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Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model. Elizabeth Clark 1 , Paul Bates 2 , Matthew Wilson 3 , Delwyn Moller 4 , Ernesto Rodriguez 4 , Dennis Lettenmaier 1 , Doug Alsdorf 5. University of Washington University of Bristol - PowerPoint PPT Presentation
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Assimilation of Remotely-Sensed Surface Water Observations into a
Raster-based Hydraulics Model
Elizabeth Clark1, Paul Bates2, Matthew Wilson3, Delwyn Moller4, Ernesto Rodriguez4, Dennis Lettenmaier1, Doug Alsdorf5
1. University of Washington2. University of Bristol3. University of Exeter in Cornwall4. Jet Propulsion Laboratory5. Ohio State University
Purpose• Globally, discharge measurements are
sparse and non-continuous• Knowledge of global discharge aids in:
• Closing the global water balance• Transboundary water management• Prediction of biogeochemical fluxes• Estimation of freshwater fluxes to the Arctic
• Satellite altimetry is able to estimate water level of rivers, reservoirs, lakes, and wetlands
• We would like to extract discharge from water level
Water Elevation Retrieval
• Also see Doug Alsdorf’s talk tomorrow at 11:55 am Lido Room
Image from Ernesto Rodriguez
• Ka-band SAR (synthetic aperture radar) with two 50 km swaths
• Uses low incidence angle (<4o) to increase the brightness signal of water relative to land
• Produces heights and co-registered imagery
• IS IT POSSIBLE TO OBTAIN DISCHARGE FROM WATER LEVEL?
Heritage: Estimation of Streamflow from Water Level• Stage-discharge relationships
derived for several locations in Congo River basin (Coe and Birkett, 2004)
• Regression models, generally based on Manning’s equation (Bjerklie et al., 2003)
Heritage: Hydrologic Data Assimilation
• Soil moisture (e.g. Margulis et al., 2002; Crow and Wood, 2003; Reichle et al., 2002)
• Snow water equivalent (Andreadis et al., in review; Durand and Margulis, 2004)
Context: Virtual Mission
Conceptual Design• Truth model:
• Hydrologic model to generate lateral inflows and boundary conditions
• Hydrodynamic model to generate ‘true’ stage
• Measurements:• Instrument simulator to add measurement
error
• Inversion problem:• Now can we estimate the ‘true’ inflows from
the synthetic measurements?
Context: Virtual MissionHydrologic Model (VIC)
Hydraulics Model
(LISFLOOD-FP)
Simulated Surface Water Extent and
Elevation
NASA/JPL Instrument Simulator
Simulated Interferometric
Altimeter Swaths
Spatial and Temporal Resolution
Tradeoffs
Back Calculation of Discharge
(Data Assimilation)
Simulated Streamflow
Lateral inflows and boundary conditions
“Truth”
Inversion
Measurement Error
Study Domain
• Ohio River flood during 1996
• 14 km hydrologic model resolution
• 270 m DEM for hydraulics model
• 50 m simulated satellite sampling resolution
Model Inputs• Discharge
(lateral inflows and boundary conditions) generated by VIC model
LISFLOOD-FP1) 1-D finite difference
solutions of the full St. Venant equations
2) 2-D finite difference and finite element diffusion wave representation of floodplain flow
Qij=AijRij2/3Sij1/2/n,i= upstream cellj= downstream celln varies (channel vs. floodplain)
Simulated Truth
• Water depth and discharge from 1995-1998
• 20 s time step
• Output for every ~11 hours
80.6
km
• Generated by JPL Instrument Simulator
ObservationsObserved “True” Error
Frequency 34.9 to 35.1 GHz Mean Error 0 cm
Repeat Cycle 16 Days Std. Dev. Error 10-15 cm
Water elevation (m) Error (m)
39.2oN, 81.7oW
38.5oN, 82.3oW
Data Assimilation: Ensemble Kalman Filter
1.Boundary condition (BC) and lateral inflow (LI) ensemble members represent propagation through VIC model of input errors from:
• Precipitation (Nijssen and Lettenmaier, 2003)
• Temperature (Andreadis, 2004)
2.LISFLOOD-FP propagates error from these BCs and LIs
3.Observations synthesized to minimize model errors versus normally-distributed measurement errors
4. Water level and discharge (states) updated
Prospects for Data Assimilation
Schematic of Ensemble Kalman Filter
2-D System Model
Lisflood-FP
Perturbed INPUTS
•Simulated discharge from VIC
•Manning’s n
UPDATED STATE
•Water depth
•Spatially distributed discharge
Error is introduced into model
Model propagates
error
STATE
•Water depth
•Spatially distributed discharge
Kalman Filter Analysis Step
OUTPUTS
•Estimated water depth
•Estimated discharge
•Associated error distributions
Filter incorporates available
measurements to minimize error
MEASUREMENTS
• Swaths of remotely-sensed water elevation with known error distribution
What will we learn from this exercise?
• Feasibility of recovering discharge with little to no in-situ data
• Evaluation of trade-offs between acceptable error and spatial resolution• Will elevation recovery work for
streams of different sizes?• How fast does the ability to recover
discharge degrade with spatial resolution?
Conclusions
• Satellites have a great potential for measuring the stage of inland waters.
• The use of data assimilation has been effective in other hydrologic applications and will likely play a role streamflow estimation.
• Results of this exercise will show the extent to which discharge can be recovered from surface water elevations.
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