Abstract
The independently-developed distributed hydrological model EasyDHM, supplies four different runoff-generation simulation methods and three different conflux simulation methods. Each has its own advantages and disadvantages. In order to increase the accuracy of flood forecast of EasyDHM, this paper studied on several multi-model combination techniques for these different simulation methods, including the simple model average method, weighted average method, adaptive simple average method and adaptive weighted average method. These combination techniques were applied to the Taoer River in Northeast China, and evaluated from the intercom parison of results by single-model simulations and results by other combination methods in this paper. The study revealed that the multi-model simulations are generally better than any single-member model simulations. Furthermore, different multi-model combination techniques also showed different affection to the accuracy levels of flood forecasting.
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References
Zhao, R.: Watershed Hydroloigcal Simulation. Hydraulic and Electric Power Press, Beijing (1984)
Jia, Y., Wang, H., Ni, G., Yang, D., Wang, J., Qing, D.: The Theory and Application of Distributed Hydrological Model. China Water Resources and Hydropower Press, Beijing (2005)
Abbott, M.B., Bathurst, J.C., Cunge, J.A., O’Connell, P.E., Rasmussen, J.: An Introduction to the European System: Systeme Hydrologique Europeen (SHE). Journal of Hydrology 87, 61–77 (1986)
Newsha, K.A., Duan, Q., Gao, X., Sorooshian, S.: Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results. Journal of Hydrometeorology 7, 755–768 (2006)
Asaad, Y.S., O’Connor, K.M.: A real-time combination method for the outputs of different rainfall-runoff models. Hydrological Sciences Journal 44(6), 895–912 (1999)
Boyle, D.P., Gupta, H.V., Sorooshian, S., Koren, V., Zhang, Z., Smith, M.: Toward improved streamflow forecast: value of semi-distributed modeling. Water Resources Research 37(11), 2749–2759 (2001)
Shamseldin, A.Y., Liang, G.C.: Methods for combining the outputs of different rainfall Crunoff models. J. Hydrol. 197, 203–229 (1997)
Shamseldin, A.Y., O’Connor, K.M.: A real-time combination method for the outputs of different rainfall Crunoff models. Hydrol. Sci. J. 44, 895–912 (1999)
Lei, X., Jiang, Y., Wang, H.: The distributed hydrological model: EasyDHM. China Water Power Press, Beijing (2010)
Lei, X., Wang, Y., Liao, W., Jiang, Y., Tian, Y., Wang, H.: Development of efficient and cost-effective distributed hydrological modeling tool MWEasyDHM based on open-source MapWindow GIS. Computers and Geosciences 37(9), 1476–1489 (2011)
Georgakakos, K.P., Seo, D.J., Gupta, H., Schake, J., Butts, M.B.: Characterizing streamflow simulation uncertainty through multimodel ensembles. J. Hydrol. 298, 222–241 (2004)
Kachroo, R.K., Natale, L.: Non-linear modelling of rainfall Crunoff transformation. J. Hydrol. 135, 341–369 (1992)
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Liao, W., Lei, X. (2012). Multi-model Combination Techniques for Flood Forecasting from the Distributed Hydrological Model EasyDHM. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_44
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DOI: https://doi.org/10.1007/978-3-642-34289-9_44
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