The Highway-Runoff Database (HRDB) was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) to provide planning-level information for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation’s receiving waters. The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. This data release provides highway-runoff data, including information about monitoring sites, precipitation, runoff, and event-mean concentrations of water-quality constituents. The dataset was compiled from 37 studies as documented in 113 scientific or technical reports. The dataset includes data from 242 highway sites across the country. It includes data from 6,837 storm events with dates ranging from April 1975 to November 2017. Therefore, these data span more than 40 years; vehicle emissions and background sources of highway-runoff constituents have changed markedly during this time. For example, some of the early data is affected by use of leaded gasoline, phosphorus-based detergents, and industrial atmospheric deposition. The dataset includes 106,441 concentration values with data for 414 different water-quality constituents. This dataset was assembled from various sources and the original data was collected and analyzed by using various protocols. Where possible the USGS worked with State departments of transportation and the original researchers to obtain, document, and verify the data that was included in the HRDB. This new version (1.1.0) of the database contains software updates to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. However, inclusion in this dataset does not constitute endorsement by the USGS or the FHWA. People who use this data are responsible for ensuring that the data are complete and correct and that it is suitable for their intended purposes.

The Highway-Runoff Database (HRDB) 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 for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation's receiving waters (Granato and Cazenas, 2009; Granato, 2013; 2019; Granato and others, 2018; Granato and Friesz, 2021). The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. The HRDB was first published as version 1.0 in cooperation with the FHWA in 2009 (Granato and Cazenas, 2009). The second version (1.0.0a) was published in cooperation with the Massachusetts Department of Transportation Highway Division to include data from Ohio and Massachusetts (Smith and Granato, 2010). The third version (1.0.0b) was published in cooperation with FHWA to include a substantial amount of additional data (Granato and others, 2018; Granato and Jones, 2019). The fourth version (1.1.0) was updated with additional data and modified to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. The fifth version (1.1.0a) was published in cooperation with the California Department of Transportation to add highway-runoff data collected in California. The sixth version published in this release (1.2.0) has been updated to include additional data, correct data-transfer errors in previous versions, add new parameter information, and modify the statistical output. This version includes data from 270 highway sites across the country (26 states); data from 8,108 storm events; and 119,224 concentration values with data for 418 different water-quality constituents or parameters.

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.

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 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.

This data release documents the data and models used to assess flows, concentrations, and loads of highway and urban runoff and of stormwater within receiving streams in southern New England. There are more than 48,000 locations in southern New England where roads cross streams and many more locations where runoff from developed areas may discharge to receiving streams; information about runoff discharges and the quantity and quality of stormflow upstream and downstream of discharge points is needed to inform resource-management decisions. This analysis was done with a version 1.1.1 of the Stochastic Empirical Loading and Dilution Model (SELDM) that was populated with regional statistics for southern New England. SELDM uses basin properties and hydrologic statistics to simulate runoff from a site of interest, which may be a highway site or another developed (urban) area, and concurrent stormflow from an upstream basin to calculate downstream values, which are the sum of contributions from the site of interest and the upstream basin. Because there are few monitoring sites with data relative to the number of potential sites of interest, the probability that data will be available at a site of interest is low. Furthermore, much of the data available at monitored sites is not sufficient to characterize long-term stormwater-quality conditions because most water-quality monitoring sites have less than one year of data. The statistics for highway and upstream basin properties, hydrologic variables, and stormwater quality provided in this data release can be used to represent long-term conditions throughout southern New England. The simulated populations of flows, concentrations, and loads documented in this data release represent long-term conditions at representative sites of interest. This data release also documents results of sensitivity analyses designed to guide the selection of input variables for runoff quality simulations and selected example simulations that illustrate use of simulation results for decision making. The methods and statistics in this study were developed for use with SELDM but may be used with other models. The information provided here can be used for robust decision making by highway practitioners, regulators, and decisionmakers. The project described in this data release was conducted in cooperation with the Federal Highway Administration and the Connecticut, Massachusetts, and Rhode Island Departments of Transportation. The data release contains twenty-one compressed (zip) files, one ReadMe file pertaining to the data release as a whole (ReadMe.txt), and a diagram to illustrate the organization of the zip files and subfolders (ReadMeDiagram.pdf). Please refer to ReadMe files within the zip files and subfolders for more detailed metadata pertaining to the data, statistics, and software provided.

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.

A previously developed groundwater flow model (https://doi.org/10.5066/P9051RUT) was slightly modified to estimate the risk-based discrete relation between groundwater extraction and surface-water/groundwater exchange. Previously, the concept of a ''capture map'' has been put forward as a means to effectively summarize this relation for decision-making consumption. While capture maps have enjoyed success in the environmental simulation industry, they are deterministic, ignoring uncertainty in the underlying model. Furthermore, capture maps are not typically calculated in a manner that facilitates analysis of varying combinations of extraction locations and/or reaches. That is, they are typically constructed with focus on a single reach or group of reaches. The former of these limitations is important for conveying risk to decision makers, while the latter is important for decision-making support related to surface-water management, where future foci may include reaches that were not the focus of the original capture analysis. Herein, we use a MODFLOW-NWT groundwater/surface-water model of the lower San Antonio River, Texas, USA to demonstrate a technique to estimate risk-based and spatially discrete streamflow depletion potential. This USGS data release contains all of the input and output files for the simulations described in the associated journal article (https://doi.org/10.1111/gwat.13080)

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

The U.S. Geological Survey, in cooperation with Broward County Environmental Planning and Resilience Division, has developed a groundwater/surface-water model to evaluate the response of the drainage infrastructure and groundwater system in Broward County to increases in sea level and potential changes in precipitation. The model was constructed using a modified version of MODFLOW-NWT, with the surface-water system represented using the Surface-Water Routing process and the Urban Runoff Process. The surface-water drainage system within this newly developed model actively simulates the extensive canal network using level-pool routing and active structures representing gates, weirs, culverts, and pumps. Steady-state and transient simulation results represented historical conditions (2013-17). Simulation results incorporating increased sea level and precipitation were used to evaluate the effects on the surface-water drainage system and wet season groundwater levels. Four future sea-level scenarios were simulated by modifying the historical inputs for both the steady-state and the transient models to represent mean sea levels of 0.5, 2.0, 2.5, and 3.0 ft above the North American Vertical Datum of 1988. This USGS data release contains all of the input and output files for the simulations described in the associated model documentation report. (https://doi.org/10.3133/sir20225074)