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http://dx.doi.org/10.15681/KSWE.2020.36.6.568

Effects of Hydro-Climate Conditions on Calibrating Conceptual Hydrologic Partitioning Model  

Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Seo, Jiyu (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Won, Jeongeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University)
Lee, Okjeong (School of Integrated Science for Sustainable Earth & Environmental Disaster, Pukyong National University)
Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
Publication Information
Journal of Korean Society on Water Environment / v.36, no.6, 2020 , pp. 568-580 More about this Journal
Abstract
Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.
Keywords
Calibration; Conceptual hydrologic partitioning model; Hydro-climate condition; Markov chain - Monte Carlo; Uncertainty;
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