benefits of satellite altimetry for transboundary basins
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27 Jul 2011 IGARSS 2011 - session WE2.T10 1
Benefits of satellite altimetry for Benefits of satellite altimetry for transboundary basinstransboundary basins
S. Biancamaria 1,2, F. Hossain 3, D. P. Lettenmaier 4, N. Pourthié 2 and C. Lion 1,2
1 LEGOS, Toulouse, France2 CNES, Toulouse, France
3 CEE, Tennessee Tech University, Cookeville, TN, USA4 CEE, University of Washington, Seattle, WA, USA
Transboundary basinsTransboundary basins
• 256 river basins are shared among 2 or more countries (Wolf et al., 1999) = 45% land surfaces
27 Jul 2011 IGARSS 2011 - session WE2.T10 2
OutlineOutline
27 Jul 2011 IGARSS 2011 - session WE2.T10 3
1. Forecasting Brahmaputra/Ganges water elevations using satellite altimetry
2. Monitoring Indus reservoirs with SWOT
Brahmaputra and Ganges basins Brahmaputra and Ganges basins • Brahmaputra: drainage area=574,000km2;
population=30 Millions; unmanaged.• Ganges: drainage area=1,065,000km2;
population=500 Millions; 34 dams/diversions.
27 Jul 2011 IGARSS 2011 - session WE2.T10 4
Bangladesh
IssueIssue• 90% of water flowing in Bangladesh comes from
India.• No India/Bangladesh real time data sharing.• Using in-situ measurements at its border ->
forecast in Bangladesh only with 2 or 3 days lead time.
• Study purpose: Use satellite-based water elevation upstream in India to forecast water elevation at the gauge locations (India/Bangladesh border).
27 Jul 2011 IGARSS 2011 - session WE2.T10 5
Data used: in-situ measurementsData used: in-situ measurements
27 Jul 2011 IGARSS 2011 - session WE2.T10 6
Data used: satellite altimetryData used: satellite altimetry• Topex/Poseidon (T/P) satellite altimeter.• Overlap with in-situ: January 2000/August
2002.• Data downloaded from HYDROWEB:
http://www.legos.obs-mip.fr/en/soa/hydrologie/hydroweb/
27 Jul 2011 IGARSS 2011 - session WE2.T10 7
242_1166_1
014_1
116_2
T/P Virtual station
Distance from gage
Mean time between obs.
242_1 550 km 14 days
166_1 250 km 16 days
Mean time between obs.
Distance from gage
T/P Virtual station
12 days 1560 km 116_2
22 days 530 km 014_1
Methodology 1/2Methodology 1/2
• Compute the cross-correlation between upstream T/P and in-situ measurements:
27 Jul 2011 IGARSS 2011 - session WE2.T10 8
Water level Correlation
Time Lead time00
0.6
k)(th(t)h
k)(th(t),h=n(k)Correlatio
altiinsitu
altiinsitu
stdevstdev
covwith k=lead time
k
0.8
k
Upstream:halti(t) Downstream;
hinsitu(t)
Methodology 2/2Methodology 2/2
• Compute scatter plot in-situ measurements & T/P measurements k days earlier.
• Use linear fit to forecast water level at gauge location from T/P measurements.
27 Jul 2011 IGARSS 2011 - session WE2.T10 9
hin-situ(t)
0 halti(t-k)
Linear fit of hinsitu(t)=f[halti(t-k)]
Water level
0 Time
hinsitu (downstream)
k day lead time forecast
Results on the BrahmaputraResults on the Brahmaputra
• 5-day lead time Forecasts:
27 Jul 2011 IGARSS 2011 - session WE2.T10 10
T/P virtual station 250 km upstream: T/P virtual station 550 km upstream:
5-day forecast RMSE ~ 0.5 m
5-day forecast RMSE ~ 0.5 m
Brahmaputra water elevation543210
-1-2-32000 2001 2002
Wat
er e
leva
tion
(m)
Brahmaputra water elevation543210
-1-2-32000 2001 2002
Wat
er e
leva
tion
(m)
In-situ
T/P forecast
Legend:
Results on the GangesResults on the Ganges
27 Jul 2011 IGARSS 2011 - session WE2.T10 11
• 10-day lead time forecast:T/P virtual station 530 km upstream:
5-day forecast RMSE ~ 0.6 m
Ganges water elevation
6
4
2
0
-2
-42001 2001.4 2001.8
Wat
er e
leva
tion
(m)
10-day forecast RMSE ~ 0.9 m
Ganges water elevation
6
4
2
0
-2
-42001 2001.4 2001.8
Wat
er e
leva
tion
(m)
T/P virtual station 1560 km upstream:
• 5-day lead time forecast:
In-situ
T/P forecast
Legend:
SWOT and the Brahmaputra/GangesSWOT and the Brahmaputra/Ganges
27 Jul 2011 IGARSS 2011 - session WE2.T10 12
• SWOT = Water mask + water elevation (and river slope) with 2 or more observations per 22 days
Expected benefits from SWOTExpected benefits from SWOT• Higher precision on measurements -> better
forecasts.• More observations on the basin -> better time
sampling.• Water extent will improve inundation forecast:
27 Jul 2011 IGARSS 2011 - session WE2.T10 13
Brahmaputra water elevation543210
-1-2-32000 2001 2002
Wat
er e
leva
tion
(m)
Brahmaputra water elevation
Dis
char
ge (
104 m
3 .S
-1) 7
6
5
4
3
2
1
02000 2001 2002
Conclusion for Brahmaputra/GangesConclusion for Brahmaputra/Ganges
• Forecasting water elevation from nadir altimeters with lead time between 5 day and 10 day.
• Expected improvement from SWOT due to water elev. + extent, better accuracy, global observation.
• Fore more details: Biancamaria et al., GRL, 38, L11401, “Forecasting transboundary river water elevations from space” (June 2011).
27 Jul 2011 IGARSS 2011 - session WE2.T10 14
OutlineOutline
27 Jul 2011 IGARSS 2011 - session WE2.T10 15
1. Forecasting Brahmaputra/Ganges water elevations using satellite altimetry
2. Monitoring Indus reservoirs with SWOT
SWOT and world lakes/reservoirsSWOT and world lakes/reservoirs
27 Jul 2011 IGARSS 2011 - session WE2.T10 16
2 4 6 8 10SWOT visits per repeat cycle
3
2.5
2
1.5
1
0.5
0Su
rfac
e ar
ea s
een
(10
6 km
2 )
Indus reservoirsIndus reservoirs
27 Jul 2011 IGARSS 2011 - session WE2.T10 17
• Indus basin=1.14x106km2, 53% to Pakistan, 34% to India.
• 2008 filling of Baglihar reservoir by India.
• 2009 construction of Kishenganga dam.
-> Lack of information = difficulties in downstream water management
SWOT and Indus reservoirsSWOT and Indus reservoirs
27 Jul 2011 IGARSS 2011 - session WE2.T10 18
• Reservoirs are seen between 2 to 3 times per 22 days
Baglihar dam Baglihar dam
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• Baglihar dam: 450 MW Run-of-river type– Pondage volume = 37.5 x 106 m3
– Full pondage level – dead storage level = 5 m-> Pondage area > 1 km2
5 m
37.5x106 m3
• SWOT requirements on lakes and reservoirs = 10 cm error on 1 km2 area.
-> SWOT should be able to observe Baglihar dam
Baglihar damBaglihar dam
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• Reservoir in mountainous region = SWOT might be affected by layover.
• Layover=geometric distortion when radar beam reaches top of a tall feature before it reaches the base.
• Layover modeled by SARVisor for ALOS/PalSAR, 7° incidence angle (yellow=layover):
BagliharBaglihar
Ascending track: Descending track:
Kishenganga projectKishenganga project
• 330 MW hydro-power plant.• Layover modeled by SARVisor for ALOS/PalSAR,
7° incidence angle (yellow=layover):
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Kishenganga
Ascending track: Descending track:
Kishenganga
Conclusion for Indus reservoirsConclusion for Indus reservoirs
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• Hydro-electric reservoirs are needed to respond to the growing demand on electricity.
• Water management for downstream country is more difficult.
• Huge potential of SWOT to provide reservoir water volume changes
• Ongoing study to characterize layover on SWOT data and better quantifying SWOT accuracy and time sampling.
Thank you for your attentionThank you for your attention
27 Jul 2011 IGARSS 2011 - session WE2.T10 23
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