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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.

Abstract:Simulink, an extension of MATLAB, is a graphics-based model development environment for system modeling and simulation. Simulink's user-friendly features, including block (data process) and arrow (data transfer) objects, a large number of existing blocks, no need to write codes, and a drag and drop interface, provide modelers with an easy development environment. In this study, a Tank model was developed using Simulink and applied to a rainfall-runoff simulation for a study watershed to demonstrate the potential of Simulink as a tool for hydrological analysis. In the example given here, the Tank model was extended by two sub-modules representing evapotranspiration and storage-runoff distribution. In addition, model pre-and post-processing, such as input data preparation and results plotting, was carried out in MATLAB. Moreover, model parameters were calibrated using MATLAB optimization tools without any additional programming for linking the calibration algorithms and the model. The graphical representation utilized in the Simulink version of the Tank model helped us to understand the hydrological interactions described in the model, and the modular structure of the program facilitated the addition of new modules and the modification of existing modules as needed. From the study, we found that Simulink could be a useful and convenient environment for hydrological analysis and model development.

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