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


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outdated climate pattern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


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 -


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


• 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


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


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


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


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


Projected daily climate

30rlz


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


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



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


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


DK

NPL KY

Z4

Z15

Z38

Stream gauge

Impact on water resourcesEffects at reservoirs


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


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)


Further developments

Generating daily weatherfor short-term climate prediction

MLR modelDaily weather generation


Teleconnection• SSTs• Ocean Indices

Hydrological simulation at ungagged basin

Hydrologic model


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.


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

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