Assessing the effects of climate change on urban watersheds: a review and call for future research
Bibliographic record
Abstract
Considerable efforts have been made to control and manage the hydrology and water quality of watersheds impacted by urban development through the construction of stormwater control measures (SCMs). Climate change (CC) could, however, undermine these efforts by intensifying precipitation and hydrologic extremes. Although the impact of CC on water resources has been well-documented, its impact on urban hydrology remains less studied. CC may complicate sustainable urban hydrology, which can cause a reduction in the efficiency of SCMs with changes in precipitation patterns (i.e., changes in duration, frequency, depth, and intensity). More intense precipitation may result in reduced runoff reduction and treatment efficiency, given that SCMs have a finite surface storage volume and surface infiltration capacity. Determining the functionality of various SCMs under future climate projections is important to better understand the impact of CC on urban stormwater and how well these practices can build resiliency into our urban environment. The purpose of this review is to provide the needs and opportunities for future research on quantifying the effect of CC on urban SCMs and to characterize the performance and effectiveness of these systems under existing and projected climate scenarios. A summary of the modeled constituents as well as the stormwater and climate models applied in these studies is provided. We concluded that there are still limitations in exploring the impact of future changes in meteorological variables that will influence the operation of SCMs in the long-term. Previous studies mostly focused on the impacts of CC on urban runoff quantity, and only a handful of studies have explored water quality impacts from CC, such as potential changes in water temperature, metals, and pathogens. Assessing the pollutant-removal efficiency of SCMs, such as bioretention, infiltration trenches, dry and wet swales, rooftop disconnections, and wet and dry ponds, which are common practices in urban watersheds, also needs more attention. Analysis of the cost of adapting SCMs for CC to maintain the same performance as current climate conditions is also recommended for future research.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".