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Record W3017235799 · doi:10.3390/w12041112

Impacts of Hydrological Processes on Stream Temperature in a Cold Region Watershed Based on the SWAT Equilibrium Temperature Model

2020· article· en· W3017235799 on OpenAlexaffabout
Xinzhong Du, Greg G. Goss, Monireh Faramarzi

Bibliographic record

VenueWater · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnvironmental scienceSurface runoffSoil and Water Assessment ToolStreamflowHydrology (agriculture)WatershedStream flowPrecipitationSoil waterDrainage basinSoil scienceMeteorologyEcologyGeologyGeography

Abstract

fetched live from OpenAlex

Variance in stream temperature from historical norms, which reflects the impacts from both hydrological and meteorological factors, is a significant indicator of the stream ecosystem health. Therefore, it is imperative to study the hydrological processes controlling stream temperature in the watershed. The impacts of hydrological processes on stream temperature in the cold region of Western Canada were investigated based on the previously developed Soil and Water Assessment Tool (SWAT) equilibrium temperature model. The model was calibrated and validated for streamflow and stream temperature based on the observations and a global parameter sensitivity analysis conducted to identify the most important hydrological process governing the stream temperature dynamics. The precipitation and air temperature lapse rates were found to be the most sensitive parameters controlling the stream temperature, followed by the parameters regulating the processes of soil water dynamics, surface runoff, and channel routing. Our analysis showed an inverse relationship between streamflow volume and stream temperature, and different runoff components have different impacts on temporal regimes of stream temperatures. This study elaborates on the response of the stream temperature to changes in hydrological processes at the watershed scale and indicates that hydrological processes should be taken into account for prediction of stream temperatures.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.407

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)0.0000.000

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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations22
Published2020
Admission routes2
Has abstractyes

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