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Using Bayesian Electrical Resistivit...

Shahid, Saffat,

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Using Bayesian Electrical Resistivity Inversion to Reveal Hillslope Dry-Up Process in a Mediterranean Climate /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Using Bayesian Electrical Resistivity Inversion to Reveal Hillslope Dry-Up Process in a Mediterranean Climate // Saffat Shahid.
Author:	Shahid, Saffat,
Description:	1 electronic resource (80 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-07.
Contained By:	Masters Abstracts International86-07.
Subject:	Geophysics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31761892
ISBN:	9798302837462

Using Bayesian Electrical Resistivity Inversion to Reveal Hillslope Dry-Up Process in a Mediterranean Climate /
Shahid, Saffat,

Using Bayesian Electrical Resistivity Inversion to Reveal Hillslope Dry-Up Process in a Mediterranean Climate /Saffat Shahid. - 1 electronic resource (80 pages)

Source: Masters Abstracts International, Volume: 86-07.

Hydrologic dynamics in hillslopes is essential for comprehending the processes that shape landscape evolution and sustain the Earth's critical zone. Electrical resistivity (ER) is considered as one of the best geophysical methods to observe these dynamics due to its sensitivity to subsurface water content. To understand hillslope water dynamics and mitigating the risks of slope instability caused by extreme weather events, we studied how subsurface hydrological processes are being influenced by variations in vegetation type across different aspects of hillslopes. Thus, how accurately ER can quantify the dry-up process during the growing season on hillslopes becomes critical, particularly in regions with distinct dry summers and wet winters (i.e. Mediterranean climates). The Blue Oaks Ranch Reserve (BORR) in Central California provides an ideal location for this study due to its consistent ridge-valley systems, which well represents the regional climatic and topographic conditions. Previous work at BORR used active source seismic refraction (SR) to constrain subsurface structure. To additionally investigate moisture content in regolith, we conduct ER surveys with Schlumberger and Dipole-Dipole configurations to invert for resistivity using Transdimensional Hierarchical Bayesian (THB) inversion framework with reversible-jump Markov Chain Monte Carlo (THB rj-MCMC). We also performed 2D synthetic tests to evaluate how well THB can recover a synthetic model with imposed data uncertainty. The results indicate that Schlumberger outperforms Dipole-Dipole in the THB rj-MCMC inversion. However, these results also reveal limited depth resolution to ~10 m depth using current ERT configurations. Finally, we adopt the THB approach for a series of ER surveys at BORR between June and September 2023. The findings suggest a distinct increase in resistivity on the North-facing slope during growing seasons, indicating reduced moisture content particularly in areas with presences of oak trees as they draw water from deep regolith. On the South-facing slope, resistivity remained stable due to the dominance of grass that lacks deep roots for consuming deep moisture. Our resistivity results show that vegetation type particularly trees play a critical role in regolith moisture distribution. To compare and correlate changes in resistivity over dry periods, we analyzed soil probe data previously collected at the site. The correlation suggested that increases in resistivity are related to decreases in volumetric moisture content. Additionally, we compared ERT data with seismic survey data to better understand changes in subsurface properties like porosity and saturation along depth, as ERT and seismic velocity is sensitive to moisture content and material porosity.

English

ISBN: 9798302837462Subjects--Topical Terms:

535228
Geophysics.
Subjects--Index Terms:

Bayesian inversion framework

Using Bayesian Electrical Resistivity Inversion to Reveal Hillslope Dry-Up Process in a Mediterranean Climate /
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520 $a Hydrologic dynamics in hillslopes is essential for comprehending the processes that shape landscape evolution and sustain the Earth's critical zone. Electrical resistivity (ER) is considered as one of the best geophysical methods to observe these dynamics due to its sensitivity to subsurface water content. To understand hillslope water dynamics and mitigating the risks of slope instability caused by extreme weather events, we studied how subsurface hydrological processes are being influenced by variations in vegetation type across different aspects of hillslopes. Thus, how accurately ER can quantify the dry-up process during the growing season on hillslopes becomes critical, particularly in regions with distinct dry summers and wet winters (i.e. Mediterranean climates). The Blue Oaks Ranch Reserve (BORR) in Central California provides an ideal location for this study due to its consistent ridge-valley systems, which well represents the regional climatic and topographic conditions. Previous work at BORR used active source seismic refraction (SR) to constrain subsurface structure. To additionally investigate moisture content in regolith, we conduct ER surveys with Schlumberger and Dipole-Dipole configurations to invert for resistivity using Transdimensional Hierarchical Bayesian (THB) inversion framework with reversible-jump Markov Chain Monte Carlo (THB rj-MCMC). We also performed 2D synthetic tests to evaluate how well THB can recover a synthetic model with imposed data uncertainty. The results indicate that Schlumberger outperforms Dipole-Dipole in the THB rj-MCMC inversion. However, these results also reveal limited depth resolution to ~10 m depth using current ERT configurations. Finally, we adopt the THB approach for a series of ER surveys at BORR between June and September 2023. The findings suggest a distinct increase in resistivity on the North-facing slope during growing seasons, indicating reduced moisture content particularly in areas with presences of oak trees as they draw water from deep regolith. On the South-facing slope, resistivity remained stable due to the dominance of grass that lacks deep roots for consuming deep moisture. Our resistivity results show that vegetation type particularly trees play a critical role in regolith moisture distribution. To compare and correlate changes in resistivity over dry periods, we analyzed soil probe data previously collected at the site. The correlation suggested that increases in resistivity are related to decreases in volumetric moisture content. Additionally, we compared ERT data with seismic survey data to better understand changes in subsurface properties like porosity and saturation along depth, as ERT and seismic velocity is sensitive to moisture content and material porosity.
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650 4 $a Geophysics. $3 535228
650 4 $a Geology. $3 516570
650 4 $a Hydrologic sciences. $3 3168407
653 $a Bayesian inversion framework
653 $a Electrical resistivity tomography
653 $a Hydrology
653 $a Landscape evolution
653 $a Mediterranean climates
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710 2 $a University of Maryland, College Park. $b Geology. $e degree granting institution. $3 3765704
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773 0 $t Masters Abstracts International $g 86-07.
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