The datasets include hydrological parameters such as streamflow, soil moisture and water temperature, and meteorological data such as precipitation, max and min temperature, evaporation from 2002 to 2017 for Lake Erie. This dataset is associated with the following publication: Feng Chang, C., V. Cover, C. Tang, P. Vlahos, D. Wanik, J. Yan, J. Bash, and M. Astitha. Linking multi-media modeling with machine learning to assess and predict lake chlorophyll a concentrations. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 47(6): 1656-1670, (2021).

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).

This data release contains the model input and output data, and supporting files, from hydrologic simulations of streamflow conditions in the upper Klamath River Basin using the Precipitation-Runoff Modeling System (PRMS). The model was calibrated for the portion of the basin draining into Upper Klamath Lake. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. The model calibration period spanned water years 2000 through 2015, and the model validation period spanned water years 1984 through 1999. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets.

This data release contains the model input and output data, and supporting files, from hydrologic simulations of streamflow conditions in the upper Klamath River Basin using the Precipitation-Runoff Modeling System (PRMS). The model was calibrated for the portion of the basin draining into Upper Klamath Lake. It simulates daily streamflow, snow, solar radiation, evapotranspiration, surface-water, and groundwater processes within the basin. The model calibration period spanned water years 2000 through 2015, and the model validation period spanned water years 1984 through 1999. The model was calibrated and validated using measured streamflow, snowpack, evapotranspiration, and solar radiation data sets.

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.

The U.S. Geological Survey (USGS), in cooperation with the Houlton Band of Maliseet Indians (HBMI), has developed tools to assess the effects climate change on hydrology and water temperatures in the Meduxnekeag River Watershed in Maine. A USGS Scientific Investigations Report (SIR) report documents tools and datasets developed by the USGS to evaluate how climate change will affect the hydrology and water temperature in the watershed. Future hydrologic climate projections were developed based on changes to the precipitation and air-temperature input to the USGS Precipitation Runoff Modeling System (PRMS) (Markstrom and others, 2015) version 5.1.0. The baseline input data sets to the model were precipitation and air temperature data from 1980 to 2016. Future scenarios included increases in precipitation by 0-, 5- , 10, and 15-percent and increase in air temperature by 0, 3.6, 7.0, and 10.4 degrees Fahrenheit. The data include the input to and output from the PRMS developed to provide streamflow and water temperature simulations under selected changes in air temperature and precipitation. The data also includes shapefiles of the hydrologic response units and stream segments with identifiers matching those in the PRMS model. Markstrom, S.L., Regan, R.S., Hay, L.E., Viger, R.J., Webb, R.M.T., Payn, R.A., and LaFontaine, J.H., 2015, PRMS–IV, the precipitation-runoff modeling system, version 4: U.S. Geological Survey Techniques and Methods, book 6, chap. B7, 158 p., accessed October 1, 2018, at https://doi.org/10.3133/tm6B7.

The U.S. Geological Survey (USGS), in cooperation with the Houlton Band of Maliseet Indians (HBMI), has developed tools to assess the effects climate change on hydrology and water temperatures in the Meduxnekeag River Watershed in Maine. A USGS Scientific Investigations Report (SIR) report documents tools and datasets developed by the USGS to evaluate how climate change will affect the hydrology and water temperature in the watershed. Future hydrologic climate projections were developed based on changes to the precipitation and air-temperature input to the USGS Precipitation Runoff Modeling System (PRMS) (Markstrom and others, 2015) version 5.1.0. The baseline input data sets to the model were precipitation and air temperature data from 1980 to 2016. Future scenarios included increases in precipitation by 0-, 5- , 10, and 15-percent and increase in air temperature by 0, 3.6, 7.0, and 10.4 degrees Fahrenheit. The data include the input to and output from the PRMS developed to provide streamflow and water temperature simulations under selected changes in air temperature and precipitation. The data also includes shapefiles of the hydrologic response units and stream segments with identifiers matching those in the PRMS model. Markstrom, S.L., Regan, R.S., Hay, L.E., Viger, R.J., Webb, R.M.T., Payn, R.A., and LaFontaine, J.H., 2015, PRMS–IV, the precipitation-runoff modeling system, version 4: U.S. Geological Survey Techniques and Methods, book 6, chap. B7, 158 p., accessed October 1, 2018, at https://doi.org/10.3133/tm6B7.

This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.

This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.

This dataset contains 3,219 feather files with time series of all model inputs for machine learning models predicting streamflow drought across the conterminous United States (CONUS). Files contain weekly time series of streamflow percentiles, meteorology, snow water equivalent, forecast meteorology, estimated water use, soil moisture, as well as lagged versions of these datasets. Values in these files were assembled from existing published datasets as explained in the data quality and processing steps sections.