Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally
Bibliographic record
Abstract
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 R2, 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".