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Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence-Based Analysis: Colorado River Case Study
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  • Homa Salehabadi,
  • David Gavin Tarboton,
  • Kevin Guy Wheeler,
  • Rebecca Smith,
  • Sarah Baker
Homa Salehabadi
Utah State University

Corresponding Author:[email protected]

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David Gavin Tarboton
Utah State University
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Kevin Guy Wheeler
University of Oxford
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Rebecca Smith
USBR
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Sarah Baker
US Bureau of Reclamation
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Abstract

Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non-stationarity, and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence-based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree-based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented and their classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non-stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages.
11 Apr 2024Submitted to ESS Open Archive
15 Apr 2024Published in ESS Open Archive
Jul 2024Published in Water Resources Research volume 60 issue 7. https://doi.org/10.1029/2024WR037225