grain size evoluti<strong>on</strong> and implicati<strong>on</strong>s for remote sensing <strong>of</strong>snow mass will be discussed.SANTOS DA SILVA, JoecilaALTIMETRY OF THE AMAZON BASIN RIVERSSANTOS DA SILVA, Joecila 1 ; Calmant, Stéphane 2 ; Seyler,Frédérique 3 ; Moreira, Daniel M. 2, 41. Centro de Estudos do Trópico Úmido – CESTU,Universidade do Estado do Amaz<strong>on</strong>as – UEA, Manaus,Brazil2. UMR 5566 LEGOS CNES/CNRS/IRD/UT3, Institut deRecherche pour le Développement – IRD, Toulouse,France3. UMR ESPACE-DEV, Institut de Recherche pour leDéveloppement – IRD, M<strong>on</strong>tpellier, France4. Universidade Federal do Rio de Janeiro - UFRJ, Rio deJaneiro, BrazilAltimetry <strong>of</strong> rivers all al<strong>on</strong>g <strong>the</strong>ir course is majorinformati<strong>on</strong> in hydrology, whatever it is for runninghydrological model, determine <strong>the</strong> amount <strong>of</strong> surface waterstored, and predict <strong>the</strong> c<strong>on</strong>sequences <strong>of</strong> extreme events.Satellite altimetry can be used in many ways to retrievec<strong>on</strong>sistent altimetry informati<strong>on</strong> throughout <strong>the</strong> course <strong>of</strong>rivers. Now, it is now well known as a useful tool to retrieve<strong>the</strong> space and time variati<strong>on</strong>s <strong>of</strong> <strong>the</strong> water surface. Besidesthis basic use <strong>of</strong> satellite altimetry, it can also be used to: 1/level gauges, so making a c<strong>on</strong>sistent dataset merging hightemporal sampling from gauges and dense sampling from<strong>the</strong> crossings between satellite tracks and river network; 2/densify climatic series (mean value per m<strong>on</strong>th <strong>of</strong> <strong>the</strong> year) allal<strong>on</strong>g <strong>the</strong> river course when series are l<strong>on</strong>g enough and, bycomparis<strong>on</strong> with time series, evidence extreme events; 3/detail <strong>the</strong> altitudinal changes <strong>of</strong> <strong>the</strong> river course which, whencompared to a DTM, inform over <strong>the</strong> basin hypsometry; 4/level bathymetric pr<strong>of</strong>iles in order to obtain altitudinalchanges <strong>of</strong> <strong>the</strong> river bed; 4/ check for errors in <strong>the</strong> gaugeseries or in <strong>the</strong> metadata informati<strong>on</strong> related to a gauge. In<strong>the</strong> present study, we present examples <strong>of</strong> such applicati<strong>on</strong>s<strong>of</strong> satellite altimetry for <strong>the</strong> major c<strong>on</strong>tributors <strong>of</strong> <strong>the</strong>Amaz<strong>on</strong> basins. In this basin, more than 500 series havebeen computed from <strong>the</strong> ERS2 & ENVISAT missi<strong>on</strong>s in <strong>the</strong><strong>on</strong>e hand (1995-2010) and from <strong>the</strong> T/P & JASON2 missi<strong>on</strong>sin <strong>the</strong> o<strong>the</strong>r hand (1992-2002 / 2008-). All series have beencarefully checked manually and we present statistics <strong>of</strong>comparis<strong>on</strong> with ground-truth, i.e. water levels from GPSleveledgauges. Rivers <strong>of</strong> very different widths have beensampled, ranging from several km wide to less than 100mwide. For some <strong>of</strong> <strong>the</strong> rivers, altimetry series are <strong>the</strong> <strong>on</strong>lypossibility to get stage and slope informati<strong>on</strong> since <strong>the</strong>serivers are devoid <strong>of</strong> in-situ measurement or <strong>the</strong>measurements are not available, in particular out <strong>of</strong> Brazil.Schroeder, Dustin M.<strong>Remote</strong> <strong>Sensing</strong> <strong>of</strong> Subglacial Water Networks withIce Penetrating RadarSchroeder, Dustin M. 1 ; Blankenship, D<strong>on</strong>ald D. 1 ; Young,Duncan A. 11. University <strong>of</strong> Texas Institute for Geophysics, Austin, TX,USAThe subglacial water systems beneath outlet glaciers <strong>of</strong>c<strong>on</strong>tinental marine ices sheets is an important and difficultto c<strong>on</strong>strain c<strong>on</strong>trol <strong>on</strong> ice sheet mass balance and sea levelrise estimates and <strong>the</strong>ir role in <strong>the</strong> global hydrologic cycle.The net subglacial water flux is <strong>the</strong> dominant unknown inrec<strong>on</strong>ciling satellite gravity and inSAR derived surfacevelocity measurements <strong>of</strong> ice sheet mass balance. It is also akey parameter in predicting sub-ice-shelf circulati<strong>on</strong> andcollapse as well as glacial surge and retreat initiated by <strong>the</strong>dynamics <strong>of</strong> subglacial lakes. Successfully modeling <strong>the</strong>sephenomena and <strong>the</strong>ir effects requires understanding not<strong>on</strong>ly <strong>the</strong> locati<strong>on</strong>s <strong>of</strong> individual subglacial lakes andc<strong>on</strong>duits but <strong>the</strong> entire subglacial hydrologic network. Sincedirect observati<strong>on</strong>s <strong>of</strong> <strong>the</strong> basal hydrology <strong>of</strong> ice sheets areboth extremely limited in area and prohibitively expensive(e.g. drilling, seismic), airborne radar sounding is <strong>the</strong> <strong>on</strong>lypractical means <strong>of</strong> acquiring basin-scale observati<strong>on</strong>s <strong>of</strong>subglacial water systems. Airborne ice penetrating radarsounding has been used with variable success to identify andcharacterize basal water systems and <strong>the</strong>ir sedimentaryc<strong>on</strong>text by <strong>the</strong> strength <strong>of</strong> <strong>the</strong> return from <strong>the</strong> basalinterface. Specularity is a parameterizati<strong>on</strong> <strong>of</strong> <strong>the</strong> angularitydependent echo intensity that measures how tightly or“mirror-like” <strong>the</strong> energy is distributed with observing angle.The specularity <strong>of</strong> <strong>the</strong> basal return can indicate <strong>the</strong> presence,extent, and c<strong>on</strong>figurati<strong>on</strong> <strong>of</strong> subglacial water and sedimentindependent <strong>of</strong> <strong>the</strong> temperature pr<strong>of</strong>ile and impurityc<strong>on</strong>centrati<strong>on</strong> <strong>of</strong> <strong>the</strong> ice column, which complicatetraditi<strong>on</strong>al amplitude based interpretati<strong>on</strong>s. We use multipleradar focusing windows to produce a basal specularity mapfrom a gridded aerogeophysical survey <strong>of</strong> West Antarctica’sThwaites Glacier catchment (over 150,000 square kilometers)using a 60 MHz coherent ice penetrating radar with a 15MHz bandwidth linear frequency modulated waveform. Wefind that regi<strong>on</strong>s <strong>of</strong> high specularity correlate with modeledhydrologic pathways and indicate an extensive water networkbetween <strong>the</strong> ice and bed. We dem<strong>on</strong>strate how variati<strong>on</strong>s in<strong>the</strong> strength <strong>of</strong> <strong>the</strong> specularity signal with <strong>the</strong> survey-line towater-flow-path angle can be used to c<strong>on</strong>strain <strong>the</strong> size,geometry and flow-regime <strong>of</strong> <strong>the</strong> water system using physicaloptics.Using <strong>the</strong>se results, we present an interpretati<strong>on</strong> <strong>of</strong><strong>the</strong> basal hydrology and morphology <strong>of</strong> Thwaites Glacier in<strong>the</strong> c<strong>on</strong>text <strong>of</strong> <strong>the</strong> hydrologic gradient, surface slope,inferred basal melt, and inferred basal shear stress. Thisinterpretati<strong>on</strong> provides insights into <strong>the</strong> current andpotential role <strong>of</strong> subglacial water in <strong>the</strong> Thwaites Glaciersystem and dem<strong>on</strong>strates <strong>the</strong> ability <strong>of</strong> specularity analysisto provide informati<strong>on</strong> about <strong>the</strong> basal boundary c<strong>on</strong>diti<strong>on</strong>at a scale that is inaccessible to traditi<strong>on</strong>al amplitude basedradio echo sounding analysis.130
Selkowitz, DavidExploring Landsat-derived Snow Covered Area(SCA) Probability Distributi<strong>on</strong>s for DownscalingMODIS-derived Fracti<strong>on</strong>al SCASelkowitz, David 1, 21. Alaska Science Center, USGS, Anchorage, AK, USA2. Department <strong>of</strong> Geography, University <strong>of</strong> Utah, Salt LakeCity, UT, USAIn this study, Landsat TM data from Landsat path-rowsfor three mountain regi<strong>on</strong>s, <strong>the</strong> Sierra Nevada in California,<strong>the</strong> Cascades in Washingt<strong>on</strong>, and <strong>the</strong> Rockies in M<strong>on</strong>tana,were analyzed to determine <strong>the</strong> feasibility <strong>of</strong> using Landsatsnow covered area (SCA) data to downscale 500 m fracti<strong>on</strong>alsnow covered area (fSCA) estimates from MODIS to 30 mspatial resoluti<strong>on</strong>. Snow cover probability distributi<strong>on</strong>s at 30m resoluti<strong>on</strong> were generated for 500 m MODIS-like gridcells based <strong>on</strong> binary SCA maps from all available LandsatTM scenes acquired between 2000 and 2006 for <strong>the</strong> studypath-rows. The probability distributi<strong>on</strong>s were <strong>the</strong>n used todownscale Landsat-derived 500 m snow cover fracti<strong>on</strong>s foreach 500 m grid cell for all available scenes acquired between2007 and 2009. Results indicate this approach was effectivein <strong>the</strong> majority <strong>of</strong> cases for each <strong>of</strong> <strong>the</strong> three regi<strong>on</strong>s, with<strong>the</strong> highest accuracy observed for grid cells above treeline,grid cells with rugged topography, and grid cellsencompassing abrupt land cover transiti<strong>on</strong>s. Lower accuracywas observed for grid cells dominated by dense,homogeneous forest cover. Results from this study suggestthat SCA patterns at 30 m spatial resoluti<strong>on</strong> mapped byLandsat tend to remain stable over multiple years for <strong>the</strong>regi<strong>on</strong>s sampled. For areas where this is <strong>the</strong> case,downscaling 500 m fSCA estimates from MODIS to a 30 mspatial resoluti<strong>on</strong> binary SCA product should be possible,though <strong>the</strong> accuracy <strong>of</strong> this approach will depend partially<strong>on</strong> how well <strong>the</strong> MODIS fSCA estimates match <strong>the</strong> number<strong>of</strong> 30 m pixels mapped as snow covered by Landsat within<strong>the</strong> 500 m MODIS grid cells. The interannual stability <strong>of</strong>SCA patterns may also allow for downscaling from MODISfSCA to Landsat scale fSCA, though a more complexapproach would be necessary.Semmens, Kathryn A.<strong>Remote</strong> <strong>Sensing</strong> <strong>of</strong> Snowmelt - Understanding aChanging (and Melting) FutureSemmens, Kathryn A. 1 ; Ramage, Joan 11. EES, Lehigh University, Bethlehem, PA, USASnowmelt has a significant influence <strong>on</strong> terrestrialhydrology in snowmelt dominated basins with spring run<strong>of</strong>fand associated flooding <strong>the</strong> most significant hydrologicevent <strong>of</strong> <strong>the</strong> year. Changes in snowmelt timing andstreamflow seas<strong>on</strong>ality impact <strong>the</strong> availability <strong>of</strong> waterresources for populati<strong>on</strong>s in <strong>the</strong>se areas. Understanding andm<strong>on</strong>itoring <strong>the</strong> diurnal amplitude variati<strong>on</strong> (DAV) betweenmorning and night sheds light <strong>on</strong> <strong>the</strong> dynamics <strong>of</strong> transiti<strong>on</strong>periods, providing informati<strong>on</strong> <strong>on</strong> timing <strong>of</strong> water release in<strong>the</strong> terrestrial hydrologic cycle. Without remote sensing, thisproperty is difficult to c<strong>on</strong>tinuously observe over largespatial areas and for inaccessible, remote regi<strong>on</strong>s. The DAVapproach provides temporally high resoluti<strong>on</strong> informati<strong>on</strong><strong>on</strong> melting and refreezing, <strong>the</strong> timing <strong>of</strong> which affects <strong>the</strong>progressi<strong>on</strong> <strong>of</strong> meltwater through a basin and peaksnowmelt run<strong>of</strong>f. This is <strong>of</strong> critical importance for hydrologyand related ecosystem processes as <strong>the</strong> timing <strong>of</strong> <strong>the</strong> end <strong>of</strong>melt-refreeze is closely linked to green-up in high latituderegi<strong>on</strong>s. In additi<strong>on</strong>, tracking and studying snowmelt stagesfrom winter through <strong>the</strong> transiti<strong>on</strong> seas<strong>on</strong> and green-up canprovide useful landscape scale informati<strong>on</strong> <strong>on</strong> prec<strong>on</strong>diti<strong>on</strong>ingfor wildfires and for run<strong>of</strong>f predicti<strong>on</strong>s. Passivemicrowave radiometers such as <strong>the</strong> Advanced MicrowaveScanning Radiometer – EOS (AMSR-E) and Special SensorMicrowave/Imager (SSM/I) that observe brightnesstemperatures (Tb), a product <strong>of</strong> physical temperature andemissivity, are used to detect snowpack properties, fromsnowmelt <strong>on</strong>set to snow water equivalent (SWE). We reviewresearch pertaining to remote sensing <strong>of</strong> snowmelt timingand run<strong>of</strong>f with particular attenti<strong>on</strong> paid to <strong>the</strong> technique<strong>of</strong> m<strong>on</strong>itoring DAV. This technique involves determining wetsnow when <strong>the</strong> difference between ascending and descendingmeasurements exceeds a fixed threshold |TbAsc-TbDes| > A,in c<strong>on</strong>juncti<strong>on</strong> with a brightness temperature thresholdTb>B. For AMSR-E (2002-2011) <strong>the</strong> 36.5 GHz V thresholdsare A=18K, B=252K. For SSM/I (1987-present) <strong>the</strong> 37 GHz Vthresholds are A=10K, B=242K. Variati<strong>on</strong>s range from adynamic threshold to utilizati<strong>on</strong> <strong>of</strong> different frequencies andpolarizati<strong>on</strong>s. For instance, some researchers utilize <strong>the</strong> 37GHz V due to its sensitivity to wetness, while o<strong>the</strong>rs c<strong>on</strong>sider19 GHz for indicati<strong>on</strong> <strong>of</strong> melt penetrati<strong>on</strong>. Combiningfrequencies allows for characterizati<strong>on</strong> <strong>of</strong> surface and nearsurfacedynamics, important for detecting hydrologicparameters such as melt, infiltrati<strong>on</strong>, and run<strong>of</strong>f. Thesetypes <strong>of</strong> data and m<strong>on</strong>itoring must be c<strong>on</strong>tinued in futuremissi<strong>on</strong>s with an effective transiti<strong>on</strong> between past and futurehydrologic remote sensing. The Joint Polar Satellite System(JPSS) may serve to c<strong>on</strong>tinue and supplement <strong>the</strong> legacy <strong>of</strong>SSM/I and AMSR-E. The Advanced Technology MicrowaveSounder (ATMS), <strong>on</strong>e <strong>of</strong> <strong>the</strong> instruments <strong>on</strong> <strong>the</strong> NPOESSPreparatory Project (NPP) missi<strong>on</strong> (launched October 2011)and <strong>the</strong> future JPSS, has 22 channels from 23 GHz to 183GHz. Importantly, <strong>the</strong> ATMS instrument has imagingchannels relevant to c<strong>on</strong>tinuing <strong>the</strong> current algorithmsutilized for SWE and snowmelt detecti<strong>on</strong> (23V, 37VH)enabling c<strong>on</strong>tinuous data collecti<strong>on</strong> <strong>of</strong> snow parametersimportant for terrestrial hydrology.131
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Alfieri, Joseph G.The Factors Influ
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Montana and Oregon. Other applicati
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further verified that even for conv
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Courault, DominiqueAssessment of mo
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storage change solutions in the for
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Famiglietti, James S.Getting Real A
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can be thought of as operating in t
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mission and will address the follow
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Gan, Thian Y.Soil Moisture Retrieva
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match the two sets of estimates. Th
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producing CGF snow cover products.
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performance of the AWRA-L model for
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Euphorbia heterandena, and Echinops
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the effectiveness of this calibrati
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