Low impact development technologies for mitigating climate change: Summary and prospects
Bibliographic record
Abstract
\nMany cities are adopting low impact development (LID) technologies (a type of nature-based solution) to sustainably manage urban stormwater in future climates. LIDs, such as bioretention cells, green roofs, and permeable pavements, are developed and applied at small-scales in urban and peri-urban settings. There is an interest in the large-scale implementation of these technologies, and therefore assessing their performance in future climates, or conversely, their potential for mitigating the impacts of climate change, can be valuable evidence in support of stormwater management planning. This paper provides a literature review of the studies conducted that examine LID function in future climates. The review found that most studies focus on LID performance at over 5 km2 scales, which is quite a bit larger than traditional LID sizes. Most paper used statistical downscaling methods to simulate precipitation at the scale of the modelled LID. The computer model used to model LIDs was predominantly SWMM or some hybrid version of SWMM. The literature contains examples of both vegetated and un-vegetated LIDs being assessed and numerous studies show mitigation of peak flows and total volumes to high levels in even the most extreme climates (characterized by increasing rainfall intensity, higher temperatures, and greater number of dry days in the inter-event period). However, all the studies recognized the uncertainty in the projections with greatest uncertainty in the LID’s ability to mitigate storm water quality. Interestingly, many of the studies did not recognize the impact of applying a model intended for small-scale processes at a much larger scale for which it is not intended. To explore the ramifications of scale when modelling LIDs in future climates, this paper provides a simple case study of a large catchment on Vancouver Island in British Columbia, Canada, using the Shannon Diversity Index. PCSWMM is used in conjunction with providing regional climates for impacts studies (PRECIS) regional climate model data to determine the relationship between catchment hydrology (with and without LIDs) and the information loss due to PCSWMM’s representation of spatial heterogeneity. The model is applied to five nested catchments ranging from 3 to 51 km2 and with an RCP4.5 future climate to generate peak flows and total volumes in 2022, and for the period of 2020–2029. The case study demonstrates that the science behind the LID model within PC stormwater management model (PCSWMM) is too simple to capture appropriate levels of heterogeneity needed at larger-scale implementations. The model actually manufactures artificial levels of diversity due to its landuse representation, which is constant for every scale. The modelling exercise demonstrated that a simple linear expression for projected precipitation vs. catchment area would provide comparable estimates to PCSWMM. The study found that due to the spatial representation in PCSWMM for landuse, soil data and slope, slope (an important factor in determining peak flowrates) had the highest level of information loss followed by soil type and then landuse. As the research scale increased, the normalized information loss index (NILI) value for landuse exhibited the greatest information loss as the catchments scaled up. The NILI values before and after LID implementation in the model showed an inverse trend with the predicted LID mitigating performance.\n
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".