Optimising Rainfall Characteristics for Determining Landslide Thresholds

This work contributes a new framework for establishing data-driven rainfall thresholds in high-risk, data-limited contexts. Rainfall thresholds are commonly used to characterise the precipitation needed to trigger landslides in a region. However, these empirical relationships are sensitive to the exact definition of a “rainfall event”, especially how the minimum inter-rainfall event time (MIT) and triggering-event (TE) are defined. Using Bayesian inference (BI) and nonlinear least-square (NLS) techniques, this study evaluates how variations in MIT and TE definitions affect rainfall threshold estimation considering both Event Rainfall–Duration (E–D) and Intensity–Duration (I–D) spaces. The dataset includes 15-minute rainfall measurements from 52 gauges recorded from 2005-2023, as well as a regional landslide dataset compiled from British Geological Survey records covering the South Wales coal fields.

Findings reveal that BI-derived thresholds are more stable than NLS-based thresholds, showing smaller parameter changes and fewer unrealistic curves, particularly in I–D space where NLS often produces near-flat thresholds. Overall, both BI and NLS approaches demonstrate their strongest performance at MIT = 48 h, emphasising the role of extended antecedent rainfall in triggering spoil tip failures. This study demonstrates how the integration of robust Bayesian methods facilitate downscaling of global thresholds to data scarce regions and how careful event-delineation practices can improve landslide prediction.