Multi-source landslide inventories for susceptibility assessments: study case in Concepcion Metropolitan Area, Chile

Francisco Castro-Venegas ^1,2* Edilia Jaque ^2,3 Jorge Quezada ⁴ José Luis Palma ⁴ Alfonso Fernandez ^3,5

• ¹ PhD. Program in Geological Sciences, Faculty of Chemical Sciences, University of Concepción, Concepción, Chile
• ² Multihazards Biobio Study Group, University of Concepción, Concepción, Chile
• ³ Department of Geography, Faculty of Architecture, Urban Planning and Geography, University of Concepcion, Concepción, VIII Biobío Region, Chile
• ⁴ Department of Earth Sciences, Faculty of Chemical Sciences, University of Concepcion, Concepción, VIII Biobío Region, Chile
• ⁵ Mountain Research Hub, University of Concepción, Concepción, Chile

The Concepción Metropolitan Area (CMA) in South-Central Chile presents a complex interplay of climatic conditions, tectonic activity, and varied topography that heightens landslide susceptibility. The CMA is characterized by steep escarpments and sloping valleys atop tectonic blocks. This complex setting creates landslide-prone areas as urban development extends into unstable hillslopes. Unfortunately, current landslide inventories are limited and inconsistent, hindering effective susceptibility zoning and urban planning efforts. The objective of this study was to improve quantitative landslide susceptibility assessments in the CMA by developing a comprehensive landslide inventory spanning from 1990 to 2023. The methods we implemented included compiling a multitemporal and multi-source comprehensive landslide inventory for the CMA, integrating historical and recent data. The inventory consolidates detailed records from the Chilean Geological Survey (SERNAGEOMIN), encompassing materials, conditioning factors, anthropogenic influences, and other relevant variables. To test the potential of our inventory for landslide susceptibility, we compared its performance relative to existing compilations using the Frequency Ratio method. Three slide susceptibility models were compared, two using previous databases, and one using the inventory developed in this study. A comparative analysis highlighted differences in predictive accuracy due to inventory completeness. Our findings show that the model using our landslide inventory exhibited the highest predictive accuracy and spatial specificity, emphasizing the benefits of a detailed, curated landslide inventory for more reliable localized assessments. Additionally, this study is novel for the region and shows that detailed inventories significantly improve accuracy of landslide susceptibility models, providing a more reliable foundation for risk-informed urban planning and land-use management in vulnerable regions.