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Record W7010479233

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

2022· dissertation· en· W7010479233 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandHydrology (agriculture)Climate changeDrainageDrainage basinSurface runoffWater storageFlooding (psychology)Hydrological modellingStructural basin
DOInot available

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.130
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.165
Teacher spread0.159 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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