Detection and forecasting of shallow landslides: lessons from a natural laboratory

Bainbridge, Rupert; Lim, Michael; Dunning, Stuart; Winter, M G; Díaz‐Moreno, Alejandro; Martin, James E.; Torun, Hamdi; Sparkes, Bradley; Khan, Muhammad Waqas; Jin, Nanlin

doi:10.1080/19475705.2022.2041108

Cited by 10 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Chiang et al [10], for large-scale landslide areas in Taiwan, identified a rainfall threshold under controlled conditions ranging from 780 mm/day (20-year recurrence interval) to 820 mm/day (25-year recurrence interval). Bainbridge et al [14] elaborated on an empirical antecedent precipitation (>62 mm) and intensity-duration (>10 h) threshold over which shallow landslides occur along a strategic road in Scotland. Despite the fact that empirical thresholds change according to local geomorphological and climatic conditions, they supply basic information that can be properly interfaced with different types of "local" data to provide a useful tool for the management of LTR.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Data Analyses for Characterizing Rainfall-Induced Landslide Scenarios: The Example of Catanzaro Municipality (South Italy)

Petrucci

Scarcella

Conforti

2023

Water

View full text Add to dashboard Cite

This paper presents a GIS-based approach to create a multilevel data system for detailed knowledge of landslide occurrences in small territorial units such as municipalities. The main aim is to collect all the available data (geological, geomorphological, and climatic data, as well as landslide inventory maps and catalogues) in a structured data management system and perform further analyses to identify the typical landslide scenarios of the study area that can be useful in landslide risk management. We demonstrated the use of the methodology analyzing landslide risk in the municipality of Catanzaro (southern Italy), having a surface of 111.7 km2, 20.5% of which was affected by landslides. The spatial and temporal distribution of landslides highlighted that in several cases, they are reactivations of pre-existing phenomena. In fact, in the municipality, approximately 17% of the buildings fall within landslides-affected areas, 7.9% of which are in areas where landslides are classified as active. Furthermore, active landslides involve 8.1% and 9.5% of the roads and railways, respectively. In the 1934–2020 study period, 53% of activations occurred between October and December and were triggered by daily rain which in the highest percentage of cases (49%) showed values between 50 and 100 mm. The proposed GIS platform can be easily updated in order to preserve the landslide history of the area and can be enriched with further thematic layers (i.e., layers concerning flood events, which often occur simultaneously with major landslide events). The case study demonstrates how the platform can support landslide risk management in terms of monitoring, planning remedial works, and the realization/updating of civil protection plans.

show abstract

Section: Introductionmentioning

confidence: 99%