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Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction

arXiv:2603.03531v1 Announce Type: new 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

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Yiming Sun, Runlong Yu, Rongchao Dong, Shuo Chen, Licheng Liu, Youmi Oh, Qianlai Zhuang, Yiqun Xie, Xiaowei Jia · · 1 min read · 8 views
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arXiv:2603.03531v1 Announce Type: new 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.

Executive Summary

This study proposes Role-Aware Conditional Inference (RACI), a novel learning framework for predicting terrestrial ecosystem carbon fluxes under spatiotemporal heterogeneity. RACI employs hierarchical temporal encoding and role-aware spatial retrieval to disentangle slow regime conditions from fast dynamic drivers, enabling improved accuracy and spatial generalization across diverse environmental regimes. The framework is evaluated across multiple ecosystem types and carbon fluxes, outperforming competitive spatiotemporal baselines in all settings. This study contributes significantly to the field of ecosystem carbon flux prediction, offering a more nuanced understanding of the complex relationships between environmental covariates and ecosystem responses.

Key Points

  • ▸ RACI is a novel learning framework for predicting terrestrial ecosystem carbon fluxes under spatiotemporal heterogeneity.
  • ▸ RACI employs hierarchical temporal encoding and role-aware spatial retrieval to disentangle slow and fast dynamic drivers.
  • ▸ RACI is evaluated across multiple ecosystem types and carbon fluxes, outperforming competitive spatiotemporal baselines.

Merits

Strength in Addressing Spatiotemporal Heterogeneity

RACI explicitly models distinct functional roles, enabling a model to adapt its predictions across diverse environmental regimes without training separate local models or relying on fixed spatial structures.

Demerits

Limitation in Data Requirements

RACI may require large datasets with detailed information on environmental covariates, which may be challenging to obtain, especially for observational measurements.

Expert Commentary

RACI's innovative approach to modeling spatiotemporal heterogeneity holds significant promise for improving the accuracy of ecosystem carbon flux predictions. However, its reliance on large datasets with detailed information on environmental covariates may limit its practical application. Nevertheless, this study contributes meaningfully to the field, offering a more nuanced understanding of the complex relationships between environmental covariates and ecosystem responses. Future research should focus on developing more efficient and scalable methods for incorporating role-aware spatial retrieval and hierarchical temporal encoding, as well as exploring the application of RACI to other environmental prediction tasks.

Recommendations

  • ✓ Future research should prioritize the development of more efficient and scalable methods for incorporating role-aware spatial retrieval and hierarchical temporal encoding.
  • ✓ RACI should be applied to a broader range of ecosystem types and carbon fluxes to further evaluate its generalizability and adaptability.

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