An understanding of the processes that influence the transport and fate of microorganisms in porous media is needed to protect water resources from pathogenic microorganisms and contaminants associated with the colloidal phase, and to optimize bioremediation strategies. As the physical and chemical factors influencing the transport of microorganisms have been well understood in homogeneous systems, accurate description of microorganism transport in field scale is hampered by the existence of preferential flow.

The objective of this research was to better understand and quantify physical and chemical factors that influence microorganism transport and fate in soils that exhibit preferential flow. E. coli D21g and coliphage phiX174 were selected in this study as the representative bacterial pathogen and the surrogate for human viruses, respectively. The transport of microorganisms was examined in well defined and controlled soil columns containing artificial macropores (sand lenses) of different length and configuration under different solution ionic strength (IS).

Transport experiments demonstrated that retention of E. coli D21g and φX174 increased with IS in both homogeneous and heterogeneous systems. The importance of preferential flow on microbe transport was found to be enhanced at higher IS, even though the overall transport decreased. Deposition profiles revealed significant cell retention at the interface of the coarse sand lens and fine sand matrix as a result of mass transfer. The length and configuration of the artificial macropore proved to have a great impact on the transport of E.coli D21g, especially under high ionic strength conditions. At low ionic strength, more extensive transport in the preferential path and earlier arrival time were observed for E.coli D21g compared to bromide as a result of size exclusion. Cell release from the preferential flow system with a reduction of solution IS exhibited multi-pulse breakthrough behavior that was strongly dependent on the initial amount of cell retention, especially at the lens-matrix interface, and the lens configuration. Simulations in 2D models were capable to describe the transport and deposition, and the release process during transients in chemistry in preferential flow systems. Dual-permeability models were also successfully applied to simulate microorganism transport in preferential flow systems with different configuration and length of preferential path, and correlations found between the parameters of dual-permeability model and preferential path characters could promote the upcaling from local to field scale for microorganism transport in preferential flow systems.

Contaminants in water at the soil surface can be rapidly transported to streams or locations of surface water storage by runoff. This has been reported to be the primary transport route for contaminants on sloping fields and hillslopes. Generally, contaminants are transported faster and in larger amounts with runoff, and much slower and in smaller amounts with infiltration. Runoff transports are strongly affected by surface topography, soil properties, vegetation, and weather. The risk of transport is also highly dependent on the contaminant load at the soil surface, interactions with soil particles, and transformations. An understanding of and the ability to predict processes that influence the transport and fate of contaminants in runoff water are therefore needed to assess and mitigate risks of contamination of surface water supplies on human health.

Physically-based, spatially-distributed models have the potential to be an efficient tool to examine and optimize the removal of contaminants from agricultural runoff through land-use changes and best management practices. In this research, the existing subsurface version of HYDRUS-1D was adapted to simulate uniform or physical nonequilibrium flow and reactive solute transport processes during runoff at the soil surface. The numerical results obtained by the new model produced an excellent agreement with an analytical solution for the kinematic wave equation. Additional model tests further demonstrated the applicability of the adapted model to simulate the transport and fate of many different solutes (non-adsorbing tracers, nutrients, pesticides, microbes, and sediments) that undergo equilibrium and/or kinetic sorption and desorption, and first- or zero-order reactions.

Along with PBMs, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models when there is a lack of required data. Here we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. Several machine learning techniques including Linear Regression (LR), k-Nearest Neighbor regression (kNN), Support Vector Machine with linear (SVM-L) and non-linear (SVM-NL) kernels, and Deep Neural Networks (DNN) (Neural Networks with multiple hidden layers) were explored to find input - output functional relations. The results indicated that the Deep Neural Network (DNN) model with two hidden layers performed the best among selected data-driven models. This DNN model accurately predicted runoff water quantity over a wide range in parameters. It also predicted well runoff water quality for near-equilibrium solute transport over a wide range in parameters.

Disruptions of the hydrological and nitrogen cycles in arid regions can seriously impact desert ecosystems by altering the fire cycle and exacerbating shifts in vegetation. With the potential for nitrogen deposition, spread of invasive species, and climate change to alter the hydrological and nitrogen cycles, understanding these cycles is necessary for assessing the threats to desert ecosystems. This dissertation explores the connections between the hydrological and nitrogen cycles in sandy soils in arid ecosystems. An analysis of the hydrological cycle suggested that the water flux at highly negative potentials during the dry summers contributes significantly to the total annual water flux at two study sites in the Sonoran desert. Because commonly-used soil hydraulic models do not accurately approximate the retention curve at highly negative potentials, predictions of liquid water flow at moderate potentials during the growing season were inaccurate due to very dry initial conditions. An examination of how monthly nitrate measurements throughout the top 100cm of soil varied with precipitation at the same study sites showed how strongly the hydrological cycle affects the nitrogen cycle in the desert. After rapid nitrate creation followed the first rains of the growing season, the leaching of nitrate with subsequent rainfalls was likely a major factor causing nitrogen limitation in annual plants. Significantly greater nitrate in the soil in plots fertilized with ammonium nitrate relative to unfertilized plots throughout the dry summer suggested that a considerable amount of nitrate in the soil was not taken up by plants. Variations in ammonium measurements with precipitation in the soil at the sites were similarly analyzed. Results suggest that the adsorption of ammonium is likely an important factor in increasing nitrogen availability to short-rooted plants through inhibiting leaching. Identifying how the hydrological cycle influences the nitrogen cycle in arid ecosystems is critical for predicting how deserts will change in the future.