Abstract
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.
Highlights
Hydrological models are used to predict low flows, floods, and groundwater recharge, and to develop safe and sustainable water resource strategies [1]
Three research questions were addressed: (i) does the complexity of hydrological models enhance model performance? (ii); does the complexity of Potential Evapotranspiration (PET) estimation methods have analogous effects on optimized parameters?; and, (iii) does increasing the complexity of PET estimation methods and hydrological models enhance robustness? We applied five hydrological models: Génie Rural à 4 paramètres Journalier (GR4J), Simplified HYDROLOG (SIMHYD), Catchment Hydrological Cycle Assessment Tool (CAT), TANK, and Sacramento Soil Moisture Accounting (SAC-SMA) models that considered the combined, radiation, and temperature-based PET estimation methods in a sample of 10 catchments that are located in South Korea (8.5–6648 km2)
The performances of the five hydrological models for each PET input fell within satisfactory criteria for all the tested catchments in the validation periods (Table A2 in Appendix A), as shown in Figures 5 and A6 (Appendix B)
Summary
Hydrological models are used to predict low flows, floods, and groundwater recharge, and to develop safe and sustainable water resource strategies [1]. As a result, understanding the water resources systems and reliable information play a vital role to achieve environmentally sound and sustainable water management [2,3,4]. Horne [5] addressed the importance of upgrading water information systems (data series) to enhance sustainable water management. The benefits and limitations of using a parsimonious model over an inadequate complexity were discussed by Perrin et al [7], and in a similar study, the authors addressed model over-parameterization and parameter uncertainty problems of an inadequate complexity. The term complexity hereafter refers to “the number of parameters optimized during the calibration phase” [7] for the hydrological models and data requirements for the PET estimation methods. Due to the complex nature of the hydrological systems, each hydrologist might not necessarily agree with the rainfall-runoff process, but there exist general themes in common, which agree about the dominant hydrological processes [9]
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