Advancing Stochastic Analysis of Field-Scale Transport Parameters using Hydrogeophysics

利用水文地球物理学推进现场尺度输运参数的随机分析

• 批准号: 1907555
• 负责人: Christopher Lowry
• 金额: $ 29.95万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907555&HistoricalAwards=false
• 关键词: Advancing; Stochastic; Analysis; Field; Scale

Groundwater constitutes a significant component of fresh water supplies in the United States. With changing climate, the dependence on groundwater is only expected to grow, especially in areas with limited surface water supplies. Because of their susceptibility to contamination, assertive management and cleanup efforts of groundwater systems require proper understanding of how contaminants move in these systems. Field-scale prediction of contaminants transport in complex groundwater systems is a long-standing research challenge. This project combines numerical modeling of contaminant transport with geophysical methods to advance understanding and enhance the ability to monitor and predict field-scale contaminant transport in highly complex groundwater systems. The project also contributes to the development of a diverse scientific workforce and benefits society by supporting an early career faculty member and postdoctoral researcher, providing research opportunities to undergraduate students, engaging students from underrepresented groups in STEM, and exposing middle-school students to geoscience education and opportunities.Proper management of groundwater systems and mitigation of health risks posed by contaminated aquifers requires an understanding of field-scale solute migration, particularly small-scale transport processes in highly heterogeneous aquifers. The clasic advection-dispersion model, commonly used to predict solute migration, often fails to reproduce field measurements due to the lack of knowledge and uncertainty of solute transport parameters (STPs). Traditional well-based sampling methods employed to gain insights into field-scale transport parameters provide spatially limited information. Their invasive nature may also disturb the natural small-scale transport behavior that needs to be understood. Hydrogeophysics provides opportunities to rapidly characterize spatially continuous, field-scale solute plume migration for quantitative evaluation of transport parameters using minimally-invasive methods. Hydrogeophysical estimation requires prior information about the spatial distribution of the target solute plume for computational stability. The conventional prior constraints applied in hydrogeophysics, however, lack information about the physics of the target transport process (e.g., advection-dispersion) that is driving the evolution of the contaminant plume, resulting in inaccurate estimation of the transport parameters. Given the complex heterogeneity and uncertainty in hydrogeological systems, stochastic methods are well suited for solute transport prediction in these systems. Standard stochastic sampling methods can, however, become computationally intractable in spatially-distributed, high-dimensional hydrological problems. The goal of this project is to develop and test novel stochastic estimation strategies that: 1) incorporates prior physics-based constraints of the solute transport process (i.e., accounts for multiple scales of plume dispersion and complexity) to improve velocity, plume-dispersion, and mass estimations; and 2) performs stochastic estimation in the reduced-hydrologic-process parameter space to improve computational efficiency and enable field-scale characterization of small-scale transport processes in highly heterogeneous aquifers.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.

地下水是美国淡水供应的重要组成部分。随着气候变化，对地下水的依赖预计只会增加，特别是在地表水供应有限的地区。由于它们对污染的敏感性，地下水系统的果断管理和清理工作需要正确理解污染物如何在这些系统中移动。复杂地下水系统中污染物运移的现场预测是一个长期存在的研究挑战。该项目将污染物运移的数值模拟与地球物理方法相结合，以提高对高度复杂的地下水系统中现场尺度污染物运移的监测和预测能力。该项目还通过支持早期职业教师和博士后研究人员，为本科生提供研究机会，吸引STEM中代表性不足的群体的学生，而暴露在中间-适当管理地下水系统和减轻受污染含水层造成的健康风险，了解现场规模的溶质迁移，特别是在高度异质含水层的小规模运输过程。 经典的对流-弥散模型，通常用于预测溶质运移，往往无法重现现场测量，由于缺乏知识和溶质运移参数（STPs）的不确定性。传统的井为基础的采样方法，用于深入了解现场规模的传输参数提供空间有限的信息。它们的入侵性质也可能扰乱需要了解的自然小规模迁移行为。水文物理学提供了机会，快速表征空间连续的，现场规模的溶质羽流迁移的定量评估的传输参数，使用微创方法。水文地球物理估计需要关于目标溶质羽流的空间分布的先验信息以保证计算的稳定性。然而，在水文物理学中应用的常规先验约束缺乏关于目标传输过程的物理学的信息（例如，对流-扩散），这是驱动污染物羽流的演变，导致不准确的估计传输参数。由于水文地质系统具有复杂的非均匀性和不确定性，随机方法非常适合于这些系统中的溶质运移预测。然而，标准的随机抽样方法在空间分布的高维水文问题中可能变得难以计算。该项目的目标是开发和测试新的随机估计策略，该策略：1）结合溶质运移过程的先前基于物理的约束（即，考虑到羽流扩散和复杂性的多个尺度），以改进速度、羽流扩散和质量估计;以及2）在简化的水文过程参数空间中执行随机估计，以提高计算效率，并实现小尺度水文过程的场尺度表征。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。