This data release consists of four datasets which were used for evaluating winter drawdown (WD) lakes to follow the Massachusetts general WD guidelines. The first dataset ("Water level observations.csv") provides water level monitoring data of 21 (18 WD and 3 non-WD) recreational lakes in Massachusetts from 2014 to 2018. The water levels were measured by paired nonvented pressure transducers (HOBO U20L-01) and processed by ContDataQC package to remove potential inaccurate observations. For better comparison between lakes, the water level was relativized to each lake's normal pool level. This dataset was used for understanding the hydrology of WD and non-WD lakes and validating the hydrological model that we developed for WD lakes. Details of the hydrological model were described in the software release (https://doi.org/10.5066/P9C8BVY2). The second dataset ("Water level simulations.csv") is the hydrological model simulated daily water level (relative to each lake's normal pool level) time series of the WD lakes from the first dataset (15 lakes, 2014-2018). To validate the applicability of the model on simulating water levels in WD lakes, the actual drawdown rules were set in the model to recreate the historical water levels and compare with the in-situ observations in the first dataset. The third dataset ("Guideline_Eval_Dec1drawdown.csv") contains the probability of each WD lake reaching the drawdown level by Dec 1 which is required by the guidelines when selecting different drawdown magnitudes (1-6ft) by Dec 1 in 2015. 2016 and 2017. The fourth dataset (“Guidleine_Eval_Apr1refill.csv”) consists of the latest refill starting dates of each lake with different designed drawdown magnitude (1-6 ft) to ensure over 95% possibility for each WD lake to be fully refilled by Apr 1 in 2016, 2017, 2018.

This data release consists of four datasets that were used for evaluating winter drawdown patterns in 166 Massachusetts lakes greater than 0.3 km2 surface area. The first dataset (“Water area and level.csv”) provides water area and water level time series data of 166 lakes from 2016 to 2021. Water area and water level time-series data were derived from European Space Agency’s Sentinel 1 synthetic aperture radar satellite sensor using the JavaScript code in Google Earth Engine platform. Details of this code were described in the software release (https://doi.org/10.5066/P9ZA5I1U). The second dataset (“Water area interpolated.csv”) is the linearly-interpolated daily water area time series data of the 166 lakes from the first dataset that were used in winter drawdown classification model as input files. The third dataset (“Winter drawdown classification.csv”) is the winter drawdown classification model derived binary classification (1 for winter drawdown and 0 for non-winter drawdown) of 166 lakes for 5 years (2016–2021). The fourth dataset (“Winter drawdown metrics_2016.csv”, “Winter drawdown metrics_2017.csv”, “Winter drawdown metrics_2018.csv”, (“Winter drawdown metrics_2019.csv”, and “Winter drawdown metrics_2020.csv”) are the winter drawdown metrics such as timing, duration, and magnitude of drawdown derived for the winter drawdown lakes from the water area time series (second dataset) for 5 years. The codes used for the classification model and drawdown metrics are also available in the software release (https://doi.org/10.5066/P9ZA5I1U).

This data release consists of four datasets that were used for evaluating winter drawdown patterns in 166 Massachusetts lakes greater than 0.3 km2 surface area. The first dataset (“Water area and level.csv”) provides water area and water level time series data of 166 lakes from 2016 to 2021. Water area and water level time-series data were derived from European Space Agency’s Sentinel 1 synthetic aperture radar satellite sensor using the JavaScript code in Google Earth Engine platform. Details of this code were described in the software release (https://doi.org/10.5066/P9ZA5I1U). The second dataset (“Water area interpolated.csv”) is the linearly-interpolated daily water area time series data of the 166 lakes from the first dataset that were used in winter drawdown classification model as input files. The third dataset (“Winter drawdown classification.csv”) is the winter drawdown classification model derived binary classification (1 for winter drawdown and 0 for non-winter drawdown) of 166 lakes for 5 years (2016–2021). The fourth dataset (“Winter drawdown metrics_2016.csv”, “Winter drawdown metrics_2017.csv”, “Winter drawdown metrics_2018.csv”, (“Winter drawdown metrics_2019.csv”, and “Winter drawdown metrics_2020.csv”) are the winter drawdown metrics such as timing, duration, and magnitude of drawdown derived for the winter drawdown lakes from the water area time series (second dataset) for 5 years. The codes used for the classification model and drawdown metrics are also available in the software release (https://doi.org/10.5066/P9ZA5I1U).

Lake and landscape data were compiled from the US Environmental Protection Agency National Lakes Assessment 2007 and 2012 surveys and LakeCat geospatial dataset. Additional climate variables were summarized from national PRISM and NOAA data layers following the same geoprocessing steps used in the LakeCat creation. The compiled dataset includes a derived metric that characterizes the degree of human-related water management presence on a lake that has the potential to significantly alter lake hydrology. The HydrAP metric (anthropogenic hydrological-alteration potential) uses information from the National Inventory of Dams and National Land Cover Database and is described in detail in Fergus et al. 2021. The compiled dataset includes all lake sites in the NLA 2007 survey and only new lake sites in NLA 2012 (i.e., not resampled lake sites during the two survey periods). We retained VISIT_NO = 1 observations for the analyses for a total of 1716 observations for unique lake sites distributed across the conterminous US.

Lake and landscape data were compiled from the US Environmental Protection Agency National Lakes Assessment 2007 and 2012 surveys and LakeCat geospatial dataset. Additional climate variables were summarized from national PRISM and NOAA data layers following the same geoprocessing steps used in the LakeCat creation. The compiled dataset includes a derived metric that characterizes the degree of human-related water management presence on a lake that has the potential to significantly alter lake hydrology. The HydrAP metric (anthropogenic hydrological-alteration potential) uses information from the National Inventory of Dams and National Land Cover Database and is described in detail in Fergus et al. 2021. The compiled dataset includes all lake sites in the NLA 2007 survey and only new lake sites in NLA 2012 (i.e., not resampled lake sites during the two survey periods). We retained VISIT_NO = 1 observations for the analyses for a total of 1716 observations for unique lake sites distributed across the conterminous US.

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

This data release contains a data compilation and analysis of the hydrogeology in the U.S. portion of the Lake Superior watershed, for the purpose of providing background data for future study and modeling of groundwater and contaminant movement in the watershed. The data support an analysis of groundwater contributions to the water budget of Lake Superior and provide hydrogeologic context for future modeling and sampling of groundwater in the region. The data release contains three child items: Baseflow analysis for tributaries to Lake Superior from 1946 to 2020; Geohydrology data for groundwater analysis in the Lake Superior Watershed; and Groundwater wells from Minnesota, Wisconsin, and Michigan state databases and the U.S. Geological (USGS) National Water Information System (NWIS) database with static water level data within 10km of the Lake Superior watershed.

The U.S. Geological Survey has developed tools for projecting twenty-first century climate and hydrologic risk in Massachusetts in collaboration with Cornell University and Tufts University. These tools included a Stochastic Weather Generator (SWG). Output from the SWG is in this data release. The release includes daily precipitation and minimum and maximum air temperature for a 64-year period in the Nashua River watershed (that includes the Squannacook River) in Massachusetts and New Hampshire. There are 100 ensembles from the SWG for warming scenarios of 0 to 8 degrees Celsius in 0.5-degree increments. The SWG data were converted to a format utilized by the Precipitation-Runoff Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms) and input to a PRMS model for the Squannacook River watershed. The PRMS input and output files for the 100 ensembles of each of the 17 warming scenarios are also included in this data release. The 1,700 PRMS output files were utilized by a Stochastic Watershed Modeling tool to correct modeling biases that are inherent with a deterministic model such as PRMS. This data release includes the output from this Stochastic Watershed Model (SWM). For each of the 100 ensembles, the SWM was used to generate 10,000 ensembles, resulting in 1 million ensembles of 64-year periods for each of the warming scenarios. For each ensemble, streamflow characteristics of the annual maximum daily discharge at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval and of the annual 7-day low flow at the 2- and 10-year recurrence interval were determined.

The U.S. Geological Survey has developed tools for projecting twenty-first century climate and hydrologic risk in Massachusetts in collaboration with Cornell University and Tufts University. These tools included a Stochastic Weather Generator (SWG). Output from the SWG is in this data release. The release includes daily precipitation and minimum and maximum air temperature for a 64-year period in the Nashua River watershed (that includes the Squannacook River) in Massachusetts and New Hampshire. There are 100 ensembles from the SWG for warming scenarios of 0 to 8 degrees Celsius in 0.5-degree increments. The SWG data were converted to a format utilized by the Precipitation-Runoff Modeling System (PRMS; https://www.usgs.gov/software/precipitation-runoff-modeling-system-prms) and input to a PRMS model for the Squannacook River watershed. The PRMS input and output files for the 100 ensembles of each of the 17 warming scenarios are also included in this data release. The 1,700 PRMS output files were utilized by a Stochastic Watershed Modeling tool to correct modeling biases that are inherent with a deterministic model such as PRMS. This data release includes the output from this Stochastic Watershed Model (SWM). For each of the 100 ensembles, the SWM was used to generate 10,000 ensembles, resulting in 1 million ensembles of 64-year periods for each of the warming scenarios. For each ensemble, streamflow characteristics of the annual maximum daily discharge at the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval and of the annual 7-day low flow at the 2- and 10-year recurrence interval were determined.

This data set archives all inputs, outputs and scripts needed to reproduce the findings of W.H. Farmer and R.M. Vogel in the 2016 Water Resources Research article entitled "On the Deterministic and Stochastic Use of Hydrologic Model". Input data includes observed streamflow values, in cubic feet per second, for 1225 streamgages over the period from 01 October 1980 through 30 September 2011. Estiamted streamflows, for the same streamgages and periods, is provided from a general calibration of the Precipitation Runoff Modeling System. Output data includes the same with alternate realizations of streamflow generated following the descriptions in the associated report. These results can be regenerated by using the included scripts. Data are provided in several files: (1) observedStreamflow.csv contains observed streamflows, in cubic feet per second, for all 1225 streamgages; (2) prmsModeledStreamflow.csv contains streamflows modeled with the Precipitation Runoff Modeling Streamflow (Markstrom et al., 2015; DOI 10.3133/tm6B7); (3) outputData.zip contains CSV files of observed, PRMS-modeled and stochastically-generated streamflows, in cubic feet per second, for all 1225 streamgages; (4) README.txt describes the contents of this archive and execution of model scripts; (5) simulation.R is a computer script in in the R programming lanaguage and is capable of reproducing the results in outputData.zip from observedStreamflow.csv and prmsModeledStreamflow.csv; (6) analysis.R is another R script capable of reproducing the figures in the associated report from the results in outputData.zip.