Bayesian geomorphology. Issue 1 (21st September 2020)

• Record Type: Journal Article
• Authors: Korup, Oliver
• Is Part Of: Earth surface processes and landforms. Volume 46:Issue 1(2021)
• Page Start: 151
• Page End: 172
• Publication Date: 2020-09-21
• Subjects: Bayes' rule -- probability -- uncertainty -- prediction; Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.4995
• Languages: English
• ISSNs: 0197-9337

Abstract

Summary: The rapidly growing amount and diversity of data are confronting us more than ever with the need to make informed predictions under uncertainty. The adverse impacts of climate change and natural hazards also motivate our search for reliable predictions. The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Instead, many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. Yet seemingly fixed estimates of channel heads, sediment rating curves or glacier equilibrium lines, for example, are all prone to uncertainties. Neighbouring scientific disciplines such as physics, hydrology or ecology have readily embraced Bayesian methods to fully capture and better explain such uncertainties, as the necessary computational tools have advanced greatly. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd Abstract : Geomorphologists rarely use Bayesian methods in their research, often relying instead on model averages that largely miss out on the variability of form and process. The aim of this article is to introduce the Bayesian toolkit to scientists concerned with Earth surface processes and landforms, and to show how geomorphic models might benefit from probabilistic concepts. I briefly review the use of Bayesian reasoning in geomorphology, and outline the corresponding variants of regression and classification in several worked examples.