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Record W3162262406 · doi:10.5194/egusphere-egu21-3565

Exploring the future hydrology of a Canadian Rockies glacierized catchment and its sensitivity to meteorological forcings

2021· article· en· W3162262406 on OpenAlexaffabout
Caroline Aubry‐Wake, John W. Pomeroy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCanmore Museum and Geoscience Centre
Fundersnot available
KeywordsStreamflowWeather Research and Forecasting ModelGlacierEnvironmental sciencePrecipitationDrainage basinClimate changeClimatologySnowForcing (mathematics)Climate modelHydrology (agriculture)Physical geographyGeologyMeteorologyGeographyGeomorphology

Abstract

fetched live from OpenAlex

Glacierized mountain areas are witnessing strong changes in their streamflow generation processes, influencing their capacity to provide crucial water resources to downstream environments. Shifting precipitation patterns, a warming climate, changing snow dynamics and retreating glaciers are occurring simultaneously, driven by complex physical feedbacks. To predict and diagnose future hydrological behaviour in these glacierized catchments, a semi-distributed, physically-based hydrological model including both on and off-glacier process representation was applied to Peyto basin, a 21 km2 glacierized alpine catchment in the Canadian Rockies. The model was forced with bias-corrected outputs from a dynamically downscaled, 4-km resolution Weather and Research Forecasting (WRF) simulation, for the 2000-2015 and 2085-2100 period. The future WRF runs had boundary conditions perturbed using RCP8.5 late century climate. The simulations show by the end-of-century, the catchment shifts from a glacial to a nival regime. The increase in precipitation nearly compensates for the decreased ice melt associated with glacier retreat, with a decrease in annual streamflow of only 7%. Peak flow shifts from July to June and August streamflow is reduced by 68%. Changes in blowing snow transport and sublimation, avalanching, evaporation and subsurface water storage also contribute to the strong hydrological shift in the Peyto catchment. A sensitivity analysis to uncertainty in forcing meteorology reveals that streamflow volume is more sensitive to variations in precipitation whereas streamflow timing and variability are more sensitive to variations in temperature. The combination of the temperature and precipitation variations caused substantial changes both in the future snowpack and in the streamflow pattern. By including high-resolution atmospheric modelling and unprecedented both on and off-glacier process-representation in a physically-based hydrological model, the results provide a particularly comprehensive evaluation of the hydrological changes occurring in high-mountain environments in response to climate change.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.058
GPT teacher head0.215
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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