Upscaling of geophysical measurements: A methodology for the estimation of the total ground ice content at two study sites in the dry Andes of Chile and Argentina
Bibliographic record
Abstract
With climate change and the associated continuing recession of glaciers, water security, especially in regions depending on the water supply from glaciers, is threatened. In this context, the understanding of permafrost distribution and its degradation is of increasing importance as it is currently debated whether ground ice can be considered as a significant water reservoir and as an alternative resource of fresh water that could potentially moderate water scarcity during dry seasons in the future. Thus, there is a pressing need to better understand how much water is stored as ground ice in areas with extensive permafrost occurrence and how meltwater from permafrost degradation may contribute to the hydrological cycle in the region. Although permafrost and permafrost landforms in the Central Andes are considered to be abundant and well developed, the data is scarce and understanding of the Andean cryosphere lacking, especially in areas devoid of glaciers and rock glaciers. In the absence of boreholes and test pits, geophysical investigations are a feasible and cost-effective technique to detect ground ice occurrences within a variety of landforms and substrates. In addition to the geophysical surveys themselves, upscaling techniques are needed to estimate ground ice content, and thereby future water resources, on larger spatial scales. To contribute to reducing the data scarcity regarding ground ice content in the Central Andes, this study focuses on the permafrost distribution and the ground ice content (and its water equivalent) of two catchments in the semi-arid Andes of Chile and Argentina. Geophysical methods (Electrical Resistivity Tomography, ERT and Refraction Seismic Tomography, RST) were used to detect and quantify ground ice in the study regions in the framework of environmental impact assessments in mining areas. Where available, ERT and RST measurements were quantitatively combined to estimate the volumetric ground ice content using the Four Phase Model (Hauck et al., 2011). Furthermore, we developed one of the first methodologies for the upscaling of these geophysical-based ground ice quantifications to an entire catchment in order to estimate the total ground ice volume in the study areas. In this contribution we will present the geophysical data, the upscaling methodology used to estimate total ground ice content (and water equivalent) of permafrost areas, and some first estimates of total ground ice content in rock glacier and rock glacier free areas and compare them to conventional estimates using remotely sensed data. Hauck, C., Böttcher, M., and Maurer, H. (2011). A new model for estimating subsurface ice content based on combined electrical and seismic datasets, The Cryosphere, 5: 453-468.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".