Chatchai Chingchanagool ID:109713

1. IMPACT OF FUTURE CLIMATE ON TRENDS OF INFLOW TO UBOLRATANA DAM, THAILAND 6th AIT Masters Theses Competition (16 May 2011) ChatchaiChingchanagool ID:109713 Committee : Dr. SangamShrestha (Chairperson) Dr. SutatWeesakul (Co-chairperson)   Dr. Mukand S. Babel (Member)   Dr. Roberto S. Clemente (Member) SCHOOL OF ENGINEERING AND TECHNOLOGY ASIAN INSTITUTE OF TECHNOLOGY

2. Contents Regional climate model Climate trend analysis Objective of the study Rainfall-Runoff model Study framework Conclusions Background Study area

3. Study area and Data collection Background Ubolratana dam • It is located at the northeast of Thailand. The dam is a multipurpose dam for development of electricity, irrigation and flood control. • However, the several studies in Thailand reveal that climate change is taking place in this country. • Changes of rainfall and temperature will affect the inflow and impact on the dam operation which we don’t know How much inflow? and When it will occur?

4. Objective of the study To assess the impact of climatic change on the inflow area of Ubolratanadam, Thailand To analyze the trend in historical rainfall, temperature and streamflow. To compute the projected amount of rainfall, temperature in the study area under climate change condition. To investigate the impacts of climate change on the inflow of the dam

5. Study framework Historical Data Future Climate Observed hydrological data collection Statistical analysis Collecting RCM data (PRECIS) Rainfall, Temperature SRES A2, B2 Extreme indices <Annual> -Rainfall -Temperature -Streamflow <Daily> -Rainfall -Temperature Bias correction Summary and analyze climate projection Hilbert-Huang Transform Generating rainfall-runoff model For inflow of Ubolratana dam Mann-Kendall

6. Study area Annual rainfall = 1,175 mm Average annual temperature = 27.5°C This study is confined by nine sub-basins of Chi river basin

7. Climate trend analysis Extreme indices data and methodology Daily rainfall, maximum and minimum daily temperature data for five stations More than 40 years • A total of 27 extreme rainfall and temperature indices are calculated on annual basis for all station by using “RClimDex” • RClimDex is a software calculation that identify the climate extremes indices from daily time series of input data. • Mann-Kendall trend test (non-parametric) is applied for trend estimation assuming the 95% on a probability will be the level of significance.

8. Results and discussion: Trend of Rainfall Notrend IncreaseinR40, SDII IncreaseinPRCPTOT, R10, R80, R99p, SDII Notrend DecreaseinR99p R## = No. of days when rainfall more than ## mm PRCPTOT = Annual rainfall R99p = Annual rainfall when rainfall > 99th percentile SDII = Annual rainfall divided by the number of wet days Summaryofextremerainfallindicesineachstation

9. Results and discussion Trend of Temperature Decrease in TN10p, TX10p Increase in TR25, TN90p, TNn Decrease in CSDI, DTR, TN10p, TX10p Increase in TR25, TN90p, TNn, TNx, WSDI Decrease in DTR, TN10p, TX10p Increase in TR25, TN90p, TNn Decrease in TX10p Increase in DTR, SU35, TX90p, TXx, WSDI Decrease in CSDI, TN10p, TX10p Increase in SU35, TR25, TN90p, TNn, TNx, TX90p N = Minimum, X = Maximum, p = Percentile TR25 = Annual count when TN > 25ºC, SU35 = Annual count when TX > 35ºC CSDI = Annual count with at least 6 consecutive days when TN < 10th percentile WSDI = Annual count with at least 6 consecutive days when TX > 90th percentile DTR = Monthly mean difference between TX and TN Summaryofextremetemperatureindicesineachstation

10. Analysis of annual streamflow plus inflow • Mann kendall is applied for trend test of the streamflowquantile • and mean value. Increase in Q100, Mean Notrend Increase in Q100 Increase in Q80 Notrend Notrend Increase in Q20, Q30, Q40, Q50, Q60 Notrend Notrend Summaryofextremestreamflowindicesineachstation

11. The empirical mode decomposition method (EMD) • Method for processing nonstationary signals and signals produced by nonlinear processes • Decomposition of the signal into a set of Intrinsic Mode Functions (IMF)to represent the original data, that admit well behaved Hilbert Transforms Results and discussion Loei Udonthani Khonkaen Mahasarakham Chaiyaphum

12. Trend test of segments derived from IMFs KhonkaenMax.Temp. Segment-1 Segment-2 Segment-3 The detection of trend change in time series is not easy to find. Highest order of IMF captures slow oscillation modes which can perform in segmentation 1994 • Trend test with… • Linear regression slope • IMF slope • Mann Kendall Summary results

13. The turning point of residual trend in monthly rainfall Only the results of residual trend are not enough to conclude that the turning point of graph is the transition point of climate. Loei Udonthani Khonkaen Mahasarakham Chaiyaphum • So, I divide it into 2 sections and test with… • Linear regression • slope • Residual slope • Mann Kendall Summary results

14. Regional climate model Data and methodology RCM : Observed data : Rainfall 39 stations Temperature 1 station Control period: 1976-2005 Future period : 2010-2039, 2040-2069 and 2070-2099

15. How to compare measure rainfall and rainfall from RCM (1976-2005) RRCM=0.14R1+0.33R2+0.43R3+0.10R4 The weights were determined as the fraction of the Thiessen polygon area falling within a specific grid box from RCM

16. Bias correction The power law transform method was developed by Leander and Buishand (2006) For rainfall, corrects the coefficient of variation (CV) and mean. and For temperature, corrects the standard deviation(SD) and mean. • Model assessment criteria • Coefficient of determination, R2 • Root mean square error, RMSE • Efficiency index, EI

17. The example of bias correction for precipitation @ Grid 1681024 SRES A2 (1976-2005) Very high extreme value Similar Observation data No Bias correction Raw RCM gives the overvaluation from Jun. to Oct. Bias correction Mean monthly rainfall Future data from RCM, they have some errors. So, The bias correction method is needed

18. Results: Rainfall The 2nd peak value shifts backward from Sep. to Aug. % change from 1976-2005 Wet season: May to Oct Dry season: Nov to Apr

19. Results: Rainfall 1175 (Average annual rainfall) In 1976-2005, 60% probability of annual rainfall less than 1175 mm (mean) but in period 2010-2039, the probability reduce to 40% • Why probabilistic projection graph was plotted? • We cannot give a single answer. • Giving a range of possible climate outcomes is better and can help with making robust adaptation decisions.

20. Results: Maximum Temperature New peak on Jul. Value change from 1976-2005

21. Rainfall and Elevation Annual rainfall (1976-2005) continuing to increase with elevation increase or increasing 63 mm/100m In the future, annual rainfall at the low elevation will increase more than at the high elevation level

22. Rainfall-Runoff model Mike11 NAM model is used to predict inflow to Ubolratana dam Rain Grid Model parameters

23. Mike11 NAM model Calibration 2003-2007 Drought Validation 1998-2002 Flood Drought

24. Results: Inflow Failure (Inflow higher than 80th percentile) 0 Q80 1 Satisfactory Reliability, Resilience and Vulnerability Q20 0 Failure (Inflow lower than 20thpercentile) • Even though rainfall will extremely increase, there still has drought years. • The quantile analysis show that low and moderate flows are seemed to change (increase) more than high flow.

25. Conclusions • The significant increase trend of rainfall indices are appeared on Mahasarakham an Loei province. • Trends in the extreme temperature are revealed on all stations by increasing in term of magnitude, no. of hot day and decrease in no. of cool day. • Streamflow trends have occurred in high flow at the north of upstream area. • Residual trends of daily temperature trend are increase in all station. • Rainfall in future increase 35% to 42% for A2 and 19% to 40% for B2. • Max. Temp. increase 0.5°C to 2.5°C for A2 and 0.5°C to 1.9°C for B2. • Min. Temp. increase 0.6°C to 3.1°C for A2 and 0.6°C to 2.3°C for B2. • Inflows increase for all periods and both emission scenarios with the greatest change in low and moderate flow quantile ranges.

26. Thank you