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Record W2921075492 · doi:10.1111/1752-1688.12737

Does Wetland Location Matter When Managing Wetlands for Watershed‐Scale Flood and Drought Resilience?

2019· article· en· W2921075492 on OpenAlexafffund
Ali Ameli, Irena F. Creed

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Saskatchewan
FundersAlberta Environment and Parks
KeywordsWetlandBaseflowWatershedEnvironmental scienceFlood mythHydrology (agriculture)Watershed areaFlooding (psychology)StreamflowFloodplainHydrological modellingWater resource managementGeographyDrainage basinEcologyGeology

Abstract

fetched live from OpenAlex

Abstract Wetland protection and restoration strategies that are designed to promote hydrologic resilience do not incorporate the location of wetlands relative to the main stream network. This is primarily attributed to the lack of knowledge on the effects of wetland location on wetland hydrologic function (e.g., flood and drought mitigation). Here, we combined a watershed‐scale, surface–subsurface, fully distributed, physically based hydrologic model with historical, existing, and lost (drained) wetland maps in the Nose Creek watershed in the Prairie Pothole Region of North America to (1) estimate the hydrologic functions of lost wetlands and (2) estimate the hydrologic functions of wetlands located at different distances from the main stream network. Modeling results showed wetland loss altered streamflow, decreasing baseflow and increasing stream peakflow during the period of the precipitation events that led to major flooding in the watershed and downstream cities. In addition, we found that wetlands closer to the main stream network played a disproportionately important role in attenuating peakflow, while wetland location was not important for regulating baseflow. The findings of this study provide information for watershed managers that can help to prioritize wetland restoration efforts for flood or drought risk mitigation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.049
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

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

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 head0.003
GPT teacher head0.189
Teacher spread0.186 · 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 teacher head, 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

Citations81
Published2019
Admission routes2
Has abstractyes

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