Global seasonal lake dynamics and phenology revealed by SWOT observations
Bibliographic record
Abstract
Lakes and reservoirs are among the most widespread inland water repositories. A conservative estimate from the SWOT Prior Lake Database (PLD) identifies nearly 6 million permanent and intermittent lakes larger than 1 ha worldwide, of which at least 8-9% are artificial reservoirs. These water bodies span diverse landscapes and climate zones, with their storage dynamics reflecting a complex interplay of hydrological processes and human interventions. However, the sheer number of lakes presents a significant observational challenge for geodetic satellites. Consequently, most global studies on lake storage dynamics have focused either on interannual trends in a limited number of large lakes (typically a few thousand) or on intra-annual variability across a broader set of lakes but with sparse temporal resolution (often averaging few than five observations per lake per year). While tracking interannual trends remains essential, improving intra-annual observations is critical for capturing the role of lakes in regulating seasonal flow regimes, buffering terrestrial water storage, and mediating carbon and energy fluxes. For reservoirs, tracking intra-annual variability is especially important for inferring operational practices and mitigating seasonal water disparities. Here, we leverage the first two years of SWOT science data to advance the understanding of Earth’s transient seasonal water storage in millions of lakes and reservoirs, through two complementary typological frameworks: origin-based and behavior-based. First, we adopt a conventional, origin-based typology, which differentiates among reservoirs, glacial lakes, and other natural lakes, to assess intra-annual storage dynamics across these predefined lake classes. This approach helps reveal spatial patterns and aids interpretation of their underlying mechanisms. However, we argue that origin-based typology alone is insufficient to capture the intertwined complexity of global lake behaviors. To address this limitation, we introduce the first global lake typology based on observed water level phenology. This behavior-based typology provides a novel lens for understanding the diverse ways in which lakes respond to, and modulate, regional hydroclimatic variability and anthropogenic influences. Together, these two perspectives demonstrate SWOT’s unprecedented capability to illuminate nuanced dimensions of seasonal lake dynamics, offering an essential step forward for limnological science and the monitoring of lacustrine environments at the global scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".