Analyzing Low Impact Development Strategies Using Continuous Fully Distributed Coupled Groundwater and Surface Water Models
Bibliographic record
Abstract
Low impact development (LID) strategies have received attention in recent years as offering a potential way to mitigate the adverse effects of urbanization on hydrologic flow and water quality.The obvious benefits of reduced peak flows and the sizing of storm water ponds can be simulated in models such as USEPA's SWMM; however, the analysis of LID system interaction with the groundwater system is challenging.Addressing questions related to local infiltration capacity, feedback from the groundwater system (i.e.rejected recharge and saturation-excess runoff), and ecological benefits, including the preservation of baseflow and wetland hydroperiod, requires a spatially distributed and integrated analysis of the hydrologic system.The purpose of this chapter is to illustrate the challenges and insights that the authors have encountered while simulating and comparing the effectiveness and ecological benefits of different LID scenarios using an integrated groundwater and surface water model.Additional discussion addresses how this approach can be used to complement storm water modeling and provide for a complete analysis of LID functionality.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".