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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 1. University of Washington 2. University of Bristol 3. University of Exeter in Cornwall 4. Jet Propulsion Laboratory 5. Ohio State University

Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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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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Page 1: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 2: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 3: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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?

Page 4: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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)

Page 5: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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)

Page 6: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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?

Page 7: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 8: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

Study Domain

• Ohio River flood during 1996

• 14 km hydrologic model resolution

• 270 m DEM for hydraulics model

• 50 m simulated satellite sampling resolution

Page 9: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

Model Inputs• Discharge

(lateral inflows and boundary conditions) generated by VIC model

Page 10: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics 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)

Page 11: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

Simulated Truth

• Water depth and discharge from 1995-1998

• 20 s time step

• Output for every ~11 hours

Page 12: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 13: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 14: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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

Page 15: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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?

Page 16: Assimilation of Remotely-Sensed Surface Water Observations into a Raster-based Hydraulics Model

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.