Diagnosis of the Past, Present and Future Hydrology of a Glaciated High Mountain Headwater Basin in Central Asia
Bibliographic record
Abstract
ABSTRACT This study used the Canadian physically based hydrological land surface scheme MESH for a comprehensive representation of high mountain hydrological processes such as glacier energy balance and ablation, blowing snow, energy balance snowmelt and frozen ground in Kyrgyzstan's partly glacierised basin Ala‐Archa. Historical and future changes in the basin's hydrology were diagnosed through inter‐comparisons of the hydrological processes in three periods of past (1961–1980), current (1991–2010) and future (2081–2100), with respect to the dynamics in climate and glacier coverage. Glacier maps from 1970 and 2000 were used for glacier configurations of the model in the past and present periods, respectively. Impacts of future glacier changes were evaluated through a static assumption to a fully retreated assumption. For historical and present simulations, the MESH model was forced by the EM‐Earth (0.1°) and ERA‐5 (0.25°) reanalysis data, whilst for the future simulation, monthly perturbations in temperature and precipitation were applied to the observations in 1991–2010 using the average delta changes derived from outcomes of an RCP 8.5 scenario in the CMIP5‐AR5 subset (40 GCMs). Results show that the annual peak SWE has declined by 25% from the 1960s to the 2010s, whilst that in the future would show a much smaller decrease (5%). However, the timing of peak SWE in the 2100s is predicted to advance about 1 month and the snow cover duration to decline by 2 months in comparison to the 2010s. The timing of peak streamflow is expected to advance from July to June, and the annual and summer streamflow volume would decrease by 52% and 67%, respectively, under the fully retreated glacier assumption. These results underline the need for renewed diagnostic assessments of water supply in high mountain headwaters of Central Asia to inform adaptation to climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".