A hydrologic assessment of using low impact development to mitigate the impacts of climate change in Victoria, BC, Canada
Bibliographic record
Abstract
The purpose of this study is to determine if Low Impact Development (LID) can effectively mitigate flooding under projected climate scenarios. LID relies on runoff management measures that seek to control rainwater volume at the source by reducing imperviousness and retaining, infiltrating and reusing rainwater. An event-driven hydrologic/hydraulic model was developed to simulate how climate change, land use and LID scenarios may affect runoff response in the Bowker Creek watershed, a 10km2 urbanized catchment located in the area of greater Victoria, British Columbia, Canada. The first part of the study examined flood impacts for the 2050s (2040-2069) following the A2 emissions scenario. For the 24-hour, 25-year local design storm, results show that projected changes in rainfall intensity may increase flood extents by 21% to 50%. When combined with continued urbanization flood extents may increase by 50% to 72%. The second part of the study identified potential locations for three LID treatments (green roofs, rain gardens and top soil amendments) and simulated their effect on peak in-stream flow rates and flood volumes. Results indicate that full implementation of modeled LID treatments can alleviate the additional flooding that is associated with the median climate change projection for the 5-year, 10-year and 25-year rainfall events. For the projected 100-year event, the volume of overland flood flows is expected to increase by 1%. This compares favourably to the estimated 29% increase without LID. In term of individual performance, rain gardens had the greatest hydrologic effect during more frequent rainfall events; green roofs had minimal effect on runoff for all modelled events; and top soil amendments had the greatest effect during the heaviest rainfall events. The cumulative performance of LID practices depends on several variables including design specifications, level of implementation, location and site conditions. Antecedent soil moisture has a considerable influence on LID performance. The dynamic nature of soil moisture means that at times LID could meet the mitigation target and at other times it may only partially satisfy it. Future research should run continuous simulations using an appropriately long rainfall record to establish the probabilities of meeting performance requirements. In general, simulations suggest that if future heavy rainfall events follow the median climate change projection, then LID can be used to maintain or reduce flood hazard for rainfall events up to the 25-year return period. This study demonstrates that in a smaller urban watershed, LID can play an important role in reducing the flood impacts associated with climate change.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".