Contrasting glacier responses to climate change in Central Asian Basins
Bibliographic record
Abstract
This study investigated the responses of glaciers to changes in air temperature and precipitation in two Central Asian glacierized basins, namely the Ala-Archa basin in Kyrgyzstan, where 16% of the area is covered by glaciers, and the Tailan River basin in China, with 33% glacier coverage. A ∆h-parameterization approach was coupled with the Cold Regions Hydrological Model (CRHM) to simulate glacier dynamics. CRHM uses physically based algorithms to simulate the full range of mountain hydrocryospheric processes such as energy balance snow and ice melt, slope/aspect influence on irradiance, energy balance precipitation phase, blowing snow transport and sublimation, avalanches, firnification and firn to ice conversion, subsurface storage and runoff processes, surface water detention, actual evapotranspiration and hydrograph routing. The Randolph Glacier Inventory (RGI) versions from 1.0 to 7.0 were employed to validate the modeled glacier changes. Bias-corrected ERA5 reanalysis data were used to reconstruct the meteorological and energy conditions on glaciers over the historical period from 1950 to the present. Thanks to the robust physical foundation of CRHM, which requires minimal effort in parameter identification, the contrasting glacier responses in the two basins can be predominantly attributed to differences in local climate, surrounding terrain, and energy processes. The preliminary results suggest a strong dependence of the glacier area response to climate change on terrain characteristics such as slope, aspect, and self-shadowing. Meanwhile, the response of glacier thickness is more sensitive to elevation and the distance from the central flow line. The total glacier area in the Tailan River basin is much less sensitive to warming compared to that in the Ala-Archa basin due to its greater mean glacier thickness. In contrast, the streamflow response in the Tailan River basin is more sensitive to climate warming because of its larger glacier coverage. These modeling findings offer valuable insights into how local glaciation, snow, firn and ice exposure, terrain and climate condition the streamflow response to climate change in Central Asian glacierized basins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".