Fuzzy Rough Set algorithm with Binary Shuffled Frog-Leaping (BSFL-FRSA): An innovative approach for identifying main drivers of carbon exchange in temperate deciduous forests

Abstract

The spatial variability, driving forces, and uncertainties of the net ecosystem exchange (NEE) of carbon between the temperate forests and the atmosphere remain elusive. Here, we proposed a fuzzy rough set algorithm with binary shuffled frog leaping (BSFL-FRSA) to identify main driving variables and define the contribution rate of main drivers to NEE. As a case study, we applied the approach to nine deciduous forest eddy covariance flux sites in the northeastern United States. The results show that the BSFL-FRSA effectively retained most information using just a few variables, and it performed better than the GA-FRSA (fuzzy rough set with genetic algorithm). Temperature, radiation, and soil water content were identified as the most influential variables (impact in descending order) to NEE across all sites. Soil temperature was the most important variable explained 59.6% of the NEE variance. Soil temperature and net radiation together, explained 72.7% of the NEE variance, was the most important two variables among all possible two-variable combinations. The most influential three variables on NEE among all possible three-variable combinations were soil temperature, net radiation, and soil water content or relative humidity (explained 81.1% of the NEE variance). The variance attribution approach presented here is generic and can be applied to other studies; the dominant influence of soil temperature begs for accurate characterization of soil temperature dynamics in time and space particularly in the global warming context.