Towards a global assessment of future glacial lakes and related hazards, risks and opportunities
Bibliographic record
Abstract
The formation of new lakes in areas uncovered by retreating glaciers is a phenomenon that is often accompanying glacier retreat. On the one hand, such glacial lakes constitute a potential source of hazards and risks in the form of glacial lake outburst floods (GLOFs), but also amplify the potential reach of other mass movements when involved in cascading process chains. On the other hand, these new lakes might provide opportunities as well, as they are attractive elements in changing mountain landscapes and provide a significant water storage and hydropower potential. Here, we present an approach to establish the first global assessment of the characteristics, risks and opportunities provided by the formation of new lakes in glacierized mountain regions. This study is currently in the phase of concept development, in our contribution we present the planned methodological steps and some preliminary results. In our approach, we draw on recently published datasets of ice thickness distributions of all glaciers around the world to detect the sites of potential future lake formation and extract general characteristics, such as lake depth and volume, as well as the elevation distribution. In combination with a new global glacial lake inventory, we estimate the total number of glacial lakes for each of the world’s mountain ranges, and contrast the share of already existing glacial lakes with the share of potential future glacial lakes. In combination with a global glacier evolution model (GloGEM), formation dates of these future lakes are estimated, considering different RCPs. A major focus will be put on the assessment of regional hazards and risks. By analyzing the topographic potential around all future lakes from digital elevation information and a globally complete glacier inventory (RGI), the susceptibility for mass movement impacts is assessed in a generic way for each lake. Simple flow routing modeling will be used to evaluate the potential downstream impact. In combination with census data and other socio-economic indicators, a preliminary danger or risk assessment can be made in order to identify future hotspots of GLOF risks. In combination with globally available data on glacier runoff contributions to streamflow, regions are identified where more detailed evaluations of the water storage potential provided by such new lakes are of particular relevance. The results of this work will allow anticipating hotspots of potential future GLOF hazards and risks at a local to global level. Further, important information to decision makers will be provided for long term planning regarding risk and water resources management as well as climate change adaptation measures and taking advantage of the opportunities provided by the formation of new 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".