EXCESS: The role of excess topography and peak ground acceleration on earthquake-preconditioning of landslides

滑坡是导致岩石和土壤崩塌并向下移动的物理过程的统称。当陡峭的斜坡因暴雨、地震、自然过程(例如河流)移走斜坡底部或人类活动导致山坡上的物质坍塌等因素而不稳定时，便会发生山泥倾泻。全球每年发生数千起山体滑坡，造成数千人死亡(例如，从2004年到2016年，55997人在4862起不同的山体滑坡事件中丧生)，并严重破坏基础设施、扰乱经济和阻碍国际发展。尽管进行了广泛的研究，但预测何时何地发生山体滑坡的能力仍然是一个根本性的科学挑战。这在一定程度上是因为科学家们曾认为，某个地区的滑坡速度每年都是恒定的，而这些景观中类似的地方也会发生滑坡。如果是这样的话，就很容易理解山体滑坡最有可能在何时何地发生，也就是说，山体滑坡是“可以预测的”。不幸的是，最近的研究表明，这样的假设是不正确的，事实上，暴风雨和地震等突发极端事件会改变滑坡的速度和模式。因此，能够预测滑坡风险升高的地区仍然是灾害管理的一个迫切需要的前沿领域。地震不仅会因为地震期间的地面变形和震动而引发滑坡，而且在地震发生后，在接下来的1-10年里，随后发生的滑坡数量会增加--这一过程被称为“地震预适应”。这一现象构成了一种额外的危险和风险，在很大程度上没有被认识到，也没有被量化。我们最近在尼泊尔进行的开创性研究表明，地震强度与过度地形(高于稳定门槛坡度的地区)和随后的山体滑坡之间存在联系。如果这种关系在世界其他地区是真实的，我们将有一种高度创新的方法来定位风险较高的地区。这个项目将通过研究最近发生的事件和计算机模拟来解决这一关键的研究前沿。首先，我们将使用高分辨率(&lt；5m)卫星图像，在最近的大地震之前、期间和之后为六个不同的地区创建新的滑坡目录。这些高分辨率数据使我们能够准确地确定每个地区滑坡的长期平均发生率，并自信地确定地震后滑坡增加的时期的大小和持续时间。选定的区域和地震跨越了一系列气候、构造环境和地震大小，使我们能够调查影响，并确定不同控制因素(如降雨、坡度、地形、地震大小)在全球范围内的相对重要性，确保研究成果具有广泛的适用性。然后，这些数据集将用于区域一级的滑坡易感性模型，以形成可供国家/区域政府和机构用于减轻灾害和风险的产出。其次，我们将开发一种新的基于过程的计算机模型来研究地震景观破坏的机制，以及这种破坏如何随着时间的变化而导致观测到的滑坡模式。与经验统计模型不同，基于过程的模型可以显式地模拟滑坡发生的驱动因素，并且可以考虑环境突变和快速变化的影响。模型的结果将通过敏感性图得到验证，能够模拟10到1000年的多次地震将导致对地震引发的和地震预处理的滑坡在长期地貌演变中的作用的新见解，最终提高准确预测地震周期中滑坡位置的能力。

Landsliding is a collective term for physical processes that cause rock and soil to fail and move down slope. Landslides occur when steep slopes are destabilised by factors such as heavy rainfall, earthquakes, the removal of the base of the slope by natural processes (e.g., by rivers) or by the action of people causing material on the hillside to collapse. Many thousands of landslides occur globally each year, killing thousands of people (e.g., from 2004 and 2016; 55,997 people died in 4,862 separate landslide events) and significantly damaging infrastructure, disrupting economies and hindering international development. Despite extensive research, the ability to forecast when and where a landslide will occur remains a fundamental scientific challenge. This is partly because scientists had thought that the rate of landsliding in a certain area is constant from year to year, and that landslides would occur in similar places in those landscapes. If this were the case, then it would be straightforward to understand where and when landslides would most likely occur, i.e., they would be 'predictable'. Unfortunately, recent research shows that such assumptions are incorrect and in fact sudden extreme events such as storms and earthquakes will change the rates and patterns of landsliding. Being able to predict areas of elevated landslide risk thus remains an imperative frontier in hazard management. Earthquakes not only induce landslides because of ground deformation and shaking during the event, but also after an earthquake there are increased numbers of subsequent landslides over the next 1-10 years - this process has been termed "earthquake-preconditioning". This phenomenon poses an additional hazard and risk that is largely unrecognised and unquantified. Our recent ground-breaking research in Nepal suggests that there is a link between the strength of an earthquake and excess topography (areas in the landscape that are above a stable threshold slope) and subsequent landsliding. If this relationship is true in other parts of the world, we will have a highly innovative way of locating areas at higher risk.This project will address this critical research frontier through the study of recent events and computer modelling. Firstly, we will create new landslide catalogues before, during and after recent large earthquakes for six different regions, using high-resolution (<5m) satellite imagery. These high-resolution data allow us to accurately determine the long-term average rate of landslide occurrence in each region and confidently identify the size and duration of periods of increased landsliding following an earthquake. The regions and earthquakes selected span a range of climates, tectonic settings, and earthquake sizes to enable us to investigate the influence, and determine the relative importance that different control factors (e.g., rainfall, slope, topography, earthquake size) have at a global level, ensuring that the research outputs have wide applicability. These datasets will then be used in landslide susceptibility models at regional level to form outputs that can be used in hazard and risk mitigation by national/regional governments and agencies. Secondly, we will develop a new process-based computer model to investigate the mechanism of earthquake landscape damage and how this changes through time to cause observed patterns of landslides. Unlike empirical statistical models, process-based models explicitly simulate the drivers of landslide occurrence and can consider the impact of sudden and rapid environmental changes. The results of the model will be validated by the susceptibility maps, and the ability to model multiple earthquakes over 10s to 1000s of years will lead to new insights into the role of earthquake-induced and earthquake-preconditioned landslides in long-term landscape evolution, ultimately increasing the ability to accurately forecast the location of landslides across earthquake cycles.