Identifying compound weather prototypes of forest mortality with β-VAE

Forest health is affected by many interacting and correlated weather variables over multiple temporal scales. Climate change affects weather conditions and their dependencies. To better understand future forest health and status, an improved scientific  understanding of the complex relationships between weather conditions and forest mortality is required. Explainable AI (XAI) methods are increasingly used to understand and simulate physical processes in complex environments given enough data. In this work, an hourly weather generator (AWE-GEN) is used  to simulate 200,000 years of daily weather conditions representative of central Germany. It is capable of simulating low and high-frequency characteristics of weather variables and also captures the inter-annual variability of precipitation. These data are then used to drive an individual-based forest model (FORMIND) to simulate the dynamics of a beech, pine, and spruce forest. A variational autoencoder β-VAE is used to learn representations of the generated weather conditions, which include radiation, precipitation and temperature. We learn shared and specific variable latent representations using a decoder network which remains the same for all the weather variables. The representation learning is completely unsupervised. Using the output of the forest model, we identify single and compounding weather prototypes that are associated with extreme forest mortality. We find that the prototypes associated with extreme mortality are similar for pine and spruce forests and slightly different for beech forests. Furthermore, although the compounding weather prototypes represent a larger sample size (2.4%-3.5%) than the single prototypes (1.7%-2.2%), they are associated with higher levels of mortality on average. Overall, our research illustrates how deep learning frameworks can be used to identify weather patterns that are associated with extreme impacts.