Loss structure by institutional sectors for the scenario HU Tizsa + Bodrog G H Tab. 5.103f: Loss structure by institutional sectors and industry branches for the scenario HU I Tizsa + Bodrog house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 7 301 223 531 10.5% C (Mining and quarrying) 0 15 0 15 0.3% M D (Manufacturing) 0 670 9 679 13.4% public E (Electricity, gas and water N private supply) 1 292 0 292 5.8% households F (Construction) 2 101 5 108 2.1% O G (Wholesale and retail trade; repair) 1 262 12 275 5.4% Fig. 5.144j: Structure of the loss for the scenario HU Tizsa + Bodrog by industry H (Hotels and restaurant) 1 32 1 35 0.7% branches and institutional sectors I (Transport, storage and communications) 24 295 2 321 6.3% J (Financial intermediation) 0 26 0 27 0.5% K (Real estate, renting, 0 200 400 600 800 1 000 1 200 1 400 research) 77 146 1 092 1 315 26.0% L (Public administration 1 022 0 0 1 022 20.2% A+B M (Education) 178 0 13 191 3.8% N (Health and social work) 101 1 4 105 2.1% C O (Other community, social and personal services) 38 15 88 140 2.8% D TOTAL 1 452 2 156 1 447 5 056 % 28.7% 42.7% 28.6% E F Tab. 5.103g: Loss structure by asset categories and industry branches, sc. HU Tizsa + Bodrog G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 384 147 0 0 531 10.5% C 9 6 0 0 15 0.3% J D 234 329 116 0 679 13.4% E 199 88 6 0 292 5.8% K F 56 30 23 0 108 2.1% G 87 69 119 0 275 5.4% L H 29 5 1 0 35 0.7% I 172 137 12 0 321 6.3% M building J 21 6 0 0 27 0.5% equipment K 1 049 34 34 198 1 315 26.0% N inventories L 901 53 68 0 1 022 20.2% household equip. M 170 20 0 0 191 3.8% O N 81 24 0 0 105 2.1% O 130 9 0 0 140 2.8% Fig. 5.144k: Structure of the loss for the scenario HU Tizsa + Bodrog by industry TOTAL 3 522 957 379 198 5 056 branches and asset categories % 69.7% 18.9% 7.5% 3.9% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Raba 3 500 Loss [mil. EUR] Total loss Loss on public property 3 000 2 500 2 000 1 500 1 000 500 0 Fig. 5.145b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.104a: LEC and survival function for the scenario HU Raba Fig. 5.145a: LEC function for the scenario HU Raba Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 85 18 Probability 20 Total loss 50 2.0% 944 201 Loss on public property 100 1.0% 1 678 357 5.0% 250 0.4% 2 741 584 500 0.2% 3 304 703 4.0% Loss structure for the RTP 250: Tab. 5.104b: Regional loss structure for the scenario HU Raba Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Gyor-Moson-Sopron 1 294 47% 2.8% Vas 1 121 41% 4.1% Veszprém 314 11% 1.2% 2.0% Zala 11 0% 0.0% TOTAL 2 741 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Loss [mil. EUR] Fig. 5.145c: Survival function for the scenario HU Raba Fig. 5.145d: Regional loss structure for the scenario HU Raba (% of total loss) Fig. 5.145e: Loss intensity in provinces for the scenario HU Raba (% of property in the province) infrastructure- Tab. 5.104c: Losses by sector / purpose classification for the scenario HU Raba public Loss 19.4% Category % [mil EUR] infrastructure-public 533 19.4% enterprise-public enterprise-public 51 1.9% 1.9% Public 584 21.3% infrastructure-non-public 49 1.8% enterprise-non-public 2 108 76.9% TOTAL 2 741 infrastructure- non-public 1.8% enterprise-non- public 76.9% Fig. 5.145f: Loss by sector / purpose classification the scenario HU Raba Tab. 5.104d: Loss structure by asset categories for the scenario HU Raba Household Loss eguipment Category code % Inventories [mil EUR] 9% 4% Dwellings Dwellings AN.1111 601 22% 22% Other buildings Machinery and and structures AN.1112 1 160 42% equipment Machinery and 23% equipment AN.1113 622 23% Inventories AN.12 242 9% Household eguipment - 116 4% TOTAL 2 741 Other buildings and structures 42% Fig. 5.145g: Loss structure by asset categories for the scenario HU Raba building Tab. 5.104e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 594 48 21 116 779 28.4% private 659 527 192 0 1 378 50.3% private public 509 47 29 0 584 21.3% TOTAL 1 761 622 242 116 2 741 % 64.3% 22.7% 8.8% 4.2% households public households 21% 28% 0 200 400 600 800 1 000 1 200 1 400 1 600 Fig. 5.145h: Loss structure by institutional sectors and asset categories for the scenario HU Raba 0 100 200 300 400 500 600 700 800 900 A+B C D private 51% E F Fig. 5.145i: Loss structure by institutional sectors for the scenario HU Raba G H Tab. 5.104f: Loss structure by institutional sectors and industry branches for the scenario HU I Raba house- J Industry branch public private Total loss holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 2 71 96 170 6.2% C (Mining and quarrying) 0 0 17 17 0.6% M D (Manufacturing) 0 10 700 710 25.9% public E (Electricity, gas and water N private supply) 0 0 106 106 3.9% households F (Construction) 1 2 44 47 1.7% O G (Wholesale and retail trade; repair) 1 6 118 125 4.6% Fig. 5.145j: Structure of the loss for the scenario HU Raba by industry branches and H (Hotels and restaurant) 1 1 23 25 0.9% institutional sectors I (Transport, storage and communications) 14 1 169 184 6.7% J (Financial intermediation) 0 0 12 13 0.5% K (Real estate, renting, 0 100 200 300 400 500 600 700 800 900 research) 46 638 86 769 28.1% L (Public administration 394 0 0 394 14.4% A+B M (Education) 67 5 0 72 2.6% N (Health and social work) 39 1 0 40 1.5% C O (Other community, social and personal services) 19 43 7 69 2.5% D TOTAL 584 779 1 378 2 741 % 21.3% 28.4% 50.3% E F Tab. 5.104g: Loss structure by asset categories and industry branches, sc. HU Raba G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 123 47 0 0 170 6.2% C 10 7 0 0 17 0.6% J D 245 344 121 0 710 25.9% E 72 32 2 0 106 3.9% K F 24 13 10 0 47 1.7% G 39 31 54 0 125 4.6% L H 20 4 1 0 25 0.9% I 98 78 7 0 184 6.7% M building J 10 3 0 0 13 0.5% equipment K 613 21 20 116 769 28.1% N inventories L 348 21 26 0 394 14.4% household equip. M 64 8 0 0 72 2.6% O N 31 9 0 0 40 1.5% O 64 5 0 0 69 2.5% Fig. 5.145k: Structure of the loss for the scenario HU Raba by industry branches and TOTAL 1 761 622 242 116 2 741 asset categories % 64.3% 22.7% 8.8% 4.2% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Sio 3 000 Loss [mil. EUR] Total loss Loss on public property 2 500 2 000 1 500 1 000 500 0 0 100 200 300 400 500 Fig. 5.146b: Area affected by the scenario RTP [years] Tab. 5.105a: LEC and survival function for the scenario HU Sio Fig. 5.146a: LEC function for the scenario HU Sio Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 62 14 Probability 20 Total loss 50 2.0% 718 162 Loss on public property 100 1.0% 1 277 289 5.0% 250 0.4% 2 089 473 500 0.2% 2 562 581 4.0% Loss structure for the RTP 250: Tab. 5.105b: Regional loss structure for the scenario HU Sio Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Fejér 802 38% 1.8% Veszprém 322 15% 1.2% Tolna 309 15% 1.5% 2.0% Somogy 273 13% 1.4% Zala 249 12% 1.1% Baranya 105 5% 0.3% Vas 25 1% 0.1% 1.0% TOTAL 2 085 0.0% 0 500 1 000 1 500 2 000 2 500 3 000 Loss [mil. EUR] Fig. 5.146c: Survival function for the scenario HU Sio Fig. 5.146d: Regional loss structure for the scenario HU Sio (% of total loss) Fig. 5.146e: Loss intensity in provinces for the scenario HU Sio (% of property in the province) infrastructure- Tab. 5.105c: Losses by sector / purpose classification for the scenario HU Sio public 21% Category Loss % [mil EUR] infrastructure-public 437 21% enterprise-public enterprise-public 36 2% 2% Public 473 23% infrastructure-non-public 137 7% enterprise-non-public 1 479 71% TOTAL 2 089 infrastructure- non-public 7% enterprise-non- public 70% Fig. 5.146f: Loss by sector / purpose classification the scenario HU Sio Tab. 5.105d: Loss structure by asset categories for the scenario HU Sio Loss Household Category code % [mil EUR] eguipment Inventories Dwellings 4% 8% 22% Dwellings AN.1111 469 11985% Other buildings and structures AN.1112 908 23235% Machinery and Machinery and equipment equipment AN.1113 452 11575% 22% Inventories AN.12 174 4460% Household eguipment - 86 2194% TOTAL 2 089 Other buildings and structures 44% Fig. 5.146g: Loss structure by asset categories for the scenario HU Sio building Tab. 5.105e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 449 37 15 86 587 28.1% private 513 380 136 0 1 029 49.2% private public 415 35 23 0 473 22.6% TOTAL 1 377 452 174 86 2 089 % 65.9% 21.7% 8.3% 4.1% households households public 28% 23% 0 200 400 600 800 1 000 1 200 Fig. 5.146h: Loss structure by institutional sectors and asset categories for the scenario HU Sio 0 100 200 300 400 500 600 A+B C D private 49% E F Fig. 5.146i: Loss structure by institutional sectors for the scenario HU Sio G H Tab. 5.105f: Loss structure by institutional sectors and industry branches for the scenario HU I Sio house- J Industry branch public private Total loss holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 2 92 68 161 7.7% C (Mining and quarrying) 0 10 0 10 0.5% M D (Manufacturing) 0 466 6 472 22.6% public E (Electricity, gas and water N private supply) 0 132 0 133 6.3% households F (Construction) 1 36 2 38 1.8% O G (Wholesale and retail trade; repair) 0 93 4 97 4.6% Fig. 5.146j: Structure of the loss for the scenario HU Sio by industry branches and H (Hotels and restaurant) 1 14 0 15 0.7% institutional sectors I (Transport, storage and communications) 8 109 0 118 5.6% J (Financial intermediation) 0 8 0 8 0.4% K (Real estate, renting, 0 100 200 300 400 500 600 research) 33 63 473 568 27.2% L (Public administration 328 0 0 328 15.7% A+B M (Education) 56 0 4 59 2.8% N (Health and social work) 32 0 1 33 1.6% C O (Other community, social and personal services) 13 5 30 48 2.3% D TOTAL 473 1 029 587 2 089 % 22.6% 49.2% 28.1% E F Tab. 5.105g: Loss structure by asset categories and industry branches, sc. HU Sio G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 117 44 0 0 161 7.7% C 6 4 0 0 10 0.5% J D 163 229 80 0 472 22.6% E 90 40 3 0 133 6.3% K F 20 10 8 0 38 1.8% G 31 24 42 0 97 4.6% L H 13 2 0 0 15 0.7% I 63 50 4 0 118 5.6% M building J 7 2 0 0 8 0.4% equipment K 454 14 14 86 568 27.2% N inventories L 290 17 22 0 328 15.7% household equip. M 53 6 0 0 59 2.8% O N 26 7 0 0 33 1.6% O 45 3 0 0 48 2.3% Fig. 5.146k: Structure of the loss for the scenario HU Sio by industry branches and TOTAL 1 377 452 174 86 2 089 asset categories % 65.9% 21.7% 8.3% 4.1% Detailed Results for Catchment-based Scenario, Hungary Scenario: HU Koros 1 600 Loss [mil. EUR] Total loss 1 400 Loss on public property 1 200 1 000 800 600 400 200 0 Fig. 5.147b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.106a: LEC and survival function for the scenario HU Koros Fig. 5.147a: LEC function for the scenario HU Koros Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 35 10 Probability 20 Total loss 50 2.0% 402 110 Loss on public property 100 1.0% 715 196 5.0% 250 0.4% 1 170 321 500 0.2% 1 402 385 4.0% Loss structure for the RTP 250: Tab. 5.106b: Regional loss structure for the scenario HU Koros Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Hajdú-Bihar 523 45% 1.3% Békés 432 37% 1.9% Jász-Nagykun-Szolnok 188 16% 0.7% 2.0% Szabolcs-Szatmár-Bereg 26 2% 0.1% TOTAL 1 170 1.0% 0.0% 0 200 400 600 800 1 000 1 200 1 400 1 600 Loss [mil. EUR] Fig. 5.147c: Survival function for the scenario HU Koros Fig. 5.147d: Regional loss structure for the scenario HU Koros (% of total loss) Fig. 5.147e: Loss intensity in provinces for the scenario HU Koros (% of property in the province) Tab. 5.106c: Losses by sector / purpose classification for the scenario HU Koros infrastructure- public Loss Category % 26% [mil EUR] infrastructure-public 302 26% enterprise-public 19 2% Public 321 27% enterprise-public infrastructure-non-public 48 4% 2% enterprise-non-public 801 68% TOTAL 1 170 infrastructure- non-public enterprise-non- 4% public 68% Fig. 5.147f: Loss by sector / purpose classification the scenario HU Koros Tab. 5.106d: Loss structure by asset categories for the scenario HU Koros Loss Category code % [mil EUR] Household Inventories eguipment 8% Dwellings AN.1111 271 23% 4% Other buildings Machinery and and structures AN.1112 525 45% equipment Dwellings Machinery and 20% 23% equipment AN.1113 237 20% Inventories AN.12 93 8% Household eguipment - 43 4% TOTAL 1 170 Other buildings and structures 45% Fig. 5.147g: Loss structure by asset categories for the scenario HU Koros building Tab. 5.106e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 247 25 8 43 324 27.7% private 268 187 70 0 525 44.9% private public 281 24 15 0 321 27.4% TOTAL 796 237 93 43 1 170 % 68.1% 20.3% 8.0% 3.7% households households public 28% 27% 0 100 200 300 400 500 600 Fig. 5.147h: Loss structure by institutional sectors and asset categories for the scenario HU Koros 0 50 100 150 200 250 300 350 A+B C D private 45% E F Fig. 5.147i: Loss structure by institutional sectors for the scenario HU Koros G H Tab. 5.106f: Loss structure by institutional sectors and industry branches for the scenario HU I Koros house- J Industry branch public private Total loss holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 2 77 57 135 11.5% C (Mining and quarrying) 0 4 0 4 0.4% M D (Manufacturing) 0 205 3 208 17.8% public E (Electricity, gas and water N private supply) 0 45 0 45 3.9% households F (Construction) 0 23 1 24 2.1% O G (Wholesale and retail trade; repair) 0 59 2 61 5.2% Fig. 5.147j: Structure of the loss for the scenario HU Koros by industry branches and H (Hotels and restaurant) 0 7 0 7 0.6% institutional sectors I (Transport, storage and communications) 5 65 0 70 6.0% J (Financial intermediation) 0 5 0 5 0.5% K (Real estate, renting, 0 50 100 150 200 250 300 350 research) 17 32 238 287 24.5% L (Public administration 224 0 0 224 19.2% A+B M (Education) 42 0 3 44 3.8% N (Health and social work) 23 0 1 24 2.0% C O (Other community, social and personal services) 8 3 19 30 2.6% D TOTAL 321 525 324 1 170 % 27.4% 44.9% 27.7% E F Tab. 5.106g: Loss structure by asset categories and industry branches, sc. HU Koros G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 98 37 0 0 135 11.5% C 3 2 0 0 4 0.4% J D 72 101 35 0 208 17.8% E 31 13 1 0 45 3.9% K F 13 7 5 0 24 2.1% G 19 15 27 0 61 5.2% L H 6 1 0 0 7 0.6% I 38 30 3 0 70 6.0% M building J 4 1 0 0 5 0.5% equipment K 229 7 7 43 287 24.5% N inventories L 198 12 15 0 224 19.2% household equip. M 40 5 0 0 44 3.8% O N 18 5 0 0 24 2.0% O 29 2 0 0 30 2.6% Fig. 5.147k: Structure of the loss for the scenario HU Koros by industry branches and TOTAL 796 237 93 43 1 170 asset categories % 68.1% 20.3% 8.0% 3.7% Detailed Results for Pan Regional Scenario, Hungary Scenario: HU Danube Large 18 000 Loss [mil. EUR] Total loss 16 000 Loss on public property 14 000 12 000 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.148b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.107a: LEC and survival function for the scenario HU Danube Large Fig. 5.148a: LEC function for the scenario HU Danube Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 404 96 Probability 20 Total loss 50 2.0% 4 503 1 081 Loss on public property 100 1.0% 8 001 1 920 5.0% 250 0.4% 13 070 3 138 500 0.2% 15 947 3 835 4.0% Loss structure for the RTP 250: Tab. 5.107b: Regional loss structure for the scenario HU Danube Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Budapest 2 879 22% 0.7% Gyor-Moson-Sopron 1 791 14% 3.9% Pest 1 772 14% 1.6% 2.0% Vas 1 149 9% 4.2% Fejér 1 128 9% 2.6% Komárom-Esztergom 919 7% 3.5% Baranya 810 6% 2.5% 1.0% Veszprém 655 5% 2.5% Tolna 563 4% 2.7% Zala 491 4% 2.1% 0.0% Bács-Kiskun 490 4% 1.3% Somogy 415 3% 2.1% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 Loss [mil. EUR] Nógrád 8 0% 0.1% TOTAL 13 070 Fig. 5.148c: Survival function for the scenario HU Danube Large Fig. 5.148d: Regional loss structure for the scenario HU Danube Large (% of total loss) Fig. 5.148e: Loss intensity in provinces for the scenario HU Danube Large (% of property in the province) infrastructure- Tab. 5.107c: Losses by sector / purpose classification for the scenario HU Danube Large public Loss 22% Category % [mil EUR] infrastructure-public 2 875 22% enterprise-public 262 2% enterprise-public Public 3 138 24% 2% infrastructure-non-public 584 4% enterprise-non-public 9 349 72% TOTAL 13 070 infrastructure- non-public 4% enterprise-non- public 72% Fig. 5.148f: Loss by sector / purpose classification the scenario HU Danube Large Tab. 5.107d: Loss structure by asset categories for the scenario HU Danube Large Household Loss Category code % eguipment [mil EUR] Inventories 5% 8% Dwellings Dwellings AN.1111 2 985 23% 23% Other buildings Machinery and and structures AN.1112 5 763 44% equipment Machinery and 20% equipment AN.1113 2 616 20% Inventories AN.12 1 092 8% Household eguipment - 614 5% TOTAL 13 070 Other buildings and structures 44% Fig. 5.148g: Loss structure by asset categories for the scenario HU Danube Large building Tab. 5.107e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 3 051 216 111 614 3 992 30.5% private 2 956 2 164 821 0 5 941 45.5% private public 2 741 236 161 0 3 138 24.0% TOTAL 8 748 2 616 1 092 614 13 070 % 66.9% 20.0% 8.4% 4.7% households public households 24% 31% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 Fig. 5.148h: Loss structure by institutional sectors and asset categories for the scenario HU Danube Large 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 A+B C D private 45% E F Fig. 5.148i: Loss structure by institutional sectors for the scenario HU Danube Large G H Tab. 5.107f: Loss structure by institutional sectors and industry branches for the scenario HU I Danube Large house- J Industry branch public private Total loss holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 9 361 310 679 5.2% C (Mining and quarrying) 0 43 17 60 0.5% M D (Manufacturing) 0 1 852 725 2 578 19.7% public E (Electricity, gas and water N private supply) 1 506 106 613 4.7% households F (Construction) 4 160 52 216 1.7% O G (Wholesale and retail trade; repair) 3 543 144 691 5.3% Fig. 5.148j: Structure of the loss for the scenario HU Danube Large by industry H (Hotels and restaurant) 4 64 25 93 0.7% branches and institutional sectors I (Transport, storage and communications) 68 657 173 899 6.9% J (Financial intermediation) 0 71 14 85 0.7% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 research) 242 1 006 2 831 4 079 31.2% L (Public administration 2 239 0 0 2 239 17.1% A+B M (Education) 300 5 16 321 2.5% N (Health and social work) 176 2 5 184 1.4% C O (Other community, social and personal services) 91 71 173 334 2.6% D TOTAL 3 138 5 342 4 591 13 070 % 24.0% 40.9% 35.1% E F Tab. 5.107g: Loss structure by asset categories and industry branches, sc. HU Danube Large G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 492 187 0 0 679 5.2% C 35 24 1 0 60 0.5% J D 889 1 249 439 0 2 578 19.7% E 417 184 12 0 613 4.7% K F 111 60 46 0 216 1.7% G 219 173 299 0 691 5.3% L H 77 13 3 0 93 0.7% I 481 383 35 0 899 6.9% M building J 66 19 0 0 85 0.7% equipment K 3 249 109 107 614 4 079 31.2% N inventories L 1 973 117 148 0 2 239 17.1% household equip. M 287 34 0 0 321 2.5% O N 142 42 0 0 184 1.4% O 310 23 1 0 334 2.6% Fig. 5.148k: Structure of the loss for the scenario HU Danube Large by industry TOTAL 8 748 2 616 1 092 614 13 070 branches and asset categories % 66.9% 20.0% 8.4% 4.7% Detailed Results for Pan Regional Scenario, Hungary Scenario: HU Tisza Large 9 000 Loss [mil. EUR] Total loss 8 000 Loss on public property 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.149b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.108a: LEC and survival function for the scenario HU Tisza Large Fig. 5.149a: LEC function for the scenario HU Tisza Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 216 61 Probability 20 Total loss 50 2.0% 2 448 698 Loss on public property 100 1.0% 4 353 1 246 5.0% 250 0.4% 7 116 2 035 500 0.2% 8 514 2 437 4.0% Loss structure for the RTP 250: Tab. 5.108b: Regional loss structure for the scenario HU Tisza Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Csongrád 1 740 24% 5.0% Borsod-Abaúj-Zemplén 1 458 20% 3.2% Jász-Nagykun-Szolnok 978 14% 3.5% 2.0% Szabolcs-Szatmár-Bereg 653 9% 2.1% Békés 547 8% 2.5% Hajdú-Bihar 546 8% 1.4% Heves 520 7% 2.2% 1.0% Bács-Kiskun 299 4% 0.8% Pest 279 4% 0.3% Nógrád 95 1% 0.8% TOTAL 7 116 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 Loss [mil. EUR] Fig. 5.149c: Survival function for the scenario HU Tisza Large Fig. 5.149d: Regional loss structure for the scenario HU Tisza Large (% of total loss) Fig. 5.149e: Loss intensity in provinces for the scenario HU Tisza Large (% of property in the province) Tab. 5.108c: Losses by sector / purpose classification for the scenario HU Tisza Large infrastructure- Loss public Category % [mil EUR] 27% infrastructure-public 1 914 27% enterprise-public 122 2% Public 2 036 29% infrastructure-non-public 458 6% enterprise-non-public 4 622 65% enterprise-public TOTAL 7 116 2% infrastructure- non-public 6% enterprise-non- public 65% Fig. 5.149f: Loss by sector / purpose classification the scenario HU Tisza Large Household Tab. 5.108d: Loss structure by asset categories for the scenario HU Tisza Large Inventories eguipment Loss Dwellings Category code % 8% 4% [mil EUR] 24% Machinery and equipment Dwellings AN.1111 1 682 24% 19% Other buildings and structures AN.1112 3 252 46% Machinery and equipment AN.1113 1 369 19% Inventories AN.12 539 8% Household eguipment - 273 4% TOTAL 7 116 Other buildings and structures 45% Fig. 5.149g: Loss structure by asset categories for the scenario HU Tisza Large building Tab. 5.108e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 522 146 50 273 1 991 28.0% private 1 635 1 065 389 0 3 089 43.4% private public 1 778 158 100 0 2 036 28.6% TOTAL 4 934 1 369 539 273 7 116 % 69.3% 19.2% 7.6% 3.8% households households public 28% 29% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 Fig. 5.149h: Loss structure by institutional sectors and asset categories for the scenario HU Tisza Large 0 500 1 000 1 500 2 000 A+B C private D 43% E F Fig. 5.149i: Loss structure by institutional sectors for the scenario HU Tisza Large G H Tab. 5.108f: Loss structure by institutional sectors and industry branches for the scenario HU I Tisza Large house- J Industry branch public private Total loss holds % [mil EUR] [mil EUR] [mil EUR] K [mil EUR] A+B (Agriculture, hunting L and forestry) 10 408 301 719 10.1% C (Mining and quarrying) 0 22 0 22 0.3% M D (Manufacturing) 0 1 006 13 1 019 14.3% public E (Electricity, gas and water N private supply) 1 437 0 437 6.1% households F (Construction) 3 142 7 152 2.1% O G (Wholesale and retail trade; repair) 1 361 16 378 5.3% Fig. 5.149j: Structure of the loss for the scenario HU Tisza Large by industry branches H (Hotels and restaurant) 2 46 1 49 0.7% and institutional sectors I (Transport, storage and communications) 33 410 2 445 6.3% J (Financial intermediation) 0 36 0 36 0.5% K (Real estate, renting, 0 500 1 000 1 500 2 000 research) 106 201 1 505 1 812 25.5% L (Public administration 1 433 0 0 1 433 20.1% A+B M (Education) 252 0 18 270 3.8% N (Health and social work) 143 1 5 149 2.1% C O (Other community, social and personal services) 53 20 122 194 2.7% D TOTAL 2 036 3 089 1 991 7 116 % 28.6% 43.4% 28.0% E F Tab. 5.108g: Loss structure by asset categories and industry branches, sc. HU Tisza Large G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 520 198 0 0 719 10.1% C 13 9 0 0 22 0.3% J D 351 494 174 0 1 019 14.3% E 298 131 9 0 437 6.1% K F 78 42 32 0 152 2.1% G 120 95 164 0 378 5.3% L H 41 7 1 0 49 0.7% I 238 190 17 0 445 6.3% M building J 29 8 0 0 36 0.5% equipment K 1 445 46 47 273 1 812 25.5% N inventories L 1 264 75 95 0 1 433 20.1% household equip. M 241 29 0 0 270 3.8% O N 115 34 0 0 149 2.1% O 181 13 1 0 194 2.7% Fig. 5.149k: Structure of the loss for the scenario HU Tisza Large by industry branches TOTAL 4 934 1 369 539 273 7 116 and asset categories % 69.3% 19.2% 7.6% 3.8% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Vistula Upper 10 000 Loss [mil. EUR] Total loss 9 000 Loss on public property 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.109a: LEC and survival function for the scenario PL Vistula Upper Fig. 5.109a: LEC function for the scenario PL Vistula Upper Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 221 76 Probability 20 Total loss 50 2.0% 1 116 382 Loss on public property 100 1.0% 3 836 1 329 5.0% 250 0.4% 6 998 2 432 500 0.2% 8 595 2 991 4.0% Loss structure for the RTP 250: Tab. 5.109b: Regional loss structure for the scenario PL Vistula Upper Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] MaÅ‚opolskie 4 178 60% 4.3% ÅšlÄ…skie 1 712 24% 1.1% Podkarpackie 562 8% 1.1% 2.0% ÅšwiÄ™tokrzyskie 547 8% 1.6% TOTAL 6 998 1.0% 0.0% 0 2 000 4 000 6 000 8 000 10 000 Loss [mil. EUR] Fig. 5.109c: Survival function for the scenario PL Vistula Upper Fig. 5.109d: Regional loss structure for the scenario PL Vistula Upper (% of total loss) Fig. 5.109e: Loss intensity in provinces for the scenario PL Vistula Upper (% of property in the province) Tab. 5.109c: Losses by sector / purpose classification for the scenario PL Vistula Upper infrastructure- public Loss Category % 23% [mil EUR] infrastructure-public 1 580 23% enterprise-public 852 12% Public 2 432 35% infrastructure-non-public 496 7% enterprise-non-public 4 070 58% TOTAL 6 998 enterprise-public 12% enterprise-non- infrastructure- public non-public 58% 7% Fig. 5.109f: Loss by sector / purpose classification the scenario PL Vistula Upper Tab. 5.109d: Loss structure by asset categories for the scenario PL Vistula Upper Household Loss Category code % eguipment [mil EUR] Inventories 6% Dwellings 8% 24% Dwellings AN.1111 1 681 24% Other buildings and structures AN.1112 2 916 42% Machinery and equipment AN.1113 1 389 20% Inventories AN.12 589 8% Household eguipment - 423 6% TOTAL 6 998 Machinery and equipment 20% Other buildings and structures 42% Fig. 5.109g: Loss structure by asset categories for the scenario PL Vistula Upper Tab. 5.109e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 413 96 51 423 1 983 28.3% private 1 499 714 370 0 2 583 36.9% private public 1 685 579 169 0 2 432 34.8% TOTAL 4 596 1 389 589 423 6 998 % 65.7% 19.8% 8.4% 6.0% building equipment households inventories household equip. public households 0 500 1 000 1 500 2 000 2 500 3 000 35% 28% Loss [mil. EUR] Fig. 5.109h: Loss structure by institutional sectors and asset categories for the scenario PL Vistula Upper Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 3 000 A+B C D private 37% E F Fig. 5.109i: Loss structure by institutional sectors for the scenario PL Vistula Upper G H Tab. 5.109f: Loss structure by institutional sectors and industry branches for the scenario PL Vistula Upper I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 27 162 32 220 3.1% C (Mining and quarrying) 32 49 2 83 1.2% M D (Manufacturing) 455 683 24 1 162 16.6% public E (Electricity, gas and water N private supply) 294 443 15 752 10.7% households F (Construction) 85 69 13 168 2.4% O G (Wholesale and retail trade; repair) 14 516 70 600 8.6% Fig. 5.109j: Structure of the loss for the scenario PL Vistula Upper by industry branches H (Hotels and restaurant) 14 58 8 80 1.1% and institutional sectors I (Transport, storage and communications) 268 82 9 359 5.1% J (Financial intermediation) 10 79 1 89 1.3% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 215 391 1 797 2 402 34.3% L (Public administration 545 3 1 549 7.8% A+B M (Education) 223 7 3 233 3.3% N (Health and social work) 115 14 6 135 1.9% C O (Other community, social and personal services) 135 29 2 166 2.4% D TOTAL 2 432 2 583 1 983 6 998 % 34.8% 36.9% 28.3% E F Tab. 5.109g: Loss structure by asset categories and industry branches, sc. PL Vistula Upper G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 189 32 0 0 220 3.1% C 50 30 3 0 83 1.2% J D 430 500 232 0 1 162 16.6% E 516 217 19 0 752 10.7% K F 78 52 37 0 168 2.4% G 222 151 226 0 600 8.6% L H 67 11 2 0 80 1.1% I 186 170 2 0 359 5.1% M building 52 32 5 0 89 1.3% J equipment K 1 890 67 22 423 2 402 34.3% N 480 33 36 0 549 7.8% inventories L household equip. M 215 17 1 0 233 3.3% O N 92 42 2 0 135 1.9% O 128 35 2 0 166 2.4% Fig. 5.109k: Structure of the loss for the scenario PL Vistula Upper by industry branches TOTAL 4 596 1 389 589 423 6 998 and asset categories % 65.7% 19.8% 8.4% 6.0% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Vistula Middle 18 000 Loss [mil. EUR] Total loss 16 000 Loss on public property 14 000 12 000 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.110a: LEC and survival function for the scenario PL Vistula Middle Fig. 5.151a: LEC function for the scenario PL Vistula Middle Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 1 387 473 Probability 20 Total loss 50 2.0% 6 975 2 382 Loss on public property 100 1.0% 10 050 3 461 5.0% 250 0.4% 14 158 4 898 500 0.2% 16 721 5 790 4.0% Loss structure for the RTP 250: Tab. 5.110b: Regional loss structure for the scenario PL Vistula Middle Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Mazowieckie 12 228 86% 4.7% Lubelskie 1 144 8% 2.1% Å?ódzkie 389 3% 0.5% 2.0% ÅšwiÄ™tokrzyskie 364 3% 1.1% ÅšlÄ…skie 29 0% 0.0% MaÅ‚opolskie 3 0% 0.0% Podkarpackie 1 0% 0.0% 1.0% TOTAL 14 158 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 Loss [mil. EUR] Fig. 5.151c: Survival function for the scenario PL Vistula Middle Fig. 5.151d: Regional loss structure for the scenario PL Vistula Middle (% of total loss) Fig. 5.151e: Loss intensity in provinces for the scenario PL Vistula Middle (% of property in the province) infrastructure- Tab. 5.110c: Losses by sector / purpose classification for the scenario PL Vistula Middle public 23% Loss Category % [mil EUR] infrastructure-public 3 261 23% enterprise-public 1 637 12% Public 4 898 35% infrastructure-non-public 948 7% enterprise-non-public 8 313 59% TOTAL 14 158 enterprise-public 12% infrastructure- enterprise-non- non-public public 7% 58% Fig. 5.151f: Loss by sector / purpose classification the scenario PL Vistula Middle Tab. 5.110d: Loss structure by asset categories for the scenario PL Vistula Middle Household Loss eguipment Category code % [mil EUR] Inventories 6% 7% Dwellings Dwellings AN.1111 3 776 27% 27% Other buildings and structures AN.1112 5 812 41% Machinery and equipment AN.1113 2 689 19% Inventories AN.12 1 012 7% Household eguipment - 870 6% TOTAL 14 158 Machinery and equipment 19% Other buildings and structures 41% Fig. 5.151g: Loss structure by asset categories for the scenario PL Vistula Middle Tab. 5.110e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 2 933 214 107 870 4 124 29.1% private 3 094 1 375 667 0 5 137 36.3% private public 3 560 1 100 238 0 4 898 34.6% TOTAL 9 587 2 689 1 012 870 14 158 % 67.7% 19.0% 7.2% 6.1% building equipment households inventories household equip. public households 0 1 000 2 000 3 000 4 000 5 000 6 000 35% 29% Loss [mil. EUR] Fig. 5.151h: Loss structure by institutional sectors and asset categories for the scenario PL Vistula Middle Loss [mil. EUR] 0 1 000 2 000 3 000 4 000 5 000 6 000 A+B C private D 36% E F Fig. 5.151i: Loss structure by institutional sectors for the scenario PL Vistula Middle G H Tab. 5.110f: Loss structure by institutional sectors and industry branches for the scenario PL Vistula Middle I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 63 379 74 516 3.6% C (Mining and quarrying) 33 45 2 80 0.6% M D (Manufacturing) 465 634 22 1 122 7.9% public E (Electricity, gas and water N private supply) 526 756 26 1 308 9.2% households F (Construction) 77 141 27 245 1.7% O G (Wholesale and retail trade; repair) 29 1 204 165 1 398 9.9% Fig. 5.151j: Structure of the loss for the scenario PL Vistula Middle by industry branches H (Hotels and restaurant) 11 95 13 119 0.8% and institutional sectors I (Transport, storage and communications) 895 576 67 1 537 10.9% J (Financial intermediation) 140 311 3 454 3.2% Loss [mil. EUR] K (Real estate, renting, 0 1 000 2 000 3 000 4 000 5 000 6 000 research) 820 803 3 693 5 316 37.5% L (Public administration 1 104 2 0 1 106 7.8% A+B M (Education) 327 31 11 369 2.6% N (Health and social work) 151 25 11 188 1.3% C O (Other community, social and personal services) 257 134 9 401 2.8% D TOTAL 4 898 5 137 4 124 14 158 % 34.6% 36.3% 29.1% E F Tab. 5.110g: Loss structure by asset categories and industry branches, sc. PL Vistula Middle G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 442 74 0 0 516 3.6% C 48 29 3 0 80 0.6% J D 415 483 224 0 1 122 7.9% E 898 377 33 0 1 308 9.2% K F 114 77 54 0 245 1.7% G 518 353 527 0 1 398 9.9% L H 100 16 3 0 119 0.8% I 797 730 11 0 1 537 10.9% M building J 264 165 24 0 454 3.2% equipment K 4 246 150 50 870 5 316 37.5% N 967 66 74 0 1 106 7.8% inventories L household equip. M 341 27 1 0 369 2.6% O N 128 58 2 0 188 1.3% O 311 84 6 0 401 2.8% Fig. 5.151k: Structure of the loss for the scenario PL Vistula Middle by industry branches TOTAL 9 587 2 689 1 012 870 14 158 and asset categories % 67.7% 19.0% 7.2% 6.1% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Vistula Lower 9 000 Loss [mil. EUR] Total loss 8 000 Loss on public property 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.111a: LEC and survival function for the scenario PL Vistula Lower Fig. 5.152a: LEC function for the scenario PL Vistula Lower Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 398 137 Probability 20 Total loss 50 2.0% 2 004 689 Loss on public property 100 1.0% 4 015 1 390 5.0% 250 0.4% 6 352 2 202 500 0.2% 7 636 2 648 4.0% Loss structure for the RTP 250: Tab. 5.111b: Regional loss structure for the scenario PL Vistula Lower Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Mazowieckie 3 508 55% 1.3% Kujawsko-pomorskie 1 520 24% 3.0% Pomorskie 635 10% 0.9% 2.0% Å?ódzkie 614 10% 0.8% WarmiÅ„sko-mazurskie 75 1% 0.2% TOTAL 6 352 1.0% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 9 000 Loss [mil. EUR] Fig. 5.152c: Survival function for the scenario PL Vistula Lower Fig. 5.152d: Regional loss structure for the scenario PL Vistula Lower (% of total loss) Fig. 5.152e: Loss intensity in provinces for the scenario PL Vistula Lower (% of property in the province) infrastructure- Tab. 5.111c: Losses by sector / purpose classification for the scenario PL Vistula Lower public 23% Loss Category % [mil EUR] infrastructure-public 1 437 23% enterprise-public 766 12% Public 2 202 35% infrastructure-non-public 450 7% enterprise-non-public 3 699 58% TOTAL 6 352 enterprise-public 12% infrastructure- enterprise-non- non-public public 7% 58% Fig. 5.152f: Loss by sector / purpose classification the scenario PL Vistula Lower Household Dwellings Tab. 5.111d: Loss structure by asset categories for the scenario PL Vistula Lower Inventories eguipment 25% Loss Category code % 8% 6% [mil EUR] Dwellings AN.1111 1 602 25% Other buildings and structures AN.1112 2 612 41% Machinery and equipment AN.1113 1 276 20% Inventories AN.12 483 8% Household eguipment - 378 6% TOTAL 6 352 Other buildings Machinery and and structures equipment 41% 20% Fig. 5.152g: Loss structure by asset categories for the scenario PL Vistula Lower Tab. 5.111e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 272 91 45 378 1 787 28.1% private 1 388 659 315 0 2 362 37.2% private public 1 554 525 123 0 2 202 34.7% TOTAL 4 214 1 276 483 378 6 352 % 66.4% 20.1% 7.6% 6.0% building equipment households inventories household equip. public households 0 500 1 000 1 500 2 000 2 500 35% 28% Loss [mil. EUR] Fig. 5.152h: Loss structure by institutional sectors and asset categories for the scenario PL Vistula Lower Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 A+B C D private 37% E F Fig. 5.152i: Loss structure by institutional sectors for the scenario PL Vistula Lower G H Tab. 5.111f: Loss structure by institutional sectors and industry branches for the scenario PL Vistula Lower I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 27 161 32 220 3.5% C (Mining and quarrying) 23 36 1 60 0.9% M D (Manufacturing) 316 506 17 840 13.2% public E (Electricity, gas and water N private supply) 238 375 13 626 9.9% households F (Construction) 30 63 12 105 1.7% O G (Wholesale and retail trade; repair) 13 470 64 547 8.6% Fig. 5.152j: Structure of the loss for the scenario PL Vistula Lower by industry branches H (Hotels and restaurant) 5 37 5 47 0.7% and institutional sectors I (Transport, storage and communications) 373 192 22 588 9.3% J (Financial intermediation) 44 97 1 142 2.2% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 research) 308 349 1 607 2 265 35.7% L (Public administration 449 1 0 451 7.1% A+B M (Education) 161 10 4 175 2.7% N (Health and social work) 82 11 5 98 1.5% C O (Other community, social and personal services) 134 53 4 190 3.0% D TOTAL 2 202 2 362 1 787 6 352 % 34.7% 37.2% 28.1% E F Tab. 5.111g: Loss structure by asset categories and industry branches, sc. PL Vistula Lower G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 188 32 0 0 220 3.5% C 36 22 2 0 60 0.9% J D 311 361 167 0 840 13.2% E 430 180 16 0 626 9.9% K F 49 33 23 0 105 1.7% G 203 138 206 0 547 8.6% L H 40 6 1 0 47 0.7% I 305 279 4 0 588 9.3% M building J 83 51 8 0 142 2.2% equipment K 1 801 64 21 378 2 265 35.7% N inventories L 394 27 30 0 451 7.1% household equip. M 161 13 0 0 175 2.7% O N 66 30 1 0 98 1.5% O 147 40 3 0 190 3.0% Fig. 5.152k: Structure of the loss for the scenario PL Vistula Lower by industry branches TOTAL 4 214 1 276 483 378 6 352 and asset categories % 66.4% 20.1% 7.6% 6.0% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Bug 8 000 Loss [mil. EUR] Total loss 7 000 Loss on public property 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.112a: LEC and survival function for the scenario PL Bug Fig. 5.153a: LEC function for the scenario PL Bug Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 341 117 Probability 20 Total loss 50 2.0% 1 718 590 Loss on public property 100 1.0% 3 462 1 209 5.0% 250 0.4% 5 634 1 980 500 0.2% 6 876 2 420 4.0% Loss structure for the RTP 250: Tab. 5.112b: Regional loss structure for the scenario PL Bug Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Mazowieckie 4 180 74% 1.6% Podlaskie 619 11% 2.0% Lubelskie 432 8% 0.8% 2.0% WarmiÅ„sko-mazurskie 403 7% 1.2% TOTAL 5 634 1.0% 0.0% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 Loss [mil. EUR] Fig. 5.153c: Survival function for the scenario PL Bug Fig. 5.153d: Regional loss structure for the scenario PL Bug (% of total loss) Fig. 5.153e: Loss intensity in provinces for the scenario PL Bug (% of property in the province) infrastructure- Tab. 5.112c: Losses by sector / purpose classification for the scenario PL Bug public Loss 24% Category % [mil EUR] infrastructure-public 1 336 24% enterprise-public 643 11% Public 1 980 35% infrastructure-non-public 356 6% enterprise-non-public 3 298 59% TOTAL 5 634 enterprise-public 11% infrastructure- enterprise-non- non-public public 6% 59% Fig. 5.153f: Loss by sector / purpose classification the scenario PL Bug Inventories Household Tab. 5.112d: Loss structure by asset categories for the scenario PL Bug 7% eguipment Dwellings Loss 6% Category code % 26% [mil EUR] Dwellings AN.1111 1 491 26% Other buildings and structures AN.1112 2 306 41% Machinery and equipment AN.1113 1 083 19% Inventories AN.12 404 7% Household eguipment - 350 6% TOTAL 5 634 Machinery and Other buildings equipment and structures 19% 42% Fig. 5.153g: Loss structure by asset categories for the scenario PL Bug Tab. 5.112e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 175 83 41 350 1 648 29.3% private 1 202 540 263 0 2 006 35.6% private public 1 420 460 100 0 1 980 35.1% TOTAL 3 797 1 083 404 350 5 634 % 67.4% 19.2% 7.2% 6.2% building equipment households inventories household equip. public households 0 500 1 000 1 500 2 000 2 500 35% 29% Fig. 5.153h: Loss structure by institutional sectors and asset categories for the scenario PL Bug 0 500 1 000 1 500 2 000 2 500 A+B C D private 36% E F Fig. 5.153i: Loss structure by institutional sectors for the scenario PL Bug G H Tab. 5.112f: Loss structure by institutional sectors and industry branches for the scenario PL Bug I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 25 147 29 201 3.6% C (Mining and quarrying) 15 23 1 39 0.7% M D (Manufacturing) 217 318 11 545 9.7% public E (Electricity, gas and water N private supply) 192 286 10 488 8.7% households F (Construction) 26 53 10 90 1.6% O G (Wholesale and retail trade; repair) 11 444 61 515 9.1% Fig. 5.153j: Structure of the loss for the scenario PL Bug by industry branches and H (Hotels and restaurant) 4 37 5 46 0.8% institutional sectors I (Transport, storage and communications) 387 199 23 609 10.8% J (Financial intermediation) 49 106 1 156 2.8% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 research) 296 323 1 486 2 106 37.4% L (Public administration 448 1 0 449 8.0% A+B M (Education) 137 11 4 153 2.7% N (Health and social work) 66 10 4 79 1.4% C O (Other community, social and personal services) 106 48 3 157 2.8% D TOTAL 1 980 2 006 1 648 5 634 % 35.1% 35.6% 29.3% E F Tab. 5.112g: Loss structure by asset categories and industry branches, sc. PL Bug G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 172 29 0 0 201 3.6% C 23 14 1 0 39 0.7% J D 202 235 109 0 545 9.7% E 335 141 12 0 488 8.7% K F 42 28 20 0 90 1.6% G 191 130 195 0 515 9.1% L H 39 6 1 0 46 0.8% I 316 289 4 0 609 10.8% M building J 91 57 8 0 156 2.8% equipment K 1 677 59 20 350 2 106 37.4% N inventories L 392 27 30 0 449 8.0% household equip. M 141 11 0 0 153 2.7% O N 54 24 1 0 79 1.4% O 121 33 2 0 157 2.8% Fig. 5.153k: Structure of the loss for the scenario PL Bug by industry branches and TOTAL 3 797 1 083 404 350 5 634 asset categories % 67.4% 19.2% 7.2% 6.2% Detailed Results for Catchment-based Scenario, Poland Scenario: PL San 1 200 Loss [mil. EUR] Total loss Loss on public property 1 000 800 600 400 200 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.113a: LEC and survival function for the scenario PL San Fig. 5.154a: LEC function for the scenario PL San Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 26 9 Probability 20 Total loss 50 2.0% 130 48 Loss on public property 100 1.0% 465 171 5.0% 250 0.4% 847 311 500 0.2% 1 041 382 4.0% Loss structure for the RTP 250: Tab. 5.113b: Regional loss structure for the scenario PL San Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Podkarpackie 739 87% 1.4% Lubelskie 108 13% 0.2% TOTAL 847 2.0% 1.0% 0.0% 0 200 400 600 800 1 000 1 200 Loss [mil. EUR] Fig. 5.154c: Survival function for the scenario PL San Fig. 5.154d: Regional loss structure for the scenario PL San (% of total loss) Fig. 5.154e: Loss intensity in provinces for the scenario PL San (% of property in the province) infrastructure- Tab. 5.113c: Losses by sector / purpose classification for the scenario PL San public Loss 24% Category % [mil EUR] infrastructure-public 203 24% enterprise-public 108 13% Public 311 37% infrastructure-non-public 55 7% enterprise-non-public 480 57% TOTAL 847 enterprise-public 13% enterprise-non- public infrastructure- 56% non-public 7% Fig. 5.154f: Loss by sector / purpose classification the scenario PL San Household Tab. 5.113d: Loss structure by asset categories for the scenario PL San eguipment Loss Inventories Category code % 6% Dwellings [mil EUR] 8% 23% Dwellings AN.1111 198 23% Other buildings and structures AN.1112 346 41% Machinery and equipment AN.1113 184 22% Inventories AN.12 67 8% Household eguipment - 52 6% TOTAL 847 Machinery and Other buildings equipment and structures 22% 41% Fig. 5.154g: Loss structure by asset categories for the scenario PL San Tab. 5.113e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 171 11 5 52 238 28.1% private 170 87 40 0 297 35.1% private public 203 86 22 0 311 36.8% TOTAL 544 184 67 52 847 % 64.2% 21.7% 7.9% 6.1% building equipment households inventories household equip. households public 0 50 100 150 200 250 300 350 28% 37% Fig. 5.154h: Loss structure by institutional sectors and asset categories for the scenario PL San 0 50 100 150 200 250 300 A+B C D private 35% E F Fig. 5.154i: Loss structure by institutional sectors for the scenario PL San G H Tab. 5.113f: Loss structure by institutional sectors and industry branches for the scenario PL San I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 4 18 4 25 3.0% C (Mining and quarrying) 5 8 0 14 1.6% M D (Manufacturing) 77 112 4 193 22.8% public E (Electricity, gas and water N private supply) 35 51 2 87 10.3% households F (Construction) 2 6 1 9 1.0% O G (Wholesale and retail trade; repair) 2 37 5 43 5.1% Fig. 5.154j: Structure of the loss for the scenario PL San by industry branches and H (Hotels and restaurant) 1 2 0 3 0.4% institutional sectors I (Transport, storage and communications) 51 7 1 60 7.0% J (Financial intermediation) 1 3 0 4 0.5% K (Real estate, renting, 0 50 100 150 200 250 300 research) 16 48 220 285 33.6% L (Public administration 56 0 0 57 6.7% A+B M (Education) 25 1 0 26 3.1% N (Health and social work) 15 1 1 17 2.1% C O (Other community, social and personal services) 21 2 0 23 2.7% D TOTAL 311 297 238 847 % 36.8% 35.1% 28.1% E F Tab. 5.113g: Loss structure by asset categories and industry branches, sc. PL San G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 22 4 0 0 25 3.0% C 8 5 0 0 14 1.6% J D 72 83 39 0 193 22.8% E 60 25 2 0 87 10.3% K F 4 3 2 0 9 1.0% G 16 11 16 0 43 5.1% L H 3 0 0 0 3 0.4% I 31 28 0 0 60 7.0% M building J 2 1 0 0 4 0.5% equipment K 222 8 3 52 285 33.6% N inventories L 49 3 4 0 57 6.7% household equip. M 24 2 0 0 26 3.1% O N 12 5 0 0 17 2.1% O 18 5 0 0 23 2.7% Fig. 5.154k: Structure of the loss for the scenario PL San by industry branches and TOTAL 544 184 67 52 847 asset categories % 64.2% 21.7% 7.9% 6.1% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Odra Upper 12 000 Loss [mil. EUR] Total loss Loss on public property 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.114a: LEC and survival function for the scenario PL Odra Upper Fig. 5.155a: LEC function for the scenario PL Odra Upper Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 330 132 Probability 20 Total loss 50 2.0% 1 661 664 Loss on public property 100 1.0% 5 055 2 003 5.0% 250 0.4% 8 966 3 540 500 0.2% 10 912 4 307 4.0% Loss structure for the RTP 250: Tab. 5.114b: Regional loss structure for the scenario PL Odra Upper Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] DolnoÅ›lÄ…skie 4 907 55% 5.0% ÅšlÄ…skie 1 534 17% 0.9% Opolskie 1 397 16% 4.2% 2.0% Wielkopolskie 726 8% 0.7% Lubuskie 402 4% 1.3% TOTAL 8 966 1.0% 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 Loss [mil. EUR] Fig. 5.155c: Survival function for the scenario PL Odra Upper Fig. 5.155d: Regional loss structure for the scenario PL Odra Upper (% of total loss) Fig. 5.155e: Loss intensity in provinces for the scenario PL Odra Upper (% of property in the province) Tab. 5.114c: Losses by sector / purpose classification for the scenario PL Odra Upper infrastructure- public Loss Category % 25% [mil EUR] infrastructure-public 2 231 25% enterprise-public 1 309 15% Public 3 540 39% infrastructure-non-public 786 9% enterprise-non-public 4 640 52% TOTAL 8 966 enterprise-public 15% infrastructure- enterprise-non- non-public public 9% 51% Fig. 5.155f: Loss by sector / purpose classification the scenario PL Odra Upper Household Tab. 5.114d: Loss structure by asset categories for the scenario PL Odra Upper Inventories eguipment Dwellings Loss 8% 5% Category code % 21% [mil EUR] Dwellings AN.1111 1 928 21% Other buildings and structures AN.1112 3 930 44% Machinery and equipment AN.1113 1 943 22% Inventories AN.12 731 8% Household eguipment - 434 5% TOTAL 8 966 Machinery and Other buildings equipment and structures 22% 44% Fig. 5.155g: Loss structure by asset categories for the scenario PL Odra Upper Tab. 5.114e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 471 111 54 434 2 071 23.1% private 1 920 988 448 0 3 355 37.4% private public 2 467 844 229 0 3 540 39.5% TOTAL 5 858 1 943 731 434 8 966 % 65.3% 21.7% 8.2% 4.8% building equipment households inventories household equip. households 23% 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 public 40% Fig. 5.155h: Loss structure by institutional sectors and asset categories for the scenario PL Odra Upper 0 500 1 000 1 500 2 000 2 500 3 000 A+B C D private 37% E F Fig. 5.155i: Loss structure by institutional sectors for the scenario PL Odra Upper G H Tab. 5.114f: Loss structure by institutional sectors and industry branches for the scenario PL Odra Upper I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 41 207 41 289 3.2% C (Mining and quarrying) 50 73 2 125 1.4% M D (Manufacturing) 697 1 020 35 1 752 19.5% public E (Electricity, gas and water N private supply) 491 723 25 1 239 13.8% households F (Construction) 25 95 18 138 1.5% O G (Wholesale and retail trade; repair) 18 504 69 591 6.6% Fig. 5.155j: Structure of the loss for the scenario PL Odra Upper by industry branches H (Hotels and restaurant) 5 51 7 63 0.7% and institutional sectors I (Transport, storage and communications) 421 123 14 558 6.2% J (Financial intermediation) 15 95 1 111 1.2% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 459 401 1 845 2 704 30.2% L (Public administration 749 1 0 750 8.4% A+B M (Education) 243 6 2 251 2.8% N (Health and social work) 137 19 9 165 1.8% C O (Other community, social and personal services) 191 36 2 229 2.6% D TOTAL 3 540 3 355 2 071 8 966 % 39.5% 37.4% 23.1% E F Tab. 5.114g: Loss structure by asset categories and industry branches, sc. PL Odra Upper G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 247 42 0 0 289 3.2% C 75 46 4 0 125 1.4% J D 649 754 349 0 1 752 19.5% E 850 357 31 0 1 239 13.8% K F 64 43 30 0 138 1.5% G 219 149 223 0 591 6.6% L H 53 8 2 0 63 0.7% I 289 265 4 0 558 6.2% M building J 65 40 6 0 111 1.2% equipment K 2 168 77 26 434 2 704 30.2% N inventories L 656 45 50 0 750 8.4% household equip. M 232 18 1 0 251 2.8% O N 112 51 2 0 165 1.8% O 178 48 3 0 229 2.6% Fig. 5.155k: Structure of the loss for the scenario PL Odra Upper by industry branches TOTAL 5 858 1 943 731 434 8 966 and asset categories % 65.3% 21.7% 8.2% 4.8% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Odra Lower 2 500 Loss [mil. EUR] Total loss Loss on public property 2 000 1 500 1 000 500 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.115a: LEC and survival function for the scenario PL Odra Lower Fig. 5.156a: LEC function for the scenario PL Odra Lower Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 181 76 Probability 20 Total loss 50 2.0% 908 381 Loss on public property 100 1.0% 1 434 619 5.0% 250 0.4% 1 973 865 500 0.2% 2 261 996 4.0% Loss structure for the RTP 250: Tab. 5.115b: Regional loss structure for the scenario PL Odra Lower Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Lubuskie 1 002 51% 3.4% Zachodniopomorskie 971 49% 1.8% TOTAL 1 973 2.0% 1.0% 0.0% 0 500 1 000 1 500 2 000 2 500 Loss [mil. EUR] Fig. 5.156c: Survival function for the scenario PL Odra Lower Fig. 5.156d: Regional loss structure for the scenario PL Odra Lower (% of total loss) Fig. 5.156e: Loss intensity in provinces for the scenario PL Odra Lower (% of property in the province) Tab. 5.115c: Losses by sector / purpose classification for the scenario PL Odra Lower enterprise-non- infrastructure- Loss public public Category % 32% [mil EUR] 51% infrastructure-public 639 32% enterprise-public 226 11% Public 865 44% infrastructure-non-public 127 6% enterprise-non-public 981 50% TOTAL 1 973 enterprise-public 11% infrastructure- non-public 6% Fig. 5.156f: Loss by sector / purpose classification the scenario PL Odra Lower Household Tab. 5.115d: Loss structure by asset categories for the scenario PL Odra Lower Inventories eguipment Loss 7% 6% Category code % Dwellings [mil EUR] 24% Dwellings AN.1111 471 24% Other buildings and structures AN.1112 884 45% Machinery and equipment AN.1113 373 19% Inventories AN.12 135 7% Household eguipment - 111 6% TOTAL 1 973 Machinery and equipment Other buildings 19% and structures 44% Fig. 5.156g: Loss structure by asset categories for the scenario PL Odra Lower Tab. 5.115e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 370 24 12 111 517 26.2% private 359 155 76 0 591 29.9% private public 625 193 47 0 865 43.9% TOTAL 1 355 373 135 111 1 973 % 68.6% 18.9% 6.8% 5.6% building equipment households inventories household equip. households 26% 0 200 400 600 800 1 000 public Fig. 5.156h: Loss structure by institutional sectors and asset categories for the scenario 44% PL Odra Lower 0 100 200 300 400 500 600 700 A+B C private D 30% E F Fig. 5.156i: Loss structure by institutional sectors for the scenario PL Odra Lower G H Tab. 5.115f: Loss structure by institutional sectors and industry branches for the scenario PL Odra Lower I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 11 43 8 62 3.1% C (Mining and quarrying) 7 9 0 16 0.8% M D (Manufacturing) 96 127 4 227 11.5% public E (Electricity, gas and water N private supply) 98 112 4 214 10.9% households F (Construction) 8 17 3 28 1.4% O G (Wholesale and retail trade; repair) 7 111 15 133 6.7% Fig. 5.156j: Structure of the loss for the scenario PL Odra Lower by industry branches H (Hotels and restaurant) 3 18 3 24 1.2% and institutional sectors I (Transport, storage and communications) 147 28 3 179 9.0% J (Financial intermediation) 4 9 0 13 0.7% K (Real estate, renting, 0 100 200 300 400 500 600 700 research) 90 103 473 666 33.7% L (Public administration 251 0 0 251 12.7% A+B M (Education) 54 1 0 55 2.8% N (Health and social work) 31 5 2 37 1.9% C O (Other community, social and personal services) 59 8 1 68 3.5% D TOTAL 865 591 517 1 973 % 43.9% 29.9% 26.2% E F Tab. 5.115g: Loss structure by asset categories and industry branches, sc. PL Odra Lower G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 53 9 0 0 62 3.1% C 10 6 1 0 16 0.8% J D 84 98 45 0 227 11.5% E 147 62 5 0 214 10.9% K F 13 9 6 0 28 1.4% G 49 34 50 0 133 6.7% L H 20 3 1 0 24 1.2% I 93 85 1 0 179 9.0% M building J 8 5 1 0 13 0.7% equipment K 529 19 6 111 666 33.7% N inventories L 219 15 17 0 251 12.7% household equip. M 51 4 0 0 55 2.8% O N 25 11 0 0 37 1.9% O 53 14 1 0 68 3.5% Fig. 5.156k: Structure of the loss for the scenario PL Odra Lower by industry branches TOTAL 1 355 373 135 111 1 973 and asset categories % 68.6% 18.9% 6.8% 5.6% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Warta 10 000 Loss [mil. EUR] Total loss 9 000 Loss on public property 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.116a: LEC and survival function for the scenario PL Warta Fig. 5.157a: LEC function for the scenario PL Warta Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 175 60 Probability 20 Total loss 50 2.0% 883 302 Loss on public property 100 1.0% 3 692 1 271 5.0% 250 0.4% 7 023 2 411 500 0.2% 8 806 3 017 4.0% Loss structure for the RTP 250: Tab. 5.116b: Regional loss structure for the scenario PL Warta Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Wielkopolskie 3 913 56% 3.6% Å?ódzkie 2 216 32% 2.9% ÅšlÄ…skie 856 12% 0.5% 2.0% Lubuskie 19 0% 0.1% Opolskie 13 0% 0.0% Kujawsko-pomorskie 6 0% 0.0% TOTAL 7 023 1.0% 0.0% 0 2 000 4 000 6 000 8 000 10 000 Loss [mil. EUR] Fig. 5.157c: Survival function for the scenario PL Warta Fig. 5.157d: Regional loss structure for the scenario PL Warta (% of total loss) Fig. 5.157e: Loss intensity in provinces for the scenario PL Warta (% of property in the province) infrastructure- Tab. 5.116c: Losses by sector / purpose classification for the scenario PL Warta public 23% Loss Category % [mil EUR] infrastructure-public 1 624 23% enterprise-public 787 11% Public 2 411 34% infrastructure-non-public 603 9% enterprise-non-public 4 009 57% TOTAL 7 023 enterprise-public 11% enterprise-non- infrastructure- public non-public 57% 9% Fig. 5.157f: Loss by sector / purpose classification the scenario PL Warta Tab. 5.116d: Loss structure by asset categories for the scenario PL Warta Loss Household Category code % Inventories [mil EUR] eguipment 8% Dwellings 6% 25% Dwellings AN.1111 1 722 25% Other buildings and structures AN.1112 2 935 42% Machinery and equipment AN.1113 1 401 20% Inventories AN.12 545 8% Household eguipment - 420 6% TOTAL 7 023 Other buildings Machinery and equipment and structures 20% 41% Fig. 5.157g: Loss structure by asset categories for the scenario PL Warta Tab. 5.116e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 1 411 100 51 420 1 982 28.2% private 1 550 725 355 0 2 630 37.4% private public 1 696 575 140 0 2 411 34.3% TOTAL 4 657 1 401 545 420 7 023 % 66.3% 19.9% 7.8% 6.0% building equipment households inventories household equip. public households 0 500 1 000 1 500 2 000 2 500 3 000 34% 28% Fig. 5.157h: Loss structure by institutional sectors and asset categories for the scenario PL Warta 0 500 1 000 1 500 2 000 2 500 3 000 A+B C D private E 38% F Fig. 5.157i: Loss structure by institutional sectors for the scenario PL Warta G H Tab. 5.116f: Loss structure by institutional sectors and industry branches for the scenario PL Warta I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 31 185 36 252 3.6% C (Mining and quarrying) 27 44 1 72 1.0% M D (Manufacturing) 376 617 21 1 014 14.4% public E (Electricity, gas and water N private supply) 339 549 19 907 12.9% households F (Construction) 48 139 27 214 3.0% O G (Wholesale and retail trade; repair) 10 469 64 543 7.7% Fig. 5.157j: Structure of the loss for the scenario PL Warta by industry branches and H (Hotels and restaurant) 5 31 4 40 0.6% institutional sectors I (Transport, storage and communications) 330 114 13 457 6.5% J (Financial intermediation) 14 41 0 55 0.8% K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 research) 277 388 1 784 2 448 34.9% L (Public administration 458 3 1 462 6.6% A+B M (Education) 213 10 3 226 3.2% N (Health and social work) 98 12 5 114 1.6% C O (Other community, social and personal services) 186 30 2 218 3.1% D TOTAL 2 411 2 630 1 982 7 023 % 34.3% 37.4% 28.2% E F Tab. 5.116g: Loss structure by asset categories and industry branches, sc. PL Warta G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 216 36 0 0 252 3.6% C 43 26 2 0 72 1.0% J D 376 436 202 0 1 014 14.4% E 623 262 23 0 907 12.9% K F 100 67 47 0 214 3.0% G 201 137 205 0 543 7.7% L H 34 5 1 0 40 0.6% I 237 217 3 0 457 6.5% M building J 32 20 3 0 55 0.8% equipment K 1 937 68 23 420 2 448 34.9% N inventories L 403 28 31 0 462 6.6% household equip. M 209 17 1 0 226 3.2% O N 78 35 1 0 114 1.6% O 169 46 3 0 218 3.1% Fig. 5.157k: Structure of the loss for the scenario PL Warta by industry branches and TOTAL 4 657 1 401 545 420 7 023 asset categories % 66.3% 19.9% 7.8% 6.0% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Notec 1 400 Loss [mil. EUR] Total loss Loss on public property 1 200 1 000 800 600 400 200 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.117a: LEC and survival function for the scenario PL Notec Fig. 5.158a: LEC function for the scenario PL Notec Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 19 7 Probability 20 Total loss 50 2.0% 98 35 Loss on public property 100 1.0% 450 161 5.0% 250 0.4% 926 333 500 0.2% 1 231 444 4.0% Loss structure for the RTP 250: Tab. 5.117b: Regional loss structure for the scenario PL Notec Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Wielkopolskie 431 46% 0.4% Kujawsko-pomorskie 261 28% 0.5% Zachodniopomorskie 149 16% 0.3% 2.0% Lubuskie 62 7% 0.2% Pomorskie 23 3% 0.0% TOTAL 926 1.0% 0.0% 0 200 400 600 800 1 000 1 200 1 400 Loss [mil. EUR] Fig. 5.158c: Survival function for the scenario PL Notec Fig. 5.158d: Regional loss structure for the scenario PL Notec (% of total loss) Fig. 5.158e: Loss intensity in provinces for the scenario PL Notec (% of property in the province) Tab. 5.117c: Losses by sector / purpose classification for the scenario PL Notec infrastructure- public Loss 25% Category % [mil EUR] infrastructure-public 231 25% enterprise-public 102 11% Public 333 36% infrastructure-non-public 71 8% enterprise-non-public 522 56% TOTAL 926 enterprise-public 11% enterprise-non- public infrastructure- 56% non-public 8% Fig. 5.158f: Loss by sector / purpose classification the scenario PL Notec Tab. 5.117d: Loss structure by asset categories for the scenario PL Notec Loss Household Category code % [mil EUR] eguipment Inventories 6% Dwellings 8% 23% Dwellings AN.1111 218 23% Other buildings and structures AN.1112 394 43% Machinery and equipment AN.1113 188 20% Inventories AN.12 74 8% Household eguipment - 53 6% TOTAL 926 Machinery and equipment Other buildings 20% and structures 43% Fig. 5.158g: Loss structure by asset categories for the scenario PL Notec Tab. 5.117e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 178 13 7 53 250 27.0% private 198 96 48 0 343 37.0% private public 235 79 19 0 333 36.0% TOTAL 612 188 74 53 926 % 66.0% 20.3% 7.9% 5.7% building equipment households inventories household equip. households public 27% 0 50 100 150 200 250 300 350 400 36% Fig. 5.158h: Loss structure by institutional sectors and asset categories for the scenario PL Notec 0 50 100 150 200 250 300 350 A+B C D private 37% E F Fig. 5.158i: Loss structure by institutional sectors for the scenario PL Notec G H Tab. 5.117f: Loss structure by institutional sectors and industry branches for the scenario PL Notec I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 4 23 4 32 3.4% C (Mining and quarrying) 3 6 0 10 1.1% M D (Manufacturing) 48 91 3 142 15.3% public E (Electricity, gas and water N private supply) 35 64 2 102 11.0% households F (Construction) 7 17 3 27 2.9% O G (Wholesale and retail trade; repair) 1 62 8 72 7.8% Fig. 5.158j: Structure of the loss for the scenario PL Notec by industry branches and H (Hotels and restaurant) 1 5 1 7 0.7% institutional sectors I (Transport, storage and communications) 54 14 2 70 7.5% J (Financial intermediation) 2 5 0 7 0.7% K (Real estate, renting, 0 50 100 150 200 250 300 350 research) 35 49 225 309 33.4% L (Public administration 73 0 0 74 8.0% A+B M (Education) 28 1 0 29 3.2% N (Health and social work) 14 2 1 17 1.8% C O (Other community, social and personal services) 26 4 0 31 3.3% D TOTAL 333 343 250 926 % 36.0% 37.0% 27.0% E F Tab. 5.117g: Loss structure by asset categories and industry branches, sc. PL Notec G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 27 4 0 0 32 3.4% C 6 4 0 0 10 1.1% J D 53 61 28 0 142 15.3% E 70 29 3 0 102 11.0% K F 13 8 6 0 27 2.9% G 27 18 27 0 72 7.8% L H 6 1 0 0 7 0.7% I 36 33 0 0 70 7.5% M building J 4 2 0 0 7 0.7% equipment K 245 9 3 53 309 33.4% N inventories L 64 4 5 0 74 8.0% household equip. M 27 2 0 0 29 3.2% O N 11 5 0 0 17 1.8% O 24 6 0 0 31 3.3% Fig. 5.158k: Structure of the loss for the scenario PL Notec by industry branches and TOTAL 612 188 74 53 926 asset categories % 66.0% 20.3% 7.9% 5.7% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Nysa+Bobr 500 Loss [mil. EUR] Total loss 450 Loss on public property 400 350 300 250 200 150 100 50 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.118a: LEC and survival function for the scenario PL Nysa+Bobr Fig. 5.159a: LEC function for the scenario PL Nysa+Bobr Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 13 5 Probability 20 Total loss 50 2.0% 67 26 Loss on public property 100 1.0% 215 84 5.0% 250 0.4% 384 150 500 0.2% 472 184 4.0% Loss structure for the RTP 250: Tab. 5.118b: Regional loss structure for the scenario PL Nysa+Bobr Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] DolnoÅ›lÄ…skie 228 59% 0.2% Lubuskie 156 41% 0.5% TOTAL 384 2.0% 1.0% 0.0% 0 100 200 300 400 500 Loss [mil. EUR] Fig. 5.159c: Survival function for the scenario PL Nysa+Bobr Fig. 5.159d: Regional loss structure for the scenario PL Nysa+Bobr (% of total loss) Fig. 5.159e: Loss intensity in provinces for the scenario PL Nysa+Bobr (% of property in the province) Tab. 5.118c: Losses by sector / purpose classification for the scenario PL Nysa+Bobr infrastructure- Loss public Category % [mil EUR] 27% infrastructure-public 104 27% enterprise-public 46 12% Public 150 39% infrastructure-non-public 36 9% enterprise-non-public 198 52% TOTAL 384 enterprise-public 12% enterprise-non- public infrastructure- 52% non-public 9% Fig. 5.159f: Loss by sector / purpose classification the scenario PL Nysa+Bobr Tab. 5.118d: Loss structure by asset categories for the scenario PL Nysa+Bobr Loss Household Category code % [mil EUR] eguipment Dwellings Inventories 5% 23% Dwellings AN.1111 88 23% 7% Other buildings and structures AN.1112 171 44% Machinery and equipment AN.1113 77 20% Inventories AN.12 28 7% Household eguipment - 20 5% TOTAL 384 Machinery and equipment Other buildings 20% and structures 45% Fig. 5.159g: Loss structure by asset categories for the scenario PL Nysa+Bobr Tab. 5.118e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 67 5 2 20 94 24.4% private 83 40 18 0 140 36.6% private public 109 33 8 0 150 39.0% TOTAL 258 77 28 20 384 % 67.3% 20.2% 7.4% 5.2% building equipment households inventories household equip. households 24% 0 20 40 60 80 100 120 140 160 public 39% Fig. 5.159h: Loss structure by institutional sectors and asset categories for the scenario PL Nysa+Bobr 0 20 40 60 80 100 120 140 A+B C private D 37% E F Fig. 5.159i: Loss structure by institutional sectors for the scenario PL Nysa+Bobr G H Tab. 5.118f: Loss structure by institutional sectors and industry branches for the scenario PL Nysa+Bobr I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 2 9 2 13 3.4% C (Mining and quarrying) 1 3 0 4 1.0% M D (Manufacturing) 19 36 1 56 14.6% public E (Electricity, gas and water N private supply) 17 33 1 52 13.4% households F (Construction) 1 3 1 5 1.3% O G (Wholesale and retail trade; repair) 1 23 3 27 7.1% Fig. 5.159j: Structure of the loss for the scenario PL Nysa+Bobr by industry branches H (Hotels and restaurant) 0 3 0 3 0.8% and institutional sectors I (Transport, storage and communications) 22 5 1 28 7.3% J (Financial intermediation) 1 4 0 5 1.3% K (Real estate, renting, 0 20 40 60 80 100 120 140 research) 20 18 84 123 32.0% L (Public administration 38 0 0 38 9.8% A+B M (Education) 11 0 0 11 3.0% N (Health and social work) 6 1 0 8 2.0% C O (Other community, social and personal services) 10 1 0 11 2.9% D TOTAL 150 140 94 384 % 39.0% 36.6% 24.4% E F Tab. 5.118g: Loss structure by asset categories and industry branches, sc. PL Nysa+Bobr G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 11 2 0 0 13 3.4% C 2 1 0 0 4 1.0% J D 21 24 11 0 56 14.6% E 35 15 1 0 52 13.4% K F 2 2 1 0 5 1.3% G 10 7 10 0 27 7.1% L H 3 0 0 0 3 0.8% I 15 13 0 0 28 7.3% M building J 3 2 0 0 5 1.3% equipment K 98 3 1 20 123 32.0% N inventories L 33 2 2 0 38 9.8% household equip. M 11 1 0 0 11 3.0% O N 5 2 0 0 8 2.0% O 9 2 0 0 11 2.9% Fig. 5.159k: Structure of the loss for the scenario PL Nysa+Bobr by industry branches TOTAL 258 77 28 20 384 and asset categories % 67.3% 20.2% 7.4% 5.2% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Baltic West 1 400 Loss [mil. EUR] Total loss Loss on public property 1 200 1 000 800 600 400 200 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.119a: LEC and survival function for the scenario PL Baltic West Fig. 5.160a: LEC function for the scenario PL Baltic West Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 21 9 Probability 20 Total loss 50 2.0% 107 43 Loss on public property 100 1.0% 478 201 5.0% 250 0.4% 925 392 500 0.2% 1 192 504 4.0% Loss structure for the RTP 250: Tab. 5.119b: Regional loss structure for the scenario PL Baltic West Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Zachodniopomorskie 471 51% 0.9% Pomorskie 454 49% 0.7% TOTAL 925 2.0% 1.0% 0.0% 0 200 400 600 800 1 000 1 200 1 400 Loss [mil. EUR] Fig. 5.160c: Survival function for the scenario PL Baltic West Fig. 5.160d: Regional loss structure for the scenario PL Baltic West (% of total loss) Fig. 5.160e: Loss intensity in provinces for the scenario PL Baltic West (% of property in the province) infrastructure- Tab. 5.119c: Losses by sector / purpose classification for the scenario PL Baltic West enterprise-non- public public 30% Loss Category % 52% [mil EUR] infrastructure-public 278 30% enterprise-public 114 12% Public 392 42% infrastructure-non-public 58 6% enterprise-non-public 475 51% TOTAL 925 enterprise-public 12% infrastructure- non-public 6% Fig. 5.160f: Loss by sector / purpose classification the scenario PL Baltic West Household Tab. 5.119d: Loss structure by asset categories for the scenario PL Baltic West Inventories Dwellings Loss eguipment Category code % 6% 26% [mil EUR] 6% Dwellings AN.1111 245 26% Other buildings and structures AN.1112 401 43% Machinery and equipment AN.1113 164 18% Inventories AN.12 60 6% Household eguipment - 56 6% TOTAL 925 Machinery and equipment 18% Other buildings and structures 44% Fig. 5.160g: Loss structure by asset categories for the scenario PL Baltic West Tab. 5.119e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 187 12 6 56 260 28.1% private 171 69 33 0 273 29.5% private public 288 83 21 0 392 42.4% TOTAL 646 164 60 56 925 % 69.8% 17.7% 6.4% 6.1% building equipment households inventories household equip. households 0 50 100 150 200 250 300 350 400 450 28% public Fig. 5.160h: Loss structure by institutional sectors and asset categories for the scenario 42% PL Baltic West 0 50 100 150 200 250 300 350 400 A+B C private D 30% E F Fig. 5.160i: Loss structure by institutional sectors for the scenario PL Baltic West G H Tab. 5.119f: Loss structure by institutional sectors and industry branches for the scenario PL Baltic West I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 5 21 4 30 3.2% C (Mining and quarrying) 3 3 0 6 0.7% M D (Manufacturing) 44 41 1 87 9.3% public E (Electricity, gas and water N private supply) 39 47 2 87 9.4% households F (Construction) 3 10 2 14 1.6% O G (Wholesale and retail trade; repair) 3 53 7 64 6.9% Fig. 5.160j: Structure of the loss for the scenario PL Baltic West by industry branches H (Hotels and restaurant) 2 9 1 12 1.3% and institutional sectors I (Transport, storage and communications) 63 18 2 84 9.0% J (Financial intermediation) 2 7 0 9 0.9% K (Real estate, renting, 0 50 100 150 200 250 300 350 400 research) 53 52 239 344 37.2% L (Public administration 114 0 0 114 12.3% A+B M (Education) 27 1 0 28 3.1% N (Health and social work) 14 2 1 17 1.8% C O (Other community, social and personal services) 21 9 1 30 3.3% D TOTAL 392 273 260 925 % 42.4% 29.5% 28.1% E F Tab. 5.119g: Loss structure by asset categories and industry branches, sc. PL Baltic West G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 25 4 0 0 30 3.2% C 4 2 0 0 6 0.7% J D 32 37 17 0 87 9.3% E 60 25 2 0 87 9.4% K F 7 4 3 0 14 1.6% G 24 16 24 0 64 6.9% L H 10 2 0 0 12 1.3% I 43 40 1 0 84 9.0% M building J 5 3 0 0 9 0.9% equipment K 275 10 3 56 344 37.2% N inventories L 99 7 8 0 114 12.3% household equip. M 26 2 0 0 28 3.1% O N 11 5 0 0 17 1.8% O 23 6 0 0 30 3.3% Fig. 5.160k: Structure of the loss for the scenario PL Baltic West by industry branches TOTAL 646 164 60 56 925 and asset categories % 69.8% 17.7% 6.4% 6.1% Detailed Results for Catchment-based Scenario, Poland Scenario: PL Baltic East 500 Loss [mil. EUR] Total loss 450 Loss on public property 400 350 300 250 200 150 100 50 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.120a: LEC and survival function for the scenario PL Baltic East Fig. 5.161a: LEC function for the scenario PL Baltic East Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 4 2 Probability 20 Total loss 50 2.0% 21 8 Loss on public property 100 1.0% 149 56 5.0% 250 0.4% 336 127 500 0.2% 450 170 4.0% Loss structure for the RTP 250: Tab. 5.120b: Regional loss structure for the scenario PL Baltic East Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] WarmiÅ„sko-mazurskie 315 94% 0.9% Podlaskie 20 6% 0.1% TOTAL 336 2.0% 1.0% 0.0% 0 100 200 300 400 500 Loss [mil. EUR] Fig. 5.161c: Survival function for the scenario PL Baltic East Fig. 5.161d: Regional loss structure for the scenario PL Baltic East (% of total loss) Fig. 5.161e: Loss intensity in provinces for the scenario PL Baltic East (% of property in the province) Tab. 5.120c: Losses by sector / purpose classification for the scenario PL Baltic East Loss infrastructure- Category % [mil EUR] public 29% infrastructure-public 98 29% enterprise-public 28 8% Public 127 38% infrastructure-non-public 22 7% enterprise-non-public 187 56% TOTAL 336 enterprise-public 8% enterprise-non- infrastructure- public non-public 56% 7% Fig. 5.161f: Loss by sector / purpose classification the scenario PL Baltic East Household Tab. 5.120d: Loss structure by asset categories for the scenario PL Baltic East eguipment Loss Category code % 7% [mil EUR] Inventories 6% Dwellings Dwellings AN.1111 93 28% 28% Other buildings and structures AN.1112 139 41% Machinery and Machinery and equipment equipment AN.1113 61 18% 18% Inventories AN.12 20 6% Household eguipment - 23 7% TOTAL 336 Other buildings and structures 41% Fig. 5.161g: Loss structure by asset categories for the scenario PL Baltic East Tab. 5.120e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] building households 76 4 2 23 105 31.3% equipment private 66 26 13 0 104 31.0% private public 91 30 5 0 127 37.7% inventories TOTAL 233 61 20 23 336 household equip. % 69.3% 18.0% 5.9% 6.8% households public households 0 20 40 60 80 100 120 140 38% 31% Fig. 5.161h: Loss structure by institutional sectors and asset categories for the scenario PL Baltic East 0 20 40 60 80 100 120 140 A+B C D private 31% E F Fig. 5.161i: Loss structure by institutional sectors for the scenario PL Baltic East G H Tab. 5.120f: Loss structure by institutional sectors and industry branches for the scenario PL Baltic East I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 2 9 2 12 3.6% C (Mining and quarrying) 1 1 0 2 0.6% M D (Manufacturing) 9 21 1 31 9.1% public E (Electricity, gas and water N private supply) 9 20 1 30 8.8% households F (Construction) 0 3 0 3 1.0% O G (Wholesale and retail trade; repair) 1 18 2 21 6.3% Fig. 5.161j: Structure of the loss for the scenario PL Baltic East by industry branches H (Hotels and restaurant) 0 4 0 4 1.3% and institutional sectors I (Transport, storage and communications) 33 4 0 38 11.3% J (Financial intermediation) 1 1 0 2 0.6% K (Real estate, renting, 0 20 40 60 80 100 120 140 research) 14 21 98 133 39.6% L (Public administration 31 0 0 31 9.1% A+B M (Education) 11 0 0 11 3.4% N (Health and social work) 6 0 0 7 2.1% C O (Other community, social and personal services) 8 2 0 10 3.0% D TOTAL 127 104 105 336 % 37.7% 31.0% 31.3% E F Tab. 5.120g: Loss structure by asset categories and industry branches, sc. PL Baltic East G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 10 2 0 0 12 3.6% C 1 1 0 0 2 0.6% J D 11 13 6 0 31 9.1% E 20 9 1 0 30 8.8% K F 2 1 1 0 3 1.0% G 8 5 8 0 21 6.3% L H 4 1 0 0 4 1.3% I 20 18 0 0 38 11.3% M building J 1 1 0 0 2 0.6% equipment K 105 4 1 23 133 39.6% N inventories L 27 2 2 0 31 9.1% household equip. M 11 1 0 0 11 3.4% O N 5 2 0 0 7 2.1% O 8 2 0 0 10 3.0% Fig. 5.161k: Structure of the loss for the scenario PL Baltic East by industry branches TOTAL 233 61 20 23 336 and asset categories % 69.3% 18.0% 5.9% 6.8% Detailed Results for Real Event-based Scenario, Poland Scenario: PL 1997 25 000 Loss [mil. EUR] Total loss Loss on public property 20 000 15 000 10 000 5 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.121a: LEC and survival function for the scenario PL 1997 Fig. 5.162a: LEC function for the scenario PL 1997 Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 678 254 Probability 20 Total loss 50 2.0% 3 416 1 278 Loss on public property 100 1.0% 9 891 3 704 5.0% 250 0.4% 17 302 6 474 500 0.2% 21 015 7 862 4.0% Loss structure for the RTP 250: Tab. 5.121b: Regional loss structure for the scenario PL 1997 Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] DolnoÅ›lÄ…skie 4 598 27% 4.7% MaÅ‚opolskie 4 023 23% 4.2% ÅšlÄ…skie 3 924 23% 2.4% 2.0% Opolskie 1 278 7% 3.9% Kujawsko-pomorskie 741 4% 1.5% Lubuskie 621 4% 2.1% Å?ódzkie 493 3% 0.6% 1.0% Mazowieckie 476 3% 0.2% ÅšwiÄ™tokrzyskie 446 3% 1.3% Wielkopolskie 402 2% 0.4% 0.0% Podkarpackie 200 1% 0.4% Lubelskie 94 1% 0.2% 0 5 000 10 000 15 000 20 000 25 000 Loss [mil. EUR] Zachodniopomorskie 7 0% 0.0% TOTAL 17 302 Fig. 5.162c: Survival function for the scenario PL 1997 Fig. 5.162d: Regional loss structure for the scenario PL 1997 (% of total loss) Fig. 5.162e: Loss intensity in provinces for the scenario PL 1997 (% of property in the province) infrastructure- Tab. 5.121c: Losses by sector / purpose classification for the scenario PL 1997 public Loss 24% Category % [mil EUR] infrastructure-public 4 098 24% enterprise-public 2 376 14% Public 6 474 37% infrastructure-non-public 1 377 8% enterprise-non-public 9 451 55% TOTAL 17 302 enterprise-public 14% infrastructure- enterprise-non- non-public public 8% 54% Fig. 5.162f: Loss by sector / purpose classification the scenario PL 1997 Tab. 5.121d: Loss structure by asset categories for the scenario PL 1997 Inventories Household Loss eguipment Dwellings Category code % 8% [mil EUR] 5% 23% Dwellings AN.1111 3 933 1971% Other buildings and structures AN.1112 7 388 3702% Machinery and equipment AN.1113 3 619 1814% Inventories AN.12 1 426 715% Household eguipment - 936 469% TOTAL 17 302 Machinery and Other buildings equipment and structures 21% 43% Fig. 5.162g: Loss structure by asset categories for the scenario PL 1997 Tab. 5.121e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 3 147 224 113 936 4 420 25.5% private 3 695 1 835 878 0 6 408 37.0% private public 4 479 1 560 435 0 6 474 37.4% TOTAL 11 321 3 619 1 426 936 17 302 % 65.4% 20.9% 8.2% 5.4% building equipment households inventories household equip. households 26% public 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 37% Fig. 5.162h: Loss structure by institutional sectors and asset categories for the scenario PL 1997 0 1 000 2 000 3 000 4 000 5 000 6 000 A+B C private D 37% E F Fig. 5.162i: Loss structure by institutional sectors for the scenario PL 1997 G H Tab. 5.121f: Loss structure by institutional sectors and industry branches for the scenario PL 1997 I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 74 400 78 552 3.2% C (Mining and quarrying) 91 131 4 226 1.3% M D (Manufacturing) 1 283 1 832 63 3 178 18.4% public E (Electricity, gas and water N private supply) 858 1 248 43 2 149 12.4% households F (Construction) 114 171 33 318 1.8% O G (Wholesale and retail trade; repair) 36 1 095 149 1 280 7.4% Fig. 5.162j: Structure of the loss for the scenario PL 1997 by industry branches and H (Hotels and restaurant) 19 115 16 150 0.9% institutional sectors I (Transport, storage and communications) 751 236 27 1 014 5.9% J (Financial intermediation) 32 187 2 221 1.3% K (Real estate, renting, 0 1 000 2 000 3 000 4 000 5 000 6 000 research) 726 864 3 977 5 568 32.2% L (Public administration 1 367 5 1 1 373 7.9% A+B M (Education) 494 16 5 515 3.0% N (Health and social work) 270 36 16 322 1.9% C O (Other community, social and personal services) 358 73 5 436 2.5% D TOTAL 6 474 6 408 4 420 17 302 % 37.4% 37.0% 25.5% E F Tab. 5.121g: Loss structure by asset categories and industry branches, sc. PL 1997 G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 473 79 0 0 552 3.2% C 136 83 7 0 226 1.3% J D 1 177 1 368 633 0 3 178 18.4% E 1 475 620 54 0 2 149 12.4% K F 148 99 70 0 318 1.8% G 474 323 483 0 1 280 7.4% L H 126 20 4 0 150 0.9% I 526 481 7 0 1 014 5.9% M building J 129 80 12 0 221 1.3% equipment K 4 423 156 52 936 5 568 32.2% N inventories L 1 200 82 91 0 1 373 7.9% household equip. M 476 38 1 0 515 3.0% O N 219 99 4 0 322 1.9% O 338 91 7 0 436 2.5% Fig. 5.162k: Structure of the loss for the scenario PL 1997 by industry branches and TOTAL 11 321 3 619 1 426 936 17 302 asset categories % 65.4% 20.9% 8.2% 5.4% Detailed Results for Large Complex Geographical Units-based Scenario, Poland Scenario: PL Vistula Large 45 000 Loss [mil. EUR] Total loss 40 000 Loss on public property 35 000 30 000 25 000 20 000 15 000 10 000 5 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.122a: LEC and survival function for the scenario PL Vistula Large Fig. 5.163a: LEC function for the scenario PL Vistula Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 2 372 812 Probability 20 Total loss 50 2.0% 11 944 4 090 Loss on public property 100 1.0% 21 827 7 561 5.0% 250 0.4% 33 988 11 823 500 0.2% 40 869 14 231 4.0% Loss structure for the RTP 250: Tab. 5.122b: Regional loss structure for the scenario PL Vistula Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] Mazowieckie 19 916 59% 7.6% MaÅ‚opolskie 4 181 12% 4.3% ÅšlÄ…skie 1 741 5% 1.1% 2.0% Lubelskie 1 684 5% 3.1% Kujawsko-pomorskie 1 520 4% 3.0% Podkarpackie 1 302 4% 2.5% Å?ódzkie 1 004 3% 1.3% 1.0% ÅšwiÄ™tokrzyskie 911 3% 2.7% Pomorskie 635 2% 0.9% Podlaskie 619 2% 2.0% WarmiÅ„sko-mazurskie 478 1% 1.4% 0.0% TOTAL 33 988 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 40 000 45 000 Loss [mil. EUR] Fig. 5.163c: Survival function for the scenario PL Vistula Large Fig. 5.163d: Regional loss structure for the scenario PL Vistula Large (% of total loss) Fig. 5.163e: Loss intensity in provinces for the scenario PL Vistula Large (% of property in the province) Tab. 5.122c: Losses by sector / purpose classification for the scenario PL Vistula Large infrastructure- Category Loss % public [mil EUR] 23% infrastructure-public 7 817 23% enterprise-public 4 007 12% Public 11 823 35% infrastructure-non-public 2 305 7% enterprise-non-public 19 860 58% TOTAL 33 988 enterprise-public 12% infrastructure- enterprise-non- non-public public 7% 58% Fig. 5.163f: Loss by sector / purpose classification the scenario PL Vistula Large Tab. 5.122d: Loss structure by asset categories for the scenario PL Vistula Large Household Loss Inventories Category code % eguipment [mil EUR] 8% 6% Dwellings AN.1111 8 747 26% Dwellings Machinery and Other buildings 26% equipment and structures AN.1112 13 992 41% 19% Machinery and equipment AN.1113 6 621 19% Inventories AN.12 2 556 8% Household eguipment - 2 073 6% TOTAL 33 988 Other buildings and structures 41% Fig. 5.163g: Loss structure by asset categories for the scenario PL Vistula Large Tab. 5.122e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 6 964 495 248 2 073 9 781 28.8% private 7 354 3 374 1 656 0 12 384 36.4% private public 8 421 2 751 652 0 11 823 34.8% TOTAL 22 739 6 621 2 556 2 073 33 988 % 66.9% 19.5% 7.5% 6.1% building equipment households inventories household equip. public households 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 35% 29% Fig. 5.163h: Loss structure by institutional sectors and asset categories for the scenario PL Vistula Large 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 A+B C D private 36% E F Fig. 5.163i: Loss structure by institutional sectors for the scenario PL Vistula Large G H Tab. 5.122f: Loss structure by institutional sectors and industry branches for the scenario PL Vistula Large I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 146 867 170 1 183 3.5% C (Mining and quarrying) 109 161 5 275 0.8% M D (Manufacturing) 1 531 2 253 78 3 862 11.4% public E (Electricity, gas and water N private supply) 1 285 1 911 66 3 261 9.6% households F (Construction) 220 332 65 617 1.8% O G (Wholesale and retail trade; repair) 68 2 670 365 3 103 9.1% Fig. 5.163j: Structure of the loss for the scenario PL Vistula Large by industry branches H (Hotels and restaurant) 34 229 32 296 0.9% and institutional sectors I (Transport, storage and communications) 1 974 1 057 122 3 153 9.3% J (Financial intermediation) 243 596 6 844 2.5% K (Real estate, renting, 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 research) 1 656 1 914 8 805 12 374 36.4% L (Public administration 2 603 7 2 2 612 7.7% A+B M (Education) 873 61 21 955 2.8% N (Health and social work) 429 61 27 517 1.5% C O (Other community, social and personal services) 653 265 18 936 2.8% D TOTAL 11 823 12 384 9 781 33 988 % 34.8% 36.4% 28.8% E F Tab. 5.122g: Loss structure by asset categories and industry branches, sc. PL Vistula Large G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 1 013 170 0 0 1 183 3.5% C 165 100 9 0 275 0.8% J D 1 430 1 662 770 0 3 862 11.4% E 2 239 940 82 0 3 261 9.6% K F 288 193 136 0 617 1.8% G 1 150 783 1 171 0 3 103 9.1% L H 249 39 8 0 296 0.9% I 1 634 1 496 22 0 3 153 9.3% M building J 492 307 45 0 844 2.5% equipment K 9 836 348 117 2 073 12 374 36.4% N inventories L 2 282 156 174 0 2 612 7.7% household equip. M 883 70 2 0 955 2.8% O N 351 160 6 0 517 1.5% O 726 196 14 0 936 2.8% Fig. 5.163k: Structure of the loss for the scenario PL Vistula Large by industry branches TOTAL 22 739 6 621 2 556 2 073 33 988 and asset categories % 66.9% 19.5% 7.5% 6.1% Detailed Results for Large Complex Geographical Units-based Scenario, Poland Scenario: PL Odra Large 25 000 Loss [mil. EUR] Total loss Loss on public property 20 000 15 000 10 000 5 000 0 Fig. 5.161b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.123a: LEC and survival function for the scenario PL Odra Large Fig. 5.164a: LEC function for the scenario PL Odra Large Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 718 279 Probability 20 Total loss 50 2.0% 3 618 1 409 Loss on public property 100 1.0% 10 848 4 139 5.0% 250 0.4% 19 273 7 300 500 0.2% 23 683 8 948 4.0% Loss structure for the RTP 250: Tab. 5.123b: Regional loss structure for the scenario PL Odra Large Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] DolnoÅ›lÄ…skie 5 135 27% 5.2% Wielkopolskie 5 070 26% 4.6% ÅšlÄ…skie 2 391 12% 1.5% 2.0% Å?ódzkie 2 216 11% 2.9% Lubuskie 1 642 9% 5.5% Opolskie 1 410 7% 4.3% Zachodniopomorskie 1 120 6% 2.1% 1.0% Kujawsko-pomorskie 267 1% 0.5% Pomorskie 23 0% 0.0% TOTAL 19 273 0.0% 0 5 000 10 000 15 000 20 000 25 000 Loss [mil. EUR] Fig. 5.164c: Survival function for the scenario PL Odra Large Fig. 5.164d: Regional loss structure for the scenario PL Odra Large (% of total loss) Fig. 5.164e: Loss intensity in provinces for the scenario PL Odra Large (% of property in the province) Tab. 5.123c: Losses by sector / purpose classification for the scenario PL Odra Large infrastructure- public Loss 25% Category % [mil EUR] infrastructure-public 4 829 25% enterprise-public 2 470 13% Public 7 300 38% infrastructure-non-public 1 622 8% enterprise-non-public 10 351 54% TOTAL 19 273 enterprise-public 13% infrastructure- enterprise-non- non-public public 8% 54% Fig. 5.164f: Loss by sector / purpose classification the scenario PL Odra Large Household Tab. 5.123d: Loss structure by asset categories for the scenario PL Odra Large Inventories eguipment Loss 5% Dwellings Category code % 8% [mil EUR] 23% Dwellings AN.1111 4 426 23% Other buildings and structures AN.1112 8 314 43% Machinery and equipment AN.1113 3 982 21% Inventories AN.12 1 513 8% Household eguipment - 1 039 5% TOTAL 19 273 Machinery and equipment Other buildings 21% and structures 43% Fig. 5.164g: Loss structure by asset categories for the scenario PL Odra Large Tab. 5.123e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 3 498 252 125 1 039 4 914 25.5% private 4 110 2 004 945 0 7 059 36.6% private public 5 132 1 725 443 0 7 300 37.9% TOTAL 12 740 3 982 1 513 1 039 19 273 % 66.1% 20.7% 7.9% 5.4% building equipment households inventories household equip. households 25% public 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000 38% Fig. 5.164h: Loss structure by institutional sectors and asset categories for the scenario PL Odra Large 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 A+B C private D 37% E F Fig. 5.164i: Loss structure by institutional sectors for the scenario PL Odra Large G H Tab. 5.123f: Loss structure by institutional sectors and industry branches for the scenario PL Odra Large I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 89 467 91 648 3.4% C (Mining and quarrying) 88 135 5 227 1.2% M D (Manufacturing) 1 236 1 890 65 3 191 16.6% public E (Electricity, gas and water N private supply) 980 1 482 51 2 513 13.0% households F (Construction) 89 270 53 412 2.1% O G (Wholesale and retail trade; repair) 38 1 168 159 1 366 7.1% Fig. 5.164j: Structure of the loss for the scenario PL Odra Large by industry branches H (Hotels and restaurant) 14 108 15 138 0.7% and institutional sectors I (Transport, storage and communications) 974 285 33 1 292 6.7% J (Financial intermediation) 35 153 1 190 1.0% K (Real estate, renting, 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 research) 881 959 4 411 6 251 32.4% L (Public administration 1 569 5 1 1 574 8.2% A+B M (Education) 549 18 6 573 3.0% N (Health and social work) 286 38 17 341 1.8% C O (Other community, social and personal services) 472 80 5 557 2.9% D TOTAL 7 300 7 059 4 914 19 273 % 37.9% 36.6% 25.5% E F Tab. 5.123g: Loss structure by asset categories and industry branches, sc. PL Odra Large G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 555 93 0 0 648 3.4% C 137 83 7 0 227 1.2% J D 1 182 1 373 636 0 3 191 16.6% E 1 725 725 63 0 2 513 13.0% K F 192 129 91 0 412 2.1% G 506 344 516 0 1 366 7.1% L H 116 18 4 0 138 0.7% I 670 613 9 0 1 292 6.7% M building J 111 69 10 0 190 1.0% equipment K 4 977 176 59 1 039 6 251 32.4% N inventories L 1 376 94 105 0 1 574 8.2% household equip. M 530 42 1 0 573 3.0% O N 232 105 4 0 341 1.8% O 432 117 8 0 557 2.9% Fig. 5.164k: Structure of the loss for the scenario PL Odra Large by industry branches TOTAL 12 740 3 982 1 513 1 039 19 273 and asset categories % 66.1% 20.7% 7.9% 5.4% Detailed Results for Cross-border Scenario Scenario: V4 Odra / Oder 18 000 Loss [mil. EUR] Total loss 16 000 Loss on public property 14 000 12 000 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.165b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.124a: LEC and survival function for the scenario V4 Odra / Oder Fig. 5.165a: LEC function for the scenario V4 Odra / Oder Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 20 5.0% 591 230 Probability Total loss 50 2.0% 3 350 1 289 Loss on public property 100 1.0% 8 044 3 075 5.0% 250 0.4% 13 501 5 152 500 0.2% 16 298 6 228 4.0% Loss structure for the RTP 250: Tab. 5.124b: Regional loss structure for the scenario V4 Odra / Oder Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] PL DolnoÅ›lÄ…skie 4 907 36.3% 5.0% CZ Moravskoslezský 2 425 18.0% 4.5% PL ÅšlÄ…skie 1 534 11.4% 0.9% 2.0% PL Lubuskie 1 404 10.4% 4.7% PL Opolskie 1 397 10.3% 4.2% PL Zachodniopomorskie 971 7.2% 1.8% PL Wielkopolskie 726 5.4% 0.7% 1.0% CZ Olomoucký 122 0.9% 0.5% CZ Královéhradecký 15 0.1% 0.1% TOTAL 13 501 0.0% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 18 000 Loss [mil. EUR] Fig. 5.165c: Survival function for the scenario V4 Odra / Oder Fig. 5.165d: Regional loss structure for the scenario V4 Odra / Oder (% of total loss) Fig. 5.165e: Loss intensity in provinces for the scenario V4 Odra / Oder (% of property in the province) Tab. 5.124c: Losses by sector / purpose classification for the scenario V4 Odra / Oder enterprise-non- infrastructure- public Loss public Category % 55% [mil EUR] 26% infrastructure-public 3 499 26% enterprise-public 1 653 12% Public 5 152 38% infrastructure-non-public 977 7% enterprise-non-public 7 373 55% TOTAL 13 501 enterprise-public 12% infrastructure- non-public 7% Fig. 5.165f: Loss by sector / purpose classification the scenario V4 Odra / Oder Tab. 5.124d: Loss structure by asset categories for the scenario V4 Odra / Oder Household Loss eguipment Dwellings Category code % Inventories 20% [mil EUR] 5% 8% Dwellings AN.1111 2 743 20% Other buildings and structures AN.1112 6 036 45% Machinery and equipment AN.1113 2 952 22% Inventories AN.12 1 143 8% Household eguipment - 626 5% TOTAL 13 501 Machinery and equipment 22% Other buildings and structures 45% Fig. 5.165g: Loss structure by asset categories for the scenario V4 Odra / Oder Tab. 5.124e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 2 100 161 80 626 2 968 22.0% private 2 928 1 684 770 0 5 382 39.9% private public 3 752 1 107 293 0 5 152 38.2% TOTAL 8 780 2 952 1 143 626 13 501 % 65.0% 21.9% 8.5% 4.6% building equipment households inventories households household equip. 22% public 0 1 000 2 000 3 000 4 000 5 000 6 000 38% Loss [mil. EUR] Fig. 5.165h: Loss structure by institutional sectors and asset categories for the scenario V4 Odra / Oder Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 A+B C D private 40% E F Fig. 5.165i: Loss structure by institutional sectors for the scenario V4 Odra / Oder G H Tab. 5.124f: Loss structure by institutional sectors and industry branches for the scenario V4 Odra / Oder I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 53 263 51 367 2.7% C (Mining and quarrying) 123 190 3 316 2.3% M D (Manufacturing) 808 2 142 78 3 027 22.4% public E (Electricity, gas and water N private supply) 688 885 29 1 602 11.9% households F (Construction) 33 131 24 188 1.4% O G (Wholesale and retail trade; repair) 26 714 96 836 6.2% Fig. 5.165j: Structure of the loss for the scenario V4 Odra / Oder by industry branches H (Hotels and restaurant) 8 76 10 94 0.7% and institutional sectors I (Transport, storage and communications) 729 189 21 939 7.0% J (Financial intermediation) 19 110 1 130 1.0% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 3 500 4 000 4 500 research) 583 591 2 639 3 813 28.2% L (Public administration 1 172 1 0 1 174 8.7% A+B M (Education) 408 7 3 418 3.1% N (Health and social work) 206 28 12 246 1.8% C O (Other community, social and personal services) 296 55 3 354 2.6% D TOTAL 5 152 5 382 2 968 13 501 % 38.2% 39.9% 22.0% E F Tab. 5.124g: Loss structure by asset categories and industry branches, sc. V4 Odra / Oder G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 311 56 0 0 367 2.7% C 196 110 10 0 316 2.3% J D 1 126 1 297 603 0 3 027 22.4% E 1 116 447 39 0 1 602 11.9% K F 86 60 41 0 188 1.4% G 316 203 317 0 836 6.2% L H 80 12 2 0 94 0.7% I 551 383 5 0 939 7.0% M building J 78 46 7 0 130 1.0% equipment K 3 042 110 35 626 3 813 28.2% N inventories L 1 031 65 77 0 1 174 8.7% household equip. M 391 26 1 0 418 3.1% O N 173 71 2 0 246 1.8% O 283 66 5 0 354 2.6% Fig. 5.165k: Structure of the loss for the scenario V4 Odra / Oder by industry branches TOTAL 8 780 2 952 1 143 626 13 501 and asset categories % 65.0% 21.9% 8.5% 4.6% Detailed Results for Cross-border Scenario Scenario: V4 Dunaj / Danube 25 000 Loss [mil. EUR] Total loss Loss on public property 20 000 15 000 10 000 5 000 0 Fig. 5.166b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.125a: LEC and survival function for the scenario V4 Dunaj / Danube Fig. 5.166a: LEC function for the scenario V4 Dunaj / Danube Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 369 86 Probability 20 Total loss 50 2.0% 3 669 1 015 Loss on public property 100 1.0% 7 187 2 058 5.0% 250 0.4% 15 171 4 434 500 0.2% 20 031 5 881 4.0% Loss structure for the RTP 250: Tab. 5.125b: Regional loss structure for the scenario V4 Dunaj / Danube Loss Loss % of total Province intensity [mil EUR] loss 3.0% [%] SK Bratislavský kraj 4 984 32.9% 7.3% SK Trnavský kraj 2 697 17.8% 9.3% HU Budapest 2 671 17.6% 0.6% 2.0% HU Pest 1 644 10.8% 1.5% HU Komárom-Esztergom 849 5.6% 3.3% SK Nitriansky kraj 594 3.9% 1.9% HU Gyor-Moson-Sopron 461 3.0% 1.0% 1.0% HU Bács-Kiskun 455 3.0% 1.3% HU Fejér 302 2.0% 0.7% HU Tolna 236 1.6% 1.2% 0.0% HU Baranya 190 1.3% 0.6% SK TrenÄ?iansky kraj 64 0.4% 0.2% 0 5 000 10 000 15 000 20 000 25 000 Loss [mil. EUR] HU Veszprém 17 0.1% 0.0% HU Nógrád 7 0.0% 0.0% Fig. 5.166c: Survival function for the scenario V4 Dunaj / Danube TOTAL 15 171 Fig. 5.166d: Regional loss structure for the scenario V4 Dunaj / Danube (% of total loss) Fig. 5.166e: Loss intensity in provinces for the scenario V4 Dunaj / Danube (% of property in the province) Tab. 5.125c: Losses by sector / purpose classification for the scenario V4 Dunaj / Danube infrastructure- public Loss Category % 26% [mil EUR] infrastructure-public 3 961 26% enterprise-public 473 3% Public 4 434 29% infrastructure-non-public 714 5% enterprise-non-public 10 024 66% enterprise-public TOTAL 15 171 3% infrastructure- non-public enterprise-non- 5% public 66% Fig. 5.166f: Loss by sector / purpose classification the scenario V4 Dunaj / Danube Tab. 5.125d: Loss structure by asset categories for the scenario V4 Dunaj / Danube Inventories Household Loss eguipment Category code % 8% [mil EUR] 5% Dwellings 24% Dwellings AN.1111 3 595 24% Machinery and Other buildings equipment and structures AN.1112 6 755 45% 19% Machinery and equipment AN.1113 2 810 19% Inventories AN.12 1 240 8% Household eguipment - 771 5% TOTAL 15 171 Other buildings and structures 44% Fig. 5.166g: Loss structure by asset categories for the scenario V4 Dunaj / Danube building Tab. 5.125e: Loss structure by institutional sectors and industry branches equipment AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public inventories buildings equipment equipment % sector ries [mil EUR] household equip. [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 3 244 188 117 771 4 321 28.5% private 3 447 2 025 944 0 6 417 42.3% private public 3 658 597 178 0 4 434 29.2% TOTAL 10 349 2 810 1 240 771 15 171 % 68.2% 18.5% 8.2% 5.1% households public households 29% 28% 0 1 000 2 000 3 000 4 000 5 000 6 000 7 000 Loss [mil. EUR] Fig. 5.166h: Loss structure by institutional sectors and asset categories for the scenario V4 Dunaj / Danube Loss [mil. EUR] 0 1 000 2 000 3 000 4 000 5 000 6 000 A+B C D private 43% E F Fig. 5.166i: Loss structure by institutional sectors for the scenario V4 Dunaj / Danube G H Tab. 5.125f: Loss structure by institutional sectors and industry branches for the scenario V4 Dunaj / Danube I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 17 257 129 403 2.7% C (Mining and quarrying) 38 93 0 132 0.9% M D (Manufacturing) 24 2 740 74 2 838 18.7% public E (Electricity, gas and water N private supply) 746 665 0 1 411 9.3% households F (Construction) 3 159 14 176 1.2% O G (Wholesale and retail trade; repair) 8 843 77 928 6.1% Fig. 5.166j: Structure of the loss for the scenario V4 Dunaj / Danube by industry H (Hotels and restaurant) 5 81 6 92 0.6% branches and institutional sectors I (Transport, storage and communications) 574 609 15 1 198 7.9% J (Financial intermediation) 5 144 2 150 1.0% Loss [mil. EUR] K (Real estate, renting, 0 1 000 2 000 3 000 4 000 5 000 6 000 research) 374 778 3 863 5 015 33.1% L (Public administration 2 094 0 0 2 094 13.8% A+B M (Education) 242 0 11 253 1.7% N (Health and social work) 167 9 7 182 1.2% C O (Other community, social and personal services) 138 40 122 299 2.0% D TOTAL 4 434 6 417 4 321 15 171 % 29.2% 42.3% 28.5% E F Tab. 5.125g: Loss structure by asset categories and industry branches, sc. V4 Dunaj / Danube G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 299 104 2 0 405 2.7% C 104 24 315 0 444 2.9% J D 1 065 1 242 243 0 2 550 16.8% E 1 033 346 19 0 1 398 9.2% K F 82 56 216 0 354 2.3% G 341 211 185 0 737 4.9% L H 73 16 5 0 94 0.6% I 755 418 26 0 1 199 7.9% M building J 106 40 22 0 168 1.1% equipment K 4 044 111 114 771 5 041 33.2% N inventories L 1 807 149 90 0 2 047 13.5% household equip. M 232 21 1 0 254 1.7% O N 144 38 1 0 183 1.2% O 264 33 1 0 298 2.0% Fig. 5.166k: Structure of the loss for the scenario V4 Dunaj / Danube by industry TOTAL 10 349 2 810 1 240 771 15 171 branches and asset categories % 68.2% 18.5% 8.2% 5.1% Detailed Results for Cross-border Scenario Scenario: V4 Tisza 16 000 Loss [mil. EUR] Total loss 14 000 Loss on public property 12 000 10 000 8 000 6 000 4 000 2 000 0 Fig. 5.167b: Area affected by the scenario 0 100 200 300 400 500 RTP [years] Tab. 5.126a: LEC and survival function for the scenario V4 Tisza Fig. 5.167a: LEC function for the scenario V4 Tisza Probability of Loss on Return perid Total Loss the loss public [years] [mil EUR] exceedance [mil EUR] 6.0% 5.0% 471 149 Probability 20 Total loss 50 2.0% 4 034 1 306 Loss on public property 100 1.0% 6 908 2 218 5.0% 250 0.4% 11 098 3 556 500 0.2% 13 444 4 327 4.0% Loss structure for the RTP 250: Tab. 5.126b: Regional loss structure for the scenario V4 Tisza Loss Loss % of total intensity [mil EUR] loss 3.0% Province [%] SK KoÅ¡ický kraj 2 788 25% 6.9% HU Csongrád 1 643 15% 4.9% HU Borsod-Abaúj-Zemplén 1 377 12% 3.1% 2.0% SK PreÅ¡ovský kraj 1 254 11% 4.7% HU Jász-Nagykun-Szolnok 924 8% 3.4% HU Szabolcs-Szatmár-Bereg 616 6% 2.0% HU Békés 517 5% 2.4% 1.0% HU Hajdú-Bihar 515 5% 1.4% HU Heves 491 4% 2.2% SK Banskobystrický kraj 337 3% 1.2% 0.0% HU Bács-Kiskun 283 3% 0.8% HU Pest 264 2% 0.2% 0 2 000 4 000 6 000 8 000 10 000 12 000 14 000 16 000 Loss [mil. EUR] HU Nógrád 90 1% 0.8% TOTAL 11 098 Fig. 5.167c: Survival function for the scenario V4 Tisza Fig. 5.167d: Regional loss structure for the scenario V4 Tisza (% of total loss) Fig. 5.167e: Loss intensity in provinces for the scenario V4 Tisza (% of property in the province) Tab. 5.126c: Losses by sector / purpose classification for the scenario V4 Tisza infrastructure- public Loss Category % 30% [mil EUR] infrastructure-public 3 305 30% enterprise-public 252 2% Public 3 557 32% infrastructure-non-public 689 6% enterprise-non-public 6 852 62% TOTAL 11 098 enterprise-public 2% infrastructure- enterprise-non- non-public public 6% 62% Fig. 5.167f: Loss by sector / purpose classification the scenario V4 Tisza Tab. 5.126d: Loss structure by asset categories for the scenario V4 Tisza Inventories Household Loss eguipment Category code % 7% Dwellings [mil EUR] 4% 23% Dwellings AN.1111 2 522 23% Machinery and Other buildings equipment and structures AN.1112 5 189 47% 19% Machinery and equipment AN.1113 2 112 19% Inventories AN.12 826 7% Household eguipment - 448 4% TOTAL 11 098 Other buildings and structures 47% Fig. 5.167g: Loss structure by asset categories for the scenario V4 Tisza Tab. 5.126e: Loss structure by institutional sectors and industry branches AN.12 AN.1111-2 AN.1113 household Ownership invento- Total loss public buildings equipment equipment % sector ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] households 2 157 172 68 448 2 845 25.6% private 2 568 1 509 619 0 4 696 42.3% private public 2 985 432 140 0 3 557 32.0% TOTAL 7 711 2 112 826 448 11 098 % 69.5% 19.0% 7.4% 4.0% building equipment households inventories household equip. public households 32% 26% 0 1 000 2 000 3 000 4 000 5 000 Loss [mil. EUR] Fig. 5.167h: Loss structure by institutional sectors and asset categories for the scenario V4 Tisza Loss [mil. EUR] 0 500 1 000 1 500 2 000 2 500 3 000 3 500 A+B C D private 42% E F Fig. 5.167i: Loss structure by institutional sectors for the scenario V4 Tisza G H Tab. 5.126f: Loss structure by institutional sectors and industry branches for the scenario V4 Tisza I house- J public private Total loss Industry branch holds % [mil EUR] [mil EUR] [mil EUR] [mil EUR] K A+B (Agriculture, hunting L and forestry) 16 469 310 795 7.2% C (Mining and quarrying) 18 52 0 70 0.6% M D (Manufacturing) 9 1 758 38 1 806 16.3% public E (Electricity, gas and water N private supply) 450 657 0 1 107 10.0% households F (Construction) 3 163 9 174 1.6% O G (Wholesale and retail trade; repair) 2 535 35 572 5.2% Fig. 5.167j: Structure of the loss for the scenario V4 Tisza by industry branches and H (Hotels and restaurant) 2 60 2 64 0.6% institutional sectors I (Transport, storage and communications) 410 488 8 906 8.2% J (Financial intermediation) 1 63 0 64 0.6% Loss [mil. EUR] K (Real estate, renting, 0 500 1 000 1 500 2 000 2 500 3 000 3 500 research) 202 418 2 297 2 917 26.3% L (Public administration 1 843 0 0 1 843 16.6% A+B M (Education) 323 0 18 341 3.1% N (Health and social work) 190 5 6 200 1.8% C O (Other community, social and personal services) 89 27 122 238 2.1% D TOTAL 3 557 4 696 2 845 11 098 % 32.0% 42.3% 25.6% E F Tab. 5.126g: Loss structure by asset categories and industry branches, sc. V4 Tisza G AN.12 AN.1111-2 AN.1113 household invento- Total loss H Industry branch buildings equipment equipment % ries [mil EUR] [mil EUR] [mil EUR] [mil EUR] [mil EUR] I A+B 582 213 0 0 795 7.2% C 56 14 1 0 70 0.6% J D 667 808 331 0 1 806 16.3% E 800 284 23 0 1 107 10.0% K F 87 50 37 0 174 1.6% G 201 132 240 0 572 5.2% L H 54 9 1 0 64 0.6% I 569 319 19 0 906 8.2% M building J 48 15 1 0 64 0.6% equipment K 2 355 61 54 448 2 917 26.3% N inventories L 1 606 116 120 0 1 843 16.6% household equip. M 311 30 0 0 341 3.1% O N 158 42 0 0 200 1.8% O 218 19 1 0 238 2.1% Fig. 5.167k: Structure of the loss for the scenario V4 Tisza by industry branches and TOTAL 7 711 2 112 826 448 11 098 asset categories % 69.5% 19.0% 7.4% 4.0%