Benefits of satellite altimetry for transboundary basins S. Biancamaria  1,2 , F. Hossain  3 , D. P. Lettenmaier  4 , N. P...
Transboundary basins <ul><li>256 river basins are shared among 2 or more countries (Wolf et al., 1999) = 45% land surfaces...
Outline <ul><li>Forecasting Brahmaputra/Ganges water elevations using satellite altimetry </li></ul><ul><li>Monitoring Ind...
Brahmaputra and Ganges basins  <ul><li>Brahmaputra: drainage area=574,000km 2 ; population=30 Millions; unmanaged. </li></...
Issue <ul><li>90% of water flowing in Bangladesh comes from India . </li></ul><ul><li>No India/Bangladesh real time data s...
Data used: in-situ measurements 27 Jul 2011 IGARSS 2011 - session WE2.T10
Data used: satellite altimetry <ul><li>Topex/Poseidon (T/P) satellite altimeter. </li></ul><ul><li>Overlap with in-situ: J...
Methodology 1/2 <ul><li>Compute the cross-correlation between upstream T/P and in-situ measurements: </li></ul>27 Jul 2011...
Methodology 2/2 <ul><li>Compute scatter plot in-situ measurements & T/P measurements k days earlier. </li></ul><ul><li>Use...
Results on the Brahmaputra <ul><li>5-day lead time Forecasts:  </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 T/P vir...
Results on the Ganges 27 Jul 2011 IGARSS 2011 - session WE2.T10 <ul><li>10-day lead time forecast: </li></ul>T/P virtual s...
SWOT and the Brahmaputra/Ganges <ul><li>SWOT = Water mask + water elevation (and river slope) with 2 or more observations ...
Expected benefits from SWOT <ul><li>Higher precision on measurements -> better forecasts. </li></ul><ul><li>More observati...
Conclusion for Brahmaputra/Ganges <ul><li>Forecasting water elevation from nadir altimeters with lead time between 5 day a...
Outline <ul><li>Forecasting Brahmaputra/Ganges water elevations using satellite altimetry </li></ul><ul><li>Monitoring Ind...
SWOT and world lakes/reservoirs 27 Jul 2011 IGARSS 2011 - session WE2.T10 2  4  6  8  10 SWOT visits per repeat cycle 3 2....
Indus reservoirs 27 Jul 2011 IGARSS 2011 - session WE2.T10 <ul><li>Indus basin=1.14x10 6 km 2 , 53% to Pakistan, 34% to In...
SWOT and Indus reservoirs <ul><li>Reservoirs are seen between 2 to 3 times per 22 days </li></ul>27 Jul 2011 IGARSS 2011 -...
Baglihar dam  <ul><li>Baglihar dam: 450 MW Run-of-river type </li></ul><ul><ul><li>Pondage volume = 37.5 x 10 6  m 3 </li>...
Baglihar dam <ul><li>Reservoir in mountainous region = SWOT might be affected by  layover . </li></ul><ul><li>Layover =geo...
Kishenganga project <ul><li>330 MW hydro-power plant. </li></ul><ul><li>Layover  modeled by SARVisor for ALOS/PalSAR, 7° i...
Conclusion for Indus reservoirs <ul><li>Hydro-electric reservoirs are needed to respond to the growing demand on electrici...
Thank you for your attention 27 Jul 2011 IGARSS 2011 - session WE2.T10
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  • Pondage=Live storage for run-of-river dam and designed to meet fluctuations in the discharge of turbine arising from variations in daily and weekly loads of plant
  • Biancamaria_etal_IGARSS2011_22Jul2011.ppt

    1. 1. Benefits of satellite altimetry for transboundary basins S. Biancamaria 1,2 , F. Hossain 3 , D. P. Lettenmaier 4 , N. Pourthié 2 and C. Lion 1,2 1 LEGOS, Toulouse, France 2 CNES, Toulouse, France 3 CEE, Tennessee Tech University, Cookeville, TN, USA 4 CEE, University of Washington, Seattle, WA, USA 27 Jul 2011 IGARSS 2011 - session WE2.T10
    2. 2. Transboundary basins <ul><li>256 river basins are shared among 2 or more countries (Wolf et al., 1999) = 45% land surfaces </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    3. 3. Outline <ul><li>Forecasting Brahmaputra/Ganges water elevations using satellite altimetry </li></ul><ul><li>Monitoring Indus reservoirs with SWOT </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    4. 4. Brahmaputra and Ganges basins <ul><li>Brahmaputra: drainage area=574,000km 2 ; population=30 Millions; unmanaged. </li></ul><ul><li>Ganges: drainage area=1,065,000km 2 ; population=500 Millions; 34 dams/diversions. </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 Bangladesh
    5. 5. Issue <ul><li>90% of water flowing in Bangladesh comes from India . </li></ul><ul><li>No India/Bangladesh real time data sharing . </li></ul><ul><li>Using in-situ measurements at its border -> forecast in Bangladesh only with 2 or 3 days lead time. </li></ul><ul><li>Study purpose: Use satellite-based water elevation upstream in India to forecast water elevation at the gauge locations (India/Bangladesh border). </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    6. 6. Data used: in-situ measurements 27 Jul 2011 IGARSS 2011 - session WE2.T10
    7. 7. Data used: satellite altimetry <ul><li>Topex/Poseidon (T/P) satellite altimeter. </li></ul><ul><li>Overlap with in-situ: January 2000/August 2002. </li></ul><ul><li>Data downloaded from HYDROWEB: http://www.legos.obs-mip.fr/en/soa/hydrologie/hydroweb/ </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 242_1 166_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
    8. 8. Methodology 1/2 <ul><li>Compute the cross-correlation between upstream T/P and in-situ measurements: </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 Water level Correlation Time Lead time 0 0 0.6 with k=lead time k 0.8 k Upstream: h alti (t) Downstream; h insitu (t)
    9. 9. Methodology 2/2 <ul><li>Compute scatter plot in-situ measurements & T/P measurements k days earlier. </li></ul><ul><li>Use linear fit to forecast water level at gauge location from T/P measurements. </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 h in-situ (t) 0 h alti (t-k) Linear fit of h insitu (t)=f[h alti (t-k)] Water level 0 Time h insitu (downstream) k day lead time forecast
    10. 10. Results on the Brahmaputra <ul><li>5-day lead time Forecasts: </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 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 elevation 5 4 3 2 1 0 -1 -2 -3 2000 2001 2002 Water elevation (m) Brahmaputra water elevation 5 4 3 2 1 0 -1 -2 -3 2000 2001 2002 Water elevation (m) In-situ T/P forecast Legend:
    11. 11. Results on the Ganges 27 Jul 2011 IGARSS 2011 - session WE2.T10 <ul><li>10-day lead time forecast: </li></ul>T/P virtual station 530 km upstream: 5-day forecast RMSE ~ 0.6 m Ganges water elevation 6 4 2 0 -2 -4 2001 2001.4 2001.8 Water elevation (m) 10-day forecast RMSE ~ 0.9 m Ganges water elevation 6 4 2 0 -2 -4 2001 2001.4 2001.8 Water elevation (m) T/P virtual station 1560 km upstream: <ul><li>5-day lead time forecast: </li></ul>In-situ T/P forecast Legend:
    12. 12. SWOT and the Brahmaputra/Ganges <ul><li>SWOT = Water mask + water elevation (and river slope) with 2 or more observations per 22 days </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    13. 13. Expected benefits from SWOT <ul><li>Higher precision on measurements -> better forecasts. </li></ul><ul><li>More observations on the basin -> better time sampling. </li></ul><ul><li>Water extent will improve inundation forecast: </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 Brahmaputra water elevation 5 4 3 2 1 0 -1 -2 -3 2000 2001 2002 Water elevation (m) Brahmaputra water elevation Discharge (10 4 m 3 .S -1 ) 7 6 5 4 3 2 1 0 2000 2001 2002
    14. 14. Conclusion for Brahmaputra/Ganges <ul><li>Forecasting water elevation from nadir altimeters with lead time between 5 day and 10 day. </li></ul><ul><li>Expected improvement from SWOT due to water elev. + extent, better accuracy, global observation. </li></ul><ul><li>Fore more details: Biancamaria et al., GRL, 38, L11401, “Forecasting transboundary river water elevations from space” (June 2011). </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    15. 15. Outline <ul><li>Forecasting Brahmaputra/Ganges water elevations using satellite altimetry </li></ul><ul><li>Monitoring Indus reservoirs with SWOT </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    16. 16. SWOT and world lakes/reservoirs 27 Jul 2011 IGARSS 2011 - session WE2.T10 2 4 6 8 10 SWOT visits per repeat cycle 3 2.5 2 1.5 1 0.5 0 Surface area seen (10 6 km 2 )
    17. 17. Indus reservoirs 27 Jul 2011 IGARSS 2011 - session WE2.T10 <ul><li>Indus basin=1.14x10 6 km 2 , 53% to Pakistan, 34% to India. </li></ul><ul><li>2008 filling of Baglihar reservoir by India. </li></ul><ul><li>2009 construction of Kishenganga dam. </li></ul>-> Lack of information = difficulties in downstream water management
    18. 18. SWOT and Indus reservoirs <ul><li>Reservoirs are seen between 2 to 3 times per 22 days </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    19. 19. Baglihar dam <ul><li>Baglihar dam: 450 MW Run-of-river type </li></ul><ul><ul><li>Pondage volume = 37.5 x 10 6 m 3 </li></ul></ul><ul><ul><li>Full pondage level – dead storage level = 5 m </li></ul></ul><ul><ul><li>-> Pondage area > 1 km 2 </li></ul></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 5 m 37.5x10 6 m 3 <ul><li>SWOT requirements on lakes and reservoirs = 10 cm error on 1 km 2 area. </li></ul><ul><li>-> SWOT should be able to observe Baglihar dam </li></ul>
    20. 20. Baglihar dam <ul><li>Reservoir in mountainous region = SWOT might be affected by layover . </li></ul><ul><li>Layover =geometric distortion when radar beam reaches top of a tall feature before it reaches the base. </li></ul><ul><li>Layover modeled by SARVisor for ALOS/PalSAR, 7° incidence angle (yellow= layover ): </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 Baglihar Baglihar Ascending track: Descending track:
    21. 21. Kishenganga project <ul><li>330 MW hydro-power plant. </li></ul><ul><li>Layover modeled by SARVisor for ALOS/PalSAR, 7° incidence angle (yellow= layover ): </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10 Kishenganga Ascending track: Descending track: Kishenganga
    22. 22. Conclusion for Indus reservoirs <ul><li>Hydro-electric reservoirs are needed to respond to the growing demand on electricity. </li></ul><ul><li>Water management for downstream country is more difficult. </li></ul><ul><li>Huge potential of SWOT to provide reservoir water volume changes </li></ul><ul><li>Ongoing study to characterize layover on SWOT data and better quantifying SWOT accuracy and time sampling. </li></ul>27 Jul 2011 IGARSS 2011 - session WE2.T10
    23. 23. Thank you for your attention 27 Jul 2011 IGARSS 2011 - session WE2.T10

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