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Record W2619612306 · doi:10.5194/hess-2017-252

On the Relationship Between Flood and Contributing Area

2017· article· en· W2619612306 on OpenAlexafffundabout
Christopher Spence, S. G. Mengistu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of AlbertaEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsFlood mythStreamflowHydrology (agriculture)Environmental scienceDrainage basinWatershedSurface runoff100-year floodPower functionMagnitude (astronomy)Watershed areaPower lawGeologyStatisticsGeographyMathematicsPhysicsCartography

Abstract

fetched live from OpenAlex

Abstract. While it is well known that the vast majority of the time only a portion of any watershed contributes runoff to the outlet, this extent is rarely documented. The power-law form of the streamflow and contributing area (Q-Ac) relationship has been known for a half century, but it is uncommon for it to be quantified or its controls evaluated. In this study a semi-distributed hydrological model (MESH-PDMROF) that can simulate contributing area and streamflow was employed to compare contributing area and flood frequency distributions in a southern Manitoba, Canada catchment and test the hypothesis that the relationship between a catchment’s floods and contributing area is a power function that influences the form of regional flood-area relationships. The model simulated streamflow reasonably well (Nash Sutcliffe values = 0.62). Modelled estimates of the area contributing to the mean annual flood were much lower (0.3) than those derived from independent topographic analysis (0.9) described in earlier literature, even after bias and error corrections. Estimates of the coefficient and exponent of the Q-Ac power law function ranged from 0.08–0.14 and 0.9–1.12, respectively. Lower exponent values of regional flood frequency curves suggest they are a construct of Q-Ac curves from individual basins. The non-linear nature of this relationship implies any contributing area change will have a profound impact on flood magnitude. The mean annual flood of the major river in this region, the Red, has increased 33 % since 1987. Applying the coefficient and exponent ranges above suggests this is associated with an expansion in contributing areas of 29–38 %. There are implications for the attribution of causes and mitigation of nutrient transport from regional watersheds. However, how physiography and land and water management could change Q-Ac power law exponents is poorly known and MESH-PDMROF does not provide explicit estimates of the spatial distribution of contributing area. These are areas encouraged for future research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.084
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.259
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 source (direct Gemma or distilled Codex), 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

Citations2
Published2017
Admission routes3
Has abstractyes

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