Future Change Analysis of Extreme Floods atthe Indochinese Peninsula Using Large
Ensemble Climate Simulation Data

Yasuto TACHIKAWA, Patinya HANITTINAN,Kazuaki YOROZU, Yutaka ICHIKAWA
Hydrology and Water Resources Research Lab.
Dept. of Civil & Earth Resources Engineering, Kyoto University

New Climate Change
Research Program
TOUGOU Program, 2017-2021

Integrated Climate Model Advanced Research Programsupported by MEXT
2TOUGOU Program, 2017-2021
Theme iv) Water-related Hazard Prediction
in Southeast Asia and the Pacific
MRI, Meteorological Research Institute in Japan provides
 multi-ensemble 60km resolution atmospheric and hydrologic
dataset for percent and future climate projections, and new GCM outputs simulated with 60km AGCM, 20km AGCM, and
5km regional climate model under RCP8.5 scenario for Southeast
Asia and the pacificfor Southeast Asia and the Pacific region.

1. Water-related hazard and water resources analysis
under climate change
2. Flood and draught hazard and risk assessment

iv. Water-related hazard prediction for Southeast Asia and the pacificiv(a) Water-related hazard prediction(Kyoto University）
1. Indochinese Peninsula: Hydrologicprediction (low flow and high flow)applying a newly developed bias
collection method,
2. Chao Phraya River basin (Thailand):Water resources prediction applying anew land surface model incorporating
irrigation scheme,
3. Batanhari River basin (Indonesia): Floodprediction and development of flood
hazard mapping,
4. Red River basin (Viet Nam): Floodprediction and development of floodrisk mapping.

iv(b) Prototype
development for
supporting climatechange adaptation
measures (ICHARM)

Collaboration with MRI
for GCM projections

d4PDF
AGCM20NHRCM05

CORDEX－SEA IISoutheast Asia
DDS data

Prototype development for supporting climate change adaptation
implementation in Philippines and Indonesia.
:

1. Risk assessment of water-related disasters;
2. Field survey for needs and abilities for climate change
adaptation; and3. Supporting climate change adaptation for local stakeholders.

A new opportunity for extreme hydrologic
prediction researchusing Large Ensemble Climate Simulation Datathe database for Policy Decision making
for Future climate change (d4PDF)

http://www.miroc-gcm.jp/~pub/d4PDF/index_en.html
5

database for Policy Decision making
for Future climate change (d4PDF)

The database consists of atmospheric simulations for the globe using MRI-AGCM 3.2H with
60km spatial resolution (Mizuta et al. 2012) and dynamically downscaled simulations using
NHRCM with 20km resolution (Sasaki et al. 2011).
The entire globe experimentDownscaled local experiment

 Historical experiment 1951～2011×100
ensemble members
 4℃ increase experiment 2051～2111×90
ensemble members

 Historical experiment 1951～2011×50 ensemble
members
 4℃ increase experiment 2051～2111×90 ensemble
members「地球温暖化対策に資するアンサンブル気候予測データベース」利用手引きより

database for Policy Decision making
for Future climate change (d4PDF)

MRI-NHRCM, 20km spatial resolution
 Present Climate Experiments: 50 ensembles of 60 years climate simulation under different
boundary conditions, which provides 3,000 years hydrologic time series data.
 The End of 21st Century Climate Experiments (4 degree increase): 15 ensembles of 60years climate simulation under different initial and boundary conditions for 6 SST settings,
which provides 5,400years (= 900years times 6 SSTs) hydrologic time series data.
「地球温暖化対策に資するアンサンブル気候予測データベース」利用手引きより

MRI-NHRCM, 20km spatial resolution

Developed by Mr. Kato at Graduate School of Engineering, Kyoto Univ.

8Future Change Analysis of Extreme Floods at theIndochinese Peninsula Using d4PDF

9Analysis atIndochinese
Peninsula109.5o E
34.0o NSalween
River basin
Irrawaddy
River basin

Red Riverbasin

Mekong Riverbasin
Chao Phraya
River basin
91.0o E
5.0o N

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River Flow Routing Model
Elevation

Flow direction

Hydrological Projections at the Indochinese Peninsula underclimate change using the d4PDF datasets (P. Hanittinan, 2017)
Future Projections of streamflow: mean of the annual maximum dischargea) CCSM4 (CC)d) MIROC5 (MI)b) GFDL-CM3 (GF)c) HadGEM2-AO (HA)e) MPI-ESM-MR (MP)f) MRI-CGCM3 (MR)Hydrological Projections at the Indochinese Peninsula underclimate change using the d4PDF datasets (P. Hanittinan, 2017)
Future Projections of streamflow: standard deviation of the annual maximum dischargea) CCSM4 (CC)b) GFDL-CM3 (GF)

d) MIROC5 (MI)e) MPI-ESM-MR (MP)c) HadGEM2-AO (HA)f) MRI-CGCM3 (MR)Hydrological Projections at the Indochinese Peninsula underclimate change using the d4PDF datasets (P. Hanittinan, 2017)Concluding Remarks
River discharge projections and its statistical significance••

We found increase change in future river discharge in terms of mean of the annual maximum riverdischarge, standard deviation, and Q95 in all of the SST warming patterns at Mekong River Delta, Red
River Basin’s mouth, and the Southern Indochinese Peninsula (changes ratio ranged from 1.10 – 3.00)
We analyzed the statistical significance of these changes and found the difference at 5% significancelevel at the Mekong Delta and the Southern Indochinese Peninsula

River discharge projections and its statistical significance under SST warming patterns CCSM4 (CC) scenarioRiverDischarge ratio

Statisticalsignificance: U-test

Statisticalsignificance: L-test

Statisticalsignificance: A-D test

Conclusions
1) Future flood changes essentially showed a similar trend
notwithstanding of SST patterns.
2) Strong increase signals were found at Mekong Delta andsouthern Indochinese Peninsula for all of the d4PDF SST
scenarios.
3) The changes in mean of the annual maximum discharge
were statistically significant at 95% confidence level in theMekong Delta, the southern Indochinese Peninsula, and themouth of the Red River basin for all of the SST patterns4) Based on the overall simulated results and the past studies,
we found a clear and increasing risk of future floods at theIndochinese Peninsula, especially at the low-lying Mekong
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Delta and the Southern Indochina regions.

New opportunity for future hydrologic
prediction and design
1) To examine the change of probability
distributions of hydrologic extremes ispossible using the large ensemble climate
simulation data.
2) To estimate over 1,000 year-annual maximum
extreme events is possible using the large
ensemble climate simulation data.
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Thank you very much for your attention

Datasets: Input Forcing data

Present Climate(1951 – 2010)

20th Century

Future Climate)(2051 – 2110)
21st Century