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

地表连通，例如河网中河道之间的连通，对于了解人口稠密的沿海河流三角洲等地貌的发展和演变非常重要。地下连通对了解地下水流动和溶质运移至关重要。事实上，优先流道可以迅速将污染物输送到供水水井，这在人口稠密的沿海地区是一个特别重要的问题。建立地表和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移动的能力，从而改善获得清洁水的机会，并限制污染和健康风险。我们建议定量地研究地表网络的动力学如何创建地下网络，从而确定如何利用地表信息来预测地下网络的性质。这将使我们能够更好地预测可持续性和管理人口稠密的三角洲的水资源，如恒河-雅鲁藏布江三角洲，在那里，高浓度的砷普遍存在于上游三角洲的地下水中，盐碱化问题在下游三角洲普遍存在。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放源码发布，我们将与孟加拉国机构合作传播我们的研究结果。我们的驱动假设是，可以结合(I)现代地表网络快照、(Ii)地表网络运动学(即其时间演化)和(Iii)可容纳和净沉积的信息来预测3D地下结构。我们进一步假设：(4)地表-地下转换的性质对结构连通性和通过所产生的含水层系统的溶质运输产生重大影响。我们的目标是开发新的方法来转换地表沟道网络，以获得地下建筑以及流动和传输过程的定量模型。我们将采用实验、数值模拟和观测相结合的方法进行分析，并将通过收集恒河-雅鲁藏布江三角洲的岩性和地球化学数据来验证我们的发现。我们的发现将提供关于给定地表水道网络及其运动学的地下结构的可预测性的关键信息，并将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发展地表网络和地下3D结构和流动路径的连通性的定量度量，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，在这些系统中，空间异质性和大的空间范围阻碍了通过现场观察来充分表征。通过将这种结构理解扩展到动态溶质迁移行为，拟议的研究将提高高度非均质系统中污染物迁移的可预测性。

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.