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Using stable water isotopes and isotope-enabled hydrologic modelling to quantify water in Central and Northeastern Ontario

2023· dissertation· en· W6987825827 sur OpenAlexaboutno aff

Notice bibliographique

RevueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueHydrology and Watershed Management Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPrecipitationWater cycleClimate changeHydrology (agriculture)StreamflowHydrological modelling
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The understanding of hydrologic processes in Central and Northern Ontario's mesoscale
\nwatersheds, located within the Precambrian Shield region, remains limited, posing challenges for
\naccurate hydrological modeling and assessment of climate change impacts on water resources.
\nThis study focuses on Central and Northeastern Ontario, typically characterized by granitic
\nbedrock, small depressions, and shallow acidic soils, where annual precipitation exceeds
\nevapotranspiration, resulting in abundant surface waters. Changes in hydrological processes in this
\nregion can have significant consequences for the local ecosystem of mesoscale watersheds.
\nTherefore, investigating the effects of climate change on water quantity is crucial.
\nThis research utilizes stable water isotopes (SWIs) as cost-effective tools to improve our
\nunderstanding of hydrologic processes and flowpaths in mesoscale Precambrian Shield
\nwatersheds. By analyzing long-term meteorological, hydrometric, and SWI data from the Sturgeon
\nRiver, French River, and Muskoka River watersheds, valuable insights are gained regarding the
\nimpacts of climate change on hydrological processes in these regions. The study employs a new
\nisotope-enabled distributed hydrologic model, isoWATFLOOD, which provides a good
\n
\nrepresentation of fluxes, storages, and their changes due to climate change in mesoscale and large-
\nscale watersheds.
\n
\nThe research objectives include exploring the key controls and importance of surface water
\nstorage (lakes and wetlands) on hydrologic function in the Sturgeon River-Lake Nipissing-French
\nRiver (SNF) and Muskoka watersheds, evaluating isoWATFLOOD hydrologic model's
\nperformance in simulating streamflow and isotope values in the Sturgeon River-Lake Nipissing
\n(SN) watershed, evaluating the importance of wetland connectivity representation in
\nisoWATFLOOD performance across the SN watershed, and assessing the impacts of climate
\nchange on streamflow and hydrologic partitioning in the SN watershed using the isoWATFLOOD
\nhydrologic model.
\nPCA and HCPC approaches are used to identify variation in controls on hydrologic function
\nin SNF and Muskoka watersheds using combination of hydrometric, geology, landscape and
\nisotopic metrics. The findings reveal greater evaporative enrichment impacts in Muskoka
\ncompared to the SNF catchments, with Muskoka exhibiting less variability in streamflow isotopes.
\nThe study identifies a positive correlation between wetland area and damping ratio (coefficient of
\nvariation of isotopes in streamflow to coefficient of variation of isotopes in precipitation), suggesting that wetland connection/disconnection and varying evaporation impacts contribute to
\nisotopic value variability in catchments with higher wetland coverage. Muskoka and SNF
\ncatchments generally fall into separate clusters, primarily influenced by wetland and lake area
\npercentages, mean slope, and the extent of glacialacustrine and glaciofluvial outwash deposits. The
\ncombination of catchment classification analyses and stable isotopes (δ
\n
\n18O and δ
\n
\n2H) proved
\neffective in studying how different catchment characteristics influence variations in hydrometric
\nresponse.
\nAn application of isoWATFLOOD was set up for Sturgeon River-Lake Nipissing (SN)
\nwatershed. Five separate models with varied connected wetland (CW) ratios between 10% to 50%
\nare set up to evaluate the importance of CW ratio in model performance. The SN isoWATFLOOD
\nmodel, calibrated using isotope and streamflow data, successfully simulates streamflow and
\nisotope values (KGE > 0.6) across 11 catchments. Wetland connectivity percentage significantly
\ninfluences streamflow and isotope simulations, particularly during the calibration period. The most
\naccurate streamflow simulations occur with 40% wetland connectivity, improving baseflow
\nrepresentation. This study advances isotope-enabled hydrologic simulations using
\nisoWATFLOOD and provides insights into wetland connectivity representation, a critical
\nlandscape aspect of Precambrian Shield watersheds. Stable isotopes prove valuable in addressing
\nthe challenge of equifinality.
\nUsing the SN isoWATFLOOD model and considering 16 global climate model (GCM)-
\nemission (RCP) models, findings project a future characterized by warmer and wetter climatic
\nconditions (2020-2082) compared to the baseline period (1990-2019). On average, the study
\npredicts an annual discharge increase ranging from 4.8% to 11.5%, with elevated winter and fall
\nstreamflow across the watershed. These changes result from warmer fall and winter seasons,
\nreduced freezing days, increased annual precipitation, and more frequent extreme precipitation
\nevents. Additionally, the simulations indicate an earlier spring freshet peakflow, accompanied by
\na reduced peak flow rate. Furthermore, climate change will impact hydrological partitioning,
\nleading to alterations in the contributions of annual average daily baseflow to streamflow.
\nMoreover, there will be a rise in average annual daily direct runoff due to intensified annual
\nprecipitation, more frequent extreme precipitation events, and rain-on-snow occurrences within
\nthe watershed. The results highlight the significance of integrating climate change impacts into water resources management planning, specifically concerning peak flow timing, seasonality, and
\nchanges in flow volume during different seasons.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,559
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,203
Écart entre enseignants0,184 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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