Title

Geochemical methods to infer landscape response to Quaternary climate change and land use in depositional archives: A review

RIS ID

143791

Publication Details

Francke, A., Holtvoeth, J., Codilean, A., Lacey, J., Bayon, G. & Dosseto, A. (2020). Geochemical methods to infer landscape response to Quaternary climate change and land use in depositional archives: A review. Earth-Science Reviews, 207

Abstract

© 2020 Elsevier B.V. Understanding and quantifying the processes and geochemical cycles associated with catchment erosion, the development of soils and weathering horizons, and terrestrial habitat change beyond the scales of modern observations remain challenging. Such research, however, has become increasingly important to help predict future landscape change in light of increasing land use and rapid global warming. We herein review organic and inorganic geochemical tools applied to depositional archives to better understand various aspects of landscape evolution on geological time scales. We highlight the potentials and limitations of inorganic geochemical analytical methods, such as major element geochemistry, metal and radiogenic isotopes, and in-situ cosmogenic nuclides, as qualitative, semi-quantitative, and quantitative proxies for the transformation of bedrock material via regolith and soils to sediments. We also show how stable isotope geochemistry applied to lacustrine endogenic carbonates can be used to infer rock-water interactions, vegetation change, and soil development in limestone-rich catchments. Proxies focusing on the silicilastic element of sediment formation, transport and deposition are ideally combined with organic geochemical proxies for vegetation change and soil organic matter evolution in a catchment to gain a comprehensive picture of the Critical Zone's evolution over time. Multi-proxy and multidisciplinary research combining organic and inorganic geochemical techniques from several sedimentary archives in the same catchment have high potential to provide comprehensive information on Quaternary landscape evolution and thus improve the robustness of associated forecasting models.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.earscirev.2020.103218