Browsing by Department "Earth and Climate Sciences"

Wetlands are invaluable ecosystems that provide critical ecological services and contribute to global climate regulation as large carbon sinks. The Southeastern (SE) United States (US), especially its coastal regions, is rich in wetlands. Compared to their northern counterparts, wetlands in the SE US are more vulnerable to climate change, facing threats from global warming, changes in rainfall patterns, and sea-level rise. These climate change-driven disturbances can profoundly alter the hydrological processes within these wetlands, resulting in wetland degradation and a substantial reduction in their carbon storage capacity. As a result, masses of greenhouse gases like carbon dioxide (CO2) and methane (CH4) are likely to be released into the atmosphere, potentially shifting wetlands from net carbon sinks to carbon sources and further exacerbating global warming. Despite the importance of wetlands, there remains a lack of good understanding regarding the spatiotemporal patterns of wetland degradation and carbon fluxes, as well as the underlying factors and mechanisms driving these changes, especially on regional scales such as the SE US.

This dissertation aims to advance our understanding of climate change-driven wetland degradation and carbon emissions in the Southeastern United States by answering two key questions: 1) How has climate change impacted wetlands on a regional scale? and 2) What is the feedback of wetlands to the climate through their carbon emissions? To address these questions, I established a framework for monitoring regional wetland degradation, investigated the primary regulators and processes of wetland degradation, developed high-resolution and long-term wetland carbon flux datasets, and identified key environmental factors and mechanisms controlling the spatiotemporal patterns of wetland carbon fluxes in the SE US.

Specifically, by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data from Landsat, a new framework was developed to detect the spatial and temporal patterns of wetland degradation. This framework was applied at the Alligator River National Wildlife Refuge in coastal North Carolina, uncovering spatiotemporal patterns of coastal wetland degradation between 1995 and 2019. Most degradation occurred within two kilometers of the shoreline over the past five years (2015−2019), primarily due to accelerated sea-level rise. To further identify the key hydrological factors driving different types of coastal wetland degradation detected by the framework, random forest classification models were employed. The analysis underscored the varying importance of specific hydrological drivers depending on the wetland type, with woody wetlands being vulnerable to saltwater intrusion and emergent herbaceous wetlands to inundation and droughts. Distances to canals played a key role in determining the status of woody wetlands after degradation.

Additionally, to investigate how wetland carbon fluxes evolved with climate change, we integrated FLUXNET/AmeriFlux data, machine learning methods, and the process-based biogeochemical model, Forest-DNDC, to pinpoint the crucial factors and processes influencing wetland carbon fluxes across the SE US. Variable importance analysis revealed that temperature and water table levels collectively regulate methane emissions from subtropical freshwater wetlands, different from high-latitude peatlands where CH4 emissions are primarily sensitive to temperature, and tropical wetlands, where CH4 emissions are predominantly sensitive to water table levels. Moreover, we constructed the first-ever high-spatial-resolution (~1 km × 1 km) and long-term (1982-2010) monthly gridded regional wetland CH4 flux product for the SE US, estimating annual methane emissions from subtropical freshwater wetlands in the region at 5.07 ± 0.12 Tg CH4 yr-1. We also detected a significant increasing trend in annual wetland CH4 emissions, with an approximate increase of 0.006 Tg CH4 per year. Our ongoing study is exploring critical environmental variables and mechanisms governing wetland CO2 fluxes while also developing a regional-scale, long-term, and high-spatial-resolution CO2 flux data product for the SE US subtropical wetlands.

In conclusion, this dissertation offers valuable insights into the spatiotemporal patterns and primary drivers and mechanisms of wetland degradation and carbon fluxes in the Southeastern United States. Frameworks and methodologies developed in this dissertation—including a remote sensing-hydrologic model integrated scheme, a machine learning-hydrological model coupled method, and the upscaling of site-level wetland carbon fluxes using eddy covariance-based flux tower measurements, remote sensing data, and machine learning—are readily applicable to wetland regions worldwide. The knowledge gained and the datasets developed in this dissertation not only enrich our comprehension of wetlands’ role in climate feedback mechanisms but also inform strategic wetland conservation efforts. As wetlands continue to face the threats of climate change, the findings from this dissertation are essential for guiding wetland management efforts and for leveraging wetlands as potent nature-based solutions to mitigate climate change.

The Biological Carbon Pump (BCP) is a natural mechanism in the ocean that exports carbon in the deep ocean and is estimated to transfer between 5 and 12 Pg C from the surface to the deep ocean annually. While the underlying mechanisms of this process – primary producers create organic carbon from CO2 through photosynthesis, some of this organic carbon is recycled in the surface ocean, while some of it is exported to depth via physical or biological processes – have been identified for decades, this process remains difficult to quantify and predict. We estimate the carbon export potential from the surface ocean by estimating net community production (NCP) from continuously measured in situ O2/Ar ratios. In this dissertation, I aimed to assess the coherence of many methods of measuring NCP and determine factors, both physical and biological, that drive changes in NCP. Together, these goals allowed me to offer suggestions to improve modeling efforts to estimate the BCP from autonomous or remote sensing observations. To explore these topics, I used many different methods. In Chapter 2, I showed that measurements of NCP collected from different methods were consistent around Ocean Station Papa in the North Pacific after accounting for spatial heterogeneity. I compared estimates of NCP from shipboard O2/Ar measurements; O2, NO3-, particulate organic carbon (POC), and dissolved inorganic carbon (DIC) measurements from autonomous platforms, and shipboard incubations based on changes in Chl a and NO3-. I used a generalized additive mixed model to compare the datasets when spatial and temporal differences in the measurements were considered. In Chapter 3, I explored drivers of NCP by comparing how NCP related to various in situ biomarkers and biogeochemical rates measurements. I used moving Pearson’s correlations to assess how continuous measurements of biomarkers such as Chl a, POC, phytoplankton carbon, temperature, and community particle size distribution correlated to changes in continuous NCP. In addition, I showed that NCP was likely driven by changes in production, rather than respiration, in both the North Pacific and North Atlantic by comparing NCP with incubation-based estimates of gross primary production (GPP), net primary production (NPP), and microbial community respiration (mCR). Finally, I modeled NCP from the available biomarker data and determined that POC is a better proxy for estimating NCP than Chl a, in both locations. Finally, in Chapter 4, I examined how changes in the microbial community (from 16S and 18S amplicon sequencing) paired with changes in NCP in the North Pacific. I showed that at coarse taxonomic groupings, such as Phylum, Class, or plankton functional type, had no correlation to changes in NCP, while individual amplicon sequencing variants (ASVs) had strong correlations to changes in the surface ocean organic carbon balance. This indicates a need for increased granularity in microbial community composition estimates to effectively model NCP or carbon export from surface ocean microbial communities. Altogether, my research increases confidence in global NCP estimates from various platforms, presents potential improvements to biogeochemical modelling efforts, and suggests that respiration does not drive changes in NCP in the ocean.