Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States
Streamflow duration information underpins many management decisions. However, hydrologic data are rarely available where needed. Rapid streamflow duration assessment methods (SDAMs) classify reaches based on indicators that are measured in a single brief visit. We evaluated a proposed framework for...
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oai:doaj.org-article:89fa56bc459d46dea37e76bf836a87cc2021-11-25T19:16:44ZImplementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States10.3390/w132233102073-4441https://doaj.org/article/89fa56bc459d46dea37e76bf836a87cc2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4441/13/22/3310https://doaj.org/toc/2073-4441Streamflow duration information underpins many management decisions. However, hydrologic data are rarely available where needed. Rapid streamflow duration assessment methods (SDAMs) classify reaches based on indicators that are measured in a single brief visit. We evaluated a proposed framework for developing SDAMs to develop an SDAM for the Arid West United States that can classify reaches as perennial, intermittent, or ephemeral. We identified 41 candidate biological, geomorphological, and hydrological indicators of streamflow duration in a literature review, evaluated them for a number of desirable criteria (e.g., defensibility and consistency), and measured 21 of them at 89 reaches with known flow durations. We selected metrics for the SDAM based on their ability to discriminate among flow duration classes in analyses of variance, as well as their importance in a random forest model to predict streamflow duration. This approach resulted in a “beta” SDAM that uses five biological indicators. It could discriminate between ephemeral and non-ephemeral reaches with 81% accuracy, but only 56% accuracy when distinguishing 3 classes. A final method will be developed following expanded data collection. This Arid West study demonstrates the effectiveness of our approach and paves the way for more efficient development of scientifically informed SDAMs.Raphael D. MazorBrian J. ToppingTracie-Lynn NadeauKen M. FritzJulia E. KelsoRachel A. HarringtonWhitney S. BeckKenneth S. McCuneAaron O. AllenRobert LeidyJames T. RobbGabrielle C. L. DavidMDPI AGarticleclassificationflow durationstreamsperennialintermittentephemeralHydraulic engineeringTC1-978Water supply for domestic and industrial purposesTD201-500ENWater, Vol 13, Iss 3310, p 3310 (2021) |
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classification flow duration streams perennial intermittent ephemeral Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
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classification flow duration streams perennial intermittent ephemeral Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Raphael D. Mazor Brian J. Topping Tracie-Lynn Nadeau Ken M. Fritz Julia E. Kelso Rachel A. Harrington Whitney S. Beck Kenneth S. McCune Aaron O. Allen Robert Leidy James T. Robb Gabrielle C. L. David Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
description |
Streamflow duration information underpins many management decisions. However, hydrologic data are rarely available where needed. Rapid streamflow duration assessment methods (SDAMs) classify reaches based on indicators that are measured in a single brief visit. We evaluated a proposed framework for developing SDAMs to develop an SDAM for the Arid West United States that can classify reaches as perennial, intermittent, or ephemeral. We identified 41 candidate biological, geomorphological, and hydrological indicators of streamflow duration in a literature review, evaluated them for a number of desirable criteria (e.g., defensibility and consistency), and measured 21 of them at 89 reaches with known flow durations. We selected metrics for the SDAM based on their ability to discriminate among flow duration classes in analyses of variance, as well as their importance in a random forest model to predict streamflow duration. This approach resulted in a “beta” SDAM that uses five biological indicators. It could discriminate between ephemeral and non-ephemeral reaches with 81% accuracy, but only 56% accuracy when distinguishing 3 classes. A final method will be developed following expanded data collection. This Arid West study demonstrates the effectiveness of our approach and paves the way for more efficient development of scientifically informed SDAMs. |
format |
article |
author |
Raphael D. Mazor Brian J. Topping Tracie-Lynn Nadeau Ken M. Fritz Julia E. Kelso Rachel A. Harrington Whitney S. Beck Kenneth S. McCune Aaron O. Allen Robert Leidy James T. Robb Gabrielle C. L. David |
author_facet |
Raphael D. Mazor Brian J. Topping Tracie-Lynn Nadeau Ken M. Fritz Julia E. Kelso Rachel A. Harrington Whitney S. Beck Kenneth S. McCune Aaron O. Allen Robert Leidy James T. Robb Gabrielle C. L. David |
author_sort |
Raphael D. Mazor |
title |
Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
title_short |
Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
title_full |
Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
title_fullStr |
Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
title_full_unstemmed |
Implementing an Operational Framework to Develop a Streamflow Duration Assessment Method: A Case Study from the Arid West United States |
title_sort |
implementing an operational framework to develop a streamflow duration assessment method: a case study from the arid west united states |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/89fa56bc459d46dea37e76bf836a87cc |
work_keys_str_mv |
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