Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction

TL;DR

This paper introduces RACI, a novel framework for predicting ecosystem carbon fluxes that explicitly models different environmental roles, improving accuracy and generalization across diverse ecosystems and data sources.

Contribution

RACI is the first approach to explicitly disentangle slow and fast environmental influences using role-aware inference, enhancing prediction across heterogeneous ecosystems.

Findings

RACI outperforms existing models in accuracy across multiple ecosystem types.

RACI demonstrates superior spatial generalization in heterogeneous environments.

RACI effectively integrates process-based simulations and observational data.

Abstract

Accurate prediction of terrestrial ecosystem carbon fluxes (e.g., CO$_2$, GPP, and CH$_4$) is essential for understanding the global carbon cycle and managing its impacts. However, prediction remains challenging due to strong spatiotemporal heterogeneity: ecosystem flux responses are constrained by slowly varying regime conditions, while short-term fluctuations are driven by high-frequency dynamic forcings. Most existing learning-based approaches treat environmental covariates as a homogeneous input space, implicitly assuming a global response function, which leads to brittle generalization across heterogeneous ecosystems. In this work, we propose Role-Aware Conditional Inference (RACI), a process-informed learning framework that formulates ecosystem flux prediction as a conditional inference problem. RACI employs hierarchical temporal encoding to disentangle slow regime conditioners from fast dynamic drivers, and incorporates role-aware spatial retrieval that supplies functionally similar and geographically local context for each role. By explicitly modeling these distinct functional roles, RACI enables a model to adapt its predictions across diverse environmental regimes without training separate local models or relying on fixed spatial structures. We evaluate RACI across multiple ecosystem types (wetlands and agricultural systems), carbon fluxes (CO$_2$, GPP, CH$_4$), and data sources, including both process-based simulations and observational measurements. Across all settings, RACI consistently outperforms competitive spatiotemporal baselines, demonstrating improved accuracy and spatial generalization under pronounced environmental heterogeneity.