A synthetic aperture radar–based model to assess historical changes in lowland floodplain hydroperiod
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".