Sensitivity analysis of the SWAP (Soil-Water-Atmosphere-Plant) model under different nitrogen applications and root distributions in saline soils

Sensitivity analysis is important for determining the parameters in the model calibration process. In our study, a variance-based global sensitivity analysis (extended Fourier amplitude sensitivity test, EFAST) was applied to an agro-hydrological model (the SWAP (Soil-Water-Atmosphere-Plant model) model). The sensitivities of 20 parameters belonging to 4 categories (soil hydraulics, solute transport, root water uptake, and environmental stresses) for the simulated accumulated transpiration, dry matter (DM), and yield of sunflowers were analyzed under three nitrogen application rates (N1, N2, and N3), four salinity levels (S1, S2, S3, and S4), and three root distributions (R1, R2, and R3). The results indicated that for predominantly loamy soils, the high-impact parameters for accumulated transpiration, DM, and yield were the soil hydraulic parameters (α and n), critical stress index for compensatory root water uptake (ω_c), the salt level at which salt stress starts (Pi), the decline of root water uptake above Pi (SSF), residual water content (θ_r), saturated water content (θ_s), and relative uptake of solutes by roots (TSCF). We also found that nitrogen application did not change the order of the parameter impacts on accumulated transpiration, DM, and yield. However, TSCF replaced α as the highest-impact parameter for the accumulated transpiration, DM, and yield at high salinity levels (S2 and S3). Furthermore, α was also the highest-impact parameter for DM and yield under different root distributions, but the highest-impact parameters for transpiration were ω_c, α, and θ_s under R1, R2, and R3, respectively. Nitrogen application could be neglected when considering the interactive effects of nitrogen application, salinity level, and root distribution on the transpiration, DM, and yield. Additionally, the mean values and uncertainties of the transpiration, DM, and yield were similar in all scenarios, except S3, which showed a sharp decrease in the mean values. We suggest determining the above eight parameters (α, n, ω_c, Pi, SSF, θ_r, θ_s, and TSCF) and the saturated vertical hydraulic conductivity (K_S) based on rigorous calibrations with direct or indirect local measurements using economical methods (e.g., a literature review), with limited observations for other parameters when using the SWAP model and other similar agro-hydrological models.