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Coastal wetlands are being lost at an alarming rate and thus wetland restoration is an important way to recruit coastal wetlands as an ally in our fight against climate change. To better understand and maximize the effectiveness of wetland restoration, it is important to evaluate the success of these restoration projects. In this study, we measured sediment accumulation and soil carbon density in North Campus Open Space, a recently restored wetland near the campus of UC Santa Barbara. The objectives of this study were to 1) assess rates of sediment accumulation since restoration, 2) evaluate spatial heterogeneity in sediment accumulation and soil carbon density, and 3) use this information to better understand the mechanisms that define the relationship between sediment accumulation and surface soil carbon storage to better predict carbon accumulation. Sediment accumulation rate averaged 2.16 mm/year, with a range of 0.4 mm/yr to 5.4 mm/yr. Sediment accumulation, surface soil carbon density, and subsurface carbon density all varied significantly by plot location. The spatial dynamics of vertical accretion were primarily driven by mineral rather than organic material, whereas surface soil carbon was primarily driven by aboveground plant biomass. Sediment accumulation was greatest downstream, whereas soil carbon accumulation was greatest upstream at the freshwater stream inputs. Some areas showed a negative relationship between sediment accumulation and soil carbon density, with high sediment accumulation associated with low surface soil carbon density. Although wetlands are touted to mitigate sea level rise and climate change through sediment accretion and carbon sequestration, these results suggest that sediment accumulation and carbon accumulation do not necessarily co-occur. The results imply that sediment accumulation alone cannot reliably predict carbon accumulation.

The heatwave and drought stresses induced by rapid climate change can alter the emission of isoprene from terrestrial ecosystems. This, in turn, affects climate and air quality by modifying photochemistry and forming secondary organic aerosols. Understanding the complex interactions and feedback loops between climate and isoprene emissions is a challenging yet urgent task. This study integrates laboratory experiments and in-situ measurements to investigate and model these impacts within the Model of Emissions of Gases and Aerosols from Nature (MEGAN).In the first chapter, an empirical algorithm was developed to simulate drought effects on isoprene emissions, revealing an 11% global decrease in isoprene in 2012 due to drought. This algorithm improved the agreement between model simulations and satellite formaldehyde observations during droughts, as formaldehyde is widely used as a proxy for isoprene. However, its performance was limited by the model's ability to accurately capture drought severity.The second and third chapters focus on Arctic ecosystems, where rapid warming is accelerating isoprene emissions. The second chapter characterizes the temperature response of Arctic willows, finding that their hourly temperature response curve is similar to that of temperate plants. Isoprene emissions increase with rising temperature, reaching an optimal level before declining due to enzyme denaturation. Additionally, the isoprene capacity of willows could increase rapidly with rising ambient temperatures from the previous day. During heatwaves, Arctic willows exhibited a 66% higher isoprene emission when using a modified algorithm based on my measurements.The third chapter investigates sedges, another major Arctic isoprene emitter, and finds that their temperature response is notably stronger compared to other isoprene emitters. Integrating these findings into MEGAN improved the capacity of model to reproduce observations. The omission of these strong temperature responses from both willows and sedges led to a 20% underestimation of isoprene emissions in high-latitude regions between 2000 and 2009, and a 55% underestimation of long-term trends from 1960 to 2009. Therefore, rapid warming in the Arctic could significantly increase isoprene emissions, altering local chemistry and impacting the climate.

The carbon and water cycles in global terrestrial ecosystems hold significant importance due to their unique influence on biosphere-climate interactions. In my dissertation, I adopted a data-driven method to deepen our understanding of these global cycles. In the first chapter, I enhanced the AmeriFlux data product, resulting in the creation of a publicly accessible dataset named ONEFlux BETA. This process entailed the downscaling, gap-filling, and partitioning of flux observations from 63 distinct flux tower sites. Subsequent chapters employed this dataset, paired with other global data such as FLUXNET 2015, MODIS observations of phenology, and remote sensing observations of vegetation indices, to examine ecosystem autumn phenology and water use efficiency. Specifically, in the second chapter, I investigated the connection between photosynthesis during the growth season and end-of-season (EOS) senescence across various ecosystems, using data from 40 flux tower sites. This analysis challenged existing theories by revealing no significant negative correlation between growing season photosynthesis and EOS. On the contrary, more productive growth seasons are often associated with a delayed EOS in diverse ecosystems. The third chapter shifted the focus to water use efficiency (WUE) and its responses to water stress, factoring in variables such as vapor pressure deficit (VPD) and soil moisture. The results indicated a more consistent response to VPD across most sites. My dissertation showcases a data-driven approach to understanding ecosystem dynamics, with a particular emphasis on the carbon and water cycles of terrestrial ecosystems, harnessing data from global flux tower sites.

Low-level cloud microphysical properties are very sensitive to changes in the properties of aerosol, particularly in clean marine regions where small relative number concentration changes are amplified in low background conditions. Aerosol-cloud measurements from Ascension Island, St. Helena, a remote marine site in the tropical South Atlantic, were used to evaluate the effects of clean and smoky marine aerosol on low-level cloud microphysical properties during the 2016-2017 DOE ARM Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign. Measured aerosol size distributions were fit with three modes representing Aitken, accumulation, and sea spray aerosol, with the sea spray mode retrieved from a new technique combining submicron mass size distributions and constraints on supermicron mass from three-wavelength nephelometer scattering using Mie Theory. To investigate cloud supersaturation, the “Hoppel minimum” diameter was retrieved from the modal fits and hygroscopicity estimated from submicron composition. Accumulation-mode aerosol contributed 75% of the cloud condensation nuclei number at supersaturation

Climate change is altering both the supply of and demand for water in ecosystems across the globe. Despite the importance of understanding how ongoing changes in water availability will impact ecosystems, critical measures of tree water use and indicators of tree water stress are lacking. This dissertation uses novel tools and theories to characterize the dynamics of plant water use at multiple scales. First, two novel approaches are presented for estimating evapotranspiration at fine scales using thermal imagery and a suite of micrometeorological sensors mounted on an unmanned aerial vehicle (UAV) platform. In a comparison of UAV retrievals and eddy covariance data across 50 UAV flights, integrating atmospheric profiles of heat and moisture into a surface energy balance algorithm is shown to reduce potential uncertainty in evapotranspiration estimates by up to 50%. Next, this approach is implemented in a desert riparian ecosystem where trees are subject to significant water and temperature stress. Retrievals of canopy temperature, conductance, and transpiration are used to characterize how individual trees co-regulate water use and temperature in response to changing water supply and demand. Desert trees have different responses to supply- and demand-driven water stress, but similar responses to thermal stress. This work reveals that under most conditions, plants must make tradeoffs between hydraulic function and avoiding thermal stress, but when both supply and demand are high, regulation of water use and canopy temperature can become decoupled. Finally, soil moisture dynamics derived from a global soil moisture dataset are used to characterize plant responses to water limitation and examine the sensitivity of these responses to environmental conditions. Using a novel model to describe plant water use as a function of soil moisture, plant responses are compared by vegetation type, productivity, and antecedent aridity. Grasslands show more aggressive water use patterns and greater sensitivity to ecological and hydrological competition than woody vegetation. As climate change scenarios project an increase in the frequency and severity of droughts, studying plant adaptations to water limitation provides insights into the responses we may expect to see across a range of ecosystems. Functional understanding of the vulnerability and resilience of plants to water stress will facilitate predictions of how ecosystems might respond to warmer, drier, more variable conditions.

Climate change and its associated environmental impacts pose immense challenges that require innovative approaches to address. This dissertation presents three distinct studies that showcase the application of advanced modeling and machine learning methods to investigate critical issues at the intersection of changing human and natural systems.In Chapter 1, I employ a novel modeling framework to analyze the complex relationship between vegetation dynamics and hydroclimate variability across East Africa. Empirical dynamic modeling is a data-driven approach for studying state-dependent dynamics and interactions within complex systems, enabling the identification of key driving variables and the prediction of future system behaviors. Adopting this method, the study provides insights into how the stability and vulnerability of ecosystems vary with environmental conditions, land cover type, and seasonality. In Chapter 2, I explore how various factors contribute to human displacement, focusing on the environmental drivers and mechanisms of migration in Somalia. Gravity models are a class of spatial interaction models that estimate the flow or movement between locations based on the attractiveness of the destinations and the impedance between the origin and destination. I use these models to examine the connections between climate, socio-economic, and political factors influencing population movements. Notably, I find that livelihood is an important differentiating factor in determining whether the climate strongly impacts individuals' migration patterns. In Chapter 3, I implement advanced natural language processing techniques to develop an automated system for classifying global multi-hazard disaster events from humanitarian news articles and reports. Large language models are a form of artificial intelligence and deep learning that can process, understand, and generate human language by learning from vast amounts of textual data, enabling them to perform a wide range of natural language processing tasks. By employing these models, the study demonstrates the potential of emerging technologies in improving the efficiency of disaster information retrieval and response. With a geographical framework that unifies perspectives from environmental, social, and computer science, these chapters collectively contribute to developing data-driven solutions for understanding environmental stressors.

The actions to mitigate climate change lag behind the ambitions to limit the increase of the global average temperature by 2ºC. Socioeconomic challenges play an important role in slowing the progress of the low-carbon transition. However, while socioeconomic factors are pivotal in the low-carbon transition of the energy system, it is unclear how these factors quantitatively change the benefits and costs at national, organizational and individual levels. Here, I quantify the distribution of costs and benefits across time and space, and explore how the allocation of costs and benefits shape different stances toward the low-carbon transition. To address the socioeconomic challenges, I further examine how innovations in policy and technology enable politically and economically feasible pathways towards a low-carbon energy system. In the first chapter, I quantify the spatial distribution of stranded asset costs together with that of the GDP benefits stemming from climate change mitigation. To limit the average global temperature increase within 2°C, 95% of the global net benefits are shouldered by low and lower-middle income countries, while 90% of the stranded assets costs are borne by higher income countries. In the second chapter, I analyze the lifetime costs and benefits of climate change mitigation by age cohorts across countries under the Paris Agreement. My results show that the age cohorts born prior to 1960 generally experience a net reduction in lifetime net benefits. Age cohorts born after 1990 will gain net benefits from climate change mitigation in most lower income countries, while no age cohorts enjoy net benefits regardless of the birth year in many higher income countries. In the third chapter, I examine whether global transcontinental power pools address the unequal distribution of benefits and costs caused by heterogeneous resource endowments of renewable energy across countries. Employing an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, I assess whether transcontinental power pools reliably meet the growing global demand for renewable electricity and concurrently reduce system costs. I find that transcontinental power pools enable renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Transitioning to the next two chapters, I dissect socioeconomic barriers at the regional level, focusing on China. The fourth chapter quantifies the spatial distribution of health and employment outcomes of low-carbon electricity pathways in China. I integrate an electricity system planning model (GridPath), a health impact model (InMAP), and a multiregional input-output model to quantify China’s provincial-level impacts of electricity system decarbonization on costs, health outcomes, employment, and labor compensation. I find that disparities in health impacts across provinces narrow as fossil fuels are phased out, whereas disparities in labor compensation widen. Wealthier East Coast provinces reap the greatest benefits in labor compensation because of materials and equipment manufacturing, and offshore wind deployment. In the last chapter, I investigate whether the innovation of the hydrogen technology enables an economically feasible pathway in the low-carbon transition. I leverage an electricity planning model, GridPath, to quantify the cost implications of hydrogen penetrations, and further demonstrate how hydrogen interplay with other zero-carbon technologies and hard-to-abate sectors. I find that hydrogen reduces the cost of a zero-carbon electricity system by 16%, compared with a scenario without hydrogen. Apart from the role of long-term storage, hydrogen from the zero-carbon electricity system can be used to meet hydrogen demand in hard-to-abate sectors, while incurring a marginal decrease in the unit cost of energy demand. My dissertation reveals the socioeconomic barriers inherent in the low-carbon transition of the energy system, and calls for more actions to address the socioeconomic issues towards a sustainable energy system.

Ecosystems and societies face increasing pressures from global change, including climate and land use changes. Subsequent impacts to ecosystems are compounded through the interactions, dependencies, and feedbacks with human systems (i.e. social-ecological systems). The interdependence of ecosystems and society is manifested through the invaluable contributions that nature provides to people, commonly referred to as ecosystem services. As climate change alters the structure and functioning of ecosystems, there are corresponding impacts on ecosystem services, consequently impacting human well-being. While climate change impacts on ecosystems are well studied, the subsequent impacts on the services they provide to people are less understood, especially in the case of the non-material benefits people derive from nature (i.e. cultural ecosystem services). Thus, we lack a holistic understanding of the implications of climate change on human well-being. My dissertation draws on a multitude of disciplines to unveil and test innovative methods, tools, and data to overcome historical limitations in studying cultural ecosystem services and assess their vulnerability to climate change. Specifically, I focus on using social sensing data (i.e. sensing/data collected from humans and/or their devices) and machine learning to map, model, and value climate change impacts on cultural ecosystem services in a holistic social-ecological fashion and in understudied data-poor regions of the world. Ultimately, this work develops and tests new approaches to accounting for the non-material benefits of nature to people in order to create a more comprehensive understanding of how climate change will impact human well-being.The first chapter of this dissertation systematically reviews the literature to assess how machine learning and big data are being used in ecosystem service research to overcome major gaps in the field. I find that although cultural ecosystem services are understudied across the general literature, studies that are starting to incorporate these tools are able to address historical limitations to better account for the subjectivity, nonlinearity, and complexity of cultural ecosystem services. The second chapter develops and tests new methods, informed by the insights of chapter one, using machine learning (Random Forest) and social sensing data (Flickr) to map recreational ecosystem services across California, model the social-ecological drivers of recreation, and model the future impacts of climate change on recreation. I find that the social sensing data effectively represents recreational ecosystem service patterns across the landscape. This allowed me to use machine learning (i.e. Random Forest) to connect ecosystem service use to social-ecological drivers, showing access to cultural ecosystem service supply and climate play significant roles in driving flows to people. Further, our machine learning model predicts that climate change will exacerbate peak season recreational patterns, with highly popular regions becoming more suitable for cultural ecosystem service use and vice versa. The third chapter expands on this methodology to test its use at a large scale in a historically understudied and data-poor region. Social sensing data from eBird is used to represent the use of cultural ecosystem services across the continent of Africa, Random Forest is used to explore the social-ecological drivers of cultural ecosystem services, and Maximum Entropy Modeling is used to map cultural ecosystem service suitability and predict future suitability. I find that social sensing data from eBird is a globally available and effective proxy for cultural ecosystem service use. Further, I find that climate change will increasingly constrain the flow of cultural ecosystem services to people across Africa, with biodiversity change also playing a large role, and land use change playing a much more moderate role. Overall, I demonstrate how these tools, data, and methods can be utilized to scale and implement the study of ecosystem service flows and future impacts to them across the world. The fourth chapter expands on chapter three by integrating environmental economics methods to value the non-market utility of cultural ecosystem services and future impacts. I use social sensing data from the eBird citizen science project, machine learning (i.e. Maximum Entropy modeling), and econometric methods (i.e. the travel cost method) to map, model, and value current and future birding cultural ecosystem services across a biodiversity hotspot, South Africa. Leveraging social sensing data, I reveal national patterns of birding-related cultural ecosystem servcies and identify the beneficiaries, enabling the valuation of non-market ecosystem service demand. Additionally, through using both the social sensing data and Maximum Entropy modeling, I discern variations in the value of cultural ecosystem services, the social-ecological drivers, and the differential effects of anticipated climate, biodiversity, and land cover changes on domestic and international beneficiaries. This analysis and valuation showcases the potential of emerging data and tools to enhance and scale more holistic social-ecological analyses that account for non-market non-material ecosystem service value, improving the integration of ecosystem services in management and policy for a future shaped by global change.

Human activities release greenhouse gases and air pollutants into the atmosphere, causing global climate change and other widespread impacts on the Earth System. These emissions are concentrated in cities, where most humans live and where most transportation, energy generation and consumption occur. While many cities are taking action to reduce their emissions, verifying the success of such efforts is difficult, especially at local scales in complex urban environments. Without reliable monitoring of urban emissions trends, it is uncertain whether attempted solutions are effective and our ability to steer climate policy is limited.In my dissertation, I provide novel information about spatial and temporal patterns of anthropogenic gas emissions and our capacity to monitor emissions in urban environments. In my first study, I used a mobile laboratory to measure on-road carbon monoxide (CO) and carbon dioxide (CO2). The ratio of these two gases (CO/CO2) is a useful metric for assessing the success of regulations intended to reduce air pollutant emissions from vehicles. The results show that California’s policies and technological advancements have made the Los Angeles traffic fleet more efficient. However, combustion efficiency worsened during the COVID-19 pandemic and in Salt Lake City, likely because of changes to traffic conditions and fleet composition that offset progress in reducing vehicle CO emissions.In Chapter 2, I focus on quantifying fossil fuel CO2 (ffCO2) emission reductions that occurred during the COVID-19 pandemic using the mobile measurements from Chapter 1 and a community-sourced dataset of plant radiocarbon (14C). These two datasets reveal a significant reduction in ffCO2 in California’s urban areas in 2020 due to social distancing measures imposed by the pandemic. Furthermore, ffCO2 emissions rebounded to pre-pandemic levels by 2021, but not uniformly, with some areas taking longer to return to “normal” than others. The study demonstrated the capacity for plant 14C samples to capture ffCO2 emission reductions with shifts in human behaviors. This implied that plant sampling could be an informative and accessible tool for ffCO2 monitoring in cities that lack CO2 monitoring infrastructure as climate change mitigation policies take effect. This motivated us to conduct further tests comparing ffCO2 patterns indicated by plant 14C analysis with more established CO2 monitoring approaches.In Chapter 3, I collected turfgrass 14C samples along an urban to rural gradient in Southern California alongside measurements of in situ surface CO2 and remotely sensed total column CO2. The ffCO2 patterns indicated by each of these metrics agreed well with each other, suggesting that plant 14C analysis can independently provide similar quality information about urban ffCO2 emissions, but at a finer resolution since it is more operationally feasible to sample plants. In conjunction with surface CO2, plant 14C can also provide insight on biogenic fluxes in CO2, but more work is needed to inform nature-based climate solutions in cities. Future work should use the insights in these studies to monitor trends in urban ffCO2 emissions and guide policymakers during the transition away from fossil fuels.

Groundwater is a fundamental resource for humans and represents a major volumetric phase of the terrestrial water cycle. Observations indicate that climate change is altering precipitation patterns globally and inducing an increase in the frequency and magnitude of extreme events. These extreme precipitation events may buffer water resources long-term in the face of increasing droughts by contributing significant recharge to aquifers. Nonetheless, uncertainties remain regarding the influence of local geological variability and the unpredictability of regional weather patterns on extreme precipitation induced groundwater recharge at the catchment scale. This complexity necessitates further exploration and requires both targeted testing and extended observational studies. This study applied interferometric synthetic aperture radar (InSAR) to characterize groundwater fluxes in the ungauged endorheic basin Death Valley, California, from December 2018 through December 2023. Over this period there was mean upward vertical displacement (uplift) of ~1.5 to 2 cm across the basin caused by recharge. T-mode principal component analysis was used to isolate dominant deformation signals coinciding with extreme precipitation events. This study demonstrates the applicability of InSAR as a viable tool to assess groundwater fluxes in response to precipitation variability, furthering our understanding of terrestrial water cycling in a warming world.

Anthropogenic greenhouse gas emissions have resulted in profound changes in the physical and chemical properties of the ocean that have serious implications for all levels of ecological organization. Concomitant risk to fisheries, economies, and coastal livelihoods that depend on marine species, coupled with projections of intensifying environmental changes, challenge society’s capacity to adapt and manage effectively. The variety of complex impact mechanisms and uncertainties associated with interactions between biological and socio-economic components of fisheries necessitate integrated assessments of vulnerability and adaptive capacity at scales necessary for local-scale adaptation planning. This dissertation integrates fishery-dependent commercial landings data, regional oceanographic anomaly data, semi-structured interviews with fishermen, and fishermen feedback sessions to understand the how the commercial sectors of two of California’s most economically valuable fisheries (California spiny lobster and California market squid) will respond to future change, informed by their past responses to short-term historical climate variability, fishermen’s perceptions of constraints to adaptive capacity, and their perceptions of the efficacy of fisheries management. Given that risk and/or adaptive capacity have not been evaluated for either fishery, and considering the notable differences in these fisheries, they were ideal study species for a comparative research assessment. Chapter 1 shows that market squid fishermen have been able to adapt to dramatic shifts in the geographic range of the fishery given their high mobility. However, fishermen’s responses to change are highly contingent on how a given change manifests in the fishery, as well as characteristics of individual fishermen. Chapter 2 revealed important similarities and differences with regard to the likelihood that spiny lobster and market squid fishermen would perceive a given factor as a constraint, as well as the extent to which different domains of adaptive capacity influence their perceptions of constraints. Constraints relating to fishery governance were the most commonly perceived constraints in both fisheries; however, there were clear differences in perceptions of individual-level constraints (i.e., mobility and knowledge). Chapter 3 shows that market squid and spiny lobster fishermen are generally supportive of fishery management, as well as specific management or conservation tools, with lack of support often indicative of a desire for stronger, scientifically-backed management measures and inclusive decision-making processes. This dissertation highlights the multiple dimensions of adaptive capacity in two highly valuable fisheries, and the critical importance of designing management and decision-making processes that contribute to resilience. Key differences between these fisheries brings explicit attention to the multitude of factors that influence fishermen’s responses and perceptions, and the critical need for multi-species approaches to studying fisheries. This research also highlights the need for contextual, place-based management policies and adaptation strategies that integrate the knowledge, lived experiences, and perceptions of resource users to proactively address climate change impacts on fishermen’s livelihoods.

Decades of environmental legislation and emission reduction efforts have resulted in remarkable improvements in air quality in the United States, but we continue to find additional benefits to further abating atmospheric concentrations of toxic air pollutants such as particulate matter. This task, however, is becoming increasingly arduous on account of the current climatic transition. The atmospheric warming associated with climate change is dramatically altering the landscape of particulate matter pollution: influencing precursor emission rates, the chemical processes regulating its production, and the meteorology controlling its accumulation and dispersion. This effect is even more pronounced in urban areas, where warming is occurring fastest and where public exposure potential is greatest. Thus, continued air quality improvements in the face of climate change will necessitate a more precise understanding of the drivers of aerosol pollution that do or do not fall under our control. Focusing on summertime in the Eastern U.S., I begin by showing how the current enhancement of particulate matter with temperature is dominated by the behavior of organic aerosols, arguing that this represents a significant regulatory challenge on account of the myriad emission sources responsible for generating this subset of particulate matter. I continue by assessing the role of chemical transport models for investigating the enhancement of organic aerosols with temperature, showing how current model predictions are lacking in the representation of aerosol speciation at high temperatures, and making suggestions for future model evaluation strategies. Finally, I explore the role of meteorology in exacerbating the accumulation of pollutants, illustrating that the connection between ambient temperature and atmospheric stagnation can be exceptionally consistent, representing an amplifying factor to pollution that falls outside of our direct control but within our prognostic capabilities. Through this work, I inform strategies for probing the nature of air pollution in a warmer future, outlining the circumstances in which human intervention will be more or less effective in lessening exposure.

Rapid, global development of renewable energy, especially solar energy, is increasingly playing a pivotal role in mitigating climate change and meeting both national and global decarbonization goals. While transitioning from fossil fuels to renewable energy is necessary to address climate change and adhere to international climate accords, solar energy can have impacts—both positive and negative—on the environment and require large amounts of land. As such, novel solar energy applications, such as floating solar energy photovoltaics (FPV), have been gaining traction globally as the technology increases accessibility to solar energy to land-limited populations and also may provide non-energy benefits to the host water body. However, FPV technology is still relatively new, with little understanding of the technology on environmental and hydrological conditions. Additionally, there is a dearth of understanding towards how FPV installations relate to ground-mounted PV technologies in terms of performance and land use. The main goal of this dissertation is ultimately to explore the land use implications of solar energy technologies and, more specifically, investigate the environmental and hydrological implications of floating solar photovoltaics. While this dissertation mainly focuses on the hydrological, environmental, and land-use impacts associated with floating solar photovoltaic energy installations, it also evaluates the impact of ground-mounted solar energy-land-use interactions to form a comprehensive understanding of the interplay between solar energy and the environment. As such, this research consists of four portions:1.Using a systematic literature review on solar energy-land use metrics to propose a standardized suite of metrics for communicating solar energy-land interactions,2.Evaluating the land sparing benefits of floating solar energy installations and propose metrics for communicating the spatial footprint of FPVs,3.Investigating the impacts of FPV on continuous water temperature and dissolved oxygen dynamics at multiple locations across the United States, 4.Investigating the impacts of FPV on major water quality parameters and algae growth dynamics at multiple FPV locations across unique climatic zones.In the first chapter, a systematic literature review is conducted that evaluates the current state of solar energy-land use metrics across disciplines. Specifically, this chapter focuses on the disconnect between the metric term and associated units across studies and how this disconnect leads to the inaccurate dissemination of findings across studies. This review quantifies metric term and unit usage and uses these findings to propose standardized metrics in three distinct categories: capacity-based, generation-based, and population-based solar energy-land use metrics. By doing so, the goal of this chapter is to minimize the use of an unnecessary number of unique metrics to define identical parameters and, ultimately, aid in the accurate dissemination of findings across disciplines. In the second chapter, an evaluation of the land sparing aspects of floating solar installations along with quantifying the water surface use efficiency and water surface transformation of four FPV installations across the United States is conducted. Metrics for both generation-based and capacity-based surface use by FPV installations is proposed following similar naming mechanisms identified in chapter 1 for ground-mounted installations. The two metrics are then calculated for the four sites in this study and compared to ground-mounted installations on a per-unit of PV area basis. The results of this study indicate that the four FPVs in this study are more efficient on a per m2 of installation basis in both generation and capacity. While these findings demonstrate the technical benefits of FPVs, the chapter also provides a path for future comparative studies between FPV and other renewable energy technologies. In chapter three, a one-year field study is conducted that continuously monitors water temperature and dissolved oxygen at three FPV locations across the United States. The study compares water column temperature at multiple depths and near-surface dissolved oxygen below the FPV compared to open water locations of the same host water body. The results of this study indicate that high-coverage FPV installations compared to the water surface area can have significantly lower temperatures beneath the FPV installation than in the open water body. Dissolved oxygen is also found to be less beneath all FPV installations. Additionally, this study identifies ways in which FPVs shift the temperature range of the water column on a diel basis and shifts warming patterns towards later in the day. These findings can better inform future modeling efforts of FPV installations, specifically on shallow bodies of water, and help elucidate the impact FPV could have at helping mitigate the impacts of warming urban waterways due to climate change and urbanization. In the final chapter, a seasonal spot sampling field campaign is conducted that evaluates water quality parameters and algal growth dynamics below FPV installations compared to open water portions of the host water body. Specifically, temperature, dissolved oxygen, pH, phycocyanin, chlorophyll-a, and conductivity measurements were collected via handheld sonde over the course of four seasons from 2021-2022. Each season consisted of four days of twice-daily campaigns, one in the morning and one in the late afternoon. The findings from this study indicate that FPVs may not have a significant impact on algal growth within the host water body and further validate findings from chapter three on dissolved oxygen concentrations being lower beneath the FPV. The findings of this chapter demonstrate the need for further, continuous monitoring of water quality parameters at FPV installations and greater access to pre-FPV construction data in order to fully grasp trends that may be occurring as a response to FPV deployments.

Widespread tree mortality events occur in temperate forests during periods of severe drought. Severe droughts like the 2012-2016 California drought have become more frequent over the last several decades, threatening forests no longer aligned with their current climate conditions. To better predict what will happen to these forests in the future, we need an improved understanding of the response of forests to severe drought and the capability to predict tree mortality risk at regional scales. The increasing frequency of flight campaigns by organizations like the National Ecological Observatory Network (NEON) provides an opportunity to generate detailed maps of individual tree mortality to support risk analyses at the scale of the individual tree and support satellite-based estimates of tree mortality over a broader domain. In the first chapter of this dissertation, my coauthors and I used lidar and multispectral surface reflectance from the NEON airborne observation platform to map individual tree mortality over a 160 km2 area during and after the 2012-2016 drought for two sites in California’s Sierra National Forest. We derived tree locations and crown perimeters from the lidar point clouds and used surface reflectance and changes in crown perimeters between 2013 and 2017 to map 2017 tree mortality for more than one million trees. We found that cumulative tree mortality after the drought could be as high as 50%. In addition, we found that the subsequent effects of wildfire after the drought can be severe, with the Blue Fire of 2021 killing almost the trees within its perimeter. While tree mortality at low elevations appeared to saturate near 50%, cumulative tree mortality at higher elevations can be considerably lower (25%), with elevated rates of mortality continuing in the years after the drought subsides. In Chapter 2, we used the data set from Chapter 1 to retrospectively investigate biophysical drivers of tree mortality risk from a severe drought at the two sites in the Sierra Nevada using a machine learning method called extreme gradient boosting. Our classification models of tree mortality performed better on the lower elevation site (74% accuracy on the validation and held-out test data sets), which experienced high mortality (50%) during the drought. The most significant driving variables we explored at this site were tree height, distance to rivers, and canopy cover fraction. We found that our models trained on data from one study area did not perform well at the other, highlighting the importance of developing tree mortality benchmarking data sets, which encompass a broad domain for training predictive models of tree mortality. In Chapter 3, we aimed to create a model to estimate tree mortality fraction over a broader domain. We linked the individual tree mortality data set from Chapter 1 to the Landsat time series using one-dimensional convolutional neural networks. The R^2 values for the relationship between the mortality fraction observations and convolutional neural network predictions was 0.44 for the entire data set, including pixels with no trees, and 0.57 when we filtered for pixels with at least four trees. Our model enables the expansion of tree mortality estimates to broader spatial domains, which may help uncover fundamental interactions among biophysical drivers of tree mortality needed to generalize process-based tree mortality models at regional scales. This is important because these models are used to predict the biosphere's response to current and future climate to help predict future concentrations of atmospheric carbon.Our approaches and datasets provide a means to estimate tree mortality and predict tree mortality risk at the scale of individual trees across our study domain in the Southern Sierra Nevada to broader scales across California. These analyses may improve our understanding of forest dynamics after severe drought and subsequent wildfire to improve projections of forest structure and carbon cycling in the Anthropocene.

Riparian woodlands are hotspots of productivity and biodiversity on dryland landscapes, yet riparian tree species are also extremely vulnerable to catastrophic hydraulic damage caused by hydroclimatic change. Root zone water subsidies from shallow groundwater facilitate the high levels of productivity seen in riparian woodlands. Shallow groundwater provides a persistent source of root zone soil moisture that is somewhat decoupled from the local precipitation regime. As a result, riparian tree species are able to avoid water stress and maximize productivity throughout the prolonged summer dry seasons that are common in the southwestern United States. Groundwater declines caused by extreme drought conditions threaten the health and function of dryland riparian woodlands. If groundwater elevations drop below the root zones of riparian tree species, it is likely that riparian woodlands will experience widespread stress and mortality. However, the sensitivity of riparian tree species to changes in root zone water availability remains poorly constrained. This dissertation combines remote sensing, flux tower measurements, and ecological modeling to examine vegetation cover, evapotranspiration, and photosynthesis in riparian woodlands under changing environmental conditions. It also identifies critical physiological thresholds that are needed to maintain ecosystem structure and function under anthropogenic climate change.In the first chapter, I combine remote sensing data with measurements from groundwater monitoring wells to identify critical groundwater thresholds that are needed to maintain the health and function of riparian woodlands. The analysis examines the Santa Clara River in southern California, which experienced widespread groundwater declines during an extreme drought from 2012 to 2019. Spectral mixture analysis was used to estimate the fractional cover of green vegetation and non-photosynthetic vegetation (i.e., dead and woody plant material) in six riparian woodlands. The groundwater depth was characterized for each woodland using data from groundwater monitoring wells. The analysis revealed that riparian woodlands experience substantial decreases in green vegetation cover and substantial increases in dead/woody vegetation cover when the depth to groundwater exceeds ca. 5 m. The analysis also revealed a coherent spatial and temporal trend of riparian woodland mortality that proceeded downstream over six years and mirrored trends in groundwater elevation over space and time. The findings reveal that riparian woodlands depend on shallow groundwater access to maintain health and function, and that they experience substantial stress and mortality when they lose access to root zone water subsidies from shallow groundwater aquifers.In the second chapter, I develop a novel theoretical model to predict leaf temperature as a function of evapotranspiration. The model reveals that the difference between leaf temperature and air temperature varies as a linear function of the evaporative fraction. The model also reveals that leaf temperature converges to air temperature when the evaporative fraction equals one. The model predictions were validated using flux tower measurements from a riparian woodland and an upland savanna in southeastern Arizona. The flux tower measurements reveal that evaporative cooling reduced leaf temperature by ca. 1-5 °C in the middle of the growing season. Evaporative cooling also resulted in a ca. 15% reduction in leaf respiration. The impact of evaporative cooling on leaf carbon cycling represents a novel connection between plant water and carbon cycles via leaf energy balance that has received little attention in literature.In the third chapter, I develop a novel modeling framework to predict the vertical profiles of radiation and wind speed within forest canopies based on known physical principles. The predictions are then used to estimate the vertical profiles of leaf temperature and net photosynthesis for five flux tower sites spanning a large latitudinal gradient in North and South America. The model predictions reveal that leaf temperature decreases exponentially downward through forest canopies, and that the difference between top-of-canopy leaf temperature and bottom-of-canopy leaf temperature can exceed 7 °C. Unexpectedly, in many forest biomes, the highest levels of net photosynthesis often occur in the middle of the canopy. The analysis helps uncover the mechanistic basis for this behavior. The analysis also identifies the combinations of environmental conditions that result in critical leaf temperatures that cause irreversible damage to leaf photosynthetic infrastructure. Two different data sets provide evidence of trait coordination between leaf size and stomatal conductance to avoid critical leaf temperatures across global forest biomes.

Coral reefs are among the most biodiverse, valuable, and impacted ecosystems on the planet. Disturbances that kill coral, the foundation species of coral reef ecosystems, are becoming more frequent and more severe, thereby threatening the persistence of global reefs which provide food, jobs, and coastal protection for over one billion people worldwide. However, even the most extreme disturbances seldom cause total mortality of corals, and the spatial variability of surviving coral has consequences for how the rest of the ecosystem responds. In my dissertation work, I sought to understand 1) how and where branching corals evaded mortality during a severe marine heatwave, 2) the extent to which coral predators can amplify the impact of disturbance on coral through species interactions, and 3) whether and how the effects disturbance-driven mortality of branching coral cascaded through the ecosystem and impacted coral predators that vary in their dependency on coral as a food source. To answer these questions, I utilized and performed a combination of field experiments, in situ surveys, and statistical models. My work provides insight into the complex and nuanced relationship between coral and coral predators and improves our ability to understand and predict both how different disturbances impact coral reef ecosystem dynamics.

While individual populations wax and wane, aggregate community properties such as total biomass of organisms are often much less variable. The contrasting stability dynamics at the population and community levels have intrigued many ecologists to ask: (1) What are the underlying mechanisms maintaining long-term community stability despite the large variations in individual population size and in environmental conditions? (2) Will the mechanisms that historically undergird community stability become ineffective or even flip to destabilizing forces under changing climate? And (3) to what extent is fluctuating population dynamics chaotic in natural ecosystems? Here, I address these questions with data from freshwater ecosystems. In the first chapter, I show how climate change modifies the effectiveness of various stabilizing mechanisms—including their direction (stabilizing vs destabilizing) and strength using four-decade ecological observations in a dryland stream. In the second chapter, I focus on another stability mechanism—cross-ecosystem subsidies. Using two-year stable isotope measurements, I demonstrate the critical role of terrestrial detritus in maintaining food web stability and productivity of seasonal wetlands in two distinct and compensatory pathways. In the last chapter, I investigate whether chaos exists in lake ecosystems using time series analysis. I found evidence for high-dimension chaos in both daily and high-frequency Chlorophyll a data. The chaotic dynamics likely primarily arises from endogenous interactions within lake ecosystems.