Improving Dynamic Vegetation Modeling in Noah‐MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau

Abstract

AbstractAccurate modeling of vegetation dynamics is needed to improve our understanding of and ability to predict the impacts of vegetation changes on terrestrial water‐energy‐carbon cycles. Parameter optimization (PO) and data assimilation (DA) are widely used to improve the performance of dynamic vegetation modules in land surface models (LSMs). However, their effectiveness is unclear. Here we analyze their impacts on the performance of the dynamic vegetation module of the Noah with multiparameterization options (Noah‐MP) LSM over the Chinese Loess Plateau, which is an ideal study case because it is a large region that has undergone dramatic vegetation change. We first optimize these parameters that strongly affect the predicted vegetation dynamics based on the results of sensitivity analysis using PO. In addition, we evaluate the effect of DA by assimilating leaf area index (LAI) remote sensing data into Noah‐MP without PO. Finally, we investigate the effect of applying PO and DA together. PO increases the predicted rates of carbon assimilation and turnover and thus reduces the underestimation of LAI and the lag in vegetation seasonality. DA has a weaker impact than PO: it only reduces the root mean squared error (RMSE) of the predicted LAI in around 49.76% of the studied region and is mainly beneficial in the growing phase. Combining PO and DA compensate the limitations of each other, and gives the most significant reduction in RMSE (median: −0.24 m2/m2) and increase in R2 (+0.44). The improved vegetation dynamics with different optimization methods thus improve the modeling of water and carbon cycle processes.