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Moisture-driven landslides (MDL) are typically associated with elevated soil moisture content and sub-surface pore-water pressure due to temporal clustering of moderate to extreme precipitation over steep terrain leading to mass wasting phenomena such as rock, soil, and debris flows downward along the slope. With their quick response times and short recovery periods, these cascading hazard events are widespread in tectonically active regions, such as the Himalayas, damaging the natural and built environment systems. Due to climate and land use changes, the number of MDLs, including the mountainous Himalayas, is increasing globally. This study first uses Ripley's L-function to compare the spatial clustering of MDLs between two non-overlapping time windows 2007-2015 versus 2016-2022, assuming spatial point process information follows Poisson distribution across the Uttarakhand state (latitude: 28 degrees 42' N - 31 degrees 28' N; longitude: 77 degrees 35'E - 81 degrees 05' E), one of the most landslide-prone areas in the western Himalayas. Then, we investigate the potential physical controls of landslides by considering ranges of conditioning drivers, such as extreme rainfall indices, catchment and soil attributes. While we find evidence of marked spatial clustering of MDLs up to 80 km radial distance, which is more pronounced during the first half (2007-2015) of the time window compared to the latter half (2016-2022), we show that topographic factors contribute significantly to such events with a median contribution of 55% (range 33-60%), followed by the soil properties, and meteorological indices with median contributions lies in the tune of 20-22%. Among topographic factors, slope, form factor, stream power index, and drainage density significantly trigger MDLs. Whereas, soil factors such as cation exchange capacity and soil organic carbon content were identified as the significant factors to mediate landslides. Among meteorological drivers, the number of days with rainfall over 20 mm shows the highest confidence in triggering landslides, followed by the accumulated rainfall of more than 99th percentiles emerging as key conditioning drivers for MDLs. Understanding the spatial dynamics of landslides and their potential drivers enables stakeholders to develop early warning systems, adaptation, and planning, enhancing climate resilience in landslide-prone areas.

来源平台：NATURAL HAZARDS