andreadis_swot_igarss.pdf

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andreadis_swot_igarss.pdf

  1. 1. Hydrologic estimation from SWOT observations: Methods and implications K. Andreadis1,2 E. Rodriguez2 D. Moller3 D. Alsdorf1 M. Durand1 1 Ohio State University 2 NASA JPL 3 Remote Sensing Solutions Inc.International Geosciences and Remote Sensing Symposium 2011
  2. 2. Motivation • SWOT direct observations will include water surface elevations, widths and slopes • Water storage changes can also be observed → important for lakes, wetlands etc. • Discharge is very important for hydrology • Two algorithmic approaches: • Direct estimation through Manning’s equation • Assimilation of SWOT observations • Developing and testing of SWOT data assimilation framework for discharge estimation • Many challenges for global application
  3. 3. Testing the algorithm • “Virtual” SWOT observations • Identical twin synthetic assimilation experiment
  4. 4. Previous work • A number of simple data assimilation experiments in different study areas • Amazon River basin • 240 km reach • Relationship between inundated area and slope • Estimated WSE and bathymetry at selected locations • Ob River basin • Estimated water depths • Model errors in precipitation and temperature • Demonstrated effects of localization in assimilation • Ohio River basin • 50 km reach • Assimilation of SWOT WSE to estimate river discharge • Examined sensitivity to observation error and temporal persistence of analysis
  5. 5. Experimental design • Needed actual SWOT instrument simulator to produce synthetic observations with correct orbital and error characteristics • Study area is part of the Ohio River basin • Drains about 150,000 km2 • Topography from National Elevation Dataset (30 m) • River topology from HydroSHEDS • Channel width and depth from developed power-law Topography map of study relationships area
  6. 6. Hydraulic modeling • LISFLOOD-FP raster-based hydrodynamics model • Combination of 1-D and 2-D flow solvers Bates and deRoo (2000) • Kinematic, Diffusive and Inertial formulations • Requires information on topography, river channel characteristics and boundary inflows • Boundary inflows obtained from USGS gauge measurements • Channel width, bathymetry and roughness coefficient perturbed for the open-loop simulations
  7. 7. Data assimilation • A number of assimilation techniques available • Extended Kalman filter (EKF) • Requires explicit modeling of model error covariance, and tangent linear models • Ensemble Kalman filter (EnKF) • Requires ensemble of model states • Filtering vs Smoothing • Assimilate observations for every orbit cycle, instead of every pass Reichle et al. (2002)
  8. 8. SWOT instrument simulator • Instrument Simulator calculates inteferometric response from land and water • Produces data with correct SNR, layover and geometric decorrelation characteristics • Thermal and speckle noise added • Topographic layover is identified and appropriate errors can be assigned
  9. 9. SWOT observations • SWOT Instrument Simulator uses “true” water depths to produce WSE observations • Post-processing can produce “smoothed” WSE data, with topographic layover identified and appropriate errors assigned • Example maps of synthetic SWOT observations
  10. 10. Water depth • Water depth maps after the first SWOT overpass • Boundary inflows for open-loop simulation were much higher than “true” inflows • Assimilation is able to improve water depth estimates even in locations that SWOT didn’t observe • Flood extent was still problematic • only one overpass though! Truth Open-loop Assimilated Observed
  11. 11. Discharge • Discharge maps after single SWOT overpass • Assimilation improved discharge estimation, reducing RMSE from 783.6 m3 /s to 471.9 m3 /s • Floodplain flow was not improved as much (smaller uncertainty within the ensemble?) Truth Open-loop Assimilated
  12. 12. Main stem and tributaries • SWOT information is propagated across river network • General error reduction, but additional constraints might lead to improvement
  13. 13. Uncertainty maps • The Ensemble Kalman filter was used here, allowing for direct estimation of uncertainty • Expected discharge error (m3 /s) data product from SWOT • Map of “true” absolute discharge error • Maps of ensemble σ before & after assimilation True Abs. Error Open-loop Std.dev. Assimilated Std.dev.
  14. 14. SWOT Data products • Applying this framework globally will be challenging • Computational cost • Model uncertainties • What do hydrologists need? What could they need? • Level 3 and Level 4 data products • Discharge anomalies versus absolute discharge
  15. 15. SWOT Data products • Applying this framework globally will be challenging • Computational cost • Model uncertainties • What do hydrologists need? What could they need? • Level 3 and Level 4 data products • Discharge anomalies versus absolute discharge • Potential accuracy and additional information provided by assimilation framework would be invaluable • Optimization of forward (hydrologic/hydrodynamic) models • Leverage computational advances to deliver data (e.g. cloud-based services)
  16. 16. Ongoing & future work • Fraternal twin synthetic assimilation experiment • Evaluate different assimilation techniques and error covariance modeling approaches • Assimilate additional SWOT observables (top width, slopes) • Expand work to other river basins • Evaluate information content of SWOT observations • Estimate river channel bathymetry • Calibrate hydrodynamic and hydrologic models

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