EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
6/05/2017 | 9:15 AM - 9:30 AM | PREDICTABILITY AND SELECTION OF HYDROLOGIC METRICS IN RIVERINE ECOHYDROLOGY | 302B
PREDICTABILITY AND SELECTION OF HYDROLOGIC METRICS IN RIVERINE ECOHYDROLOGY
A multitude of hydrologic metrics have been developed to describe natural flow regimes to quantify flow alteration that provide a hydrologic foundation for environmental flow standards. Many hydrologic metric estimation applications for streams require the use of models to predict natural values of hydrologic metrics. However, the error associated with these hydrologic metric estimation applications has not been previously evaluated. The primary goal of this study is to provide guidance to river scientists and water-resource managers with the selection, use and interpretation of hydrologic metrics for stream classification and hydroecological investigations of river ecosystems. We evaluated the predictability of 612 hydrologic metrics using statistical models, and we also examined how the predictability varied among unique components of the flow regime. Roughly 40% out of 612 hydrologic metrics examined can be reliably predicted. The predictable metrics were disproportionately represented in five flow components: asymmetry, seasonality, magnitude, variability, and average monthly flows. Most metrics that represent extreme hydrologic events could not be reliably predicted. Roughly two-thirds of the evaluated hydrologic metrics were incalculable or highly biased at intermittent streams due to logarithmic transformations or scaling by other hydrologic metrics.
- C36 Water Resource Management
- C34 Science and Policy
- C14 Hydroecology
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Presenters/Authors
Ken Eng
(), U.S. Geological Survey, keng@usgs.gov;
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Ted Grantham
(), University of California, Berkeley, tgrantham@berkeley.edu;
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Daren Carlisle
(), U.S. Geological Survey, dcarlisle@usgs.gov;
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David Wolock
(), U.S. Geological Survey, dwolock@usgs.gov;
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