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Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting

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Abstract

With much more higher requirement for the precision of flood forecasting and length of forecast period in the hydrological operational predication, three coupling forecast methods which include real-time correction—combination forecast (RC-CF) method, combination forecast—real-time correction (CF-RC) method, and Integral Parameters Optimization (IPO) method are proposed in this paper for the purpose of improving the precision of flood forecasting. These coupling forecast methods are based on the real-time correction and combination forecast methods. Thereafter, two methods (method A & method B) are proposed for the purpose of prolonging the forecast period. Furthermore, indices of accuracy assessment which consist of mean absolute error, mean relative error, certainty coefficient and root-mean-square error are utilized to evaluate the forecast results of coupling forecast methods. Moreover, with a case study of Xiangjiaba station in the Jinsha River, advantages and disadvantages of these coupling forecast methods are obtained through the comparison of forecast results calculated by these methods, and they provide the basis for selection of coupling forecast methods. The result shows that the IPO method performs better than other two methods which behave undifferentiated. It is found that the IPO method combined with method B can be a viable alternative for flood forecasting of multiple hydrological models.

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References

  • Bauwens W, Vandewiele GL (1989) The real time runoff forecast models for the River Dijle. Water Resour Manag 3:1–9

    Article  Google Scholar 

  • Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE (2008) The MOGREPS short-range ensemble prediction system. Q J R Meteorol Soc 134:703–722

    Article  Google Scholar 

  • Cattivelli FS, Lopes CG, Sayed AH (2008) Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans Signal Process 56:1865–1877

    Article  Google Scholar 

  • Descroix L, Nouvelot JF, Vauclin M (2002) Evaluation of an antecedent precipitation index to model runoff yield in the western Sierra Madre (North-west Mexico). J Hydrol 263:114–130

    Article  Google Scholar 

  • Elliott G, Timmermann A (2004) Optimal forecast combinations under general loss functions and forecast error distributions. J Econ 122:47–79

    Article  Google Scholar 

  • Eum H-I, Simonovic S (2010) Integrated reservoir management system for adaptation to climate change: the Nakdong river basin in Korea. Water Resour Manag 24:3397–3417

    Article  Google Scholar 

  • Fedora MA, Beschta RL (1989) Storm runoff simulation using an antecedent precipitation index (API) model. J Hydrol 112:121–133

    Article  Google Scholar 

  • Guang-Te W, Singh VP (1994) An autocorrelation function method for estimation of parameters of autoregressive models. Water Resour Manag 8:33–55

    Article  Google Scholar 

  • Heggen R (2001) Normalized antecedent precipitation index. J Hydrol Eng 6:377–381

    Article  Google Scholar 

  • Hersbach H (2000) Decomposition of the continuous ranked probability score for ensemble prediction. Syst Weather Forecast 15:559–570

    Article  Google Scholar 

  • Hsu M-H, Fu J-C, Liu W-C (2003) Flood routing with real-time stage correction method for flash flood forecasting in the Tanshui River, Taiwan. J Hydrol 283:267–280

    Article  Google Scholar 

  • Huan G, Zhu C (2009) Daily flow estimation at ungauged regions based on regional flow duration curves. In: Advances in water resources and hydraulic engineering. Springer, Berlin, pp 50–54

    Chapter  Google Scholar 

  • Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688

    Article  Google Scholar 

  • Ioffe A (1993) A Lagrange multiplier rule with small convex-valued subdifferentials for nonsmooth problems of mathematical programming involving equality and nonfunctional constraints. Math Program 58:137–145

    Article  Google Scholar 

  • Jiang S, Ren L, Hong Y, Yang X, Ma M, Zhang Y, Yuan F (2014) Improvement of multi-satellite real-time precipitation products for ensemble streamflow simulation in a middle latitude basin in South China. Water Resour Manag 28:2259–2278

    Article  Google Scholar 

  • Kapetanios G, Shin Y, Snell A (2003) Testing for a unit root in the nonlinear STAR framework. J Econ 112:359–379

    Article  Google Scholar 

  • Krstanovic PF, Singh VP (1993) A real-time flood forecasting model based on maximum-entropy spectral analysis: II. Application. Water Resour Manag 7:131–151

    Article  Google Scholar 

  • Lee YH, Singh VP (2005) Tank model for sediment yield. Water Resour Manag 19:349–362

    Article  Google Scholar 

  • Li Q, Gowing J (2005) A daily water balance modelling approach for simulating performance of tank-based irrigation systems. Water Resour Manag 19:211–231

    Article  Google Scholar 

  • Lu G, Wu Z, Wen L, Lin C, Zhang J, Yang Y (2008) Real-time flood forecast and flood alert map over the Huaihe River Basin in China using a coupled hydro-meteorological modeling system. Sci China Ser E Technol Sci 51:1049–1063

    Article  Google Scholar 

  • Ouyang S, Zhou J, Li C, Liao X, Wang H (2014) Optimal design for flood limit water level of cascade reservoirs. Water Resour Manag :1–13

  • Peng Y, Ji C, Gu R (2014) A multi-objective optimization model for coordinated regulation of flow and sediment in cascade reservoirs. Water Resour Manag 28:4019–4033

    Article  Google Scholar 

  • Piotrowski AP, Napiorkowski JJ (2012) Product-units neural networks for catchment runoff forecasting. Adv Water Resour 49:97–113

    Article  Google Scholar 

  • Plett GL (2004) Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1. Background. J Power Sources 134:252–261

    Article  Google Scholar 

  • Rapach DE, Strauss JK, Zhou G (2009) Out-of-sample equity premium prediction: combination forecasts and links to the real economy review of financial studies

  • Ren-Jun Z (1992) The Xinanjiang model applied in China. J Hydrol 135:371–381

    Article  Google Scholar 

  • Seo D-J, Breidenbach JP (2002) Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J Hydrometeorol 3:93–111

    Article  Google Scholar 

  • Shaheen HI, Rashed GI, Cheng SJ (2009) Application of differential evolution algorithm for optimal location and parameters setting of UPFC considering power system security. Eur Trans Electr Power 19:911–932

    Article  Google Scholar 

  • Shamseldin AY, O’Connor KM, Liang GC (1997) Methods for combining the outputs of different rainfall–runoff models. J Hydrol 197:203–229

    Article  Google Scholar 

  • Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. International Computer Science Institute, Berkeley

    Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359

    Article  Google Scholar 

  • Symonds ME, Moussalli A (2011) A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav Ecol Sociobiol 65:13–21

    Article  Google Scholar 

  • Terregrossa SJ (2005) On the efficacy of constraints on the linear combination forecast model. Appl Econ Lett 12:19–28

    Article  Google Scholar 

  • Tian Y, Xu Y-P, Zhang X-J (2013) Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour Manag 27:2871–2888

    Article  Google Scholar 

  • Tian Y, Booij M, Xu Y-P (2014) Uncertainty in high and low flows due to model structure and parameter errors. Stoch Env Res Risk A 28:319–332

    Article  Google Scholar 

  • Tingsanchali T, Gautam MR (2000) Application of tank, NAM, ARMA and neural network models to flood forecasting. Hydrol Process 14:2473–2487

    Article  Google Scholar 

  • Vasan A, Raju K (2007) Application of differential evolution for irrigation planning: an indian case study. Water Resour Manag 21:1393–1407

    Article  Google Scholar 

  • Wang W, Hu S, Li Y (2011) Wavelet transform method for synthetic generation of daily streamflow. Water Resour Manag 25:41–57

    Article  Google Scholar 

  • Wang X, Zhou J, Ouyang S, Li C (2014) Research on joint impoundment dispatching model for cascade reservoir. Water Resour Manag

  • Winkler RL, Makridakis S (1983) The combination of forecasts. J R Stat Soc Ser A (Gen) 146:150–157

    Article  Google Scholar 

  • Xiong L, Shamseldin AY, O’Connor KM (2001) A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system. J Hydrol 245:196–217

    Article  Google Scholar 

  • Zhang Q, Wang B-D, He B, Peng Y, Ren M-L (2011) Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water Resour Manag 25:2683–2703

    Article  Google Scholar 

  • Zhao X-h, Chen X (2015) Auto regressive and ensemble empirical mode decomposition hybrid model for annual runoff forecasting. Water Resour Manag :1–14

Download references

Acknowledgments

This work is funded by the National Natural Science Foundation Key Project of China (51239004). Special thanks are given to the anonymous reviewers and editors for their constructive comments.

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Correspondence to Jianzhong Zhou.

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Highlights of this Paper

(1) Real-time error correction combined with multi-model combination forecast

(2) Coupling forecast methods of multiple rainfall-runoff models

(3) Obvious forecast precision improvement and forecast period prolonging

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Wu, J., Zhou, J., Chen, L. et al. Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting. Water Resour Manage 29, 5091–5108 (2015). https://doi.org/10.1007/s11269-015-1106-8

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  • DOI: https://doi.org/10.1007/s11269-015-1106-8

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