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

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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.