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

Tepegjozova, Marija, Meyer, Benjamin F., Rammig, Anja, Zang, Christian S., Czado, Claudia

arXiv.org Machine Learning 

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.

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