Flood and Drought Monitoring and Prediction by Satellite-based Microwave Remote Sensing

Regional Training Workshop "Advances in Remote Sensing Application in Water Resources Management"

Toshio KoikeDirector, 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

Definition of Remote Sensing

Remote Sensing is a technology for identifying a target and estimating its physical, chemical and biological conditions without touching by using its inherent characteristics of emission, reflection, absorption and transmission of electromagnetic wave and its radiation transfer.

MWIRVISUV frequency(Hz)

0.7um-1mm0.4-0.7um 1mm-1m

WATER

StrongDipoleMolecule

Unique Roles in the Earth Environment• Large Specific Heat of Liquid Water: Ocean as a heat transporter

• Large Heat Exchange through Liquid – Gas Phase Transition Water vapor as a heat transporter

• Solid ICE Crystal Lattice: Ice floats in water & bine drives

the great ocean conveyor belt.

OHH

HH

OO

HH- - -

+++

+++

- - -Strong Dielectric Material

ALOS-2 GPM-Core

+++

- - -

GCOM-W1

Active Passive

Microwave Remote Sensing

5

Soil Moisture Vegetation/Snow

Active & PassiveMicrowave Sensors

Precipitation

Surface Emissivity & Temp.

Microwave Remote Sensing of Land Hydrology

ALOS-2

GPM-Core

GCOM-W1

6

Satellite Precipitation Sensors

Land Surface Monitoring by SARSAR measures a intensity of microwaves reflected from the earth’s surface

The intensity is called “backscattering coefficient σ0 = f( Mv, Sd, Cl, Sv, D )

Soil moisture:Mv : Volume fraction of soil moisture (vol.)

Surface roughness:Sd : Standard deviation of surface height (cm)Cl : Surface correlation length (cm)

Soil parameter:Sv : Volume fraction of soil grains (vol.)D : mean diameter of soil grains (cm)

Surface scattering

volume scattering

Surface scattering

volume scattering

Scattering image

Nigeria Flood in September 2018

https://edition.cnn.com/2018/09/18/africa/nigeria‐flood‐national‐disaster/index.html

https://www.aljazeera.com/news/2018/09/nigeria‐floods‐kill‐100‐people‐10‐states‐180917193612830.html

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

Flood Area mapFlood Area mapFlood Area by SARFlood 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, 20181) Sep‐04, 2018 2) Sep‐16, 20182) Sep‐16, 2018 3) Sep‐22, 20183) Sep‐22, 2018 4) Sep‐28, 20184) Sep‐28, 2018

Source of back image: "NASA Worldview"

5) Oct‐10, 20185) Oct‐10, 2018 6) Oct‐22, 20186) Oct‐22, 2018 7) Nov‐03, 20187) Nov‐03, 2018 8) Nov‐15, 20188) Nov‐15, 2018

Physical Measurement Approach

O UB

O MG

O CYMONGOLIA SA

CHINA

RUSSIA

0

1

2

34

5

6

7

8

A B C D E F G H I J

AWS

ASSH

Location of AWS and ASSH in AMSR experimental fields of the study area (UB:Ulaanbaatar, CY:Choir, MG:Mandalgobi, SA:Study area)

Temporal Variation of Spatially Averaged Validation

Seasonal Variation of the Soil Moisture in the Tibetan Plateu

17

Soil Moisture Vegetation/Snow

Precipitation

Surface Emissivity & Temp.

Data Assimilation Coupled with Microwave Remote Sensing

Land Surface Scheme

Vegetation/Snow Model

CloudPhysicsModel

Microwave Remote Sensing of Land Hydrology

ALOS-2

GPM-Core

GCOM-W1

Active & PassiveMicrowave Sensors

observationRadar-AMeDAS

predictionJMA-MSM

uncertainty inthe location of

rainfall systems

Coupled Atmosphere-LandData Assimilation System (CALDAS)

Low freq.LDAS

↓

Soil Moist.Suf. Temp.

↓

High freq.ADAS

Seto, Koike, et al.JGR(2013)

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

Water-Energy BudgetHydrological Model

(WEB-DHM)

River DischargeSoil MoistureGround WaterDam Storage

Crop Production

Satellite-based Land Data Assimilation

(CLVDAS)

Real Time Data Management System

DIAS Archives

NASA GLDAS Optimized LDAS ParametersAMSR2MODIS

GFDLAPCC

Hydromet Data Satellite Seasonal Forecast Global CLVDAS Outputs

Ensemble Rainfall Prediction

Ensemble Drought Prediction

Optimized RTM Parameters

NCDC Global Met

JMA Reanalysis

LDAS Reanalysis Statistics

River DischargeSoil Moisture, Ground Water

Dam StorageCrop Production

Rainfall Pattern 1Rainfall Pattern 2

Rainfall Pattern n

Prediction Accuracy EvaluationBias Correction

Weighting

Hydrometeorology‐Agriculture Droughts Prediction System

Agricultural DroughtMonitoring-Prediction

North AfricaAqua AMSR-E

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

Marengo et al., 2017

Rainfall Anomaly

Number of DryDays

Vegetation WaterSupply Index(VWSI) Anomaly

2012 2013 2014 2015 2016

Background: Long-term Serious Droughts

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

It is virtually certain that drought will become more severe.

0

200

400

600

800

1000

rainfall mm/yea

r

Annual Average Rainfall

1986‐20002051‐2065

0

200

400

600

800

1000

rainfall mm/yea

r

Annual Average Rainfall1986‐20002051‐2065

AIN BEYA OUEDKALAAT ESSENAM

Mejerda River

29

31

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

33

Soil Moisture Vegetation/Snow

Precipitation

Surface Emissivity & Temp.

Data Assimilation Coupled with Microwave Remote Sensing

Land Surface Scheme

Vegetation/Snow Model

CloudPhysicsModel

Microwave Remote Sensing of Land Hydrology

ALOS-2

GPM-Core

GCOM-W1

Active & PassiveMicrowave Sensors

Preliminary outputsGautemala

Lake Atescata

LAI output from CLVDAS

2003 2004 2005 2006

2007 2008 2009 2010

2013 2014 2015 2016

2017 2018November 1

Gautemala spatial average

FAOSTAT

FAOSTAT

CLVDAS LAI and major products

3.3. Application: Horn of Africa drought

[FAO, 2011]

[Anderson et al., 2012]

We cannot have the access to many ground observations to develop the drought prediction system.

Leaf Area Index timeseriesC

LVD

AS(R

eal P

redi

cion

)Blue: PredictionGreen: Horn of Africa drought (reanalysis)

[Sawada and Koike, JGR-A, submitted]

Predictions: starting from 1 Oct 2010

CLV

DAS

(Rea

l Pre

dici

on) Predictions: starting from 1 Jan 2011

CLV

DAS

(Rea

l Pre

dici

on) Predictions: starting from 1 Mar 2011

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

Bias Corrected Rainfall & Hydro-met Forcing

Previous Year Hydro-met Forcing with

Modified Rainfall by Seasonal Prediction

Dec.1

Dec.15

Jan.1

Jan.15

Feb.1

Feb.15

Mar.1

Mar.15

Dec.Dec.1-31

Dec.1-31Jan.-Mar.

Jan. Feb. Mar.

Jan. Feb. Mar. Apr.

Feb. Mar. Apr. M

Nov. Dec. Jan. Feb.

Jan.1-31

Jan.1-31Feb.-Apr.

Feb.1-31

Feb.1-31Mar.-May

Monitoring Prediction

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

LAI deviation

Crop yield under irrigation condition

LAI deviation

Crop yield under rain-fed condition

DIASdrought system

DIASdrought system

Precipitation

Irrigation water volume and its supply timing

Irrigated area

Irrigated area, water volume and its supply timing: If we obtain the irrigated area, water volume and its supply timing, we can calculate LAI and crop yield under the irrigation condition by inputting precipitation and irrigation water into the DIAS drought system and can compare it with the crop yield under rain-fed condition. In consequence, we can evaluate the effectiveness of irrigation.

Effectiveness of irrigation

Input

Input

Output

Output

Application to estimate required irrigation water volume without the irrigated water volume and its supply timing

Precipitation + Irrigation water

Estimation of the required irrigation water volume: We assumed the averaged crop yield in the period between 2003 and 2017 as the target crop yield of each farmer. The required irrigation water volume was estimated by comparing the target crop yield and the relationship between assumed irrigation water and estimated crop yield. In consequence, our simulations clearly show consistent decreases in required irrigation water volume as the drought condition improved gradually in 2015 and 2016 after a severe drought in 2014.

0.100.150.200.250.300.350.40

0 1 2 3 4 5 6 7 8

P cas

hew

[t/ha

]

Irrigation [Ir, mm/day]

2014

0.100.150.200.250.300.350.40

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2015

0.100.150.200.250.300.350.40

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2016

1.94 mm/day 1.45 mm/day 0.16 mm/day

0.20

0.25

0.30

0.35

0.40

0 1 2 3 4 5 6 7 8

P bea

ns[t/

ha]

Irrigation [Ir, mm/day]

2014

0.20

0.25

0.30

0.35

0.40

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2015

0.20

0.25

0.30

0.35

0.40

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2016

1.86 mm/day 1.38 mm/day 0.06 mm/day

56789

1011

0 1 2 3 4 5 6 7 8

P cas

sava

[t/ha

]

Irrigation [Ir, mm/day]

2014

56789

101112

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2015

56789

1011

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2016

2.70 mm/day 2.10 mm/day1.07 mm/day

0.20

0.40

0.60

0.80

1.00

1.20

0 1 2 3 4 5 6 7 8

P cor

n[t/

ha]

Irrigation [Ir, mm/day]

2014

0.20

0.40

0.60

0.80

1.00

1.20

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2015

0.20

0.40

0.60

0.80

1.00

1.20

0 1 2 3 4 5 6 7 8Irrigation [Ir, mm/day]

2016

2.02 mm/day 1.52 mm/day 0.25 mm/day

Cashew nuts

Fejonbeans

Cassava

Corn