Does the Complexity of Evapotranspiration and Hydrological Models Enhance Robustness?

Dereje Birhanu, Hyeonjun Kim, Cheolhee Jang and Sanghyun Park
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Dereje Birhanu: Smart City & Construction Engineering Department, University of Science and Technology, Daejeon 34113, Korea
Hyeonjun Kim: Smart City & Construction Engineering Department, University of Science and Technology, Daejeon 34113, Korea
Cheolhee Jang: Department of Land, Water and Environment Research, Korea Institute of Civil Engineering & Building Technology, Goyang 10223, Korea
Sanghyun Park: Smart City & Construction Engineering Department, University of Science and Technology, Daejeon 34113, Korea

Sustainability, 2018, vol. 10, issue 8, 1-34

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.

Keywords: hydrological model; potential evapotranspiration; complexity; parsimony (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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