Filters: Tags: nitrate (X) > partyWithName: Jennifer C Murphy (X)
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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...
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...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Ammonia,
Calcium,
Chloride,
Delaware,
Delaware River,
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...
In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project. One of the major goals of the NAWQA project was to determine how river water quality has changed over time. To support that goal, long-term consistent and comparable monitoring has been conducted by the USGS on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water quality. In 2017, data from these multiple sources were combined to support one of the most comprehensive assessments...
The datasets provided here are the output from the Seasonal Kendall Trend (SKT) test and Weighted Regressions on Time, Discharge, and Season (WRTDS) model that characterize changes in water quality in rivers and streams across the Delaware River Basin. SKT results are compiled in "skt_out.csv" for all combinations of site, water-quality parameter, and trend period. WRTDS results are compiled in four datasets. If unspecified, generalized flow normalization (GFN) results are reported. Stationary flow normalization (SFN) results are indicated in the datasets. "wrtds_out_annResults.csv" contains the annual estimates of mean concentration and load and GFN and SFN estimates by site and parameter for the entire calibration...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Ammonia,
Calcium,
Chloride,
Delaware,
Delaware River,
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...
In 1991, the U.S. Geological Survey (USGS) began a study of more than 50 major river basins across the Nation as part of the National Water-Quality Assessment (NAWQA) project. One of the major goals of the NAWQA project was to determine how river water quality has changed over time. To support that goal, long-term consistent and comparable monitoring has been conducted by the USGS on streams and rivers throughout the Nation. Outside of the NAWQA project, the USGS and other Federal, State, and local agencies also have collected long-term water-quality data to support their own assessments of changing water quality. In 2017, data from these multiple sources were combined to support one of the most comprehensive assessments...
Trends in nutrient fluxes and streamflow for selected tributaries in the Lake Erie watershed were calculated using monitoring data at 10 locations. Trends in flow-normalized nutrient fluxes were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Site information and streamflow and water-quality records are contained in 3 zipped files named as follows: INFO (site information), Daily (daily streamflow records), and Sample (water-quality records). The INFO, Daily (flow), and Sample files contain the input data, by water-quality parameter and by site as .csv files, used to run trend analyses. These files were generated by the R (version 3.1.2) software...
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