Scholarlink

<p>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-event time (MIT) and triggering event (TE) are defined. Using Bayesian inference (BI) and nonlinear least-squares (NLS) techniques, this study evaluates how variations in MIT and TE definitions affect rainfall threshold estimation, considering both Event Rainfall–Duration <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\left( {E{-}D} \right)\)</EquationSource> </InlineEquation> and Intensity–Duration <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\left( {I{-}D} \right)\)</EquationSource> </InlineEquation> spaces. The dataset includes 15-min rainfall measurements from 52 gauges recorded from 2005 to 2023, as well as a regional landslide dataset compiled from British Geological Survey records covering the South Wales coalfields. Findings reveal that <i>BI</i>-derived thresholds are more stable than <i>NLS</i>-based thresholds, showing smaller parameter changes and fewer unrealistic curves, particularly in I–D space, where <i>NLS</i> often produces near-flat thresholds. Overall, both <i>BI</i> and <i>NLS</i> approaches demonstrate their strongest performance at MIT = 48&#xa0;h, emphasising the role of extended antecedent rainfall in triggering spoil tip failures. This study demonstrates how the integration of robust Bayesian methods facilitates the downscaling of global thresholds to data-scarce regions and how careful event delineation practices can improve landslide prediction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimising rainfall characteristics for determining landslide thresholds

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

查看全文
  • Himasha Abeysiriwardana,
  • Thomas Kjeldsen,
  • Cormac Reale

摘要

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-event time (MIT) and triggering event (TE) are defined. Using Bayesian inference (BI) and nonlinear least-squares (NLS) techniques, this study evaluates how variations in MIT and TE definitions affect rainfall threshold estimation, considering both Event Rainfall–Duration \(\left( {E{-}D} \right)\) and Intensity–Duration \(\left( {I{-}D} \right)\) spaces. The dataset includes 15-min rainfall measurements from 52 gauges recorded from 2005 to 2023, as well as a regional landslide dataset compiled from British Geological Survey records covering the South Wales coalfields. 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 facilitates the downscaling of global thresholds to data-scarce regions and how careful event delineation practices can improve landslide prediction.