The core equations of the SPARROW model (Schwarz and others, 2006) were implemented in differential form using the R programming language (R Core Team, 2017), as the basis of a tool for empirically relating a regional pattern of changes in constituent flux, over a multi-year period, to spatially referenced changes in explanatory variables over the same period. A pilot implementation was developed to explore factors influencing changes in flow-normalized flux of total nitrogen (TN) over the period 1990-2010 at 43 sites in the non-tidal Chesapeake Bay watershed. Model inputs, outputs, and code are included in this data release, and are described below.

This data release includes 5 files containing model inputs and resulting model predictions. A previously-calibrated spatially referenced regression (SPARROW) model was used to estimate effects of climate change on in-stream nitrogen (TN) loads in the Chesapeake Bay watershed between 1995 and 2025. Model scenarios were run using data for nitrogen sources and landscape characteristics from 2012, changing only temperature and runoff using climate change predictions to evaluate the change in climate on TN loads. Confidence intervals for model output predictions are also included.

This data release includes 5 files containing model inputs and resulting model predictions. A previously-calibrated spatially referenced regression (SPARROW) model was used to estimate effects of climate change on in-stream nitrogen (TN) loads in the Chesapeake Bay watershed between 1995 and 2025. Model scenarios were run using data for nitrogen sources and landscape characteristics from 2012, changing only temperature and runoff using climate change predictions to evaluate the change in climate on TN loads. Confidence intervals for model output predictions are also included.

This data release contains a boosted regression tree (BRT) model (written in the R programming language), and the input and output data from that model that were used to relate base flow nitrate concentrations in the Chesapeake Bay watershed to catchment characteristics. The input data consists of two types of information: 1) surface water nitrate concentrations collected by the USGS and partnering agencies in the Chesapeake Bay watershed between 1970 and 2013 and 2) potential predictor variables that included nitrogen sources, catchment characteristics, soil and groundwater chemistry, soil drainage and composition, and aquifer geology. The results from the BRT model were used to identify ten significant predictors of base flow nitrate concentrations in streams in the Chesapeake Bay watershed.

This data release contains a boosted regression tree (BRT) model (written in the R programming language), and the input and output data from that model that were used to relate base flow nitrate concentrations in the Chesapeake Bay watershed to catchment characteristics. The input data consists of two types of information: 1) surface water nitrate concentrations collected by the USGS and partnering agencies in the Chesapeake Bay watershed between 1970 and 2013 and 2) potential predictor variables that included nitrogen sources, catchment characteristics, soil and groundwater chemistry, soil drainage and composition, and aquifer geology. The results from the BRT model were used to identify ten significant predictors of base flow nitrate concentrations in streams in the Chesapeake Bay watershed.

The CBTN_v5 and CBTP_v5 SPARROW models were developed to support inferences about causes of observed changes in nitrogen and phosphorus (respectively) fluxes in Chesapeake Bay tributaries between 1992 and 2012. Model inputs and outputs are included in three files, which are described below. Detailed documentation of the SPARROW modeling technique is available at https://pubs.er.usgs.gov/publication/tm6B3.

This data release contains mean-annual total nitrogen (TN) loads predicted by a SPARROW model for individual stream and shoreline reaches in the Chesapeake watershed as defined by NHDPlus, a 1:100,000 scale representation of stream hydrography built upon the National Hydrography Dataset (NHD) (Horizon Systems, 2010). Also included are the input variables required to execute the model, including landscape characteristics, nutrient inputs to land, and calibration data from water quality monitoring stations. Further details on model construction and results are described in Ator (2011, https://doi.org/10.3133/sir20115167).

This data release contains mean-annual total nitrogen (TN) loads predicted by a SPARROW model for individual stream and shoreline reaches in the Chesapeake watershed as defined by NHDPlus, a 1:100,000 scale representation of stream hydrography built upon the National Hydrography Dataset (NHD) (Horizon Systems, 2010). Also included are the input variables required to execute the model, including landscape characteristics, nutrient inputs to land, and calibration data from water quality monitoring stations. Further details on model construction and results are described in Ator (2011, https://doi.org/10.3133/sir20115167).

Please review Zhang et al. (2021) for details on study design and datasets (https://doi.org/10.1016/j.watres.2022.118443). In summary, predictor and response variable data was acquired from the Chesapeake Bay Program and USGS. This data was subjected to a trend analysis to estimate the MK linear slope change for both predictor and response variables. After running a cluster analysis on the scaled TN loading time series (the response variable), the cluster assignment was paired with the slope estimates from the suite of predictor variables tied to the nutrient inventory and static geologic and land use variables. From there, an RF analysis was executed to link trends in anthropogenic driver and other contextual environmental factors to the identified trend cluster types. After calibrating the RF model, likelihood of improving, relatively static, or degrading catchments across the Chesapeake Bay were identified for the 2007 to 2018 period. Tabular data is available on the journal website and PUBMED, and the predictor/response variable data can be downloaded individually in the USGS and Chesapeake Bay Program links listed in the data access section. Portions of this dataset are inaccessible because: This data was generate by other federal entities and are housed in their respective data warehouse domains (e.g., USGS and Chesapeake Bay Program). Furthermore, the data can be accessed on the journal website as well as NCBI PUBMED (https://pubmed.ncbi.nlm.nih.gov/35461100/). They can be accessed through the following means: Combined dataset can be accessed on the journal website (https://www.sciencedirect.com/science/article/pii/S0043135422003979?via%3Dihub#ack0001) and will soon be available on NCBI (https://pubmed.ncbi.nlm.nih.gov/35461100/). The predictor variable data can be accessed from the Chesapeake Bay Program (https://cast.chesapeakebay.net/) and USGS (https://pubs.er.usgs.gov/publication/ds948 and https://www.sciencebase.gov/catalog/item/5669a79ee4b08895842a1d47). Format: Please review Zhang et al. (2021) for details on study design and datasets (https://doi.org/10.1016/j.watres.2022.118443). In summary, predictor and response variable data was acquired from the Chesapeake Bay Program and USGS. This data was subjected to a trend analysis to estimate the MK linear slope change for both predictor and response variables. After running a cluster analysis on the scaled TN loading time series (the response variable), the cluster assignment was paired with the slope estimates from the suite of predictor variables tied to the nutrient inventory and static geologic and land use variables. From there, an RF analysis was executed to link trends in anthropogenic driver and other contextual environmental factors to the identified trend cluster types. After calibrating the RF model, likelihood of improving, relatively static, or degrading catchments across the Chesapeake Bay were identified for the 2007 to 2018 period. Tabular data is available on the journal website and PUBMED, and the predictor/response variable data can be downloaded individually in the USGS and Chesapeake Bay Program links listed in the data access section. This dataset is associated with the following publication: Zhang, Q., J. Bostic, and R. Sabo. Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications. WATER RESEARCH. Elsevier Science Ltd, New York, NY, USA, 218: 1-15, (2022).

The data release documents the development of a hybrid (statistical-mechanistic) SPARROW (SPAtially Referenced Regression On Watershed attributes) model of long-term mean annual total nitrogen applied to streams and rivers of the Grande River Basin, Brazil. The model coupled observed long-term average total nitrogen loads at monitoring locations with additional explanatory variables (e.g., landscape sources, wastewater treatment plant inputs, and in-stream nitrogen losses) to estimate nitrogen loading to all reaches in the modeled area. The model was applied to estimate the effects of hypothetical changes in land use and discharge from wastewater treatment on in-stream total nitrogen loading, as described in the journal article. This USGS data release contains all of the input and output files for the execution of all of the models described in the journal article (see Table 1; https://doi.org/10.3390/w12102911). An R script is provided that allows users to execute the model.