8 - EARS - Presentation Flow Forecasting and Drought Monitoring WB-IHP-III-Sep-16

EARS Satellite data for Climate Water and Food

Meteosat Flow Forecasting &

Drought Monitoring

Andries Rosema

EARS Earth Environment Monitoring BV

Delft, The Netherlands

EARS Satellite data for Climate Water and Food

• Remote sensing company since 1977

• Delft, the Netherlands

• Energy & Water Balance Monitoring

• Using geostationary meteorological satellites

• Climate, Water and Food applications:

EARS Earth Environment Monitoring BV

River flow forecasting

Drought monitoring

Crop yield forecasting

Crop insurance

EARS Satellite data for Climate Water and Food

Energy and Water Balance

Monitoring System

EARS Satellite data for Climate Water and Food

Meteosat

MSG

FY2c

METEOSAT IOC

MTSat

EARS Satellite data for Climate Water and Food

Energy en Water Balance

Radiation

Heat Evaporation Precipitation

Flow

EARS Satellite data for Climate Water and Food

Energy and Water Balance Monitoring System (EWBMS)

Rainfall

processing Clouds

Temperature

Albedo

Energy balance

processing

WMO stations

Precipitation

Radiation

Evaporation

Crop growth model

Hydrological

model

River flow forecasting

Crop yield forecasting

Drought processing

Drought monitoring

Meteosat

VIS & TIR

EARS Satellite data for Climate Water and Food

Rainfall product

EARS Satellite data for Climate Water and Food

Actual evapotranspiration product

EARS Satellite data for Climate Water and Food

Water balance validation

EWBMS rainfall & evapotranspiration Same but cumulative

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mu

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ET (mm) PREC (mm)

y = 840.69x - 2E+06R² = 0.9982

y = 880.73x - 2E+06R² = 0.9951

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• SW Burkina Faso reported run-off: 2-8% (Mahe et al. 2008)

• EWBMS using Meteosat: 4.5%

EARS Satellite data for Climate Water and Food

Time series (daily, 10-daily)

EARS Satellite data for Climate Water and Food

Complete river basins

EARS Satellite data for Climate Water and Food

Flow Forecasting

EARS Satellite data for Climate Water and Food

Upper Yellow RiverWei River

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Wei River

Upper

Yellow River

Second largest river basin of

China

Yellow River basin project (2006-2009)

EARS Satellite data for Climate Water and Food

GMS / FY2 precipitation data

1st quarter 2000 2nd quarter 2000

3rd quarter 2000 4th quarter 2000

EARS Satellite data for Climate Water and Food

GMS / FY2 evapotranspiration data

1st quarter 2000 2nd quarter 2000

3rd quarter 2000 4th quarter 2000

EARS Satellite data for Climate Water and Food

Water balance validation

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Net precipitation 5 days-floating average

River discharge at Tangnaihai

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Upper Yellow River

EARS Satellite data for Climate Water and Food

Land component: 2-dimensional diffusion process Surface & sub-surface flow

Large Scale Hydrological Model (LSHM)

Q(t)

Ql(t)

Ql(t)

River flow component:

Muskingum-Cunge routing

Q(t)

EWBMS Precipitation &

Evapotranspiration

EARS Satellite data for Climate Water and Food

Wei River flow simulation

R2 = 0.75

Vol. error = 4%

R2 = 0.80

Vol. error = 11%

EARS Satellite data for Climate Water and Food

Wei River 24 hr forecast

RMSE = 110 m3/s RRMSE = 0.37

COE = 0.75 R2 = 0.79

EARS Satellite data for Climate Water and Food

Upper Yellow River

flow simulation

Station R2 Volume

difference

Jimai 0.80 +17.9 %

Maqu 0.82 - 0.61%

Jungong 0.80 + 0.61%

Tangnaihai 0.80 - 0.67%

EARS Satellite data for Climate Water and Food

RMSE = 161 m3/s RRMSE = 0.17

COE = 0.84 R2 = 0.93

Upper Yellow River 24 hr forecast

EARS Satellite data for Climate Water and Food

22

High level interest at the

2nd International Yellow River Forum

Zhengzhou, October 2005

EARS Satellite data for Climate Water and Food

• By a Chinese high-level scientific commission

• Classification: “World Leading Level”

• 2nd Prize China Ministry of Water Resources

Yellow River project evaluation

EARS Satellite data for Climate Water and Food

• Niger Basin Authority, Niamey, Niger

• Operational implementation

• Drought monitoring

• River flow forecasting

Niger Basin project (2014-2017)

EARS Satellite data for Climate Water and Food

• Meteosat receiver

• PC network

• Software

– Pre-processing

– EWBMS

– LSHM

– Utility GIS

• Validation

• Calibration

• Training

Project components

EARS Satellite data for Climate Water and Food

Meteosat antenna

26

EARS Satellite data for Climate Water and Food

Receiving and processing office

27

EARS Satellite data for Climate Water and Food

Training

28

EARS Satellite data for Climate Water and Food

Drought monitoring

EARS Satellite data for Climate Water and Food

• Meteorological drought SPI

• Hydrological drought EP = P – Ea

• Agricultural drought RE = LEa/ LEp

• Climatic drought (> 1 yr)

– Climatic Moisture Index (UNEP) CMI = P / LEp

– Environmental Moisture Index EMI = LEa/ LEp

EWBMS drought information

EARS Satellite data for Climate Water and Food

Evapotranspiration & crop growth

• Evapotranspiration transpiration

• CO2 uptake proportional to crop growth

1-RY = k*(1-RE)

Doorenbos & Kassam (1979)

“Yield Response to Water”

FAO Irrigation & Drainage Paper 33

EARS Satellite data for Climate Water and Food

Relative evapotranspiration (RE)

Agricultural drought ìndex used for:

• Irrigation scheduling / water allocation

• Crop yield forecasting

• Agricultural drought insurance

Difference evapotranspiration (DE)

DE = (RE-REavg)/REavg

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EARS Satellite data for Climate Water and Food

Drought monitoring East Africa

• DE East Africa

• Vuli season (Dec-Jan)

• Scale -30% to +30%

• Extreme drought in 2006

1997 2000

2003 2006

EARS Satellite data for Climate Water and Food

Irrigation patterns Niger delta

EARS Satellite data for Climate Water and Food

Start of 2014 growing season W-Africa

EARS Satellite data for Climate Water and Food

2014 Crop yield forecast

EARS Satellite data for Climate Water and Food

37

Long time series for index insurance design

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Dekad r

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tive e

vapotr

anspiratio

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RE

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Pl.Mouh. Dande

• Location of growing season (starting window)

• Quality of growing season (RE)

• Historic risk assessment insurance premium

EARS Satellite data for Climate Water and Food

• Crops and livestock

• Drought and excessive precipitation

• In 10 African countries

• A dozen of insurance partners

• 23,000 farmers insured in 2013

• Target next 6 year: 1 million farmers

Index insurance

EARS Satellite data for Climate Water and Food

• Satellite based climate monitoring system

• Precipitation and evapotranspiration data

• Daily temporal resolution

• 3-5 km spatial resolution

• Distributed, transboundary

• Uniform

• Objective

• Validated

• Cost effective

• River flow forecasting

• Drought monitoring and water allocation

• Crop yield forecasting and crop insurance

Conclusions

EARS Satellite data for Climate Water and Food

What about India?

EARS Satellite data for Climate Water and Food

Meteosat Indian Ocean Coverage

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

Thank you for your attention

andries.rosema@ears.nl

www.ears.nl

EARS Satellite data for Climate Water and Food

EARS Satellite data for Climate Water and Food

55 55

Rainfall processing

• Meteosat TIR

• Cloud top temperature

• Cloud level

• Cloud level durations (CDi)

• GTS rain gauge data (R)

• Regression: R = a0+a1CD1+a2CD2+ ….

• Calculate rainfall field

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Temperature (K)

Heig

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m)

cold

high

medium high

medium low

EARS Satellite data for Climate Water and Food

Evapotranspiration processing

Hourly TIR

TIR

VIS Cloud?

To,Ta, Ao, t

Ac , tc

In = (1-Ao) Ig – Lu

In = (1-Ao) tc Ig

H = (To-Ta) LE = In - H

LEp= 0.8 In

RE=LE/LEp

LE = 0.8*RE*In

Atm.corr.

Hourly VIS

Radiation

Sensible heat flux

Potential evaporation

Actual evaporation

Rel. evaporation

constant “Bowen ratio”