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SWOT DA estimate of river depth and slope. Why: Highly uncertain for many rivers Temporally persistent errors lead to significant uncertainty in LISFLOOD discharge uncertainty Approach : Retrospective ensemble data assimilation analysis using LISFLOOD: similar to EnKF or EnKS Advantage:
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SWOT DA estimate of river depth and slope • Why: • Highly uncertain for many rivers • Temporally persistent errors lead to significant uncertainty in LISFLOOD discharge uncertainty • Approach: • Retrospective ensemble data assimilation analysis using LISFLOOD: similar to EnKF or EnKS • Advantage: • Retrospective approach allows for rapid evaluation of various measurement characteristics (uncertainty, overpass frequency, pixel size, etc.)
Methods • Observation vector hobs is timeseries of SWOT elevations • is time invariant model input: either river slope or river depth • Generate ensemble of n=20 LISFLOOD runs from ensemble of 20 values of • Treat each of the ensemble members as truth in turn, estimate it using ensemble of n-1=19
LISFLOOD slope sensitivity: Experiment results • Model is highly sensitive to river slope • Relationship is linear for slopes > 2 cm km-1
River slope estimation • Estimated river slope using timeseries of basin mean inundated area • Each estimate is a mean across an ensemble • SWOT slope RMSE estimated at 0.36 cm km-1
Estimate river depth from floodplain observations • Define measurement window of pixels upstream from elevation node within spatial correlation length • Use timeseries of SWOT water levels in the floodplain within the measurement window
Future work • Analysis • Tradeoffs on slope/depth and discharge errors with overpass sampling frequency • Tradeoffs on errors with pixel size • Comparison of profiling and swath altimeters? • Tradeoffs with and without a radiometer • Timeline • ASLO meeting (March 1) • GRL paper (submit by March 15) • Next steps • Simultaneous river depth + floodplain flow estimation for the Amazon • Assimilate JERS measurements to estimate river slope/depth