Improved Hydrologic Modeling and Geophysical Imaging of Critical
Zones
Authors: Chen Hang 
Published: January 2025

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 from seismic imaging 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, replicating 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.