Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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Item EXPLORING AND ASSESSING LAND-BASED CLIMATE SOLUTIONS USING EARTH OBSERVATIONS, EARTH SYSTEM MODELS, AND INTEGRATED ASSESSMENT MODELS(2024) Gao, Xueyuan; Wang, Dongdong; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Anthropogenic greenhouse gas (GHG) emissions have led the global mean temperature to increase by approximately 1.1 °C since the industrial revolution, resulting in mass ice sheet melt, sea level rise, and an increase in extreme climate events, and exposing natural and human systems to uncertainties and the risks of unsustainable development. Meeting the Paris Agreement’s climate goal of keeping temperature increases well below 2 °C — even 1.5 °C — will require removing CO2 from the atmosphere beyond reducing GHG emissions. Therefore, carbon dioxide removal and the sustainable management of global carbon cycles are one of the most urgent society needs and will become the major focus of climate action worldwide. However, research on carbon dioxide removal remains in an early stage with large knowledge gaps. The global potential and scalability, full climate consequences, and potential side effects of currently suggested carbon sequestration options — afforestation and reforestation, bioenergy with carbon capture and storage (BECCS), direct air carbon capture — are uncertain. Moreover, although about 120 national governments have a net-zero emission target, few have actionable plans for developing carbon dioxide removal.This dissertation examines two major categories of land-based carbon removal and sequestration methods: nature-based solutions that rely on the natural carbon uptake of the land ecosystem, and technology-based solutions, especially BECCS. These two options were investigated using four studies with satellite and in-situ observations, Earth system models (climate models), and integrated assessment models (policy models). Study 1 provides evidence that land ecosystem is an important carbon sink, Study 2 assesses the carbon sequestration potential of forest sustainable management via numerical experiments, Study 3 monitors recent tropical landscape restoration efforts, and Study 4 extends to BECCS and explores the impacts of future climate changes on its efficacy. Overall, this dissertation (1) improved monitoring, reporting, and verification of biomass-based carbon sequestration efforts using Earth observations, (2) improved projections on biomass-based carbon sequestration potential using Earth system models and socio-economic models, and (3) provided guidance on scaling up biomass-based carbon sequestration methods to address the climate crisis.Item Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion(2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.Item Demand-Driven Climate Mitigation in the United States: Challenges and Opportunities to Reduce Carbon Footprints from Households and State-Level Actors(2022) Song, Kaihui; Baiocchi, Giovanni; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Subnational and non-governmental actors have great potential to push for bolder climate actions to limit the global average temperature increase to 1.5 degrees Celsius above pre-industrial levels. A consistent and accurate quantification of their GHG emissions is an important prerequisite for the success of such efforts. Although an increasing number of subnational actors have developed their climate mitigation plans with medium- or long- term goals, whether these progressive commitments can yield effectiveness as planned still remains unclear. This dissertation research focuses on two large groups of climate mitigation actors in the U.S. – households and state-level actors – to improve the understanding of potential mitigation challenges and shed light on climate policies. This dissertation consists of three principle essays. The first essay reveals a key challenge of emission spillover among state-level collective mitigation efforts in the U.S. It quantifies consumption-based GHG emissions at the state level and analyzes emissions embodied in interstate and international trade. By analyzing major emission transfers between states from critical sectors, this essay proposed potential policy strategies for effective climate mitigation collaboration. The second essay addresses unequal household consumption and associated carbon footprints in the U.S., with a closer look at different contributions across income groups to the national peak-and-decline trend in the U.S. This analysis further analyzes changes in consumption patterns of detailed consumed products by income groups. The third essay proposed a framework to link people’s needs and behaviors to their consumption and associated carbon footprints. This framework, built on existing models that connect carbon footprints with consumer behaviors, extends to people’s needs with simulation over time. Such an extension provides a better understanding of carbon footprints driven by various needs in the context of real-world decision-making. Based on this framework, this essay selects a basket of behavioral changes driven by changing fundamental human needs and analyzes associated carbon footprints. The dissertation identifies opportunities and challenges in demand-driven climate mitigation in the U.S. Its findings provide implications for effective climate actions from state-level actors and households.Item Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics(2021) Ma, Lei; Hurtt, George; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change.Item PARTICIPATION IN CLIMATE CHANGE ADAPTATION: THE ROLE OF SOCIAL NETWORKS IN SUPPORTING LEARNING AND COLLECTIVE ACTION(2020) Teodoro Morales, Jose Daniel; Prell, Christina; Sun, Laixiang; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Climate change is a complex problem affecting the world in different ways and posing challenges at varying governance levels. It is widely acknowledged that broad stakeholder participation is needed to adapt to increasing climate impacts. However, interactions between stakeholders are complex and not enough is known about the social processes that support stakeholder participation or how to measure its effectiveness. The main goal of this dissertation is to increase the understanding of stakeholder participation in addressing climate change problems. Using the State of Maryland (USA) as a case study, I (1) evaluate the magnitude of climate change impacts and map the stakeholder landscape in this region, and (2) I focus on a local participatory process in the eastern shore of the Chesapeake Bay, the Deal Island Peninsula Partnership (DIPP), to study how stakeholder networks facilitate learning and collective action. I found the Chesapeake Bay is experiencing severe impacts from sea-level rise, scientists and state government produce more data and indicators at larger scales, while fewer data are produced at the local level where is needed. Increasingly, participatory approaches are being employed to bridge the knowledge gap between experts, scientists, and local stakeholders. Moreover, I found that DIPP stakeholder views are predicted by their social networks of mutual understanding, respect, and influence. Finally, by modeling the co-evolution of mutual understanding ties, co-attendance, and climate change perceptions, I found that stakeholder participation enables stronger and denser social networks of mutual understanding, yet these ties do not facilitate changes in perceptions. These results suggest that fostering mutual understanding among a diverse group of stakeholders may be more relevant for collective action than changing their perceptions. This dissertation provides empirical evidence that stakeholder participation is important in climate adaptation policies and contributes to the development of measures for stakeholder participation effectiveness.