This data release presents data that were collected as part of a larger effort (Global Lake Ecological Observatory Network [GLEON] IceBlitz) to enhance understanding of the spatial and temporal variation in global lake ice properties. During January and February of 2021 ice cores were extracted from Hobart Lake, North Dakota, USA and characterized following standard procedures. Characteristics of the cores were recorded, including thickness of distinct layers and presence of visible bubbles and impurities. Surface conditions (e.g., snow, slush) were also characterized and water and air temperature were measured and recorded.

This data release presents data that were collected as part of a larger effort (Global Lake Ecological Observatory Network [GLEON] IceBlitz) to enhance understanding of the spatial and temporal variation in global lake ice properties. During January and February of 2021 ice cores were extracted from Hobart Lake, North Dakota, USA and characterized following standard procedures. Characteristics of the cores were recorded, including thickness of distinct layers and presence of visible bubbles and impurities. Surface conditions (e.g., snow, slush) were also characterized and water and air temperature were measured and recorded.

This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions.

This data release presents data that were collected as part of a larger effort to assess factors that regulate thermal stratification and mixing in small ponds. This work was part of an international collaborative effort with the Global Lake Ecological Observatory Network (GLEON). From May to October 2019, temperature and light were measured throughout the water-depth profile of two artificial ponds located near Jamestown, North Dakota. Meteorological and bathymetric data also were collected. The ponds, managed by the U.S. Geological Survey Northern Prairie Wildlife Research Center, are representative of the small inland wetlands of the Prairie Pothole Region of North America. Data from this collaborative study will be used to understand how small inland ponds differ from large lakes and coastal systems, specifically with regard to nutrient recycling, primary production, greenhouse gas emissions, and oxygen dynamics.

This data release presents data that were collected as part of a larger effort to assess factors that regulate thermal stratification and mixing in small ponds. This work was part of an international collaborative effort with the Global Lake Ecological Observatory Network (GLEON). From May to October 2019, temperature and light were measured throughout the water-depth profile of two artificial ponds located near Jamestown, North Dakota. Meteorological and bathymetric data also were collected. The ponds, managed by the U.S. Geological Survey Northern Prairie Wildlife Research Center, are representative of the small inland wetlands of the Prairie Pothole Region of North America. Data from this collaborative study will be used to understand how small inland ponds differ from large lakes and coastal systems, specifically with regard to nutrient recycling, primary production, greenhouse gas emissions, and oxygen dynamics.

This data release includes remotely sensed lake and lake chemistry and water temperature data collected from 2011 to 2014) from a series of lakes on the Arctic Coastal Plain of Alaska. Most of the data is from two sites within the Chipp River Basin.

This data release includes remotely sensed lake and lake chemistry and water temperature data collected from 2011 to 2014) from a series of lakes on the Arctic Coastal Plain of Alaska. Most of the data is from two sites within the Chipp River Basin.

Input data, executable computer program, output data, and metadata of a Soil-Water-Balance (SWB) model for the Glacial Ridge National Wildlife area, northwestern Minnesota during the years 2002 through 2015. Also included is a data set of selected hourly precipitation totals for six ditch basins used in HEC/HMS ditch-flow modelling in the the Glacial Ridge National Wildlife area, 2004–2006 and 2013–2015 described in the associated report. A soil-water balance model (SWB) was developed to estimate evapotranspiration in six ditch basins of the Glacial Ridge National Wildlife Refuge area, northwestern Minnesota, during 2002–2015. The model was used to estimate evapotranspiration in water balances in six ditch basins as part of the associated report, U.S. Geological Survey Scientific Investigations Report 2019-5041 (http://dx.doi.org/10.3133/SIR20195041). This SWB model was derived from the statewide Minnesota SWB potential recharge model, described, calibrated, and documented as part of U.S. Geological Survey Scientific Investigations Report 2015-5038 (http://dx.doi.org/10.3133/sir20155038). The data sets and calibrations from the Minnesota statewide model were used without modification except for the more detailed precipitation, water capacity, and land use input data. In this model, precipitation data were interpolated from local raingages. Water capacity data were taken from the gSSURGO soils data base. Land-use data were compiled from three sources using the most detailed data: the National Land Cover Database, the Cropland Data Layer and data from the local Natural Resources Conservation Service office. Details of the procedures used to produce these three detailed data sets can be found in U.S. Geological Survey Scientific Investigations Report 2019-5041 (http://dx.doi.org/10.3133/SIR20195041). This model was not recalibrated. All calibrated parameters remain the same as those in the statewide Minnesota SWB model. The areal resolution of this model was increased to a 60-meter square grid and the temporal period was extended through 2015 relative to the statewide SWB model. Daymet (version 2) daily surface temperature data necessary to run this SWB model are available upon request through the following link: https://doi.org/10.3334/ORNLDAAC/1219.

Input data, executable computer program, output data, and metadata of a Soil-Water-Balance (SWB) model for the Glacial Ridge National Wildlife area, northwestern Minnesota during the years 2002 through 2015. Also included is a data set of selected hourly precipitation totals for six ditch basins used in HEC/HMS ditch-flow modelling in the the Glacial Ridge National Wildlife area, 2004–2006 and 2013–2015 described in the associated report. A soil-water balance model (SWB) was developed to estimate evapotranspiration in six ditch basins of the Glacial Ridge National Wildlife Refuge area, northwestern Minnesota, during 2002–2015. The model was used to estimate evapotranspiration in water balances in six ditch basins as part of the associated report, U.S. Geological Survey Scientific Investigations Report 2019-5041 (http://dx.doi.org/10.3133/SIR20195041). This SWB model was derived from the statewide Minnesota SWB potential recharge model, described, calibrated, and documented as part of U.S. Geological Survey Scientific Investigations Report 2015-5038 (http://dx.doi.org/10.3133/sir20155038). The data sets and calibrations from the Minnesota statewide model were used without modification except for the more detailed precipitation, water capacity, and land use input data. In this model, precipitation data were interpolated from local raingages. Water capacity data were taken from the gSSURGO soils data base. Land-use data were compiled from three sources using the most detailed data: the National Land Cover Database, the Cropland Data Layer and data from the local Natural Resources Conservation Service office. Details of the procedures used to produce these three detailed data sets can be found in U.S. Geological Survey Scientific Investigations Report 2019-5041 (http://dx.doi.org/10.3133/SIR20195041). This model was not recalibrated. All calibrated parameters remain the same as those in the statewide Minnesota SWB model. The areal resolution of this model was increased to a 60-meter square grid and the temporal period was extended through 2015 relative to the statewide SWB model. Daymet (version 2) daily surface temperature data necessary to run this SWB model are available upon request through the following link: https://doi.org/10.3334/ORNLDAAC/1219.

Globally, lakes are losing ice cover, but our understanding of the rates and variability of change are biased towards few lakes with long-term records. Recent works have used remote sensing and predictive modeling to supplement the observational record, but these approaches produce large errors when estimating ice phenology for individual lakes. Our associated manuscript explores machine learning (ML) approaches for hindcasting ice phenology using daily weather drivers and lake ice records from 1980-2018 across 625 Minnesota lakes, covering 4359 lake-years of record. Most notably, this model application provides LSTM predictions of lake ice cover time series for 881 National Hydrography Dataset High Resolution (NHDHR) lakes in Minnesota from 1980 to 2018. Along with the best available predictions, we provide the trained model weights and feature scaling factors associated with the best model developed in PyTorch, an open-source deep learning library. This model application uses inputs available from a prior data release (https://doi.org/10.5066/P9PPHJE2) which are retrieved via code and provides an earlier version of Minnesota lake ice phenology data published at https://doi.org/10.13020/110f-j487, whose original source is the Minnesota Department of Natural Resources State Climatology Office. For more in-depth information on the methodology and a detailed exploration of the model(s), please refer to Diaz et al. 2025 (in review). This work was initially funded by the Integrated Information and Dissemination Division, then by the Predictive Understanding of Multiscale Processes project - both under the USGS Water Mission Area (WMA) to explore the potential of machine learning-based lake ice prediction in addition to attention-based transformers, very large models, and explainable AI (XAI). This project made use of the USGS Tallgrass (https://doi.org/10.5066/P9XE7ROJ) High Performance Computing environment for GPU acceleration of large neural network models. The methods and results from this modeling effort are described in: Jeremy Diaz, Samantha Oliver, Simon Topp, et al. Predicting Minnesota lake ice phenology with deep learning, explainable methods, and a physically based benchmark, 1980-2018. ESS Open Archive . September 05, 2025. DOI: 10.22541/essoar.175710698.87505589/v1. This is a USGS peer reviewed and approved preprint. This manuscript is also under review at Water Resources Research.