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Record W4226429111 · doi:10.1080/07011784.2022.2055496

Hydrological behaviour of an unregulated eastern slope river under changing historical climate

2022· article· en· W4226429111 on OpenAlexaffvenueabout
Yixuan Zhou, Cuauhtémoc Tonatiuh Vidrio‐Sahagún, M. Cathryn Ryan, Jianxun He

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Calgary
FundersUniversidad de Guadalajara
KeywordsClimate changeHydrology (agriculture)Environmental sciencePhysical geographyGeographyClimatologyWater resource managementGeologyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

The Elbow River is an eastern slope river with headwaters in the Rocky Mountains in Alberta whose major end-use is a critical source of municipal water for Calgary. Overwinter precipitation in its watershed falls primarily as snow and accumulates as snowpack until spring melt. Precipitation falls mainly as rain from May until October. The river is unregulated above Calgary’s water supply reservoir, and its relatively undeveloped watershed makes it ideal for examining potential climate change impacts on river hydrology. Available historical hydrometeorological data (1967 to 2015) from the basin were assessed to study its hydrological behaviour under a changing climate. The analysis showed significant upward trends in both flow and precipitation variables, especially from 1979 to 2015. Significant increases in both annual flow volume and annual maximum daily flow (AM-flow), and later seasonal occurrence of AM-flow, were not observed in other eastern slope rivers. Although these changes could attenuate predicted water supply shortages, they could also potentially increase flood magnitudes. The analysis also revealed that three sub-watersheds, which are approximately equal in geographic area, contributed differing flow volumes during the high-flow season (May to October). The upper watershed contributed most (∼68%), followed by the middle (∼26%) and lower (∼6%) watersheds, on average. Extreme high-flow events (ie >90th percentile AM-flow) were strongly related to high rainfall events, but not significantly related to snowpack loss (or melt). Moderate AM-flows were positively related to both the cumulative snowpack loss before the high-flow season and the cumulative antecedent precipitation prior to the AM-flow, suggesting that the antecedent soil moisture conditions could play a role. Predictions of climate change impacts on this eastern slope river’s hydrology should thus consider the effects of meteorological variables and the moisture conditions of the watershed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.015
GPT teacher head0.196
Teacher spread0.180 · 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 designObservational
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

Citations4
Published2022
Admission routes3
Has abstractyes

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