Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Abstract

Croplands exert a strong influence on the land–atmosphere coupling and the surface exchanges of heat, water vapor, and momentum. The current trend is incorporating crop models into process-based land surface models (LSMs) to capture photosynthesis, crop growth process, and yield process. However, the models still have shortcomings, such as the assumption of constant SLA tends to ignore specific leaf area (SLA) changes caused by the accumulation and transport of photosynthetic products between organs. In this study, a dynamic SLA, which is specified for each crop, was implemented in the Noah-Multiparameterization Land Surface Model (Noah-MP-Crop), a coupled-crop-land-surface model, with the aim of improving the accuracy of estimated leaf area index (LAI) and above-ground biomass (leaf, stem and grain yield) of crops (maize, rice, soybean and winter wheat) in China. By including the dynamic SLA, the new model (Noah-MP-Cropsla) is more than qualified for capturing the measured variability in LAI and crops’ above-ground biomass, with the mean dim (Willmott’s index of agreement for the dynamic SLA scheme) of the four crops were all above 0.7. A remarkable improvement in the simulations of crop LAI and above-ground biomass were found during the crop’s jointing stage as indicated by the mean relative change rate of RMSE (RMSE% > 50%). Excluding the dynamic process of SLA will result in higher biases in the grain yield for most of the study sites. the improvement degree of crop yield was related to the improvement degree of LAI at the jointing stage (heading stage) of maize, rice and soybean (winter wheat). With the improved SLA process, the Noah-MP-Cropsla have a solid ability to capture the spatial pattern of crop yields, the R2 of the simulated and census data of the four major crop yield in China were all above 0.5.