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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers ( Show direct descendants )

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A monthly water balance model (MWBM) was driven with precipitation and temperature using a station-based dataset for current conditions (1949 to 2010) and selected statistically-downscaled general circulation models (GCMs) for current and future conditions (1950 to 2099) across the conterminous United States (CONUS) using hydrologic response units from the Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014). Six MWBM output variables (actual evapotranspiration (AET), potential evapotranspiration (PET), runoff (RO), streamflow (STRM), soil moisture storage (SOIL), and snow water equivalent (SWE)) and the two MWBM input variables (atmospheric temperature (TAVE) and precipitation (PPT)) were summarized...
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Lidar-derived digital elevation models often contain a vertical bias due to vegetation. In areas with tidal influence the amount of bias can be ecologically significant, for example, by decreasing the expected inundation frequency. We generated a corrected digital elevation mode (DEM) for Suisun marsh using a modification of the Lidar Elevation Adjustment with NDVI (LEAN) technique (Buffington et al. 2016). GPS survey data (6912 points, collected across public and private land in 2018), Normalized Difference Vegetation Index (NDVI) derived from an airborne multispectral image (June 2018), a 1 m lidar DEM from September 2018, and a 1 m canopy surface model were used to generate models of predicted bias across the...


    map background search result map search result map Monthly Water Balance Model Futures LEAN-Corrected DEM for Suisun Marsh LEAN-Corrected DEM for Suisun Marsh Monthly Water Balance Model Futures