NH9.8 | Urbanization and natural hazards: their interaction, modeling, monitoring, and prediction, with a focus on slope stability with physical models
EDI
Urbanization and natural hazards: their interaction, modeling, monitoring, and prediction, with a focus on slope stability with physical models
Convener: Massimiliano Alvioli | Co-conveners: Elisa BozzolanECSECS, Ugur OzturkECSECS, Minu Treesa AbrahamECSECS, Marcio Augusto Ernesto de Moraes, Caroline Michellier, Faith TaylorECSECS

Urban development and infrastructure have significantly transformed the environment altering topographies, drainage systems, river morphologies, coastal dynamics, and climate patterns. These legacies influence preparatory conditions and triggers of natural hazards [1]. Urban expansion amplifies population exposure to NHs, particularly in low-to-middle-income regions, where urban planning and engineering design are often inadequate [2]. This complexity results in the omission of dynamic urban factors in hazard models, hindering the development and evaluation of effective hazard mitigation strategies.

Landslides are a relevant NH affecting urban areas and transport infrastructure. Construction of housing, roads, and drainage systems modify hydrology, strongly affecting slope stability [3]. Rapid urbanization, particularly in developing countries, often results in unregulated buildings and poor or non-existing water drainage, which cause widespread slope instability under intense rainfall. Landslide susceptibility maps based on statistical models often prove insufficient at urban scales. Physically based models, however, incorporate localized anthropogenic changes, are specialized to landslide type, include reach distance and runoff, and take into account time-dependent triggering conditions [4, 5]. All of this may lead to effective early warning systems in urban areas and along transport routes [6].

This session seeks to investigate the impacts of urbanization on natural hazards and multi-hazard scenarios. Key themes are:
- strategies that identify detrimental urban practices
- social/political barriers found to disaster-resilient urban management
- modeling landslides in urban areas and transport infrastructure
- ground movement detection with remote sensing and ground-based methods,
- integration of data analytics for real-time monitoring and early warning systems,
- interdisciplinary approaches, novel methodologies, and practical implementations in rapidly growing urban areas.

References
[1] Dille et al., Nature Geosci. (2022). DOI: 10.1038/s41561-022-01073-3
[2] Ozturk et al., Nature (2022). DOI: 10.1038/d41586-022-02141-9
[3] Bozzolan et al., Sci. Tot. Env. (2023). DOI: 10.1016/j.scitotenv.2022.159412
[4] Alvioli et al., Eng. Geol. (2021). DOI: 10.1016/j.enggeo.2021.106301
[5] Marchesini et al., Eng. Geol. (2024). DOI: 10.1016/j.enggeo.2024.107474
[6] Mendes et al., Geotech. Geol. Eng. (2017). DOI: 10.1007/s10706-017-0303-z