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.

Outputs from WRF, EPIC, VIC. Outputs and analysis from the ML-based model described in the paper. This dataset is associated with the following publication: Feng Chang, C., M. Astitha, Y. Yuan, C. Tang, P. Vlahos, V. Garcia, 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): 1-43, (2023).

This dataset allows the user to explore the potential impacts of various environmental and anthropogenic drivers on observed growing season total phosphorus concentrations in lakes and streams across the United States.

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).

Monitoring data from Minnesota and from national surveys of lakes and streams that were used in the analysis for the paper: Predicting lake chlorophyll from stream phosphorus concentrations. mod.mn.R.txt: R script for fitting TP-Chl model using Minnesota data mod.nat.R.txt: R script for fitting TP-Chl model using national data dat.nat.1.csv: National stream TP data dat.nat.2.csv: National lake Chl data dat.mn.1.csv: Minnesota lake Chl data dat.mn.2.csv: Minnesota stream TP data. Citation information for this dataset can be found in Data.gov's References section.

These files contain data for relating stream total phosphorus concentration, a nutrient, to land cover and land use variables in the East Fork of the Little Miami River watershed near Cincinnati, Ohio. Water quality grab samples were collected from June 26, 2012 to September 11, 2012, and total phosphorus concentrations were measured on those samples. The files in the jawr12543-sup-002-R_code_and outputs folder are htmls, which can be opened with any browser to view the data and work flow of the data analysis. The files in the jawr12543-sup-003-SSN_file_objects contains the dataset as an R object, which can be opened in the open-source R software. This dataset is associated with the following publication: Scown, M., M. McManus, J. Carson, and C. Nietch. Improving predictive models of in-stream phosphorus based on nationally-available spatial data coverages in a Southwestern Ohio watershed. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 53(4): 944-960, (2017).

The data are: 1) compilation of field observed nutrient and hydrometeorological data for the Upper Fish River Watershed (UFRW); 2) wetland and GIS data downloaded from national repositories for UFRW; 3) wetland nutrient data generated by the models for the UFRW; 4) output data (nutrient loads and removal rates) produced by the SWAT-WetQual (watershed-wetland) model framework for the UFRW; 5) global wetland nutrient function data obtained from literature; and 6) model data used in developing statistical regression relationships for nutrient removal rates and efficiencies. Nutrients: Nitrate and Orthophosphate.

The primary data set consists of continuous water-quality data (temperature, specific conductance, pH, dissolved oxygen, turbidity, nitrate plus nitrite, and streamflow) from in-situ equipment, and discrete water-quality samples (total nitrogen, total phosphorus, suspended sediment concentration, and suspended sediment sieve diameter) collected during site visits at the USGS streamgage Iroquois River near Foresman, Indiana, April 7, 2015 to July 19, 2018. These continuous and discrete measurements were used to develop regression models which may be used to compute concentrations and loads of total nitrogen and total phosphorus. The secondary data set consists of daily streamflow, daily nitrate, daily turbidity and daily specific conductance values collected continuously by in-situ monitors at Iroquois River near Foresman, Indiana March 20, 2015 to July 19, 2018 which serve as input explanatory variables for the developed regression models to compute total nitrogen and total phosphorus at Iroquois River near Foresman. The tertiary data set for March 20, 2015 to July 19, 2018 is the output data set that was developed by application of the regression models and includes the computed daily mean total nitrogen and total phosphorus concentrations (concentration, upper 95-percent prediction interval, and lower 95-percent prediction interval) and daily mean total nitrogen and total phosphorus loads (load, upper 95-percent prediction interval, and lower 95-percent prediction interval).

water quality loadings (nitrogen and phosphorous) from SWAT simulations on different crop expansion scenarios. This dataset is associated with the following publication: Chen, P., Y. Yuan, W. Li, S. LeDuc, T. Lark, X. Zhang, and C. Clark. Assessing the Impacts of Recent Crop Expansion on Water Quality in the Missouri River Basin Using the Soil and Water Assessment Tool. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 13(6): e2020MS002284, (2021).

The Archive.zip data set contains R source code and inputs and selected outputs associated with regression analyses used to estimate total phosphorus loads for selected sites on Great Lakes tributaries for water years 2014–2018