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 FRI  c 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 , Albay, Pampanga, Zambales, 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 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 be hazardous and flood prone. Albay is assessed as the most BATANESBATANES hazardous province in the Philippines. While typhoon prone Hazard Index provinces like , Pampanga, Zambales, Negros Occidental,

ILOCOS NORTE Bataan and Iloilo are also ranked hazardous because of these CAGAYAN ISABELA provinces have high annual average monthly rainfall values. Fig. 3 ± LA UNIONBENGUET NUEVA VIZCAYA shows the distribution map of the Hazard Index of provinces which PANGASINAN NUEVA ECIJA TARLAC are colored in red. ZAMBALES PAMPANG A BULACAN QUEZON NCRBATAAN - MANILA, FIRSTRIZAL DISTRICT CAVITE LAGUNA CAMARINES NORTE QUEZON BATANGAS 2. Exposure Index CAMARINES SUR ALBAY OCCIDENTALORIENTAL MINDORO Exposure plays a very important role in risk evaluation. If large SORSOGON MASBATE ROMBLONROMBLON NORTHERN SAMAR MASBATE PALAWAN floods take place in a highly populated area of lowland areas, there BILIRANSAMAR (WESTERN SAMAR) EASTERN SAMAR AKLAN CAPIZ ANTIQUE LEYTE would be large number of victims. That is why the population ILOILO SOUTHERN LEYTE NEGROS OCCIDENTALCEBU 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. SURIGAO DEL SUR DEL NORTE ORIENTAL AGUSAN DEL SUR MISAMIS OCCIDENTAL Distribution Map of Hazard Index DEL NORTELANAOISABELA DEL NORTE CITYBUKIDNON All_Provinces ZAMBOANGA DEL SUR DEL SUR Cavite, is calculated as high exposure province in the Philippines. (DAVAOCOMPOSTELA DEL NORTE) VALLEY Hazard as Percent of Total COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SURDAVAO ORIENTAL TAWI-TAWI DAVAO (DAVAO DEL NORTE) 0.000 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 SARANGANI SULU SULU SARANGANI 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 map of the Exposure Index. TAWI-TAWI TAWI-TAWI 1.63 - 1.91 The areas in red indicate high value areas while those areas in green 1.91 - 2.38 indicate low value of Exposure Index.

3. Basic Vulnerability Index Basic Vulnerability is defined as the basic condition of each Fig. 5 shows the distribution map of province. Batanes, Benguet, Laguna, Rizal and Cavite rank high in the Vulnerability index. Dark green the list because of good governance indicator and high human areas indicate high vulnerable areas. development index.

Fig. 4 Exposure Index Fig. 5 Vulnerability Index Fig. 6 Capacity S Index

BATANES BATANES

BATANES BATANES

BATANES BATANES BATANESBATANES BATANESBATANES Capacity H Index Vulnerability Index Exposure Index APAYAO CAGAYAN

A P ABRA A ILOCOS NORTE ILOCOS NORTEY APAYAO KALINGA A C O A G CAGAYAN ILOCOS SUR A MOUNTAIN PROVINCE YA N ISABELA A ABRA R IFUGAO B KALINGA KALINGA A LA UNIONBENGUET ILOCOS SUR ILOCOS SUR MOUNTAIN PROVINCE A MOUNTAIN PROVINCE NUEVA VIZCAYA L ISABELA QUIRINO O E A B ± IFUGAO ± ± G FU A I S I PANGASINAN LA UNIONBENGUET BENGUET AURORA NUEVA VIZCAYA NUEVA VIZCAYA QUIRINO NUEVA ECIJA QUIRINO TARLAC ZAMBALES PANGASINAN PANGASINAN AURORA PAMPANGA AURORA BULACAN NUEVA ECIJA NUEVA ECIJA QUEZON TARLAC TARLAC BATAAN RIZAL ZAMBALES ZAMBALES NCR - MANILA, FIRST DISTRICT PAMPANGA PAMPANGA BULACAN BULACAN CAVITE QUEZON QUEZON LAGUNA CAMARINES NORTE BATAAN RIZAL NCRBATAAN - MANILA, FIRSTRIZAL DISTRICT QUEZON NCR - MANILA, FIRST DISTRICT BATANGAS CATANDUANES CAMARINES SUR CAVITE CAVITE LAGUNA CAMARINES NORTE LAGUNA CAMARINES NORTE MARINDUQUE QUEZON QUEZON ALBAY BATANGAS BATANGAS CATANDUANES CATANDUANES CAMARINES SUR OCCIDENTALORIENTAL MINDORO MINDORO MASBATE CAMARINES SUR SORSOGON MARINDUQUE MARINDUQUE ROMBLON MASBATE ALBAY ALBAY ROMBLONROMBLON NORTHERN SAMAR ORIENTAL MINDORO MASBATE OCCIDENTALORIENTAL MINDORO MINDORO MASBATE MASBATE OCCIDENTAL MINDORO PALAWAN SORSOGON SORSOGON ROMBLON MASBATE ROMBLON MASBATE BILIRANSAMAR (WESTERN SAMAR) ROMBLON EASTERN SAMAR ROMBLONROMBLON NORTHERN SAMAR ROMBLON NORTHERN SAMAR AKLAN BILIRAN MASBATE MASBATE PALAWAN PALAWAN CAPIZ SAMAR (WESTERN SAMAR) SAMAR (WESTERN SAMAR) ANTIQUE LEYTE BILIRAN BILIRAN ILOILO EASTERN SAMAR EASTERN SAMAR AKLAN BILIRAN AKLAN BILIRAN Z CAPI CAPIZ GUIMARAS SOUTHERN LEYTE L LEYTE ANTIQUE O E ANTIQUE NEGROS OCCIDENTALCEBU IL Y ILOILO SURIGAO DEL NORTE LO T I E SOUTHERN LEYTE SURIGAO DEL NORTE U GUIMARAS BOHOL GUIMARAS B E SOUTHERN LEYTE SOUTHERN LEYTE PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE NEGROS OCCIDENTALC NEGROS OCCIDENTALCEBU SURIGAO DEL NORTE SURIGAO DEL NORTE SURIGAO DEL NORTE SOUTHERN LEYTE SOUTHERN LEYTE L SIQUIJOR CAMIGUIN HO SURIGAO DEL NORTE SURIGAO DEL NORTE BO BOHOL SURIGAO DEL NORTE SURIGAO DEL NORTE SURIGAO DEL SUR PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE AGUSAN DEL NORTE MISAMIS ORIENTAL SIQUIJOR SIQUIJOR CAMIGUIN AGUSAN DEL SUR CAMIGUIN MISAMIS OCCIDENTAL SURIGAO DEL SUR SURIGAO DEL SUR AGUSAN DEL NORTE AGUSAN DEL NORTE ZAMBOANGA DEL NORTELANAOISABELA DEL NORTE CITYBUKIDNON MISAMIS ORIENTAL MISAMIS ORIENTAL LANAO DEL SUR AGUSAN DEL SUR AGUSAN DEL SUR ZAMBOANGA DEL SUR DAVAO (DAVAO DEL NORTE) MISAMIS OCCIDENTAL B MISAMIS OCCIDENTAL COMPOSTELA VALLEY Distribution Map of Exposure Index U Distribution Map of Capacity H Index

K

I ZAMBOANGA DEL NORTELANAOISABELA DEL NORTE CITY D Distribution Map of Vulneratbility Index ZAMBOANGA DEL NORTELANAOISABELA DEL NORTE CITY COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SURDAVAO ORIENTAL N All_Provinces O All_Provinces TAWI-TAWI DAVAO (DAVAO DEL NORTE) ZAMBOANGA DEL SUR LANAO DEL SUR N ZAMBOANGA DEL SUR LANAO DEL SUR MAGUINDANAO DAVAO (DAVAOCOMPOSTELA DEL NORTE) VALLEY All_Provinces DAVAO (DAVAOCOMPOSTELA DEL NORTE) VALLEY Exposure as Percent of Total CapacityH as Percent of Total BASILAN DAVAO ORIENTAL DAVAO ORIENTAL SULTAN KUDARAT COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SUR Vulnerabil as Percent of Total COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SUR 0.00 - 0.20 TAWI-TAWI DAVAO (DAVAO DEL NORTE) TAWI-TAWI DAVAO (DAVAO DEL NORTE) 0.000 SULU SOUTH COTABATO MAGUINDANAO 0.54 - 0.79 MAGUINDANAO 0.20 - 0.73 0.00 - 0.83 SULU SULU SARANGANISARANGANI BASILAN BASILAN SULU SULTAN KUDARAT 0.79 - 0.98 SULTAN KUDARAT 0.73 - 1.01 SULU SOUTH COTABATO SULU SOUTH COTABATO 0.83 - 1.03 SULU 0.98 - 1.13 1.01 - 1.21 SULU SULU SARANGANISARANGANI SULU SULU SARANGANISARANGANI 1.03 - 1.19 TAWI-TAWITAWI-TAWI SULU SULU TAWI-TAWI 1.13 - 1.22 TAWI-TAWI 1.21 - 1.34 SULU SULU 1.192 - 1.35 TAWI-TAWI 1.22 - 1.32 TAWI-TAWITAWI-TAWI TAWI-TAWITAWI-TAWI 1.34 - 1.51 TAWI-TAWI TAWI-TAWI 1.35 - 1.50 TAWI-TAWI 1.32 - 1.45 TAWI-TAWI 1.51 - 1.81 TAWI-TAWI TAWI-TAWI 1.50 - 1.66 1.45 - 1.62 1.81 - 2.31 1.66 - 1.93 1.62 - 2.01 4. Capacity Hard Countermeasure Index Fig. 7 Capacity H Index

BATANES BATANES Provinces like Pangasinan, Pampanga, Tarlac and Rizal rank high in BATANESBATANES the Capacity H Index because of the provinces’ good investment for flood mitigation projects as outlined in the Medium Term Philippine Capacity S Index ILOCOS NORTE APAYAO Development Plan (2005-2010). Isabela topped the ranking CAGAYAN ABRA KALINGA ILOCOS SUR MOUNTAIN PROVINCE ISABELA considering that it has good sizeable forest cover area. Forest cover is IFUGAO LA UNIONBENGUET ± NUEVA VIZCAYA a good indicator for hard countermeasures against floods. Devastated QUIRINO PANGASINAN AURORA NUEVA ECIJA TARLAC ZAMBALES forests cause rapid rainfall runoff, which is contributory to floods. PAMPANG A BULACAN QUEZON NCRBATAAN - MANILA, FIRSTRIZAL DISTRICT Ifugao, Ilocos Norte, Apayao and Quirino on the other hand complete CAVITE LAGUNA CAMARINES NORTE QUEZON BATANGAS CATANDUANES CAMARINES SUR the ranking (Fig. 6) because of its good basic delivery of services MARINDUQUE ALBAY OCCIDENTALORIENTAL MINDORO MINDORO MASBATE SORSOGON through implementation of basic infrastructures. ROMBLON MASBATE ROMBLONROMBLON NORTHERN SAMAR MASBATE PALAWAN

BILIRANSAMAR (WESTERN SAMAR) EASTERN SAMAR AKLAN BILIRAN CAPIZ ANTIQUE LEYTE 5. Capacity Soft Countermeasure Index ILOILO GUIMARAS SOUTHERN LEYTE NEGROS OCCIDENTALCEBU SURIGAO DEL NORTE Education of the people in itself is enabling capacity and power that SOUTHERN LEYTE BOHOL SURIGAO DEL NORTE SURIGAO DEL NORTE PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE will pump prime the community into action for preparedness against SIQUIJOR CAMIGUIN SURIGAO DEL SUR AGUSAN DEL NORTE MISAMIS ORIENTAL AGUSAN DEL SUR disaster. If the people are not well educated - access to information MISAMIS OCCIDENTAL LANAO DEL NORTE Distribution Map of Capacity S Index ZAMBOANGA DEL NORTE ISABELA CITYBUKIDNON ZAMBOANGA DEL SUR LANAO DEL SUR DAVAO (DAVAOCOMPOSTELA DEL NORTE) VALLEY will amount to nothing - the people cannot interpret and will not know All_Provinces COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SURDAVAO ORIENTAL DAVAO (DAVAO DEL NORTE) CapacityH as PercentTAWI-TAWI of Total MAGUINDANAO 0.00 what action to do with it. Provinces like Camarines Sur, Batangas, BASILAN SULTAN KUDARAT 0.00 - 0.83 SULU SOUTH COTABATO SULU SULU SARANGANISARANGANI Laguna, Leyte and Ilocos Norte topped the ranking because of its high 0.83 - 1.03 SULU 1.03 - 1.19 SULU TAWI-TAWITAWI-TAWI 1.192 - 1.35 TAWI-TAWI TAWI-TAWI literacy and access to information indicators. TAWI-TAWI 1.35 - 1.50

1.50 - 1.66 The above results are deemed acceptable, convincing and agreeable 1.66 - 1.93 with the result of similar past studies.

Fig. 8 Philippine Flood Risk Index Philippine Flood Risk Index (P-FRIc) Fig. 8 shows the distribution map of P-FRIc. Provinces in dark brown P-FRIc indicate high flood risk while in that in yellow show low flood risk. ILOCOS NORTE Philippine Flood Risk Index APAYAO CAGAYAN ABRA Metro Manila is assessed as the most flood risk province in the KALINGA ILOCOS SUR MOUNTAIN PROVINCE ISABELA IFUGAO country having high vulnerability and exposure. This was confirmed LA UNIONBENGUET NUEVA VIZCAYA QUIRINO ± PANGASINAN AURORA on September 2009 when 80% of the capital was submerged by NUEVA ECIJA TARLAC ZAMBALES PAM PANGA BULACAN flashflood caused by Typhoon Ketsana that killed more than 300 QUEZON NCRBATAAN - MANILA, FIRSTRIZAL DISTRICT

CAVITE CAMARINES NORTE LAGUNA people. QUEZON BATANGAS CATANDUANES CAMARINES SUR MARINDUQUE ALBAY nd OCCIDENTALORIENTAL MINDORO MINDORO MASBATE SORSOGON Albay province comes in the 2 place mainly due to its high Hazard ROMBLON MASBATE ROMBLONROMBLON NORTHERN SAMAR MASBATE PALAWAN

BILIRANSAMAR (WESTERN SAMAR) and Exposure. Albay has been hit with destructive debris flow in EASTERN SAMAR AKLAN BILIRAN CAPIZ ANTIQUE LEYTE November 2006 when Typhoon Durian caused widespread debris flow ILOILO GUIMARAS SOUTHERN LEYTE NEGROS OCCIDENTALCEBU SURIGAO DEL NORTE over Mt. Mayon that killed 1,150 people and buried infrastructures SOUTHERN LEYTE BOHOL SURIGAO DEL NORTE SURIGAO DEL NORTE PALAWAN NEGROS ORIENTAL SURIGAO DEL NORTE and houses. SIQUIJOR CAMIGUIN SURIGAO DEL SUR AGUSAN DEL NORTE MISAMIS ORIENTAL AGUSAN DEL SUR rd MISAMIS OCCIDENTAL Several provinces occupying the top 10 are: Zambales at 3 , ZAMBOANGA DEL NORTELANAOISABELA DEL NORTE CITYBUKIDNON ZAMBOANGA DEL SUR LANAO DEL SUR DAVAO (DAVAO DEL NORTE) th th th Distribution Map of P-FRIc COMPOSTELA VALLEY COTABATOCOTABATO CITY (NORTHDAVAO COTABATO) DEL SURDAVAO ORIENTAL DAVAO (DAVAO DEL NORTE) Pampanga at 4 , Bulacan at 7 and Bataan at 10 . These provinces All_Provinces TAWI-TAWI MAGUINDANAO PFRIc as Percent of Total BASILAN SULTAN KUDARAT are high risk due to high Vulnerability and low Coping Capacity soft 0 - 0.39 SULU SOUTH COTABATO SULU SARANGANI 0.39 - 0.79 SULU SARANGANI SULU measures. Pangasinan although has high Hazard and Vulnerability, 0.79 - 1.19 SULU 1.19 - 1.59 TAWI-TAWITAWI-TAWI TAWI-TAWI TAWI-TAWI th 1.59 - 1.98 TAWI-TAWI comes in at 15 place only because of high Coping Capacity hard 1.98 - 2.38 2.38 - 2.78 measures. The completion of the Agno Flood Control Project has 2.78 - 3.18 addressed the perennial flooding in the province of Pangasinan.

Fig. 9 Radar Chart Showing Structure of P-FRIc COMPARATIVE ANALYSIS BETWEEN P-FRIc AND PAST FLOOD DAMAGE

The calculated Philippine Flood Risk Index (P-FRIc) is compared with the past actual flood damage data in order to verify agreement. Fig. 10 shows the scatter graph plotted by P-FRIc and composed actual Flood Damage Indicator of total killed people (FDIa_Com_K), which is calculated by using three classified flood damage indicators, based on levels of magnitude. The composed flood damage indicator FDIa_Com is calculated by simple addition of the three classified indicators: FDIa_L (low) : 10 deaths and below (K < 10) FDIa_Com_K = FDIa_L + FDIa_M + FDIa_H FDIa_M (middle): more than 10 and below 100 deaths (10 < K < 100) FDIa_H (high) : more than 100 deaths (K > 100)

P-FRIc exhibit correspondence with the past flood damages, but apparently this is not so significant (R2=0.1974). One of the reasons for this disagreement is that P-FRIc expresses the present condition of flood risk whereas FDIa_Com_K indicates the consequence of past 40 year record annual flood damage data.

Fig. 10 is the scatter graph in which all the provinces are classified into 6 groups and plotted by P-FRIc and FDIa_Com_K. The provinces in group 4 and 3 have relatively good agreement between FDIa_Com_K. While the provinces in Groups 2 and 6 have lower value P-FRIc than FDIa_Com_K. It means that there is possibility that the values of FRIc were underestimated in this aspect. The provinces in Groups 1 and 5 have large values of P-FRIc and FDIa_Com_K. It means P-FRIc may have overestimated the flood risk. This means that these provinces have the high possibility to experience from high casualty flood events in the future. Fig. 10 Scatter Graph of the Provinces Plotted by P-FRIc vs FDI_Com_K

CONCLUSION This master thesis established the Flood Risk Index for 82 provinces in Philippines, by considering the following:  Fifteen (15) indicators considering natural and social conditions of the Philippines were identified as components of Flood Risk Index. These datasets were selected based on availability and quality (length of record) of data and their relevance to characterize and represent hazard, vulnerability, exposure and coping capacity factors.  The nationwide Philippine Flood Risk Index (P-FRIc) was assessed quantitatively based on the conceptual framework of Pressure and Release (PAR) Model using five indexes. P-FRIc assessed flood risk at present time even without utilizing past flood damage data. It presented the structure of flood risk in comparative and quantitative approaches.  P-FRIc assessed flood risk at present time even without utilizing past flood damage data. It is observed that the comparison of radar charts indicates the different composition of strengths and weaknesses in risk components by province. The methodology to assess the flood risk is a new attempt and is very informative for disaster managers. It is hoped that the outcomes of this thesis will advance the knowledge of disaster managers and planners in the Philippines in flood risk assessment and motivate the people, who are at risk to the negative impact of floods to enhance their flood risk administration. ACKNOWLEDGEMENT I would like to express my sincere gratitude to my Advisor, Prof. Kuniyoshi Takeuchi, for his scholarly advice, guidance and encouragement and the staff of ICHARM led by Chief Researcher Mr. Katsuhito Miyake, Dr. Tadashi Nakasu and Dr. Ali Chavosian for their sincere cooperation and valuable review of this thesis.

REFERENCES Ben Wisner,et.al. At Risk; 2nd Edition, Natural Hazards, People's Vulnerability and Disasters. Routledge, 2004. National Statistical Coordination Board http://www.nscb.gov.ph/ Yasuo Kannami, Master Thesis for GRIPPS / ICHARM . Establishment of Country Based Flood Risk Index: ICHARM, 2008. Yuko Arai, Master Thesis for University of Tokyo . Establishment of a Global Flood Risk Index: University Of Tokyo, 2008.