Speakers
Description
Climate change, driven by human activity, has intensified extreme weather events, causing significant losses, especially in vulnerable communities (IPCC, 2023). In Brazil, data from 2023 indicate that around 73% of the population lives in municipalities with high risk of flooding, flash floods, or landslides (Anjos, 2024). In this context, the integration between environmental risk management and urban management has gained more prominence nationally and internationally (Cabral; Cândido, 2019). However, its translation into effective public policies still faces limitations regarding the understanding of vulnerability or resilience states in cities.
Research has advanced to identify "risk," and numerous quantitative and qualitative methods have been developed to model these processes. Background conditions leading to a "disaster" have been documented by frameworks like the Risk-Hazard and Pressure and Release models (Turner et al., 2003; Blaikie et al., 1994). Advances have also been made in mapping, measuring, and classifying areas of social vulnerability to risks (Cutter et al., 2003).
Insights into resilience in socio-ecological systems can contribute to a convergence of historically distinct research lines (Adgar, 2006). Resilience, from complex systems' perspective, is defined as the ability to absorb disturbances before the system shifts to a different state and the capacity for self-(re)organization and adaptation (Mochizuki et al., 2018). Vulnerability refers to the degree a system is susceptible and unable to cope with adverse effects (Mochizuki et al., 2018). Traditional vulnerability analysis relies on static mapping and quantifying vulnerable areas, often ignoring the dynamic aspect of risk and the role of social actors in changing vulnerability states (Adger, 2006, Aerts et al., 2018).
Agent-based models (ABMs) provide insights into complex systems by modeling risk dynamics through environmental and social factors (Filatova et al., 2013). This research develops the Landslide Risk Simulation Model (LRSIM), aimed at contributing to public policies on urban management, exploring institutional actions through "what-if" scenarios. The research's main goal is to understand landslide risk mechanisms in the emergence of vulnerability or resilience states, providing insights for urban and risk management.
The model simulates landslides based on predisposing factors (environmental conditions such as slope, geology, geomorphology) and effective factors (land use, infrastructure, population density, precipitation). The empirical application area is Petrópolis, Rio de Janeiro, which has a history of large-scale landslides: 1967, 1979, 1988, 2012, 2014 and 2022. The February 2022 event resulted in 238 deaths.
The prototype was implemented in NetLogo and calibrated using two rainfall scenarios. Data comes from rain gauge records for the 2024 dates (no landslide occurrence) and 2022 rains (landslides ocorrence). As expected, the model generated more landslides for the 2022 scenario. In the sensitivity analysis, the model shows sensitivity to the population density parameter, tending to locate landslides in areas with high population density and occupation typologies such as “favelas” (slums). The LRSIM is still in sensitivity analysis and validation process. The model will be validated by comparing the model outputs and landslide scars mapped in 2022 in Petrópolis. Once validated, scenarios will be created to test different urban management policies to minimize risks.
References
Anjos, A. B. (2024). No Brasil, 3 a cada 4 vivem em municípios com mais risco de desastres causados por chuvas. Agência Pública.
Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281.
Aerts, J. C. et al. (2018). Integrating human behaviour dynamics into flood disaster risk assessment. Nature Climate Change, 8(3), 193-199.
Blaikie, P. et al. (1994). At Risk: Natural Hazards, People’s Vulnerability and Disasters. Routledge.
Cabral, L. N., & Cândido, G. A. (2019). Urbanização, vulnerabilidade, resiliência: relações conceituais e compreensões de causa e efeito. Revista Brasileira de Gestão Urbana, 11, 1-12.
Cutter, S. L. et al. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84, 242–261.
Filatova, T. et al. (2013). Spatial agent-based models for socio-ecological systems: Challenges and prospects. Environmental Modelling & Software, 45, 1-7.
Intergovernmental Panel on Climate Change, IPCC. (2023). Summary for policymakers. In: Climate Change 2023: Synthesis Report.
Mochizuki, J. et al. (2018). An overdue alignment of risk and resilience? A conceptual contribution to community resilience. Disasters, 42(2), 361-391.
Turner, B. L., et al. (2003). A framework for vulnerability analysis in sustainability science. Proceedings of the National Academy of Sciences, 100(14), 8074-8079.
| Keywords | risk; landslide; agent-based models; urban management; socio-ecological systems. |
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| Best Congress Paper Award | No |
