Bayesian Calibration and Uncertainty Assessment of HYDRUS-1D Model Using GLUE Algorithm for Simulating Corn Root Zone Salinity under Linear Move Sprinkle Irrigation System

Abstract

Soil salinization is one of the significant concerns regarding irrigation with saline waters as an alternative resource for limited freshwater resources in arid and semi-arid regions. Thus, the investigation of proper management methods to control soil salinity for irrigation with saline waters is inevitable. The HYDRUS-1D model is a well-known numerical model that can facilitate the exploration of management scenarios to mitigate the consequences of irrigation with saline waters, especially soil salinization. However, before using the model as a decision support system, it is crucial to calibrate the model and analyze the model’s parameters and outputs’ uncertainty. Therefore, the generalized likelihood uncertainty estimation (GLUE) algorithm was implemented for the HYDRUS-1D model in the R environment to calibrate the model and assess the uncertainty aspects for simulating soil salinity of corn root zone under saline irrigation with linear move sprinkle irrigation system. The results of the study have detected a lower level of uncertainty in the α, n, and θs (saturated soil water content) parameters of water flow simulations, dispersivity (λ), and adsorption isotherm coefficient (Kd) parameters of solute transport simulations comparing to the other parameters. A higher level of uncertainty was found for the diffusion coefficient as its corresponding posterior distribution was not considerably changed from its prior distribution. The reason for this phenomenon could be the minor contribution of diffusion to the solute transport process in the soil compared with advection and hydrodynamic dispersion under saline water irrigation conditions. Predictive uncertainty results revealed a lower level of uncertainty in the model outputs for the initial growth stages of corn. The analysis of the predictive uncertainty band also declared that the uncertainty in the model parameters was the predominant source of uncertainty in the model outputs. In addition, the excellent performance of the calibrated model based on 50% quantiles of the posterior distributions of the model parameters was observed in terms of simulating soil water content (SWC) and electrical conductivity of soil water (ECsw) at the corn root zone. The ranges of NRMSE for SWC and ECsw simulations at different soil depths were 0.003 to 0.01 and 0.09 to 0.11, respectively. The results of this study have demonstrated the authenticity of the GLUE algorithm to seek uncertainty aspects and calibration of the HYDRUS-1D model to simulate the soil salinity at the corn root zone at field scale under a linear move irrigation system.

Determination of Winter Irrigation Quotas for Corn and Oil Sunflower Considering Crop Salt Tolerance Threshold under Subsurface Pipe Drainage Technology

“…HYDRUS-2D is one of the most widely used software programs for the simulation of soil solute transport. It accurately models the transport of soil water, fertilizer, heat, and salt under various irrigation modes, including surface irrigation, drip irrigation, and sprinkler irrigation [27][28][29][30]. Lazarovitch et al incorporated a new boundary condition into HYDRUS-2D, which takes into account the influence of water source characteristics, inlet pressure and soil hydraulic characteristics on the calculated flow of underground irrigation sources, and verifies the rationality of the considered factors based on transient test data [20].…”