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.

翻译：滑坡灾害最被广泛采用的定义整合了滑坡位置（易发性）、威胁（强度）和频率（重现期）的空间信息。在大区域研究中通常仅考虑并估算前两个要素，且多以独立模型分别处理，频率要素极少被探究。频率与强度相互交织、彼此依赖——规模较大的事件发生频率更低，反之亦然。然而，由于缺乏多期次滑坡编目和联合统计模型，通过统一灾害模型建模此类特性一直颇具挑战且尚未被尝试。为填补这一空白，本文在坡单元尺度上构建了统一滑坡灾害评估模型。我们结合深度学习与基于极值理论的模型，分析了尼泊尔30年观测的降雨型滑坡编目，评估了多个重现期下的滑坡灾害，并进一步探讨了截至本世纪末不同气候变化情景下相同重现期的灾害特征。结果表明，所提模型性能优异，可实现滑坡灾害的统一建模。地貌学分析显示，在两种气候变化情景（SSP245和SSP885）下，喜马拉雅低海拔地区的滑坡灾害平均可能增加至两倍，中海拔地区基本保持不变，而高海拔地区则略有下降。