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Record W7018493417

The Effect of Access Road Construction on the Hydrology of Wetlands in Rock Barren Landscapes

2019· dissertation· en· W7018493417 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCulvertWetlandHydrology (agriculture)Water tablePondingSurface runoffDrainageGroundwaterDownstream (manufacturing)
DOInot available

Abstract

fetched live from OpenAlex

Dense networks of access roads can be found across the Canadian landscape. Though necessary for natural resource and mineral exploration projects, access roads are linear disturbances that can alter hydrological processes operating within the landscape. While this has previously been studied in many landscapes, research has not been conducted in wetland-dominated depressional landscapes of the Precambrian shield. As such, four wetlands were instrumented with paired monitoring wells and piezometer nests to assess hydrological change upstream and downstream of the road cut-through. In all four wetlands, the road obstructed the movement of lateral flow, resulting in ponding upstream as water was discharged to the surface. Downstream of the road, the wetland experienced a lowered water table due to reduced water inputs, especially during drought conditions. The difference between the upstream and downstream water table position (∆WT) was largest when the culvert was placed 20 cm above the surface, indicating that large water inputs and prolonged flooded conditions was required for water to be permitted downstream. Conversely, the ∆WT was smallest when the culvert was embedded 50% into the subsurface, confirming a previous suggestion (Phillips, 1997) that partial burial is the ideal culvert placement to maintain drainage patterns. In wetlands with comparable culvert placements (perched on the moss surface), the ∆WT was smallest in the wetland that received not only water input from lateral flow, but from groundwater discharge and overland flow as well. This suggests that multiple water sources are important to provide water to the bisected unit downstream of the road cut-through. Furthermore, wetlands with a groundwater connection and deeper depression depths were capable of maintaining a water table during drought conditions, and as such were not subjected to long-term aerobic conditions that can result in peat degradation. In general, the ∆WT was higher in the wetland during the fall rewetting than during the drought. Provided that culvert design was standardized throughout the road network, wetlands that received a greater contribution of overland flow would be at a greater risk for flooding and subsequently hydrological change. As such, a GIS model was created to assess the relative flooding potential of wetlands using criteria (catchment area, proportion of rock cover, stream order, surface water connection) that represents the first-order controls on runoff in Precambrian shield landscapes (T3 template; Buttle, 2006). The model output was evaluated using field data, where wetlands were ranked based on its water table position during the winter period when snowmelt was assumed to occur. The model was capable of assessing the hydroperiod of different wetland types, where the highest and lowest flooding potential was associated with marshes and bogs, respectively. A higher flooding potential was also associated with deeper depressions, which have been shown to have a higher hydroperiod (Didemus, 2016). The flooding potential of wetlands was variable throughout the landscape, and was not correlated with a particular wetland metric (i.e. wetland type, wetland area). The model results suggest that wetlands can be assessed based on flooding potential, in conjunction with other traditionally used wetland metrics. As such, an understanding of the hydrological function of wetlands, and proper selection of culvert design and wetlands for road crossing can be completed in order to minimize hydrological change associated with the construction of access roads.

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 score0.484
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.200
Teacher spread0.195 · 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
Published2019
Admission routes1
Has abstractyes

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