Quantifying the Impacts of Flash Flooding on Dominica’s Material Stocks in Buildings: A GIS-based methodological framework for Small Island States
Bibliographic record
Abstract
Economic growth is usually accompanied by extensive extraction of natural resources, especially in developing countries. From a “material-stock-flow-service” perspective, the substantial part (e.g., construction materials) of the extracted natural resources as inflows to a society get accumulated in the built environment as “material stocks” (MS). Depending on the end-use types of their containers, MS provide essential services to a society such as housing, education and transportation. When an environmental hazard strikes, MS lose their functionality due to the destruction of the physical structure of their carriers, resulting in extra construction waste that then must be cleared for recovery. To make a society more resilient to environmental hazards, which is especially important in small island states with limited natural and human resources, the knowledge of exposure of MS to hazard risk is critical. \nThis research focuses on the quantity and spatial distribution of MS in buildings in the context of intense rainfall-triggered flash flooding in Dominica, a small island state in the Caribbean region. A Geographical Information System (GIS)-based stock-driven methodology is used to quantify four typical types of construction materials: concrete, aggregates, timber, and steel. To quantify exposed MS in buildings to flash flooding, an event-based flood model is used to generate flood inundation extents at the national scale. To investigate the degrees to which the exposed households are susceptible to the impacts of environmental hazards, this research also designs a resident survey to collect social factors contributing to household vulnerability to hazards. For 2020, the total MS in the building sector is estimated at 6,574 kt, equivalent to 91 t per capita, given Dominica’s population of the year. In terms of the distributions of MS in different material categories, concrete accounts for 86% of the total MS in buildings, followed by aggregate at 7%, timber at 4% and steel at 3%. Examining the exposure of MS in buildings to flash flooding, it is found that flood events of larger magnitudes would result in more MS contained in the exposed buildings. For flash flood events with 5-year, 10-year, and 20-year return periods, the numbers of exposed buildings are 2,781, 3,030, and 3,274, respectively, which contain 17%, 18%, and 19% of the total MS in buildings in Dominica. This research demonstrates how to link the results of material stock accounting to flash flood modelling, approaching the concept of socio-economic metabolism from an environmental hazard risk perspective. Knowledge of the quantity and spatial distribution of the exposed MS in buildings can assist local governments in making cost-effective mitigation plans before a hazard event. Although the designed survey was not implemented due to travel restrictions, it is a valuable instrument to collect the information about household vulnerability to environmental hazards, which can help hazard response agencies with more-efficient rescue operations during a hazardous event.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".