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Person

Daren M Carlisle

Ecological Studies Coordinator
Office of the Chief Operating Officer

Email: dcarlisle@usgs.gov
Office Phone: 785-832-3524
Fax: 785-832-3500
ORCID: 0000-0002-7367-348X

Location
Lawrence - Main Office
1217 Biltmore Drive
Lawrence , KS 66049
US

Supervisor: Jennifer L Keisman
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Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2-7 metrics were compared for three measures of performance. The datasets in this release support the Carlisle, et al. 2022 report cited herein....
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Ecological assessment data from the USGS National Water-Quality Assessment Program and the USEPA National River and Stream Assessment were reviewed and records were retained from sampling sites co-located with active USGS stream gages. A limited amount of ancillary data, including location, physical watershed features, and basic water chemistry data for each site were also retained.
Datasets used to asses the effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate and diatom communities (2002-12)
Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biolgical condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. This data release contains all of the input and output files necessary to reproduce the results presented and discussed in the associated journal article.
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This metadata record describes monthly estimates of natural baseflow for 15,866 stream reaches, defined by the National Hydrography Dataset Plus Version 2.0 (NHDPlusV2), in the Delaware River Basin for the period 1950-2015. A statistical machine learning technique - random forest modeling (Liaw and Wiener, 2018; R Core Team, 2020) - was applied to estimate natural flows using about 150 potential predictor variables (Miller and others, 2018). Calibration data used for the random forest model are available from (Foks and others, 2020). Each model was run twice, first using all potential predictor variables, which represents a "full" model run, and a second time using the top 20 predictors from the original run, which...
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The hydrologic modeling approach used to predict functional flows relies on daily streamflow data from gages operated by the U.S. Geological Survey (USGS) in California. This dataset contains, for each of 219 gages, a collection of metrics computed on each water year for the period of record to and including Water Year 2016.
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