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Optimising rainfall characteristics for determining landslide thresholds.

Himasha Abeysiriwardana1, Thomas Kjeldsen1, Cormac Reale1

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Summary
This summary is machine-generated.

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.

Keywords:
Bayesian inferenceData scarcityEvent rainfall–durationIntensity–durationLandslidesMinimum inter–event timeRainfall thresholds

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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.