Filters: partyWithName: U.S. Geological Survey - ScienceBase (X) > partyWithName: Ayman H Alzraiee (X)
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The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. This data release contains data used in a machine learning model to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features as well as model output values by 12-digit hydrologic...
This data release is a shapefile of USGS streamgage locations in the Russian River watershed and the outflow point of the Russian River.
This data release is a subset of the 2010 LANDFIRE Existing Vegetation Cover, covering the Russian River watershed. This LANDFIRE data was downloaded and processed in 2014. The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. The existing vegetation cover (EVC) data layer depicts percent canopy cover by life form, and is an important input to other LANDFIRE mapping efforts....
This is a shapefile of the streamflow network. This dataset contains the SFR segment and reach data along with the non-calibrated Streamflow-Routing (SFR) package parameter values.
Monthly 30-year "normal" dataset covering the conterminous U.S., including the Russian River watershed, averaged over the climatological period 1981-2010. Contains spatially gridded average monthly and average annual precipitation, maximum temperature, and minimum temperature at 800m grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset was heavily peer reviewed, and is available free-of-charge on the PRISM website. The dataset was downloaded from the PRISM website in 2019
This data release is a subset of the National Hydrography Dataset (NHD) water bodies, specifically lakes Mendocino and Sonoma in the Russian River watershed. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed...
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: California,
Russian River,
United States,
hydrology,
modeling,
This data release is a raster dataset from of the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness (2011 Edition). The NLCD 2011 data was downloaded from the Multi-Resolution Land Characteristics Consortium (MRLC) in 2014. Below is the abstract from the NLCD metadata: The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture -Forest...
This data is a subset of the 2010 LANDFIRE Existing Vegetation Type, covering the Russian River watershed. This LANDFIRE data was downloaded and processed in 2017. Introduction: The LANDFIRE existing vegetation layers describe the following elements of existing vegetation for each LANDFIRE mapping zone: existing vegetation type, existing vegetation canopy cover, and existing vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees. DATA SUMMARY: The existing vegetation type (EVT) data layer represents the current distribution of the terrestrial ecological systems classification...
This data release is a subset of the National Hydrography Dataset (NHD) flowlines covering the Russian River watershed and the Russian River Integrated Hydrologic Model (RRIHM). The majority of this metadata was modified from the source metadata. Abstract from the NHD metadata: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto...
This data release is the Soil Survey Geographic (SSURGO) databases covering Mendocino County, Eastern Part and Southwestern Part of Trinity County, and Sonoma County California. This data was downloaded in 2014. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. his data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a...
This child item describes a machine learning model that was developed to estimate public-supply water use by service area boundary and 12-digit hydrologic unit code (HUC12) for the conterminous United States. This model was used to develop an annual and monthly public supply reanalysis of withdrawals for the period 2000-2020. This data release contains model input feature datasets, python codes used to develop and train the water use machine learning model, and output water use predictions by HUC12. This page includes the following files: PS_HUC12_Tot_2000_2020.csv - a csv file with monthly public supply reanalysis of withdrawals from 2000-2020 by HUC12 PS_HUC12_GW_2000_2020.csv - a csv file with estimated monthly...
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