Land System Analysis of Llaca Lake: A Tropical Moraine-Dammed Supraglacial Lake in the Cordillera Blanca, Peru
Bibliographic record
Abstract
The tropical Andes are experiencing rapid deglaciation due to climate warming, resulting in the formation and evolution of moraine-dammed glacial lakes. These lakes, while significant for hydrological and ecological processes, also pose a growing hazard due to the potential for glacial lake outburst floods (GLOFs). This study focuses on Llaca Lake, a moraine-dammed supraglacial lake situated in the Cordillera Blanca of Perú, which serves as a representative case study for understanding the dynamics and hazards associated with these tropical alpine environments.Using an integrated landsystem approach, we analyzed geomorphological, hydrological, and sedimentological processes shaping Llaca Lake and its surrounding landscape. High-resolution satellite imagery, drone-based surveys, and in situ field measurements were combined with GIS analysis to map key geomorphological features, including the moraine complex, ice-contact zones, and sediment pathways. Additionally, bathymetric surveys were conducted to delineate the lakebed morphology and evaluate its storage capacity and potential flood risk.Results indicate that Llaca Lake has undergone significant expansion over recent decades, with notable retreat of the adjacent Llaca Glacier. This retreat has exposed a dynamic moraine system characterized by steep, unstable slopes and active mass-wasting processes. Sedimentological analysis reveals that the moraine complex is composed of poorly sorted, unconsolidated material, increasing its susceptibility to breach or failure. Hydrological modeling highlights the lake's dependence on glacial meltwater inputs, which are projected to decline with ongoing glacier retreat, altering downstream water availability and ecosystem services.Hazard assessment of Llaca Lake underscores the potential for GLOF events triggered by slope instability, ice calving, or seismic activity, all of which are exacerbated by the fragile geomorphic and climatic setting. Vulnerability mapping identified downstream communities, infrastructure, and ecosystems at risk, emphasizing the need for proactive monitoring and risk mitigation strategies.This study highlights the value of a landsystem framework for understanding the interplay of geomorphic, hydrological, and climatic processes in shaping tropical moraine-dammed lakes. Llaca Lake serves as a critical case study for addressing broader implications of glacial retreat in the tropical Andes, including water security, ecosystem resilience, and disaster risk reduction. The findings contribute to regional efforts in sustainable water management and hazard mitigation, offering transferable insights for other rapidly deglaciating mountain systems worldwide.By integrating multi-disciplinary methods and a holistic perspective, this research advances our understanding of the complex dynamics of moraine-dammed glacial lakes and their role in tropical alpine environments in a warming world.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".