Application of Parameter Optimization Methods Based on Kalman Formula to the Soil—Crop System Model

Abstract

Soil–crop system models are effective tools for optimizing water and nitrogen application schemes, saving resources and protecting the environment. To guarantee model prediction accuracy, we must apply parameter optimization methods for model calibration. The performance of two different parameter optimization methods based on the Kalman formula are evaluated for a parameter identification of the soil Water Heat Carbon Nitrogen Simulator (WHCNS) model using mean bias error (ME), root-mean-square error (RMSE) and an index of agreement (IA). One is the iterative local updating ensemble smoother (ILUES), and the other is the DiffeRential Evolution Adaptive Metropolis with Kalman-inspired proposal distribution (DREAMkzs). Our main results are as follows: (1) Both ILUES and DREAMkzs algorithms performed well in model parameter calibration with the RMSE_Maximum a posteriori (RMSE_MAP) values were 0.0255 and 0.0253, respectively; (2) ILUES significantly accelerated the process to the reference values in the artificial case, while outperforming in the calibration of multimodal parameter distribution in the practical case; and (3) the DREAMkzs algorithm considerably accelerated the burn-in process compared with the original algorithm without Kalman-formula-based sampling for parameter optimization of the WHCNS model. In conclusion, ILUES and DREAMkzs can be applied to a parameter identification of the WHCNS model for more accurate prediction results and faster simulation efficiency, contributing to the popularization of the model.