FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes

2026-05-19

AI Summary

This study addresses the limited generalization of existing models in ecosystem flux upscaling under covariate and conditional distribution shifts driven by climate and ecosystem changes. The authors establish the first out-of-distribution extrapolation benchmark specifically designed for flux upscaling, incorporating ecologically informed evaluation scenarios that challenge models along temporal, spatial, and temperature-driven dimensions. They systematically quantify the impacts of these two types of distributional shifts and introduce tail error metrics alongside multi-scale evaluation criteria to better align with real-world scientific requirements. Experimental results reveal that while mainstream models exhibit comparable median hourly RMSE performance, they differ substantially in tail robustness and multi-scale predictive accuracy, demonstrating the benchmark’s effectiveness in discerning model generalization capabilities.

Abstract

We introduce FLUXtrapolation, a benchmark for extrapolating ecosystem fluxes under progressively harder distribution shifts. Ecosystem fluxes are central to understanding the carbon, water, and energy cycles, yet they can only be measured directly at sparsely located measurement towers. Producing global flux estimates therefore requires training models on observed sites using globally available covariates and predicting in unobserved regions, that is, upscaling. Flux upscaling is a challenging domain generalization problem that is affected by a shift in covariate distribution across climates, ecosystem types, and environmental conditions, as well as by conditional shift: important drivers remain unobserved at global scale. We provide a quantitative analysis of both these shifts in $P_X$ and $P_{Y\mid X}$. FLUXtrapolation is designed based on domain expertise on flux upscaling: it defines temporal, spatial, and temperature-based extrapolation scenarios and evaluates performance across held-out domains, temporal aggregations, and tail errors. In a pilot study, we find that baselines perform similarly under median hourly RMSE, but separate under the proposed tail-focused and multi-scale evaluation. FLUXtrapolation therefore poses a realistic and thus relevant challenge for machine learning methods under distribution shift; at the same time, progress on this benchmark would directly support the scientific goal of improving flux upscaling.

Problem

• ecosystem fluxes
• distribution shift
• domain generalization
• upscaling
• extrapolation

Innovation

• distribution shift
• domain generalization
• ecosystem fluxes
• extrapolation benchmark
• tail error