Advancing Stochastic Analysis of Field-Scale Transport Parameters using Hydrogeophysics

GSTDTAP

项目编号	1907555
	Advancing Stochastic Analysis of Field-Scale Transport Parameters using Hydrogeophysics
	Erasmus Oware (Principal Investigator)
主持机构	SUNY at Buffalo
项目开始年	2019
	2019-07-15
项目结束日期	2021-06-30
资助机构	US-NSF
项目类别	Standard Grant
项目经费	299515(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213586
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Erasmus Oware .Advancing Stochastic Analysis of Field-Scale Transport Parameters using Hydrogeophysics.2019.