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A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
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Optimising rainfall characteristics for determining landslide thresholds
Himasha Abeysiriwardana1, Thomas Kjeldsen1, Cormac Reale1
1Department of Architecture and Civil Engineering, Claverton Down Campus, University of Bath, Bath, BA2 7AY UK.
Natural Hazards (Dordrecht, Netherlands)
|February 23, 2026
Summary
This study introduces a new framework for setting rainfall thresholds to predict landslides in data-limited areas. Bayesian inference methods offer more stable landslide prediction thresholds than nonlinear least-squares, especially with longer antecedent rainfall periods.
Area of Science:
- Geosciences
- Hydrology
- Natural Hazards
Background:
- Landslide prediction commonly uses rainfall thresholds, but these are sensitive to event definition.
- Defining minimum inter-event time (MIT) and triggering event (TE) significantly impacts threshold accuracy.
Purpose of the Study:
- To develop a new framework for data-driven rainfall thresholds in data-limited, high-risk regions.
- To evaluate the impact of MIT and TE definitions on rainfall threshold estimation using Bayesian inference (BI) and nonlinear least-squares (NLS).
Main Methods:
- Employed Bayesian inference (BI) and nonlinear least-squares (NLS) techniques.
- Analyzed rainfall-duration and intensity-duration spaces using 15-min rainfall data (2005-2023) and landslide records from South Wales.
- Assessed variations in minimum inter-event time (MIT) and triggering event (TE) definitions.
Main Results:
- Bayesian inference (BI) derived thresholds proved more stable than nonlinear least-squares (NLS) derived thresholds.
- NLS methods often produced unrealistic, near-flat thresholds, especially in intensity-duration space.
- Both BI and NLS showed optimal performance with a 48-hour minimum inter-event time (MIT), highlighting the role of antecedent rainfall.
Conclusions:
- Robust Bayesian methods can improve landslide prediction in data-scarce regions by downscaling global thresholds.
- Careful event delineation practices are crucial for enhancing the accuracy of landslide prediction models.
- The study provides a framework for establishing reliable, data-driven rainfall thresholds for landslide early warning systems.
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