Seasonal Cycle Biases in DGVM Simulations of Double-Cropping Systems: A Case Study in the Huang-Huai-Hai Plain

Abstract. Global dynamic vegetation models (DGVMs) are essential tools for studying the changes in terrestrial ecosystems and their responses to climate change and human activities. However, these models exhibit substantial uncertainties when applied to croplands, particularly in regions with multiple cropping systems. These uncertainties arise from variations in planting types and phenology, which are influenced by sowing and harvesting schedules. This study focused on the phenological estimation errors of DGVMs in typical double - cropping agricultural regions. The Huang - Huai - Hai Plain in eastern China was chosen, which is one of the most important grain-producing areas with mainly winter wheat-summer crop rotation. A comparative analysis was conducted between the seven models from the TRENDY project and three remote sensing observations over last two decades. The results indicate that remote sensing vegetation indices consistently exhibit a typical bimodal structure in the study area, with peaks in April and August, corresponding to the growth peaks of the two-season crops. However, none of the DGVMs successfully capture this bimodal pattern. Given that multiple cropping systems are widespread in middle- and low-latitude regions with favorable water and temperature conditions, improving the simulation capabilities of DGVMs in such areas is an urgent and critical issue for advancing global vegetation modeling.