werapol bejranonda and manfred koch geohydraulics and engineering hydrology, university of kassel...

Werapol Bejranonda and Manfred KochGeohydraulics and Engineering Hydrology, University of Kassel

Aug 2005-manager.co.th

Application of Multi-sitestochastic daily Climate Generation

to assess the Impact of Climate Change in the eastern Seaboard of Thailand

Table of Contents1.Introduction

Motivation/ Study region/ Objectives/ Scope of work

2.Model development

Methodology/ Model structure

3.Evaluation & Application

Climate schemes/ Application in downscaling

4.Impacts of climate change

Climate of the 21th century/ Impact on water resources

5.Conclusions2 Introduction Development ImpactsEval. & App. Conclusions

Motivation

Aug 2005-manager.co.th

2005

Drought crisisin Eastern Seaboard

Industrial shutdown

Crop lossAbruption of Thai economy

(ICIS, 2005)

outdated climate pattern

Rainfall / Climate Water Planningtraditional management

Jan Decno storage

Res

ervo

ir s

tora

ge

monsoon storms source: eastwater.com

traditional rule

http://www.oknation.net/blog/print.php?id=222747

Water storage in reservoir (DK)

Consequences

3 Introduction Development ImpactsEval. & App. Conclusions

Study area

Eastern coastline

Major industrial zone of Thailand

Eastern Seaboard of Thailand (EST)

Thai Gulf

Rayong

Chonburi

1560 km2

DK

NPL KY

Khlong Yai basin

4 Introduction Development ImpactsEval. & App. Conclusions

Objectives

Pattern of climate changeand effects on water resources

1. Development of daily weather generation- Using statistical/stochastical techniques -

Ultimate goal

3. Investigation of climate pattern in 21st century - Assessing the impact of climate change -

2. Application in climate projection- Integrating with climate downscaling -

5 Introduction Development ImpactsEval. & App. Conclusions

Scope of workClimate models

2. Climate downscaling

1. Stochastic generation of daily climate

projectingmonthly climate in 21st century

rescalingmonthly daily climate

Parameters

Tmax, Tmin, PCP

Climate sites in EST ● 24 precipitation ● 4 temperature

Tmax, Tmin, PCP

Future monthly climate

Historic monthly & daily

climate

Performance

Existing predicting

toolsvs.New tools

developed here

Impact assessment in

EST

6 Introduction Development ImpactsEval. & App. Conclusions

• Data distribution• Extreme values• Spatial pattern• etc.

Stochastic climate

generator

Methodology (1)

multi-realizationdaily climate

30rlz

Daily attributesMonthly climate

Daily Moran’s I

Extreme daily rainfall

7 Introduction Development ImpactsEval. & App. Conclusions

Methodology (2)

Daily Moran’s I of Tmax

1.Today wet or dry ?

2.Rainfall amount 3.Temperature

Rainfall generation

Multi-site generation

Climate pattern

(Khalili et al., 2007)

dataurbanist.com

two-state Markov chain

Exponential distribution Normal distribution

Spatial Autocorrelation

Tmax & Tmin generation

Moran’s I

Positive Moran’s I Negative Moran’s I

dataurbanist.com

8 Introduction Development ImpactsEval. & App. Conclusions

wetdry

Model structure

monthly MLR model

Daily weather generation MLR + weather generationmonthly GCMs daily climateNew tool !

RainfallDaily climate

Monthly rainfall

Probability of wet day

Tmax & Tmin

Rain. occurrencegeneration

Rainfall amountgeneration

Tmax & Tmingeneration

Historicrecord

Monthly data

Parameter estimation• Moran’s I relationship

• Extreme value relationship

• Critical rainfall probability (Pc)• etc.

γk,i=1

Ik,i=1

γk,i=12

Ik,i=12… ...

m = 30 points

RmeanTmean

Textr/Tmean

30rlz

30rlz

series

Rain onwet day

Daily Tmax & Tmin on wet/dry

9 Introduction Development ImpactsEval. & App. Conclusions

Climate schemes

Long-termprojection

Daily weather generation

calibration

1971 1999

verification

1985 1986

20c3m

2096

projection

2000

Future scenarios (SRES)

1971 2000

calibration verification projection

1971-1985 1986-1999 2000-2096

GCM-baseline

1985 1986

calibration verification

calibration verification

1971-1985 1986-2000

Using GCM climate data

Using local climate data

10 Introduction Development ImpactsEval. & App. Conclusions

Multi-linear regression (MLR)

Climate projection

Monthly GCMs

Application in climate projection

A1BA2

B1

2000-2096

Scenarios

Multi-domain & High-Res GCMs ● 2.5° x 2.5° GCMs (5 domains)

● 0.5° x 0.5° High-Res. GCM

75,000 km2

3,000 km2

ECHO-G, BCCR, ECHAM5, GISS, PCM

CRU/TYN

Projected monthly climate

Daily weather generation

Projected daily climate

30rlz

11 Introduction Development ImpactsEval. & App. Conclusions

Evaluation: Daily climate generation

calibration 1971-1985verification 1986-1999

Validation schemeScatterplots of obs. and sim.monthly average climate

PCP Max temperature Min temperature

PredictorCalibration: 1971-1985   Verification: 1986-2000 residual error

NS  residual error

NSME RMSE   ME RMSE

Wet rate (% wet day) 0.36 3.32 0.71   0.70 2.89 0.80

Rainfall amount (mm/day) -0.15 0.24 0.99   0.19 0.34 0.99

Tmax (°C) -0.04 0.07 0.99   0.20 0.24 0.95

Tmin (°C) -0.01 0.08 0.99   0.08 0.21 0.99

12 Introduction Development ImpactsEval. & App. Conclusions

Evaluation: Application in downscaling

Multi-linear regression downscaling (MLR)+

Daily Weather Generation (DWG)

Cross-correlationPredicted vs observed series

Density distributionPredicted vs observed Tmax

Goal Describing climate behaviour

Best in describing climate series(correlation & distribution)

Temperature (°C) Temperature (°C)

a) SDSM b) LARS-WG

c) MLR-daily d) MLR+DWG

Temperature (°C) Temperature (°C)

Temperature (°C) Temperature (°C)

a) SDSM b) LARS-WG

c) MLR-daily d) MLR+climate generator 13 Introduction Development ImpactsEval. & App. Conclusions

Hydrol. consequences

Impact assessment

SWAT model

2

4

8

6

3

9

10

1

7 5

11

12

(Arnold et al, 1998)

Tmax & Tmax

Precipitation

Projected daily climate

30rlz

30rlz

MLR + DWG

monthly GCMs

daily climate

New tool ! Land & Soil maps

Physical properties

0

200

400

600

800

1000

1200

1400

1600

1800

2000

amou

nt o

f wat

er (m

m/y

ear)

year

Soil+Surface ETPERC PCP.obs.simET.obs.sim PERC.obs.sim

20c3m SRES

evapotranspiration

precipitation

percolation

PCP

Evaporation

Percolation

30rlz

Impact assessment

14 Introduction Development ImpactsEval. & App. Conclusions

Climate over 21st century21st century projection

2000 – 209620th century simulation

1971 – 1999

21st20th

20 th

21st

longer droughts

Extreme daily rainfall

20th

21st

more extreme

SRES A2

Prob. of rain occurrence(% of wet day)Temperature

vs

slight increase

Precipitation

% ofwet day

21st20th

Tmax

Tmin

15 Introduction Development ImpactsEval. & App. Conclusions

DK

NPL KY

Z4

Z15

Z38

Stream gauge

Impact on water resourcesEffects at reservoirs

Aug 2005-manager.co.th

A1BA2B1

Density distributionof runoff

Wet season

SRES A2

Streamflow

20th increase 21st decrease

more low-flowchange of pattern

NPLreservoir

ch

an

ge o

f m

on

thly

flow

-in

(c

ms/

year)

21st

20 th

Compared to 20th

Avg

. m

on

thly

dis

char

ge

at z

4,z1

5 an

d z

38 (

m3/s

)

21st

20th

NPLNPL reservoir

Change of inflow in 21st century

16 Introduction Development ImpactsEval. & App. Conclusions

May 2014-manager.co.th

Conclusions

DWG can be applied for :• Generating daily weather data from known monthly• Downscaling monthly GCMs into daily climate series

(in application of monthly downscaling) DWG Model performance

• DWG can describe climate fluctuation and distribution• Better performance than daily GCM downscaling (e.g.

SDSM and LARS-WG)

Daily weather generation (DWG)

Impact of climate change Climate in 21st century in study region

• Higher temperature / extreme wet spells / longer droughts• Change in mean and distribution

Impact on water resources• Less reservoir inflow / pattern change (distribution / season)

17 Introduction Development ImpactsEval. & App. Conclusions

Further developments

Generating daily weatherfor short-term climate prediction

MLR modelDaily weather generation

18 Introduction Development ImpactsEval. & App. Conclusions

Teleconnection• SSTs• Ocean Indices

Hydrological simulation at ungagged basin

Hydrologic model

Daily weather generation

Known monthly regional climate

Thanks to• Water Resources System Research Unit,

Chulalongkorn University, Thailand (WRSRU_CU)• Royal Irrigation Department, Thailand (RID)• Thai meteorological department, Thailand (TMD)

Questions & Answers

References Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part i: model development. J

Am Water Resources Assoc 34(1):73–89. Chantanusornsiri W (2012) 2011 GDP growth sinks to 0.1% on flood crisis. Bounceback of about 6% expected this year. Bangkok Post

2012 Houghton J, Ding Y, Griggs D, Noguer M, van der Linden P, Dai X, Maskell K, Johnson C (2001) Climate change 2001. The scientific

basis. Contribution of Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press.

ICIS (2005) How severe is drought in Thailand? http://www.icis.com/Articles/2005/07/25/2003310/how-severe-is-drought-in-thailand.html Khalili M, Leconte R, Brissette F (2007) Stochastic Multisite Generation of Daily Precipitation Data Using Spatial Autocorrelation. J.

Hydrometeor. 8(3):396–412. Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators

for diverse climates. Clim. Res. 10(2):95–107. Wilby RL, Dawson CW, Barrow EM (2002) SDSM — a decision support tool for the assessment of regional climate change impacts.

Environmental Modelling & Software 17(2):145–157.

19 Introduction Development ImpactsEval. & App. Conclusions