Geography

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Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change
(MDPI, 2016-01-06) Flanagan, Steven A.; Hurtt, George C.; Fisk, Justin P.; Sahajpal, Ritvik; Hansen, Matthew C.; Dolan, Katelyn A.; Sullivan, Joe H.; Zhao, Maosheng
There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.
Potential Transient Response of Terrestrial Vegetation and Carbon in Northern North America from Climate Change
(MDPI, 2019-09-18) Flanagan, Steven A.; Hurtt, George C.; Fisk, Justin P.; Sahajpal, Ritvik; Zhao, Maosheng; Dubayah, Ralph; Hansen, Matthew C.; Sullivan, Joe H.; Collatz, G. James
Terrestrial ecosystems and their vegetation are linked to climate. With the potential of accelerated climate change from anthropogenic forcing, there is a need to further evaluate the transient response of ecosystems, their vegetation, and their influence on the carbon balance, to this change. The equilibrium response of ecosystems to climate change has been estimated in previous studies in global domains. However, research on the transient response of terrestrial vegetation to climate change is often limited to domains at the sub-continent scale. Estimation of the transient response of vegetation requires the use of mechanistic models to predict the consequences of competition, dispersal, landscape heterogeneity, disturbance, and other factors, where it becomes computationally prohibitive at scales larger than sub-continental. Here, we used a pseudo-spatial ecosystem model with a vegetation migration sub-model that reduced computational intensity and predicted the transient response of vegetation and carbon to climate change in northern North America. The ecosystem model was first run with a current climatology at half-degree resolution for 1000 years to establish current vegetation and carbon distribution. From that distribution, climate was changed to a future climatology and the ecosystem model run for an additional 2000 simulation years. A model experimental design with different combinations of vegetation dispersal rates, dispersal modes, and disturbance rates produced 18 potential change scenarios. Results indicated that potential redistribution of terrestrial vegetation from climate change was strongly impacted by dispersal rates, moderately affected by disturbance rates, and marginally impacted by dispersal mode. For carbon, the sensitivities were opposite. A potential transient net carbon sink greater than that predicted by the equilibrium response was estimated on time scales of decades–centuries, but diminished over longer time scales. Continued research should further explore the interactions between competition, dispersal, and disturbance, particularly in regards to vegetation redistribution.
Changing cropland in changing climates: quantifying two decades of global cropland changes
(Institute of Physics, 2023-05-12) Kennedy, Jennifer; Hurtt, George C.; Liang, Xin-Zhong; Chini, Louise; Ma, Lei
Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis.
An Evaluation of the Climate Change Preparedness of Terrestrial Protected Areas
(2022-05-01) Panday, Frances Marie; Hurrt, George; Lamb, Rachel
The rate at which the climate changes and the direction of these shifts is highly variable across the landscape. As proposed by Loarie et al. (2009), the concept of a climate change velocity (CV) adds a spatial component to the rate at which the temperature increases across the landscape. Identifying where regions will experience the most significant changes in climate conditions is highly valuable for the management of areas with high ecological and societal value, such as protected areas (PAs). To examine the relationship between climate velocity and protected areas, Loarie et al. (2009) proposes the concept of a climate residence time (CRT), which estimates the length of time current climate conditions will remain in a given spatial location before shifting. Current infrastructure design managing protected areas is outdated and may be ill-equipped to handle future changes in climate. Current work examining the relationship between protected area and the CV is relatively new, but results are promising. Here, we evaluate the climate-change preparedness of terrestrial protected areas in MD by first, quantifying the magnitude of future changes using the climate residence time, and second, evaluating their capacity to manage changes by qualitatively scoring their associated management plans for climate adaptation and/or mitigation language. This two-fold approach showed that most PAs have climate residence times less than or equal to 1.5 years and had plans with little to no language addressing climate change and its associated impacts. This suggests that PAs in MD are poorly prepared for future changes in climate. Given these results, including CVs and CRTs within PA management plans would improve a park’s adaptive capacity but also signal the need for a cross-coordinated management effort that transcends different management and governance scales.
Campus Forest Carbon Project Technical Guidance Document
(2022-08-11) Panday, Frances Marie; Howerton, Michael; Kopp, Katelyn; Hurtt, George; Lamb, Rachel
The technical guidance document was created for the Office of Sustainability to support the inclusion of forest carbon into UMD's Greenhouse Gas Inventory. This document outlines the Campus Forest Carbon's project role within UMD's climate action plan and the approach to calculating forest carbon dynamics on UMD managed and owned properties.
Quantifying the Spatial and Temporal Variation of Land Surface Warming Using in situ and Satellite Data
(2019) Rao, Yuhan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The global mean surface air temperature (SAT) has demonstrated the “unequivocal warming”. To understand the impact of the global warming, it is very important to quantify the spatial and temporal patterns of the surface air temperature change. Currently, most observational studies rely on in situ temperature measurements over the land and ocean. But the uneven and sparse nature of these temperature measurements may cause large uncertainty for the climate analysis especially at local and regional scales. With the rapid development of satellite data, it is possible to estimate spatial complete surface air temperature from satellite data using advanced statistical models. The satellite data-based estimation can serve as a better data source for local and regional climate analysis to reduce analysis uncertainty. In this dissertation, I firstly examined the uncertainty of four mainstream gridded SAT datasets over the global land area (i.e., BEST-LAND, CRU-TEM4v, NASA-GISS, NOAA-NCEI). The comprehensive assessment of these datasets concludes that different data coverage may cause remarkable differences (i.e., -0.4 ~ 0.6°C) of calculated large scale (i.e., global, hemispheric) average SAT anomaly using different datasets. Moreover, these datasets show even larger differences at regional and local scale (5°×5°). The local and regional data differences can lead to statistically significant differences on linear trends of SAT estimated using different datasets. The correlation analysis shows strong relationship between the uncertainty of estimated SAT trends and the density of in situ measurements across different regions. To reduce the uncertainty of surface air temperature data, I developed a statistical modelling framework which can estimate daily surface air temperature using remote sensing land surface temperature and radiation products. The framework uses machine learning models (i.e., rule-based Cubist regression model and multivariate adaptive regression spline) to characterize the physical difference between land surface temperature and surface air temperature by including radiation products at both surface and the top of the atmosphere. The model was firstly developed for the Tibetan Plateau using Cubist model trained with Chinese Meteorological Administration station measurements. Comprehensive evaluation show that the Cubist model can estimate the surface air temperature with nearly zero degree Celsius bias and small RMSEs between 1.6 °C ~ 2.1 °C. The estimated SAT over the entire Plateau for 2000-2015 show that the warming of the western part of the Plateau has been more prominent than the rest of the region. This result show the potential underestimation of conventional station measurements based studies because there are no station measurements to represent the rapid warming region. The machine learning model is then extended to the northern high latitudes with necessary modification to account for the regional difference of the diurnal temperature cycle as well as the large data volume of the northern high latitudes. The MARS model trained using data over the northern high latitudes from the Global Historical Climatology Network daily data archive show a reasonable model performance with the bias of around -0.2 °C and the RMSE ranging between 2.1 – 2.6 °C. Further evaluation shows that the model performs worse over permanent snow and ice surface due to the insufficient training data to represent this specific surface conditions. Overall, this research demonstrated that leveraging advanced statistical methods and satellite products can help generating high quality surface air temperature data which can provide much needed spatial details to reduce the uncertainty of local and regional climate analysis. The model developed in this research is generic and can be further extended to other regions with proper modification and training using high quality local data.
QUANTIFYING FIRE-INDUCED SURFACE FORCING IN SIBERIAN LARCH FORESTS
(2017) Chen, Dong; Loboda, Tatiana V.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Wildfires are a common disturbance agent in the global boreal forests. In the North American boreal forests, they have been shown to exert a strong cooling effect through post-fire changes in surface albedo that has a larger overall impact on the climate system than associated carbon emissions. However, these findings are not directly applicable to the Siberian larch forests, a major component of the boreal biome where species composition are dominated by a deciduous needleleaf species and fire regimes are characterized by the common occurrence of both stand-replacing and less-severe surface fires. This dissertation quantifies the post-fire surface forcing imposed by both fire types in these forests over 14 years since fire, and determines that both surface and stand replacing fires impose cooling effects through increased albedo during snow season. The magnitude of the cooling effect from stand replacing fires is much larger than that of surface fires, and this is likely a consequence of higher levels of canopy damage after stand-replacing fires. At its peak (~ year 11 after fire occurrence), the cooling magnitude is similar to that of the North American boreal fires. Strong cooling effect and the wide-spread occurrence of stand-replacing fires lead to a net negative surface forcing over the entire region between 2002 and 2013. Based on the extended albedo trajectory which was made possible by developing a 24-year stand age map, it was shown that the cooling effect of stand-replacing fires lasts for more than 26 years. The overall cooling effect of surface fires is of lower magnitude and is likely indicative of damages not only to the canopies but also the shrubs in the understory. Based on the identified difference in their influences on post-fire energy budget, this dissertation also identified a remote sensing method to separate surface fires from stand-replacing fires in Siberian larch forests with an 88% accuracy. The insights gained from this dissertation will allow for accurate representation of wildfires in the regional or global climate models in the future.
Plant Migrations Impact on Potential Vegetation and Carbon Redistribution in Northern North America from Climate Change
(2016) Flanagan, Steven; Hurtt, George C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Forests have a prominent role in carbon storage and sequestration. Anthropogenic forcing has the potential to accelerate climate change and alter the distribution of forests. How forests redistribute spatially and temporally in response to climate change can alter their carbon sequestration potential. The driving question for this research was: How does plant migration from climate change impact vegetation distribution and carbon sequestration potential over continental scales? Large-scale simulation of the equilibrium response of vegetation and carbon from future climate change has shown relatively modest net gains in sequestration potential, but studies of the transient response has been limited to the sub-continent or landscape scale. The transient response depends on fine scale processes such as competition, disturbance, landscape characteristics, dispersal, and other factors, which makes it computational prohibitive at large domain sizes. To address this, this research used an advanced mechanistic model (Ecosystem Demography Model, ED) that is individually based, but pseudo-spatial, that reduces computational intensity while maintaining the fine scale processes that drive the transient response. First, the model was validated against remote sensing data for current plant functional type distribution in northern North America with a current climatology, and then a future climatology was used to predict the potential equilibrium redistribution of vegetation and carbon from future climate change. Next, to enable transient calculations, a method was developed to simulate the spatially explicit process of dispersal in pseudo-spatial modeling frameworks. Finally, the new dispersal sub-model was implemented in the mechanistic ecosystem model, and a model experimental design was designed and completed to estimate the transient response of vegetation and carbon to climate change. The potential equilibrium forest response to future climate change was found to be large, with large gross changes in distribution of plant functional types and comparatively smaller changes in net carbon sequestration potential for the region. However, the transient response was found to be on the order of centuries, and to depend strongly on disturbance rates and dispersal distances. Future work should explore the impact of species-specific disturbance and dispersal rates, landscape fragmentation, and other processes that influence migration rates and have been simulated at the sub-continent scale, but now at continental scales, and explore a range of alternative future climate scenarios as they continue to be developed.
DYNAMICS OF THE SHORT TERM AND LONG TERM AVIAN DISTRIBUTIONS IN NORTH AMERICA - THE ROLES OF VEGETATION HEIGHT HETEROGENEITY AND CLIMATIC FACTORS
(2015) Huang, Qiongyu; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Understanding how biodiversity spatially distribute over both the short term and long term, and what factors are affecting the distribution, are critical for modeling the spatial pattern of biodiversity as well as for promoting effective conservation planning and practices. This dissertation aims to examine factors that influence short-term and long-term avian distribution from the geographical sciences perspective. The research develops landscape level habitat metrics to characterize forest height heterogeneity and examines their efficacies in modelling avian richness at the continental scale. Two types of novel vegetation-height-structured habitat metrics are created based on second order texture algorithms and the concepts of patch-based habitat metrics. I correlate the height-structured metrics with the richness of different forest guilds, and also examine their efficacies in multivariate richness models. The results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of two forest bird guilds. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. The second and the third projects focus on analyzing centroids of avian distributions, and testing hypotheses regarding the direction and speed of these shifts. I first showcase the usefulness of centroids analysis for characterizing the distribution changes of a few case study species. Applying the centroid method on 57 permanent resident bird species, I show that multi-directional distribution shifts occurred in large number of studied species. I also demonstrate, plain birds are not shifting their distribution faster than mountain birds, contrary to the prediction based on climate change velocity hypothesis. By modelling the abundance change rate at regional level, I show that extreme climate events and precipitation measures associate closely with some of the long-term distribution shifts. This dissertation improves our understanding on bird habitat characterization for species richness modelling, and expands our knowledge on how avian populations shifted their ranges in North America responding to changing environments in the past four decades. The results provide an important scientific foundation for more accurate predictive species distribution modeling in future.
MODELING OF SEASONAL TRACE GAS AND PARTICULATE EMISSIONS FROM VEGETATION FIRES IN SOUTHERN AFRICA
(2004-04-29) Korontzi, Stefania; Justice, Christopher O; Geography
Fire is widespread in southern African savannas with important implications for tropical and global atmospheric chemistry. However, previous regional emission studies have not fully accounted for the variability of the emissions throughout the burning season and the associated impacts on emissions quantification. The main aim of this study is to address this gap. The complexity of the emissions process is described using a spatially and temporally explicit modeling approach that integrates recently published satellite-driven fuel load amounts, satellite burned area products, and empirically derived parameterizations of combustion completeness and emission factors. To represent fire behavior characteristics, land cover is classified into grasslands and woodlands, using a satellite-derived percent tree cover product. The combustion completeness is modeled as a function of grass fuel moisture and the emission factors as a function of grass fuel moisture in grasslands and fuel mixture in woodlands. Fuel moisture is derived from a fuel load model and by using satellite vegetation index time series. A sensitivity analysis with respect to three satellite burned area products reveals large differences in emissions due to differences in their amounts and spatial distribution. The analysis at the regional scale shows, that early burning in grasslands may lead to higher amounts of products of incomplete combustion despite the lower amounts of fuel consumed, compared with late dry season burning. In contrast, early burning in woodlands results in lower emissions because less fuel gets consumed. These seasonal emissions trends become more pronounced when the fuels are wetter. Burning in woodlands dominates the regional emissions budgets. Emissions estimates for various atmospheric species, many of which are modeled for the first time, are reported and compared with other regional sources of pyrogenic emissions and global biomass burning and fossil fuel emissions. The modeled estimates for 2000 are (in Tg): 537 CO2, 23.2 CO, 0.726 CH4, 0.661 NMHC, 2.4 particulates (< 2.5 micron), 1.0 NOx and account for significant fractions of regional emissions from all pyrogenic sources. Especially high is the previously undetermined contribution of Oxygenated Volatile Organic Compounds (1.8 Tg). The methodology and results have direct implications for national reporting of savanna fire emissions.

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