A global inventory of potential future glacial lakes
Bibliographic record
Abstract
A prominent phenomenon accompanying glacier retreat is the formation of new lakes. Such glacial lakes are the subject of numerous studies and investigations due to their potential to produce far-reaching glacial lake outburst floods (GLOFs), but also because they might provide opportunities for water resource management and energy production. Here we present a first global inventory of potential future glacial lakes, along with expected formation times under different RCP scenarios. From published datasets of ice thickness distributions of all glaciers of the world, we identified glacier bed overdeepenings and extracted parameters of potential future lakes, such as area, depth and volume. The consideration of the ensemble of ice thicknesses allowed for a first-order quantification of uncertainties. We identified 67,000 (ranging from 55,000 to 87,000) overdeepenings with volumes larger than 1 x 106 m3, the total surface area and volume of corresponding potential lakes is 61,000 (56,000 to 64,000) km2 and 4,600 (3,100 to 7,200) km3, respectively. However, these numbers are based on the assumption of fully water-filled overdeepenings and therefore represent upper bound estimates. Global results are strongly influenced by very large depressions identified beneath (flat) polar glaciers and ice caps. We then combined potential future lake sites with estimated future glacier extents from a global glacier evolution model (GloGEM), in order to estimate formation periods of these future lakes, considering different RCPs. Strong regional differences are also found in the anticipated formation periods: While in the low latitudes most future lakes are expected to form in the current decade, irrespective of the RCP, Arctic regions have highest lake formation rates towards the end of the 21st century, with the majority of bed overdeepening not being exposed by glacier retreat until 2100. In mid latitude mountain regions, large differences between RCP2.6 and RCP8.5 exist in regard of the timing of lake formation and the amount of total uncovered overdeepenings. In addition to geometric properties and expected formation periods, the topographic potential for impacting mass movements, such as rock or ice avalanches, is determined for each overdeepening. In combination with potential lake volume and watershed area of the lake, these characteristics can be used for a first order estimation of lake outburst susceptibility. With a basic flow routing algorithm, potential outburst trajectories are modeled for each overdeepening. In combination with information on population density, settlements and further socio-economic and environmental datasets, this information can be used for future analyses of hazards, risks and opportunities associated with these potential future glacial lakes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.003 | 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".