Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges
Understanding forest resilience to drought with Shapley values
Stenka Vulova · Alby Duarte Rocha · Akpona Okujeni · Johannes Vogel · Michael Förster · Patrick Hostert · Birgit Kleinschmit
Keywords: [ Ecosystems and biodiversity ] [ Interpretable ML ] [ Earth observations and monitoring ] [ Extreme weather ] [ Earth science ] [ Classification, regression, and supervised learning ] [ Forestry and other land use ]
Increases in drought frequency, intensity, and duration due to climate change are threatening forests around the world. Climate-driven tree mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more resilient to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate drought indices. In this study, we aim to gain a better understanding of the temporal and spatial drivers of drought-induced changes in forest vitality using Shapley values, which allow for the relevance of predictors to be quantified locally. A better understanding of the contribution of meteorological and environmental factors to trees’ response to drought can support forest managers aiming to make forests more climate-resilient.