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A spatio-temporal modelling framework for assessing the fluctuations of avalanche occurrence resulting from climate change: application to 60 years of data in the northern French Alps

机译:时空建模框架,用于评估气候变化导致的雪崩发生的波动:在法国北部阿尔卑斯山的60年数据中的应用

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摘要

Based on a previous township-scale model, a spatio-temporal framework is proposed to study the fluctuations of avalanche occurrence possibly resulting from climate change. The regional annual component is isolated from the total variability using a two-factor nonlinear analysis of variance. Moreover, relying on a Conditional AutoRegressive sub-model for the spatial effects, the structured time trend is distinguished from the random noise with different time series sub-models including autocorrelative, periodic and change-point models. The hierarchical structure obtained takes into account the uncertainty related to the estimation of the annual component for the quantification of the time trend. Bayesian inference is performed using Monte Carlo simulations. This allows a comparison of the different time series models and the prediction of future activity in an explicit unsteady context. Application to the northern French Alps illustrates the information provided by the model’s different components, mainly the spatial and temporal terms as well as the spatio-temporal fluctuation of the relative risk. For instance, it shows no strong modifications in mean avalanche activity or in the number of winters of low or high activity over the last 60 years. This suggests that climate change has recently had little impact on the avalanching rhythm in this region. However, significant temporal patterns are highlighted: a complex combination of abrupt changes and pseudo-periodic cycles of approximately 15 years. For anticipating the future response of snow avalanches to climate change, correlating them with fluctuations of the constraining climatic factors is now necessary.
机译:基于先前的乡镇规模模型,提出了一个时空框架来研究可能由气候变化引起的雪崩发生的波动。使用两因素方差非线性分析,将区域年度成分与总可变性隔离开来。此外,依赖于条件自回归子模型的空间效应,结构化的时间趋势与具有不同时间序列子模型(包括自相关模型,周期性模型和变化点模型)的随机噪声有所区别。所获得的层次结构考虑了与估计时间趋势的年度分量的估计有关的不确定性。贝叶斯推断是使用蒙特卡洛模拟进行的。这样可以比较不同的时间序列模型,并在明显的不稳定情况下预测未来的活动。在法国北部的阿尔卑斯山中的应用说明了该模型的不同组成部分提供的信息,主要是时空条件以及相对风险的时空波动。例如,在过去的60年中,平均雪崩活动或低活动或高活动的冬季数量没有明显变化。这表明,气候变化最近对该地区的雪崩节律影响很小。但是,突出了重要的时间模式:突然变化和大约15年的伪周期性周期的复杂组合。为了预测雪崩对气候变化的未来响应,现在需要将其与约束性气候因素的波动联系起来。

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