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Carol L Luukkonen

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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...
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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...
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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...
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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 presents the total estimated monthly public-supply water withdrawal by 12-digit hydrologic unit code (HUC12) in the conterminous United States for 2015. Public-supply water use was estimated by spatially and temporally downscaling available data from each state. The total represents combined groundwater and surface water withdrawals for 83,178 watersheds. Public supply refers to water withdrawn by public and private water suppliers that provide water for cities, towns, rural water districts, mobile-home parks, Native American Indian reservations, and military bases. Public-supply facilities are classified under the Standard Industrial Classification (SIC) 4941 and provide water to at least 25 people...
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