High-Fidelity Deep Approximation of Ecosystem Simulation over Long-Term at Large Scale.

Ecosystem services, such as carbon sequestration, biodiversity, and climate regulation, play essential roles in combating climate change. Projection of ecosystem dynamics under various scenarios is critical in understanding potential impacts and informing policies and mitigation strategies. Ecosystem Demography (ED) model is a major mechanistic model for ecosystem dynamics projection, but its computational cost has been a major bottleneck in performing large-scale (e.g., global, national) projections at very high spatial resolution. We aim to approximate the ED model using deep neural networks at operational high accuracy to assist large-scale climate studies. The deep approximation is non-trivial due to challenges by long-term error accumulation (e.g., 40 years), highly diverse scenarios, and high cost in training data generation. We propose a Deep-ED approximation model to address the challenges with a multi-scale cumulative loss reduction structure, significance-based scenario partitioning, self-guided forwarding, and physics-aware active learning strategies. Experiment results in the northeastern US demonstrate the high accuracy of Deep-ED and its potential in large-scale ecosystem projection.