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Bayesian geomorphology
(2020)
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.
Bayesian geomorphology
(2020)
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.
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
Many of the volcanic plateau margins of the eastern, formerly glaciated, foreland of the Patagonian Andes are undermined by giant landslides (>= 10(8) m(3)). One cluster of such landslides extends along the margin of the Meseta del Lago Buenos Aires (MLBA) plateau that is formed mainly by Neogene-Quaternary basalts. The dry climate is at odds with numerous >2-km long earthflows nested within older and larger compound landslides. We present a hydrological analysis, a detailed geomorphic map, interpretations of exposed landslide interiors, and radiocarbon dating of the El Mirador landslide, which is one of the largest and morphologically most representative landslide. We find that the presence of lakes on top of the plateau, causing low infiltration rates, correlates negatively with the abundance of earthflows on compound landslides along the plateau margins. Field outcrops show that the pattern of compound landslides and earthflows is likely controlled by groundwater seepage at the contact between the basalts and underlying soft Miocene molasse. Numerous peat bogs store water and sediment and are more abundant in earthflow-affected areas than in their contributing catchment areas. <br /> Radiocarbon dates indicate that these earthflows displaced metre-thick layers of peat in the late Holocene (<2.5 ka). We conclude that earthflows of the MLBA plateau might be promising proxies of past hydroclimatic conditions in the Patagonian foreland, if strong earthquakes or gradual crustal stress changes due to glacioisostatic rebound can be ruled out.