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Record W2935557676 · doi:10.3808/jei.201900407

Water Quality Management of a Cold Climate Region Watershed in Changing Climate

2019· article· en· W2935557676 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

VenueJournal of Environmental Informatics · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsAthabasca University
FundersAlberta Innovates
KeywordsEnvironmental scienceSoil and Water Assessment ToolWatershedClimate changeSWAT modelWater qualityStreamflowHydrology (agriculture)Water resourcesDrainage basinEcologyGeography

Abstract

fetched live from OpenAlex

Cold climate regions provide a multitude of ecosystem services. However, cold regions under a changing climate could be more vulnerable than others because their glaciers, freezing soils and peatlands are sensitive to the slightest of changes in climate. This has posed serious threats to the water resources, sustainable goods production and ecosystem services that depend on regional water quality. Therefore, proper watershed management is imperative. In this paper, we investigate this issue in a cold climate watershed in central Alberta, Canada with the main objective of quantifying the impacts of climate change on water quality status. We modified specific water quality related processes of a process-based model – Soil and Water Assessment Tool (SWAT) with a view of better representing the reality of cold climate regions. A SWAT model is then built-up, followed up by a multi-site and multi-objective (streamflow, sediment and water quality) calibration, validation and uncertainty analysis in a baseline period (1983 - 2013). The calibrated and validated model is then fed with a high spatial resolution (25 km) daily future climate data – the CanRCM4. Improvements on stream water temperature (Ts) and dissolved oxygen (DO) simulations justified the modifications. This model is able to simulate the dynamics of other water quality variables (carbonaceous biochemical oxygen demand – cBOD, total nitrogen – TN and phosphorus – TP) with a wide range of accuracy (very good to satisfactory) in the base period. Agriculture areas account for the highest amount of annual TN (11.16 kgN/ha) and TP (2.88 kgP/ha) yield rate in the base period leading to poor water quality status in the immediate downstream reaches. The situation would be further exacerbated (16.52 kgN/ha and 4.89 kgP/ha) in future. Finally, we tested different alternative management options to compare the water quality status of the Athabasca River Basin (ARB) under a changing climate. Significant reduction in future nutrient concentrations (~ 20% on TN and 60% on TP) can be achieved using a certain combination of management practices and the ecological status of the basin can be improved. This demonstrates that the modified SWAT model can be applied to other cold climate regions, and that the results can be translated to help in managing the ARB in a more holistic way.

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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)0.001
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 score0.120
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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