Abstract. Landslide inventories are used for multiple purposes including landscape characterisation and monitoring, and landslide susceptibility, hazard and risk evaluation. Their quality and completeness can depend on the data and the methods with which they were produced. In this work we evaluate the effects of a variable visibility of the territory to map on the spatial distribution of the information collected in different landslide inventories prepared using different approaches in a study area. The method first classifies the territory in areas with different visibility levels from the paths (roads) used to map landslides and then estimates the landslide density reported in the inventories into the different visibility classes. Our results show that (1) the density of the information is strongly related to the visibility in inventories obtained through fieldwork, technical reports and/or newspapers, where landslides are under-sampled in low-visibility areas; and (2) the inventories obtained by photo interpretation of images suffer from a marked under-representation of small landslides close to roads or infrastructures. We maintain that the proposed procedure can be useful to evaluate the quality and completeness of landslide inventories and then properly orient their use.
<p>India is heavily affected by rainfall-induced landslides that cause fatalities and damage. Therefore, the development of effective and reliable models for the landslide forecasting and their possible integration in early warning systems (LEWSs) is necessary to mitigate the risk posed by such phenomena. Within the LANDSLIP (LANDSLIde Multi-Hazard Risk Assessment, Preparedness and Early Warning in South Asia: Integrating Meteorology, Landscape and Society; www.landslip.org) project, we developed threshold-based forecasting models to predict the occurrence of rainfall-induced landslides. The models were calibrated&#160; in two Indian pilot areas: the Darjeeling and Nilgiris districts, in the states of West Bengal and Tamil Nadu, respectively. For the purpose, we built&#160; two catalogs of 84 and 116 rainfall conditions likely responsible for landslide triggering in Darjeeling and Nilgiris, respectively, and daily rainfall measurements, which were used to define frequentist rainfall thresholds at different non-exceedance probabilities by means of an automatic tool (CTRL-T). A revision of the methodology to identify the rainfall conditions that triggered the failures was necessary due to possible inaccuracies in the landslide occurrence date and the daily temporal resolution of rainfall measurements in India. Triggering rainfall conditions were also related to the different monsoon regimes in the study areas. For a few uncertain events, the rainfall conditions automatically reconstructed by CTRL-T were revised after a consensus among several investigators. In agreement with the rainfall regimes of the two pilot areas, the thresholds for Darjeeling are higher than those for Nilgiris; regardless of the rainfall duration, a larger amount of rainfall is necessary to trigger landslides in the Darjeeling area.&#160;</p><p>Despite some limitations, mostly due to the daily temporal resolution of rainfall data and the spatial and temporal distribution of the reported landslides, the uncertainties of the calculated thresholds were acceptable (also thanks to the double checking) to allow their implementation in the LANDSLIP prototype LEWS.&#160;</p><p>The thresholds require ongoing evaluation and refinement. For the purpose, additional landslide and rainfall data were used to validate thresholds and improve forecasts.</p>
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