Zahra Ghahremani Thesis Defense

Title: Investigating the Distribution of Soil Inorganic Carbon, and the Role of Dust Deposition in Dryland Soils.

Abstract:The importance of dust deposition in the biogeochemical cycling of nutrients, sediment redistribution, and soil formation in the Critical Zone is well-known. Anthropogenic activities, such as changing land use and agricultural practices, can influence dust flux deposition rates, carbon content in the dust, and the chemical composition of dust. This study examines the influence of agricultural activities on aeolian deposition in a cold desert system. We investigate seasonal and spatial variations in dust deposition rates, dust carbon content, and dust geochemistry using passive dust traps. The Reynolds Creek Experimental Watershed (RCEW) in the Owyhee Mountains, Idaho, and the Northwest Irrigation and Soils Research Lab in the Snake River Plain, Idaho, are considered as non-agricultural (native) and agricultural (heavily managed) sites respectively. In chapter one, I focus on the impact of human activities on aeolian processes in a semi-arid desert. Dust is also a well-known source of calcium, contributing to the formation of soil inorganic carbon (SIC) in arid and semi-arid areas. SIC plays a vital role in shaping global carbon cycles, influencing hydrological processes, and affecting climate models. However, it is somewhat surprising that there has been a notable dearth of modeling studies dedicated to comprehensively understanding the formation, dynamics, and distribution of SIC in North America. The second chapter of this thesis focuses on utilizing machine learning models to predict and map SIC within the top 1 meter of soil across the Contiguous United States (CONUS). To accomplish this, we utilized the World Soil Information Service (WoSIS) dataset (Batjes et al., 2017) and several covariates, including land surface parameters like vegetation density and land use, soil properties, climatic variables, and geographical information as inputs for the models. Overall, this chapter demonstrates the capacity of machine learning models to predict SIC within the top one meter of soil on a large scale.

Zoom Link: https://boisestate.zoom.us/j/96823510207

Advisor: Jen Pierce

Committee Members: Dave Huber, Linda Reynard