Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-07-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR037225 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850211848957198336 |
|---|---|
| author | Homa Salehabadi David G. Tarboton Kevin G. Wheeler Rebecca Smith Sarah Baker |
| author_facet | Homa Salehabadi David G. Tarboton Kevin G. Wheeler Rebecca Smith Sarah Baker |
| author_sort | Homa Salehabadi |
| collection | DOAJ |
| description | 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, along with the 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. |
| format | Article |
| id | doaj-art-a727bef6d67141ca93f31cb89d9c931a |
| 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-a727bef6d67141ca93f31cb89d9c931a2025-08-20T02:09:28ZengWileyWater Resources Research0043-13971944-79732024-07-01607n/an/a10.1029/2024WR037225Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case StudyHoma Salehabadi0David G. Tarboton1Kevin G. Wheeler2Rebecca Smith3Sarah Baker4Department of Civil and Environmental Engineering Utah Water Research Laboratory Utah State University Logan UT USADepartment of Civil and Environmental Engineering Utah Water Research Laboratory Utah State University Logan UT USAEnvironmental Change Institute University of Oxford Oxford UKU.S. Bureau of Reclamation Boulder CO USAU.S. Bureau of Reclamation Boulder CO USAAbstract 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, along with the 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.https://doi.org/10.1029/2024WR037225streamflow ensemblestatistical metricsstochastic hydrologytime serieswater managementColorado River Basin |
| spellingShingle | Homa Salehabadi David G. Tarboton Kevin G. Wheeler Rebecca Smith Sarah Baker Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study Water Resources Research streamflow ensemble statistical metrics stochastic hydrology time series water management Colorado River Basin |
| title | Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study |
| title_full | Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study |
| title_fullStr | Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study |
| title_full_unstemmed | Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study |
| title_short | Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence‐Based Analysis: Colorado River Case Study |
| title_sort | quantifying and classifying streamflow ensembles using a broad range of metrics for an evidence based analysis colorado river case study |
| topic | streamflow ensemble statistical metrics stochastic hydrology time series water management Colorado River Basin |
| url | https://doi.org/10.1029/2024WR037225 |
| work_keys_str_mv | AT homasalehabadi quantifyingandclassifyingstreamflowensemblesusingabroadrangeofmetricsforanevidencebasedanalysiscoloradorivercasestudy AT davidgtarboton quantifyingandclassifyingstreamflowensemblesusingabroadrangeofmetricsforanevidencebasedanalysiscoloradorivercasestudy AT kevingwheeler quantifyingandclassifyingstreamflowensemblesusingabroadrangeofmetricsforanevidencebasedanalysiscoloradorivercasestudy AT rebeccasmith quantifyingandclassifyingstreamflowensemblesusingabroadrangeofmetricsforanevidencebasedanalysiscoloradorivercasestudy AT sarahbaker quantifyingandclassifyingstreamflowensemblesusingabroadrangeofmetricsforanevidencebasedanalysiscoloradorivercasestudy |