Daniel R. Wise 20250630 application/service https://doi.org/10.5066/P143TXNR The USGS Spatially Referenced Regression on Watershed attributes (SPARROW) model is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the supply, transport, and fate of contaminants in streams and surface-water impoundments (Schwarz and others, 2006). This data release includes a set of 20 regional models that were converted from previously developed SAS-based models for the Midwest, Northeast, Pacific, Southeast, and Southwest Regions of the United States (Ator, 2019; Hoos and Roland, 2019; Robertson and Saad, 2019; Wise, 2019; Wise and others, 2019) to the R version of SPARROW (Sanisaca and Alexander, 2023). The original SAS models were developed to estimate streamflow, suspended-sediment, total nitrogen, and total phosphorus conditions. The models represent mean annual conditions for water years 2000 to 2014, centered on a modeling base year of 2012. No changes were made to the original SAS model inputs or settings during the conversion of the SAS models to RSPARROW, but there were small differences between the SAS and RSPARROW results. This was due to differences between the optimization routines used by the SAS SPARROW and RSPARROW models for minimizing residuals. The data release consists of five child items that contain the modeling files and metadata for each of the five regions. The RSPARROW models in this data release were converted from previously developed SAS-based models to provide publicly available versions of those models using the free and open-source R programming language. The models in this data release are not operational in their current form, but the Readme.txt file in each child item explains how to create working models for each region using the files included with the data release. All the models in this data release provide the basic functionality of RSPARROW, meaning that users can calibrate the models and generate tabular predictions. Although the control files for the models are configured to initiate the RSHINY interactive Decision Support System (DSS) mapper (Wang and others, 2025), and all geospatial data needed to run that application are included with the data release, the RSHINY mapper might not work for all users depending on the user’s local operating system settings. Users should run the most recent version of RSPARROW (available from https://code.usgs.gov/water/stats/rsparrow) using the model files located in the UserTutorial folder for each regional constituent model. 19991001 20140930 ground condition Complete None planned -126.5600 -65.0400 50.1800 24.0500 ISO 19115 Topic Category inlandWaters environment USGS Thesaurus hydrology surface water quality USGS Metadata Identifier USGS:67abb66fd34e329fb2045862 Common geographic areas Alabama Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Alaska Florida Georgia Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming None. Please see 'Distribution Info' for details. These data are in the public domain in accordance with Creative Commons Zero v1.0 Universal Public Domain Dedication (CC0-1.0) and have no use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Daniel Wise U.S. Geological Survey Hydrologist mailing and physical

601 SW Second Ave, Suite 1950

Portland Oregon 97205 U.S. 971-413-0036 gs-w-or_sciencebase@usgs.gov The author thanks Lillian Gorman-Sanisaca, Rich Alexander, and Kyle Hurley for their help with creating RSPARROW versions of the SAS regional SPARROW models. The models in this data release were created using RSPARROW 2.0.1 (available from https://code.usgs.gov/water/stats/rsparrow) operating with R version 4.3.1. Gregory Schwarz Anne B. Hoos R.B. Alexander R.A. Smith 2006 publication n/a US Geological Survey https://doi.org/10.3133/tm6B3 Lillian E Gorman Sanisaca Richard B. Alexander 2023 dataset https://www.sciencebase.gov U.S. Geological Survey https://code.usgs.gov/water/stats/rsparrow W. Chang J. Cheng J. Allaire C. Sievert B. Schloerke Y. Xie J. Allen J. McPherson A. Dipert B. Borges 2025 application/service https://shiny.posit.co/ Scott W. Ator 2019 publication n/a US Geological Survey https://doi.org/10.3133/sir20195118 Anne B. Hoos Victor L. Roland II 2019 publication n/a US Geological Survey https://doi.org/10.3133/sir20195135 Dale M. Robertson David A. Saad 2019 publication n/a US Geological Survey https://doi.org/10.3133/sir20195114 Daniel R. Wise 2019 publication n/a US Geological Survey https://doi.org/10.3133/sir20195112 Daniel R. Wise David W. Anning Olivia L. Miller 2019 publication n/a US Geological Survey https://doi.org/10.3133/sir20195106 The model coefficients estimated by RSPARROW were compared to the attributes estimated by the original SAS models, and the results for each region are included with the child item for that region. No formal logical consistency tests were conducted. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. Not applicable. Not applicable. The creation of each set of regional models is described in the metadata for that region. 2025 The entities and associated attributes for this data release are described in the metadata xml file and data dictionary found in each child item. The data dictionaries include the source for each attribute, but users should consult those sources to obtain more details on how those attributes were compiled and processed. Not applicable GS ScienceBase U.S. Geological Survey mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P143TXNR None 20250630 Daniel Wise U.S. Geological Survey Hydrologist mailing and physical

601 SW Second Ave, Suite 1950

Portland Oregon 97205 U.S. 971-413-0036 gs-w-or_sciencebase@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998