Andreadis, K. M., University of Washington, Seattle, USA, kostas@hydro.washington.edu
Lettenmaier, D. P., University of Washington, Seattle, USA, dennisl@u.washington.edu
Alsdorf, D. ., Ohio State University, Columbus, USA, alsdorf.1@osu.edu

RIVER DISCHARGE ESTIMATION THROUGH ASSIMILATION OF REMOTELY-SENSED WATER SURFACE ELEVATIONS

New methods of measuring surface water elevations from space have the potential to revolutionize the type, frequency, and spatial scale of global observations. Although satellite altimeters can measure surface water elevation directly, river discharge (and consequently freshwater inflow to oceans) cannot be directly measured. Data assimilation offers the potential to indirectly estimate river discharge by ingesting satellite observations into a hydrodynamics model. Our work demonstrates the potential of such an approach in an identical twin data assimilation experiment. Early results revealed some of the limitations that need to be overcome for an operational implementation of such an estimation system. Simulated water elevation profiles for the Ohio River were generated by the JPL Instrument Simulator to represent satellite measurements of surface water with errors representative of those that would be inherent in observations from a dual-sensor Ka-band wide swath altimeter. Model errors are represented by introducing errors in model parameters, channel geometry, as well as precipitation that is used to drive a hydrologic model which produces boundary inflow conditions for LISFLOOD. Model and observation errors are evaluated through a simultaneous state-parameter estimation Ensemble Kalman filter. Boundary conditions are shown to have a large effect on the estimation process, and ways to estimate their errors are explored through error covariance bias correction.

Oral presentation

Presentation is given by student: Yes
Session #:006
Date: 03-05-2008
Time: 16:45