Filters: partyWithName: Ayman H. Alzraiee (X)
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This child item describes Python code used to estimate average yearly and monthly tourism per 1000 residents within public-supply water service areas. Increases in population due to tourism may impact amounts of water used by public-supply water systems. This data release contains model input datasets, Python code used to develop the tourism information, and output estimates of tourism. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature in the public supply delivery and water use machine learning models. This page includes the following files: tourism_input_data.zip...
This child item describes a public supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to commercial, industrial, institutional, and irrigation (CII) users. This model predicts total, domestic, and CII per capita rates for public-supply water service areas within the conterminous United States for 2009-2020. This child item contains model input datasets, code used to build the delivery machine learning model, and national predictions. This dataset is part of a larger data release using machine learning to predict public-supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following...
This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input...
This dataset is a comma separated file of agricultural fields, ponds, surface diversions and wells used to generate the Agricultural package (AG).
This dataset provides a flow accumulation raster for the stream network. It displays the accumulated weight of all cells flowing into each downslope cell in a raster. The flow accumulation raster was generated using a flow direction raster, which was created from an elevation raster.
This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020....
This child item describes Python code used to retrieve gridMET climate data for a specific area and time period. Climate data were retrieved for public-supply water service areas, but the climate data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the climate data collector code were used as input feature variables in the public supply delivery and water use machine learning models. This page includes the following file: climate_data_collector.zip - a zip file containing the climate data collector Python code used to retrieve...
This child item describes R code used to determine water source fractions (groundwater (GW), surface water (SW), or spring (SP)) for public-supply water service areas, counties, and 12-digit hydrologic unit codes (HUC12) using information from a proprietary dataset from the U.S. Environmental Protection Agency. Water-use volumes per source were not available from public-supply systems so water source fractions were calculated by the number of withdrawal source types (GW/SW). For example, for a public supply system with three SW intakes and one GW well, the fractions would be 0.75 SW and 0.25 GW. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic...
This dataset provides a flow accumulation raster for the stream network. It displays the accumulated weight of all cells flowing into each downslope cell in a raster. The flow accumulation raster was generated using a flow direction raster, which was created from an elevation raster.
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.
A three-dimensional, GSFLOW model was developed to simulate and quantify the hydrologic system of the Yucaipa subbasin. The model was calibrated to conditions from 1970 to 2014, the period for which data are most complete and reliable. The model was used to (1) quantify the effects of historical and potential water-resource development induced by climate changes and human-related activities, and (2) to derive components of the daily water budgets for the components of the integrated model for calendar years 1970–2014 (long-term average), with particular attention given to groundwater budgets for dry and wet periods. The model also was used to assess the effects of pumping and climate stresses on hydrologic-budget...
This dataset provides a shapefile of the surface water diversions to agricultural ponds in the Russian River Coupled Groundwater and Surface-Water Flow Model (GSFLOW) model.
This dataset provides a flow accumulation raster for the stream network. It displays the accumulated weight of all cells flowing into each downslope cell in a raster. The flow accumulation raster was generated using a flow direction raster, which was created from an elevation raster.
This dataset provides a shapefile of agricultural ponds used in the Russian River Coupled Groundwater and Surface-Water Flow Model (GSFLOW) model.
This dataset provides a shapefile containing the the agricultural wells used in the Russian River Coupled Groundwater and Surface-Water Flow Model (GSFLOW) model.
National watershed boundary (HUC12) dataset for the conterminous United States, retrieved 10/26/2020
This child item provides a snapshot of the watershed boundary dataset which consists of a shapefile with 87,020 12-digit hydrologic unit codes (HUC12) for the conterminous United States retrieved 10/26/2020. The National Watershed Boundary Dataset (WBD) is a comprehensive set of digital spatial data that represents the surface drainages areas of the United States. Although versions of the WBD are published as part of U.S. Geological Survey National Hydrography Products, the version used to produce the water-use reanalysis was not archived and is provided here. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Public-supply...
This dataset provides a shapefile of the fraction of each Coupled Groundwater and Surface-Water Flow Model (GSFLOW) grid cell covered by agricultural fields.
This dataset provides crop coefficients from the Sonoma County water agency. Crop coefficients are used to estimate the water requirements of different crops and are essential for efficient irrigation management. The data in this dataset can be used by farmers and water managers to optimize water use and improve crop yields in Sonoma County.
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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