Quantifying the mitigation effect of natural landscapes during an extraordinary flood: the prominent role of the Otter Creek wetlands to middlebury, Vermont, USA, during Tropical Storm Irene
Bibliographic record
Abstract
In 2011, Tropical Storm Irene caused significant damage in the northeastern states of the USA. However, the Otter Creek watershed in Vermont stood out for experiencing reduced flooding damages in its downstream region, attributed to the presence of a large riparian wetland and floodplain complex. This paper addresses three main questions: (i) Can the PHYSITEL/HYDROTEL hydrological modeling platform reproduce the Tropical Storm Irene hydrographs observed in the Otter Creek watershed? (ii) How well do the best calibrated models perform on annual (2011) and multiannual (1992–2013) bases? (iii) What would have been the impact of different wetland loss scenarios on the flood at Middlebury? Using OSTRICH and ParaPADDS optimization algorithms with various calibration strategies, our results demonstrate that by redefining the calibration method to include wetland attributes, including connectivity parameters, the platform can accurately reproduce the observed hydrographs during Tropical Storm Irene, as well as those in 2011 and over the 1992–2013 period. The strategy called HCR-HWCM performed best, while revealing certain limitations such as manual adjustments required to simulate wetland connectivity and constraints in representing water flow dynamics. Overall, our results explicitly reinstate the hydrological services provided by wetlands and advocate for their specific, yet informed, integration into hydrological modeling platform.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".