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Enregistrement W7010479233

The influence of climate change and wetland managment on prairie hydrology - insights from Smith Creek, Saskatchewan

2022· dissertation· en· W7010479233 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueHydrology and Watershed Management Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWetlandHydrology (agriculture)Climate changeDrainageDrainage basinSurface runoffWater storageFlooding (psychology)Hydrological modellingStructural basin
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Internally drained depressional wetlands are critical landscape features in the Prairie Pothole Region (PPR) of North America. They provide important ecosystems services such as flood attenuation, improved downstream water quality, and diverse species habitat, however they are frequently drained by agriculture producers to manage excess surface water, access more farmland, or improve operational efficiencies. After recent flooding in the Canadian Prairies, there is increased interest in understanding the relative influence of climate change and wetland drainage on prairie hydrology to ensure sustainable economic and social development in the region. Future climate projections show increasing air temperatures and rainfall in the Canadian Prairies, while wetland drainage is expected to persist due to rising land prices. As such, the purpose of this thesis is to determine the influence of wetland drainage and climate change on prairie basin hydrology and develop future wetland management strategies that preserve agricultural land and mitigate downstream impacts during wet periods in the PPR. The objectives are therefore to 1) improve surface water storage capacity estimation methods from high-resolution digital elevation models (DEMs) of agriculturally dominated prairie basins, 2) advance prairie hydrological modelling through improved representation of wetland characteristics, and 3) evaluate the influence of wetland management and projected climate change on prairie basin hydrological responses. A case study of the instrumented and partially drained Smith Creek Research Basin (SCRB) is presented in this thesis. First, surface water storage capacity estimates of depressional wetlands were improved through manual breaching of roads to simulate the function of culverts in surface water drainage and storage modelling, using a 2-m resolution digital elevation model (DEM). Road-breaching at presumed culvert locations was found to decrease estimates of depressional wetland area by 29% and surface water storage capacity by 48% compared to estimates with roads-intact from automated depressional wetland delineation using the 2-m resolution DEM. Importantly, the roads-breached simulation provided wetland area and surface water storage capacity estimates that were 150% higher than estimates from aerial-photos. This result suggests that current prairie hydrological models are subject to uncertainty in estimates of wetland areas and storage capacities depending on wetland delineation methods, which may impact wetland drainage or restoration scenarios modelling results. Next, a new prairie hydrological model was developed for SCRB using the Cold Regions Hydrological Modelling Platform. This model uses primarily physically-based algorithms to simulate cold-regions prairie-specific hydrological processes including precipitation phase, wind redistribution of snow, snow sublimation, snowmelt, infiltration into frozen and unfrozen soils, crop growth, evapotranspiration, soil moisture balance, surface water storage in depressions or wetlands, and runoff routing. The new model, builds upon previous work conducted in the SCRB, but offers improved representation of wetland characteristics using depressional wetlands delineated from the 2-m roads-breached DEM, updated parameters to support multi-year simulations, a new macro to prevent soils from re-freezing after large snowfall events in the late spring, and a novel link to a hydraulic model to simulate culvert-restricted streamflow that occurs in roadside ditches and along stream channels during high runoff events in the SCRB. Finally, the new model was used to evaluate the influence of climate change and wetland drainage on the hydrology of the SCRB. Current and projected future weather variables from the Weather Research and Forecasting model were used to simulate the influence of climate change in the SCRB towards the end of the 21st century. Results suggest that a significantly warmer (5.5 ⁰C) and wetter (44 mm) projected future climate, with less snowfall and more extreme rainfall, will increase mean annual streamflow volume by 26%, with spring peak discharge decreasing by 34% and summer peak discharge increasing by 161%. If wetland drainage continues in the SCRB and wetland area drops below 9% of the basin area, streamflow volume could increase above the climate projected increase. This suggests that continued wetland drainage in prairie basins may have more influence on future streamflow volumes than projected climate change. Wetland restoration to near-historical extents was found to increase storage volumes sufficiently to offset climate projected increases in streamflow volumes, but even complete wetland restoration to historically maximum levels did not offset projected increases in summer peak daily discharge. This means that additional infrastructure upgrades or emergency response plans beyond wetland management strategies will likely be needed to manage future flood risk in the Canadian Prairies. The new methods, analysis, and results presented in this thesis are expected to be relevant to those interested in wetland management in cold-region prairie basins, including policy makers, basin stewardship groups, conservation organisations, water resources engineers, agriculture producers and the public.

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 enseignants0,130
Score d'incertitude au seuil0,262

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
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,006
Tête enseignante GPT0,165
Écart entre enseignants0,159 · 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

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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