This dissertation investigates the complex dynamics of semi-dilute inertial particles suspended in vortices using the Eulerian-Lagrangian method. The study explores the modulation of flow induced by inertial particles, focusing on the characteristics of a single vortex, instability analysis within particle-laden flows, and the merging process of co-rotating vortices. Simulations reveal a preferential concentration mechanism, where inertial particles cluster around a void fraction bubble, accelerating the decay of the vortex tube. Small-scale clusters, arising from particle-trajectory crossings, induce significant gradients in the fluid vorticity field, contributing to rapid vortex breakdown. Within a specific Stokes number range, increased particle inertia results in faster vortex decay and stronger inhomogeneity in the particle phase. The instability mechanism in particle-laden flows is explored using a Rankine vortex model. Two-way coupling triggers azimuthal perturbations, leading to the breakdown of the vortex structure. Linear Stability Analysis and Two-Fluid modeling demonstrate that the dusty vortex flow exhibits unstable modes, with growth rates increasing with wavenumber. Eulerian-Lagrangian simulations validate these results, showing excellent agreement between computed and predicted growth rates. The dissertation also delves into the co-rotating vortex merger in a semi-dilute dusty flow. For weak inertial effects, merger experiences a delay compared to particle-free vortices. Under moderate inertial conditions, the merger process exhibits repulsion, increased separation, and eventual convective merger stages. Highly inertial particles stretch the vortex core, initiating a merger with an outcome of a particle-free vortex core surrounded by a halo of concentrated particles. In conclusion, the feedback force from the dispersed phase induces instability and significantly influences the dynamics of vortices in particle-laden flows. The findings contribute to a deeper understanding of the intricate interactions between inertial particles and vortical structures.

The hydrologic cycle in drylands is complex with large spatiotemporal variationsacross scales and is particularly vulnerable to changes in climate and land cover. To address the challenges posed by hydrologic changes, a synergistic approach that combines numerical models, ground and remotely sensed observations, and data analysis is crucial. This dissertation uses innovative detection and modeling techniques to assess key hydrologic variables in drylands, including irrigated water use, streamflow, and snowpack conditions, answering following research questions that also have broad societal implications: (1) What are the individual and combined effects of future climate and land use change on irrigation water use (IWU) in the Phoenix Metropolitan Area (PMA)?; (2) How can temporal changes in streamflow and the impacts of flash flooding be detected in dryland rivers?; and (3) What are the impacts of rainfall-snow partitioning on future snowpack and streamflow in the Colorado River Basin (CRB)? Firstly, I conducted a scenario modeling using the Variable Infiltration Capacity (VIC) model under future climate and land use change scenarios. Results showed that future IWU will change from -0.5% to +6.8% in the far future (2071-2100) relative to the historical period (1981-2010). Secondly, I employed CubeSat imagery to map streamflow presence in the Hassayampa River of Arizona, finding that the imaging capacity of CubeSats enabled the detection of ephemeral flow events using the surface reflectance of the near-infrared (NIR) band. Results showed that 12% of reaches were classified as intermittent, with the remaining as ephemeral. Finally, I implemented a physically-based rainfall-snow partitioning scheme in the VIC model that estimates snowfall fraction from the wet-bulb temperature using a sigmoid function. The new scheme predicts more significant declines in snowfall (-8 to -11%) and streamflow (-14 to -27%) by the end of the 21st century over the CRB, relative to historical conditions. Overall, this dissertation demonstrates how innovative technologies can enhance the understanding of dryland hydrologic changes and inform decision-making of water resources management. The findings offer important insights for policymakers, water managers, and researchers who seek to ensure water resources sustainability under the effects of climate and land use change.

The current level of carbon dioxide in ambient air is increasing and reinforcing the severity of global warming. Several techniques have been developed to capture the gas directly from the air. Moisture swing absorption (MSA) is a mechanism through which a reactive surface, namely resin beads, absorbs carbon dioxide when dry and releases it when wet. The ionic complexity of the surface of the bead interacts with CO2 when H2O contents are low, and CO2 diffuses as bicarbonate or carbonate. Hence, diffusion-drift-reaction equations describe the moving species behavior MS sorbent. A numerical model has been developed previously applying finite difference scheme (FDS) to estimate the evolution of species concentrations over uniform time and space intervals. The methodology was based on a specific membrane and bead geometry. In this study, FDS was employed again with modifications over the boundary conditions. Neumann boundary condition was replaced by Robin boundary condition which enforced diffusion and drift fluxes at the center of the sorbent. Furthermore, the generic equations were approximated by another numerical scheme, Finite volume scheme (FVS), which discretizes the spatial domain into cells that conserves the mass of species within. The model was predicted to reduce the total carbon mass loss within the system. Both schemes were accommodated with a simulated model of isolated chamber that contained arbitrary sorbent. Moreover, to derive the outcomes of absorption/desorption cycles and validate the performance of FVS, Langmuir curve was utilized to obtain CO2 saturation in the sorbent and examine two scenarios: one by varying the partial pressure of CO2 (PCO2) in the chamber at constant H2O (PH2O), or changing PH2O at constant PCO2. The results from FDS approximation, when adjusting the center with Robin boundary condition, show 0.11% lower carbon mass gain than when applying Neumann boundary condition. On the other hand, FVS minimizes the mass loss by 0.3% lower than the original total carbon mass and achieves sorbent saturation without any adjustment. Moreover, the isotherm curve demonstrates that increasing PH2O reduces CO2 saturation and is dependent on the linear and non-linear correlations used to estimate water concentration on the surface.

The fast pace of global urbanization makes cities the hotspots of population density and anthropogenic activities, leading to intensive emissions of heat and carbon dioxide (CO2), a primary greenhouse gas. Urban climate scientists have been actively seeking effective mitigation strategies over the past decades, aiming to improve the environmental quality for urban dwellers. Prior studies have identified the role of urban green spaces in the relief of urban heat stress. Yet little effort was devoted to quantify their contribution to local and regional CO2 budget. In fact, urban biogenic CO2 fluxes from photosynthesis and respiration are influenced by the microclimate in the built environment and are sensitive to anthropogenic disturbance. The high complexity of the urban ecosystem leads to an outstanding challenge for numerical urban models to disentangling and quantifying the interplay between heat and carbon dynamics.This dissertation aims to advance the simulation of thermal and carbon dynamics in urban land surface models, and to investigate the role of urban greening practices and urban system design in mitigating heat and CO2 emissions. The biogenic CO2 exchange in cities is parameterized by incorporating plant physiological functions into an advanced single-layer urban canopy model in the built environment. The simulation result replicates the microclimate and CO2 flux patterns measured from an eddy covariance system over a residential neighborhood in Phoenix, Arizona with satisfactory accuracy. Moreover, the model decomposes the total CO2 flux from observation and identifies the significant CO2 efflux from soil respiration. The model is then applied to quantify the impact of urban greening practices on heat and biogenic CO2 exchange over designed scenarios. The result shows the use of urban greenery is effective in mitigating both urban heat and carbon emissions, providing environmental co-benefit in cities. Furthermore, to seek the optimal urban system design in terms of thermal comfort and CO2 reduction, a multi-objective optimization algorithm is applied to the machine learning surrogates of the physical urban land surface model. There are manifest trade-offs among ameliorating diverse urban environmental indicators despite the co-benefit from urban greening. The findings of this dissertation, along with its implications on urban planning and landscaping management, would promote sustainable urban development strategies for achieving optimal environmental quality for policy makers, urban residents, and practitioners.

The world has been continuously urbanized and is currently accommodating more than half of the human population. Despite that cities cover only less than 3% of the Earth’s land surface area, they emerged as hotspots of anthropogenic activities. The drastic land use changes, complex three-dimensional urban terrain, and anthropogenic heat emissions alter the transport of mass, heat, and momentum, especially within the urban canopy layer. As a result, cities are confronting numerous environmental challenges such as exacerbated heat stress, frequent air pollution episodes, degraded water quality, increased energy consumption and water use, etc. Green infrastructure, in particular, the use of trees, has been proved as an effective means to improve urban environmental quality in existing research. However, quantitative evaluations of the efficacy of urban trees in regulating air quality and thermal environment are impeded by the limited temporal and spatial scales in field measurements and the deficiency in numerical models.

This dissertation aims to advance the simulation of realistic functions of urban trees in both microscale and mesoscale numerical models, and to systematically evaluate the cooling capacity of urban trees under thermal extremes. A coupled large-eddy simulation–Lagrangian stochastic modeling framework is developed for the complex urban environment and is used to evaluate the impact of urban trees on traffic-emitted pollutants. Results show that the model is robust for capturing the dispersion of urban air pollutants and how strategically implemented urban trees can reduce vehicle-emitted pollution. To evaluate the impact of urban trees on the thermal environment, the radiative shading effect of trees are incorporated into the integrated Weather Research and Forecasting model. The mesoscale model is used to simulate shade trees over the contiguous United States, suggesting how the efficacy of urban trees depends on geographical and climatic conditions. The cooling capacity of urban trees and its response to thermal extremes are then quantified for major metropolitans in the United States based on remotely sensed data. It is found the nonlinear temperature dependence of the cooling capacity remarkably resembles the thermodynamic liquid-water–vapor equilibrium. The findings in this dissertation are informative to evaluating and implementing urban trees, and green infrastructure in large, as an important urban planning strategy to cope with emergent global environmental changes.

Soil moisture (θ) is a fundamental variable controlling the exchange of water and energy at the land surface. As a result, the characterization of the statistical properties of θ across multiple scales is essential for many applications including flood prediction, drought monitoring, and weather forecasting. Empirical evidences have demonstrated the existence of emergent relationships and scale invariance properties in θ fields collected from the ground and airborne sensors during intensive field campaigns, mostly in natural landscapes. This dissertation advances the characterization of these relations and statistical properties of θ by (1) analyzing the role of irrigation, and (2) investigating how these properties change in time and across different landscape conditions through θ outputs of a distributed hydrologic model. First, θ observations from two field campaigns in Australia are used to explore how the presence of irrigated fields modifies the spatial distribution of θ and the associated scale invariance properties. Results reveal that the impact of irrigation is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas. Second, a physically-based distributed hydrologic model is applied in a regional basin in northern Mexico to generate hyperresolution θ fields, which are useful to conduct analyses in regions and times where θ has not been monitored. For this aim, strategies are proposed to address data, model validation, and computational challenges associated with hyperresolution hydrologic simulations. Third, analyses are carried out to investigate whether the hyperresolution simulated θ fields reproduce the statistical and scaling properties observed from the ground or remote sensors. Results confirm that (i) the relations between spatial mean and standard deviation of θ derived from the model outputs are very similar to those observed in other areas, and (ii) simulated θ fields exhibit the scale invariance properties that are consistent with those analyzed from aircraft-derived estimates. The simulated θ fields are then used to explore the influence of physical controls on the statistical properties, finding that soil properties significantly affect spatial variability and multifractality. The knowledge acquired through this dissertation provides insights on θ statistical properties in regions and landscape conditions that were never investigated before; supports the refinement of the calibration of multifractal downscaling models; and contributes to the improvement of hyperresolution hydrologic modeling.

This dissertation research studies long-term spatio-temporal patterns of surface urban heat island (SUHI) intensity, urban evapotranspiration (ET), and urban outdoor water use (OWU) using Phoenix metropolitan area (PMA), Arizona as the case study. This dissertation is composed of three chapters. The first chapter evaluates the SUHI intensity for PMA using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product and a time-series trend analysis to discover areas that experienced significant changes of SUHI intensity between 2000 and 2017. The heating and cooling effects of different urban land use land cover (LULC) types was also examined using classified Landsat satellite images. The second chapter is focused on urban ET and the impacts of urban LULC change on ET. An empirical model of urban ET for PMA was built using flux tower data and MODIS land products using multivariate regression analysis. A time-series trend analysis was then performed to discover areas in PMA that experienced significant changes of ET between 2001 and 2015. The impact of urban LULC change on ET was examined using classified LULC maps. The third chapter models urban OWU in PMA using a surface energy balance model named METRIC (Mapping Evapotranspiration at high spatial Resolution with Internalized Calibration) and time-series Landsat Thematic Mapper 5 imagery for 2010. The relationship between urban LULC types and OWU was examined with the use of very high-resolution land cover classification data generated from the National Agriculture Imagery Program (NAIP) imagery and regression analysis. Socio-demographic variables were selected from census data at the census track level and analyzed against OWU to study their relationship using correlation analysis. This dissertation makes significant contributions and expands the knowledge of long-term urban climate dynamics for PMA and the influence of urban expansion and LULC change on regional climate. Research findings and results can be used to provide constructive suggestions to urban planners, decision-makers, and city managers to formulate new policies and regulations when planning new constructions for the purpose of sustainable development for a desert city.

The Colorado River Basin (CRB) is the primary source of water in the

southwestern United States. A key step to reduce the uncertainty of future streamflow

projections in the CRB is to evaluate the performance of historical simulations of General

Circulation Models (GCMs). In this study, this challenge is addressed by evaluating the

ability of nineteen GCMs from the Coupled Model Intercomparison Project Phase Five

(CMIP5) and four nested Regional Climate Models (RCMs) in reproducing the statistical

properties of the hydrologic cycle and temperature in the CRB. To capture the transition

from snow-dominated to semiarid regions, analyses are conducted by spatially averaging

the climate variables in four nested sub-basins. Most models overestimate the mean

annual precipitation (P) and underestimate the mean annual temperature (T) at all

locations. While a group of models capture the mean annual runoff at all sub-basins with

different strengths of the hydrological cycle, another set of models overestimate the mean

annual runoff, due to a weak cycle in the evaporation channel. An abrupt increase in the

mean annual T in observed and most of the simulated time series (~0.8 °C) is detected at

all locations despite the lack of any statistically significant monotonic trends for both P

and T. While all models simulate the seasonality of T quite well, the phasing of the

seasonal cycle of P is fairly reproduced in just the upper, snow-dominated sub-basin.

Model performances degrade in the larger sub-basins that include semiarid areas, because

several GCMs are not able to capture the effect of the North American monsoon. Finally,

the relative performances of the climate models in reproducing the climatologies of P and

T are quantified to support future impact studies in the basin.

As inhabitants of a desert, a sustainable water source has always been and will continue to be a crucial component in developing the cities Arizonans call home. Phoenix and the surrounding municipalities make up a large metropolitan area that continues to grow in spatial size and population. However, as climate change becomes more of an evident challenge, Arizona is forced to plan and make decisions regarding its ability to safely and efficiently maintain its livelihood and/or growth. With the effects of climate change in mind, Arizona will need to continue to innovatively and proactively address issues of water management and the effects of urban heat island (UHI). The objective of this thesis was to study the socioeconomic impacts of four extreme scenarios of the future Phoenix metropolitan area. Each of the scenarios showcased a different hypothetical extreme and uniquely impacted factors related to water management and UHI. The four scenarios were a green city, desert city, expanded city into desert land, and expanded city into agricultural land. These four scenarios were designed to emphasize different aspects of the urban water-energy-population nexus, as the future of the Phoenix metropolitan area is dynamic. Primarily, the Green City and Desert City served as contrasting viewpoints on UHI and water sustainability. The Expanded Cities showed the influence of population growth and land use on water sustainability. The socioeconomic impacts of the four scenarios were then analyzed. The quantitative data of the report was completed using the online user interface of WaterSim 5.0 (a program created by the Decision Center for a Desert City (DCDC) at Arizona State University). The different scenarios were modeled in the program by adjusting various demand and supply oriented factors. The qualitative portion as well as additional quantitative data was acquired through an extensive literature review. It was found that changing land use has direct water use implications; agricultural land overtaken for municipal uses can sustain a population for longer. Though, removing agricultural lands has both social and economic implications, and can actually cause the elimination of an emergency source. Moreover, it was found that outdoor water use and reclaimed wastewater can impact water sustainability. Practices that decrease outdoor water use and increase wastewater reclamation are currently occurring; however, these practices could be augmented. Both practices require changes in the publics' opinions on water use, nevertheless, the technology and policy exists and can be intensified to become more water sustainable. While the scenarios studied were hypothetical cases of the future of the Phoenix metropolitan area, they identified important circumscribing measures and practices that influence the Valley's water resources.

Utilizing an urban canopy model (UCM) developed by Zhihua Wang, Ph.D. for a research study conducted for the National Asphalt Pavement Association (NAPA), several scenarios were run in order to determine the impact on the mitigation of the urban heat island (UHI) effect. These scenarios included various roof albedo, wall albedo, ground albedo, a combination of all three albedos, roof emissivity, wall emissivity, ground emissivity, a combination of all three emissivities, and normalized building height as independent variables. Dependent variables included canyon air temperature, effective ground temperature, effective roof temperature, effective wall temperature, and sensible heat flux. It was found that emissivity does play a part in reducing the different dependent variables; however, typically emissivity values are already within a preferred range that not much can be done with them. Normalized building height has a minor impact but the impact that it does have upon the different variables is lessened with lower values of the normalized building height. Increasing the wall albedo decreased the canyon air temperature and the effective wall temperature the most compared to the other variables when considering expenses. An increase in roof albedo reduced effective roof temperature and sensible heat flux the most when taking into consideration the cost of changing the albedo of the surface. Larger values of ground albedo helped to reduce the effective ground temperature more than the other variables considered when a budget is necessary.