Water (TG1)-Agriculture (TG5)-Droughts (TG6) Joint Session

1. Inputs from each Co-chair:
1) Brief introduction to each TG
2) Advantages and issues to be shared
3) Expected cooperation:
2. Discussion towards coordination and integration:1) Mapping the advantages and issues2) Implementation design for cooperation

1

2

3

2000-2015
1980-1995

2000-2015
1980-1995

Water –Energy-Food Nexus

6

Discharge[m3/s]

Hydropower Operation Optimization
based on Ensemble Inflow Prediction
34～39hours

Discharge[m3/s]

PredictedInflow

34～39hours

Same area

Hydropower water use時間

less than the maximum power generation capacity

Increase of power generation by 16%

Higher water level at the end of flood season

Additional
Power
Generation

ReducedWaterRelease

Integrated Hydrological ModelingandSeasonal Prediction

9ICHARM: Delivering best available knowledge to local
practices

WATER-AGRICULTURE-DROUGHTS JOINT SESSION - AOGEO-2019

A Seamless Modeling Approach foreffective Climate Change Adaptation
planning
Dr. Mohamed Rasmy
Senior Researcher,
ICHARM/PWRI
Associate Professor, GRIPS

End to End Approach to Climate Change
Adaptation

 Climate change (CC) is a massive threat to sustainabledevelopment
 Reliable assessments of CC impacts very essential todraw efficient adaptation strategies for effective water
resources management and thus ensuring foodsecurity

Courtesy: Prof. Toshio
Koike

Climate modeling

Rainfall
PredictionCrop ModelingHydrological modeling

ClimateModeling

EconomicModeling• SendaiFramework• Parisagreement• SDGsclimate12

Hydrological Modeling for A SeamlessApproach

Water and Energy budget
Rainfall-Runoff-Inundation(WEB-RRI) Model
Rasmy et al., 2019: Journal ofHydrology
 Physical formulations for ET, and soil moisture  improve reliability offlood and drought Reliable responses to the water cycle variability as well as climate change
scenarios  Assessment of hydrological extremes with a greatconfidence Complete consideration of hydrological cycle with restarting function 
enable reliable real-time applications such as flood forecastingRice & Eco-Hydro Modelings

Simulation Model for RiceWeather Relations ( SIMRIW)
model developed by Prof. Horie(1987)

Eco‐hydrological Model with andvegetation dynamics by Sawada etal., 2014

SIMRIW predicts the potential
yield that can be expected froma given cultivar under a given
climateThe model solve water-vegetationinteractions and contribute to anunderstanding ecosystem responses tohydrological extremes.

Climate Change Impact Assessments on Agriculture andEcosystem

Hydrological model

ONE MONTH LONG PREDICTION
OF RAINFALL/TEMPERATURE IN
TONE-RIVER BASIN
Tomoki Ushiyama
Research Specialist
International Centre for Water Hazard and Risk Management (ICHARM)
under the auspices of UNESCO, Public Works Research Institute (PWRI)ONE MONTH LONG PREDICTION OF RAINFALLIN TONE-RIVER BASIN• For water resource risk management in Tokyo area, Tone River basin

rainfall is quite important.

• We evaluated one month prediction of rainfall/temperature provided

from Japan Meteorological Agency (JMA).

• We further downscaled the global forecasts into 15km resolution byusing WRF model.
Global Forecast

Regional Forecast model

Target Basin
Tone-River

DownscalingΔx=15km17

ACCUMULATED RAINFALL
Ensemblemean

Ensemblemean

Observation☓Observation

☓☓

☓
18

350
300
250
200
150
100
50
0

350
300
250
200
150
100
50
0

1 week2 week3 week
4 week5 week2019062500
2019062600
2019070200
2019070300
2019070900
2019071000
2019071600
2019071700
2019072300
2019072400
20190730002019073100
2019080600
2019080700
2019081300
2019081400
2019082000
2019082100
2019082700
2019082800
2019090300
2019090400
2019091000
2019091100

RMSE (mm)2019062500
2019062600
2019070200
2019070300
2019070900
2019071000
2019071600
2019071700
2019072300
2019072400
2019073000
2019073100
2019080600
2019080700
2019081300
2019081400
2019082000
2019082100
2019082700
2019082800
2019090300
2019090400
2019091000
2019091100

RMSE (mm)

RMSE FOR ACCUMULATED RAINFALLRMSE Downscaling
RMSE GCM

1 week2 week3 week
4 week• Downscaled rainfall (left) is mostly better than GCM original(right).

• There are periods of bad accuracy (23, 24 July, 27,28Aug.)19

5 weekSpread

70
60
50
40
30
20
10
0

1 week2 week3 week
4 week5 week2019062500
2019062600
2019070200
2019070300
2019070900
2019071000
2019071600
2019071700
2019072300
2019072400
2019073000
2019073100
2019080600
2019080700
2019081300
2019081400
2019082000
2019082100
2019082700
2019082800
2019090300
2019090400
2019091000
2019091100

2019062500
2019062600
2019070200
2019070300
2019070900
2019071000
2019071600
2019071700
2019072300
2019072400
2019073000
2019073100
2019080600
2019080700
2019081300
2019081400
2019082000
2019082100
2019082700
2019082800
2019090300
2019090400
2019091000
2019091100

RMSE (mm)ENSEMBLE SPREAD FOR ACCUMULATED RAINFALL
Spread Downscaling
Spread GCM

70
60
50
40
30
20
100

1 week2 week3 week
4 week• Ensemble spread is larger in downscaled rainfall (left) thanGCM original (right). (Wider probability of uncertainties)

20

5 week