  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.
