The Stochastic Empirical Loading and Dilution Model (SELDM) 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 transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks (Granato 2013; Granato and Jones, 2014). SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. The SELDM was developed as a database application with a simple graphical user interface (GUI) by using Microsoft Access® to facilitate highway and urban runoff analyses by scientists, engineers, and decisionmakers without specialized modeling skills. SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. This software archive is designed to document different versions of SELDM that have been used by the USGS, Federal and State transportation engineers, and others since version 1.0 was published as a USGS techniques and methods report (Granato 2013). Versions 1.0.1 through 1.0.3 were developed to implement minor modifications to the software. Version 1.1.0 was developed to provide an interface to run multiple analyses in one session, which facilitates use of the model for scenario and sensitivity analyses. Details about version changes are provided within SELDM’s GUI and in the “ReadMe” files within this software release.

Stochastic Empirical Loading and Dilution Model (SELDM) utilizes Microsoft Access databases to build and run model simulations. The compiled database was used for all simulations related to the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

Stochastic Empirical Loading and Dilution Model (SELDM) utilizes Microsoft Access databases to build and run model simulations. The compiled database was used for all simulations related to the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

In 2013, the U.S. Geological Survey (USGS) in partnership with the U.S. Federal Highway Administration (FHWA) published a new national stormwater quality model called the Stochastic Empirical Loading Dilution Model (SELDM; Granato, 2013). The model is optimized for roadway projects but in theory can be applied to a broad range of development types. SELDM is a statistically-based empirical model pre-populated with much of the data required to successfully run the application (Granato, 2013). The model uses Monte Carlo methods (as opposed to deterministic methods) to generate a wide range of precipitation events and stormwater discharges coupled with water-quality constituent concentrations and loads from the upstream basin and highway site. SELDM is particularly useful for stormwater managers in its ability to provide the statistical probability of a water-quality standard exceedance that could occur downstream of a stormwater discharge location during the period of record simulated as part of a SELDM analysis. SELDM can be used to model a variety of Best Management Practices (BMPs), which allows the user to evaluate the subsequent instream water-quality benefit of different stormwater treatment devices. This functionality makes the model well suited for supporting BMP-specific cost/benefit analyses. In 2015, the North Carolina Department of Transportation (NCDOT) initiated a partnership with the USGS South Atlantic Water Science Center (Raleigh, North Carolina office) to enhance the national SELDM model with additional data specific to North Carolina (NC) to improve the model’s predictive performance across the State. Specific USGS data incorporated to enhance the NC SELDM model included selected North Carolina streamflow data as well as water-quality transport curves for selected constituents. SELDM streamflow statistics (based on data through the 2015 water year) were computed for 266 continuous-record streamgages and updated in the StreamStats database, which is accessible from the USGS StreamStats application for North Carolina (available online via https://streamstats.usgs.gov/ss/). Instantaneous streamflow data available at 30 selected continuous-record streamgages across North Carolina, with drainage areas ranging from 4.12 to 63.3 square miles, were used to develop site-specific recession ratio statistics. Water-quality data through the 2016 water year were used to develop water-quality transport curves for 27 streamgages for the following constituents: suspended sediment concentration, total nitrogen, total phosphorus, turbidity, copper, lead, and zinc. The NCDOT identified NC highway-runoff research reports containing water-quality and quantity data available from non-USGS sources. These data were reviewed by USGS and – where deemed acceptable – were uploaded into the FHWA Highway-Runoff Database, the data warehouse and preprocessor for SELDM (Granato and others, 2018; Granato and Cazenas, 2009; Smith and Granato, 2010). Based on the analysis techniques documented by Granato (2014) in a national BMP study and using available water-quality sample data from selected highway-runoff and BMP site pairs, performance data from the NC highway-runoff research reports were also analyzed and incorporated into the NC SELDM model for three BMP types. Results of analyses completed during development of the NC SELDM model are documented in Weaver and others (2019). In 2018, USGS and NCDOT initiated an additional “phase 2” study for the NC SELDM model to complete numerous model simulations to develop an NC_SELDM_Catalog (Microsoft Excel spreadsheet) of outputs for a wide range of highway catchment and upstream basin variables. A total of 74,880 SELDM simulations were completed across the Piedmont, Blue Ridge, and Coastal Plain regions (24,960 per region) in North Carolina. Within each region, the completed simulations represented 12,480 design scenarios (one each using the grass swale and bioretention BMP device for treatment of

In 2013, the U.S. Geological Survey (USGS) in partnership with the U.S. Federal Highway Administration (FHWA) published a new national stormwater quality model called the Stochastic Empirical Loading Dilution Model (SELDM; Granato, 2013). The model is optimized for roadway projects but in theory can be applied to a broad range of development types. SELDM is a statistically-based empirical model pre-populated with much of the data required to successfully run the application (Granato, 2013). The model uses Monte Carlo methods (as opposed to deterministic methods) to generate a wide range of precipitation events and stormwater discharges coupled with water-quality constituent concentrations and loads from the upstream basin and highway site. SELDM is particularly useful for stormwater managers in its ability to provide the statistical probability of a water-quality standard exceedance that could occur downstream of a stormwater discharge location during the period of record simulated as part of a SELDM analysis. SELDM can be used to model a variety of Best Management Practices (BMPs), which allows the user to evaluate the subsequent instream water-quality benefit of different stormwater treatment devices. This functionality makes the model well suited for supporting BMP-specific cost/benefit analyses. In 2015, the North Carolina Department of Transportation (NCDOT) initiated a partnership with the USGS South Atlantic Water Science Center (Raleigh, North Carolina office) to enhance the national SELDM model with additional data specific to North Carolina (NC) to improve the model’s predictive performance across the State. Specific USGS data incorporated to enhance the NC SELDM model included selected North Carolina streamflow data as well as water-quality transport curves for selected constituents. SELDM streamflow statistics (based on data through the 2015 water year) were computed for 266 continuous-record streamgages and updated in the StreamStats database, which is accessible from the USGS StreamStats application for North Carolina (available online via https://streamstats.usgs.gov/ss/). Instantaneous streamflow data available at 30 selected continuous-record streamgages across North Carolina, with drainage areas ranging from 4.12 to 63.3 square miles, were used to develop site-specific recession ratio statistics. Water-quality data through the 2016 water year were used to develop water-quality transport curves for 27 streamgages for the following constituents: suspended sediment concentration, total nitrogen, total phosphorus, turbidity, copper, lead, and zinc. The NCDOT identified NC highway-runoff research reports containing water-quality and quantity data available from non-USGS sources. These data were reviewed by USGS and – where deemed acceptable – were uploaded into the FHWA Highway-Runoff Database, the data warehouse and preprocessor for SELDM (Granato and others, 2018; Granato and Cazenas, 2009; Smith and Granato, 2010). Based on the analysis techniques documented by Granato (2014) in a national BMP study and using available water-quality sample data from selected highway-runoff and BMP site pairs, performance data from the NC highway-runoff research reports were also analyzed and incorporated into the NC SELDM model for three BMP types. Results of analyses completed during development of the NC SELDM model are documented in Weaver and others (2019). In 2018, USGS and NCDOT initiated an additional “phase 2” study for the NC SELDM model to complete numerous model simulations to develop an NC_SELDM_Catalog (Microsoft Excel spreadsheet) of outputs for a wide range of highway catchment and upstream basin variables. A total of 74,880 SELDM simulations were completed across the Piedmont, Blue Ridge, and Coastal Plain regions (24,960 per region) in North Carolina. Within each region, the completed simulations represented 12,480 design scenarios (one each using the grass swale and bioretention BMP device for treatment of

This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and R.M. Vogel in the 2016 Water Resources Research article entitled "On the Deterministic and Stochastic Use of Hydrologic Model". Input data includes observed streamflow values, in cubic feet per second, for 1225 streamgages over the period from 01 October 1980 through 30 September 2011. Estiamted streamflows, for the same streamgages and periods, is provided from a general calibration of the Precipitation Runoff Modeling System. Output data includes the same with alternate realizations of streamflow generated following the descriptions in the associated report. These results can be regenerated by using the included scripts. Data are provided in several files: (1) observedStreamflow.csv contains observed streamflows, in cubic feet per second, for all 1225 streamgages; (2) prmsModeledStreamflow.csv contains streamflows modeled with the Precipitation Runoff Modeling Streamflow (Markstrom et al., 2015; DOI 10.3133/tm6B7); (3) outputData.zip contains CSV files of observed, PRMS-modeled and stochastically-generated streamflows, in cubic feet per second, for all 1225 streamgages; (4) README.txt describes the contents of this archive and execution of model scripts; (5) simulation.R is a computer script in in the R programming lanaguage and is capable of reproducing the results in outputData.zip from observedStreamflow.csv and prmsModeledStreamflow.csv; (6) analysis.R is another R script capable of reproducing the figures in the associated report from the results in outputData.zip.

This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and R.M. Vogel in the 2016 Water Resources Research article entitled "On the Deterministic and Stochastic Use of Hydrologic Model". Input data includes observed streamflow values, in cubic feet per second, for 1225 streamgages over the period from 01 October 1980 through 30 September 2011. Estiamted streamflows, for the same streamgages and periods, is provided from a general calibration of the Precipitation Runoff Modeling System. Output data includes the same with alternate realizations of streamflow generated following the descriptions in the associated report. These results can be regenerated by using the included scripts. Data are provided in several files: (1) observedStreamflow.csv contains observed streamflows, in cubic feet per second, for all 1225 streamgages; (2) prmsModeledStreamflow.csv contains streamflows modeled with the Precipitation Runoff Modeling Streamflow (Markstrom et al., 2015; DOI 10.3133/tm6B7); (3) outputData.zip contains CSV files of observed, PRMS-modeled and stochastically-generated streamflows, in cubic feet per second, for all 1225 streamgages; (4) README.txt describes the contents of this archive and execution of model scripts; (5) simulation.R is a computer script in in the R programming lanaguage and is capable of reproducing the results in outputData.zip from observedStreamflow.csv and prmsModeledStreamflow.csv; (6) analysis.R is another R script capable of reproducing the figures in the associated report from the results in outputData.zip.

This document provides guidance for using the Stochastic Empirical Loading Dilution Model (SELDM) in the state of Oregon. The document is meant as an accompaniment to the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

This document provides guidance for using the Stochastic Empirical Loading Dilution Model (SELDM) in the state of Oregon. The document is meant as an accompaniment to the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053

This data release documents statistics for simulating structural stormwater runoff best management practices (BMPs) with the Stochastic Empirical Loading and Dilution Model (SELDM). The U.S. Geological Survey (USGS) developed SELDM and the statistics documented in this report in cooperation with the Federal Highway Administration (FHWA) to indicate the risk for stormwater flows, concentrations, and loads to be above user-selected water-quality goals and the potential effectiveness of mitigation measures to reduce such risks. In SELDM, three treatment variables, hydrograph extension, volume reduction, and water-quality treatment are modeled by using the trapezoidal distribution and the rank correlation with the associated highway-runoff variables. This data release also documents statistics for estimating the minimum irreducible concentration (MIC), which is the lowest expected effluent concentration from a BMP site or a class of BMPs. These statistics are different from the statistics commonly used to characterize or compare BMPs. They are designed to provide a stochastic transfer function to approximate the quantity, duration, and quality of BMP effluent given the associated inflow values for a population of storm events. In SELDM, BMP performance is the result of random combinations of variables documented in this report and the interplay among the selected distributions and correlations to inflow variables. Granato (2014) and Granato and others (2020) describe the methods used to calculate these statistics and provide summary statistics for these variables. This data release provides the individual at-site statistics. The statistics were calculated by using data extracted from a modified copy of the December 2019 version of International Stormwater Best Management Practices Database. Sufficient data were available to estimate statistics for 8 to 12 BMP categories by using data from 44 to more than 265 monitoring sites. Water-quality treatment statistics, including trapezoidal ratios and MIC values were developed for 51 runoff-quality constituents commonly measured in highway and urban runoff studies.