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Enregistrement W6987825827

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 watersheds, located within the Precambrian Shield region, remains limited, posing challenges for accurate hydrological modeling and assessment of climate change impacts on water resources. This study focuses on Central and Northeastern Ontario, typically characterized by granitic bedrock, small depressions, and shallow acidic soils, where annual precipitation exceeds evapotranspiration, resulting in abundant surface waters. Changes in hydrological processes in this region can have significant consequences for the local ecosystem of mesoscale watersheds. Therefore, investigating the effects of climate change on water quantity is crucial. This research utilizes stable water isotopes (SWIs) as cost-effective tools to improve our understanding of hydrologic processes and flowpaths in mesoscale Precambrian Shield watersheds. By analyzing long-term meteorological, hydrometric, and SWI data from the Sturgeon River, French River, and Muskoka River watersheds, valuable insights are gained regarding the impacts of climate change on hydrological processes in these regions. The study employs a new isotope-enabled distributed hydrologic model, isoWATFLOOD, which provides a good representation of fluxes, storages, and their changes due to climate change in mesoscale and large- scale watersheds. The research objectives include exploring the key controls and importance of surface water storage (lakes and wetlands) on hydrologic function in the Sturgeon River-Lake Nipissing-French River (SNF) and Muskoka watersheds, evaluating isoWATFLOOD hydrologic model's performance in simulating streamflow and isotope values in the Sturgeon River-Lake Nipissing (SN) watershed, evaluating the importance of wetland connectivity representation in isoWATFLOOD performance across the SN watershed, and assessing the impacts of climate change on streamflow and hydrologic partitioning in the SN watershed using the isoWATFLOOD hydrologic model. PCA and HCPC approaches are used to identify variation in controls on hydrologic function in SNF and Muskoka watersheds using combination of hydrometric, geology, landscape and isotopic metrics. The findings reveal greater evaporative enrichment impacts in Muskoka compared to the SNF catchments, with Muskoka exhibiting less variability in streamflow isotopes. The study identifies a positive correlation between wetland area and damping ratio (coefficient of variation of isotopes in streamflow to coefficient of variation of isotopes in precipitation), suggesting that wetland connection/disconnection and varying evaporation impacts contribute to isotopic value variability in catchments with higher wetland coverage. Muskoka and SNF catchments generally fall into separate clusters, primarily influenced by wetland and lake area percentages, mean slope, and the extent of glacialacustrine and glaciofluvial outwash deposits. The combination of catchment classification analyses and stable isotopes (δ 18O and δ 2H) proved effective in studying how different catchment characteristics influence variations in hydrometric response. An application of isoWATFLOOD was set up for Sturgeon River-Lake Nipissing (SN) watershed. Five separate models with varied connected wetland (CW) ratios between 10% to 50% are set up to evaluate the importance of CW ratio in model performance. The SN isoWATFLOOD model, calibrated using isotope and streamflow data, successfully simulates streamflow and isotope values (KGE > 0.6) across 11 catchments. Wetland connectivity percentage significantly influences streamflow and isotope simulations, particularly during the calibration period. The most accurate streamflow simulations occur with 40% wetland connectivity, improving baseflow representation. This study advances isotope-enabled hydrologic simulations using isoWATFLOOD and provides insights into wetland connectivity representation, a critical landscape aspect of Precambrian Shield watersheds. Stable isotopes prove valuable in addressing the challenge of equifinality. Using the SN isoWATFLOOD model and considering 16 global climate model (GCM)- emission (RCP) models, findings project a future characterized by warmer and wetter climatic conditions (2020-2082) compared to the baseline period (1990-2019). On average, the study predicts an annual discharge increase ranging from 4.8% to 11.5%, with elevated winter and fall streamflow across the watershed. These changes result from warmer fall and winter seasons, reduced freezing days, increased annual precipitation, and more frequent extreme precipitation events. Additionally, the simulations indicate an earlier spring freshet peakflow, accompanied by a reduced peak flow rate. Furthermore, climate change will impact hydrological partitioning, leading to alterations in the contributions of annual average daily baseflow to streamflow. Moreover, there will be a rise in average annual daily direct runoff due to intensified annual precipitation, more frequent extreme precipitation events, and rain-on-snow occurrences within the watershed. The results highlight the significance of integrating climate change impacts into water resources management planning, specifically concerning peak flow timing, seasonality, and changes 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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,018
Score d'incertitude au seuil0,102

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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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