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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...
Categories: Data,
Data Release - Revised;
Types: Map Service,
NetCDF OPeNDAP Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: USGS Science Data Catalog (SDC),
United States,
Water Resources,
hydrology,
inlandWaters,
The U.S. Geological Survey Precipitation-Runoff Modeling System (PRMS) was used to assess the effects of changing climate and land disturbance on seasonal streamflow in the Rio Grande Headwaters (RGHW) region. Three applications of PRMS in the RGHW were used to simulate 1) baseline effects of climate, 2) effects of bark-beetle induced tree mortality, and 3) effects of wildfire, on components of the hydrologic cycle and subsequent seasonal streamflow runoff from April through September for water years 1980 through 2017. PRMS input files and select PRMS output variables for each simulation are contained in this data release to accompany the journal article.
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
Coastal wetland ecosystems are expected to migrate landward in response to accelerated sea-level rise. However, due to differences in topography and coastal urbanization extent, estuaries vary in their ability to accommodate wetland migration. The landward movement of wetlands requires suitable conditions, such as a gradual slope and land free of urban development. Urban barriers can constrain migration and result in wetland loss (coastal squeeze). For future-focused conservation planning purposes, there is a pressing need to quantify and compare the potential for wetland landward movement and coastal squeeze. For 41 estuaries in the northern Gulf of Mexico (i.e., the USA gulf coast), we quantified and compared...
Categories: Data;
Types: Citation,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Alabama,
Florida,
Gulf of Mexico,
Louisiana,
Mississippi,
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
These data were generated with MAXENT 3.3.3k freeware (Phillips et al. 2011) using climate data and fire probability data for for three time periods: reference (1900-1929), mid-century (2040-2069) and late century (2070-2099), and community occurrence point data extracted from LANDFIRE Environmental Site Potential (ESP). Future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. In MAXENT, we used the logistic output format (generating presence probabilities between 0 and 1), a random test percentage of 30 (using 70 % of the occurrence points to generate the suitability model and 30 % of the occurrence points to validate it), and a jackknife test to measure variable importance....
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Climate Change,
Drought, Fire and Extreme Weather,
Environmental Suitability Models,
Fire,
LANDFIRE,
This data release supports the study by Sexstone and others (2020) and contains simulation output from SnowModel (Liston and Elder, 2006), a well-validated process-based snow modeling system. Simulations are for water years 1984 through 2017 (October 1, 1983 through September 30, 2017) across a 11,200 square kilometer model domain in the San Juan Mountains of southwestern Colorado, United States that encompasses the Rio Grande Basin headwaters (HUC8 13010001). This data release also contains supporting field-based snow and meteorological station observations collected within the model domain during water years 2016 and 2017 that were used to evaluate SnowModel simulations. Sexstone and others (2020) provide details...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Colorado,
Rio Grande headwaters,
USGS Science Data Catalog (SDC),
Upper Rio Grande,
climatology,
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