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Record W3025270814 · doi:10.1029/2019jc015928

Present and Future Flood Hazard in the Lower Columbia River Estuary: Changing Flood Hazards in the Portland‐Vancouver Metropolitan Area

2020· article· en· W3025270814 on OpenAlexaboutno aff
Lumas Helaire, Stefan A. Talke, David A. Jay, Heejun Chang

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersOffice of Naval ResearchOffice of Naval Research GlobalDivision of Ocean SciencesPortland State UniversityNational Science Foundation
KeywordsFlood mythHydrology (agriculture)Magnitude (astronomy)EstuaryEnvironmental scienceFloodplainFluvial100-year floodWater levelGeologyGeographyOceanographyGeomorphologyCartography

Abstract

fetched live from OpenAlex

Abstract Over the next century, model projections suggest that river run‑off in the Pacific Northwest will increase during the winter season and that sea‐level rise (SLR) may exceed a meter. To investigate the resulting changes in flood hazard, we numerically model the February 1996 and January 1923 floods (the largest and third‐largest Willamette River floods since 1900) under present and potential future run‐off and sea level scenarios. First, we reproduce the actual February 1996 flood to within a root‐mean‐square error of 0.05 m ( N = 7) for peak water levels. Next, we run scenarios in which three SLR scenarios (0, 0.6, and 1.5 m) are combined with two river run‐off scenarios (0% and 10% run‐off increase). Then the slightly larger 1923 flood scenario is run, but with modern (higher than historical) Columbia River flow. The results indicate that a 10% increase in river run‐off increased the1996 flood magnitude by 0.78 m, while 1923 flow increases flood magnitude by 0.82 m. Overall, the type and magnitude of future flood hazards vary with reach. The Portland/Vancouver Metropolitan area is most sensitive to changes in run‐off, with a smaller change of ~0.2–0.26 m per meter of SLR. By contrast, coastal regions are quite sensitive to amplified sea level and exhibit nonlinear responses based on changes to river slope and tides. Between the fluvial region and the estuary, a region of compound flood hazard exists that is sensitive to changes in river discharge, sea level, tides, and storm surge.

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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.290
Teacher spread0.269 · 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 teacher head, 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

Citations30
Published2020
Admission routes1
Has abstractyes

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