PREEVENTS Track 2: Collaborative Research: Defining precursors of ground failure: a multiscale framework for early landslide prediction through geomechanics and remote sensing

人口增长、城市扩张和极端天气比以往任何时候都更加剧了脆弱性的危险。在由天气模式驱动的灾害中，山体滑坡引起的地面变形具有巨大的全球影响。地球表面的很大一部分地区面临地面故障的风险，影响到世界上相当一部分人口。山体滑坡每年造成全球数千人死亡，仅在美国每年造成的经济损失就超过10亿美元。预测地面故障的最大挑战来自于它们在缺乏观测到的前兆的情况下突然加速的能力。事实上，天然斜坡可以以多种方式变形，有时表现为缓慢移动，有时以流态化状态快速移动。这些变形模式共存于同一地点，影响彼此相邻的地形部分，并且可能在不同时间被相同的山坡所经历。目前缺乏可预测的大规模地面变形模型，这在很大程度上是地面监测数据空间复盖面较差的结果。为了克服这些障碍，该项目将依靠遥感技术的进步，能够以时空分辨率探测降雨模式和地面运动，这在十年前是不可想象的。这些观测进展有可能通过丰富的、公开的、空间分布的信息为水文和地面变形模型带来新的公式、校准和验证的可能性。具体地说，该项目的动机是，破坏前的变形与地面破坏发生的时间、方式和原因有很大关系，旨在证明分析地面的变形特征是解释山坡在变化的天气模式下以不同方式破坏的关键。如果成功，该项目将带来新的方法来解码地面不稳定的物理根源，并确定引发灾难性滑坡的可测量前兆，从而可能激励设计能够更好地保护人类生命和基础设施的创新实时预警系统。在这个项目的过程中，将从一个新的多学科角度研究地球表面和大气之间的相互作用。具体而言，这一项目将编制：(1)能够解释因环境条件动态变化而引起的滑坡速度变化的岩土材料流变学规律；(2)能够在山脉尺度上量化空间不均匀降雨输入和土壤湿度的多尺度天气-水文学模拟平台；(3)能够通过地形近端之间的力传递规律再现遥感变形演变的景观尺度地质力学模型；(4)基于复杂系统物理的地表过程网络理论，该理论可用于确定模式和确定失控不稳定的前兆。这种方法的结合将提供滑坡动态的综合表现，从而提高我们在景观尺度上预测滑坡和减轻灾害的能力。最重要的是，它将提供创新的工具来解决危险预测领域的悬而未决的问题，例如：(I)我们能否利用景观尺度的观测来推断山坡的流变学？(2)哪些地貌尺度的测量对于预测早期山体滑坡的命运最有用？(3)同时收集降雨模式和排泄率的空间分布数据是否足以确定滑坡的前兆？这些问题将通过将最先进的遥感工具(例如卫星和机载干涉合成孔径雷达、高分辨率数字高程模型和天气雷达)的数据与基于物理的土壤和岩石变形本构定律、大气-水文模型和复杂网络理论相结合来回答。可用于美国国内外各种地质环境和泥土材料的丰富数据集将用于测试拟议方法的预测能力。这一战略将提供独特的机会来验证项目核心的概念，并测试它们在广泛的地貌和气候背景下的适用性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Population growth, urban expansion, and extreme weather are contributing more than ever to hazard vulnerability. Among the hazards driven by weather patterns, ground deformation due to landslides has immense global impacts. Large portions of the Earth's surface are at risk to ground failures, affecting a considerable fraction of the world's population. Landslides cause a global annual death toll of several thousand and financial losses of more than $1B per year in the United States alone. The most formidable challenge in predicting ground failures derives from their ability to suddenly accelerate despite the lack of observed precursors. In fact, natural slopes can deform in multiple ways, sometimes by displaying slow movements, while at other times moving rapidly in a fluidized state. Such modes of deformation coexist at the same site, affect portions of terrain proximal to one another, and may be experienced by the same hillslopes at different times. The current scarcity of predictive large-scale ground deformation models is largely a consequence of the poor spatial coverage of ground-based monitoring data. To overcome these obstacles, this project will rely on technological advances in remote sensing that allow the detection of rainfall patterns and ground movements at spatiotemporal resolutions that were unthinkable just a decade ago. These observational advances have the potential to unleash new formulation, calibration and validation possibilities for hydrologic and ground deformation models by means of abundant, openly-available, spatially-distributed information. Specifically, the project is motivated by the idea that pre-failure deformations have much to say about when, how, and why ground failure occurs, and aims to demonstrate that analyzing the deformation signature of the ground is the key to explain why hillslopes fail in different ways when subjected to variable weather patterns. If successful, this project will lead to new ways to decode the physical origin of ground instability and define measurable precursors of catastrophic landslide triggering, thus potentially inspiring the design of innovative real-time early warning systems able to better protect human life and infrastructure. During the course of this project, the interaction between the Earth's surface and the atmosphere will be studied from a new multi-disciplinary perspective. Specifically, the project will formulate: (1) rheological laws for geomaterials able to explain variations in landslide velocity resulting from dynamically changing environmental conditions; (2) a multiscale weather-hydrology simulation platform able to quantify spatially heterogeneous rainfall inputs and soil moisture at the scale of mountain ranges; (3) landscape-scale geomechanical models able to reproduce the evolution of remotely sensed deformations via force-transfer laws between proximal portions of terrain; (4) a network theory for surface processes based on the physics of complex systems, by which patterns can be identified and precursors of runaway instability defined. Such a combination of methods will provide a comprehensive representation of landslide dynamics, thus improving our ability to forecast landslides and mitigate hazards at the landscape scale. Most importantly, it will provide innovative tools to address open questions in the domain of hazard forecasting, such as: (i) Can we use landscape-scale observations to infer the rheology of hillslopes? (ii) Which landscape-scale measurements are most useful for predicting the fate of incipient landslides? (iii) Is the concurrent collection of spatially-distributed data of rainfall patterns and displacement rates sufficient to identify landslide precursors? These questions will be answered by combining data from state-of-the-art remote sensing tools (e.g., satellite and airborne interferometric synthetic aperture radar, high-resolution digital elevation models, and weather radar) with physics-based constitutive laws for soil and rock deformation, atmospheric-hydrologic models, and complex network theories. Rich datasets available for a variety of geological settings and earthen materials within and outside the United States will be used to test the predictive capabilities of the proposed approaches. This strategy will offer unique opportunities to validate the concepts at the core of the project and test their applicability to a wide range of geomorphic and climatic contexts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.