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

GSTDTAP

项目编号	1854975
	PREEVENTS: Track 2: Collaborative Research: Defining precursors of ground failure: a multiscale framework for early landslide prediction through geomechanics and remote sensing
	Alexander Handwerger (Principal Investigator)
主持机构	Middlebury College
项目开始年	2019
	2019-06-01
项目结束日期	2023-05-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	260599(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213396
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Alexander Handwerger .PREEVENTS: Track 2: Collaborative Research: Defining precursors of ground failure: a multiscale framework for early landslide prediction through geomechanics and remote sensing.2019.