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Record W4322212808 · doi:10.5194/egusphere-egu23-15726

Is multidecadal prediction of flood patterns possible for infrastructure planning purposes using the 10000 year cosmogenic isotope (10Be and 14C) record?

2023· preprint· en· W4322212808 on OpenAlexaff
Michael Asten, K. G. McCracken

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsCargill (Canada)
Fundersnot available
KeywordsFlood mythMaximaPeriod (music)ClimatologyGeologyHydrology (agriculture)Environmental scienceGeographyPhysicsArchaeology

Abstract

fetched live from OpenAlex

We compare spectral decomposition of flood data from three sites in Australia (south-east coast, east-inland, east-central) with the Southern Oscillation Index (SOI), and with those from the Brahmaputra River (Bangladesh) and Nile River (Egypt). All show clear evidence of spectral maxima at medium periods approximating 50, 85, 130 and 200 years, which correspond closely to the maxima in the power spectra of the cosmogenic 10Be and 14C observations obtained from ice cores with ages covering the past 10000 years. We find that the Gleissberg cycle (85 yr period) for Australian sites is out of phase with that for the Brahmaputra River. All sites also show spectral maxima at short periods 6-20 yr as expected from the ENSO cycle; flood associations varying over these short periods are generally accepted. We explore the possibility that the medium periods can be used to assist in the prediction of flood and drought activity several decades into the future. We consider the hypothesis of a correlation or causal relationship existing between solar activity (including its effects on the intensity of galactic cosmic rays on the Earth) and flood cycles for the medium periods. The Australian SE coast and east-inland sites, and the Brahmaputra River, show strong medium-period maxima. The phase of the medium periods is obtained by optimized fitting of multiple sine curves with periods obtained from the spectra; the Australian SE coast and east-inland sites show summed sine curves with high correlation. The Brahmaputra River shows similar correlation at medium periods (in particular the Gleissberg 85-year period) but in opposite phase. The Australian east-central site (Murray-Darling Basin) and the Nile River (Egypt) show only weak evidence for the medium-period maxima which suggests ocean proximity is a factor for these influences. The short duration SOI record shows weaker evidence for medium-period spectral maxima, and the Southern Annular mode (SAM) and Indian Ocean Dipole (IOD) show no obvious correlation with observed medium-period flood patterns at the selected sites. We speculate that the strong medium-period flood patterns are associated with the solar and/or cosmic ray cycles, observed in the cosmogenic record, where the causative mechanisms are yet to be established. We conclude that the association of floods in medium-period cycles in addition to the association with the better-known short period variations associated with the ENSO cycle, provides opportunity for empirical predictions of flood patterns over ~80 years, and for the further investigation of possible causative mechanisms linking solar phenomena to oceanic indices and multi-decadal flood patterns.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.044
GPT teacher head0.295
Teacher spread0.251 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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