首页> 外文会议>World Landslide Forum 3, Volume 4: Discussion Session >Coupling a Stochastic Rainfall Generator and a Physically-Based Landslide Triggering Model to Validate the I-D Power-Law Empirical Model
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Coupling a Stochastic Rainfall Generator and a Physically-Based Landslide Triggering Model to Validate the I-D Power-Law Empirical Model

机译:耦合随机降雨产生器和基于物理的滑坡触发模型来验证I-D幂律经验模型

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Knowledge of the rainfall conditions that trigger landslides serves as a fundamental basis for landslide early warning.Empirical models, derived from the analysis of rainfall and landslide historical data, are often used for this purpose.The most extensively-used empirical relationship is the Intensity-Duration power-law model, as proved by the fact that lots of regional empirical thresholds are of this type.In spite of this, the use of such a relationship has not been at this time fully supported by physically-based considerations, and deterministic rainfall thresholds, derived from physically-based hydrological-geomechanical models reveal a relationship between critical intensity and duration that in general deviates from a power-law.Nonetheless, deterministic thresholds are generally derived under the assumption of constant-intensity rainfall (uniform hyetographs), and do not account for the real stochastic nature of rain intensity within events, which constitutes a source of uncertainty, because to a given rain duration and mean intensity may correspond different variable-intensity hyetographs.Another source of uncertainty, of more relevance, is that to a given hyetograph may correspond different initial conditions, due to the aleatory nature of antecedent rainfall.In this study, we adopt a Monte Carlo simulation frame work, that combines a stochastic rainfall generator and a physically-based hydrological and geotechnical model, to study the effect of the two above mentioned aleatory factors on threshold determination, with the aim of validating from a physically-based perspective the I-D power-law model.In particular, we derive optimal power-law thresholds through the optimation of a ROC-index, and contextually we quantify the above-mentioned uncertainty, through Receiver-Operating-Characteristics (ROC) concepts.Application of the approach to the Peloritani Mountains, an area hit by destructive shallow rapidly-moving landslide phenomena, shows that the I-D power-law relationship may have a physically-based justification in cases where soil water pore pressure memory is low, i.e.it is adequate to model the transient part of hillslope response.In other most general cases, where the hillslope has a significant upslope contributing area, the effect of antecedent precipitation should be accounted for, by modifying the I-D model.
机译:了解引发滑坡的降雨条件是滑坡预警的基本基础。为此目的,通常使用从降雨和滑坡历史数据分析得出的经验模型,其中最广泛使用的经验关系是强度-持续时间幂律模型,已被许多区域经验阈值属于这种类型的事实所证明,尽管如此,基于物理的考虑因素和确定性降雨目前尚不完全支持使用这种关系从基于物理的水文地质力学模型得出的阈值揭示了临界强度与持续时间之间的关系,通常与幂律有所不同,尽管如此,确定性阈值通常是在恒定强度降雨的假设下推导出来的(均一的断面图),以及没有考虑到事件中降雨强度的真实随机性,这构成了不可靠的来源由于给定的降雨时间和平均强度可能对应于不同的变强度海图仪,因此不确定性的另一个原因是,由于前期降雨的偶然性质,给定的海图可能对应于不同的初始条件。这项研究采用蒙特卡洛模拟框架,结合了随机降雨发生器和基于物理的水文和岩土模型,研究了上述两个偶然因素对阈值确定的影响,目的是为了验证从物理角度看待ID幂律模型。特别是,我们通过优化ROC指数得出最佳幂律阈值,并通过接收器-操作特性(ROC)概念在上下文中量化上述不确定性。该方法在佩洛里塔尼山脉上的应用,该地区被破坏性的浅层快速移动滑坡现象所击中,显示出内径在土壤水孔隙压力记忆较低的情况下,r律关系可能具有基于物理的理由,即足以模拟山坡响应的瞬态部分。在其他大多数情况下,山坡具有明显的上坡贡献面积,应通过修改ID模型来考虑前期降水的影响。

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