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

Sturm et al. (1995) introduced a seasonal snow coverclassification system for local to global applications. It hasseen considerable use since its initial development, but theoriginal dataset describing the global distribution of snowclasses was limited by its ~50 km spatial resolution.Consequently, it could not be used in high-resolutionapplications. The latest available fine-scale atmosphericforcing, topography, and land cover datasets were used torecalculate the snow classes on a global ~1 km grid. Thisnew resolution greatly increases the accuracy of the systemand opens the door for additional uses. Here we provide asummary of the new dataset and example applications thatinclude: 1) defining snow parameter values for regional andglobal snow-process and climate modeling at fine spatialscales, 2) defining parameter values to enhance snow remotesensingalgorithms, 3) categorizing and/or stratifying fieldmeasurements and/or model outputs, and 4) definingparameter values for snow-property models such as depthdensityrelationships, again at real landscape scales. See theSnowNet website (www.ipysnow.net) for access to the newclassification system.Liu, HuidongValidation of modeled lake water level variationsdue to changing climate, thermal variations andhuman activities using satellite observationsLiu, Huidong 1 ; Famiglietti, James S. 1, 2 ; Subin, Zachary M. 3, 41. Earth System Science, Univ. of California, Irvine, Irvine,CA, USA2. UC Center for Hydrologic Modeling, University ofCalifornia, Irvine, CA, USA3. Energy & Resources Group, University of California,Berkeley, Berkeley, CA, USA4. Earth Sciences Division, Lawrence Berkeley NationalLaboratory, Berkeley, CA, USALake level variations are mainly driven by changingclimate, and can also be altered by human regulations suchas dams, diversions and dredging activities. In this study,lakes are included in a coupled routing model andcatchment-based land surface model (CHARMS), which ismodified from the land-surface component (CLM4) of anEarth system model (CESM1). In the routing scheme, lakesare connected with rivers using upstream/downstreamrelationships in a lake basin. Evaporation, precipitation, andriver runoff are modeled in order to close the lake waterbudget. However, the original lake model in CLM4 poorlypredicts the lake temperature, which highly affects theevaporation and surface energy fluxes. Using an improvedlake model (CLM4-LISSS) the lake water temperature andsurface energy flux are better predicted. This new version ofCHARMS is tested on several large lakes around the world(e.g., the Great Lakes, and Lake Victoria) to evaluate itsperformance in different climate zones. The impacts ofhuman regulation on lakes will be included as a term in theoutflow. Modeled lake level time series are compared withsatellite altimetry. In order to test the ability of CHARMS tosimulating the variations of lake temperature, we comparethe amount of thermal expansion calculated from modeledlake temperature with the amount of thermal expansiondetermined from Gravity Recovery and Climate Experiment(GRACE) and satellite altimetry data.Liu, ZhaoAn Explicit Representation of High Resolution RiverNetworks using a Catchment-based Land SurfaceModel with the NHDPlus dataset for CaliforniaRegionLiu, Zhao 2 ; Kim, HyungJun 1 ; Famiglietti, James 1, 21. UC Center for Hydrologic Modeling, Univ. of California,Irvine, Irvine, CA, USA2. Dept. of Earth System Science, Univ. of California, Irvine,Irvine, CA, USAThe main motivation of this study is to characterize howaccurately we can estimate river discharge, river depth andinundation extent using an explicit representation of theriver network with a catchment-based hydrological androuting modeling system (CHARMS) framework. Here wepresent a macroscale implementation of CHARMS overCalifornia. There are two main components in CHARMS: aland surface model based on National Center AtmosphericResearch Community Land Model (CLM) 4.0, which ismodified for implementation on a catchment template; anda river routing model that considers the water transport ofeach river reach. The river network is upscaled from theNational Hydrography Dataset Plus (NHDPlus) to theHydrologic Unit Code (HUC8) river basins. Both long-termmonthly and daily streamflow simulation are generated andshow reasonable results compared with gage observations.With river cross-section profile information derived fromempirical relationships between channel dimensions anddrainage area, river depth and floodplain extent associatedwith each river reach are also explicitly represented. Resultshave implications for assimilation of surface water altimetryand for implementation of the approach at the continentalscale.Lovejoy, ShaunThe Space-time variability of precipitation frommillimeters to planetary scales, from hours tocenturies: emergent laws and multifractal cascadesLovejoy, Shaun 1 ; Schertzer, Daniel 21. Dept Physics, McGill Univ, Montreal, QC, Canada2. LEESU, École des Ponts ParisTech, Université Paris-Est,Marne-la-Vallée, FrancePrecipitation is the key input field into hydrologicalsystems and models; it displays extreme variability withcomplex structures embedded within structures, from dropto planetary scales in space and from milliseconds tomillennia in time. Numerous applications including remotesensing require detailed hypotheses about its space-timevariability: commonly they assume (unrealistically) that thesubsensor structure is homogeneous. The only hope for93