In this study, five hydrological models of increasing complexity and 12 Potential Evapotranspiration (PET) estimation methods of different data requirements were applied in order to assess their effect on model performance, optimized parameters, and robustness. The models were applied over a set of 10 catchments that are located in South Korea. The Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. The hydrological models’ performance was satisfactory for each PET input in the calibration and validation periods for all of the tested catchments. The five hydrological models’ performance were found to be insensitive to the 12 PET inputs because of the SCE-UA algorithm’s efficiency in optimizing model parameters. However, the five hydrological models’ parameters in charge of transforming the PET to actual evapotranspiration were sensitive and significantly affected by the PET complexity. The values of the three statistical indicators also agreed with the computed model evaluation index values. Similarly, identical behavioral similarities and Dimensionless Bias were observed in all of the tested catchments. For the five hydrological models, lack of robustness and higher Dimensionless Bias were seen for high and low flow as well as for the Hamon PET input. The results indicated that the complexity of the hydrological models’ structure and the PET estimation methods did not necessarily enhance model performance and robustness. The model performance and robustness were found to be mainly dependent on extreme hydrological conditions, including high and low flow, rather than complexity; the simplest hydrological model and PET estimation method could perform better if reliable hydro-meteorological datasets are applied.

In this study, GR4J and Sacramento soil moisture accounting model performance and parameters response to 14 different PE inputs is evaluated in a sample of two watersheds located in South Korea. The 14 PE inputs selected to assess the response of the two watershed models for better streamflow simulations varies from simple to more data demanding models. The result of both models in the tested watersheds was insensitive to different PE inputs, due to the ability of Shuffled Complex Evolution–University of Arizona algorithm to adapt and optimize parameters for each PE inputs in the calibration process. The parameter of both models responsible for changing the PE to actual evapotranspiration are significantly affected by the different PE inputs but the remaining parameters are relatively stable. The result of the study will provide some level of confidence for modelers to work with temperature based PE models, due to its competent efficiency as that of data demanding and complex models for areas with limited climate data.