X-MethaneWet: A Cross-scale Global Wetland Methane Emission Benchmark Dataset for Advancing Science Discovery with AI

TL;DR

This paper introduces X-MethaneWet, a comprehensive benchmark dataset combining simulated and real-world data to improve AI-based modeling of global wetland methane emissions, with experiments demonstrating enhanced prediction capabilities.

Contribution

It presents the first cross-scale global wetland methane dataset and evaluates deep learning and transfer learning methods for better methane flux prediction.

Findings

Deep learning models perform well on methane flux prediction.

Transfer learning improves model generalization from simulated to real data.

The dataset enables new AI-driven climate modeling research.

Abstract

Methane (CH$_4$) is the second most powerful greenhouse gas after carbon dioxide and plays a crucial role in climate change due to its high global warming potential. Accurately modeling CH$_4$ fluxes across the globe and at fine temporal scales is essential for understanding its spatial and temporal variability and developing effective mitigation strategies. In this work, we introduce the first-of-its-kind cross-scale global wetland methane benchmark dataset (X-MethaneWet), which synthesizes physics-based model simulation data from TEM-MDM and the real-world observation data from FLUXNET-CH$_4$. This dataset can offer opportunities for improving global wetland CH$_4$ modeling and science discovery with new AI algorithms. To set up AI model baselines for methane flux prediction, we evaluate the performance of various sequential deep learning models on X-MethaneWet. Furthermore, we explore four different transfer learning techniques to leverage simulated data from TEM-MDM to improve the generalization of deep learning models on real-world FLUXNET-CH$_4$ observations. Our extensive experiments demonstrate the effectiveness of these approaches, highlighting their potential for advancing methane emission modeling and identifying new opportunities for developing more accurate and scalable AI-driven climate models.