Skip to main content

Person

Jennifer C Murphy

Supervisory Hydrologist

Central Midwest Water Science Center

Email: jmurphy@usgs.gov
Office Phone: 815-752-2040
Fax: 815-756-9214
ORCID: 0000-0002-0881-0919

Location
Aspen Ridge Business Park
650 Peace Road
DeKalb , IL 60115

Supervisor: Amy M Russell
thumbnail
This data set includes WRTDS nutrient flux trend results and the values of daily streamflow trend results displayed in the Quantile-Kendall plots. For 1995-2015 nutrient trends, the method of generalized flow normalization (FNG) was used which explicitly addresses non-stationary streamflow conditions. For 2005-2015 nutrient trends, the WRTDS trend analyses used the method of stationary flow normalization (FNS) because streamflow nonstationarity is difficult to assess over this shorter duration time frame. The 1995-2015 annual nutrient trends were determined for all five nutrient parameters (TP, SRP, TN, NO23, TKN), and monthly trends were evaluated only for SRP. The 2005-2015 annual nutrient trends were determined...
thumbnail
The datasets provided here are the input data used to run the Seasonal Kendall Trend (SKT) tests and Weighted Regressions on Time, Discharge, and Season (WRTDS) models. SKT tests use "annualSamplingFreqs_allSites.csv" and "wqData_screenedSitesAll.csv" which includes, for all site-parameter combinations, information on annual sampling frequencies and the screened water-quality data, respectively. The WRTDS models use "DRB.wqdata.20200521.csv", "DRB.flow.20200610.zip", and "DRB.info.20200521.csv" for calibration which includes, for all site-parameter combinations, the water-quality data, streamflow data (as separate .csv files for each site), model specifications and site information, respectively. The multisource...
thumbnail
This data release contains one dataset and one model archive in support of the journal article, "Leveraging machine learning to automate regression model evaluations for large multi-site water-quality trend studies," by Jennifer C. Murphy and Jeffrey G. Chanat. The model archive contains scripts (run in R) to reproduce the four machine learning models (logistic regression, linear and quadratic discriminant analysis, and k-nearest neighbors) trained and tested as part of the journal article. The dataset contains the estimated probabilities for each of these models when applied to a training and test dataset.
thumbnail
Data provided in this release support the findings in Choquette et al. (2019), utilizing methods for evaluating water-quality and daily-streamflow trends described also in Hirsch and DeCicco (2015 and 2018a) and Hirsch (2018). The trend results and model-input data focus on 10 locations in the Lake Erie watershed that have long-term (20 or more years) water-quality and streamflow monitoring records. The trend results include the years 1987 through 2016 or specified sub-periods during this time frame. The model-input data records spanned the time period 1974 through 2016 although record lengths varied by site, data type, and trend analysis. The water-quality records were provided by the National Center for Water...
thumbnail
For about 10 years, the U.S. Geological Survey (USGS) has monitored water quality and streamflow in three agricultural drainage ditches in an effort to evaluate the influence of best management practices on water quality. These ditches are small tributaries to oxbow lakes located in the Mississippi Alluvial Plain of northwestern Mississippi--two sites (LWSR and LWT2) drain to Lake Washington and one site (BLT1) drains to Bee Lake. Streamflow was intermittent at these sites and the ditches were dry much of the year. When streamflow was present, flows were measured on 15-minute intervals and water-quality samples were collected over the course of the flow event using an automated sampler. These datasets were aggregated...
View more...
ScienceBase brings together the best information it can find about USGS researchers and offices to show connections to publications, projects, and data. We are still working to improve this process and information is by no means complete. If you don't see everything you know is associated with you, a colleague, or your office, please be patient while we work to connect the dots. Feel free to contact sciencebase@usgs.gov.