Modelling the glacier-hydrology of two large catchments in the Peruvian Andes
Bibliographic record
Abstract
Glacier meltwater is a vital component of river discharge in the Peruvian Andes, providing an important source of dry season runoff for communities, agriculture and fragile mountain ecosystems. Previous hydrochemical and modelling studies have identified the importance of glacier meltwater to downstream runoff and analysis of runoff records suggest ‘peak water’ has passed already. These studies, however, have been confined to the Rio Santa basin and the models applied have simplifications in their treatment of glacier melt and evolution. Our objectives are to i) determine the past glacier contribution to streamflow, determining when peak water passed and quantifying the recession of the glacier contribution to runoff; ii) predict future glacier evolution and its consequent impacts on water resources; and iii) to compare the hydrological response of catchments in central and southern Peru and establish their future response to glacier recession. To meet these objectives we have applied the hourly physically-oriented, glacio-hydrological model TOPKAPI-ETH to two catchments in the Peruvian Andes: the Rio Santa in the Cordillera Blanca (4953 km2) and the Rio Urubamba draining the Cordilleras Vilcanota, Urubamba and Vilcabamba (11048 km2), the two most glacierised catchments in Peru. Past glacier recession has been substantial and future temperature rise is likely to lead to further glacier retreat, threatening water security in both regions. The model is forced with hourly atmospheric inputs from high-resolution (4 km), bias-corrected Weather Research and Forecasting (WRF) model outputs, which are downscaled to the TOPKAPI-ETH model resolution (100 m), using temperature and precipitation lapse rates defined from the WRF data for all sub-catchments of each domain. To reduce equifinality in model parameters we calibrate the model in a stepwise manner, using a combination of in-situ and remotely sensed data. Melt model parameters are calibrated based on full energy balance simulations at five sites across the two domains, with albedo parameters also derived from calibration with measured data. We calibrate the temperature decrease over glacier ice in an iterative manner using the WRF air temperatures, observed weather station data and the energy balance model outputs. Precipitation undercatch is a key unknown but it is constrained by careful comparison of modelled glacier surface mass balances with those inverted from remotely sensed data, while hydrological routing parameters are identified through calibration against hourly runoff records collected within the catchments. We use the model outputs to unravel the water balance characteristics of both catchments, their main drivers, including the relative importance of glacier and snow melt components within catchment runoff, and how they vary seasonally, inter-annually and through time due to glacier recession. By applying the model to two catchments with contrasting climatologies and glacier characteristics we are also able to disentangle the reasons for their distinct future trajectories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".