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

arXiv 2310.10324 ↗First posted 16 Oct 2023stat.AP stat.ME stat.MLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 62 references that are in the map. First posted on arXiv 16 Oct 2023.

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References · 4

  1. 2018Prediction based on conditional distributions of vine copulascited by 8
  2. 2015D-vine copula based quantile regressioncited by 5
  3. 2021Nonparametric C- and D-vine based quantile regressioncited by 5
  4. 2022Bivariate vine copula based regression, bivariate level and quantile curvescited by 1

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