The US Geological Survey (USGS) conducted a study (Carlisle and others 2017) with a national-scale dataset composed of ecological data from the USGS National Water-Quality Assessment Project and the US Environmental Protection Agency, matched to USGS streamgaging sites. In a follow-up study (Carlisle and others 2019), additional data from three regional assessments conducted by USGS were combined with data from the original study, and these new data are published here. Using all of the aforementioned datasets, the follow-up study (Carlisle and others, 2019) then developed regional-scale model predictions of the relation between streamflow modification and indicators of biological integrity. These model predictions, presented as graphics, are published here.
Carlisle, D.M., T.E. Grantham, K. Eng and D. M. Wolock. 2017. Biological relevance of streamflow metrics: regional and national perspectives. Freshwater Science 36(4): 927-940.
Carlisle, D.M., T.E. Grantham, K. Eng, and D.M. Wolock. 2019. Regional-scale associations between indicators of biological integrity and indicators of streamflow modification: U.S. Geological Survey Open-File Report 2019-1088, 10 p., https://doi.org/10.3133/ofr20191088.
Citation Information
| Publication Year | 2022 |
|---|---|
| Title | Regional-scale Model Predictions of the Relation Between Biological Integrity and Streamflow Modification |
| DOI | 10.5066/P9O2ZV0M |
| Authors | Daren M Carlisle |
| Product Type | Data Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Office of Planning and Programming |
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Regional-scale associations between indicators of biological integrity and indicators of streamflow modification
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Regional-scale associations between indicators of biological integrity and indicators of streamflow modification
Although streamflow is widely recognized as a controlling factor in stream health, empirical relations between indicators of anthropogenic modification of streamflow and ecological indicators have been elusive. The objective of this report is to build upon specific findings reported in recent publications by providing a library of empirical models that describe the relations between streamflow mod - Connect