Field studies were conducted at Lincoln University of Missouri (USA) and Hokkaido University (Japan) to: (i) study the relationships between greenhouse gases emissions and soil properties, (ii) assess the influence of agricultural practices on greenhouse gas fluxes and soil properties and (iii) improve the quantification of greenhouse gases from soil in agricultural fields using geospatial technologies. Results showed that besides soil temperature (T), soil thermal properties such as thermal conductivity (K), resistivity (R) and diffusivity (D) and soil pore spaces indices such as the pore tortuosity factor and the relative gas diffusion coefficient (Ds/Do) are controlling factors for greenhouse gases emissions. Soil thermal properties correlated with greenhouse gases emissions when soil temperature could not. The study has found that predicted Ds/Do and correlate with greenhouse gas fluxes even when the air-filled porosity and the total porosity from which they are predicted did not. We have also showed that Ds/Do and can be predicted quickly from routine measurements of soil water and air and existing diffusivity models found in the literature. Agricultural practices do seriously impact greenhouse gases emissions as showed by the effect of mechanized tillage operations on soil physical properties and greenhouse gas fluxes in a corn and soybean fields. In fact, our results showed that tractor compaction increased soil resistance to penetration, water, bulk density and pore tortuosity while reducing air-filled porosity, total pore space and the soil gas diffusion coefficient. Changes in soil properties resulted in increased CO2, NO and N2O emissions. Finally, our results also confirmed that greenhouse gas fluxes vary tremendously in space and time. As estimates of greenhouse gas emissions are influenced by the data processing approach, differences between the different calculation approaches leads to uncertainty. Thus, techniques for developing better estimates are needed. We have showed that Geographic Information Systems (GIS), Global Positioning System (GPS), computer mapping and geo-statistics are technologies that can be used to better understand systems containing large amounts of spatial and temporal variability. Our GIS-based approach for quantifying CO2, CH4 and N2O fluxes from soil in agricultural fields showed that estimating (extrapolating) total greenhouse gas fluxes using the “standard” approach – multiplying the average flux value by the total field area – results in biased predictions of field total greenhouse gases emissions. In contrast, the GIS-based approach we developed produces an interpolated map portraying the spatial distribution of gas fluxes across the field from point measurements and later process the interpolated map produced to determine flux zones. Furthermore, processing, classification and modeling enables the computation of field total fluxes as the sum of fluxes in different zones, therefore taking into account the spatial variability of greenhouse gas fluxes.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

published_or_final_version / Applied Geosciences / Master / Master of Science

Climate change constitutes one of the greatest challenges facing the world today, as it will influence the way we live and work in future decades. Excessive greenhouse gas (GHG) emissions are recognized as the key contributor to climate change, and the construction sector has an indispensable role to play in emission reduction, as building facilities are energy- and emission-intensive to construct and operate. Previous research indicates that up to 30 percent of buildings’ lifecycle emissions can be minimized through the careful selection of low-carbon materials. Although building environmental assessment (BEA) tools have been widely used in identifying and mitigating the lifecycle environmental impacts of building facilities, the existing BEA tools provide no rigorous regime for assessing the embodied GHG emissions of building materials. Therefore the aim of this research is to bridge the research and practical gaps by developing an integrated BEA assessment model that comprehensively audits and benchmarks the embodied GHG emissions of building materials at product level. The research began by examining the limitations of current BEA tools, in particular their means of evaluating the embodied GHG emissions of buildings. Then, an embodied GHG emissions evaluation module model under an existing BEA scheme was proposed. The proposed model comprised (i) product category, (ii) product-based GHG auditing framework, and (iii) emissions benchmarking measure. After that, a thorough review of the relevant literature and international classification systems was carried out to establish a systematic product categorization regime for building materials. An auditing framework comprising system boundary, process map, emission sources, and a carbon auditing tool in Microsoft TM Excel has been developed by reviewing international standards on product carbon footprint assessments and eliciting knowledge from domain experts through a series of interviews. The emission benchmarks for each product category have been determined through the application of fuzzy set theory to facilitate easy comparison and decision-making. Finally, the developed product categorization regime, GHG auditing framework, and benchmarks were validated through a Delphi study, a discussion of which concluded the thesis. The research outcomes confirm that the GHG emissions embodied in a building facility can be meticulously analyzed and integrated into the BEA. The research also improves the understanding of how the materials’ embodied emissions can be accurately calculated at the product level. More importantly, it enhances existing BEA tools by incorporating embodied GHG emissions into the analysis, thus makes the lifecycle emission assessment of building facilities possible. The proposed integrated BEA model will enable clients and design teams to minimize the carbon footprints of buildings and assist users and the general public in identifying green building facilities. The originality of this research lies in the establishment of a set of emissions benchmarks for five most emission-intensive building materials using fuzzy set theory. These benchmarks provide a seamless platform allowing the assessment of materials’ embodied emissions to be integrated with the existing BEA model, thereby not only encouraging the adoption of low-carbon building materials but also fostering ongoing product carbon footprint reductions. / published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

The Asian monsoon-affected area is one of the most vulnerable regions in the world facing hydroclimate changes. Anthropogenic climate change, particularly the emissions of greenhouse gases (GHGs) and aerosols, exerts significant impacts on monsoon rainfall and circulation. Understanding the effects of external forcing on monsoon rainfall is essential for improving the predictability, constraining the uncertainty, and assessing the climate risks. In this dissertation, I use a combination of observations, outputs from multiple Coupled Model Intercomparison Project - Phase 5 (CMIP5) models, and idealized atmospheric general circulation model (AGCM) experiments to examine the Asian summer monsoon variability and change. The main focus is understanding the responses to GHGs and anthropogenic aerosols and their differences for both the historical period and future projections. The Asian monsoon is an interactive system influenced by multiple natural and anthropogenic factors. GHGs and aerosols induce significantly different changes in monsoon rainfall through both thermodynamical and dynamical processes. These changes can be further separated into the fast adjustments related to radiation and cloud processes and the slow response due to changes in sea surface temperature (SST). This thesis provides a detailed analysis of the multiple physical processes entangled in the total response, advancing our mechanistic understanding of the effects of external forcing on the Asian monsoon system and the associated uncertainties. In Chapter 2, I first analyze the monsoon-ENSO (El Nino - Southern Oscillation) relationship in observations and CMIP5 models to determine the role of natural variability. Separating the natural and forced components shows that natural variability plays a dominant role in the 20th century, however enhanced monsoon rainfall associated with global warming may contribute to a weakened ENSO-monsoon relation in the 21st century. In Chapter 3, I examine the physical mechanisms causing the changes of the Asian summer monsoon during the 20th and 21st century using observations and CMIP5 models, attributing the rainfall changes to the relative roles of thermodynamic and dynamic processes. CMIP5 models show a distinct drying of the Asian summer monsoon rainfall during the historical period but strong wetting for future projections, which can be explained by the strong aerosol-induced dynamical weakening during the 20th century and the thermodynamic enhancement due to GHGs in the 21st century. In Chapters 4 and 5, I further use multiple AGCMs to separate the total monsoon response into a fast adjustment component independent of the sea surface temperature (SST) responses, and a slow response component associated with SST feedbacks. For GHGs (Chapter 4), the fast and slow monsoon circulation changes largely oppose each other, leading to an overall weak response and large inter-model spread. For aerosols (Chapter 5), the strongly weakened monsoon circulation over land due to aerosols is largely driven by the fast adjustments related to aerosol-radiation and aerosol-cloud interactions. Finally in Chapter 6, I design idealized AGCM experiments with prescribed SSTs using the Community Atmosphere Model (CAM5) and the Geophysical Fluid Dynamic Laboratory Model (GFDL-AM3) to investigate the relative roles of uniform SST warming/cooling as well as global and regional SST patterns in shaping the differing monsoon responses. While GHGs-induced SST changes affect the monsoon largely via the uniform warming effect, for aerosols the SST spatial pattern plays the dominant role through changes in atmospheric circulation.

Mountain glaciers have influenced the surface of our planet throughout geologic time. These large reservoirs of water ice sculpt alpine landscapes, regulate downstream river flows, perturb climate-tectonic feedbacks, contribute to sea level change, and guide human migration and settlement patterns. Glaciers are especially relevant in modern times, acting as buffers which supply seasonal meltwater to densely populated downstream communities and support economies via hydropower generation. Anthropogenic warming is accelerating ice loss in most glacierized regions of the world. This has sparked concerns regarding water resources and natural hazards, and placed glaciers at the forefront of climate research. Here we provide new observations of glacier change in key mountain regions to quantify rates of ice loss, better understand climate drivers, and help establish a more unified framework for studying glacier change across timescales. In Chapter 1 we use seismic observations, numerical modeling, and geomorphic analysis to investigate a destructive glacial lake outburst flood (GLOF) which occurred in Bhutan. GLOFs are a substantial hazard for downstream communities in many vulnerable regions. Yet key aspects of GLOF dynamics remain difficult to quantify, as in situ measurements are scarce due to the unpredictability and remote source locations of these events. Here we apply cross-correlation based seismic analyses to track the evolution of the GLOF remotely (~100 km from the source region), use the seismic observations along with eyewitness reports and a downstream gauge station to constrain a numerical flood model, then assess geomorphic change and current state of the unstable lakes via satellite imagery. Coherent seismic energy is evident from 1 to 5 Hz beginning approximately 5 hours before the flood impacted Punakha village, which originated at the source lake and advanced down the valley during the GLOF duration. Our analysis highlights potential benefits of using real-time seismic monitoring to improve early warning systems. The next two chapters in this work focus on quantifying multi-decadal glacier ice loss in the Himalayas. Himalayan glaciers supply meltwater to densely populated catchments in South Asia, and regional observations of glacier change are needed to understand climate drivers and assess impacts on glacier-fed rivers. Here we utilize a set of digital elevation models derived from cold war–era spy satellite film and modern stereo satellite imagery to evaluate glacier responses to changing climate over the last four decades. In Chapter 2 we focus on the eastern Himalayas, centered on the Bhutan–China border. The wide range of glacier types allows for the first mass balance comparison between clean, debris, and lake-terminating (calving) glaciers in the area. Measured glaciers show significant ice loss, with statistically similar mass balance values for both clean-ice and debris-covered glacier groups. Chapter 3 extends the same methodology to quantify glacier change across the entire Himalayan range during 1975–2000 and 2000–2016. We observe consistent ice loss along the entire 2000-km transect for both intervals and find a doubling of the average loss rate during 2000–2016 compared to 1975–2000. The similar magnitude and acceleration of ice loss across the Himalayas suggests a regionally coherent climate forcing, consistent with atmospheric warming and associated energy fluxes as the dominant drivers of glacier change. Chapter 4 investigates millennial-scale glacier changes during the Late Glacial period (15-11 ka). Here we present a high-precision beryllium-10 chronology and geomorphic map from a sequence of well-preserved moraines in the Nendaz valley of the western European Alps, with the goal to shed light on the timing and magnitude of glacier responses during an interval of dramatic natural climate variability. Our chronology brackets a coherent glacier recession through the Younger Dryas stadial into the early Holocene, similar to glacier records from the southern hemisphere and a new chronology from Arctic Norway. These results highlight a general agreement between mountain glacier changes and atmospheric greenhouse gas records during the Late Glacial. In Chapter 5 we use a numerical glacier model to simulate glacier change across a typical alpine region in the European Alps. Model results suggest that shorter observational timespans focused on modern periods (when glaciers are far from equilibrium and undergoing rapid change) exhibit greater spatial variability of mean annual ice thickness changes, compared to intervals which extend further back in time (to include decades when climate was more stable). The model agrees with multi-decadal satellite observations of glacier change, and clarifies the positive correlation between glacier disequilibrium and spatial variability of glacier mass balance. This relationship should be taken into account in regional glacier studies, particularly when analyzing recent spatial patterns of ice loss. Advances made in this work are of practical value for societies vulnerable to glacier change. This includes potential improvements to GLOF early warning systems via seismic monitoring, better constraints on glacier-sourced water scenarios in South Asia, strengthened understanding of long-term glacier responses to baseline natural climate variability, and a clarified relationship between glacier disequilibrium and spatial variability of ice loss. When placed within a global context, our observations highlight the correlation between regional mountain glacier change and greenhouse gas forcing through time.

e transport sector is responsible for a quarter of global CO2 emissions and the emissions continue to grow rapidly. The overall objective of this study was to calculate the following greenhouse gas emissions (GHG); CO2, CH4 and N2O from the transport sector in South Africa in the base year 2009. However, in addition to the calculations of the emissions for this base year, emissions from road transport were recalculated since 2000. The available data allowed only Tier 1 method to calculate all the GHG emissions. Vehicles per type, province and distances they travelled were used to estimate the emissions, while fuel used at various airports in the country was used to determine aviation emissions. Emissions from other modes of the transport sector were calculated using the data from the national energy balances. It was estimated that 54,296 Giga grams (Gg) of CO2 equivalent (CO2-eq) emissions were emitted in 2009. Road, off- road, aviation and rail transports accounted for 80%, 13%, 6% and 1% of the emissions, respectively. Motorcars and trucks produced more than 70% of the road transport emissions. Road transport emissions increased at approximately 2.66% per year between 2000 and 2009. Gauteng province had the highest emissions. Minibus taxis were the most efficient transport mode on the basis of load carried.

The semiarid region between the North African Savanna and Sahara Desert, known as the Sahel, experienced dramatic multidecadal precipitation variability in the 20th century that was unparalleled in the rest of the world, including devastating droughts and famine in the early 1970s and 80s. Accurate predictions of this region’s hydroclimate future are essential to avoid future disasters of this kind, yet simulations from state of the art general circulation models (GCMs) do a poor job of simulating past Sahel rainfall variability, and don’t even agree on whether future precipitation will increase or decrease under global warming. Furthermore, climate scientists are still not in agreement about whether anthropogenic emissions played an important role relative to natural variability in dictating past Sahel rainfall change. Because the climate system is complex and coupled, it is difficult to determine which processes should be considered causal drivers of circulation changes and which should be considered part of the climate response, and therefore many theories for monsoon rainfall variability coexist in the literature. It is difficult to evaluate these competing theories because observational studies generally cannot be interpreted causally, but simulated experiments may not represent the dynamics of the real world. The Coupled Model Intercomparison Project (CMIP) provides a wealth of data in which GCMs maintained at research institutions worldwide perform similar experiments, allowing the researcher to reach conclusions that are robust to differences in parameterization between GCMs. The scientific community has been using a wide range of statistical techniques to analyze this data, and each has notable limitations. This dissertation explores two statistical techniques for leveraging CMIP to explore the drivers and mechanisms of historical Sahel rainfall variability: analysis of ensemble-mean responses to prescribed variables, and causal inference. In ‎Chapter 1, we give an overview of the climatology and variability of Sahel rainfall and present relevant physical theory. In ‎Chapter 2, we examine the roles of various types of anthropogenic forcing in observations and coupled simulations, using a 3-tiered multi-model mean (MMM) to extract robust climate signals from CMIP phase 5 (CMIP5). We examine “20th century” historical and single-forcing simulations—which separate the influence of anthropogenic aerosols, greenhouse gases (GHG), and natural radiative forcing on global coupled ocean-atmosphere system, and were specifically designed for attribution studies—as well as pre-Industrial control simulations, which only contain unforced internal climate variability, to investigate the drivers of simulated Sahel precipitation variability. The comparison of single-forcing and historical simulations highlights the importance of anthropogenic and volcanic aerosols over GHG in generating forced Sahel rainfall variability that reinforces the observed pattern, with anthropogenic aerosols alone responsible for the low-frequency component of simulated variability. However, the forced MMM only accounts for a small fraction of observed variance. A residual consistency test shows that simulated internal variability cannot explain the residual observed multidecadal variability, and points to model deficiency in simulating multidecadal variability in the forced response, internal variability, or both. In ‎Chapter 3, we investigate the causes for discrepancies in low-frequency Sahel precipitation variability between these ensembles and for model deficiency in reproducing observations. In the most recent version of CMIP – phase 6 of the Coupled Model Intercomparison Project (CMIP6) – the differences between observed and simulated variability are amplified rather than reduced: CMIP6 still grossly underestimates the magnitude of low-frequency variability in Sahel rainfall, but unlike CMIP5, historical mean precipitation in CMIP6 does not even correlate with observed multi-decadal variability. We continue to use a MMM to extract robust climate signals from simulations, but now additionally include sea surface temperature (SST) as a mediating variable in order to test the proposed physical processes. This partitions all influences on Sahel precipitation variability into five components: (1) teleconnections to SST; (2) atmospheric and (3) oceanic variability internal to the climate system; (4) the SST response to external radiative forcing; and (5) the “fast” (not mediated by SST) precipitation response to forcing. Though the coupled simulations perform quite poorly, in a vast improvement from previous ensembles, the CMIP6 atmosphere-only ensemble is able to reproduce the full magnitude of observed low-frequency Sahel precipitation variance when observed SST is prescribed. The high performance is due entirely to the atmospheric response to observed global SST – the fast response to forcing has a relatively small impact on Sahel rainfall, and only lowers the performance of the ensemble when it is included. Using the previously-established North Atlantic Relative Index (NARI) to approximate the role of global SST, we estimate that the strength of simulated teleconnections is consistent with observations. Applying the lessons of the atmosphere-only ensemble to coupled settings, we infer that both coupled CMIP ensembles fail to explain low-frequency historical Sahel rainfall variability mostly because they cannot explain the observed combination of forced and internal variability in SST. Though the fast response is small relative to the simulated response to observed SST variability, it is influential relative to simulated SST variability, and differences between CMIP5 and CMIP6 in the simulation of Sahel precipitation and its correlation with observations can be traced to differences in the simulated fast response to forcing or the role of other unexamined SST patterns. In this chapter, we use NARI to approximate the role of global SST because it is considered by some to be the best single index for estimating teleconnections to the Sahel. However, we show that NARI is only able to explain half of the high-performing simulated low-frequency Sahel precipitation variability in the atmospheric simulations with prescribed global SST. Statistical techniques commonly applied in the literature cannot distinguish between correlation and causality, so we cannot analyze the response of Sahel rainfall to global SST in more depth without atmospheric CMIP simulations targeted at every ocean basin of interest or a new method. In ‎Chapter 4, we turn to a novel technique called causal inference to qualify the notion that NARI can adequately represent the role of global SST in determining Sahel rainfall. We apply a causal discovery algorithm to CMIP6 pre-Industrial control simulations to determine which ocean basins influence Sahel rainfall in individual GCMs. Though we find that state of the art causal discovery algorithms for time series still struggle with data that isn’t generated specifically for algorithm evaluation, we robustly find that NARI does not mediate the full effect of global SST variability on Sahel rainfall in any of the climate simulations. This chapter lays the foundation for future work to fully-characterize the dependence of Sahel precipitation on individual ocean basins using the non-targeted simulations already available in CMIP – an approach which can be validated by comparing the composite results to the interventional historical simulations that are available. Furthermore, we hope this chapter will guide algorithm improvement efforts that are needed to increase the performance and usefulness of time series causal discovery algorithms on climate data.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Tropical forests represent major uncertainties in climate models and have the potential to act as both net carbon sources and sinks in the future. Projections that hurricanes will be an increasingly powerful disturbance in many tropical forests further complicate our ability to predict how these ecosystems will respond to climate change. By understanding how environmental variation at small spatial scales affects ecosystem processes shaping present-day forests, it may be possible to improve our predictions for how these forests will change in the future. This dissertation consists of three chapters examining the spatial patterns of tree species and soil greenhouse gas fluxes in a tropical forest in the Luquillo Experimental Forest, Puerto Rico. Disentangling the forces that drive the spatial distribution of tree species has been a foundational question in ecology and determining the relative importance of these forces is central to understanding spatial variation in soil biogeochemistry. In chapter 1, I use percolation threshold analysis to examine the clustering patterns of simulated and real tree spatial point patterns to understand the role that environmental filtering and density dependent processes play in shaping tree species distributions. I demonstrate that percolation threshold analysis successfully distinguishes thinning by random, environmental filtering, and density dependent processes. Additionally, the relative importance of these thinning processes varies by species’ traits; fast growing species with low LMA and shade intolerance have stronger evidence of density dependent processes compared to species with high LMA and shade tolerance. In chapter 2, I examine the spatial relationships between soil greenhouse gas fluxes and two proximal drivers of soil environmental variation: tree species and topography. I also examine how incorporating small-scale variation in greenhouse fluxes affects our scaled-up estimates of ecosystem greenhouse gas emissions. I show that including species effects improves estimates of soil CO2 fluxes, and including measures of topography improve estimates of CH4 and N2O fluxes. Incorporating spatial variation in GHG fluxes related to tree species and topography into our estimates of ecosystem GHG emissions decreased estimates of the total CO2-equivalent emissions in this forest by 5%. Finally, in chapter 3 I examine how the GHG fluxes in this forest change after an intense hurricane. I demonstrate that GHG emissions shift following a hurricane; this shift is primarily driven by a 176% increase in N2O emissions that represent a significant net loss of gaseous nitrogen from this forest. N2O fluxes accounted for 4.2% of the post-hurricane GHG-induced radiative forcing (compared to 1.8% pre-hurricane) and the combined increase in CO2, CH4, and N2O emissions observed translates to a 25% increase in CO2-equivalent emissions compared to pre-hurricane conditions. This dissertation focuses on the role of small-scale environmental variation in shaping forest communities and spatial patterns of GHG fluxes and aims to highlight how this variation can help us to better understand the role tropical forests play in the biosphere.