Applying a deep learning-based approach for scaling vegetation dynamics to predict changing forest regimes under future climate and fire scenarios

Abstract

The ability to anticipate future changes in terrestrial ecosystems is key for their management. New tools are required that bridge the gap between a high level of process understanding at fine spatial grain, and the increasing relevance for management at larger extents. Such a tool is SVD (Scaling Vegetation Dynamics), a scaling framework that specifically uses deep learning to learn the behavior of detailed vegetation models in response to different environmental factors. This trained deep neural network (DNN) is then applied within the framework on large spatial scales. In addition, SVD includes also explicitly modelled processes such as fire disturbances. Here we use the framework to simulate forest regime change in the 3 Mio. ha landscape of the Greater Yellowstone Ecosystem. We used four climate change scenarios and pre-defined fire events from statistical modelling, and analyzed whether prevailing forest types are able to regenerate after fire. Our results show that up to 60% of the area may undergo regime change until the end of the 21st century.