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Data‐driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions.

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Title: Data‐driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions.
Authors: Johnson, Ryan C.1 (AUTHOR) rjohnson18@ua.edu, Burian, Steven J.2 (AUTHOR), Oroza, Carlos A.3 (AUTHOR), Hansen, Carly4 (AUTHOR), Baur, Emily3 (AUTHOR), Aziz, Danyal2 (AUTHOR), Hassan, Daniyal5 (AUTHOR), Kirkham, Tracie6 (AUTHOR), Stewart, Jessie6 (AUTHOR), Briefer, Laura6 (AUTHOR)
Source: Journal of the American Water Resources Association. Apr2024, Vol. 60 Issue 2, p687-706. 20p.
Subjects: Water demand management, Municipal water supply, Water supply, Chilled water systems, Demand forecasting, Water use, Water management, Hydrological forecasting, Statistical bias
Geographic Terms: Salt Lake City (Utah)
Abstract: Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade‐offs between simple climate‐independent econometric‐based models and complex climate‐sensitive data‐driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. The climate‐independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate‐sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. −27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate‐independent model. The climate‐sensitive workflow increased model accuracy and characterized climate‐demand interactions, demonstrating a novel tool to enhance water system management. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Water Resources Association is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 176410097
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
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  Data: Data‐driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions.
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  Data: <searchLink fieldCode="AR" term="%22Johnson%2C+Ryan+C%2E%22">Johnson, Ryan C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rjohnson18@ua.edu</i><br /><searchLink fieldCode="AR" term="%22Burian%2C+Steven+J%2E%22">Burian, Steven J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Oroza%2C+Carlos+A%2E%22">Oroza, Carlos A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hansen%2C+Carly%22">Hansen, Carly</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Baur%2C+Emily%22">Baur, Emily</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aziz%2C+Danyal%22">Aziz, Danyal</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hassan%2C+Daniyal%22">Hassan, Daniyal</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kirkham%2C+Tracie%22">Kirkham, Tracie</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Stewart%2C+Jessie%22">Stewart, Jessie</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Briefer%2C+Laura%22">Briefer, Laura</searchLink><relatesTo>6</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Water+Resources+Association%22">Journal of the American Water Resources Association</searchLink>. Apr2024, Vol. 60 Issue 2, p687-706. 20p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Water+demand+management%22">Water demand management</searchLink><br /><searchLink fieldCode="DE" term="%22Municipal+water+supply%22">Municipal water supply</searchLink><br /><searchLink fieldCode="DE" term="%22Water+supply%22">Water supply</searchLink><br /><searchLink fieldCode="DE" term="%22Chilled+water+systems%22">Chilled water systems</searchLink><br /><searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Water+use%22">Water use</searchLink><br /><searchLink fieldCode="DE" term="%22Water+management%22">Water management</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrological+forecasting%22">Hydrological forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+bias%22">Statistical bias</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Salt+Lake+City+%28Utah%29%22">Salt Lake City (Utah)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade‐offs between simple climate‐independent econometric‐based models and complex climate‐sensitive data‐driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. The climate‐independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate‐sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. −27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate‐independent model. The climate‐sensitive workflow increased model accuracy and characterized climate‐demand interactions, demonstrating a novel tool to enhance water system management. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the American Water Resources Association is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=176410097
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/1752-1688.13186
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 687
    Subjects:
      – SubjectFull: Water demand management
        Type: general
      – SubjectFull: Municipal water supply
        Type: general
      – SubjectFull: Water supply
        Type: general
      – SubjectFull: Chilled water systems
        Type: general
      – SubjectFull: Demand forecasting
        Type: general
      – SubjectFull: Water use
        Type: general
      – SubjectFull: Water management
        Type: general
      – SubjectFull: Hydrological forecasting
        Type: general
      – SubjectFull: Statistical bias
        Type: general
      – SubjectFull: Salt Lake City (Utah)
        Type: general
    Titles:
      – TitleFull: Data‐driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions.
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              Text: Apr2024
              Type: published
              Y: 2024
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