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Record W2182410630 · doi:10.82308/36086

Runoff simulation in the Canagagigue Creek watershed using the MIKE SHE model

2006· article· en· W2182410630 on OpenAlexfundaboutno aff
Shalini. Oogathoo

Bibliographic record

VenueeScholarship@McGill (McGill) · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersMcGill University
KeywordsWatershedSurface runoffHydrology (agriculture)Environmental scienceGeologyGeotechnical engineeringComputer scienceEcology

Abstract

fetched live from OpenAlex

The Canagagigue Creek watershed, located in the Grand River Basin, is one of the fastest developing areas in Ontario. The watershed hydrology has changed considerably due to the increased anthropogenic activities, producing frequent floods and droughts as well as water quality problems. MIKE SHE, a watershed-scale model, was used to simulate surface runoff from the Canagagigue Creek watershed. Various management scenarios affecting the surface hydrology were also evaluated. The model was calibrated for four years (1994-95 to 1997-98) and validated for another four years (1990-91 to 19931-94). For the calibration period, the correlation between the observed and simulated daily runoff was satisfactory, as shown by the coefficient of determination value of 0.59. The coefficient of determination was 0.44 for the validation period. The Nash-Sutcliffe coefficients obtained were 59% and 40% for the calibration and validation period, respectively. Use of daily input data together with a simplistic snowmelt routine, was found to affect model performance during the winter/spring period. Henceforth, model performance can be greatly improved by adopting a more comprehensive method for simulating snowmelt and incorporating the frozen soil conditions. Overall, the model was able to simulate surface runoff reasonably well on annual, seasonal, monthly, and daily intervals, representing all the hydrological components adequately. With the various management scenarios simulated, it was found that the deforestation scenario considerably increased the total flow (11%). On the other hand, the high runoff peaks were decreased and low flows were increased considerably in the application of the tile drainage scenario. It was also observed that surface flow increased in wet years and decreased in normal and dry years in the climate change scenario. Though impacts of certain scenarios were almost negligible, their effects were significant when associated with the percentage area under transformation. Hence, it was concluded that the model can be used to simulate various management scenarios to solve hydrologic problems in the Southern Ontario climatic condition.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.233
Teacher spread0.209 · 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.

Study designSimulation or modeling
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

Citations24
Published2006
Admission routes2
Has abstractyes

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