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

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 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.

The InterpretSELDM program is a graphical post processor designed to facilitate analysis and presentation of stormwater modeling results from the Stochastic Empirical Loading and Dilution Model (SELDM), which is a stormwater model developed by the U.S. Geological Survey in cooperation with the Federal Highway Administration. SELDM simulates flows, concentrations, and loads in stormflows from upstream basins, the highway, best management practice outfalls, and in the receiving water downstream of a highway. SELDM is designed to transform complex scientific data into meaningful information about (1) the risk of adverse effects from stormwater runoff on receiving waters, (2) the potential need for mitigation measures, and (3) the potential effectiveness of management measures for reducing those risks. SELDM produces results in (relatively) easy-to-use tab delimited output files that are designed for use with spreadsheets and graphing packages. However, time is needed to learn, understand, and use the SELDM output formats. Also, the SELDM output requires post-processing to extract the specific information that commonly is of interest to the user (for example, the percentage of storms above a user-specified value). Because SELDM output files are comprehensive, the locations of specific output values may not be obvious to the novice user or the occasional model user who does not consult the detailed model documentation. The InterpretSELDM program was developed as a postprocessor to facilitate analysis and presentation of SELDM results. The program provides graphical results and tab-delimited text summaries from simulation results. InterpretSELDM provides data summaries in seconds. In comparison, manually extracting the same information from SELDM outputs could take minutes to hours. It has an easy-to-use graphical user interface designed to quickly extract dilution factors, constituent concentrations, annual loads, and annual yields from all analyses within a SELDM project. The program provides the methods necessary to create scatterplots and boxplots for the extracted results. Graphs are more effective than tabular data for evaluating and communicating risk-based information to technical and nontechnical audiences. Commonly used spreadsheets provide methods for generating graphs, but do not provide probability-plots or boxplots, which are useful for examining extreme stormflow, concentration, and load values. Probability plot axes are necessary for evaluating stormflow information because the extreme values commonly are the values of concern. Boxplots provide a simple visual summary of results that can be used to compare different simulation results. The graphs created by using the InterpretSELDM program can be copied and pasted into word processors, spreadsheets, drawing software, and other programs. The graphs also can be saved in commonly used image-file formats.

Municipal Separate Storm Sewer System (MS4) permitees including the California Department of Transportation need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff. These entities also need information about the potential effectiveness of stormwater best management practices (BMPs) used to mitigate the effects of runoff. This information is needed to address total maximum daily load (TMDL) regulations. This model archive describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for assessing long-term annual yields of highway and urban runoff in selected areas of California with version 1.1.0 of the Stochastic Empirical Loading and Dilution Model (SELDM). In this study SELDM was used to do 368 analyses to examine highway- and urban-runoff yields for 53 runoff-quality constituents. The analyses include 222 random-seed analyses, 60 regional highway-runoff analyses, 24 regional urban-runoff analyses, and 62 focused TMDL-area analyses. Results for all these analyses are provided in this model archive. Although application of results from this study may have considerable uncertainty for predicting loads from any particular stormwater outfall, the results do provide robust estimates to support basin-scale planning-level analyses in California. These analyses also provide regional estimates inside and outside California for the 12 U.S. Environmental Protection Agency level III ecoregions that lie in-whole or in-part within the state of California.

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.

This data release contains inputs for and outputs from hydrologic simulations of the southeastern U.S. using the Monthly Water Balance Model, the Precipitation Runoff Modeling System (PRMS), and statistically-based methods. These simulations were developed to provide estimates of water availability and statistics of streamflow for historical and potential future conditions for an area of approximately 1.16 million square miles. These model input and output data are intended to accompany a U.S. Geological Survey Scientific Investigations Report (LaFontaine and others, 2019); they include four types of data: 1) model input parameters, 2) model output statistics, 3) GIS files of the model hydrologic response units and stream segments, and 4) statistically-based streamflow estimates for headwater watersheds. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., 2019, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U.S. Geological Survey Scientific Investigations Report, 2019-5039, 83 p., https://doi.org/10.3133/sir20195039.

Spreadsheet used to calculated hydrograph recession statistical parameters (Minimum, Most Probable Value, and Maximum) for the Stochastic Empirical Loading Dilution Model (SELDM) . The spreadsheet was used in conjunction with the SELDM simulations used in 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, and after using the Hydrograph.xlsx spreadsheet.