Interdisciplinary Geosciences - Kamide Lecture
Title: Characterize basin scale subsurface heterogeneity using natural stimuli


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Yu-Li WANG

National Taiwan University

Speaker Biography

Dr. Wang is an Assistant Professor in the Department of Bioenvironmental Systems Engineering at National Taiwan University, Taiwan. He received his Ph.D. from the University of Arizona, USA in 2020. Dr. Wang’s research focuses on subsurface data fusion, plant-soilwater-groundwater interaction, and stochastic theory. His research group has developed a stochastic data fusion algorithm that is applicable to different field sites. These new theories and methods of subsurface exploration based on naturalk stimuli (e.g., stream stage variation and triggered lightning) have undoubtedly advanced our ability to explore the subsurface, which is a major breakthrough in geoscientific research. Dr. Wang has published several papers in the prestigious hydrology and water resources journals (e.g., WRR, JoH, GRL, STOTEN). One of them has recently received the top cited article award from Wiley in the year of 2022-2023. Dr. Wang also received the Young Professional Award from the International Society of Paddy and Water Environment Engineering in recognition of his academic achievements.


Abstract

Subsurface characteristics such as hydraulic conductivity (K) and specific storage (Ss) govern groundwater flow dynamics and influence contaminant migration within aquifers. Accurate mapping of these properties is essential for effective groundwater management and ensuring sustainable water resources. However, applying artificial stimuli like pumping or water injection for basin-scale characterization is often impractical due to their limited influence over large areas. To address this, we have developed a stochastic data fusion framework that leverages natural stimuli, such as stream stage variations and lightning, to delineate basin-scale subsurface characteristics. In regions with significant surface water-groundwater interaction, we analyze stream stage and groundwater level data collected during wet and dry periods to map the spatial distribution of K and Ss. This approach has been successfully applied to watersheds across diverse climatic zones. Additionally, in areas lacking hydrological observation networks, we have employed an innovative geophysical technique using artificially triggered lightning to preliminarily investigate subsurface electromagnetic properties. The framework demonstrates the potential of natural stimuli as cost-effective and scalable alternatives for characterizing subsurface heterogeneity, paving the way for broader applications in groundwater resource management and environmental monitoring.





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