Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally

Water temperature and ice cover are critical characteristics of the ecological, biogeochemical, and physical functioning of a lake. Site-specific observations of temperature and ice, however, are not available for most lakes in the world. Yet this information is crucial to understanding the global role of lakes in the functioning of the bio- and hydrosphere. Here, we present the LakeTEMP dataset, referring to the ~1.4 million lakes globally of the HydroLAKES database with a surface area exceeding 0.1 km2, and consisting of two subsets: (1) an observational dataset that contains lake surface water temperatures (LSWTs), derived from Landsat 8 thermal radiance observations between 2013 and 2021 extracted at the lake center points; and (2) a dataset with monthly and yearly LSWT summary statistics and predictions of average yearly ice cover durations, interpolated from the observational dataset using seasonal trendlines. All observations underwent extensive quality control and filtering, based on outlier detection, overlapping imagery removal, and the removal of observations taken from dry lake beds. Validation of the LSWT observations was carried out with in-situ data and yielded an R^2, RMSE and median of differences of 0.93, 1.71°C and 0.42°C, respectively. The global average yearly LSWT is 6.3°C, assuming 0°C during times of presumed ice cover, and 12.4°C when only considering periods of open water. About 8% of all lakes never freeze, ~6% have short or sporadic freezing periods, and ~86% freeze every year, corresponding to an estimated proportion of global lake surface area of 23%, 20%, and 57%, respectively. The warmest lakes in the world (average temperatures of up to 36°C) are all artificial lakes used in the power plant, mining, salt extraction, and aquaculture industries. LakeTEMP fills a crucial spatial data gap in large-scale limnological research, especially for the incorporation of small lakes and understudied geographies of remote regions. Moreover, easy linkage to other large-scale datasets that use the unique lake identifiers from HydroLAKES, most notably the LakeATLAS database (56 hydro-environmental variables for each lake including anthropogenic influences), allows to explore characteristics that may be correlated to or affected by LSWT and ice cover. The data are in an analysis-ready format and openly available at https://doi.org/10.6084/m9.figshare.23844660

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes

AEMON-J/DSOS Archive: "Hacking Limnology" Workshop + Virtual Summit in Data Science & Open Science in Aquatic Research

This OSF project is meant to serve as a long-term storage repository for presentations and workshop materials for the Aquatic Ecosystem Modeling-Junior (AEMON-J) and Virtual Summit: Incorporating Data Science and Open Science (DSOS) communities. Contributors in this repository include past presenters and workshop organizers. Contributors are only responsible for those individual presentations that are labeled with their surname