Carbon cycling in dryland ecosystems is complicated by three interrelated factors that pose challenges in characterizing current dynamics and predicting future change: 1) large pulses of ecosystem respiration occur when dry soils are rewetted, 2) a patchy distribution of vegetation leads to spatial heterogeneity in carbon stocks and fluxes, and 3) a large fraction of carbon is stored in belowground pools making it inherently more difficult to study. In this dissertation, I seek to address these issues through the development and application of landscape-scale ecosystem models, as well as field experiments and field observations from the Kalahari savannas of Southern Africa. In Chapter 2, I use new measurement techniques to characterize spatial and temporal variability. I compare the spatial pattern of soil carbon, woody plant canopies and root systems, which I mapped using ground penetrating radar, and use continuous in situ measurements of soil CO2 concentrations during experimental wetting treatments to determine the relationship between soil moisture and soil respiration at a fine temporal scale. Woody plant roots are the primary determinant of the spatial distribution of soil carbon, and soil respiration responds to fluctuations in soil moisture in a way that most large-scale models are unable to account for. To account for these factors I develop in Chapter 3 a steady-state, semi-analytical model of soil carbon stocks that uses a probabilistic description of vegetation structure and a multiplicative noise approximation of decomposition dynamics. The model results are sensitive to the parameters describing the spatial extent of woody plant root systems. I present the results of the excavation and mapping of complete root systems in Chapter 4 that better characterize rooting structure for modeling applications. I observed a high degree of species-level diversity that is not accounted for in the previously presented modeling framework. I conclude in Chapter 5 by developing a stochastic above- and belowground model of the abundance and distribution of biomass that incorporates these new results. It provides a framework for the future development of models of dryland carbon, water, and energy balance.
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