A Methodology for Assessing Dynamic Resilience of Coastal Cities to Climate Change Influenced Hydrometeorological Disasters
Bibliographic record
Abstract
Confronted with rapid urbanization, intensified tourism, population densification, increased migration, and climate change impacts, coastal cities are facing more challenges now than ever before. Traditional disaster management approaches are no longer sufficient to address the increased pressures facing urban areas. A paradigm shift from disaster risk reduction to disaster resilience building strategies is required to provide holistic, integrated, and sustainable disaster management looking forward. To address some of the shortcomings in current disaster resilience assessment research, a mathematical and computational framework was developed to help quantify, compare, and visualize dynamic disaster resilience. The proposed methodological framework for disaster resilience combines physical, economic, engineering, health, and social spatio-temporal impacts and capacities of urban systems in order to provide a more holistic representation of disaster resilience.\nTo capture the dynamic spatio-temporal characteristics of resilience and gauge the effectiveness of potential climate change adaptation options, a disaster resilience simulator tool (DRST) was developed to employ the mathematical framework. The DRST is applied to a case study in Metro Vancouver, British Columbia, Canada. The simulation model focuses on the impacts of climate change-influenced riverine flooding and sea level rise for three future climates based on the results of the CGCM3 global climate model and two (2) future emissions scenarios. The output of the analyses includes a dynamic set of resilience maps and graphs to demonstrate changes in disaster resilience in both space and time. The DRST demonstrates the value of a quantitative resilience assessment approach to disaster management. Simulation results suggest that various adaptation options such as access to emergency funding, provision of mobile hospital services, and managed retreat can all help to increase disaster resilience. Results also suggest that, at a regional scale, Metro Vancouver is relatively resilient to climate change influenced-hydrometeorological hazards, however it is not distributed proportionately across the region. Although a pioneering effort by nature, the methodological and computational framework behind the DRST could ultimately provide decision support to disaster management professionals, policy makers, and urban planners.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".