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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
22
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
23
(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
27
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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