evaluating the surface water ocean topography … · evaluating the surface water ocean topography...
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Evaluating the Surface Water Ocean TopographyMission Hydrologic Observations
Kostas Andreadis1,2, Michael Durand2, Sylvain Biancamaria3,Elizabeth Clark1, Ernesto Rodriguez4, Delwyn Moller5, Doug
Alsdorf2, Dennis Lettenmaier1, and Nelly Mognard3,6
1University of Washington, 2Ohio State University3Laboratoire d’Etudes en Geophysique et Oceanographie Spatiales
4NASA Jet Propulsion Laboratory, 5Remote Sensing Solutions6Centre National d’Etude Spatiales
IEEE IGARSS, Cape Town - 15 July 2009
Andreadis et al. - IGARSS 2009 SWOT Hydrology
The SWOT satellite
◮ Surface Water OceanTopography (SWOT)satellite mission
◮ Ka-band SARinterferometric system
◮ Two swaths, 10 and 60 kmon each side of the nadirtrack
◮ WSOA and SRTM heritage
◮ Produces heights andco-registered all weatherimagery
Andreadis et al. - IGARSS 2009 SWOT Hydrology
What type of measurements?
◮ SWOT will measure◮ Water surface elevation (h)◮ Temporal and spatial variability in heights (dh/dt and dh/dx)◮ Water inundation (water/no water)◮ Lake and reservoir storage (changes)
◮ Examples of similar measurements from other sensors
◮ SWOT will improve their heritage
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Ohio River widths (LandSat-derived)
Courtesy: Jon Partsch
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Virtual mission motivation
◮ What will SWOT “see”?◮ Height and width accuracy of rivers, lakes, wetlands and
reservoirs◮ How many times will rivers be observed per orbit cycle?◮ How far upstream the river network will SWOT observe?
◮ How will SWOT estimate discharge?◮ What are the expected errors and what is the contribution of
different sources?◮ “Direct” retrieval and Data assimilation◮ Estimating other parameters (e.g. bathymetry, roughness)
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Ability of SWOT to observe storage change
Height and areal errorsvary with lake area
Results from PeaceAthabasca lakes
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Courtesy: Hyongki Lee
Andreadis et al. - IGARSS 2009 SWOT Hydrology
A river characteristics global database
◮ In-situmeasurements ofriver characteristicsare sparse andinadequate
◮ Need to create arealistic global rivernetwork to evaluateSWOT observations
◮ Power lawregressions to relatedrainage area todischarge, width,depth
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Andreadis et al. - IGARSS 2009 SWOT Hydrology
Global map of number of observations per orbit cycle
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Andreadis et al. - IGARSS 2009 SWOT Hydrology
What rivers will SWOT see?
◮ Rivers with withs > 50 m will beobserved
◮ We can evaluate the characteristicsof rivers observed
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Width (m)
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Width (m)
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Estimating river discharge
◮ “Direct” retrieval
Q =1
nwS
12 z
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◮ Fast method based on Manning’sequation
◮ Similar to SRTM-based dischargeestimation over Amazon
◮ Data assimilation
◮ Coupling of hydrodynamics and hydrologic models
◮ Channel and floodplain discharge: states
◮ Water surface elevations: observations
◮ Computationally more expensive
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Discharge “direct” retrieval
◮ Synthetic test over tributaries of the Ohio River basin
◮ Depth error propagates into discharge estimates
◮ However, discharge variations are accurate despite those errors
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Assessing errors in discharge estimation from SWOT
Used in-situ dischargemeasurements to examineerrors due to (i) temporalsampling and (ii) heighterrors
σ2m
Q2=
σ2t
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σ2s
Q2
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Assimilation schematic
◮ Synthetic experiment where true WSE and discharge aresimulated
◮ Expected height errors added to “true” WSE to generateSWOT observations
◮ Open-loop and Filter simulations use corrupted model inputs
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Data assimilation - Water depth
Water depth (in meters) mapsfor different simulations on 13March 1995 (06:00)
TRUTH
TRUTH - OPEN LOOP TRUTH - FILTER
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Data assimilation - Channel discharge
◮ Channel discharge profiles for the different simulations duringtwo update times
◮ Calculated using uncertain width, roughness and depth
13 March 1995 - 06:00 22 April 1995 - 06:00
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Estimating other river characteristics
◮ Exploit SWOT information content for indirect estimation ofriver characteristics
Estimation of channel roughness coefficient(Ohio River study domain)
Estimation of river bathymetry (AmazonRiver study domain)
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Bathymetric slope estimation
◮ SWOT can measure inundated area and total storage onfloodplains
◮ Knowing these through time, allows selection of correctchannel bathymetric slope
Andreadis et al. - IGARSS 2009 SWOT Hydrology
Ongoing work
◮ Expand data assimilation studies on different river basins (e.g.Mississippi, Ob)
◮ SWOT instrument simulation over entire continent
◮ Methods for retrieval of bathymetry (e.g. slope/width todepth stochastic models)
◮ Use of detailed in-situ measurements to evaluate the dischargeestimation error budget
◮ Understand the role of long-wavelength errors (e.g. slope,geoid) affect Amazon hydrologic characterization
◮ Quantifying the impact of errors in the context of dataassimilation (precipitation, lateral inflows)
◮ Optimization of hydrodynamics model for faster simulations
◮ Product delivery considerations (computational scalability etc)
Andreadis et al. - IGARSS 2009 SWOT Hydrology