Multi-Objective Uncertainty Analysis of Agro-Hydrological Modeling of a Sugarcane Farming System with Subsurface Drainage through Restrained Monte-Carlo Simulations

The agro-hydrologic simulations are subject to varying degrees of uncertainty. In this regard, uncertainty analysis can provide useful insights into the level of robustness of the model outputs. In this study, a hybrid multi-objective uncertainty analysis scheme, GLUE-UPSO, based on strained Monte-Carlo simulations was developed, combining Generalized Likelihood Uncertainty Estimation (GLUE) and Unified Particle Swarm Optimization (UPSO). The developed scheme was used for uncertainty analysis of distributed agro-hydrological modeling with SWAP for a sugarcane farming system with subsurface drainage located at Shoaybiyeh Sugarcane Agro-industrial Company farms, Khuzestan, Iran. The results highlighted the strong nonuniqueness of most of the calibrated parameters and the importance of uncertainty analysis of the SWAP simulations. Strong parameter correlations revealed the need for the simultaneous calibration of the model parameters against diverse calibration data. The 95% prediction uncertainty bands obtained for the model's hydrology (soil water content, water table level, sub-surface drainage outflow), solute transport (soil water solute concentration and sub-surface drainage outflow salinity), and biophysical (leaf area index, cane, and sucrose dry yield) components enveloped 67%-90%, 23%-71%, and 75%-100% of the corresponding observed data (including both calibration and validation datasets), respectively, with an r-factor of 0.58-1.34, 0.43-1.07, and 0.70-0.98. The results of the study indicated the acceptable level of model uncertainty and the capability of the developed framework for simultaneous calibration and uncertainty quantification of model components.