Nonlinear responses in the dynamics of climate system could be triggered by small change of forcing. Interactions between different components of Earth’s climate system are believed to cause abrupt and catastrophic transitions, of which anthropogenic forcing is a major and the most irreversible driver. Meantime, in the face of global climate change, extreme climatic events, such as extreme precipitations, heatwaves, droughts, etc., are projected to be more frequent, more intense, and longer in duration. These nonlinear responses in climate dynamics from tipping points to extreme events pose serious threats to human society on a large scale. Understanding the physical mechanisms behind them has potential to reduce related risks through different ways. The overarching objective of this dissertation is to quantify complex interactions, detect tipping points, and explore propagations of extreme events in the hydroclimate system from a new structure-based perspective, by integrating climate dynamics, causal inference, network theory, spectral analysis, and machine learning. More specifically, a network-based framework is developed to find responses of hydroclimate system to potential critical transitions in climate. Results show that system-based early warning signals towards tipping points can be located successfully, demonstrated by enhanced connections in the network topology. To further evaluate the long-term nonlinear interactions among the U.S. climate regions, causality inference is introduced and directed complex networks are constructed from climatology records over one century. Causality networks reveal that the Ohio valley region acts as a regional gateway and mediator to the moisture transport and thermal transfer in the U.S. Furthermore, it is found that cross-regional causality variability manifests intrinsic frequency ranging from interannual to interdecadal scales, and those frequencies are associated with the physics of climate oscillations. Besides the long-term climatology, this dissertation also aims to explore extreme events from the system-dynamic perspective, especially the contributions of human-induced activities to propagation of extreme heatwaves in the U.S. cities. Results suggest that there are long-range teleconnections among the U.S. cities and supernodes in heatwave spreading. Findings also confirm that anthropogenic activities contribute to extreme heatwaves by the fact that causality during heatwaves is positively associated with population in megacities.

The Water-Energy Nexus (WEN) is a concept that recognizes the interdependence of water and energy systems. The Phoenix metropolitan region (PMA) in Arizona has significant and potentially vulnerable WEN interactions. Future projections indicate that the population will increase and, with it, energy needs, while changes in future water demand are more uncertain. Climate change will also likely cause a reduction in surface water supply sources. Under these constraints, the expansion of renewable energy technology has the potential to benefit both water and energy systems and increase environmental sustainability by meeting future energy demands while lowering water use and CO2 emissions. However, the WEN synergies generated by renewables have not yet been thoroughly quantified, nor have the related costs been studied and compared to alternative options.Quantifying WEN intercations using numerical models is key to assessing renewable energy synergy. Despite recent advances, WEN models are still in their infancy, and research is needed to improve their accuracy and identify their limitations. Here, I highlight three research needs. First, most modeling efforts have been conducted for large-scale domains (e.g., states), while smaller scales, like metropolitan regions, have received less attention. Second, impacts of adopting different temporal (e.g., monthly, annual) and spatial (network granularity) resolutions on simulation accuracy have not been quantified. Third, the importance of simulating feedbacks between water and energy components has not been analyzed. This dissertation fills these major research gaps by focusing on long-term water allocations and energy dispatch in the metropolitan region of Phoenix. An energy model is developed using the Low Emissions Analysis Platform (LEAP) platform and is subsequently coupled with a water management model based on the Water Evaluation and Planning (WEAP) platform. Analyses are conducted to quantify (1) the value of adopting coupled models instead of single models that are externally coupled, and (2) the accuracy of simulations based on different temporal resolutions of supply and demand and spatial granularity of the water and energy networks. The WEAP-LEAP integrated model is then employed under future climate scenarios to quantify the potential of renewable energy technologies to develop synergies between the PMA's water and energy systems.

Accelerated climate and land use land cover (LULC) changes are anticipated to significantly impact water resources in the Colorado River Basin (CRB), a major freshwater source in the southwestern U.S. The need for actionable information from hydrologic research is growing rapidly, given considerable uncertainties. For instance, it is unclear if the predicted high degree of interannual precipitation variability across the basin could overwhelm the impacts of future warming and how this might vary in space. Climate change will also intensify forest disturbances (wildfire, mortality, thinning), which can significantly impact water resources. These impacts are not constrained, given findings of mixed post-disturbance hydrologic responses. Process-based models like the Variable Infiltration Capacity (VIC) platform can quantitatively predict hydrologic behaviors of these complex systems. However, barriers limit their effectiveness to inform decision making: (1) simulations generate enormous data volumes, (2) outputs are inaccessible to managers, and (3) modeling is not transparent. I designed a stakeholder engagement and VIC modeling process to overcome these challenges, and developed a web-based tool, VIC-Explorer, to “open the black box” of my efforts. Meteorological data was from downscaled historical (1950-2005) and future projections (2006-2099) of eight climate models that best represent climatology under low- and high- emissions. I used two modeling methods: (1) a “top-down” approach to assess an “envelope of hydrologic possibility” under the 16 climate futures; and (2) a “bottom-up” evaluation of hydrology in two climates from the ensemble representing “Hot/Dry” and “Warm/Wet” futures. For the latter assessment, I modified land cover using projections of a LULC model and applied more drastic forest disturbances. I consulted water managers to expand the legitimacy of the research. Results showed Far-Future (2066-2095) basin-wide mean annual streamflow decline (relative to 1976-2005; ensemble median trends of -5% to -25%), attributed to warming that diminished spring snowfall and melt and year-round increased soil evaporation from the Upper Basin, and overall precipitation declines in the Lower Basin. Forest disturbances partially offset warming effects (basin-wide mean annual streamflow up to 12% larger than without disturbance). Results are available via VIC-Explorer, which includes documentation and guided analyses to ensure findings are interpreted appropriately for decision-making.

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.

Approximately 71% of the great lakes, lakes, reservoirs, and ponds, together with 51% of rivers and streams assessed in the US are impaired or threatened by pollution or do not meet the minimum water quality requirements. Pathogens, sediments, and nutrients are leading causes of impairment, with agriculture being a top source of pollution. Agricultural pollution has become a global concern overtaking urban contamination as the major factor of inland and coastal waters degradation in many parts of the world. High-yielding crop production has been achieved by the intensive use of inorganic fertilizers that are mainly composed of Nitrogen (N) and Phosphorus (P). N and P are essential nutrients for ecosystem structure, processes, and functions. However, N and P in excess can be problematic to the environment. One of the major impacts of the increasing amount of these nutrients in the environment is the global expansion of harmful algal blooms (HABs). Major agricultural nutrient pollution sources and climate change can exacerbate these risks. This dissertation aims to guide future policies to mitigate issues linked to excess nutrient loads in the U.S. by evaluating the impact of climate change on nutrient loads and assessing the environmental impact as well as the spatial patterns of one of the major agricultural sources of nutrient pollution - Concentrated Animal Feeding Operations (CAFOs). Specifically, I first investigated the impact of bias correction techniques when modeling mid-century nutrient loads in a watershed heavily impacted by CAFOs. Second, I evaluated the role of CAFOs in land use change and subsequent environmental degradation of the surrounding environment. Finally, I assessed the spatial organization of CAFOs and its links to water quality conditions. The findings revealed unique insights for future nutrient management strategies in the U.S.

Hydrologic modeling in snowfed karst watersheds is important for many communities relying on their water for municipal and agricultural use, but the complexities of karst hydrology have made this task historically difficult. Here, two Long Short-Term Memory (LSTM) models are compared to investigate this problem from a deep-learning perspective within the context of the Logan River Canyon watershed, which supplies water to Logan City, UT. One is spatially lumped and the other spatially distributed, the latter with a potential to reveal underlying spatial watershed dynamics. Both use snowmelt and rainfall to predict daily streamflow downstream. I find distributed LSTMs consistently outperform lumped LSTMs in this task. Additionally, I find that a spatial sensitivity analysis of distributed LSTMs is unpromising in revealing spatial watershed dynamics but warrants further investigation.

In recent decades animal agriculture in the U.S. has moved from small, distributed operations to large, Concentrated Animal Feeding Operations (CAFOs). CAFOs are defined by federal regulations based on animal numbers and confinement criteria. Because of the size of these operations, the excessive amount of manure generated is typically stored in lagoons, pits, or barns prior to field application or transport to other farms. Water quality near CAFOs can be impaired through the overflow of lagoons, storm runoff, or lagoon seepage. Assessing water quality impacts of CAFOs in a modeling framework has been difficult because of data paucity. A CAFO lagoon module was developed to assess lagoon overflow risk, groundwater quality, and ammonia emissions of a dairy lagoon. A groundwater quality assessment of a Dairy Lagoon in Lynden Washington was used to calibrate and validate the groundwater quality model. Groundwater down stream of the lagoon was negatively impaired. The long-term effects of this lagoon on water quality were explored as well as the effectiveness of improving the lagoon lining to reduce seepage. This model can be used to improve understanding of the impacts of CAFO lagoon seepage and develop sustainable management practices at the watershed scale for these key components of the agricultural landscape.

The National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) will provide the next generation of operational streamflow forecasts at different lead times across United States using the Weather Research and Forecasting (WRF)-Hydro hydrologic system. These forecasts are crucial for flood protection agencies and water utilities, including the Salt River Project (SRP). The main goal of this study is to calibrate WRF-Hydro in the Oak Creek Basin (OCB; ~820 km2), an unregulated mountain sub-watershed of the Salt and Verde River basins in Central Arizona, whose water resources are managed by SRP and crucial for the Phoenix Metropolitan area. As in the NWM, WRF-Hydro was set up at 1-km (250-m) resolution for the computation of the rainfall-runoff (routing) processes. Model forcings were obtained by bias correcting meteorological data from the North American Land Data Assimilation System-2 (NLDAS-2). A manual calibration approach was designed that targets, in sequence, the sets of model parameters controlling four main processes responsible for streamflow and flood generation in the OCB. After a first calibration effort, it was found that WRF-Hydro is able to simulate runoff generated after snowmelt and baseflow, as well as magnitude and timing of flood peaks due to winter storms. However, the model underestimates the magnitude of flood peaks caused by summer thunderstorms, likely because these storms are not captured by NLDAS-2. To circumvent this, a seasonal modification of soil parameters was adopted. When doing so, acceptable model performances were obtained during calibration (2008-2011) and validation (2012-2017) periods (NSE > 0.62 and RMSE = ~2.5 m3/s at the daily time scale).

The process-based calibration strategy utilized in this work provides a new approach to identify areas of structural improvement for WRF-Hydro and the NWM.

The Phoenix Metropolitan region is subject to intense summer monsoon thunderstorms that cause highly localized flooding. Due to the challenges in predicting these meteorological phenomena and modeling rainfall-runoff transformations in urban areas, the ability of the current operational forecasting system to predict the exact occurrence in space and time of floods in the urban region is still very limited. This thesis contributes to addressing this limitation in two ways. First, the existing 4-km, 1-h Stage IV and the new 1-km, 2-min Multi-Radar Multi-Sensor (MRMS) radar products are compared using a network of 365 gages as reference. It is found that MRMS products consistently overestimate rainfall during both monsoonal and tropical storms compared to Stage IV and local rain gauge measurements, although once bias-corrected offer a reasonable estimate for true rainfall at a higher spatial and temporal resolution than rain gauges can offer. Second, a model that quantifies the uncertainty of the radar products is applied and used to assess the propagation of rainfall errors through a hydrologic-hydraulic model of a small urban catchment in Downtown Phoenix using a Monte Carlo simulation. The results of these simulations suggest that for this catchment, the magnitude of variability in the distribution of runoff values is proportional to that of the input rainfall values.