Abstract
SummaryThe 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
Highlights
Bayesian geomorphologyThe range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods
Turbulence in moving fluids remains a major challenge for predicting flow properties, and many mechanistic models resort to time‐ or depth‐averaged approaches that carry some of the uncertainty by probabilistic measures of flow (Raffaele et al 2018)
Why should we care about ‘Bayesian’ geomorphology? If anything, we should be curious about a way of thinking that has changed the way how scientists deal with data, models and interpretations (Efron, 2013)
Summary
The range of statistical techniques that geomorphologists use to tackle this challenge has been growing, but rarely involves Bayesian methods. Many geomorphic models rely on estimated averages that largely miss out on the variability of form and process. 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. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd
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