Space-time explainable modelling of regional hillslope deformation, an example from the Tibetan Plateau
Jun He, Hakan Tanyaş, Da Huang, Luigi Lombardo
Abstract
The future of InSAR applications will undoubtedly involve data-driven solutions to predict deformation across space and time. Recent advancements in subsidence research have already integrated such approaches, primarily in flat to near-flat landscapes. However, in mountainous terrains, space-time InSAR modelling has so far focused mainly on individual slopes or small catchments. Here, we propose a modelling protocol based on a deep learning architecture capable of predicting InSAR-derived hillslope deformation. This approach is developed primarily using morphometric and meteorological variables over extensive mountainous areas (∼15,000 km 2 ) and extended time windows (∼7 years). By aggregating the deformation signal at the Slope Unit scale while maintaining 12-day temporal intervals consistent with Sentinel-1 acquisitions, we achieve high modelling performance (PCC = 0.7). If validated in other regions, this method could represent a crucial step towards a large-scale, consistent, and highly effective scenario-based warning system for hillslope deformation.