Three-Dimensional Landslide Rainfall Threshold Model Driven by Multi-Source Rainfall Data: Development and Performance Evaluation

Rainfall-induced landslides are one of the most widespread and destructive types of geohazards worldwide, and improving the spatiotemporal accuracy and timeliness of early warning remains a persistent challenge in disaster risk management. This study develops a three-dimensional landslide rainfall threshold model framework based on hourly rainfall time series using 216 rainfall-triggered landslide events recorded in Anhua County, China, during 2022–2024. We further provide a systematic assessment of how rainfall observations with different spatiotemporal resolutions, including regional automatic weather stations (RWS), national meteorological stations (NMS), and GPM satellite precipitation, affect threshold-model performance. The proposed three-dimensional framework is then compared against conventional two-dimensional threshold models, including the intensity–duration (I–D), cumulative event rainfall–duration (E–D), and cumulative event rainfall–intensity (E–I). The results indicate that the spatiotemporal resolution of rainfall data is the key determinant of warning performance. The three-dimensional model driven by RWS performs best, achieving a false negative rate (FNR) of 7.46% and a minimum description length (MDL) close to zero (−0.03), and significantly outperforming the counterparts based on NMS and GPM. Moreover, the proposed three-dimensional models also remain stable under both short-duration, high-intensity rainfall and prolonged, cumulative rainfall conditions, with overall performance consistently superior to that of the two-dimensional models. These findings demonstrate that an hourly three-dimensional threshold model supported by high spatiotemporal rainfall observations can substantially improve the accuracy and timeliness of landslide early warning, providing an effective methodological basis for more precise regional warning of rainfall-induced landslides.