8 - ears - presentation flow forecasting and drought monitoring wb-ihp-iii-sep-16
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8 - EARS - Presentation Flow Forecasting and Drought Monitoring WB-IHP-III-Sep-16TRANSCRIPT
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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Cu
mu
lati
ve P
REC
/ E
T (
mm
)
ET (mm) PREC (mm)
y = 840.69x - 2E+06R² = 0.9982
y = 880.73x - 2E+06R² = 0.9951
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Cu
mu
lati
ve P
REC
/ E
T (
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)
Cum. ET (mm) Cum. PREC (mm)
• 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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45
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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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recip
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tio
n (
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arg
e (
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)
Net precipitation 5 days-floating average
River discharge at Tangnaihai
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ter
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Cum. evapotranspiration
Cum. net precipitation
Cum. precipitation
Cum. river discharge
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
32
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
ela
tive e
vapotr
anspiratio
n (
RE
)
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
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
ht (k
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”