ICHARM: Delivering best available knowledge to local practices

Hydrological Modeling Framework for Climate-resilient Water
Resources and Disaster Managements under Changing Climate

On behalf of Water-related Hazard Team: Hydrology Division
Dr. Mohamed Rasmy
Senior Researcher,
Water-related Hazard Team
Professor, GRIPS

Background Water-related disasters (i.e. floods and droughts) are on
increasing trend, particularly the lower-middle income
countries become more vulnerable.
 Reliable and timely information on water-related disasters
and water availability is a key

 To develop an affordable and proactive IWRM plans and Disaster
Risk Reduction (DRR) strategies
 To ensure the security of water availability and food productions
 To achieve sustainable development goals and prosperity for all !

 ICHARM’s hydrology team is developing several cuttingedge tools and systems for implementing integrated
approaches for climate-resilient IWRM & disastermanagements under changing climate2

Development of a Simple, Inexpensive Flood Forecasting System for Small and Medium Rivers
 Real-time flood prediction models focusing on “Information on the river water levels that required evacuation“ tosupport the evacuation of residents during floods in small and medium rivers in Japan.
 Develop and provide a tool that enables prefectural river managers to handle and predict timely water-level, easily.

Observed & Predicted
RainfallUsing Analysis and Forecast ofPrecipitation provided by JMA
Multiple Observed Water
Level Data

Rainfall-Runoff-Inundation (RRI) model(sayama et al., 2012)
RRI assumes noloss/gain due toevapotranspiration,interception, andstream-aquiferinteraction• Particle filter was implemented to improvebiases in initial storage and flow
discharges and then to improve flood
simulations.

Particle filter

A-River RRI model
B-River RRI model
C-River RRI model
Data Systemfor RiverManagement

Distribution of waterrelated disaster
information to promoteresidents’ evacuation

Real time flood forecasting system
Select water level observation
point from a map of Japan・Since PF can handle non-linear models, we don’t need rewritehydrological model linear-equation like a Kalman Filter (KF).

・PF estimates state-space/parameters (water-level on slope
and in rivers).

Observed water level

Predicted water level

・Forecast: for 6 hours, Update interval: every 30 minutes
・Visualize the results on an internet based-system and allow
public access to the web-page

Seamless Modeling Approach : IWRM under changing climate
 ICHARM has developed a highly reliable hydrological model for addressing various water-related issues, tostrengthening water-related disaster resilience, water resources management and then enabling sustainable
development under climate change:
Water and Energy Budget based Rainfall-Runoff-Inundation(WEB-RRI) model (Rasmy et al., 2019)

Application 1: West- Africa(Niger & Volta basins)

Predictions in Ungauged Basins (PUB)

4-components model• Physical formulations for ET and soil moisture dynamics
for reliable of flood and drought related risk assessments

Application 2: Philippines (Pampanga)

Verification in Kalu basin, Sri Lanka

Long-term discharge simulation

Evapotranspiration estimation

Inundation

Seamless Modeling Approach

Drought monitoring and seasonal prediction system
Meteorologicalforcing
Meteorologicalforcing
forcing
Meteorological

Satellite
Satellite
Satellite
microwave
microwave
microwave
sensorsensorsensor-- -AMSR2
AMSR2
AMSR2---

Seasonal
prediction
Seasonalprediction
prediction
Seasonal

Precipitation
Precipitation
Precipitation
Precipitation

Seasonal
predicted
Seasonalpredictedpredicted
Seasonal
meteorologicalforcing
meteorologicalforcing
forcing
meteorological
Microwave
brightness
temperature
Microwave
brightness
temperature
Microwave
brightness
temperature
Microwave
brightness
temperature
Satellite
Satellite
Satellite
Satellite

RTM
RTM
RTM
RTM

Simulation
Simulation
Simulation
Simulation

Assimilation
schemeAssimilation
schemeAssimilation
schemeAssimilation
schemeParticle
Filter(GPF)-Genetic
Genetic
Particle
Filter(GPF)- --Genetic
Particle
Filter(GPF)- -- Genetic
Particle
Filter(GPF)
Minimized
Minimized
Minimized
Minimized
cost
cost
cost
cost

NONO
NONO

YES
YES
YES
YES

Cost
Cost
Cost
Cost

Landsurface
model
Land
surfacemodel
Landsurfacemodel
Landsurfacemodel-EcoHydro-SiB
EcoHydro-SiB- --EcoHydro-SiB- -- EcoHydro-SiB

Eco-hydrological
Eco-hydrological
Eco-hydrological
Eco-hydrologicalvariables
variables
variables
variables

Drought monitoring & seasonal prediction system

Drought monitoring & seasonal prediction systemLandsurfacemodel
Landsurface
surfacemodel
model
Land
EcoHydro-SiB
EcoHydro-SiB-----EcoHydro-SiB

Initialinput
Initialinput
input
Initial
from
from
from
monitoring
monitoring
monitoring

Optimized
Optimized
Optimized
Optimized
eco-hydrologicaleco-hydrologicaleco-hydrologicaleco-hydrologicalvariables
variables
variables
variables

Seasonal
predicted
Seasonalpredictedpredicted
Seasonal
eco-hydrologicaleco-hydrologicaleco-hydrologicalvariables
variables
variables

OUTPUT

Optimum irrigation waterLand surface
Soil moistureRoot-zone
Soil moisture-Locally, drought status
is determined throughmonitoring andseasonal prediction
from this system, andthe results are used toformulate farming
plans.

ET

Crop yield

Application toagricultural support
information-System monitors soilmoisture profiles andcrop growth andpredict them for up to
approximately threemonths ahead.
-It can also estimate
water requirement tomaximize crop yields.

UPDATEUPDATEUPDATEUPDATE

Initialcondition
Initialcondition
Initialcondition
Initialcondition
Eco-hydrologicalvariables
Eco-hydrologicalvariables
Eco-hydrologicalvariables
Eco-hydrologicalvariables

Brazilian Northeast.

LAI

Monitoring

Prediction-it will be further
improved to provide
information useful forfarming.

Thank you for your kind attention !!!