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

arXiv: https://arxiv.org/abs/2310.10324 · HTML: https://mlsanity.com/paper/2310.10324/

## 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.

## Builds on (most cited first, 4 of 4)

- [Prediction based on conditional distributions of vine copulas](https://mlsanity.com/paper/1807.08429/) (2018)
- [D-vine copula based quantile regression](https://mlsanity.com/paper/1510.04161/) (2015)
- [Nonparametric C- and D-vine based quantile regression](https://mlsanity.com/paper/2102.04873/) (2021)
- [Bivariate vine copula based regression, bivariate level and quantile curves](https://mlsanity.com/paper/2205.02557/) (2022)

## 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.