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Record W3035645161 · doi:10.1029/2020gl088000

Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land‐Surface Processes

2020· article· en· W3035645161 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Army Corps of EngineersBureau of ReclamationSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsFlood mythEnvironmental scienceSpatial dependenceSpatial variabilitySpatial ecologyClimatologyPhysical geographyGeographyGeologyStatistics

Abstract

fetched live from OpenAlex

Abstract Floods often affect large regions and cause adverse societal impacts. Regional flood hazard and risk assessments therefore require a realistic representation of spatial flood dependencies to avoid the overestimation or underestimation of risk. However, it is not yet well understood how spatial flood dependence, that is, the degree of co‐occurrence of floods at different locations, varies in space and time and which processes influence the strength of this dependence. We identify regions in the United States with seasonally similar flood behavior and analyze processes governing spatial dependence. We find that spatial flood dependence varies regionally and seasonally and is generally strongest in winter and spring and weakest in summer and fall. Moreover, we find that land‐surface processes are crucial in shaping the spatiotemporal characteristics of flood events. We conclude that the regional and seasonal variations in spatial flood dependencies must be considered when conducting current and future flood risk assessments.

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.

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 score0.357
Threshold uncertainty score1.000

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 head0.033
GPT teacher head0.303
Teacher spread0.270 · 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