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Record W4220898308 · doi:10.5194/egusphere-egu22-2033

On climate change affecting the dynamics of overland flow from infiltrating microcatchments

2022· preprint· en· W4220898308 on OpenAlexaboutno aff
Bernhard Schmid

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLoamSurface runoffEnvironmental scienceHydrology (agriculture)StormAtmospheric sciencesAnimal scienceSoil waterMeteorologySoil scienceGeographyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

Since the middle of the last century extreme rainfall events have intensified in many parts of the world (Martel et al., 2021), and increasing temperature is considered to let this development continue in the next decades. The Clausius-Clapeyron relationship, i.e. an about 7% increase per additional degree centigrade, yields an order of magnitude of what to expect, with convective rainfalls being likely to grow in a still more pronounced manner (Martel et al., op.cit.). Convective cells are, typically, associated with rainfalls of short duration and small spatial extent, which makes them particularly important for microcatchments. Combining Green-Ampt type infiltration with kinematic overland flow, the relationship between a square-topped hyetograph and runoff is modelled. Frequently, design rainfall duration is chosen equal to the time of concentration. In case of an infiltrating surface, however, maximum peak runoff may result from shorter rainfall. There may, thus, be partial area runoff only, in which case the Schmid (1997) design storm equation yields the critical rainfall duration needed to determine maximum peak flow. The study started from a chosen present-day IDF relationship of 20 years' return interval in Austria and a (rectangular) grass plot (hillslope) of 50 m length, 10% slope and an initial loss of 0.5 mm. Simulations were made using soil data from Columbia sandy loam, Guelph loam and Ida silt loam, in turn. Rainfall was assumed to be subject to Clausius-Clapeyron scaling and variable warming between 0.0 and 2.0 K. In the case of the most pervious soil of the three (Columbia sandy loam, vertical saturated permeability Ksv = 0.0139 mm/s) flow was laminar and described by the Dary-Weisbach friction law (K = 4000). Contributing area remained small throughout (length 0.9 m for 0 K and 8.3 m for 2 K temperature increase). Corresponding peak flow showed above-linear growth and increased strongly from 0.013 L/(s.m) for 0 K to 0.17 L/(s.m) for 2 K. The 'medium' soil, Guelph loam (Ksv = 0.00367 mm/s), was associated with contributing hillslope length growing from 35 m to the full 50 m as temperature increase varied from 0 to 2 K. Corresponding peak flows increased from 0.69 to 1.16 L/(s.m), i.e. by 68%. Flows over Guelph loam and Ida solt loam were turbulent (Manning's n = 0.4). In case of the finest soil, Ida silt loam (Ksv = 0.000292 mm/s), all of the microcatchment contributed to overland flow from the start (DT = 0 K). Peak flow increased almost linearly with temperature from 2.31 to 2.77 L/(s.m), i.e. by 20%. Consequently, it may be concluded that a future rise in temperature up to 2 K is likely to trigger strong increases in peak flows from infiltrating microcatchments. The present study indicates that Clausius-Clapeyron rainfall scaling may result in peak flows increasing much in excess of the 7% / K. References Martel, J.-L. et al.: Climate change and rainfall intensity-duration-frequency curves: overview of science and guidelines for adaption. J. Hydrol. Eng. 26(10), DOI: 10.1061/(ASCE)HE.1943-5584.0002122, 2021. Schmid, B.H.: Critical rainfall duration for overland flow from an infiltrating plane surface. J. Hydrol. 193, 45-60, 1997.

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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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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