The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are intertwined and depend on each other because larger events occur less frequently and vice versa. However, due to the lack of multi-temporal inventories and joint statistical models, modelling such properties via a unified hazard model has always been challenging and has yet to be attempted. Here, we develop a unified model to estimate landslide hazard at the slope unit level to address such gaps. We employed deep learning, combined with a model motivated by extreme-value theory to analyse an inventory of 30 years of observed rainfall-triggered landslides in Nepal and assess landslide hazard for multiple return periods. We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century. Our results show that the proposed model performs excellently and can be used to model landslide hazard in a unified manner. Geomorphologically, we find that under both climate change scenarios (SSP245 and SSP885), landslide hazard is likely to increase up to two times on average in the lower Himalayan regions while remaining the same in the middle Himalayan region whilst decreasing slightly in the upper Himalayan region areas.

Because terrain, characteristics of the rocks and soil, weather and climate all contribute to landslide activity, accurately identifying the areas most at risk at any given time can be an uphill battle. Early warning systems are generally regional – based on region-specific data provided by ground-based sensors, field observations, and rainfall totals. But what if we could identify at-risk areas anywhere in the world at any time using the combined power of space-based observations and models? The NASA global Landslide Hazard Assessment for Situational Awareness (LHASA) model, developed by a team of scientists led by Universities Space Research Association's Thomas Stanley, addresses this issue. The findings were published in Frontiers in Earth Science.