Abstract
Selecting an optimum number of calibration sites for hydrological modeling is challenging. Modelers often spend a lot of time and effort on trial and error because there is no guide. We propose a novel entropy method to automate the selection of the optimum combination of calibration sites. To illustrate, the proposed entropy method is applied using discharge data from one river basin in Korea. First, different combinations of discharge-gauging sites were grouped based on the maximum information estimated by the entropy method. Then, a hydrological model was set up for the study basin and was calibrated by estimating optimal parameters using a genetic algorithm at the discharge-gauging sites. The calibration result confirmed that the model’s performance was best when it was calibrated using the site number and combination suggested by the entropy method. In addition, the entropy method was useful in reducing the time and effort of model calibration. Therefore, we suggest and confirm the applicability of the entropy method in selecting calibration sites for hydrological modeling.
Highlights
Hydrological models are increasingly used to evaluate the impacts of climate, land use, and crop management practices on the quantity of water resources [1]
The applicability of the SWAT model was outstanding in the study basin as the coefficient of correlation (CC), Root-Mean-Square Error (RMSE), and Nash-Sutcliffe efficiency (NSE) were 0.782, 147.4, and 0.482, respectively, even in the case where no calibration was conducted (#0); it was confirmed that the results were improved slightly when the model was calibrated
The purpose of this study was to review the applicability of the entropy method in selecting the calibration sites for hydrological modeling
Summary
Hydrological models are increasingly used to evaluate the impacts of climate, land use, and crop management practices on the quantity of water resources [1]. The two main objectives of hydrological modeling are to explore the implications of making certain assumptions about the nature of the real-world system and to predict the system’s behavior under a set of naturally occurring circumstances [2]. The successful application of any hydrological model is dependent on the quality of its calibration [3]. As a result, developing calibration strategies is a requirement for their proper application in hydrological modeling. Model parameters are estimated by minimizing the deviation between the measured and simulated discharges. Researchers have suggested a number of methodologies to improve calibration-related issues [4,5,6,7,8,9,10,11]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
