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Record W2616524357 · doi:10.1061/9780784480618.039

Delineation of Homogeneous Regions Based on the Seasonal Behavior of Flood Flows: An Application to Eastern Canada

2017· article· en· W2616524357 on OpenAlexaffabout
Fahim Ashkar

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2017 · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsFlood mythHomogeneousSeasonalityHomogeneity (statistics)Environmental science100-year floodHydrology (agriculture)GeographyPhysical geographyClimatologyMeteorologyStatisticsMathematicsGeology

Abstract

fetched live from OpenAlex

We used the peaks over threshold (POT) approach to analyze the seasonal behavior of flood frequencies extracted from daily streamflow records of a group of hydrometric stations in Eastern Canada. For each record, we analyzed the distribution of the times of occurrence of flood flows above a threshold. The aim was to achieve a seasonal portioning of the year based on the time distribution of flood occurrences. It may be possible to assemble stations that are similar in their flood seasonality into geographical regions that exhibit some homogeneity. This broadly provides “homogeneous regions based on seasonality”. The number of significant “seasons” and the dates of beginning and end of each season jointly characterize the seasonal behavior of floods in a homogeneous region. We will provide examples of graphical analyses that help identify the seasons from a hydrometric record. The most important graph is based on plotting the mean number of exceedances in the time interval (zero, t] as a function of increasing t from zero to 365 days. One diagram may combine several plots for a number of threshold levels. Regional flood modeling based on grouping catchments with similar seasonal flood behavior should help reduce the uncertainty in flood forecasts at individual flood sites.

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)0.000
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 score0.167
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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