WORLDBANKGROUP GR QRI5KPROFILES EROPE ANDCENTRALA5IA (ECA) AFFECTED BY 100-YEAR AFFECTED CAPITAL LOSS BY 250-YEAR FROM 250-YEAR FLOOD EARTHQUAKE EARTHQUAKE t GDP $494 billion* P oland's population and econo- my are exp osed to e arthq uakes industry, and agriculture making a small contribution. Poland's per capita GDP BALTIC SEA RUSSIAN FEDERATION LIT U - I and floods, with floods posing was $12,700. the greater risk. The model results for present-day risk v in this risk profile This map displays GDP by province in are based on population and gross Poland, with greater color saturation Pomo domestic product (GDP) estimates for indicating greater GDP within a province. o3 Warminko-Mazurskie 2015. The estimated damage caused by The blue circles indicate the risk of expe- 7ARhodniomorskiU historical events is inflated to 2015 US riencing floods and the orange circles the o Podlaskie dollars. risk of earthquakes in terms of normal- ized annual average of affected GDP. The Kuja ko-Pomorski Just over 60 percent of Poland's pop- largest circles represent the greatest nor- ulation lives in urban environments. malized risk. The risk is estimated using The country's GDP was approximately flood and earthquake risk models. ieik kie Lubuskie US$494 billion in 2015, with more than 60 percent derived from services, The table displays the provinces at GERMANY most of the remainder generated by greatest normalized risk for each peril. In relative terms, as shown in the table, the province at greatest risk of floods is Lubeiskie Lubuskie, and the one at greatest risk of aV ki TOP AFFECTED PROVINCES earthquakes is Dolnoslaskie. In absolute terms, the province at greatest risk of polskie both floods and earthquakes is Dolno- EARTHQUAKE slaskie. ANNUAL AVERAGE OF ANNUAL AVERAGE OF CZECpPodare AFFECTED GDP (%) AFFECTED GDP (%) Lubuskie 4 Dolnoslaskie 1 Opolskie 3 Lubuskie 0 Dolnoslaskie 3 Wielkopolskie 0 Annual Average of Affected GDP (%) GDP (billions of $) Kujawsko-Pomorskie 3 Slaskie 0 Zachadnio-Pomorskie 3 Opolskie 0 4 There is a high correlation Pornorskie 2 Malopolske 0 (r=0.95) between the Podkarpackie 2 Podkarpackie 0 1 oGo population and GDP ofla 0EARTHQUAKE Malopolske 2 Lodzkie 0 EATQUK province., Wielkopolskie 1 Zachadnio-Pomorskie 0 Warminsko-Mazurskie 1 Kujawsko-Pomnrskie 0 0 Negligible Po a dWORLDBANKGROUP E|GFDRR "AN ENTRALA5A (ECA) he most devastating flood in to happen at all over a long period of Poland since 1900 occurred in time. 1997. It affected over 200,000 BALTIC SEA people and caused about $5 billion in If the 10- and 100-year bars are the damage. Another major flood event same height, then the impact of a 10- LIT I took place in 2010, affecting about year event is as large as that of a 100- 100,000 people and causing over $3 year event, and the annual average of RuSS AN FEDERATION billion in damage. Floods in 1987 and affected GDP is dominated by events 2001 each caused close to $1 billion that happen relatively frequently. in damage. If the impact of a 100-year event is much greater than that of a 10-year Pomer i This map depicts the impact of flood- event, then less frequent events make 1/4 Warminiko-Mazur,kic ing on provinces' GDPs, represented a larger contribution to the annual hadniaomErlA RU S as percentages of their annual aver- average of affected GDP. Thus, even - B age GDPs affected, with greater color if a province's annual affected GDP Podlaskie saturation indicating higher percent- seems small, less frequent and more Kuiaw Pome ages. The bar graphs represent GDP intense events can still have large affected by floods with return periods impacts. Maz ie of 10 years (white) and 100 years ilkopol W (black). The horizontal line across the The annual averge population affect- wauspta bars also shows the annual average of ed by flooding in Poland is about GDP affected by floods. 600,000 and the annual average GDP --- about $7 billion. Within the various When a flood has a 10-year return provinces, the 10- and 100-year im- _ ii period, it means the probability of pacts do not differ much, so relatively Dolnoslaskie occurrence of a flood of that magni- frequent floods have large impacts on Swietokizyskie tude or greater is 10 percent per year. these averages. Opolski A 100-year flood has a probability of occurrence of 1 percent per year. This means that over a long period of CZ EC p d i time, a flood of that magnitude will, on average, occur once every 100 years. It does not mean a 100-year 10 and GOP for flood will occur exactly once every On ad 20 retuAn pEriRLs 100 years. In fact, it is possible for a flood of any return period to occur more than once in the same year, or or10 onc inthe ameyear moretha Annual Average of Affected GDP (%) to appear in consecutive years, or not Ann ual average 4 10-year 100-year 0 2 C 9 Po anpgDR PoadWORLDBANKGROUP GFDR ROP AND A5IA(ECA) ECENTRAL RISK PROFILES Poland experienced a minor If the 10- and 100-year bars are the earthquake in 1982, affecting same height, then the impact of a 10- over 1,000 people. The event year event is as large as that of a 100- BALTIC SEA indicates that, although no major year event, and the annual average of earthquakes have been reported affected GDP is dominated by events LIT U - I there, Poland has the potential to that happen relatively frequently. experience moderate ones. If the impact of a 100-year event is RUS AN FEDERATION much greater than that of a 10-year This map depicts the impact of event, then less frequent events make earthquakes on provinces' GDPs, larger contributions to the annual av- represented as percentages of their erage of affected GDP. Thus, even if a Pomorskie annual average GDPs affected, with province's annual affected GDP seems warminsko-Mazurskie greater color saturation indicating small,Zachdnio-Pomorskie ELARU sml,less frequent and more intenseZahdi-orseBEA U higher percentages. The bar graphs events can still have large impacts. represent GDP affected by earth- PodLaskie quakes with return periods of 10 The annual average population Kujawsko-Pomrrskil years (white) and 100 years (black). affected by earthquakes in Poland is ILI Mazowieckie The horizontal line across the bars about 50,000 and the annual average also shows the annual average of GDP affected GDP about $600 million. Wietkopolskie Warsaw affected by earthquakes. The annual averages of fatalities and Lubuskie% capital losses caused by earthquakes When an earthquake has a 10-year arabutheanaot$5 _ C return period, it means the probabil- milo,rsetvl.Teftlte RINLodzkie 2 amq ity of occurrence of an earthquake illon and resetivl capital .au faalties losses caused by more GERMANYLoeu Lbesi of that magnitude or greater is 10 intense, less frequent events can be aki percent per year A 100-year earth- substantially larger than the annual Swetokrzyskie " quake has a probability of occurrence averages. For example, an earthquake Opolski oft1 percent per year. This means with a 0.4 percent annual probability 5laski that over a long period of time, an ofoccurrence (a 250-yearreturn - earthquake of that magnitude will, on period event) could cause about $700 C ECH REPUBLIC Malopolske Podkarpackie average, occur once every 100 years. million in capital loss (less than 1 It does not mean a 100-year earth- percent of GDP). Affected GDP (%) for quake will occur exactly once every 10 and 100-year return periods 100 years. In fact, it is possible for One block=1% 7 LEVA REPUL C an earthquake of any return period to occur more than once in the same year, or to appear in consecutive Annua[ average au vrg A years, or not to happen at all over a long period of time. 10-year 100-year 0 2 2 C PolndWORLDBANKGROUP ROPE AND CENTRAL E|GDR A51A(ECA) -)EARTHQUAKE EARTHQUAKE ANNUAL AVERAGE CAPITAL LOSS (MILLIONS $) ANNUAL AVERAGE FATALITIES T he rose diagrams show the provinces with the potential 9 efor greatest annual average capital losses and highest annual average numbers of fatalities, as determined using an earthquake risk model. The potential for greatest capital Podkarpackie 0.4 kopolskie 7 Podkarpackie 0 Malopolske 0 loss occurs in Dolnoslaskie, which is not surprising, given the economic importance of the province. N0 -0 -- - -- -- - -- - -- - -- - -- - -- --- - -- - -- -- - -- - -- - - -- -- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- -- - ----- --- -- - ----- ---- - - --- -- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - --- - ----- -- - ----- - - - - ---- - - - - -------------- EARTHQUAKE EXCEEDANCE PROBABILITY CURVE, 2015 AND 2080 E AV GDP E affected by, respectively, floods and earthquakes for 100 60 varying probabilities of occurrence. Values for two different 90 time periods are shown. A solid line depicts the affected 80 GDP for 2015 conditions. A diagonally striped band depicts 2080 70 40 the range of affected GDP based on a selection of climate 60 and socioeconomic scenarios for 2080. For example, if Po- 50 30 land had experienced a 100-year return period flood event 40 in 2015, the affected GDP would have been an estimated 2015 30 $30 billion. In 2080, however, the affected GDP from the 210 same type of event would range from about $40 billion to 10 about $90 billion. If Poland had experienced a 250-year earthquake event in 2015, the affected GDP would have 10 50 100 250 10 50 100 250 been about $10 billion. In 2080, the affected GDP from the Return period (years) Return period(years) same type of event would range from about $15 billion to 10 2 1 0.4 10 2 1 04 about $50 billion, due to population growth, urbanization, Probability (%) Probability (%) and the increase in exposed assets. All historical data on floods and earthqUakes are from D. Guha-Sapir, R. Below, ard Ph. Hoyois, EM-DATi International Disaster Database (Universite Catholique de Louvain, Brussels, Belgium), www.emdat.be. Damage estimates for all historical events have been inflated to 2015 US$