Collaborative Research: Using Surface Information for Quantitative Modeling of the Subsurface

协作研究：利用地表信息进行地下定量建模

• 批准号: 1719492
• 负责人: Christopher Paola
• 金额: $ 27.4万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719492&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; Surface; Information

Surface connections, such as those among channels in river networks, are important for understanding the development and evolution of landscapes, such as densely populated coastal river deltas. Connections in the subsurface are critical in understanding groundwater flow and solute transport. Preferential flowpaths, in fact, can quickly deliver contaminants to water supply wells, a particularly important problem in densely populated coastal areas. Establishing a quantitative link between surface and subsurface patterns will greatly advance our capability to predict the movement of contaminants in groundwater, thus improving access to clean water and limiting pollution and health risks. We propose to investigate quantitatively how the dynamics of surface networks create subsurface networks, and thus determine how surface information can be used to predict properties of the subsurface. This will enable us to better predict sustainability and manage water resources in densely populated deltas such as the Ganges-Brahmaputra Delta, where high concentrations of arsenic are widespread in the groundwater of the upper delta, and salinity problems are pervasive in the lower delta. The models and data analysis tools developed as part of this project will be released as open source and we will collaborate with Bangladeshi institutions to disseminate our findings. Our driving hypothesis is that the 3D subsurface structure can be predicted by combining information on (i) the modern surface network snapshot, (ii) the surface network kinematics (i.e., its temporal evolution), and (iii) accommodation and net sedimentation. We further hypothesize that (iv) the nature of the surface-to-subsurface translation exerts a major influence on structural connectivity and solute transport through the resulting aquifer system. Our goal is to develop new methods to translate surface channel networks to obtain quantitative models of subsurface architecture and flow and transport processes. We will perform this analysis with a combination of experimental, numerical modeling, and observational approaches and we will verify our findings by collecting lithologic and geochemical data in the Ganges-Brahmaputra Delta. Our findings will provide critical information about the predictability of subsurface structure given the surface channel network and its kinematics, and will allow quantification of the factors influencing this predictability. The proposed work will also further the development of quantitative metrics of connectivity of surface networks and subsurface 3D structures and flowpaths, and improve our ability to model the subsurface structure of large deltaic systems where spatial heterogeneity and large spatial extent prevent full characterization via field observations. By extending this structural understanding to dynamic solute transport behavior, the proposed research will enhance the predictability of contaminant migration in highly heterogeneous systems.

地面连接（例如河网络中的通道之间的连接）对于理解景观的发展和演变至关重要，例如人口稠密的沿海河流三角洲。地下的连接对于理解地下水流和溶质传输至关重要。实际上，优惠的流道可以迅速将污染物传递给供水井，这是人口稠密的沿海地区特别重要的问题。建立表面与地下模式之间的定量联系将大大提高我们预测地下水中污染物运动的能力，从而改善获得清洁水的获取并限制污染和健康风险。我们建议在定量上研究表面网络的动力学如何创建地下网络，从而确定如何使用表面信息来预测地下的属性。这将使我们能够更好地预测可持续性，并管理人口稠密的三角洲（例如恒河 - 布拉赫马普拉三角洲）的水资源，那里的砷在上层三角洲的地下水中很普遍，盐度问题在下层三角洲的含量广泛。作为该项目的一部分开发的模型和数据分析工具将作为开源发布，我们将与孟加拉国机构合作以传播我们的发现。我们的驾驶假设是，可以通过结合（i）现代表面网络快照，（ii）表面网络运动学（即其时间进化）以及（iii）的住宿和净沉积物来预测3D地下结构。我们进一步假设（iv）表面向外平移的性质对通过所得含水层系统的结构连通性和溶质传输产生了重大影响。我们的目标是开发新方法来翻译表面通道网络，以获取地下体系结构以及流动和传输过程的定量模型。我们将通过实验，数值建模和观察方法的结合进行此分析，我们将通过在恒河brahmaputra Delta中收集岩性和地球化学数据来验证我们的发现。我们的发现将提供有关表面通道网络及其运动学的地下结构可预测性的关键信息，并允许量化影响这种可预测性的因素。拟议的工作还将进一步开发表面网络和地下3D结构和流道的连通性的定量指标，并提高我们对大型三角洲系统的地下结构进行建模的能力，在该系统中，空间异质性和大空间范围可防止通过现场观测到完全表征。通过将这种结构性理解扩展到动态溶质传输行为，拟议的研究将增强高度异质系统中污染物迁移的可预测性。