Effects of temporal variability on HBV model calibration
This study aimed to investigate the effect of temporal variability on the optimization of the Hydrologiska Byråns Vattenbalansavedlning (HBV) model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, who...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015-10-01
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| Series: | Water Science and Engineering |
| Subjects: | |
| Online Access: | http://www.waterjournal.cn:8080/water/EN/abstract/abstract354.shtml |
| _version_ | 1828729684945272832 |
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| author | Steven Reinaldo Rusli Doddi Yudianto Jin-tao Liu |
| author_facet | Steven Reinaldo Rusli Doddi Yudianto Jin-tao Liu |
| author_sort | Steven Reinaldo Rusli |
| collection | DOAJ |
| description | This study aimed to investigate the effect of temporal variability on the optimization of the Hydrologiska Byråns Vattenbalansavedlning (HBV) model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed: short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash-Sutcliffe efficiency coefficient (NSE), relative error (RE), root mean square error (RMSE), and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, −0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temporal calibration in hydrological analysis delivers better simulation in general for water balance analysis. |
| first_indexed | 2024-04-12T14:36:11Z |
| format | Article |
| id | doaj.art-706869d43e03405ca6cc36923338e807 |
| institution | Directory Open Access Journal |
| issn | 1674-2370 2405-8106 |
| language | English |
| last_indexed | 2024-04-12T14:36:11Z |
| publishDate | 2015-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Science and Engineering |
| spelling | doaj.art-706869d43e03405ca6cc36923338e8072022-12-22T03:29:04ZengElsevierWater Science and Engineering1674-23702405-81062015-10-0184291300Effects of temporal variability on HBV model calibrationSteven Reinaldo Rusli0Doddi Yudianto1Jin-tao Liu2Civil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Bandung 40141, Indonesia; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, PR ChinaCivil Engineering Department, Faculty of Engineering, Parahyangan Catholic University, Bandung 40141, IndonesiaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, PR ChinaThis study aimed to investigate the effect of temporal variability on the optimization of the Hydrologiska Byråns Vattenbalansavedlning (HBV) model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed: short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash-Sutcliffe efficiency coefficient (NSE), relative error (RE), root mean square error (RMSE), and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, −0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temporal calibration in hydrological analysis delivers better simulation in general for water balance analysis.http://www.waterjournal.cn:8080/water/EN/abstract/abstract354.shtmlHBV modelmodel calibrationJiangwan Catchmenttemporal variability |
| spellingShingle | Steven Reinaldo Rusli Doddi Yudianto Jin-tao Liu Effects of temporal variability on HBV model calibration Water Science and Engineering HBV model model calibration Jiangwan Catchment temporal variability |
| title | Effects of temporal variability on HBV model calibration |
| title_full | Effects of temporal variability on HBV model calibration |
| title_fullStr | Effects of temporal variability on HBV model calibration |
| title_full_unstemmed | Effects of temporal variability on HBV model calibration |
| title_short | Effects of temporal variability on HBV model calibration |
| title_sort | effects of temporal variability on hbv model calibration |
| topic | HBV model model calibration Jiangwan Catchment temporal variability |
| url | http://www.waterjournal.cn:8080/water/EN/abstract/abstract354.shtml |
| work_keys_str_mv | AT stevenreinaldorusli effectsoftemporalvariabilityonhbvmodelcalibration AT doddiyudianto effectsoftemporalvariabilityonhbvmodelcalibration AT jintaoliu effectsoftemporalvariabilityonhbvmodelcalibration |