Integrating WOFOST and Noah LSM for modeling maize production and soil moisture with sensitivity analysis, in the east of The Netherlands

Abstract Optimal agricultural management relies on the availability of detailed estimates of the crop and the soil hydrology states. The World Food STudies model (WOFOST) provides optimal crop growth simulations. However, it is designed with a simplified water balance to suit crop growth simulations over large areas anywhere in the world. This manuscript aims at integrating WOFOST model with Noah Land Surface Model (Noah LSM) in order to obtain precise simulations of the crop growth and soil moisture at various depths. The soil module of the later will be used for simulating soil moisture while the vegetation module of the former is used for simulating the crop growth. The integrated model was adapted to characteristics of two fields with different soil characteristics in the east of The Netherlands, by calibrating its parameters using field measurements collected during 2013. The calibrated parameters led to overall satisfactory performance of the integrated model with goodness of fit criteria very close to the perfect fit. The modeling efficiency (EF) was higher than 0.96 with root mean square error (RMSE) of less than 9%. Values of the parameters obtained from the calibration were used to validate the model together with field measurements and meteorological data acquired during 2014. Maize production and soil moisture were assessed through comparing simulations of leaf area index (LAI), total above ground biomass (TAGP), weight of storage organs (WSO) and moisture content of three soil layers with the equivalent field measurements. For maize production, the high performance of the model is also detected where EF was higher than 0.95 with RMSE of less than 11%. Likewise, the model showed satisfactory performance in simulating soil moisture where EF was higher than 0.90 with RMSE of less than 10%. Furthermore, a two-step sensitivity analysis procedure, composed of the screening model and the Sobol’s variance-based method, was applied to 40 parameters for distinguishing and evaluating the parameters influencing the performance of the vegetation and the soil schemes of WOFOST and Noah LSM respectively. Change in WSO in response to change in the parameters was the judging criteria. It was found that changes in parameters describing the moisture state and hydraulic properties of the soil (e.g. porosity and hydraulic conductivity) showed the greatest change in WSO (∼4000 kg ha −1 ) with the highest interactions with other parameters (standard deviation > 3000 kg ha −1 ) especially under high levels of soil moisture content. Variations of parameters describing the crop phenology also showed high change of WSO (2000 kg ha −1 –4000 kg ha −1 ) with high interactions with other parameters(2000 kg ha −1  > standard deviation > 3000 kg ha −1 ), especially under conditions of good water-air balance in the soil voids.