Parameter sensitivity analysis of the AquaCrop model based on extended fourier amplitude sensitivity under different agro-meteorological conditions and application

Sensitivity analysis (SA) can be used to identify the effects of crop model input parameters on model results. Previous studies have indicated that the ranges, distributions, and properties of many parameters are difficult to estimate, though some may be acquired indirectly from literature. Parameter ranges are often adjusted to fit specific applications, but some are not representative of crop properties. The objectives of this study were to: (i) comprehensively quantify the effects of parameter variation on the sensitivity of AquaCrop output variables based on the extended Fourier amplitude sensitivity test, (ii) study the effects of parameter variation on the sensitivity of output variables over time, and (iii) calibrate and simplify the AquaCrop model based on the SA results under different agro-meteorological conditions. The results demonstrated that output variable sensitivity varied in response to crop parameter variation. The first order sensitivity index (FOSI) demonstrated that some crop parameters were sensitive to yield and maximum dry biomass. The results from the time-dependent FOSI analysis revealed that some parameters were insensitive to yield and maximum dry biomass but were sensitive to canopy cover and dry biomass during certain wheat growth stages. The total sensitivity index (TSI) and time-dependent TSI of crop parameters were more sensitive than the FOSI and time-dependent FOSI of crop parameters. The sensitivities of winter and spring wheat simulations based on FOSI and time-dependent FOSI were consistent; however, some differences existed between simulations based on TSI and time-dependent TSI. Simulations based on SA were more accurate than previously reported results. Results from the calibrated and simplified AquaCrop model revealed good consistency between simulated and measured canopy cover, biomass, and yield. Future studies should focus on analyzing the sensitivity of crop parameters to dynamic output variables over time to better calibrate and simplify the AquaCrop model.