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 water and climate data for Lake Erie, including: Soil moisture, streamflow, water temperature, evaporation, baseflow. This dataset is associated with the following publication: Chang, F., M. Astitha, Y. Yuan, C. Tang, P. Vlahos, V. Cover, and U. Khaira. A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning– and Physics-Based Modeling Systems.. Artificial Intelligence for the Earth Systems. American Meteorological Society, Boston, MA, USA, 2(3): 1-20, (2023).

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.

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2023. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2014-2023 (10 year average yield) and 2019-2023 (5 year average yield).

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2023. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2014-2023 (10 year average yield) and 2019-2023 (5 year average yield).

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2020. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2011-2020 (10 year average yield) and 2016-2020 (5 year average yield).

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2022. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2013-2022 (10 year average yield) and 2018-2022 (5 year average yield).

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2022. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Yields (representing the mass of constituent transported from a unit area of a given watershed) are used to compare the export loads from one basin to another. Yield results are obtained by dividing the annual load (pounds) of a given constituent by the respective watershed area (acres) from which the constituent was transported. Yield results presented represent the average annual per-acre loads of nitrogen, phosphorus, and suspended sediment exported from each of the Chesapeake Bay River Input Monitoring stations for two possible time periods: 2013-2022 (10 year average yield) and 2018-2022 (5 year average yield).

The water and climate data for Lake Erie, including: Soil moisture, streamflow, water temperature, evaporation, baseflow. NOTE: This dataset has been removed from public access due to revocation. Please refer inquiries regarding this dataset to the listed contact person.

Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in major rivers across the Chesapeake Bay watershed were calculated using monitoring data from the Chesapeake Bay River Input Monitoring (RIM) Network stations for the period 1985 through 2024. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). Load results represent the total mass of nitrogen, phosphorus, and suspended sediment that was exported from each of the RIM watersheds.