Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input

TL;DR

This paper introduces a physics-informed, data-driven modeling approach using DMDc to accurately estimate ecosystem respiration dynamics, incorporating environmental drivers for improved understanding of ecosystem-climate interactions.

Contribution

It presents a novel application of dynamic mode decomposition with control input for modeling ecosystem respiration as a state space system influenced by climate variables.

Findings

Prediction accuracy comparable to state-of-the-art methods

Effective modeling of ecosystem responses to climatic drivers

Potential for multi-temporal ecosystem analysis

Abstract

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.