2012 AGU Chapman Conference on Remote Sensing of the ...

Yoon, YeosangAn ensemble-based approach for estimating riverbathymetry from SWOT measurementsYoon, Yeosang 1, 5 ; Durand, Michael 2, 5 ; Merry, Carolyn 1 ;Clark, Elizabeth 3 ; Andreadis, Konstantinos 4 ; Alsdorf,Douglas 2, 51. Department of Civil and Environmental Engineering andGeodetic Science, The Ohio State University, Columbus,OH, USA2. School of Earth Sciences, The Ohio State University,Columbus, OH, USA3. Department of Civil and Environmental Engineering,University of Washington, Seattle, WA, USA4. Jet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA, USA5. Byrd Polar Research Center, The Ohio State University,Columbus, OH, USARiver discharge is an important element in many aspectsof water resources management; however, global gaugingnetworks are sparse and even have been in decline. Over thepast decade, researchers have been trying to better estimateriver discharge using remote sensing techniques tocomplement the existing in situ gage networks. Theupcoming Surface Water and Ocean Topography (SWOT)mission will directly provide simultaneous spatial mappingof inundation area and inland water surface elevation data(i.e., river, lakes, wetlands, and reservoirs), both temporallyand spatially, with the Ka-band Radar Interferometer; thismission is planned to launch in 2019. With theseobservations, the SWOT mission will provide informationfor characterizing river discharge at global scales. A key toestimating river discharge via Manning’s equation orhydrodynamic model is river bathymetry. Because SWOTwill measure water surface elevation (WSE), not the truedepth to the river bottom, the cross-sectional flow area willnot be fully measured. Note that the SWOT sensor candirectly measure the changes in water depth and crosssectionalarea above the lowest measured WSE, but absoluteriver depths will not be observed. Here, we focus onestimating river bathymetry for retrieving river dischargefrom the SWOT using a data assimilation algorithm coupledwith a hydrodynamic model. In this study, we assimilatedsynthetic SWOT observations into the LISFLOOD-FPhydrodynamic model using a local ensemble batchsmoother, simultaneously estimating river bathymetry andflow depth. First-guess estimates of bathymetry were derivedassuming a uniform spatial depth with spatially correlateddownstream variability. SWOT observations were obtainedby sampling a “true” LISFLOOD-FP simulation based on theSWOT instrument design; the “true” discharge boundarycondition was derived from USGS gages. The first-guessdischarge boundary conditions were produced by theVariable Infiltration Capacity model, with dischargeuncertainty controlled via precipitation uncertainty. Havingrealistically described uncertainties in discharge andbathymetry, we evaluate the ability of a data assimilation158algorithm to recover bathymetry and discharge using SWOTobservations.You, YaleiThe Proportionality between Surface Rainfall andVertically Integrated Water and Its Implications toSatellite Rainfall RetrievalYou, Yalei 1 ; Liu, Guosheng 11. Florida State University, Tallahassee, FL, USAThe correlation between the surface rainrate and thewater path is investigated by using the Tropical RainfallMeasuring Mission (TRMM) Precipitation Radar (PR) data.The results showed that the proportionality between thesurface rainrate and the water path varies both seasonallyand spatially. Specifically, over land the proportionally of icewater path and surface rain changes little in the tropicalregions, such as central Africa. In contrast, it changesdramatically over desert regions and monsoon-affected areas.In terms of spatial variation, it is found that theproportionality between ice water path and surface rainratehas much larger changes around Sahara desert areas. Inaddition, the proportionality between the water path andsurface rainrate has also been investigated. It seems that thelargest difference is between Sahara desert regions and otherplaces. It is likely that the lower part of the rainfall profiledominate the overall total water path. Over ocean, theproportionality between ice (liquid) water path and surfacerainrate also demonstrates similar seasonally and spatiallyvariations. These results indicate that localized methodsmaybe employed under different conditions due to the sameamount of water path corresponding to quite differentsurface rainrate.Yu, XuanUsing NLDAS-2 for Initializing IntegratedWatershed Models: Model Spin-up for theAirMOSS CampaignYu, Xuan 1 ; Duffy, Christopher 1 ; Bhatt, Gopal 1 ; Crow, Wade 2 ;Shi, Yuning 31. Civil & Environmental Engineering, Pennsylvania StateUniversity, University Park, PA, USA2. Remote Sensing Lab, United States Department ofAgriculture, Beltsville, MD, USA3. Meteorology, Pennsylvania State University, UniversityPark, PA, USAAirborne Microwave Observatory of Subcanopy andSubsurface (AirMOSS) investigation has been developed forhigh-resolution in time and space root-zone soil moistureand carbon estimation. AirMOSS will build an ultra-highfrequency (UHF) synthetic aperture radar (SAR) that has thecapability to penetrate through substantial vegetationcanopies and subsurface and retrieve information to thedepths as deep as 1.2m depending on the soil moisturecontent. To meet the high temporal and spatial resolution ofAirMOSS data Penn State Integrated Hydrologic Model(PIHM) – a fully-coupled physics-based hydrologic model is