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 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 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 contains lake and reservoir water surface temperature summary statistics calculated from Landsat 8 Analysis Ready Dataset (ARD) images available within the Conterminous United States (CONUS) from 2013-2023. All zip files within this data release contain nested directories using .parquet files to store the data. The file example_script_for_using_parquet.R contains example code for using the R arrow package (Richardson and others, 2024) to open and query the nested .parquet files. Limitations with this dataset include: - All biases inherent to the Landsat Surface Temperature product are retained in this dataset which can produce unrealistically high or low estimates of water temperature. This is observed to happen, for example, in cases with partial cloud coverage over a waterbody. - Some waterbodies are split between multiple Landsat Analysis Ready Data tiles or orbit footprints. In these cases, multiple waterbody-wide statistics may be reported - one for each data tile. The deepest point values will be extracted and reported for tile covering the deepest point. A total of 947 waterbodies are split between multiple tiles (see the multiple_tiles = “yes” column of site_id_tile_hv_crosswalk.csv). - Temperature data were not extracted from satellite images with more than 90% cloud cover. - Temperature data represents skin temperature at the water surface and may differ from temperature observations from below the water surface. Potential methods for addressing limitations with this dataset: - Identifying and removing unrealistic temperature estimates: - Calculate total percentage of cloud pixels over a given waterbody as: percent_cloud_pixels = wb_dswe9_pixels/(wb_dswe9_pixels + wb_dswe1_pixels), and filter percent_cloud_pixels by a desired percentage of cloud coverage. - Remove lakes with a limited number of water pixel values available (wb_dswe1_pixels < 10) - Filter waterbodies where the deepest point is identified as water (dp_dswe = 1) - Handling waterbodies split between multiple tiles: - These waterbodies can be identified using the "site_id_tile_hv_crosswalk.csv" file (column multiple_tiles = “yes”). A user could combine sections of the same waterbody by spatially weighting the values using the number of water pixels available within each section (wb_dswe1_pixels). This should be done with caution, as some sections of the waterbody may have data available on different dates. All zip files within this data release contain nested directories using .parquet files to store the data. The example_script_for_using_parquet.R contains example code for using the R arrow package to open and query the nested .parquet files. - "year_byscene=XXXX.zip" – includes temperature summary statistics for individual waterbodies and the deepest points (the furthest point from land within a waterbody) within each waterbody by the scene_date (when the satellite passed over). Individual waterbodies are identified by the National Hydrography Dataset (NHD) permanent_identifier included within the site_id column. Some of the .parquet files with the _byscene datasets may only include one dummy row of data (identified by tile_hv="000-000"). This happens when no tabular data is extracted from the raster images because of clouds obscuring the image, a tile that covers mostly ocean with a very small amount of land, or other possible. An example file path for this dataset follows: year_byscene=2023/tile_hv=002-001/part-0.parquet -"year=XXXX.zip" – includes the summary statistics for individual waterbodies and the deepest points within each waterbody by the year (dataset=annual), month (year=0, dataset=monthly), and year-month (dataset=yrmon). The year_byscene=XXXX is used as input for generating these summary tables that aggregates temperature data by year, month, and year-month. Aggregated data is not available for the following tiles: 001-004, 001-010, 002-012, 028-013, and 029-012, because these tiles primarily cover ocean with limited

This data release contains lake and reservoir water surface temperature summary statistics calculated from Landsat 8 Analysis Ready Dataset (ARD) images available within the Conterminous United States (CONUS) from 2013-2023. All zip files within this data release contain nested directories using .parquet files to store the data. The file example_script_for_using_parquet.R contains example code for using the R arrow package (Richardson and others, 2024) to open and query the nested .parquet files. Limitations with this dataset include: - All biases inherent to the Landsat Surface Temperature product are retained in this dataset which can produce unrealistically high or low estimates of water temperature. This is observed to happen, for example, in cases with partial cloud coverage over a waterbody. - Some waterbodies are split between multiple Landsat Analysis Ready Data tiles or orbit footprints. In these cases, multiple waterbody-wide statistics may be reported - one for each data tile. The deepest point values will be extracted and reported for tile covering the deepest point. A total of 947 waterbodies are split between multiple tiles (see the multiple_tiles = “yes” column of site_id_tile_hv_crosswalk.csv). - Temperature data were not extracted from satellite images with more than 90% cloud cover. - Temperature data represents skin temperature at the water surface and may differ from temperature observations from below the water surface. Potential methods for addressing limitations with this dataset: - Identifying and removing unrealistic temperature estimates: - Calculate total percentage of cloud pixels over a given waterbody as: percent_cloud_pixels = wb_dswe9_pixels/(wb_dswe9_pixels + wb_dswe1_pixels), and filter percent_cloud_pixels by a desired percentage of cloud coverage. - Remove lakes with a limited number of water pixel values available (wb_dswe1_pixels < 10) - Filter waterbodies where the deepest point is identified as water (dp_dswe = 1) - Handling waterbodies split between multiple tiles: - These waterbodies can be identified using the "site_id_tile_hv_crosswalk.csv" file (column multiple_tiles = “yes”). A user could combine sections of the same waterbody by spatially weighting the values using the number of water pixels available within each section (wb_dswe1_pixels). This should be done with caution, as some sections of the waterbody may have data available on different dates. All zip files within this data release contain nested directories using .parquet files to store the data. The example_script_for_using_parquet.R contains example code for using the R arrow package to open and query the nested .parquet files. - "year_byscene=XXXX.zip" – includes temperature summary statistics for individual waterbodies and the deepest points (the furthest point from land within a waterbody) within each waterbody by the scene_date (when the satellite passed over). Individual waterbodies are identified by the National Hydrography Dataset (NHD) permanent_identifier included within the site_id column. Some of the .parquet files with the _byscene datasets may only include one dummy row of data (identified by tile_hv="000-000"). This happens when no tabular data is extracted from the raster images because of clouds obscuring the image, a tile that covers mostly ocean with a very small amount of land, or other possible. An example file path for this dataset follows: year_byscene=2023/tile_hv=002-001/part-0.parquet -"year=XXXX.zip" – includes the summary statistics for individual waterbodies and the deepest points within each waterbody by the year (dataset=annual), month (year=0, dataset=monthly), and year-month (dataset=yrmon). The year_byscene=XXXX is used as input for generating these summary tables that aggregates temperature data by year, month, and year-month. Aggregated data is not available for the following tiles: 001-004, 001-010, 002-012, 028-013, and 029-012, because these tiles primarily cover ocean with limited

The comma-delimited fields in this dataset provide values for the remotely sensed variables analyzed for landscape blocks described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). The field labeled “BlockSite” links the records in this file with a set of boundaries in a shapefile called “Study_Block_Boundaries.shp” The records represent weekly measurements of normalized difference vegetation index (BlockNDVI) values and total evapotranspiration (BlockETmm), as well as the annual snow-off date (BlockDOYsnowfree) for the study blocks from January through August from 2008 to 2012.

The comma-delimited fields in this dataset provide values for the remotely sensed variables analyzed for landscape blocks described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). The field labeled “BlockSite” links the records in this file with a set of boundaries in a shapefile called “Study_Block_Boundaries.shp” The records represent weekly measurements of normalized difference vegetation index (BlockNDVI) values and total evapotranspiration (BlockETmm), as well as the annual snow-off date (BlockDOYsnowfree) for the study blocks from January through August from 2008 to 2012.

This comma-delimited dataset provides values for the remotely sensed status of snow on/off analyzed for field study sites described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). These data provide an indication of snow presence at the spatial resolution of a 500-m square cell for each eight-day interval beginning in January and ending at the start July of each year from 2008-2012. The source for the data was the MOD10A2 snow product from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. We extracted data for the cells associated with 35 field study sites for which we subsequently determined the timing of spring snow-free conditions. The data field labeled "BlockSite" links these values geospatially to a data field of the same name in an ESRI shapefile titled "Study_Block_Boundaries.shp" that delineates study blocks containing the field sites. Refer to the paper by Sadinski et al. (submitted) for details of the analyses performed.

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.