1. BACKGROUNDGlobal Climate Models (GCM) precipitation are often characterized by

biases and coarse resolution that limit direct application for basin saclehydrological modeling. The biases in GCM are a low number of dry days whichare compensated by too much drizzle, a bias in the mean and the inability toreproduce the observed heavy rainfall event. To address the bias of GCM, most ofthe climate studies couple bias correction and downscaling scheme. This studydescribes how to select appropriate GCM over the target region and a complete

Cho Thanda Nyunt1, Toshio Koike1, Pactricia Ann Jaranilla Sanchez1, Akio Yamamoto2, Toshihoro Nemoto2, Masaru Kitsuregawa2

1 Department of Civil Engineering, River and Environmental Laboratory, University of Tokyo (Bunkyo-ku, Tokyo 113-8656, Japan)

Paper No : H21F-1237

Bias Correction Method For Climate Change Impact Assessment at a Basin Scale

describes how to select appropriate GCM over the target region and a complete,efficient and comprehensive statistical bias correction method covering extremerainfall, normal rainfall and frequency of dry days. The method is applied andvalidated at the Pampanga, Angat and Kaliwa basins in Philippines. Residents ofthe Manila metropolitan area rely on Angat Dam for 97% of their water resourcesand detailed analysis of future climate change impacts on these basins areurgently required for irrigation efficiency and future water supply balancing inMetro Manila.

Department of Civil Engineering, River and Environmental Laboratory, University of Tokyo (Bunkyo ku, Tokyo 113 8656, Japan)2 Institute of Industrial Science, University of Tokyo (Meguro-ku, Tokyo 153-8505, Japan)

nyunt11@hydra.t.u-tokyo.ac.jp

4. RESULTSValidate extreme value in sorting (1981-2000)Validate extreme frequency analysis (1981-2000)Validate long term climatology inter annual variability (1981 2000)

  121°0'0"E120°0'0"E

'0"N

'0"N

±Location of Pampanga, Angat and Kaliwa Basins

3. STATISTICAL BIAS CORRECTIONSorting data decreasing orderAssign equal total rain days as observed station in GCMDefine zero correction threshold

!(

8

12

6 4

Miroc32_hires August spatial distribution (1981-2000)

!( 612 4 228 1818

Observed August spatial distribution (1981-2000)

Validate long term climatology inter-annual variability (1981-2000)Analyze recurrence level of extreme precipitation (2046-2065)Analyze total number of dry days and continuous dry spell (2046-2065)

!(

!(

!(

!(

!(

!(h

Balungao

ClsuMunoz BalerRadar

BaguioCity

DagupanCity

Pantabangan Dam

17°0

17°0

16°0

'0"N

16°0

'0"N

±Pampanga

Define zero correction threshold Define extreme threshold from top rankExtract extremes eventsCheck sensitivity ξ (be high enough to converge to the GPD fitting and low enough for determination of κ and α)Lowest of AMS of observed is defined as ξAnalyze same number of extremes in GCM gridded seriesFit GCM to GPD by itself Make correction by inverse function observed GPDExtract monthly normal rainfall between extreme and zero correction

Before correction After correction

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

1416

2

10

1820

22

2826

24

30

30

10

6

14

16

12

14

14

10

18

12

12

18

!(

!(

!(

!(!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

8

1416

18

10

2224

20

26

0

2830

12

12

16

14

30

16

12

18

2

8

6

10

GCM biases

0

50

100

150

200

1 11 21 31 41 51

rainfall mm/day

Rank

Extreme rainfall underestimation CLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1giss_aomingv_echam4

GCM seasonal rainfall pattern

200

300

mm

/day

Extreme rainfall before correction CLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1ingv echam4

200

300

mm

/day

Extreme rainfall after correctionCLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1ingv echam4

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

!(

h

Iba

Tabak

Maputi

Matulid

Infanta

Talaguio

Angat Dam

Cabanatuan

SibulSpring

SanFernando

BaiMagalang

ScienceGarden

CuyambayTanay

HaciendaLuisita

15°0

'0"N

15°0

'0"N

Pampanga

Angat

Kaliwa

h Dam

ythresholdGamma CDF mapping for correctionSame transfer function applied future projection

Extreme Rainfall Bias Correction by AMSDefine lowest of AMS as threshold of extremeFitting log-normal or Gumbel by plotting position methodFind the inverse function of in-situ station as corrected extremeC t d l h i it t ti ’ t l

!(

!(

10

8

18

Rainfall mm/dayHigh : 31.7533

Low : 0.466246

!(

!(

20

14

44

1

2

6

Rainfall mm/dayHigh : 33.0755

Low : -3.28925

0

5

10

15

20

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep

Oct

Nov Dec

rainfall mm/day

p

CLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1giss_aomingv_echam4

5

10

15

all m

m/day

Low intensity  long drizzel rain dayCLSUcsiromk3_0miroc32_medresgfdl_cm2_0fdl 2 1

10

15

20

all m

m/d

ay

GCM Raw monthly distribution CLSU

csiromk3_0

miroc32_medres

gfdl_cm2_0

gfdl_cm2_110

15

20

m/d

ay

Biascorrected monthly distribution CLSU

csiromk3_0

miroc32_medres

gfdl_cm2_0

gfdl_cm2_1

ingv echam4

0

100

1 11 21 31 41 51

rain

fall

m

Rank

g _ipsl_cm4

0

100

1 11 21 31 41 51

rain

fall

m

Rank

ingv_echam4ipsl_cm4

In the study region, we have a sufficient number of rain gauges withinor near the basin, so the bias correction can be applied to each station. Thismethod is a t pe of statistical bias correction and spatial disaggregation It has

2. GCM SELECTION21 in-situ stations data provided by Philippines Atmospheric, Geophysicald A t i l S i Ad i i t ti (PAGASA)

5. SUMMARYSix GCMs were selected According to their performances over Luzon Island in the PhilippinesReduce the uncertainty of single GCM use.Address various bias components associated with GCM output

!(

!(

TayabasAmbulong

121°0'0"E120°0'0"E

14°0

'0"N

14°0

'0"N

0 30 6015 Kilometers

!( Rain gaugesh Dam Corrected value reach in-situ stations’ extreme values

Big discrepancy in frequencies

1981-2000 2046-2065

0

5

1500 1800 2100 2400 2700 3000

rainfa

Rank

gfdl_cm2_1ingv_echam4ipsl_cm4

300

400

500

m/d

ay

Frequency analysis 2046-2065CLSUcsiromk3_0miroc32_medresgfdl_cm2_0

0

5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

rain

fa ingv_echam4

ipsl_cm4

0

5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

rain

fall

mm ingv_echam4

ipsl_cm4

0

50

100

150

200

250

1 21 41 61 81 101 121 141 161 181

rainfall mm/day

Rank

Extremes corrected by AMS

CLSU

csiromk3_0

miroc32_medres

gfdl_cm2_0

gfdl_cm2_1

ingv_echam4

ipsl_cm4

0

50

100

150

200

250

1 21 41 61 81 101 121 141 161 181

rainfall mm/day

Rank

Raw Extreme by AMS

CLSU

csiromk3_0

miroc32_medres

gfdl_cm2_0

gfdl_cm2_1

ingv_echam4

ipsl_cm4

method is a type of statistical bias correction and spatial disaggregation. It hasbeen developed and validated for both intensity at a point and basin scale forclimate change impact study.

400

500

m/d

ay

Frequency analysis 1981-2000CLSUcsiromk3_0miroc32_medresgfdl_cm2_0

Rain Bias Correction MethodExtreme Generalized Pareto Distribution

Non every year statisticsLong or short tailed fittingPeak over threshold series

and Astronomical Services Administration (PAGASA)Coupled Model Inter-comparison Project 3 (CMIP3) GCM gridded daily data1981-2000( the control period)2046-2065 (SRESA1B.)Spatial correlation and root mean square error

(Reference JRA-25 reanalysis data and GPCP )

Address various bias components associated with GCM output(extreme rainfall, normal rainfall and total frequency of dry days)

Biases remove well by fitting the GPD.Validate extreme rainfall frequency analysis against observationEstimate the maximum intesity of different return periods in near future. Solve frequency of dry days bias by sorting and matching rankTwo-parameter gamma distribution mapping monthly normal rainfall

An effective, simple and comprehensive bias correction method was developed for climate change impact assessment of basin scale hydrology, and20°N

20oN0 )(-1-1)(

1

≠⎥⎦⎤

⎢⎣⎡ −

=

⎞⎛

καξκ

ξ

κ

x

xxF

0

100

200

300

1 10 100 1000

rain

fall

mm

Return years

gfdl_cm2_1ingv_echam4ipsl_cm4

200

300

of d

ays

Number of continuous no rain day

1986-20002051-2065

6000

er o

f day

s

Total number of dry days

1981-2000

2045-2065

0

100

200

300

1 10 100 1000

rain

fall

mm

Return years

gfdl_cm2_1ingv_echam4ipsl_cm4

No rain day

Ranking order statisticsfrequency of no rain day

in GCM is same as station

developed for climate change impact assessment of basin scale hydrology, and for other impacts. Moreover, the method can be analyzed by discharge flow in further hydrological impact studies. Finally, the bias correction method should be proven by applying to different climate regions.

Selected GCMsNo GCMs Country Scores

1 fdl 2 1 USA 7

0°N160°E80°E

Large Scale area considered

115oE 130oE

10oN

Local Scale PDS) oflimit (lower parameter location parameter scale

)21

21(- parameter shape

0 1 )(

==

<<=

==⎟⎠⎞

⎜⎝⎛ −

−

ξα

κκ

κα

αξx

exF

 

5

10

15

ll mm/day

Low intensity  long drizzel rain dayCLSUcsiromk3_0miroc32_medresgfdl_cm2_0

6. REFERENCES1) Nyunt, C.T. et al: Bias correction method for climate change impact assessments in the

Philippines, JSCE, revised, 2013.2) C Piani J O Haerter E Coppola Statistical bias correction for daily precipitation in regional

PARAMETERPrecipitation

0

100

CLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1ingv_echam4ipsl_cm4

num

ber o

2000

4000

CLSUcsiromk3_0miroc32_medresgfdl_cm2_0gfdl_cm2_1ingv_echam4ipsl_cm4

num

be

100

150

200change

 %Change in precipitation % 

w.r.t  1981‐2000

csiromk3_0

miroc32_medresgfdl_cm2_0gfdl cm2 1 40

60

80

100

ion change

 (%)

Change in standard deviation % w.r.t 1981‐2000

csiromk3_0

miroc32_medres

gfdl_cm2_0

gfdl cm2 1

less than no rain day threshold change zero rainfall.

Normal Gamma Distributionmonthly CDF Gamma mapping

1 gfdl_cm2_1 USA 72 gfdl_cm2_0 USA 63 ipsl_cm4 France 54 ingv_echam4 Italy 45 csiro_mk3_0 Australia 36 miroc3_2_medres Japan 2

0

5

1500 1800 2100 2400 2700 3000

rainfa

Rank

gfdl_cm2_1ingv_echam4ipsl_cm4

GCM=0

No

rain

day

Sorting

ACKNOWLEDMENT

2) C. Piani.J.O.Haerter.E. Coppola. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99: 187-192, 2010.

3) IPCC: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Miller H. L., Cambridge University Press, Cambridge, United Kingdom and New York, 2007a.

4) M.E.Elahamy, I.A. Seierstad and A. Sorteber. Impacts of climate change on Blue Nile flows using bias-corrected GCM scenarios. Hydrol. Earth Syst. Sci., 13,551-565, 2009.

5) IPCC : The regional impacts of Climate Change: An Assessment of Vulnerability, edited by Watson R.T., Moss R.H., Zinyowera M. C., Dokken D. J., A Special report of IPCC Working Group II, 1997.

6) Gupta VK, Duckstein L.A stochastic analysis of extreme droughts. Water Resour. Res., 11:221-228. 1975.

The change in seasonal rainfall (%) for three seasons with respect to1981-2000, 2046-2065 minus 1981-2000. DJF is December-January-February,MAM is March-April-May and JJASON is wet season from June to November.For CLSU station, winter show maximum increase in precipitation and it may bedue to global warming trend in winter There is a small decrease in the monsoon

PrecipitationSea level pressureSea surface temperatureMeridonial windZonal windOLRAir Temperature

‐100

‐50

0

50

DJF MAM JJASONPrecipitation  gfdl_cm2_1

ingv_echam4ipsl_cm4Average

‐60

‐40

‐20

0

20

DJF MAM JJASON

standard deviati g _ _

ingv_echam4

ipsl_cm4

Average

y pp gto monthly CDF of station

inverse of Gamma CDF in each month is corrected rain

GCM

No

rain

day

thre

shol

d

Ext

rem

eth

resh

old

Normal rain

ACKNOWLEDMENTThis research was implemented as a part of a project of the Japan International

Cooperation Agency (JICA), "The Study of Water Security Master Plan for Metro Manila and itsAdjoining Area" by using the Data Integration and Analysis System (DIAS) funded by theMinistry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.. We aregrateful to the EDITORIA science team for permission to use the GCM time series dataset of theData Integration and Analysis System (DIAS) project.

: . 97 .7) Lana S, Burgueno A. Spatial and temporal characterization of annual extreme droughts in

Catalonia (NE Spain). International Journal of Climatology 18: 93-110. 1998b.8) Bobee B, Rasmussen P. Recent advances in flood frequency analysis. US National Report

of IUGG, 1991-1994, 1995.9) Hosking, J.R. M., and J.R. Wallis. Parameter and quantile estimation for the generalized

Pareto distribution. Techno metrics 29(3): 339-349, 1987.10) Amor V.M. Ines, James W. Hansen. Bias correction of daily GCM rainfall for crop

simulation studies. Agricultural and Forest Meteorology y138: 44-53, 2006.

due to global warming trend in winter. There is a small decrease in the monsoonseason and small increase in rainy season by averaging all GCM as in Figurebelow. Another figure compares the change signal by the standard deviationchange (%) of each season with respect to 1981-2000. It is also prominentincreases in DJF and same trend as in change in precipitation. In the rainyseason, all GCM agree in change of precipitation increase and standarddeviation change also increase means seasonal variation also robust.