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Rational Method Time of Concentration Can Underestimate Peak Discharge for Hillslopes

2021· article· en· W3189735087 on OpenAlexaff
Dana Lapides, Anneliese Sytsma, Octavia Crompton, Sally Thompson

Bibliographic record

VenueJournal of Hydraulic Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStormSurface runoffDuration (music)Return periodEnvironmental scienceHydrology (agriculture)Flow (mathematics)StormwaterMeteorologyGeologyMechanicsGeotechnical engineeringGeographyEcologyPhysics

Abstract

fetched live from OpenAlex

The Rational Method remains one of the most widely used approaches for estimating peak discharge in small catchments. In one widely used interpretation of the Rational Method, the maximum possible peak discharge produced by a storm with a given return period is predicted by setting the storm duration equal to the time of concentration. Whether the time of concentration maximizes peak flow for a rainfall return period, however, depends on the relationship between contributing area and storm duration. Here, we show that under many conditions, using the time of concentration in the Rational Method leads to an underestimation of peak discharge. This underestimation is illustrated using two case studies on idealized hillslopes on which runoff occurs as sheet flow. We suggest that practitioners should become cognizant of the differences between the critical duration (the storm duration that maximizes peak discharge) and the time of concentration within the Rational Method and be alert to morphology and land-use patterns that are likely to cause these timescales to diverge.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.239

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 head0.236
Teacher spread0.227 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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