УДК 626.80

FERRER S. B., Master Degree in Disaster Management/Senior EngineerNational Graduate Institute for Policy Studies (GRIPS)/Department of Meteoro�logy and National Irrigation Administration(NIA), the PhilippinesGUSYEV M., PhD, Lecturer/Specialist ResearcherNational Graduate Institute for Policy Studies (GRIPS)/ International Centre forWater Hazard and Risk Management (ICHARM) under the auspices of UNESCO,Public Works Research Institute (PWRI), Tsukuba, JapanHUSIEV A., Cand. Biol. Sc., Associate professorNational Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

ASSESING CURRENT AND FUTURE FLOOD IMPACTS

IN THE PAMPANGA RIVER BASIN, PHILIPPINES

Àíîòàö³ÿ. Ïðåäñòàâëåí³ ðåçóëüòàòè îö³íêè ðèçèêó ïîâåíåé ³ çàñóõç ìåòîþ îö³íêè çì³í êë³ìàòó â áàñåéí³ ð³÷êè Ïåãó, Ì’ÿíìè íà Ô³ë³ï³íàõ.Ìîäåëü îïàä³â-ñòîê³â-çàòîïëåííÿ (RRI) áóëà âèêîðèñòàíà äëÿ ìîäåëþ-âàííÿ ïîâåí³ â 2011 òà 2014 ðîêàõ. Ìèíóë³ ïîâåí³ òà çàñóõè áóëè âèçíà-÷åí³ ê³ëüê³ñíî, âèêîðèñòîâóþ÷è ñòàíäàðòèçîâàíèé ³íäåêñ îïàä³â (SPI)òà ñòàíäàðòèçîâàíèé ³íäåêñ îïàä³â åâàïîòðàíñï³ðàö³³ (SPEI).

Êëþ÷îâ³ ñëîâà: íåáåçïåêà ïîâåí³, ïîñóõà, ìîäåëü îïàä³â-ñòîê³â-ïîâåíåé (RRI), çì³íà êë³ìàòó, àòìîñôåðíà ìîäåëü çàãàëüíî¿ öèðêó-ëÿö³¿ (MRI-AGCM3.2S).

Abstract. The results of flood and drought risk assessment to assess cli-mate change in the Pampanga River Basin, Myanmar in the Philippinesare presented. The Rainfall-Drainage-Flood (RRI) model was used tomodel the floods in 2011 and 2014. Past floods and droughts have beenquantified using the Standardized Precipitation Index (SPI) and the Stan-dardized Evapotranspiration Precipitation Index (SPEI).

Keywords: flood hazard, drought, rainfall model (RRI), climatechange, atmospheric model of general circulation (MRI-AGCM3.2S).

Introduction. Flooding is a frequent disaster in the low lying areas ofthe Pampanga River basin affecting people’s lives and causing damagesto rain-fed and irrigated rice production [1–4]. The Pampanga Riverbasin located in the Central Luzon Region is the fourth largest basin inthe Philippines with a catchment area of 10,545 km2 and is home for 5.6 million people [5, 6]. According to [1], Nueva Ecija, Pampanga and

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Bulacan provinces have had the largest number of flood events from1970 to 2010 while the Pampanga province is included in the top listconsidering flood casualties. In the Pampanga basin, future climate mayhave water-related extreme events with increased frequency and magni-tude [7, 8]. This requires an assessment of flood inundation depth andarea for the vulnerability communities under climate change.

Study area. This study utilizes flood hazard (H), flood exposure (E),and flood vulnerability (V) to calculate flood risk (R) as R = H E V(Figure 1). Flood inundation depth was identified as hazard and expo-sure is based on population and agricultural area that are inundated du-ring flood event of selected return period. The vulnerability was identi-fied as potential fatalities rates considering the demographic conditionof potential affected population and fragility (or damage) curve, whichprovides a relationship between economic losses and flood inundationdepth.

The Rainfall-Runoff-Inundation(RRI) model was used to simulatethe flood inundation extent and depth in the Pampanga River basin.The RRI is a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously using the 2D diffusive wavemodel to calculate the flow on the slope and 1D diffusive wave modelto calculate the channel flow[9]. Lateral subsurface flow is based on thedischarge-hydraulic gradient relationship while the vertical infiltration isestimated based on the Green-Ampt model. In addition, the effect ofdam was considered in the RRI using a simple water balance rule,which computes the dam storage volume based on the simulated inflow,outflow and dam water storage at previous time step. Once there is noavailable dam water storage, the dam outflow equals to the dam inflowutilizing maximum flood discharge of dam operation.

Flood hazard assessment. The flood discharge and inundation depthwere simulated with the 15-arcsec (about 0.45-km) grid Rainfall-Runoff-Inundation (RRI) model [9], which has been applied for flood hazardassessment in large and small river basins [10–12]. For the Pampangariver basin in Figure 2, the RRI model was developed using 15-arcsecresolution Digital Elevation Model (DEM), flow accumulation and flowdirection data were obtained from HydroSHEDS [13] and global landcover data were used to identify the paddy field, cropland and othersland cover types [14] and applied to simulate the flood discharge andinundation areas of the past four flood events as part of the calibration

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24

Fig

ure

1. M

etho

dolo

gica

l ap

proa

ch in

this s

tudy

(year 2011) and validation (year 2009, 2012, and 2013). As part of cali-bration, the observed hydrograph in Sapang Buho, Mayapyap, and SanIsidro stations was compared to the RRI model simulated dischargeusing the Nash-Sutcliffe Efficiency (NSE) [15]. In addition, the simu-lated result showing the extent of inundation areas was compared to thePAGASA post flood report for flood event of 2011 and to the MODISTerra Level-3 8-day composite products (MOD09A1) [16] for floodevents of 2009, 2012 and 2013. To verify the formula and appliedapproach in quantifying the vulnerability in this study, the flood event of2011 was used and the simulated result of the affected population andagriculture was compared to the report prepared by NDRRMC [12].

Climate change assessment. The MRI-AGCM3.2S rainfall data atabout 20-km grid of 7-days rainfall for 25 years considering the currentclimate condition (CCC) (1979–2003) and Representative Concentra-tion Pathways (RCP) 8.5 future climate condition (FCC) (2075–2099)[17, 18]. The rainfall data were dynamically downscaled from 20-km to5-km grid resolution for better rainfall distribution across the basin anda simple correction approach developed as the difference betweenobserved and MRI-AGCM3.2S rainfall CCC data. The correction coef-ficient was applied to both climate conditions for 10-, 25-, 50-, and100-year return period of 24-hour rainfall and the calibrated RRI simu-lated flood inundation maps using these rainfall for each of these returnperiods. The 2010 census data were used for analyzing the affected po-pulation and potential fatalities was calculated based on the equationdeveloped by ICHARM [3, 4] and three heights of river crop stagessuch as stage1 (vegetative), stage2 (reproductive), and stage3 (maturing)(Figure 2A-B).

Results and discussion. Figure 3 demonstrates RRI simulated andobserved river discharge during the 2012 and 2009 flood events at theSan Isidro river gauge station, which is demonstrated in Figure 4. The0.45-km RRI model was calibrated for the 2011 flood and was com-pared to the observed hydrograph at Sapang Buho, Mayapyap, and SanIsidro stations resulting in the acceptable NSE values of 0.63, 0.51, and0.68, respectively. For validation, the calibrated RRI mode simulatedthe 2009, 2012 and 2013 floods and the MODIS image is compared tothe RRI simulated flood inundation extent[12]. In addition, the 2011flood affected people and agricultural area are compared to the NDR-RMC report (Figure 3B) and lead to 1,192,067 people affected in 953

25

Fig

ure

2. T

he P

ampa

nga

Riv

er b

asin

ele

vation

[13

] A),

gro

wth

sta

ges

of p

alay

[12

] B

),

and

regu

lar

crop

ping

cal

enda

r sh

owin

g th

e th

ree

stag

es o

f pa

lay

C)

26

barangays while the NDRRMC reports 1,471,228 affected people in1,041 barangays indicating a good agreement between the simulated andreported data. While the affected irrigated area analyzing the provinceof Nueva Ecija, the simulation resulted to have a loss of 53 millionUSD compared to the reported value of 85 million USD. The differ-ence can be accounted brought by wind because the above mentionedflood event was due to typhoon.

For assessing the impact of climate change, the 5-km downscaledand bias-corrected MRI-AGCM3.2S rainfall data are utilized for fre-quency analysis of CCC and FCC with (case 1) and without (case 2)outlier (Figure 4) and demonstrate an increasing trend of precipitationin the study area with a bigger impact in case 2. These extreme 24-hourrainfall is used in the calibrated RRI model to produce flood inunda-tion maps with precipitation of 10-, 25-, 50-, and 100-year return peri-od. For example, flood inundation extent of 50-year return period isdemonstrated for CCC in Figure 3C and for FCC-case 1 in Figure 3D.For 261,247 hectares of irrigated area in the basin, 44 % to 72 % of thearea is affected by 10- and 100-year floods under CCC while 59–88 %and 48–80 % are affected under FCC given the three stages of crops,two cases and return period as mentioned (Table 1). In terms of mon-etary values, crop under stage 2 (reproductive) causes the highest mon-etary losses equivalent to 2,914 USD per hectare as compared to othertwo stages.

Conclusions. We conducted flood hazard assessment under climatechange using a rainfall-runoff-inundation (RRI) model in the Pampan-ga River basin. The RRI model was applied to past floods matching the2011 flood discharge and inundation depth as well as the 2011 floodextent estimated from satellite images. These flood inundation maps canbe utilized for mitigation measures of future disaster prevention activi-ties and conducting flood risk assessment in terms of agricultural dam-ages to paddies and affected people in the Pampanga River basin.Although RRI has proven to be effective in use for the risk assessmentin the Pampanga river basin, there are still some limitations of themodel that need to improve in future studies. For example, the simpli-fied dam operation of the RRI model can be modified by implement-ing the operation rule of flood control and the importance of structur-al measures such as levee, embankment, and diversion channels need tobe incorporated. Using only MRI-AGCM3.2S rainfall does not includeuncertainty and other General Circulation Model rainfall should be

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Tab

le 1

Flo

od a

ffec

ted

popu

lation

and

irr

igat

ed a

rea

of t

hree

ric

e st

ages

for

ret

urn

period

s

28

Ret

urn

Per

iod

Clim

ate

Con

dition

Affec

ted

Pop

ulat

ion

% a

ffec

ted

to t

he t

otal

po

pula

tion

Sta

ge 1

(Veg

etat

ive)

Sta

ge 2

(R

epro

duct

ive)

Sta

ge 3

(M

atur

ity)

Affec

ted

area

, ha

% a

ffec

ted

to t

he t

otal

Affec

ted

area

, ha

% a

ffec

ted

to t

he t

otal

Affec

ted

area

, ha

% a

ffec

ted

to t

he t

otal

10-y

ear

CC

C2,

585,

716

46 %

153,

606

59 %

124,

514

48 %

113,

648

44 %

FC

C-C

ase

13,

190,

068

57 %

216,

130

83 %

183,

662

70 %

175,

352

67 %

FC

C-C

ase

22,

759,

170

49 %

173,

463

66 %

135,

670

52 %

125,

012

48 %

25-y

ear

CC

C2,

840,

844

51 %

168,

805

65 %

141,

177

54 %

131,

115

50 %

FC

C-C

ase

13,

477,

558

62 %

224,

153

86 %

194,

096

74 %

186,

715

71 %

FC

C-C

ase

23,

024,

987

54 %

189,

135

72 %

152,

518

58 %

142,

746

55 %

50-y

ear

CC

C3,

013,

317

54 %

179,

034

69 %

151,

982

58 %

142,

086

54 %

FC

C-C

ase

13,

650,

110

65 %

224,

153

86 %

194,

096

74 %

186,

715

71 %

FC

C-C

ase

23,

174,

095

56 %

189,

263

72 %

164,

317

63 %

153,

656

59 %

100-

year

CC

C3,

142,

931

56 %

187,

469

72 %

162,

440

62 %

150,

810

58 %

FC

C-C

ase

13,

768,

634

67 %

230,

051

88 %

203,

374

78 %

195,

933

75 %

FC

C-C

ase

23,

314,

300

59 %

207,

737

80 %

172,

772

66 %

163,

452

63 %

29

Fig

ure

2. R

RI

mod

el s

imul

ated

riv

er d

isch

arge

at

the

San

Isidr

o ga

ugin

g st

atio

n fo

r 20

12 flo

od A

) an

d 20

09 flo

od B

)

Fig

ure

3. T

he 2

011

max

imum

flo

od inu

ndat

ion

dept

h fr

om R

RI

mod

el A

) an

d fie

ld o

bser

vation

of 20

11 flo

od inu

ndat

ion

dept

h B

), a

nd flo

od inu

ndat

ion

exte

nt o

f 50

-yea

r re

turn

per

iod

for

curr

ent

C)

and

RC

P8.

5 fu

ture

D)

30

Fig

ure

4. P

roba

bilit

y an

alys

is b

etwee

n gr

ound

rai

nfal

l an

d th

e M

RI-

AG

CM

3.2S

cur

rent

clim

ate

cond

itio

n (C

CC

) A),

betw

een

CC

C a

nd fut

ure

clim

ate

cond

itio

n ca

se-1

(FC

C-c

ase1

),

and

betw

een

CC

C a

nd fut

ure

clim

ate

cond

itio

n ca

se-2

(FC

C-c

ase2

)

considered in the Pampanga basin. In addition, the future vulnerabilityof increased population and agricultural area may also lead to the floodrisk increase under climate change.

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