water (tg1)-agriculture (tg5) -droughts (tg6) joint sessionwater (tg1)-agriculture (tg5) -droughts...
TRANSCRIPT
1
Water (TG1)-Agriculture (TG5)-Droughts (TG6) Joint Session
1. Inputs from each Co-chair:1) Brief introduction to each TG2) Advantages and issues to be shared3) Expected cooperation:
2. Discussion towards coordination and integration:1) Mapping the advantages and issues2) Implementation design for cooperation
2
3
1980-19952000-2015
1980-19952000-2015
6
Water –Energy-Food Nexus
Hydropower Operation Optimization based on Ensemble Inflow Prediction
34～39hours
Discharge[m3/s]
34～39hours
時間
Same area
PredictedInflow
Hydropower water use
less than the maximum power generation capacity
Discharge[m3/s]
Higher water level at the end of flood season Increase of power generation by 16%
Reduced Water Release
AdditionalPower
Generation
9
Integrated Hydrological Modelingand
Seasonal Prediction
Dr. Mohamed RasmySenior Researcher,
ICHARM/PWRIAssociate Professor, GRIPS
ICHARM: Delivering best available knowledge to local practices
WATER-AGRICULTURE-DROUGHTS JOINT SESSION - AOGEO-2019
A Seamless Modeling Approach for effective Climate Change Adaptation
planning
End to End Approach to Climate Change Adaptation
Climate change (CC) is a massive threat to sustainable development
Reliable assessments of CC impacts very essential to draw efficient adaptation strategies for effective water resources management and thus ensuring food security
Courtesy: Prof. Toshio Koike
12
g pp g g climate
Rainfall Predictio
n
Climate Modeling
Hyd
rolo
gica
l mod
elin
gCr
op M
odel
ing
Econ
omic
M
odel
ing
Clim
ate
mod
elin
g•
Send
ai
Fram
ewor
k•
Paris
ag
reem
ent
•SD
Gs
Hydrological Modeling for A Seamless Approach
Water and Energy budget Rainfall-Runoff-Inundation
(WEB-RRI) Model
Physical formulations for ET, and soil moisture improve reliability of flood and drought
Reliable responses to the water cycle variability as well as climate change scenarios Assessment of hydrological extremes with a great confidence
Complete consideration of hydrological cycle with restarting function enable reliable real-time applications such as flood forecasting
I ibl i h li d i l l d l
Rasmy et al., 2019: Journal of Hydrology
Rice & Eco-Hydro Modelings
Simulation Model for Rice-Weather Relations ( SIMRIW) model developed by Prof. Horie(1987)
SIMRIW predicts the potential yield that can be expected from a given cultivar under a given climate
Eco‐hydrological Model with and vegetation dynamics by Sawada et al., 2014
The model solve water-vegetation interactions and contribute to an understanding ecosystem responses to hydrological extremes.
Climate Change Impact Assessments on Agriculture and Ecosystem
Hydrological model
ONE MONTH LONG PREDICTION OF RAINFALL/TEMPERATURE IN TONE-RIVER BASIN
Tomoki UshiyamaResearch SpecialistInternational Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, Public Works Research Institute (PWRI)
ONE MONTH LONG PREDICTION OF RAINFALL IN 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 by using WRF model.
17
Regional Forecast model
Δx=15km Downscaling
Global ForecastTone-River
Target Basin
ACCUMULATED RAINFALL
18
Ensemble mean
Observation
Ensemble mean
Observation
☓
☓
☓
☓
RMSE FOR ACCUMULATED RAINFALL
19
• Downscaled rainfall (left) is mostly better than GCM original (right).
• There are periods of bad accuracy (23, 24 July, 27,28Aug.)
050
100150200250300350
2019
0625
0020
1906
2600
2019
0702
0020
1907
0300
2019
0709
0020
1907
1000
2019
0716
0020
1907
1700
2019
0723
0020
1907
2400
2019
0730
0020
1907
3100
2019
0806
0020
1908
0700
2019
0813
0020
1908
1400
2019
0820
0020
1908
2100
2019
0827
0020
1908
2800
2019
0903
0020
1909
0400
2019
0910
0020
1909
1100
RMSE GCM
1 week 2 week 3 week 4 week 5 week
050
100150200250300350
2019
0625
0020
1906
2600
2019
0702
0020
1907
0300
2019
0709
0020
1907
1000
2019
0716
0020
1907
1700
2019
0723
0020
1907
2400
2019
0730
0020
1907
3100
2019
0806
0020
1908
0700
2019
0813
0020
1908
1400
2019
0820
0020
1908
2100
2019
0827
0020
1908
2800
2019
0903
0020
1909
0400
2019
0910
0020
1909
1100
RMSE Downscaling
1 week 2 week 3 week 4 week 5 week
RMSE
(mm
)
RMSE
(mm
)
ENSEMBLE SPREAD FOR ACCUMULATED RAINFALL
20
• Ensemble spread is larger in downscaled rainfall (left) than GCM original (right). (Wider probability of uncertainties)
RMSE
(mm
)
Spre
ad
010203040506070
2019
0625
0020
1906
2600
2019
0702
0020
1907
0300
2019
0709
0020
1907
1000
2019
0716
0020
1907
1700
2019
0723
0020
1907
2400
2019
0730
0020
1907
3100
2019
0806
0020
1908
0700
2019
0813
0020
1908
1400
2019
0820
0020
1908
2100
2019
0827
0020
1908
2800
2019
0903
0020
1909
0400
2019
0910
0020
1909
1100
Spread Downscaling
1 week 2 week 3 week 4 week 5 week
010203040506070
2019
0625
0020
1906
2600
2019
0702
0020
1907
0300
2019
0709
0020
1907
1000
2019
0716
0020
1907
1700
2019
0723
0020
1907
2400
2019
0730
0020
1907
3100
2019
0806
0020
1908
0700
2019
0813
0020
1908
1400
2019
0820
0020
1908
2100
2019
0827
0020
1908
2800
2019
0903
0020
1909
0400
2019
0910
0020
1909
1100
Spread GCM
1 week 2 week 3 week 4 week 5 week