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Record W3210442066 · doi:10.3389/fenvs.2021.742901

Risk Assessment of Dam-Breach Flood Under Extreme Storm Events

2021· article· en· W3210442066 on OpenAlexafffund
Xiajing Lin, Guohe Huang, Guoqing Wang, Denghua Yan, Xiong Zhou

Bibliographic record

VenueFrontiers in Environmental Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Regina
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaMitacsNational Key Research and Development Program of ChinaCanada Excellence Research Chairs, Government of Canada
KeywordsFlood mythStormEnvironmental scienceHydrology (agriculture)PrecipitationExtreme weatherClimatologyStorm surgeAtlantic hurricaneStructural basinClimate changeMeteorologyGeologyGeographyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

In recent years, as a result of increasingly intensive rainfall events, the associated water erosion and corrosion have led to the increase in breach risk of aging dams in the United States. In this study, a hydrodynamic model was used to the inundation simulation under three hypothetical extreme precipitation-induced homogeneous concrete dam-breach scenarios. All hydraulic variables, including water depth, flow velocity, and flood arriving time over separated nine cross-sections in the Catawba River, were calculated. The hypothetical simulation results illustrate that the impact of Hurricane Florence’s rainfall is far more severe over the downstream of hydraulic facilities than that of the Once-in-a-century storm rainfall event. Although Hurricane Florence’s rainfall observed in Wilmington had not historically happened near the MI Dam site, the river basin has a higher probability to be attacked by such storm rainfall if more extreme weather events would be generated under future warming conditions. Besides, the time for floodwaters to reach cross-section 6 under the Hurricane Gustav scenario is shorter than that under the Once-in-a-century rainfall scenario, making the downstream be inundated in short minutes. Since the probability can be quantitatively evaluated, it is of great worth assessing the risk of dam-break floods in coastal cities where human lives are at a vulnerable stage.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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 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

Citations9
Published2021
Admission routes2
Has abstractyes

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