Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria

TL;DR

This study evaluates the individual and joint risks of late-frost and drought in Bavarian forests from 1952 to 2020 using vine copula models, highlighting regions vulnerable to climate change impacts.

Contribution

It introduces a novel application of vine copula models for joint risk assessment of climate extremes in forests, with new conditional probability risk measures.

Findings

Identification of at-risk regions in Bavaria.

Demonstration of vine copulas for non-Gaussian dependency modeling.

Quantification of joint late-frost and drought risks.

Abstract

In light of climate change's impacts on forests, including extreme drought and late-frost, leading to vitality decline and regional forest die-back, we assess univariate drought and late-frost risks and perform a joint risk analysis in Bavaria, Germany, from 1952 to 2020. Utilizing a vast dataset with 26 bioclimatic and topographic variables, we employ vine copula models due to the data's non-Gaussian and asymmetric dependencies. We use D-vine regression for univariate and Y-vine regression for bivariate analysis, and propose corresponding univariate and bivariate conditional probability risk measures. We identify "at-risk" regions, emphasizing the need for forest adaptation due to climate change.