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Record W2019170118 · doi:10.1002/hyp.6205

Snowmelt contribution to discharge from a large mountainous catchment in subarctic Canada

2006· article· en· W2019170118 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowmeltSubarctic climateHydrographSurface runoffEnvironmental sciencePrecipitationArcticSnowStreamflowHydrology (agriculture)Drainage basinClimatologyGeologyMeteorologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Snowmelt is responsible for much of the annual runoff and most of the peak discharges in subarctic mountainous regions. It also provides a significant amount of freshwater inflow to the polar seas, which has implications for Arctic Ocean circulation. Owing to considerable topographic contrasts in large mountainous basins, snow accumulation and melt patterns are highly variable in time and space, but the scarcity of data in these regions prevents the patterns from being discerned. Application of a macro‐scale hydrological model (using reanalysis data from the European Centre for Medium‐Range Weather Forecasts, the National Centers for Environmental Prediction and the North American Regional Reanalysis) offers one suitable approach to estimate the magnitude and timing of snowmelt contribution to discharge from large mountainous catchments. The Liard basin, subarctic Canada, is used as an example and the SLURP (Semi‐distributed Land‐use‐based Runoff Processes) model allows hydrograph simulation for the Liard and its sub‐basins. Three sets of reanalysis temperature and precipitation data provide inputs to assess the sensitivity of model simulation. The spatial patterns of snowmelt, runoff and stream discharge for four water years were simulated. The SLURP model was found to be sensitive to a plausible range of input conditions as depicted by the three sets of reanalysis data. Despite differences in detail among the three sets of simulation results, several generalities emerged. A comparison of simulated snow cover with satellite data confirms that there are altitudinal delays in spring flow generation though latitude has no apparent influence. Runoff lags snowmelt while the catchment integrates flows of its tributaries, yet different combinations of winter snowfall and spring melt rates cause large interannual variations in snowmelt discharge. Streamflow measured and simulated at four stations along the main river permits an evaluation of runoff contribution from various sectors of the basin. The overall pattern of melt runoff generation and the modelling approach used in this investigation are applicable to other large mountainous basins in high latitudes. Copyright © 2006 John Wiley & Sons, Ltd.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.202
Teacher spread0.193 · 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