Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

Abstract

Hydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models.

Variations in Bedrock and Vegetation Cover Modulate Subsurface Water Flow Dynamics of a Mountainous Hillslope

“…Hence, the geophysical parameterization allows for a more detailed understanding of the flow processes and how bedrock and vegetation variability are impacting upon the subsurface flow dynamics. Similar conclusions have recently been drawn from a seismic refraction study that used the geophysics derived subsurface property distribution to inform and calibrate a hydrological model of a mountainous headwater catchment (Chen et al, 2023). Note that this model is not accounting for overland flow.…”

Influence of Subsurface Critical Zone Structure on Hydrological Partitioning in Mountainous Headwater Catchments

“…The headwater catchment in Chen, Niu, Mendieta, et al (2023) is used here, and it occupies an area of approximately 0.02 km 2 . The elevation of the catchment ranges between 1,590 and 1,650 m (Figure S1a in Supporting Information S1).…”

“…In the simulations, each structural layer of the subsurface was treated as homogenous, and its hydrologic properties were calibrated using field measurements (Chen, Niu, Mendieta, et al, 2023). The variation ranges of these model parameters are summarized in Table S1 in Supporting Information S1.…”

“…In this study, we performed a Monte Carlo simulation to estimate the uncertainty related to the simulated hydrologic fluxes and storages based on the variations in model parameters. Other model parameters were not calibrated, and their values were either taken from other studies or assigned based on experiences (Chen, Niu, Mendieta, et al, 2023). In the modeling, we followed the procedures in Maxwell and Kollet (2008) to run a spin-up phase, which forces the model to reach equilibrium by using the same hydrologic forcing repeatedly until the simulated hydraulic head change falls below a threshold.…”

Identifying spring recharge areas using stable isotope and geophysical methods: A case study of the Ababi Mountain Region, Bali, Indonesia

“…While isotope techniques can trace water sources (Zhou et al, 2023), they do not delineate subsurface ow paths. Conversely, geophysical surveys map subsurface structures but cannot independently identify hydrologic connections (Chen et al, 2023).…”