Filters: partyWithName: Carol L. Luukkonen (X)
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A regional groundwater flow model (https://pubs.usgs.gov/sir/2009/5244/) was updated to reflect 2017 pumping conditions in the Tri-County Region covering most of Clinton, Eaton, and Ingham Counties, Michigan. This model was developed to simulate the regional hydrologic system in Tri-County area and continues to be used for planning and protection of area water supplies. Revised contributing area delineations in response to recent pumping conditions were needed for local wellhead protection area programs. The model was calibrated to water level observations for 2017 from well driller logs, average water levels for 2012-17 from active USGS observation wells, and estimated baseflow for 2012-16 from USGS streamgaging...
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 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...
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 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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