Title: Improved Hydrologic Modeling And Geophysical Imaging Of Critical Zones: Incorporating Structural And Temporal Constraints
Program: Doctor of Philosophy in Geophysics
Advisor: Dr. Qifei Niu, Geosciences
Committee Members: Dr. Dylan Mikesell, College of Arts and Sciences; Dr. Alejandro Flores, Geosciences; and Dr. Jodi Mead, Mathematics
The Earth’s critical zone (CZ) is a life-sustaining layer extending from the canopy of the tree to the bottom of the active groundwater circulation, providing valuable resources to terrestrial life, including nutrients, minerals, and fresh water. There are many challenges in studying various processes within and the function of the CZ. This dissertation addresses two common challenges in studying the CZ hydrology and watershed function, which are critical for a predictive understanding of the water dynamics in mountainous terrain and for an informed management of future water resources under rapid climate change. These challenges include (1) accurate quantification of hydrological partitioning and (2) developing affordable and reliable field approaches for measuring subsurface water storage in deep subsurface.
For hydrological partitioning, hydrologic modeling has been proven an essential tool, but its accuracy largely relies on the correct representation of the subsurface, which is inherently heterogamous due to a number of processes involved, such as weathering and biological activities. This study proposes to extract the subsurface CZ structure from seismic refraction tests to improve the representation of the subsurface of mountainous catchments. A catchment in the Dry Creek Experimental Watershed, Idaho, is utilized to demonstrate the improved estimation of hydrological partitioning in mountainous catchments, and the study is presented in Chapter 3. With this new method, the influence of various subsurface CZ structures on streamflow generation, groundwater recharge, and subsurface storage is also explored. It is found that the geometry of the weathered bedrock in the CZ, which is influenced by topography, climate, and tectonic stress, significantly influences both the time and hydrological processes. The details of this study are presented in Chapter 4.
To address the second challenge, the electrical resistivity method has shown promise in estimating the spatial and temporal water content in the subsurface, including both soil and fractured rock layers. Despite wider applications, the inversion-induced biases pose a significant uncertainty for data interpretation. The inversion-induced biases mainly result from the imposed regularizations (in both spatial and time domains), which are necessary to stabilize the optimization and to find a unique solution. To minimize these biases, this study proposed to use a subsurface CZ structure to relax the traditional spatial smoothness-constrained inversion. Both synthetic and field data are used to demonstrate the effectiveness of this method, and the related work is presented in Chapter 5. The results indicate that incorporating additional structural information can improve the estimation of moisture content from resistivity data. For time-lapse resistivity inversion, this study proposes to use a prior resistivity change, which can be obtained from hydrologic measurements, to replace the traditionally used temporal smoothness constraints. A synthetic example is designed based on coupling integrated hydrologic modeling and resistivity forward modeling. The inversion results show that the time-lapse resistivity inversion constrained by hydrologic data gives more realistic resistivity images in both time and spatial domains, reprinting the major water dynamics occurring in the subsurface. The related work is presented in Chapter 6.
In summary, this dissertation has improved the hydrologic modeling and geophysical imaging of mountainous catchments, and they can be used for various CZ hydrologic studies, from hydrological partitioning quantification to estimating rock moisture in mountainous terrains. The novel hydrogeophysical approaches presented here will improve our ability to predict the hydrological responses under various climate conditions, thus contributing to a more informed management of future water resources under a changing climate.