MétaCan
Menu
Back to cohort
Record W4408755701 · doi:10.1029/2024wr037179

Searching for Functional Simplicity of Stormflow Generation

2025· article· en· W4408755701 on OpenAlexafffundabout
Hamed Karimi Sharif, Ali Ameli

Bibliographic record

VenueWater Resources Research · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSimplicityComputer scienceEnvironmental scienceHydrology (agriculture)GeologyGeotechnical engineeringPhilosophy

Abstract

fetched live from OpenAlex

Abstract Our study proposes a data‐driven framework that identifies the level of functional simplicity of catchment's stormflow generation during dormant/growing seasons, using daily scale observations of streamflow and precipitation. We classify 619 rain‐dominated catchments across Canada, the United States, Great Britain, and Australia into three behavioral classes—simple, intermediate, and complex—based on the validity of (segmented) linear models in explaining the inter‐event relationship between precipitation volume and stormflow volume. Results reveal that simple stormflow generation behavior occurs at 108 catchments during dormant seasons with the linear model explaining most variability of inter‐event relationship between precipitation and stormflow volumes (median R2 of 0.81). These simple catchments are typically steep with wet/out‐of‐phase climate and strong precipitation persistence. The functional simplicity of simple catchments is further explored using spectral (coherency) analysis, which indicates the level of synchronicity between daily scale precipitation and streamflow time‐series. Simple catchments exhibit a strong coherency value at high frequencies, resembling the dynamic of a nearly Linear Time‐Invariant system. Indeed, the portion of precipitation volume that becomes stormflow tends to remain constant during dormant seasons, since the transfer function translating precipitation pulses to the streamflow hydrograph is nearly linear and time‐invariant. Complex catchments, in contrast, exhibit nonlinear relationships and time‐variant transfer functions, with weak coherency between precipitation and streamflow time‐series. Our results guide modeling frameworks to adjust the simplicity/complexity level with the catchment's “observation‐based” functional behavior. By synthesizing the causes/drivers and empirical equations relevant to simple stormflow behavior, our study contributes to the development of a unified hydrologic theory of stormflow generation.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.151
GPT teacher head0.344
Teacher spread0.193 · 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

Citations4
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueWater Resources ResearchSame topicMeteorological Phenomena and SimulationsFrench-language works237,207