Impacts of climate change on the hydrological processes in the Mekong River. Hui Lu & Wei Wang Tsinghua University

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1 1 Impacts of climate change on the hydrological processes in the Mekong River Hui Lu & Wei Wang Tsinghua University

2 Outline Backgrounds Method and data Model calibration Temperature and rainfall trends Runoff trends Summary Acknowledgement 2

3 Backgrounds climate change will likely change rainfall amounts and patterns and the frequency and extent of extreme weather events Evaluation of the hydrological impacts of climate change become a research hotspot As changes in weather patterns are being felt across the Lower Mekong Basin, the impacts of climate change have become a topic of strong public interest The IPCC 5th Assessment Report has been released this year 3

4 Method and data Build a distribute model in Mekong Hydrological model validation Bias correction for GCM simulation Using downscale GCM outputs to force the model validation period data TRMM bias correction spin up simulate projection spin up simulation

5 Model and data We used a geomorphology based hydrological model (GBHM) A physical distributed hydrological model a flow-interval hillslope scheme is applied to simplify the complex two-dimensional water kinematics to one-dimensional 5

6 Method and data Data for building the digital basin DEM data, Land use and land cover from the USGS Soil type and soil depth data are from FAO NDVI from MODIS 6

7 Method and data Data for driving the model Calibration data: remote sensing rainfall in grid TRMM3b42 Future projection driven by output of 5 GCM in 4 RCP: 0.25 downscaling daily data from Max Planck Institute GFDL-ESM2M HadGEM2-ES IPSL-CM5a-LR MIROC-ESM-CHEM NorESM1-M 7

8 Model calibration Calibration by TRMM (1998) Daily Runoff m^3/s x 104 x x 104 Luang Stung Prabang Treng Chiang Mukdahan Sean x 104 obs obs obs simobs sim Pakse sim nash= nash=0.57 nash=0.82 nash= nash= Chiang Sean Luang Prabang Mukdahan Pakse Stung Tren Day of year 8

9 Model calibration Model performance in validation ( ) x 104 obs obs obs sim sim Pakse obs sim obs sim 3.5 sim Monthly Runoff m^3/s 6 x 104 Chiang Mukdahan Sean Stung Treng Luang Prabang nash=0.89 nash=0.86 nash=0.81 nash=0.86 nash=0.88 Chiang Sean Luang Prabang Mukdahan Pakse Stung Treng Year 9

10 station GCM bias bias_r CORR nash RMSE Model calibration GFDL Had Chiang Sean IPSL MIROC Bias correction by history data Nor GFDL Use the history data of GCMs to test the GCMs Mukdahan output in Had IPSL MIROC Calculate the ratio of simulated runoff with observation: b Nor GFDL Had Pakse Use b to IPSL correct the projected 0.02 runoff to remove the errors brought MIROC by GCMs and hydrological model Nor GFDL Had Stung Treng IPSL MIROC Nor

11 Temperature and rainfall trends rcp2.6 rcp2.6 GFDL rcp2.6 HadESM 30 rcp2.6 IPSL 27 temperature The temperature will rise projected by all GCMs rcp2.6 MIROC rcp2.6 NorGEM rcp4.5 rcp4.5 GFDL rcp4.5 HadESM rcp4.5 IPSL rcp4.5 MIROC rcp4.5 NorGEM GFDL rcp2 GFDL rcp4 24 GFDL rcp6 GFDL rcp8 IPSL rcp2 IPSL rcp4 23 IPSL rcp6 IPSL rcp HadESM rcp2 HadESM rcp4 22 HadESM rcp6 HadESM rcp8 27 MIROC rcp2 MIROC rcp4 21 MIROC rcp6 MIROC rcp8 NorGEM rcp2 NorGEM rcp4 20 NorGEM rcp6 NorGEM rcp8 2000s 2010s 2020s 2030s s 2090s rcp rcp6.0 GFDL rcp6.0 HadESM rcp6.0 IPSL rcp6.0 MIROC rcp6.0 NorGEM rcp8.5 rcp8.5 GFDL rcp8.5 HadESM rcp8.5 IPSL rcp8.5 MIROC rcp8.5 NorGEM s nd Mekong Climate 2010s Change 2020sForum2030s 2060s 2090s 11 20

12 Temperature and rainfall trends rcp The GFDL HadGEM rainfall IPSL trends MIROC NorESM show great disagreement rainfall with each other The NorESM increase as 1500 time going The GFDL and IPSL fluctuate 1400 dramatically The HadGEM and MIROC first 2000s decease 2010s 2020s then 2030s increase 2060s 2090s 1400 rcp6.0 rianfall (mm) rcp4.5 GFDL HadGEM IPSL MIROC NorESM rcp GFDL HadGEM IPSL MIROC NorESM GFDL rcp2 GFDL rcp4gfdl GFDL HadGEM rcp6 IPSLGFDL rcp8 MIROC IPSL NorESM rcp IPSL rcp IPSL rcp6 IPSL rcp8 HadESM rcp2 HadESM rcp HadESM rcp HadESM rcp8 MIROC rcp2 MIROC rcp4 MIROC rcp MIROC rcp NorGEM rcp2 NorGEM rcp4 NorGEM rcp6 NorGEM rcp8 2000s 2010s s 2030s 2060s 2090s s 2 nd 2010s 2020s 2030s Mekong Climate Change Forum 2060s 2090s

13 Runoff trends-chiang Saen GFDL G2 G4 G6 G HadGEM H2 H4 H6 H IPSL I2 I4 I6 I MIROC NorESM average M2 M4 M6 M N2 N4 N6 N rcp2.6 rcp4.5 rcp6.0 rcp8.5 13

14 Runoff trends-mukdahan GFDL HadGEM IPSL I2 I4 I6 I G2 G4 G6 G8 2010s 2010s 2020s 2030s 2060s 2090s H2 H4 H6 H8 2010s 2010s 2020s 2030s 2060s 2090s s 2010s 2020s 2030s 2060s 2090s MIROC M2 M4 M6 M8 NorESM N2 N4 N6 N8 rcp2.6 rcp6.0 average rcp4.5 rcp s 2010s 2020s 2030s 2060s 2090s s 2010s 2020s 2030s 2060s 2090s s 2010s 2020s 2030s 2060s 2090s 14

15 Runoff trends-pakse GFDL HadGEM IPSL G2 G4 G6 G8 MIROC H2 H4 H6 H8 NorESM I2 I4 I6 I8 average M2 M4 M6 M8 N2 N4 N6 N rcp2.6 rcp6.0 rcp4.5 rcp s 2010s 2020s 2030s 2060s 2090s 15

16 Runoff trends-stung Treng GFDL HadGEM IPSL I2 I4 I6 I G2 G4 G6 G8 H2 H4 H6 H MIROC M2 M4 M6 M NorESM N2 N4 N6 N rcp2.6 rcp6.0 average rcp4.5 rcp s 2010s 2020s 2030s 2060s 2090s 16

17 Runoff trends- in 2090s Runoff increase biggest in RCP4.5 Chiang Saen increase biggest for most scenarios Stung Treng increase smallest for all scenarios Chiang Sean Mukdahan Pakse Stung Treng RCP % 11.1% 8.7% 6.5% RCP % 18.1% 14.2% 12.5% RCP % 7.2% 7.7% 7.6% RCP % 10.0% 10.3% 8.4% 17

18 Runoff trends-inter-annual variations Chiang Sean average of std There has a trend of inter-annual 2800 variations increasing especially in 2300Chiang Saen Mukdahan average of std rcp2.6 rcp4.5 rcp6.0 rcp rcp2.6 rcp4.5 rcp6.0 rcp Pakse average of std Stung Treng average of std rcp2.6 rcp4.5 rcp6.0 rcp rcp2.6 rcp4.5 rcp6.0 rcp8.5

19 Summary It is obviously that the average of temperature in the basin will increase in the future For the precipitation, the GCMs shows great disagreement from each other For the runoff, it depends largely on precipitation trends, but it will increase on average The inter-annual variations will increase especially in Chiang Saen, the discharge in Chiang Saen increase biggest for most scenarios the upper Mekong is more vulnerable to climate change 19

20 Acknowledgement This work was jointly supported by the National 863 Program of China (2012AA12A309), the National Natural Science Foundation of China ( , and ), Tsinghua University Initiative Research Program ( ), and the Beijing Higher Education Young Elite Teacher Project (YETP0132). Thank the International Scientific & Technical Data Mirror Site, Computer Network Information Center, CAS for RS data and MRC for situ data. Thank the High Performance Computing (HPC), Tsinghua University for the computation support. 20

21 21 Thank you