Armed conflict as a catalyst for increasing flood risk
Bibliographic record
Abstract
Abstract Armed conflict has many adverse impacts beyond violence such as increasing risks of natural hazards. Analyses of the interactions between flood risks and armed conflict are essential for developing effective policies and strategies to address both challenges. This study aims to develop conceptual and analytical socio-hydrological frameworks for assessing how armed conflict can impact flood risks. The frameworks postulate a link between armed conflict and flood vulnerability, given that armed conflict creates unique challenges that exacerbate the effects of floods. Our conceptual framework identifies routes through which armed conflict affects vulnerability to floods, such as damage to infrastructure, population displacement and density, weak governance, and less awareness, resulting in lower resilience, higher susceptibility, and increased flood vulnerability and risk. Our analytical framework uses flood modeling to evaluate flood hazards and incorporates spatial data related to armed conflict zones, nighttime light, population classification by age, land price, land cover, and rural/urban areas classification. We take Khartoum, the capital city of Sudan, as a case study in view of its armed conflict that erupted in 2023. By highlighting the linkages between armed conflict and flood risk, this study contributes to conceptualizing the broader interlinkages between conflict and environmental systems. The study emphasizes the importance of integrating conflict analysis with disaster risk management strategies. We encourage collaboration between humanitarian, environmental, and security sectors to improve preparedness, response, and resilience in conflict-affected regions. While our analysis for Khartoum is based on conflict zones in the early stages of the conflict and uses simple estimates for conflict vulnerability contribution, the proposed frameworks provide groundwork for assessing changes in flood risk in Sudan and other conflict regions around the world.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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; both teacher heads agree on what is shown here.
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".