1. Werapol Bejranonda and Manfred Koch Geohydraulics and Engineering Hydrology, University of Kassel Aug 2005-manager.co.th Application of Multi-site stochastic daily Climate Generation to assess the Impact of Climate Change in the eastern Seaboard of Thailand

2. Table of Contents1.Introduction Motivation/ Study region/ Objectives/ Scope of work Motivation/ Study region/ Objectives/ Scope of work 2.Model development Methodology/ Model structure Methodology/ Model structure 3.Evaluation & Application Climate schemes/ Application in downscaling Climate schemes/ Application in downscaling 4.Impacts of climate change Climate of the 21th century/ Impact on water resources Climate of the 21th century/ Impact on water resources5.Conclusions 2 Introduction Development Impacts Eval. & App. Conclusions

3. Motivation Aug 2005-manager.co.th 2005 Drought crisis in Eastern Seaboard Industrial shutdown Crop loss Abruption of Thai economy (ICIS, 2005) outdated climate pattern Rainfall / ClimateWater Planning traditional management http://www.oknation.net/blog/print.php?id=222747 Water storage in reservoir (DK) Consequences 3 Introduction Development Impacts Eval. & App.Conclusions

4. Study area Eastern coastline Major industrial zone of Thailand Eastern Seaboard of Thailand (EST) Thai Gulf Rayong Chonburi 1560 km 2 DK NPL KY Khlong Yai basin 4 Introduction Development Impacts Eval. & App.Conclusions

5. Objectives Pattern of climate change and effects on water resources Development 1. Development of daily weather generation - Using statistical/stochastical techniques - Ultimate goal Investigation 3. Investigation of climate pattern in 21 st century - Assessing the impact of climate change - Application 2. Application in climate projection - Integrating with climate downscaling - 5 Introduction Development Impacts Eval. & App.Conclusions

6. Scope of work Climate models 2. Climate downscaling 1. Stochastic generation of daily climate projecting monthly climate in 21 st century rescaling monthly  daily climate Parameters Tmax, Tmin, PCP Climate sites in EST Climate sites in EST ● 24 precipitation ● 4 temperature Tmax, Tmin, PCP Future monthly climate Historic monthly & daily climate Performance Existing predicting tools vs. New tools developed here Impact assessment in EST 6 Introduction Development Impacts Eval. & App.Conclusions

7. Data distribution Extreme values Spatial pattern etc. Stochastic climate generator Methodology (1) multi-realization daily climate 30rlz Daily attributes Monthly climate Daily Moran’s I Extreme daily rainfall 7 Introduction Development Impacts Eval. & App.Conclusions

8. 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 Impacts Eval. & App.Conclusions

9. wet dry Model structure monthly MLR model Daily weather generation MLR + weather generation monthly GCMs  daily climate New tool ! Rainfall Daily climate Monthly rainfall Probability of wet day Tmax & Tmin Rain. occurrence generation Rainfall amount generation Tmax & Tmin generation Historic record Monthly data Parameter estimation Moran’s I relationship Extreme value relationship Critical rainfall probability (Pc) etc. 30rlz series Rain on wet day Daily Tmax & Tmin on wet/dry 9 Introduction Development Impacts Eval. & App.Conclusions

10. Climate schemes Long-term projection Daily weather generation calibration 1971 1999 verification 1985 1986 20c3m 2096 projection 2000 Future scenarios (SRES) 19712000 calibrationverificationprojection 1971-1985 1986-19992000-2096 GCM-baseline 1985 1986 calibrationverification calibrationverification 1971-1985 1986-2000 Using GCM climate data Using local climate data 10 Introduction Development Impacts Eval. & App. Conclusions

11. Multi-linear regression (MLR) Climate projection Monthly GCMs Application in climate projection A1B A2 B1 Future scenarios 2000-2096 Scenarios A1B A2 B1 (Houghton et al.,2001) Greenhouse gas Multi-domain & High-Res GCMs Multi-domain & High-Res GCMs ● 2.5° x 2.5° GCMs (5 domains) ● 0.5° x 0.5° High-Res. GCM 75,000 km 2 3,000 km 2 ECHO-G, BCCR, ECHAM5, GISS, PCM CRU/TYN Projected monthly climate Daily weather generation Projected daily climate 30rlz 11 Introduction Development Impacts Eval. & App. Conclusions

12. Evaluation: Daily climate generation calibration1971-1985 verification1986-1999 Validation scheme Scatterplots of obs. and sim. monthly average climate PCPMax temperatureMin temperature Predictor Calibration: 1971-1985 Verification: 1986-2000 residual error NS residual error NS MERMSE MERMSE Wet rate (% wet day)0.363.32 0.71 0.702.890.80 Rainfall amount (mm/day)-0.150.240.99 0.190.340.99 Tmax (°C)-0.040.070.99 0.200.240.95 Tmin (°C)-0.010.080.99 0.080.210.99 12 Introduction Development Impacts Eval. & App. Conclusions

13. Evaluation: Application in downscaling Multi-linear regression downscaling (MLR) + Daily Weather Generation (DWG) Cross-correlation Predicted vs observed series Density distribution Predicted vs observed Tmax Goal  Describing climate behaviour Best in describing climate series (correlation & distribution) 13 Introduction Development Impacts Eval. & App. Conclusions

14. Hydrol. consequences Impact assessment SWAT model (Arnold et al, 1998) Tmax & Tmax Precipitation Projected daily climate 30rlz MLR + DWG monthly GCMs daily climate New tool ! Land & Soil maps Physical properties PCP Evaporation Percolation 30rlz Impact assessment 14 Introduction Development Impacts Eval. & App.Conclusions

15. Climate over 21 st century 21 st century projection 2000 – 2096 20 th century simulation 1971 – 1999 21 st 20 th 21 st longer droughts Extreme daily rainfall 20 th 21 st more extreme SRES A2 Prob. of rain occurrence (% of wet day) Temperature vs slight increase Precipitation % of wet day 21 st 20 th Tmax Tmin 15 Introduction Development Impacts Eval. & App.Conclusions

16. Impact on water resources Effects at reservoirs Aug 2005-manager.co.th A1B A2 B1 Density distribution of runoff Wet season SRES A2 Streamflow 20 th increase  21 st decrease more low-flow change of pattern NPLreservoir change of monthly flow-in (cms/year) 21 st 20 th Compared to 20 th Avg. monthly discharge at z4,z15 and z38 (m 3 /s) 21 st 20 th NPL NPL reservoir Change of inflow in 21 st century 16 Introduction Development Impacts Eval. & App.Conclusions May 2014-manager.co.th

17. 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 21 st 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 Impacts Eval. & App.Conclusions

18. Further developments Generating daily weather for short-term climate prediction MLR model Daily weather generation 18 Introduction Development Impacts Eval. & App.Conclusions Teleconnection SSTs Ocean Indices Hydrological simulation at ungagged basin Hydrologic model Daily weather generation Known monthly regional climate

19. 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 Impacts Eval. & App.Conclusions