Application of Regularized Dynamic System Response Curve for Runoff Correction Based on HBV Model: Case Study of Shiquan Catchment, China
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 4
Abstract
Error correction is an important postprocess technique in hydrological modeling. The paper aims to investigate the effectiveness and suitability of a recently proposed error correction method, the regularized dynamic system response curve (RDSRC) method in the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. In the HBV model, the effective precipitation can reflect the error of model inputs so it is used for runoff error correction. Three commonly used statistics [Nash-Sutcliffe efficiency (NSE) coefficient, root-mean-squared error (RMSE), and percent bias (PBIAS)] were used to evaluate the model performance. In this study, the RDSRC method was tested by a synthetic case and a real case. Results of the synthetic case showed that the NSE, RMSE, and PBIAS of runoff simulation before correction were 0.943, , and , respectively, while those statistics after correction by the RDSRC method were , and 0.002%, respectively. In the real case, the HBV model was used for daily runoff modeling in the Shiquan catchment in China; meanwhile, the RDSRC method and the AR(2) model were compared for runoff error correction. The corrected daily runoff by the RDSRC method had much better accuracy than the second-order autoregressive [AR(2)] model and the HBV model. After correction of the RDSRC method, the NSE of the HBV model increased from 0.83 to 0.946, whereas the RMSE of the HBV model decreased by 43.6%, from 246.37 to . This case study provides two main conclusions: (1) the RDSRC method had better performance in runoff error correction than the AR(2) model, and (2) the RDSRC method is an effective alternative for daily runoff error correction when used for the HBV model.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request (models of HBV and RDSRC, rainfall and runoff data of Shiquan catchment).
Acknowledgments
This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0467), the Fundamental Research Funds for the Central Universities (B200203053, B200201027), National Natural Science Foundation of China (51709077), and Open Research Fund of the Yellow River Sediment Key Laboratory of Ministry of Water Resources (201804). The China Scholarship Council (No. 202106710059) is also gratefully acknowledged. The authors would like to thank reviewers for their in-depth reviews and constructive suggestions. The remarks and summary of reviewer comments provided by the editor and reviewers are also greatly appreciated, and facilitated major improvements in this paper.
References
Aghakouchak, A., and E. Habib. 2010. “Application of a conceptual hydrologic model in teaching hydrologic processes.” Int. J. Eng. Educ. 26: 963–973.
Ahsan, M., and K. M. O’Connor. 1994. “A reappraisal of the Kalman filtering technique, as applied in river flow forecasting.” J. Hydrol. 161 (1): 197–226. https://doi.org/10.1016/0022-1694(94)90129-5.
Babovic, V., R. Caňizares, H. R. Jensen, and A. Klinting. 2001. “Neural networks as routine for error updating of numerical models.” J. Hydraul. Eng. 127 (3): 181–193. https://doi.org/10.1061/(ASCE)0733-9429(2001)127:3(181).
Bergström, S., and A. Forsman. 1973. “Development of a conceptual deterministic rainfall-runoff model.” Hydrol. Res. 4 (3): 147–170. https://doi.org/10.2166/nh.1973.0012.
Butts, M. B., J. T. Payne, M. Kristensen, and H. Madsen. 2004. “An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation.” J. Hydrol. 298 (1–4): 242–266. https://doi.org/10.1016/j.jhydrol.2004.03.042.
Chen, L., Y. C. Zhang, J. Z. Zhou, V. P. Singh, S. L. Guo, and J. H. Zhang. 2015. “Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting.” J. Hydrol. 521 (Feb): 157–169. https://doi.org/10.1016/j.jhydrol.2014.11.053.
Dastorani, M., M. Mirzavand, M. T. Dastorani, and S. J. Sadatinejad. 2016. “Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition.” Nat. Hazard. 81 (3): 1811–1827. https://doi.org/10.1007/s11069-016-2163-x.
Delft, G. V., G. Serafy, and A. W. Heemink. 2009. “The ensemble particle filter (EnPF) in rainfall-runoff models.” Stochastic Environ. Res. Risk Assess. 23 (8): 1203–1211. https://doi.org/10.1007/s00477-008-0301-z.
Farr, T. G., and M. Kobrick. 2000. “Shuttle radar topography mission produces a wealth of data.” Eos, Trans. Am. Geophys. Union 81 (48): 583–585. https://doi.org/10.1029/EO081i048p00583.
Fraga, I., L. Cea, and J. Puertas. 2019. “Effect of rainfall uncertainty on the performance of physically-based rainfall-runoff models.” Hydrol. Processes 33 (1): 160–173. https://doi.org/10.1002/hyp.13319.
Grigonytė, E., and E. Butkevičiūtė. 2016. “Short-term wind speed forecasting using ARIMA model.” Energetika 62 (1–2): 42–55. https://doi.org/10.6001/energetika.v62i1-2.3313.
Guingla, D. P., R. D. Keyser, G. D. Lannoy, L. Giustarini, P. Matgen, and V. Pauwels. 2013. “Improving particle filters in rainfall-runoff models: Application of the resample-move step and the ensemble Gaussian particle filter.” Water Resour. Res. 49 (7): 4005–4021. https://doi.org/10.1002/wrcr.20291.
Gupta, H. V., M. P. Clark, J. A. Vrugt, G. Abramowitz, and M. Ye. 2012. “Towards a comprehensive assessment of model structural adequacy.” Water Resour. Res. 48: W08301. https://doi.org/10.1029/2011WR011044.
Gupta, H. V., S. Sorooshian, and P. O. Yapo. 1999. “Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration.” J. Hydrol. Eng. 4 (2): 135–143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135).
Han, S. S., P. Coulibaly, and D. Biondi. 2019. “Assessing hydrologic uncertainty processor performance for flood forecasting in a semiurban watershed.” J. Hydrol. Eng. 24 (9): 05019025. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001828.
Jiang, X., Z. Liang, M. Qian, X. Zhang, Y. Chen, B. Li, and X. Fu. 2019. “Method for probabilistic flood forecasting considering rainfall and model parameter uncertainties.” J. Hydrol. Eng. 24 (12): 04019056. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001861.
Kahl, B., and H. P. Nachtnebel. 2008. “Online updating procedures for a real-time hydrological forecasting system.” IOP Conf. Ser.: Earth Environ. Sci. 4 (1): 012001. https://doi.org/10.1088/1755-1307/4/1/012001.
Lee, H. S., M. W. Jeon, D. Balin, and M. Rode. 2009. “Application of rainfall runoff model with rainfall uncertainty.” J. Korea Water Resour. Assoc. 42 (10): 773–783. https://doi.org/10.3741/JKWRA.2009.42.10.773.
Liang, Z. M., Y. X. Huang, V. P. Singh, Y. M. Hu, B. Q. Li, and J. Wang. 2021. “Multi-source error correction for flood forecasting based on dynamic system response curve method.” J. Hydrol. 594 (Mar): 125908. https://doi.org/10.1016/j.jhydrol.2020.125908.
Lindstrom, G., B. Johansson, M. Persson, M. Gardelin, and S. Bergstrom. 1997. “Development and test of the distributed HBV-96 hydrological model.” J. Hydrol. 201 (1–4): 272–288. https://doi.org/10.1016/S0022-1694(97)00041-3.
Liu, Z. J., S. L. Guo, H. G. Zhang, D. D. Liu, and G. Yang. 2016. “Comparative study of three updating procedures for real-time flood forecasting.” Water Resour. Manage. 30 (7): 2111–2126. https://doi.org/10.1007/s11269-016-1275-0.
Ma, Q. M., L. H. Xiong, D. D. Liu, C. Y. Xu, and S. L. Guo. 2018. “Evaluating the temporal dynamics of uncertainty contribution from satellite precipitation input in rainfall-runoff modeling using the variance decomposition method.” Remote Sens. 10 (12): 1876. https://doi.org/10.3390/rs10121876.
McMillan, H., B. Jackson, M. Clark, D. Kavetski, and R. Woods. 2011. “Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models.” J. Hydrol. 400 (1–2): 83–94. https://doi.org/10.1016/j.jhydrol.2011.01.026.
Moriasi, D. N., J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith. 2007. “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.” Trans. ASABE 50 (3): 885–900. https://doi.org/10.13031/2013.23153.
Pagano, T. C., Q. J. Wang, P. Hapuarachchi, and D. Robertson. 2011. “A dual-pass error-correction technique for forecasting streamflow.” J. Hydrol. 405 (3–4): 367–381. https://doi.org/10.1016/j.jhydrol.2011.05.036.
Pulukuri, S., V. R. Keesara, and P. Deva. 2017. “Application of fuzzy updating algorithm for real-time forecast.” ISH J. Hydraul. Eng. 24 (2): 213–221. https://doi.org/10.1080/09715010.2017.1408039.
Ray, S., R. Ray, M. H. Khondekar, and K. Ghosh. 2018. “Scaling analysis and model estimation of solar corona index.” Adv. Space Res. 61 (8): 2214–2226. https://doi.org/10.1016/j.asr.2018.01.036.
Reichle, R. H., W. T. Crow, and C. L. Keppenne. 2008. “An adaptive ensemble Kalman filter for soil moisture data assimilation.” Water Resour. Res. 44: W03423. https://doi.org/10.1029/2007WR006357.
Renard, B., D. Kavetski, G. Kuczera, M. Thyer, and S. W. Franks. 2010. “Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors.” Water Resour. Res. 46: W05521. https://doi.org/10.1029/2009WR008328.
Shamseldin, A. Y., and K. M. O’Connor. 2001. “A non-linear neural network technique for updating of river flow forecasts.” Hydrol. Earth Syst. Sci. 5 (4): 577–598. https://doi.org/10.5194/hess-5-577-2001.
Shen, J. C., C. H. Chang, S. J. Wu, C. T. Hsu, and H. C. Lien. 2015. “Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method.” Stochastic Environ. Res. Risk Assess. 29 (7): 1903–1920. https://doi.org/10.1007/s00477-015-1074-9.
Si, W., W. M. Bao, and H. V. Gupta. 2015. “Updating real-time flood forecasts via the dynamic system response curve method.” Water Resour. Res. 51 (7): 5128–5144. https://doi.org/10.1002/2015WR017234.
Si, W., H. V. Gupta, W. M. Bao, P. Jiang, and W. Z. Wang. 2019. “Improved dynamic system response curve method for real-time flood forecast updating.” Water Resour. Res. 55 (9): 7493–7519. https://doi.org/10.1029/2019WR025520.
Sun, Y., W. Bao, P. Jiang, X. Ji, S. Gao, Y. Xu, Q. Zhang, and W. Si. 2018a. “Development of multivariable dynamic system response curve method for real-time flood forecasting correction.” Water Resour. Res. 54 (7): 4730–4749. https://doi.org/10.1029/2018WR022555.
Sun, Y. Q., W. M. Bao, P. Jiang, W. Si, J. W. Zhou, and Q. Zhang. 2018b. “Development of a regularized dynamic system response curve for real-time flood forecasting correction.” Water 10 (4): 450. https://doi.org/10.3390/w10040450.
Weimin, B., S. Wei, and Q. Simin. 2014. “Flow updating in real-time flood forecasting based on runoff correction by a dynamic system response curve.” J. Hydraul. Eng. 19 (4): 747–756. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000848.
WMO (World Meteorological Organisation). 1992. “Simulated real-time intercomparison of hydrological models.” In Operational hydrology. Geneva: WMO.
Worqlul, A. W., E. K. Ayana, B. H. Maathuis, C. Macalister, W. D. Philpot, J. M. O. Leyton, and T. S. Steenhuis. 2017. “Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the Upper Blue Nile Basin, Ethiopia.” J. Hydrol. 556 (Jan): 1182–1191. https://doi.org/10.1016/j.jhydrol.2017.01.058.
Xie, X. H., and D. X. Zhang. 2013. “A partitioned update scheme for state-parameter estimation of distributed hydrologic models based on the ensemble Kalman filter.” Water Resour. Res. 49 (11): 7350–7365. https://doi.org/10.1002/2012WR012853.
Xiong, L. H., and K. M. O’Connor. 2002. “Comparison of four updating models for real-time river flow forecasting.” Hydrol. Sci. J. 47 (4): 621–639. https://doi.org/10.1080/02626660209492964.
Yoo, C. S., J. H. Hwang, and J. H. Kim. 2012. “Use of the extended kalman filter for the real-time quality improvement of runoff data: 1. Algorithm construction and application to one station.” J. Korea Water Resour. Assoc. 45 (7): 697–711. https://doi.org/10.3741/JKWRA.2012.45.7.697.
Zhang, X. Q., W. M. Bao, and Y. Q. Sun. 2021. “Enhancing the hydrologic system differential response method for flood forecasting correction.” J. Hydrol. 592 (Jan): 125793. https://doi.org/10.1016/j.jhydrol.2020.125793.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 5, 2021
Accepted: Jan 11, 2022
Published online: Feb 15, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 15, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Jian Wang, Weimin Bao, Zhangling Xiao, Qingping Wang, Yiqun Sun, Wei Si, Objectivity verification experiment of the dynamic system response curve method for streamflow simulation, Journal of Hydrology, 10.1016/j.jhydrol.2022.128969, 617, (128969), (2023).
- Muhammad Jehanzaib, Muhammad Ajmal, Mohammed Achite, Tae-Woong Kim, Comprehensive Review: Advancements in Rainfall-Runoff Modelling for Flood Mitigation, Climate, 10.3390/cli10100147, 10, 10, (147), (2022).