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Establishment of Philippine Flood Risk Index by Province Based on Natural and Social Factors

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Establishment of Philippine Flood Risk Index by Province Based on Natural and Social Factors ESTABLISHMENT OF PHILIPPINE FLOOD RISK INDEX BY PROVINCE BASED ON NATURAL AND SOCIAL FACTORS Jerry Austria FANO Supervisor: Prof. Kuniyoshi TAKEUCHI MEE 09206 ABSTRACT This thesis offers a measure to formulate a Philippine Flood Risk Index (P-FRIc) by province in coverage, on the basis of Pressure and Release (PAR) Model which consists of five (5) key Indexes: Hazard (H), Exposure (E), Vulnerability (V), Coping Capacity using Soft countermeasures (CS) and Hard countermeasures (CH). The basic equation "Risk = Hazard × Vulnerability" (Wisner, B. et. al., 2004) is modified (Kannami, Arai et. al, Master Thesis, 2004) to calculate the P-FRIc, expressed as: Hazard Exposure Vulnerability HEV P FRIc Capacity ()Soft Measures Hard Measure CSH C This study identified and analyzed the indicators based on natural and social conditions that compose each index of P-FRIc. It quantified the indexes that include hazards (e.g., proneness to typhoons, topography), exposure (e.g., lowland population density, population growth per province) and progression of vulnerability (e.g., peoples’ socio- economic conditions) and coping capacity (e.g., hard and soft measures). It also analyzed the distribution of indexes for different provinces comparatively to draw the usable policies to be considered in future flood mitigation administration. Each index is composed of three (3) kinds of datasets which are called Indicators. P-FRIc is then used to assess the current potential risk to floods for the 82 provinces. The results of analysis indicated high flood-risk for provinces such as Metro Manila, Albay, Pampanga, Zambales, Negros Occidental, Cavite, etc. The assessment of Metro Manila as high flood risk area was confirmed on September 2009 when 80% of the capital was submerged by flashflood caused by Typhoon Ketsana that killed more than 300 people (Nilo & Espinueva, 2009). This implies the effectiveness of flood risk index. Key words: Philippine Flood Risk Index, Hazard, Exposure, Vulnerability, Coping Capacity, PAR model INTRODUCTION The Hyogo Framework for Action in the World Conference on Disaster Reduction (WCDR) held in Kobe, Japan, on January 2005 stated that “the development of indicator systems for disaster risk and vulnerability is one of the key activities enabling decision makers to assess the possible impacts of disasters”. Therefore to be able to apply to this study – the assessment of the flood risk in a flood prone area is the first step in motivating the government and the people to enhance their capabilities in flood risk management. The government of the Philippines can hardly allocate ample resources (money, manpower, machinery, etc.) for the appropriate implementation of flood disaster mitigation measures due to its limited information of areas perennially affected by floods, numerous organizational structures, differing laws as well as budgetary constraints. These reasons make the flood management system in the Philippines too complicated to put into simple terms both comparatively and quantitatively. Inasmuch that most flood disaster preparedness such as construction of critical infrastructures like national roads and bridges, dikes, sabo dams, pumping stations, diversion channels, etc., are undertaken in the national level, flooding occurs at the local level. More so, there is difficulty in knowing the level of flood risk by the regional, province or even barangay level which is not usually expressed explicitly. Most related studies in the past deal mostly by river basin. Assistant Section Chief (Engineer III), Flood Control and Sabo Engineering Center, Department of Public Works and Highways (FCSEC-DPWH), Philippines ** Director, International Centre for Water Hazard and Risk Management (ICHARM), Japan ANALYSIS OF THE PAST FLOOD DAMAGE As result of the comparative analysis of three kinds of water related disaster databases related to Philippines: the Philippines Annual Flood Disaster Damage database as compiled by its National Disaster Coordinating Council (NDCC) and the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) is accepted for data analysis as compared to two global disaster database which are the Dartmouth Flood Observatory and the Centre for Research on the Epidemiology of Disaster (CRED) through its Emergency Events Database (EM-DAT). The Philippines Annual Flood Disaster Damage is a comprehensive database that lists a 40-year flood record (1970~present) that includes the typhoon name, the affected regions and provinces, inclusive dates and the number of casualties – death tolls, missing and those injured. Also included are the amount, in PhP Peso the damage to properties, infrastructures and agriculture. Three kinds of data were used for the measurement; number of events, killed people, and average killed people per event. All events with one killed people or over were classified into three classes by the size of death toll. To make the provinces comparable, the variables were converted to indicators, namely Flood Damage Indicator (FDIa), using the following formula: FDIa = [(LN(x) – LN(Min(x)]/[(LN(Max(x)) - LN(Min(x))] Eq. (1) Where FDIa : Flood Damage Indicator (actual) x : variables (number of events (noted by N), number of killed people (noted by K), and killed people per event (noted by KperN)) Max(x): actual maximum value Min(x) : actual minimum value Figure 1 Annual Flood Casualties Caused by Water Related Disasters ESTABLISHMENT OF PHILIPPINE FLOOD RISK INDEX (P-FRIc) There are a lot of conceptual frameworks, which help us to assess the complicated structure of flood risk. One of the most common and simple conceptual framework is the Pressure and Release Model (PAR Model), which is based on the equation ‘Risk = Hazard x Vulnerability’. Basically this concept defines “disaster as the intersection of two opposing forces which are hazard and vulnerability” (Wisner, B. et al, 2004). This concept is used in this thesis to calculate the Philippine Flood Risk Index (P-FRIc). P-FRIc considers the five aspect indexes: Hazard, Fig. 2 Structure of Philippine Flood Risk Index Exposure, Basic Vulnerability, Capacity Soft countermeasures and Capacity Hard countermeasures. Each index is then composed of three kinds of datasets which are the most representative indicators (see Fig. 2). To calculate for P-FRIc the following datasets for indicators and indices are expressed as follows: HEV Eq. (2) PFRI c CCSH Where: H : Hazard Index E : Exposure Index V : Basic Vulnerability Index Cs : Capacity Soft Index CH : Capacity Soft Index Indicator : [(LN(x) – LN(Min(x)]/[(LN(Max(x)) - LN(Min(x))] Eq. (3) Index : Indicator 1 + Indicator 2 + Indicator 3 Eq. (4) INDICATORS: Indicators are selected by quantitative method: discussion with ICHARM experts, consideration of availability of natural and social statistical data and review of past related studies. Selected indicators and datasets are shown in Table 1. Data are collected from various kinds of sources such as the National Disaster Coordinating Council (NDCC), National Statistical Coordination Board (NSCB) and Center for International Earth Science Information Network (CIESIN). Table 1 Datasets for Indicators INDEXES 1. Hazard Index Fig. 3 Hazard Index If a province is frequently affected by typhoons, then the more it will BATANES BATANES be hazardous and flood prone. Albay is assessed as the most BATANESBATANES hazardous province in the Philippines. While typhoon prone Hazard Index provinces like Benguet, Pampanga, Zambales, Negros Occidental, ILOCOS NORTE APAYAO Bataan and Iloilo are also ranked hazardous because of these CAGAYAN ABRA KALINGA ILOCOS SUR MOUNTAIN PROVINCE ISABELA provinces have high annual average monthly rainfall values. Fig. 3 IFUGAO ± LA UNIONBENGUET NUEVA VIZCAYA shows the distribution map of the Hazard Index of provinces which QUIRINO PANGASINAN AURORA NUEVA ECIJA TARLAC are colored in red. ZAMBALES PAMPANG A BULACAN QUEZON NCRBATAAN - MANILA, FIRSTRIZAL DISTRICT CAVITE LAGUNA CAMARINES NORTE QUEZON BATANGAS CATANDUANES 2. Exposure Index CAMARINES SUR MARINDUQUE ALBAY OCCIDENTALORIENTAL MINDORO MINDORO MASBATE Exposure plays a very important role in risk evaluation. If large SORSOGON ROMBLON MASBATE ROMBLONROMBLON NORTHERN SAMAR MASBATE PALAWAN floods take place in a highly populated area of lowland areas, there BILIRANSAMAR (WESTERN SAMAR) EASTERN SAMAR AKLAN BILIRAN CAPIZ ANTIQUE LEYTE would be large number of victims. That is why the population ILOILO GUIMARAS SOUTHERN LEYTE NEGROS OCCIDENTALCEBU SURIGAO DEL NORTE density in low elevation coastal zones (LECZ, below 10m elevation) SOUTHERN LEYTE BOHOL SURIGAO DEL NORTE SURIGAO DEL NORTE PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE is used as for this thesis. SIQUIJOR CAMIGUIN SURIGAO DEL SUR AGUSAN DEL NORTE MISAMIS ORIENTAL AGUSAN DEL SUR MISAMIS OCCIDENTAL Distribution Map of Hazard Index ZAMBOANGA DEL NORTELANAOISABELA DEL NORTE CITYBUKIDNON All_Provinces ZAMBOANGA DEL SUR LANAO DEL SUR Cavite, is calculated as high exposure province in the Philippines. DAVAO (DAVAOCOMPOSTELA DEL NORTE) VALLEY Hazard as Percent of Total COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SURDAVAO ORIENTAL DAVAO (DAVAO DEL NORTE) 0.000 TAWI-TAWI MAGUINDANAO Metro Manila, Bulacan, Rizal and Mindoro Oriental also ranked at BASILAN 0.00 - 0.75 SULTAN KUDARAT SULU SOUTH COTABATO 0.75 - 1.00 SULU SULU SARANGANISARANGANI high position because of these provinces have rapid population 1.00 - 1.23 SULU SULU 1.23 - 1.45 TAWI-TAWITAWI-TAWI 1.45 - 1.63 TAWI-TAWI growth rate. Fig. 4 shows the distribution
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