Lake-TopoCat: A global lake drainage topology and catchment database
Bibliographic record
Abstract
Abstract. Lakes and reservoirs are ubiquitous across global landscapes, functioning as the largest repository of liquid surface freshwater, hotspots of carbon cycling, and “sentinels” of climate change. Although typically considered as lentic (hydrologically stationary) environments, lakes are an integral part of global drainage networks. Through perennial and intermittent hydrological connections, lakes often communicate with each other, and these connections actively affect water mass, quality, and energy balances in both lacustrine and fluvial systems. Deciphering how global lakes are hydrologically interconnected, or the so-called “lake drainage topology”, is not only important to lake change attribution, but also increasingly critical to discharge, sediment, and carbon modeling. Despite the proliferation of river hydrography data, lakes remain poorly represented in routing models, partially because there has been no global-scale hydrography dataset tailored to lake drainage basins and networks. Here, we introduce the global Lake drainage Topology and Catchment database, or “Lake-TopoCat”, which reveals detailed lake hydrography information with a careful consideration of possible multifurcation. Lake-TopoCat contains the outlet(s) and catchment(s) of each lake, the inter-connecting reaches among lakes, and a wide suite of attributes depicting lake drainage topology such as upstream and downstream relationship, drainage distance between lakes, and a priori drainage type and connectivity with river networks. Using the HydroLAKES (v1.0) global lake mask, the Lake-TopoCat v1.0 identifies ~1.46 million outlets for ~1.43 million lakes larger than 10 ha and delineates 77.5 million km2 of lake catchments covering 57 % of the Earth’s landmass except Antarctica. The global lakes are interconnected by ~3 million reaches, derived from MERIT Hydro (v1.0.1), stretching a total distance of ~10 million km, ~80 % of which are shorter than 10 km. With such unprecedented lake hydrography details, Lake-TopoCat may facilitate a variety of limnological applications including water quality diagnosis, agriculture and fisheries, lacustrine connectivity monitoring, and integrated lake-river modeling. It is freely accessible at https://doi.org/10.5281/zenodo.7420810 (Sikder et al., 2022).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".