Fluvial palaeohydrology in the 21st century and beyond

• Published in: Earth Surface Processes and Landforms
• Main Authors: Baker, Victor R., Benito, Gerardo, Brown, Antony G., Carling, Paul A., Enzel, Yehouda, Greenbaum, Noam, Herget, Jürgen, Kale, Vishwas S., Latrubesse, Edgardo M., Macklin, Mark G., Nanson, Gerald C., Oguchi, Takashi, Thorndycraft, Varyl R., Ben Dor, Yoav, Zituni, Rami
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2021
• Subjects: Pacific; Fennoscandia
• Online Access: http://dx.doi.org/10.1002/esp.5275; https://onlinelibrary.wiley.com/doi/pdf/10.1002/esp.5275; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/esp.5275

Abstract Professor Kenneth J. Gregory was a major contributor to fluvial palaeohydrological research. Beginning in the early 1980s, under his influence, rapid international growth of the discipline was accompanied by major advances in research methods and techniques. Current research emphases include applications of quantitative modelling and meta‐analysis; the correlation of fluvial events to other records, notably palaeolacustine records; and methods for application to diverse issues of river engineering and management. The international expansion and detailed analyses of fluvial palaeohydrology are exemplified by recent studies done in Fennoscandia, the Mediterranean region, India, Israel, Australia, Pacific humid island arcs, and South America. Future developments will involve expanded work with other academic disciplines, such as archaeology, as well as applications to practical problems arising from future climatic change and related environmental hazards, particularly extremes. Remote sensing and high‐resolution topography data and tools (e.g. LiDAR) will facilitate new discoveries of ancient exceptional flooding phenomena (megaflooding and superfloods) on Earth and on the palaeofluvial forms of Earth‐like planets. New opportunities will also arise from the increased use of machine learning and artificial intelligence for analyses of ‘big data’.