geo and geoss……....2021/06/03 · weighting hydrometeorology-agriculture droughts prediction...

Importance of in-situ and remote sensing data for decision-making in water management with special focus on drought management

IMPROVING URBAN WATER MANAGEMENT IN WESTERN AND CENTRAL ASIA WORKSHOP SERIAL (2021-2024)High Level Panel and Kick-off Workshop (online)

Essential Quality Assured Data and Information forIntegrated Urban Water Management

Toshio KoikeExecutive Director, International Centre for Water Hazard and Risk Management (ICHARM)

Council Member, Science Council of Japan (SCJ), Cabinet Office of Japan Professor Emeritus, the University of Tokyo

Chair, River Council of Japan

2

Soil Moisture Vegetation/Snow

Active & PassiveMicrowave Sensors

Precipitation

Surface Emissivity & Temp.

Microwave Remote Sensing of Land Hydrology

ALOS-2

GPM-Core

GCOM-W1

3

Satellite Precipitation Sensors

Identify inundation area on Sep-22, 2018MODIS (optical) Sentinel1 (SAR)

Flood Area mapFlood Area by SAR

Source of back image: "NASA Worldview"

Source:Copernicus Sentinel Data

Target area

Easily identification & high frequency but covered with cloud

All weather & high spatial resolution but low frequency

Niger River Flood Area Map

1) Sep-04, 2018 2) Sep-16, 2018 3) Sep-22, 2018 4) Sep-28, 2018

Source of back image: "NASA Worldview

5) Oct-10, 2018 6) Oct-22, 2018 7) Nov-03, 2018 8) Nov-15, 2018

Seasonal Variation of the Soil Moisture in the Tibetan Plateu

8


Precipitation


Data Assimilation Coupled with Microwave Remote Sensing

Land Surface Scheme

Vegetation/Snow Model

CloudPhysicsModel


ALOS-2

GPM-Core

GCOM-W1


The grant which financed this Pilot for Agriculture Drought Monitoring and Prediction in Brazil was received under the Japan-Bank Program for Main-streaming DRM in Developing Countries which is financed by the Government of Japan

Background:ScientificContribution

＋

Dynamic Vegetation Model

AMSR-E AMSR2

Yang, Koike, et al. JMSJ (2007)

Electronic-MagneticWave

Sawada & Koike, JGR (2014)

Data AssimilationCoupled

Natural climate variability

Precipitation deficiency, high temperature etc…

Soil water deficiency

Plant water stress, reduced biomass and yield

Reduced stream flow, inflow to reservoirs, Groundwater deficiency

Economic, Social, and Environmental impacts

Met

eoro

logi

cal

Ecol

ogic

al &

Ag

ricul

tura

lH

ydro

logi

cal

Relationship between ecological and hydrological processes is important for analyzing drought process.

Water-Energy BudgetHydrological Model

(WEB-DHM)

River DischargeSoil MoistureGround WaterDam Storage

Crop Production

Real Time Data Management System

DIAS Archives

NASA GLDAS Optimized LDAS ParametersAMSR2MODIS

GFDLAPCC

Hydromet Data Satellite Seasonal Forecast Global CLVDAS Outputs

Optimized RTM Parameters

NCDC Global Met

JMA Reanalysis

LDAS Reanalysis Statistics

Hydrometeorology-Agriculture Droughts Prediction System

Satellite-based Land Data Assimilation

(CLVDAS)

Drought analysisWheat production

LAI anomaly from CLVDAS

2007 Morocco Drought

Morocco Algeria Tunisia

from Sawada & Ikoma

Prediction


(WEB-DHM)


Crop, Irrigation


(CLVDAS)


DIAS Archives


GFDLAPCC


Ensemble Rainfall Prediction

Ensemble Drought Prediction


NCDC Global Met

JMA Reanalysis


River DischargeSoil Moisture, Ground Water

Dam StorageCrop, Irrigation

Rainfall Pattern 1Rainfall Pattern 2

Rainfall Pattern n

Prediction Accuracy EvaluationBias Correction

Weighting


Agricultural DroughtMonitoring-Prediction

North AfricaAqua AMSR-E

Applicability of the Coupled Land and Vegetation Data Assimilation System (CLVDAS) to various drought regions

Gautemala

West Africa

Prediction


(WEB-DHM)


Crop, Irrigation


(CLVDAS)


DIAS Archives


GFDLAPCC


Ensemble Rainfall Prediction

Ensemble Drought Prediction


NCDC Global Met

JMA Reanalysis


River DischargeSoil Moisture, Ground Water

Dam StorageCrop, Irrigation

Rainfall Pattern 1Rainfall Pattern 2

Rainfall Pattern n

Prediction Accuracy EvaluationBias Correction

Weighting


It is virtually certain that drought will become more severe.

0

200

400

600

800

1000

rain

fall

mm

/yea

r

Annual Average Rainfall

1986-20002051-2065

0

200

400

600

800

1000

rain

fall

mm

/yea

r

Annual Average Rainfall1986-20002051-2065

AIN BEYA OUEDKALAAT ESSENAM

Mejerda River

17

19

m

00.40.81.21.62

0

2

4

6 Bir Lakhdar Souk Sebt 2

cm

Groundwater Level Variations (satellite-observed)

Groundwater Level Variations (ground-observed)

-10-505

10

2002 2003 2004 2005 2006 2007 2008

Future GW Variations

-1.3mm/year

GW

sto

rage

ano

mal

y (c

m)

18/20G

W s

tora

ge a

nom

aly

(cm

)GW (TWS – SM)GW (LDAS-WEBDHM)Inter-comparison

AgricultureWinter, 2007 Summer, 2007

Sate

llite

Prel

imin

ary

AgricultureWinter, 2007 Summer, 2007

Sate

llite

Impr

oved

Irrigation

Ground Water

Land Use

Prel

imin

ary

Model

statistics

Model

Model

Model

③High spatial resolution method for assimilated soil moisture etc.

①Estimation method for soil moisture etc. using satellite remote sensing and data assimilation method

②Estimation method for high

spatial resolution soil moisture etc.

using hydrological runoff model

④Method for inputting assimilated high spatial

soil moisture etc. and optimized land

parameters into hydrological runoff

model as initial conditions.

River Discharge

Land parametersU. S. Geological Survey

Hydrological runoff model

High spatial resolution soil moisture etc.

(1km)

LAI

Soil moisture

Land data assimilation system based on satellite remote sensing

Optimized land surface parameters

Optimization by assimilation

LAI

Assimilated soil moisture etc. (25km)

Assimilation

Soil moisture

Study a method to use initial condition obtained from the CLVDASin hydrological runoff model

Crop CalendarIrrigation

24

Availability of Gauges and Bias Correction of SPPs

Zhou, L.; Koike, T.; Takeuchi, K.; Rasmy, M.; Onuma, K.; Ito, H.; Selvarajah, H.; Li, X.; Liu, L.; Ao, T. Availability of Ground Observations and Its Impacts on Bias Correction of Satellite Precipitation Products and Hydrologic Simulation Efficiency

4 stations900 km2/gauge New bias correction method Improved

much

25


Precipitation


Data Assimilation Coupled with Microwave Remote Sensing

Land Surface Scheme

Vegetation/Snow Model

CloudPhysicsModel


ALOS-2

GPM-Core

GCOM-W1


Data: Rainfall, River Discharge, Crop, Irrigation, Ground Water, Dam Storage, Soil Moisture

geo and geoss……....2021/06/03 · weighting hydrometeorology-agriculture droughts prediction...

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