Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria
Applications
2023-10-17 v1 Methodology
Machine Learning
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.
Keywords
Cite
@article{arxiv.2310.10324,
title = {Assessing univariate and bivariate risks of late-frost and drought using vine copulas: A historical study for Bavaria},
author = {Marija Tepegjozova and Benjamin F. Meyer and Anja Rammig and Christian S. Zang and Claudia Czado},
journal= {arXiv preprint arXiv:2310.10324},
year = {2023}
}