Waves, levees and impact pressures in snow avalanches

雪崩是山区的一个主要自然灾害，对全世界许多国家的人民和基础设施构成重大风险。雪崩通常有一个稀释的粉末云，掩盖了下面的密集流动。这种密集的流动往往造成大部分的损害。因此，雪科学家花了多年时间开发GEODAR，多普勒和FMCW雷达，以及仪表塔，以可视化内部密集流动态（Köhler等人。这些技术表明，密集的雪崩发展浪涌和内波（图。3)（Sovilla等人，2010年）。此外，实地观察（图4）表明，很大一部分雪崩形成堤坝。这些波浪和堤坝很重要，因为（i）它们强烈增强了流动性和流动性（Edwards et al. 2021，罗查et al. 2019），（ii）它们极大地集中了雪崩中的能量，极大地放大了它们对建筑物和障碍物的冲击力（见图4）。尽管它们很重要，但人们对雪崩中产生波浪和堤坝的基本机制知之甚少，因此目前的模型不能很好地预测它们。这项建议将密集雪崩可视化方面的突破与浅水流中波浪和堤坝形成的新通用理论结合起来，该理论最初是在曼彻斯特为小尺度干颗粒流开发的（Gray & Edwards 2014，Edwards & Gray 2015，Viroulet et al. 2018，罗查等人2019，Edwards等人2021）。这一理论框架将使我们能够使用雷达观测的波的振幅，波长和粗化动力学，提供重要的约束的流变学性质的雪崩，这是强烈的温度依赖性。Köhler等人（2018年）确定了七种主要类型的雪崩，本提案侧重于三种主要的密集流类型：（i）冷切变，（ii）暖切变和（iii）暖塞。冷流是无凝聚力的颗粒流，而暖流中含有一些液态水，这使得大雪球能够通过凝聚力聚集在一起（Steinkogler等人，2015）。雪球显著地增加了平均颗粒尺寸，然后，暖剪切流有在流出区形成巨大堤坝的趋势（图4a）。这表明这些流动可能与曼彻斯特的小规模自通道化流动非常相似（图4b），我们已经开发了一个定量模型（罗查等人，2019）。因此，该提案旨在为雪崩模型开发一种新的摩擦定律，该定律将捕获波的自发形成和增长以及流出区的自通道化。我们还将研究自通道化和波浪形成如何能够将冲击力集中在结构上并增强跑出。

Snow avalanches are a major natural hazard in mountainous regions and pose a significant risk affecting people and infrastructure in many countries throughout the world. Avalanches typically have a dilute powder cloud that obscures the underlying dense flow beneath. This dense flow often causes much of the damage. Snow scientists have therefore spent many years developing GEODAR, Doppler and FMCW radar, as well as instrumented pylons, to visualize the internal dense flow dynamics (Köhler et al. 2016; 2018). These technologies shows that dense avalanches develop surges and internal waves (figs. 3) (Sovilla et al. 2010). In addition, field observations (fig. 4) show that a significant proportion of avalanches develop levees. These waves and levees are important because (i) they strongly enhance the mobility and run-out of the flow (Edwards et al. 2021, Rocha et al. 2019) and (ii) they vastly concentrate the energy within avalanches, dramatically magnifying the impact forces they exert on buildings and obstacles in their path (see figure 4). Despite their importance, the underlying mechanisms that cause waves and levees in snow avalanches are poorly understood, and consequently they are not well predicted by current models.This proposal combines the breakthroughs in visualisation of dense snow avalanches with a new general theory for wave and levee formation in shallow flows, originally developed in Manchester for small-scale dry granular flows (Gray & Edwards 2014, Edwards & Gray 2015, Viroulet et al. 2018, Rocha et al. 2019, Edwards et al. 2021). This theoretical framework will allow us to use the radar observations of wave amplitude, wavelength and coarsening dynamics, to provide important constraints on the rheological properties of snow avalanches, which are strongly temperature dependent. Köhler et al. (2018) identified seven main types of snow avalanche and this proposal focuses on the three main dense-flow types: (i) cold shear, (ii) warm shear and (iii) warm plug. Cold flows are cohesionless granular flows, while warm flows have some liquid water in them, which allows large snowballs to agglomerate by cohesive forces (Steinkogler et al. 2015). The snowballs dramatically increase the mean particle size, and warm-shear flows then have a tendency to form huge levees in the run-out zone (fig. 4a). This suggests that these flows may be closely analogous to the small-scale self-channelizing flows in Manchester (fig. 4b), for which we have developed a quantitative model (Rocha et al. 2019). This proposal therefore aims to develop a new friction law for snow-avalanche models, that will capture the spontaneous formation and growth of waves, as well as self-channelization in the run-out zone. We will also examine how self-channelization and wave formation are able to concentrate the impact forces on structures and enhance run out.