The Effect of Training Data Series Length on the Performance of the Tank Model for Transforming Rainfall into Runoff Data Series

Abstract

The Tank model by Sugawara is included in the lumped model category. As with other types of lumped models, the effectiveness of the application of the Tank model is largely determined by the parameter optimization method applied and the quantity of training data involved in the calibration process. This article proposes the Tank-DE model to transform rain data series into discharge in a watershed. The Tank-DE model is built from a combination of a simulation equation system based on the Tank model and a multi-parameter optimization equation system based on the Differential evolution (DE) Algorithm. This article also examines the sensitivity analysis of the model to study the effect of the length of the training data series involved in the calibration process on the predictive discharge quality generated by the Tank-DE model. Thus, the minimum length of the training data series can be recommended, related to the application of the model. The results of the analysis show that the Tank-DE model can present the relationship between rainfall data series and daily period discharge very well. The results of the sensitivity analysis show that there is an indication that the longer the training data series, the more quantitatively positive impact on the performance of the model. The calibration process involving a training data set for 1 year produces a very good value of the coefficient of determination (r2 = 0.94), but the indicator decreases drastically at the validation stage. The calibration process involving a relatively long training data series produces a more consistent value of the coefficient of determination. This indicates that the Tank-DE model can be an alternative solution to solve the problem of scarcity of discharge data series which is a classic problem in water resource development activities.