GSA Connects 2021 in Portland, Oregon

Paper No. 32-4
Presentation Time: 2:20 PM


PASSALACQUA, Paola1, HARIHARAN, Jayaram1, MICHAEL, Holly2, PAOLA, Chris3, XU, Zhongyuan4, STEEL, Elisabeth5, CHADWICK, Austin J.6 and KHAN, Mahfuzur R.7, (1)Civil, Architectural and Environmental Engineering, The University of Texas at Austin, 1 University Station C1786, Austin, TX 78712-0276, (2)Univ of Delaware Geological Sciences, 255 Academy St, Newark, DE 19716-7599, (3)Earth and Envrionmental Sciences, University of Minnesota, 116 Church Street, SE, Minneapolis, MN 55455, (4)Univ of DelawareGeological Sciences, 255 Academy St, Newark, DE 19716-7599, (5)Department of Geological Sciences and Geological Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada, (6)Geological and Planetary Sciences, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, (7)Department of Geology, University of Dhaka, Dhaka, DE, Bangladesh

Hundreds of millions of people live in coastal areas and rely on groundwater resources. In many deltas, however, these resources are polluted with contaminants such as arsenic and salt, putting the safety and well-being of coastal communities at risk. Groundwater and contaminant transport is regulated by the presence, distribution, and connectivity of sand geobodies in the subsurface. Thus, quantifying subsurface structure is fundamental for accurate predictions of groundwater and subsurface contaminant transport. The subsurface structure is the result of surface network structure and kinematics through time. While information on surface networks is overall abundant, observations of the subsurface are scarce and expensive to obtain. We thus ask the question: Can subsurface information be obtained from the far more abundant surface information? We address this question by leveraging numerical modeling, remotely sensed data, and field observations to explore the connectivity of subsurface structure with surface network information. In particular, we use numerical modeling to establish metrics to connect surface patterns and the subsurface under various forcings. Using the example of the Ganges-Brahmaputra-Meghna Delta, one of the largest and most populated deltas in the world, we show that the surface network carries information on the processes acting on the system and can be used to predict the structure of the shallow subsurface. In a companion presentation, we show how this knowledge of surface-subsurface connectivity can be used to analyze the dynamics of groundwater flow and contaminant transport under various forcings.