CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Prediction

2026-05-29

AI Summary

Accurate estimation of global methane fluxes is hindered by strong spatiotemporal heterogeneity across ecosystems, site-specific characteristics, and the difficulty of modeling interannual dynamics. To address these challenges, this work proposes CHAM-net, a novel framework that integrates contrastive learning with a hierarchical adaptive meta-network for the first time. CHAM-net employs a hierarchical encoder–decoder architecture to explicitly model site-specific features and interannual evolution patterns, enabling dynamic conditional prediction grounded in historical context. Experimental results demonstrate that the proposed method substantially outperforms baseline approaches on both simulated and observational data, achieving a normalized root mean square error (nRMSE) of 0.43 (R² = 0.97) for methane emission prediction and an nRMSE of 0.88 (R² = 0.68) for consumption prediction, with markedly enhanced generalization capability.

Abstract

Methane is a potent greenhouse gas that significantly contributes to global warming. However, accurately estimating global methane emissions and consumption remains challenging due to the complex interactions among environmental drivers that may vary across spatial and temporal scales. Prior data-driven methods often overlook the inherent spatiotemporal heterogeneity of ecosystems, failing to explicitly capture site-specific characteristics and cross-year evolutionary dynamics. To address these issues, we propose the Contrastive Hierarchical Adaptive Meta-network (CHAM-net), a novel framework that explicitly learns from historical context to capture site-specific dynamics. CHAM-net employs a hierarchical encoder-decoder architecture, in which the encoder captures site-specific characteristics from historical data and then dynamically conditions the decoder to generate the final prediction. Experimental results demonstrate that CHAM-net consistently outperforms all baseline methods on both simulation and observational datasets for methane emission and consumption, achieving nRMSE values as low as 0.43 and 0.88 with corresponding R2 scores up to 0.97 and 0.68 for emission prediction.

Problem

• methane flux prediction
• spatiotemporal heterogeneity
• site-specific dynamics
• environmental drivers
• global methane emissions

Innovation

• Contrastive learning
• Hierarchical adaptive meta-network
• Spatiotemporal heterogeneity
• Methane flux prediction
• Site-specific dynamics