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REMOTE SENSING FOR WATER BUDGET MONITORING: THE NILE RIVER BASIN
Ben Zaitchik
Johns Hopkins University
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OBJECTIVES
Apply Earth Observations to:
Estimate the distributed water balance of the Nile Basin
Improve and evaluate hydrological models used in water resource analysis
Monitor and understand variability in hydrologically complex regions
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NASA SVS
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CHALLENGES
In situ data are sparse
In situ data are often politically sensitive
The basin is evaporation dominated
There is considerable meteorological and hydrological complexity
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SELECTED REMOTE SENSING STUDIES OF THE NILE
Remotely sensed water balance analysis
The Nile Land Data Assimilation System
Wetland mapping and monitoring
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SELECTED REMOTE SENSING STUDIES OF THE NILE
Remotely sensed water balance analysis
The Nile Land Data Assimilation System
Wetland mapping and monitoring
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REMOTELY SENSED WATER BALANCE ANALYSIS
1st order terrestrial approach:
ππππππππ‘ππ‘πππ β πΈπ£ππππ‘ππππ πππππ‘πππ β π·ππ πβππππ = βππ‘πππππ
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REMOTELY SENSED WATER BALANCE ANALYSIS
π·ππππππππππππ β πΈπ£ππππ‘ππππ πππππ‘πππ β π·ππ πβππππ = βππ‘πππππ
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REMOTELY SENSED WATER BALANCE ANALYSIS
ππππππππ‘ππ‘πππ β π¬πππππππππππππππππ β π·ππ πβππππ = βππ‘πππππ
Martha Anderson, USDA
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REMOTELY SENSED WATER BALANCE ANALYSIS
Martha Anderson, USDA
ππππππππ‘ππ‘πππ β π¬πππππππππππππππππ β π·ππ πβππππ = βππ‘πππππ
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REMOTELY SENSED WATER BALANCE ANALYSIS
ππππππππ‘ππ‘πππ β πΈπ£ππππ‘ππππ πππππ‘πππ β π·ππ πβππππ = βππ‘πππππ
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REMOTELY SENSED WATER BALANCE ANALYSIS
ππππππππ‘ππ‘πππ β πΈπ£ππππ‘ππππ πππππ‘πππ β π·ππ πβππππ = βπΊππππππ
The Gravity
Recovery and
Climate
Experiment
(GRACE)
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BASIN SCALE WATER BALANCE
The
Sudd
The Grand
Ethiopian
Renaissance
Dam
Aswan High
Dam
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BASIN SCALE WATER BALANCE
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P β E β DS = RIVER DISCHARGE
Units: Billion Cubic Meters per year
Rainfall Land ET dS Lake E Residual
Equatorial Lakes 574.8 Β±46.9 392.3 Β±19.6 -3.3 Β±2.8 130.4 55.4
Blue Nile 302.1 Β±20.3 247.6 Β±12.4 -3.0 Β±3.6 3.9 53.6
Lower Nile 40.7 Β±12.2 80.9 Β±4.0 -3.7 Β±2.8 11.0 -47.5
Sudd Wetlands 42.4 Β±3.4 66.4 Β±3.3 - - 0.0 -24.0
Entire Nile basin 1939.8 Β±196.9 1797.3 Β±89.9 -20.7 Β±12.4 149.8 13.5
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SELECTED REMOTE SENSING STUDIES OF THE NILE
Remotely sensed water balance analysis
The Nile Land Data Assimilation System
Wetland mapping and monitoring
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WHAT IS A LAND DATA ASSIMILATION SYSTEM?
A Land Data Assimilation System (LDAS) is a tool that merges models and
observation.
Principle: integrated analysis yields more reliable and more
meaningfulinformation.
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LDAS
Land Surface Model
Meteorological Data
Landscape Information
Update Observations LDAS Output
β’ Hydrological fluxes and storage
β’ Localized meteorology
β’ Vegetation status
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LAND SURFACE MODEL
http://www.jsg.utexas.edu/noah-mp
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LDAS
Land Surface Model
Meteorological Data
Landscape Information
Update Observations LDAS Output
β’ Hydrological fluxes and storage
β’ Localized meteorology
β’ Surface energy balance
Data Assimilation
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LDAS AROUND THE WORLD
The Global LDAS (GLDAS)
The North American LDAS (NLDAS)
The South American LDAS (SALDAS)
The South Asia LDAS (South Asia LDAS)
The Famine Early Warning System LDAS (FLDAS)
And more . . . .
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THE NASA LAND INFORMATION SYSTEM
The NASA Land Information System is a software framework to support flexible use of advanced land surface models and land data assimilation.
LIS is an integration tool that can be used to exchange and enhance information across projects
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CUSTOMIZING LDAS FOR THE NILE BASIN
What meteorological products should we use?
How will we account for irrigation?
What information is available on land cover, soils, etc.?
How will we evaluate the system?
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EVALUATION: EVAPOTRANSPIRATION
2009 FEBRUARY
ALEXI LDAS24
ALEXI
LDAS
MODIS ET
LDAS (no
irrig)
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SELECTED REMOTE SENSING STUDIES OF THE NILE
Remotely sensed water balance analysis
The Nile Land Data Assimilation System
Wetland mapping and monitoring
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THE SUDD
Main Stem
Nile
Atbara
Blue
Nile
SobatWhite
NileThe
Lakes
Sudd
Bahr
el-Ghazal
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THE SUDD
Main Stem
Nile
Atbara
Blue
Nile
SobatWhite
NileThe
Lakes
Sudd
Bahr
el-Ghazal
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THE SUDD
Main Stem
Nile
Atbara
Blue
Nile
SobatWhite
NileThe
Lakes
Sudd
Bahr
el-Ghazal
Jonglei Canal
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THE SUDD
Main Stem
Nile
Atbara
Blue
Nile
SobatWhite
NileThe
Lakes
Sudd
Bahr
el-Ghazal
Jonglei Canal
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THE SUDD
Main Stem
Nile
Atbara
Blue
Nile
SobatWhite
NileThe
Lakes
Sudd
Bahr
el-Ghazal
Jonglei Canal
β’To deliver on the order of 4-5
BCM/yr, Jonglei would need to draw
~20 MCM/day.
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FLOODED AREA: SYNTHETIC APERTURE RADAR
Dry Wet
Brightness
indicates
intensity of
radar
backscatter
Red and green
areas are
locations of
known land
cover
ASAR imagery
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Townsend (2001)
Bright:
Flooded Vegetation
Medium:
Dry Land
Dark:
Open Water
SAR AND FLOODED VEGETATION
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FLOODED AREA: SYNTHETIC APERTURE RADAR
Dry Wet
Brightness
indicates
intensity of
radar
backscatter
Red and green
areas are
locations of
known land
cover
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CLASSIFICATION OF SAR IMAGERY
Based on backscatter thresholds we can classify open water, dry land and flooded vegetation for every date when SAR imagery is available.
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MONITORING SUDD AREA & EVAPOTRANSPIRATION
ET (m
m/m
o)
Flooded A
rea (G
m2)
Wilusz et al. (2017)
Correlation between Evapotranspiration and Area allows us to
link wetland area and the water balance
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PREDICTING AREA
πππ βπππ’π‘ + π β πΈ =ππ
ππ‘
πππ β πππ’π‘ + π β (πππ΄ + πΆ) = ππ ππ΄
ππ‘
πππ,π β πππ’π‘,π + ππ β .001385π΄ β .869 = .0003988ππ΄
ππ‘
Based on Sutcliffe & Park 1987
1. Define Water Balance Equation
2. Use Area vs. ET relationship
3. Solve
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THE JONGLEI CANAL
Wilusz et al. (2017)
Use these
equations to
estimate impacts
that the Jonglei
Canal would
have on Sudd
Area
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SELECTED REMOTE SENSING STUDIES OF THE NILE
Remotely sensed water balance analysis
The Nile Land Data Assimilation System
Wetland mapping and monitoring
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IN SUMMARY . . .
Remote sensing can contribute to understanding, monitoring, and predicting the water balance of large, poorly instrumented basins.
There is power in merging data streams, both through multi-sensor approaches and data assimilation.
Uncertainties are substantial and should not be understated.
Collaborative analysis can, sometimes, overcome skepticism of remotely sensed products.
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THANK YOU