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对灾害脆弱性的影响比以往任何时候都更大。在天气模式造成的灾害中，山体滑坡造成的地面变形具有巨大的全球影响。地球表面的大部分地区都面临着地面故障的风险，影响着世界上相当一部分人口。山体滑坡每年在全球造成数千人死亡，仅在美国每年就造成超过 10 亿美元的经济损失。预测地面故障最艰巨的挑战来自于它们在缺乏观察到的先兆的情况下突然加速的能力。事实上，自然斜坡可以以多种方式变形，有时表现出缓慢的运动，而有时则以流化状态快速运动。这种变形模式在同一地点共存，影响彼此邻近的地形部分，并且可能在不同时间经历相同的山坡。目前大规模地面变形预测模型的缺乏很大程度上是由于地面监测数据的空间覆盖范围较差造成的。为了克服这些障碍，该项目将依靠遥感技术的进步，以时空分辨率检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制定、校准和验证的可能性。具体来说，该项目的动机是，破坏前变形对地面破坏发生的时间、方式和原因有很大影响，旨在证明分析地面变形特征是解释为什么山坡在受到多变的天气模式影响时会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，并定义灾难性山体滑坡触发的可测量前兆，从而可能激发创新实时预警系统的设计，从而更好地保护人类生命和基础设施。在该项目过程中，将从新的多学科角度研究地球表面与大气之间的相互作用。具体来说，该项目将制定：（1）岩土材料的流变定律，能够解释动态变化的环境条件导致的滑坡速度的变化； （2）多尺度天气水文模拟平台，能够量化山脉尺度的空间异质降雨输入和土壤湿度； (3) 景观尺度地质力学模型能够通过地形近端部分之间的力传递定律再现遥感变形的演化； （4）基于复杂系统物理学的表面过程网络理论，通过该理论可以识别模式并定义失控不稳定的前兆。这种方法的组合将提供滑坡动力学的全面表征，从而提高我们预测滑坡和减轻景观尺度灾害的能力。最重要的是，它将提供创新工具来解决灾害预测领域的开放性问题，例如：（i）我们可以使用景观尺度的观测来推断山坡的流变吗？ (ii) 哪些景观尺度测量对于预测初期滑坡的命运最有用？ (iii) 同时收集降雨模式和位移率的空间分布数据是否足以识别滑坡前兆？这些问题将通过将来自最先进的遥感工具（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。适用于美国境内外各种地质环境和土质材料的丰富数据集将用于测试所提出方法的预测能力。该战略将提供独特的机会来验证项目核心概念，并测试其在广泛的地貌和气候环境中的适用性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值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.