Exploring driving mechanism of water stress on vegetation growth by interpretable machine learning methods: main effect and interactive effect

Disentangling the individual and interactive effects of soil water content (SWC) and vapor pressure deficit (VPD) on vegetation growth remains challenging owing to tight land-plant-atmosphere interaction and lagged water stress effects. We developed a Random Forest-based dual-interpretation framework (SHAP-ALE, Shapley Additive Explanations – Accumulated Local Effects) to quantify the main effects of SWC, VPD, and historical SWC (HSWC), as well as interaction effects between water and heat stress, across six plant function types (PFTs) using the FLUXNET2015 dataset. The performance of modes was enhanced by incorporating HSWC, with an increase in the average determination coefficient of 0.13. Dual interpretation methods reveal that HSWC, which significantly affects GPP with high intensity and a wider effective range, is the dominant water stress for PFTs except for savanna and grassland, for which VPD is the dominant one. SWC benefits GPP intensely only below critical thresholds, while VPD exerts a gradual, widespread negative impact. Furthermore, heat-water stress interactions outweigh VPD-SWC interactions for most PFTs. These biome-specific effects provide insights into the refinement of vegetation parameterization in land surface models, thereby advancing ecosystem response quantification to environmental stresses.