Hydro-Meteorological Landslide Inventory for Sustainable Urban Management in a Coastal Region of Brazil.
Saved in:
| Title: | Hydro-Meteorological Landslide Inventory for Sustainable Urban Management in a Coastal Region of Brazil. |
|---|---|
| Authors: | Hader, Paulo Rodolpho Pereira, Horta, Isabela Taici Lopes Gonçalves, da Silva do Valle, Victor Arroyo, Irigaray, Clemente |
| Source: | Sustainability (2071-1050); Aug2025, Vol. 17 Issue 16, p7487, 29p |
| Abstract: | Comprehensive, standardised, multi-temporal inventories of rainfall-induced landslides linked to soil moisture remain scarce, especially in tropical regions. Addressing this gap, we present a multi-source urban inventory for Brazil's Baixada Santista region (1988–2024). A key advance is the introduction of geographical and temporal confidence classifications, which indicates precisely how each landslide's location and occurrence date are known, thereby addressing a previously overlooked criterion in Brazil's landslide data treatment. The inventory comprises 2534 records categorised by spatial (G1–G3) and temporal (T1–T3) confidence. Notable findings include the following: (i) confidence classifications enhance inventory reliability for research and early warning, though precise temporal data remains challenging; (ii) multi-source integration with UAV validation is key to robust inventories in urban tropical regions; (iii) soil moisture complements rainfall-based warnings, but requires local calibration for satellite-derived estimates; (iv) data gaps and biases underscore the need for standardised landslide documentation; and (v) the framework is transferable, providing a scalable model for Brazil and worldwide. Despite limitations, the inventory provides a foundation for (i) susceptibility and hazard modelling; (ii) empirical thresholds for early warning; and (iii) climate-related trend analyses. Overall, the framework offers a sustainable, practical, transferable method for worldwide and contributes to strengthening disaster information systems and early warning capacities. [ABSTRACT FROM AUTHOR] |
| Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!