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University of Oxford (2018)

New methods for identifying dune system reactivation drivers and responses, Nebraska Sandhills

Buckland, Catherine

Titre : New methods for identifying dune system reactivation drivers and responses, Nebraska Sandhills

Auteur : Buckland, Catherine

Université de soutenance : University of Oxford

Grade : Doctor of Philosophy (PhD) 2018

Résumé
Two billion people living in drylands are affected by land degradation and the destabilisation of surface sediments. However, a detailed understanding of the combined effects of natural and anthropogenic factors in contributing to sediment remobilisation is absent from the literature. A quantified awareness of vegetation cover sensitivities and resultant land degradation to forcing factors is needed if the vegetation and landscape response to future climatic changes and human pressure are to be better predicted. Measuring past environmental response in a location with a known disturbance history allows us to identify thresholds and explore the relationship between disturbance forces and sedimentary response within a dryland environment. The Nebraska Sandhills, located in the Central Great Plains, is a semi-arid dune field with a reactivation history spanning the last 10,000 years and a well-documented history of forcing factors over recent decades. Whilst regional reactivations in the Central Great Plains have largely been attributed to palaeoclimatic change, the role of humans (e.g. The Dust Bowl of the 1930s), in particular over grazing, has also been cited as a potential cause of land degradation and sediment reactivation. Using quartz luminescence dating, secondary datasets of forcing factors and statistical inference methods, this thesis identifies the record of sediment deposition in near-surface aeolian stratigraphies in the northern remits of the Nebraska Sandhills and explores the relationship between environmental sensitivity and external disturbance factors. High-resolution luminescence techniques allow us to construct a detailed chronological history of sedimentary deposition events over the last 900 years, producing a record of the environmental response across a range of aeolian features within the semi-arid setting. Combined with a detailed history of climatic, grazing and wildfire pressures, supervised machine learning techniques explore the relationship between forcing factor and environmental response, highlighting the importance of both regional and localised conditions in contributing to the heterogeneous sedimentary response found in the record.

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