Lessons from Assessing Uncertainty in Agricultural Water Supply Estimation for Sustainable Rice Production

Agricultural water supply (AWS) estimation is one of the first and fundamental steps of developing agricultural management plans, and its accuracy must have substantial impacts on the following decision-making processes. In modeling the AWS for paddy fields, it is still common to determine parameter values, such as infiltration rates and irrigation efficiency, solely based on literature and rough assumptions due to data limitations; however, the impact of parameter uncertainty on the estimation has not been fully discussed. In this context, a relative sensitivity index and the generalized likelihood uncertainty estimation (GLUE) method were applied to quantify the parameter sensitivity and uncertainty in an AWS simulation. A general continuity equation was employed to mathematically represent the paddy water balance, and its six parameters were investigated. The results show that the AWS estimates are sensitive to the irrigation efficiency, drainage outlet height, minimum ponding depth, and infiltration, with the irrigation efficiency appearing to be the most important parameter; thus, they should be carefully selected. Multiple combinations of parameter values were observed to provide similarly good predictions, and such equifinality produced the substantial amount of uncertainty in AWS estimates regardless of the modeling approaches, indicating that the uncertainty should be counted when developing water management plans. We also found that agricultural system simulations using only literature-based parameter values provided poor accuracy, which can lead to flawed decisions in the water resources planning processes, and then the inefficient use of public investment and resources. The results indicate that modelers' careful parameter selection is required to improve the accuracy of modeling results and estimates from using not only information from the past studies but also modeling practices enhanced with local knowledge and experience.