97119 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridors Project Component 1: Natural Hazard Risk Assessment Synthesis [Year] Report May 2015 Submitted by RMSI A-8, Sector 16 Noida 201301, INDIA Tel: +91-120-251-1102, 2101 Fax: +91-120-251-1109, 0963 www.rmsi.com rakesh.ranjan Contact: [Type the name] Gupta Sushil company Project Team Leader [Pick the date] Email: Sushil.Gupta@rmsi.com Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project For the attention of: Shyam KC Task Team Lead The World Bank Email: skc@worldbank.org Company Information: Name RMSI Private Limited CIN U74899DL1992PTC047149 Registered Office Address SEATING 3, UNIT NO. 119, FIRST FLOOR, VARDHMAN STAR CITI MALL, SECTOR-7, DWARKA NEW DELHI Delhi-110075 INDIA Corporate Office Address A-8, Sector-16 NOIDA, 201 301 India Tel:+91 120 251 1102, 251 2101 Fax:+91 120 251 1109, 251 0963 E-mail: info@rmsi.com The program is supported by the ACP-EU Natural Disaster Risk Reduction Programme, an initiative of the African, Caribbean and Pacific group, funded by the European Union and managed by the Global Facility for Disaster Reduction and Recovery. Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project  The World Bank; UNDP and various 1. Introduction departments like NDMD; Public Works; Agriculture and Fisheries; Finance; Timor-Leste is vulnerable to natural hazards Transport and Communication; including floods, strong winds, landslides, Environment, Commerce and Industry. earthquakes, and tsunamis. These hazards are  Directorate General of Rural Governance. common causing significant damages to the  Ministry of Agriculture and Fisheries and country. There is a need to evaluate these University National of Timor-Leste. natural hazards and associated risks. Forty- nine sucos of the four districts (Ainaro, Aileu, Ermera, and Manufahi) of Timor-Leste, which intersect Dili-Ainaro-linked road corridor, were selected for the study. 49 sucos across Ainaro, Aileu, Ermera and Manufahi districts Population: 136,209 persons (projected 2014 of Census 2010) Area: 1,356 sq km Number of Households: about 19,000 (estimated 2014) Number of Buildings: 47,846 (RMSI, 2014) Objectives of the Study  To assess the hazards and the risks to assets and people along Dili-Ainaro and Linked Road Corridor and develop technical capacity of the concerned Su co s u nd e r s tu d y stakeholders to use quantified data for better understanding of risks. Challenges Faced  To carry out assessments of hazards,  Absence of appropriate geo-spatial data in vulnerability, and risks of the selected National Directorate of Statistics (Census) study area focusing on landslides, floods, for building footprints and their details. and strong wind hazards, strengthen  Lack of adequate historical flow and institutional capacity and disseminate hydro-meteorological data. knowledge.  Non-standard data resolution across Stakeholders sucos  Absence of Timor-Leste’s official key The country focal points of this infrastructure information such as project - National Disaster Management location and structural details of the Directorate (NDMD) and other key buildings. stakeholders were consulted and were part of  Non-availability of Data related to the training and capacity building activities of agriculture at suco level and the project. Following are the key landownership posed difficulty in stakeholders consulted: assessing the livelihood vulnerability of  District Administrations of Aileu, Ermera, the people. Manufahi, and Ainaro. Synthesis Report Page 1 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 2. Flood Hazard Analysis Flood Hazard Mapping Flood hazard mapping has been presented in The study area faces mainly two types of terms of flood depth and flood extent. Based floods - riverine flood and flash floods. The on the return-period flows, a maximum flood main causative factors of flooding in Timor- depth of 3.9 meters was estimated for a 100- Leste include heavy rains; rapid excessive year return period event for suco Talitu of runoff from the slopes to streams; and high district Aileu. For example, the maximum mountainous ranges having steep slopes and flood inundation area covered by such an low soil permeability. event was estimated at 10.5% of the total for The flood hazard analysis was carried out in suco Liurai of district Ainaro. the 49 selected Sucos to understand the frequencies, extent and depth of flooding. In addition to flow data, morphological variables including the catchment area, river network, river cross sections at various locations, water levels at various river streams, land-use land- cover classes, etc. were considered for the flood analysis. History of Flooding Major flood events in Timor-Leste were reported in 2001, 2003, and 2007, which affected several thousand people in the country. 10 0- Y e ar Re tu r n Pe r i od f o r S u c o Liu r ai i n A il e u D is t r ic t Findings  Letefoho, Riheu, Poetete, Leolima, and Ainaro are the most flood affected Sucos while Acumau and Fahisoi are least affected Sucos.  Average Annual Loss of USD 166,430 for Riheu Suco and of USD 45,180 for Letefoho Suco among all Sucos have been estimated.  In terms of residential sector, Letefoho Suco registers average annual loss of USD S ou rc e: EM - D AT 29,942 due to flooding. Page 2 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 3. Strong Wind Hazard Analysis 500 m x 500 m could efficiently resolve the impacts of local topography and orography on The strong wind hazard analysis was done to wind fields. evaluate the frequency and severity of various strong wind events at different recurrence intervals or return periods ranging from more frequent to rare events based on historical events. History of Strong Wind Hazard Strong wind is one of the most destructive hazards in the studied area. Strong wind events normally occur during March-April and September-October. The country experienced about 19 strong wind events (2002-2011) affecting 2015 individuals and damaging 1,863 houses. Simulation Results: Strong Wind Hazard Analysis Simulation results of strong wind hazard for 100-year return period (RP) shows that Ainaro district is likely to have 15 sq. km of area affected by high wind speeds of 100 100-Year RP Strong Wind Hazard Map km/h. While considering 25, 50, and 100- year Findings return periods, the analysis indicates that the north-western part of Ainaro district is prone  Wind speed increases from low severity to strong winds (>75 km/hr). event (2-Year RP) to high severity event (100-Year RP) The moderate wind speed of 42-59 km/hr is  For lower return periods (2 to 25 years) expected to affect the northern and central low wind speed extents are limited to parts of the Aileu district. For a 2-year return southern part of the study area period (a low severity event), the wind speed  For higher return periods (>25 years) high is likely to vary between 5 km/hr to 32 km/hr, wind speed extents cover west-central while for 100 year return period (a high part of the study area located in Ainaro severity event) the potential wind speed is district likely to vary between 7 km/hr to 122 km/hr.  Most affected district for 100-year RP - The higher wind speeds could be attributed to Ainaro local topography and mountain ranges  Most Affected Sucos for 100 Year RP – present in these districts. The wind speed Ainaro, Manutasi, Mau-Ulo, and Nuno- increases over the mountainous region due to Mogue. positive pressure gradient, i.e., the pressure  Areas under high wind speed zones in decreases with increase in altitude. Hence, Ainaro district could be due to local when wind blows from areas of high pressure topography and mountain ranges located to areas of low pressure, its speed increases. in the surroundings. Results clearly indicate that the high- resolution WRF model with spatial grids of Synthesis Report Page 3 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 4. Landslide Hazard Analysis moderate, and low landslide susceptibility zones, respectively. Ainaro, Aitutu, Beboi An extensive analysis was done to evaluate the Leten, Catrai Craic Cotolau, Edi, and Fatisi landslide susceptibility of the study area. The Sucos are the most susceptible to landslide. RMSI team carried out field investigations to understand the parameters, which are responsible for landslides in the study area. History of Landslide Hazard Historical information on landslide occurrences is one of the most important considerations in landslide hazard assessment as it gives insight into the frequency of occurrence, their spatial distribution and their types, and the damage that they have caused. Following are the sources for the history of landslide events in the study area:  NDMD and Desinventar (10 landslides)  RMSI Field Survey (30 landslides)  High Resolution satellite and Google Earth Images (773 landslides)  Melbourne Energy Institute Hi st o ri c al l a nd sl id e l oc a ti o ns su p e r imp o sed ov e r th e l a nd sl id e A Landslide event on January 12, 2012, in suco su s cep t ibl e m ap Mulo, Ainaro district, caused damage to 70 The analysis suggests that the areas where houses, affected 20people. Similarly, another thick deposits of clayey soil and weathered Landslide on the same day in suco Faturasa, rocks are present on gentle slopes, when Aileu district caused damage to 15 houses, combined with prolonged high rainfall, affected 15 people. generally become unstable because of Key Factors of Landslide Hazard increase in pore water pressure. Factors influencing landslide in the study area Findings were selected based on available literature,  Historically, by 813 landslide incidents of expert opinion, and historical landslide data. various degrees occurred in the area. Based on the outcome, six factors have been  14 percent of the area falls in high to very considered for landslide susceptibility high landslide susceptibility zones. mapping, viz. Slope angle, Geology/ Lithology,  Impact wise Letefoho Suco of Manufahi Soil, and Land use-land cover, Rainfall, and district, and Ainaro, Aitutu, Mulo, Nuno- Seismicity. Mogue, Leolima Sucos of Ainaro district Landslide Susceptibility Analysis are most susceptible to landslide hazard.  Lowest landslide susceptibility is The analysis shows that approximately 76% observed in Fahiria Suco of Aileu District. of the total area of study area is susceptible to some level of landslide. Of this, 4%, 10%, 23%, and 38% areas lie in the very high, high, Page 4 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 5. Exposure Data Development and primarily classified on occupancy types and Analysis and structural types. The number of building footprints captured (47,846) for the study Being a critical component of risk assessment, area using high-resolution satellite images which is subjected to potential losses, was greater when compared to dwelling units exposure data such as population, built provided by Census 2010. environment, systems that support infrastructure and livelihood functions, or other elements present in the hazard zone have been developed. Modeling vulnerability of a system to natural hazards involves establishing a relationship between the potential damageability of critical exposure elements and different levels of local hazard intensity for the hazard of interest. Exposure Data Development Administrative Critical Buildings Demography Transport Boundaries Facilities District Population Health Residential • Age Centres Commercial Roads Sub-district • Sex Industrial Bridges Others • Gender Schools Suco Data Collection Data Data Exposure Processing Development Values • Data Requirement List • Identify Data Gaps • Compilation • Unit • Data Gap Filling • Structural Details Replacemen • Data Acquiring • Quality Checks t Costs • Data Inventory • Data Validation • Exposure Data in • Total Costs GIS format # 13 www. w r msi.c om w w .rmsi.c om Building Exposure Data: Occupancy Types Analysis of Exposure Elements Demographic Analysis  Total Population of 49 sucos: 136,209 persons (projected 2014 figure)  Fahisoi (52%), Horai-Quic (51%) and Aisirimou (51%) sucos have more percentage of female population  Fahiria Suco of Aileu district has the lowest percentage of female population (about 44%)  Poetete Suco (Ermera district) has the highest population (7% of total population), while Mau-Ulu Suco (Ainaro) Building Exposure Data: Structural Types has the least population (0.4% of total population).  Highest number of residential houses: Leolima Suco of Ainaro district. Buildings exposure analysis  Lowest number of residential houses: The footprints of residential, commercial, Cotolau Suco of Aileu district public, and industrial buildings were captured Synthesis Report Page 5 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project  Highest percentage of residential houses: bridges were considered for exposure Aileu district (84% of total.) analysis. The road data, collected from  Highest percentage of industrial and Government of Timor-Leste (2014 vintage), commercial buildings: Manufahi district which constitute important attributes like (about 4% and 16% of total, respectively) types of roads, length, administrative area and Critical Facilities Exposure Analysis replacement costs, were used for exposure analysis. Educational Institutes The building footprints of educational institutions which were captured indicate that 13 primary schools fall beyond 1-km distance from the motorable road network. Total number of educational institutions is 170 in the study area. Building Exposure Data: Health Facilities Transport Network: Roads  Total road length : 1,205 km  Metalled road length: 775 km Building Exposure Data: Educational  Highest length of roads: Suco Liurai of Ainaro district (185 km) Institutions  Lowest length of motorable roads: Seloi Craic Suco, 57 km Health Facilities  Suco with no motorable roads: Suro-Craic Suco of Ainaro district  Highest road density: Talimoro There are 77 major health centers located in Suco of Ermera district, 330 m/sqkm the study area and comprise of community w w w . r m s ii. w .c om co health centers, clinics, health posts, and In the study area, most of the bridges are mobile clinics. situated on the Dili-Ainaro highway corridor. Locations of bridges at suco level were Transportation Network captured from available bridge location data During disasters, the transportation network of 2012 from ALGIS division of Ministry of plays an important role in rescue and recovery Agriculture and Fisheries and maps received operations in suco/ district. The roads and from the transport department and were updated using high-resolution Pleiades Page 6 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project satellite images and field survey data collected Estimation of Exposure Values during the present study. Field survey data, consultations with stakeholders, and literature survey were used Agricultural Crop Exposure in the estimation of different structural types, The spatial distribution of key cash crops, average built up areas and unit costs of their types, and associated replacement costs building structures. were captured to create the crop exposure database. The major cash crops are The total estimated value of exposure in all categorized into four classes such as coconut categories in the study area is more than 570 million USD. Out of this, residential exposure crop, coconut forest, cultivated land, and rice. accounts for about 42.5% of the total value, Rice cultivation occupies the maximum transport exposure (roads and bridges) for agricultural land for production (about 51%) about 38.2%, commercial exposure for about in the study area, followed by Maize/ Corn 10.9%, educational exposure for about 3.7%, (about 41%) whereas coconut crops and industrial exposure for about 1.6%, health coconut forests account for only 6% and 1% of exposure for about 0.8%, and crop exposure about 0.7%, respectively. the crop areas, respectively. Es ti m a ted val u e s f or ex p osu r e s For exposure related to education sector, Ainaro suco of Ainaro district has the highest exposure value. Letefoho suco of Manufahi district and Liurai soco of Ainaro district are the next important sucos with higher educational exposure values. Maubisse suco of Ainaro district has the highest exposure value related to health sector followed by Seloi Sp a ti al d is t ri bu ti o n o f cr op typ e s Malere and Ainaro sucos of Aileu and Ainaro districts, respectively. Similarly, for the Beboi Leten suco of Ermera district has the studied crops exposure, Holarua suco of highest agricultural land for rice cultivation Manufahi district has the highest exposure followed by Mulo suco of Ainaro district. For value, followed by Letefoho suco of Manufahi corn/ maize cultivation, Holarua and Letefoho district and Maubisse suco of Ainaro district. suco of Manufahi district have the highest agricultural area. Leolima suco of Ainaro district has the highest amount of coconut plantation in the study area. Synthesis Report Page 7 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 6. Social Vulnerability residential structures damaged. In addition to Assessment the factors contributing to social vulnerability, access to road infrastructure seems to be Social vulnerability is considered as not only a critical in determining vulnerability. function of exposure to natural hazards, but also the sensitivity and resilience of the society to prepare, respond, and recover from the natural disasters. Livelihood and economic status influence sensitivity while skills, awareness, social security, etc. improve resilience of the community. A combination of demographic and economic variables was considered for social vulnerability assessment. Social Vulnerability Index (SoVI) Social Vulnerability Index was developed to identify people, households, groups, and communities with different levels of susceptibility to disasters and drive their ability to respond to various types of hazards that Sucos face. It is an index-based approach selecting socio-economic indicators that have strong influence on the community’s well SoV I a cr o ss th e 49 S u co s being, sensitivity, and resilience and Findings categorizing the study area into varying groups – high, medium, and low. Indicators at  Poor access to critical facilities (schools the lowest administrative unit, i.e., suco level and hospitals) and markets deter the were considered and the indices were economic growth of the sucos and thus assigned ranks based on the level of influence. increase social vulnerability.  Lack of roads restrains quick response as Suco-specific Vulnerability to Natural Hazards well as retards the development activities of the region. Sucos categorized as very The sucos were analyzed based on reported high SoVI have either poor roads or steep hazards for the period 2010-2013 (affected topography restraining community access population) along with the economic status of to markets and other critical facilities. local communities. For comparison of sucos,  The social vulnerability is high specifically poverty index was developed considering key in sucos in the mountainous areas with economic activities and converting them into poor access to health services, markets, monetary terms. Analysis indicates that roads, and financial services. strong wind is the dominant hazard in the  There is a high dependency on rainfed region. agriculture as other sources of income are During the field investigation, it was observed limited and irrigated areas are scant. that weak building structures and poorly maintained buildings (vulnerable to strong winds) and houses located on unstable slopes (vulnerable to landslides) were the main Page 8 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 7. Hazard Risk Modeling Natural hazard events such as floods, strong winds, and landslides of different severity can cause significant casualties, property damage, and business interruption to communities; ultimately impacting the people, the economy, the environment, and the long-term development of a region. Hazard risk modeling offers valuable information to assist local governments and communities to determine their risks and make more informed risk management decisions. Hazard Risk Modeling Framework Hazard risk modeling for the risk assessment of the 49 selected sucos in the four districts has taken into consideration the hazard risk equation. Risk is the uncertainty of future losses. The amount of losses are rather somewhat uncertain as the causative hazard events (e.g. Ha z ard Ri sk M od el i ng F r am e wo r k flood, landslide, strong wind, etc.); their locations, dates and times of occurrence; and the degree or amount of damage to assets caused by these events is uncertain. It has thus become imperative to assess what losses accrue due to damage by these natural hazard events. State-of-the-art methodology for hazard risk modeling and assessment were adopted and followed in the present study. While Hazard Risk Equation describing each hazard risk profile, the exposure elements being common to all the three hazards were described before the hazard risk profile of each hazard. Following is the framework of hazard risk modeling developed and followed in the present study: Synthesis Report Page 9 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 8. Risk Assessment Results Flood Risk/ Loss PML for different Year Return Period Flood Return Period Losses (USD) Since risks are uncertain, they must be stated Years Residential Commercial Industrial probabilistically which is expressed in terms 2 187,505 49,482 7,964 of a Loss Exceedance Curve (LEC). 5 281,964 74,752 11,909 10 360,741 94,843 15,416 Risk Matrices by Hazard 25 460,208 121,732 19,082 50 553,070 146,490 22,652 100 661,158 174,933 26,591 Probable Maximum Loss (PML): AAL for Flood Hazards provides an estimate of losses that are AAL likely to occur, considering existing Residential Commercial Industrial mitigation features, due to a single hazard USD Exposure USD Exposure USD Exposure scenario event with one or several return- Affected Affected Affected periods. 1,27,439 0.05% 33,658 0.05% 5,379 0.06% Loss Exceedance Curve (LEC): plots the consequences (losses) against the probability for different scenario events with different return periods. Average Annualized Loss (AAL): is the estimated long-term value of losses to assets in any single year within the study area. Risk metrics (LEC, PML, and AAL and Loss Cost) were used to estimate of the losses and damages attributable to each hazard in the Sucos at risk. Loss is the decrease in asset value due to damage, typically quantified as the replacement or repair cost. Loss estimation is one of the most important tasks in risk analysis. Based on the hazard, exposure, and vulnerability assessment, the risks in terms of economic losses for different sectors under various scenarios were calculated. PM L f o r 1 0 0 Ye a r R e t u r n P er i od Fl o od Risks Due to Flood Hazard fo r R es id en t ia l Bu il d i ng s Probable Maximum Losses (PML) for general Risks Due to Landslide Hazard occupancy (residential, industrial, and For landslide risk assessment, the hazard commercial) classes due to floods were component is most difficult to assess due to calculated. Losses are presented for six key the absence of a clear magnitude-frequency return-periods (2, 5, 10, 25, 50, and 100 relation at a particular location. During the years). The table shows that probable analysis, the hazard map was directly overlaid maximum losses are to the order of USD with a building footprint map and all buildings 661,158 for residential buildings for a 100- that fell within the high hazard zone were year return period event. However, considered to be at high risk. corresponding numbers for industrial and commercial buildings are not that significant. Page 10 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project Landslide Risk/ Loss different sectors could incur under key return Probable affected exposure from landslide hazard Probable Affected Probable Affected period scenarios. Occupancy Exposure (Million USD) Exposure % Residential 22.84 10% Strong Wind Risk/ Loss Commercial 5.37 7% PML for the Strong Wind Hazard Industrial 0.66 7% Schools 1.74 8% Return Period Losses (USD) Hospitals 0.48 11% Years Residential Commercial Industrial Power Stations 0.00075 2% 2 414,195 38,794 5,228 Power Substations 0.074 25% 5 810,054 72,888 9,433 Roads 43.41 21% Landslide Vulnerability 10 958,714 82,331 10,282 Vulnerability 25 1,007,578 83,867 10,427 Persons Buildings Roads 50 1,061,457 85,324 10,561 Debris slides, flows and 100 1,141,987 89,114 10,779 rock fall, > 25º slope 0.9 1 1 Rotational slides and slumps, < 25º slope 0.05 0.25 0.3 AAL for Strong Wind Hazard Small debris slides, flows, slumps and rock falls 0.05 0.25 0.3 AAL Residential Commercial Industrial Exposure Exposure Exposure USD USD USD Affected Affected Affected After this, the building footprint was analyzed 304,973 0.13% 27,289 0.04% 3,550 0.04% with the landslide susceptibility map and Australian Geological Survey Organization AGSO vulnerability classes were applied to Comparative Risk Assessment at Suco calculate the affected exposure. Level To better understand the situation of hazard risks at suco level, the team carried out an exercise to compare the probable maximum loss (PML) for each hazard category. In addition, the team also looked at the Social Vulnerability Index (SoVI) and displayed them together with the risks associated with these hazards. This will be very useful in understanding the coping capacity of the community in terms of hazard risks and disaster management planning and preparedness. The table below presents the comparative overview of risk assessment at suco level. All PML shown in the table below are for 100 year return period and the loss value are in US$. Higher SOVI number represents higher social vulnerability and vice a versa. Also, range (loss in US$) of low, medium, and high risk category are defined in Hi gh - ri s k l a nd sl id e z o ne s the lower part of table below. Risks Due to Strong Wind Hazard The risks due to strong wind hazard and associated losses were calculated based on hazard, exposure, and vulnerability for different return periods ranging from more frequent to rare events. The risk were calculated in terms of economic losses Synthesis Report Page 11 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project Landslide District Flood Risk PML- Wind Risk SoVI Suco Name PML- Flood Risk PML- Wind SoVI Name Category Landslide Category Category Category Ainaro Ainaro 62760 High 7135180 High 110761 HIGH 6.11 Low Suro-Craic Ainaro 5582 Low 213100 Low 4510 LOW 4.89 Medium Soro Ainaro 7800 Low 935989 Low 7370 LOW 4.75 Medium Manutasi Ainaro 5263 Low 540423 Low 20338 LOW 6.49 Low Cassa Ainaro 11993 Medium 871267 Low 15902 LOW 6.17 Low Mau-Ulo Ainaro 21 Low 1022578 Low 9183 LOW 5.09 Medium Mau-Nuno Ainaro 1496 Low 719641 Low 11873 LOW 4.53 Medium Mulo Ainaro 11497 Medium 6039511 High 129031 HIGH 4.09 Medium Nuno-Mogue Ainaro 9395 Low 3619932 High 107130 HIGH 4.01 Medium Mau-Chiga Ainaro 4396 Low 1186329 Low 16107 LOW 3.92 High Maubisse Ainaro 16164 Medium 3571851 Medium 70815 MEDIUM 2.68 High Aitutu Ainaro 14346 Medium 6306068 High 76270 MEDIUM 2.97 High Edi Ainaro 5721 Low 1871877 Medium 30082 MEDIUM 3.06 High Maulau Ainaro 10245 Medium 2719446 Medium 29549 MEDIUM 3.61 High Horai-Quic Ainaro 4284 Low 458540 Low 20110 LOW 3.06 High Suco Liurai Ainaro 1655 Low 324340 Low 4559 LOW 2.51 High Fatu-Besi Ainaro 2628 Low 818611 Low 8839 LOW 4.35 Medium Leolima Ainaro 65561 High 1679099 Medium 40899 MEDIUM 5.25 Low Aisirimou Aileu 32776 Medium 283380 Low 21456 LOW 7.46 Low Bandudato Aileu 13060 Medium 464358 Low 17961 LOW 7.28 Low Fahiria Aileu 12432 Medium 89154 Low 6669 LOW 7.16 Low Fatubosa Aileu 4997 Low 1418756 Low 24004 LOW 4.22 Medium Lahae Aileu 3087 Low 791066 Low 7643 LOW 5.32 Low Lausi Aileu 2329 Low 500194 Low 5067 LOW 4.67 Medium Hoholau Aileu 2908 Low 120212 Low 6468 LOW 4.82 Medium Seloi Malere Aileu 2580 Low 781219 Low 37884 MEDIUM 6.41 Low Seloi Craic Aileu 5006 Low 2429447 Medium 29953 MEDIUM 6.33 Low Saboria Aileu 6016 Low 508742 Low 7299 LOW 7.39 Low Suco Liurai Aileu 28980 Medium 1126396 Low 61847 MEDIUM 7.66 Low Acumau Aileu 0 Low 2276332 Medium 31615 MEDIUM 5.16 Medium Fahisoi Aileu 0 Low 504286 Low 13799 LOW 4.68 Medium Cotolau Aileu 891 Low 91435 Low 4900 LOW 5.03 Medium Talitu Aileu 4819 Low 1531740 Low 24727 LOW 4.72 Medium Madabeno Aileu 325 Low 1241234 Low 14695 LOW 5.19 Medium Tohumeta Aileu 462 Low 642118 Low 8029 LOW 6.11 Low Fatisi Aileu 15940 Medium 610280 Low 7325 LOW 5.22 Medium Tocoluli Ermera 15762 Medium 358967 Low 8397 LOW 6.45 Low Fatuquero Ermera 46290 Medium 124543 Low 16837 LOW 5.21 Medium Railaco Craic Ermera 4385 Low 537343 Low 10792 LOW 5.42 Low Railaco Leten Ermera 3072 Low 622826 Low 9367 LOW 3.81 High Samalete Ermera 4028 Low 742989 Low 8348 LOW 6.19 Low Poetete Ermera 83172 High 1385110 Low 55061 MEDIUM 4.68 Medium Talimoro Ermera 1157 Low 1137439 Low 11317 LOW 5.89 Low Riheu Ermera 183205 High 429766 Low 38928 MEDIUM 5.44 Low Lauala Ermera 24498 Medium 510927 Low 18795 LOW 4.34 Medium Catrai-Craic Ermera 3350 Low 1381052 Low 20162 LOW 1.62 High Beboi Leten Ermera 3668 Low 1139905 Low 17090 LOW 2.52 High Letefoho Manufahi 166430 High 7288707 High 35174 MEDIUM 6.07 Low Holarua Manufahi 20857 Medium 1203558 Low 15915 LOW 3.25 High Hazard Type Social Vulnerability Probable Maximum Loss (US$) Index Risk Category Flood Landslide Strong Wind SoVI High 83,172 - 183,205 3,619,932 -7,288,707 107,130 – 129,031 1.62 – 3.92 Medium 32,776 - 83,172 1,679,099 -3,619,932 29,549 – 76,270 4.01 – 5.22 Low 0 - 32,776 89,154 - 1,679,099 4,510 – 24,727 5.25 – 7.66 Comparative overview of risk assessment at suco level Page 12 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project published by the National Directorate of 9. GIS Database Development Statistics, Timor-Leste and population growth rates. The detailed overview of the development of  Developed and improved hazard models the database of population, buildings, for flood, strong wind, and landslide. infrastructure assets, and crops for the 49  Developed Social Vulnerability Index sucos in the study area distributed over four (SoVI) for each suco for a comparative districts of Timor-Leste. Data management analysis at suco level. and inventory of such vulnerable buildings,  Develop suco level hazard and risk profile infrastructure, demographics, and other  All the GIS data, outputs, and the Suco level asset elements (e.g., crops) present in hazard Risk Atlas have been uploaded to a zones that were considered for risk GeoNode created by SOPAC/SPC team for assessment have been developed. Timor-Leste. Following are important details:  Collected, collated, and updated all the data available from different sources.  High-resolution (0.5m) Pleiades satellite images have been procured through the World Bank. RMSI team processed these imageries and used them to capture building footprints (2014) for the entire study area. A total of 47,846 building footprints in the study area were captured  Improved Land Use Land Cover by updating all the data of building level footprints  Developed Digital Terrain Model (DTM) using 20m interval contours, high resolution DSM, and Spot-heights for the study  Estimated 2014 population distribution at suco level using Census 2010 data Synthesis Report Page 13 Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project 10. Capacity Building and Feedbacks from training were received from Knowledge Transfer the participants through structured questionnaires during different sessions. The The technical capacity building under this participants found the training program project is viewed as a building block for useful and rated it from good to excellent. subsequent strengthening of NDMD capacity Field training to undertake risk assessment exercises. The capacity building and knowledge transfer was NDMD staffs were also trained on-the job divided into three specific categories: in-class during field-surveys and data collection for intensive training with hands-on, on-the job landslide, flood, and social vulnerability. training through participation to field data Officers from NDMD participated in the initial collection and field survey, and finally through study area observations on May 7, 2014. workshops. Officers joined the social vulnerability assessment and landslide field-data collection Training during May 23- June 7, 2014 to improve their capacity. Workshops Training Session for Stakeholders at Timor- Leste As part of their technical capacity development, the staffs at NDMD as well as key stakeholders within ministries were trained within a total of seven days of Workshop with Stakeholders at Timor-Leste intensive sessions that included hands-on The project has adopted an inclusive approach exercises and one-to-one support was since the start. Workshops were organized to conducted in Dili, Timor-Leste for these select massively disseminate information about the participants. Participants were introduced to project to stakeholders and beneficiaries. In the concept of multi-hazard risk assessment total, three one-day workshops were with specific sessions on landslides conducted at inception, mid-term, and final assessment and participation to the social stage of the project. The objectives of the vulnerability assessment survey. workshops were to communicate about the progress and main findings of the project, and The GIS training were focused on the basics of to obtain feedback from stakeholders and QGIS, open-source geospatial software that is beneficiaries. Representatives from different now installed on all of NDMD’s computers. ministries of the Government of Timor Leste, Participants were also trained on the use of donor and partners, NGOs and Civil Societies the GeoNode PacRIS platform. This GeoNode participated in the workshops. platform has been specifically developed for Representatives from the studied Districts Timor Leste, in collaboration with SOPAC/SPC and Sucos also attended each workshop. team. Page 14 Synthesis Report Building Climate and Disaster Resilience in Communities along Dili-Ainaro and Linked Road Corridor Project Page 15 Synthesis Report