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Record W1666764420 · doi:10.1029/2001wr001046

A synthetic aperture radar–based model to assess historical changes in lowland floodplain hydroperiod

2002· article· en· W1666764420 on OpenAlexfundno aff
Philip A. Townsend, Jane R. Foster

Bibliographic record

VenueWater Resources Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersCanadian Space AgencyU.S. Geological SurveyNature Conservancy
KeywordsFloodplainHydrology (agriculture)Riparian zoneEnvironmental scienceFlood mythWetlandFlooding (psychology)Physical geographyGeologyGeographyHabitatEcology

Abstract

fetched live from OpenAlex

The hydrology of riparian wetlands worldwide has been altered extensively owing to the construction and operation of dams. We developed a model for the Roanoke River floodplain (United States) to simulate flood extent and duration based on a power law correlation between inundation area A , as mapped from synthetic aperture radar (SAR) imagery, and river discharge Q . Model fit was 0.955 for the upper portion of the study area and 0.789 for the tidally influenced lower section. We then compared hydroperiod simulated for predam (1912–1949) and postdam (1965–1995) periods. Topographically wet areas are now flooded longer than before damming, and dry areas are now drier. Similarly, hydrologically wet years experience longer floods, whereas the driest years are drier. Most importantly, spring hydroperiod regimes are now wetter than prior to damming. Our results suggest that the intermediate zone of the hydrologic gradient has been squeezed to either wetter or drier conditions. The model presented represents a simple but effective empirical method to simulate hydroperiod regimes at the landscape scale in large lowland systems where the data necessary to develop more complex physical models are not available.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.087
GPT teacher head0.302
Teacher spread0.215 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations34
Published2002
Admission routes1
Has abstractyes

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