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

Abstract

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.

The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin

“…In literature, several approaches are evident. Established approach such as Bayesian decomposition (Ohn et al, 2020) may be more comprehensive but is very time intensive as tens of thousands of iterations may be required which may hinder its application in a large scale physically-based hydrological modeling. A simple yet robust method, based on Maximum Entropy (ME) principle was suggested by Gay and Estrada (2010).…”