Prospects for river discharge and depth estimation through assimilation of swath‐altimetry into a raster‐based hydrodynamics model

Surface water elevation profiles for a reach of the Ohio River were produced by the Jet Propulsion Laboratory Instrument Simulator to represent satellite measurements representative of those that would be observed by a wide swath altimeter being considered jointly by U.S. and European space agencies. The Ensemble Kalman filter with a river hydrodynamics model as its dynamical core was used to assimilate the water elevation synthetic observations, and to estimate river discharge. The filter was able to recover water depth and discharge, reducing the discharge RMSE from 23.2% to 10.0% over an 84‐day simulation period, relative to a simulation without assimilation. An autoregressive error model was instrumental in correcting boundary inflows, and increasing the persistence of error reductions between times of observations. The nominal 8‐day satellite overpass produced discharge relative errors of 10.0%, while 16‐day and 32‐day overpass frequencies resulted in errors of 12.1% and 16.9% respectively.

[1]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[2]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[3]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[4]  Dennis McLaughlin,et al.  Recent developments in hydrologic data assimilation , 1995 .

[5]  Paul R. Ehrlich,et al.  Human Appropriation of Renewable Fresh Water , 1996, Science.

[6]  Erik Stokstad,et al.  Scarcity of Rain, Stream Gages Threatens Forecasts , 1999, Science.

[7]  P. D. Batesa,et al.  A simple raster-based model for flood inundation simulation , 2000 .

[8]  P. Bates,et al.  Predicting floodplain inundation: raster‐based modelling versus the finite‐element approach , 2001 .

[9]  D. Lettenmaier,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States* , 2002 .

[10]  Dennis P. Lettenmaier,et al.  Tracking Fresh Water from Space , 2003, Science.

[11]  José N. Onuchic,et al.  SCIENCE EDUCATION: Enhanced: Educating Future Scientists , 2003 .

[12]  Bart Nijssen,et al.  Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites , 2004 .

[13]  G. Evensen Sampling strategies and square root analysis schemes for the EnKF , 2004 .

[14]  D. Lettenmaier,et al.  Measuring surface water from space , 2004 .

[15]  Henrik Madsen,et al.  Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure , 2005 .