Global Non-Gaussian Stochastic Partial Differential Equation Models for Assessing Future Health of Ecohydrologic Systems

用于评估生态水文系统未来健康状况的全局非高斯随机偏微分方程模型

基本信息

批准号：
2014166
负责人：
Stefano Castruccio
金额：
$ 15万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014166&HistoricalAwards=false
关键词：
Global Non Gaussian Stochastic Partial

项目摘要

In recent decades, the dramatic increase of computational power, coupled with technological advances in portable and remote sensing devices has exponentially increased the volume, variety and velocity of data, facilitating new scientific and engineering breakthroughs. This project focuses on global spatio-temporal data, a data type highly affected by this Big Data revolution and aims to develop a new global dynamical model for processes monitored at high resolution in time (daily or hourly). The application will focus on the occurrence and intensity of rainfall, and the model will be applied to assess risks faced by diverse hydrologic systems, including lakes, wetlands, and the surface/groundwater interaction zone. The proposed global statistical model will be flexible enough to explain floods and drought events governed by large scale atmospheric/oceanic patterns (for example, the El Niño Southern Oscillation) that a local model could miss. This application will focus on four regions in the continental USA known to be sensitive to precipitation events. Outreach activities at different levels, from lectures to high school students to events for the local community, are planned to increase awareness on the value of healthy hydrological systems, and a computer program will allow users to explore which areas in the United States are at higher risk of floods and droughts. The graduate student support will be used on interdisciplinary research and writing codes. Models for global data represent a theoretical challenge, as there are restrictions in defining valid processes over the sphere and time. Practical and computational challenges also exist as these models must be both flexible enough to capture non-trivial data structure across the globe, and be able to fit the extremely large size of modern data sets (billions of points). A latent Gaussian model for global spatio-temporal data is proposed, which will control the spatial dependence by a Stochastic Partial Differential Equation with an operator able to capture non-stationarity with a local tensor deformation, and changing behavior across land and ocean to allow for a smooth transition across the two domains. The model will be solved with a finite volume approach which will guarantee sparsity of the precision matrix in the latent process, thus allowing scalability for extremely large data sets. The application will address the critical issue in hydrology of the assessment of the uncertainty in future health of ecohydrological systems. Global simulations of daily precipitation and a mass conservation equation will provide estimates of the future risk to droughts and floods in four regions in the United States.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.

近几十年来，计算能力的急剧增长，加上便携式和遥感设备的技术进步，数据的数量、种类和速度呈指数级增长，促进了新的科学和工程突破。一种受大数据革命影响很大的数据类型，旨在开发一种新的全球动态模型，用于以高分辨率及时（每日或每小时）监测过程。该应用程序将重点关注降雨的发生和强度，该模型将是用于评估不同水文面临的风险拟议的全球统计模型将足够灵活，可以解释由大规模大气/海洋模式（例如厄尔尼诺南方涛动）控制的洪水和干旱事件。本地模型可能会错过。该应用程序将重点关注美国大陆已知对降水事件敏感的四个地区，计划开展不同级别的外展活动，从针对高中生的讲座到针对当地社区的活动，以提高人们对降水事件的认识。健康水文学的价值系统和计算机程序将允许用户探索美国哪些地区面临更高的洪水和干旱风险。研究生的支持将用于跨学科研究和编写全球数据模型，这是一项理论挑战。在定义范围和时间上的有效过程方面也存在限制，因为这些模型必须足够灵活以捕获全球范围内的重要数据结构，并且能够适应极大的现代数据。集（十亿个点）。提出了全球时空数据的潜在高斯模型，该模型将通过随机偏微分方程控制空间依赖性，其中算子能够捕获局部张量变形的非平稳性，并改变陆地和海洋的行为，以允许该模型将使用有限体积方法进行求解，这将保证潜在过程中精度矩阵的稀疏性，从而允许极大数据集的可扩展性。水文学中评估生态水文系统未来健康的不确定性的关键问题对每日降水和质量守恒方程的全球模拟将提供对美国四个地区未来干旱和洪水风险的估计。该奖项反映了美国国家科学基金会的贡献。法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。