Opportunities for Leveraging Existing Hydrologic and Hydraulic Models Developed for Water Quantity Management to Mitigate Flooding Due to Extreme Precipitation
Bibliographic record
Abstract
With a focus on a specific flood-prone community in Camden, NJ, this research utilizes a detailed hydraulic and hydrologic (H&H) model to assesses the impacts of climate change on Combined Sewer Overflows (CSOs) and localized flooding under two different infrastructure scenarios. In the US, the Clean Water Act compels regulated utilities to develop Long-Term Control Plans to reduce combined sewer overflows (CSOs), but there is no parallel mandate to simultaneously reduce flooding within the associated service areas. With different control measures in place, H&H models are frequently used to evaluate CSO volumes and frequencies under historical climate conditions. However, precipitation intensification and sea level rise (SLR) will also modify CSO volumes. This study uses a calibrated and validated 1D and 2D Personal Computer Stormwater Management Model (PCSWMM) simulation to predict both CSO discharges and flooding under different climate and infrastructure scenarios. A total of ten climate change scenarios comprising a range of plausible climate futures are considered. The infrastructure scenario that is tested would divert stormwater generated in an upstream municipality (Pennsauken, NJ) away from Camden’s combined sewer system. Without the disconnection, increases in precipitation will increase CSOs, whereas SLR primarily increases flooding. The proposed mitigation strategy can immediately reduce both CSOs and flooding, but with diminishing effectiveness over time, as climate change demonstrates the need for supplemental measures. Areas for further analysis regarding alternative mitigation methods and future research are outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".