  Ecosystem respiration (Reco) represents a major component of the global
carbon cycle, and accurate characterization of its dynamics is essential for a
comprehensive understanding of ecosystem-climate interactions and the impacts
of climate extremes on the ecosystem. This paper presents a novel data-driven
and physics-aware method for estimating Reco dynamics using the dynamic mode
decomposition with control input (DMDc) technique, an emerging tool for
analyzing nonlinear dynamical systems. The proposed model represents Reco as a
state space model with an autonomous component and an exogenous control input.
The control input can be any ecosystem driver(s), such as air temperature, soil
temperature, or soil water content. This unique modeling approach allows
controlled intervention to study the effects of different inputs on the system.
Experimental results using Fluxnet2015 data show that the prediction accuracy
of Reco dynamics achieved with DMDc is comparable to state-of-the-art methods,
making it a promising tool for analyzing the dynamic behavior of different
vegetation ecosystems on multi-temporal scales in response to different
climatic drivers.
