Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks
Abstract Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial...
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| Format: | Article |
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Wiley
2024-07-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036632 |
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| author | Ayman Alzraiee Richard Niswonger Carol Luukkonen Josh Larsen Donald Martin Deidre Herbert Cheryl Buchwald Cheryl Dieter Lisa Miller Jana Stewart Natalie Houston Scott Paulinski Kristen Valseth |
| author_facet | Ayman Alzraiee Richard Niswonger Carol Luukkonen Josh Larsen Donald Martin Deidre Herbert Cheryl Buchwald Cheryl Dieter Lisa Miller Jana Stewart Natalie Houston Scott Paulinski Kristen Valseth |
| author_sort | Ayman Alzraiee |
| collection | DOAJ |
| description | Abstract Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios. |
| format | Article |
| id | doaj-art-3facb219efc44e36a1fecd0e8c7ea319 |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-3facb219efc44e36a1fecd0e8c7ea3192025-08-20T02:36:39ZengWileyWater Resources Research0043-13971944-79732024-07-01607n/an/a10.1029/2023WR036632Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational FrameworksAyman Alzraiee0Richard Niswonger1Carol Luukkonen2Josh Larsen3Donald Martin4Deidre Herbert5Cheryl Buchwald6Cheryl Dieter7Lisa Miller8Jana Stewart9Natalie Houston10Scott Paulinski11Kristen Valseth12California Water Science Center U.S. Geological Survey (USGS) Sacramento CA USAUSGS Water Mission Area Menlo Park CA USAUSGS Upper Midwest Water Science Center in Lansing Lansing MI USACalifornia Water Science Center U.S. Geological Survey (USGS) Sacramento CA USACalifornia Water Science Center U.S. Geological Survey (USGS) Sacramento CA USACalifornia Water Science Center U.S. Geological Survey (USGS) Sacramento CA USAUSGS Upper Midwest Water Science Center in Lansing Lansing MI USAUSGS Maryland‐Delaware‐DC Water Science Center Catonsville MD USAUSGS Colorado Water Science Center San Diego CA USAUSGS Upper Midwest Water Science Center in Lansing Lansing MI USAUSGS Oklahoma‐Texas Water Science Center Austin TX USACalifornia Water Science Center U.S. Geological Survey (USGS) Sacramento CA USAUSGS Oklahoma‐Texas Water Science Center Austin TX USAAbstract Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.https://doi.org/10.1029/2023WR036632national water usepublic supplymachine learninguncertaintywater withdrawaldata driven modeling |
| spellingShingle | Ayman Alzraiee Richard Niswonger Carol Luukkonen Josh Larsen Donald Martin Deidre Herbert Cheryl Buchwald Cheryl Dieter Lisa Miller Jana Stewart Natalie Houston Scott Paulinski Kristen Valseth Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks Water Resources Research national water use public supply machine learning uncertainty water withdrawal data driven modeling |
| title | Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks |
| title_full | Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks |
| title_fullStr | Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks |
| title_full_unstemmed | Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks |
| title_short | Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks |
| title_sort | next generation public supply water withdrawal estimation for the conterminous united states using machine learning and operational frameworks |
| topic | national water use public supply machine learning uncertainty water withdrawal data driven modeling |
| url | https://doi.org/10.1029/2023WR036632 |
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