Bayesian analysis to detect abrupt changes in extreme hydrological processes

Jo, Seongil; Kim, Gwangsu; Jeon, Jong-June

doi:10.1016/j.jhydrol.2016.03.065

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…Future research will apply the proposed Bayesian method to meteorological/hydrological variables that are not expected to follow a normal distribution, e.g., maximum precipitation which may be assumed to follow a generalized extreme value distribution as in Jo et al (2016). Another potential extension of the proposed Bayesian method is to treat datasets for multiple regions within a single Bayesian model.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Ohn

Seo

Kim

et al. 2020

CSAM

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A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

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Section: Discussionmentioning

confidence: 99%

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Ohn

Seo

Kim

et al. 2020

CSAM

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“…Change-point models have been used, for example, in the context of objectively tracking the onset of induced earthquakes in Oklahoma (Fiedler et al, 2018). Hydrological time series are also amenable to change-point analysis, and Jo et al (2016) used a Bayesian extreme-value model with a Markovian structure to detect changes in maximum yearly precipitation. These and other Bayesian models based on piecewise regression (Gallagher et al, 2011) have the advantage of explicitly handling the uncertainties in parameters and previous knowledge.…”

Section: Methodsmentioning

confidence: 99%

Bayesian Detection of Streamflow Response to Earthquakes

Korup

Mohr

Manga

2021

Water Resources Research

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Pliny the Elder lived in the 1st century CE and was among the first to report in writing that the amount of water discharged at Earth's surface can change following earthquakes. Following the Great Lisbon Earthquake in 1755, Immanuel Kant described sudden changes in surface hydrology that occurred "in the same minutes as the earthquake devastated the coasts of Portugal." He noted that "in just (…) minutes, the mineral water at Töplitz in Bohemia (Czech Republic) suddenly stopped and returned to blood red," while "in the Kingdom of Fez (Morocco) in Africa, a subterranean force split a mountain and poured blood-red streams out of its mouth" (Kant, 1756). Many other similar observations show that streamflow can increase or decrease following strong seismic shaking and thus reflects responses of groundwater being supplied to streams (Mohr et al., 2017). Earthquakes modify crustal stresses, hydraulic heads, and physical properties such as the permeability of the subsurface that all control water flux (Wang & Manga, 2014). Altered river discharges following earthquakes have been reported in the near-and intermediate-field, that is, within one or a few rupture lengths away from the earthquake source (Rojstaczer et al., 1995). The magnitude of streamflow responses can involve the release of >1 km 3 of excess water into streams (Mohr et al., 2017) and sustain enhanced streamflow for months (

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“…2015; Jo et al . 2016), although geomorphologists have invested somewhat less in this approach. Yet rarely do we encounter data, problems or predictions that are free of uncertainty.…”

Section: Acknowledging Uncertainty In Geomorphologymentioning

confidence: 99%

“…Bayesian theory offers the tools to measure these uncertainties, and this article intends to introduce some of the underlying concepts. Fields such as ecology, hydrology, meteorology or seismology have been embracing Bayesian methods for some time now (Dose and Menzel, 2004;Seidou et al 2006;Silva et al 2015;Jo et al 2016), although geomorphologists have invested somewhat less in this approach. Yet rarely do we encounter data, problems or predictions that are free of uncertainty.…”

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confidence: 99%

Bayesian geomorphology

Korup

2020

Earth Surf Processes Landf

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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

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Bayesian analysis to detect abrupt changes in extreme hydrological processes

Cited by 10 publications

References 30 publications

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Bayesian Detection of Streamflow Response to Earthquakes

Bayesian geomorphology

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