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Subsampling and space-filling metrics to test ensemble size for robustness analysis with a demonstration in the Colorado River Basin.

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Title: Subsampling and space-filling metrics to test ensemble size for robustness analysis with a demonstration in the Colorado River Basin.
Authors: Bonham, Nathan1,2 (AUTHOR) nathan.bonham@colorado.edu, Kasprzyk, Joseph1,2 (AUTHOR), Zagona, Edith1,2 (AUTHOR), Rajagopalan, Balaji1,2,3 (AUTHOR)
Source: Environmental Modelling & Software. Jan2024, Vol. 172, pN.PAG-N.PAG. 1p.
Subjects: Watersheds, Decision making
Geographic Terms: Colorado
Abstract: Decision Making Under Deep Uncertainty often uses prohibitively large scenario ensembles to calculate robustness and rank policies' performance. This paper contributes a framework using subsampling algorithms and space-filling metrics to determine how smaller ensemble sizes impact the accuracy of robustness rankings. Subsampling methods create smaller scenario ensembles of varying sizes. We evaluate ranking sensitivity to the ensemble size and calculate accuracy relative to a baseline ranking. Then, metrics of scenario set quality predict ranking accuracy. Notably, the metrics and subsampling methods do not require additional model simulations. We demonstrate the framework with a case study of shortage policies for Lake Mead in the Colorado River Basin (CRB). Results suggest that fewer scenarios than previous studies can accurately rank Lake Mead policies, and results depend on the type of objective and robustness metric. Smaller ensembles could reduce the computational burden of robustness analyses in the ongoing CRB policy renegotiation. • Framework tests sensitivity of policy robustness rankings to scenario ensemble size. • Subsampling methods reevaluate policy rankings without additional simulations. • Model-free metrics of scenario ensemble quality predict rank accuracy. • Case study: shortage operation policies of Lake Mead in the Colorado River Basin. • Fewer scenarios than previous studies can accurately rank Lake Mead policies. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Modelling & Software is the property of Elsevier B.V. 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
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Header DbId: egs
DbLabel: Engineering Source
An: 174760094
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Subsampling and space-filling metrics to test ensemble size for robustness analysis with a demonstration in the Colorado River Basin.
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  Data: Decision Making Under Deep Uncertainty often uses prohibitively large scenario ensembles to calculate robustness and rank policies' performance. This paper contributes a framework using subsampling algorithms and space-filling metrics to determine how smaller ensemble sizes impact the accuracy of robustness rankings. Subsampling methods create smaller scenario ensembles of varying sizes. We evaluate ranking sensitivity to the ensemble size and calculate accuracy relative to a baseline ranking. Then, metrics of scenario set quality predict ranking accuracy. Notably, the metrics and subsampling methods do not require additional model simulations. We demonstrate the framework with a case study of shortage policies for Lake Mead in the Colorado River Basin (CRB). Results suggest that fewer scenarios than previous studies can accurately rank Lake Mead policies, and results depend on the type of objective and robustness metric. Smaller ensembles could reduce the computational burden of robustness analyses in the ongoing CRB policy renegotiation. • Framework tests sensitivity of policy robustness rankings to scenario ensemble size. • Subsampling methods reevaluate policy rankings without additional simulations. • Model-free metrics of scenario ensemble quality predict rank accuracy. • Case study: shortage operation policies of Lake Mead in the Colorado River Basin. • Fewer scenarios than previous studies can accurately rank Lake Mead policies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. 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=174760094
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      – Type: doi
        Value: 10.1016/j.envsoft.2023.105933
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Colorado
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              M: 01
              Text: Jan2024
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              Y: 2024
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