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This metadata record documents 3 sets of comma delimited tables representing the amount of reported best management practice (BMP) implementation within the Chesapeake Bay watershed as well as output data from scenarios of the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The scenario data were used to estimate the effects of BMPs on water quality. The data are organized by three themes; 1) BMP implementation and definitions, 2) isolation scenarios, and 3) "Progress" and "No Action" scenarios.

This metadata record documents 3 sets of comma delimited tables representing the amount of reported best management practice (BMP) implementation within the Chesapeake Bay watershed as well as output data from scenarios of the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The scenario data were used to estimate the effects of BMPs on water quality. The data are organized by three themes; 1) BMP implementation and definitions, 2) isolation scenarios, and 3) "Progress" and "No Action" scenarios.

This metadata record documents 11 comma delimited tables representing the amount of reported best management practice (BMP) implementation for the years from 1985 to 2014 at three geographic scales: county or land-river modeling segment, River Input Monitoring (RIM) station drainage areas, and the entire Chesapeake Bay Watershed (CBWS). Data originated from the Chesapeake Bay Watershed jurisdictions including Maryland, Pennsylvania, Virginia, Delaware, New York, West Virginia, and the District of Columbia. Data were reported to the Chesapeake Bay Program for an annual review of progress toward meeting nitrogen, phosphorus, and sediment reduction goals.

This metadata record documents a comma-delimited table representing scenario output from the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The annual effect of best management practices (BMP) on water quality in the Chesapeake Bay Watershed (CBWS) is estimated through a series of model scenarios. The model output data tables for each year from 1985 to 2014 are included with estimated mass of nitrogen, phosphorus, and sediment. Each year has an output table for a “Progress” scenario, which is a model run with all BMPs active, and a “No Action” scenario, which is a model run with all BMPs deactivated. Model output is provided at the Watershed Model land-river segment scale.

This metadata record documents a comma-delimited table representing scenario output from the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The annual effect of best management practices (BMP) on water quality in the Chesapeake Bay Watershed (CBWS) is estimated through a series of model scenarios. The model output data tables for each year from 1985 to 2014 are included with estimated mass of nitrogen, phosphorus, and sediment. Each year has an output table for a “Progress” scenario, which is a model run with all BMPs active, and a “No Action” scenario, which is a model run with all BMPs deactivated. Model output is provided at the Watershed Model land-river segment scale.

This metadata record documents 2 comma delimited tables representing output from the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The effect of best management practices (BMP) in 2014 in the Chesapeake Bay Watershed (CBWS) is estimated through a series of model scenarios that isolate the effect of individual BMPs. Data include a table describing the series of isolation scenarios and the summarized output of all scenarios. Scenario output include the estimated nitrogen, phosphorus, and sediment mass reductions for each BMP. Analysis of BMP implementation over time can provide insight to water quality restoration progress.

This metadata record documents 2 comma delimited tables representing output from the Chesapeake Bay Program Phase 5.3.2 Watershed Model. The effect of best management practices (BMP) in 2014 in the Chesapeake Bay Watershed (CBWS) is estimated through a series of model scenarios that isolate the effect of individual BMPs. Data include a table describing the series of isolation scenarios and the summarized output of all scenarios. Scenario output include the estimated nitrogen, phosphorus, and sediment mass reductions for each BMP. Analysis of BMP implementation over time can provide insight to water quality restoration progress.

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S.

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S.