Geography
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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 Impacts of a changing fire frequency on soil carbon stocks in interior Alaskan boreal forests(2014) Hoy, Elizabeth Embury; Kasischke, Eric S; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Increasing temperatures and drier conditions, related to climate change, have resulted in changes to the fire regime in interior Alaskan boreal forests, including increases in burned area and fire frequency. These fire regime changes alter carbon storage and emissions, especially in the thick organic soils of black spruce (Picea mariana) forests. While there are ongoing studies of the size and severity of fire using ground- and remote-based studies in mature black spruce forests, a better understanding of fire regime changes to immature black spruce forests is needed. The goal of this dissertation research was to assess impacts of changing fire frequency on soil organic layer (SOL) carbon consumption during wildland fires in recovering Alaskan black spruce forests using a combination of geospatial and remote sensing analyses, field-based research, and modeling. The research objectives were to 1) quantify burning in recovering vegetated areas; 2) analyze factors associated with variations in fire frequency; 3) quantify how fire frequency affects depth of burning, residual SOL depth, and carbon loss in the SOL of black spruce forests; and 4) analyze how fire frequency impacts carbon consumption in these forests. Results showed that considerable burning in the region occurs in stands not yet fully recovered from earlier fire events (~20% of burned areas are in immature stands). Additionally, burning in recovering black spruce forests (~40 yrs old) resulted in SOL depth of burn similar to that in mature forests which have burned. Incorporating these results into a modeling framework (through adding an immature black spruce fuel type and associated ground-layer carbon consumption values) resulted in higher ground-layer carbon consumption (and thus total carbon consumed) for areas that burned in 2004 and 2005 than that of a previous version of the model. This research indicated that the dominant controls on fire behavior in this system were fuel type and amount, not fuel condition, and that changes in vegetation associated with more frequent fire (shift to deciduous and shrub vegetation which does not traditionally burn as readily) may represent a long-term negative feedback on burned area. These new results provide insight into the fire-climate-vegetation dynamics within the region and could be used to both inform and validate modeling efforts to better estimate soil carbon pools and emissions as climate continues to change.Item TESTING FOR OPTIMAL LARGE-SCALE VEGETATION PROPERTIES FOR MAXIMUM TERRESTRIAL PRODUCTIVITY AND QUANTIFYING FUTURE UNCERTAINTY OF VEGETATION RESPONSE TO ANTICIPATED CLIMATE CHANGE(2007-07-10) Pavlick, Ryan; Dubayah, Ralph; Kleidon, Axel; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this study, I present a new approach to quantifying a range of uncertainty associated with the carbon-climate feedback over the period 1850 to 2100 within an earth system model of intermediate complexity. The degree to which terrestrial vegetation adaptively self-organizes to shape its own climatic conditions is still an open question. Nonetheless, one can simulate a 'best case' scenario, in which terrestrial productivity is periodically maximized with respect to several macroscopic vegetation parameters, commonly held constant in other models such as maximum stomatal conductance. The results of this 'dynamically optimized' simulation are compared to a simulation where the vegetation parameters are held static at the values optimized for pre-industrial conditions. With this comparison, the degree to which terrestrial productivity is underestimated when vegetation parameterizations remain static compared to those reflecting optimal adaptation to new conditions can be quantified.