Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 12 4 2008 10.5194/hess-12-1121-2008 http://www.hydrol-earth-syst-sci.net/12/1121/2008/ http://www.hydrol-earth-syst-sci.net/12/1121/2008/hess-12-1121-2008.html http://www.hydrol-earth-syst-sci.net/12/1121/2008/hess-12-1121-2008.pdf 1121 1127 2008-08-25 Estimation of streamflow by slope regional dependency function A. Altunkaynak altunkay@itu.edu.tr Istanbul Technical University, Faculty of Civil Engineering, Maslak 34469, Istanbul, Turkey Kriging is one of the most developed methodologies in the regional variable modeling. However, one of its drawbacks is that the influence radius can not be determined by this method. In which distance and in what ratio that pivot station is influenced from adjacent sites is rather often encountered problem in practical applications. Regional weighting functions obtained from available data consist of several broken lines. Each line has different slopes which represent the similarity and the contribution of adjacent stations as a weighting coefficient. The approach in this study is called as Slope Regional Dependency Function (SRDF). The main idea of this approach is to express the variability in value differences γ and distances together. Originally proposed SRDF and Trigonometric Point Cumulative Semi-Variogram (TPCSV) methods are used to predict streamflow. TPCSV and Point Cumulative Semi-Variogram (PCSV) approaches are also compared with each other. Prediction performance of all the three methods revealed a relative error less than 10% which is acceptable for most engineering applications. It is shown that SRDF outperforms PCSV and TPCSV with very high differences. It can be used for missing data completion, determination of measurement sites location, calculation of influence radius, and determination of regional variable potential. The proposed method is applied for the 38 stream flow measurement sites located in the Mississippi River basin. Altunkaynak, A.: Significant wave height prediction by using a spatial model, Ocean. Eng., 32(8–9), 924–936, 2005. Altunkaynak, A. and Özger, M.: Spatial significant wave height variation assessment and its estimation, J. Waterw. Port C.-ASCE, 131(6), 277–282, 2005. Altunkaynak, A., Özger, M., and Sen, Z.: Regional stream flow estimation by Standard regional dependence function approach, J. Hydraul. Eng.-ASCE, 131(11), 1001–1006, 2005. Altunkaynak, A., Özger, M., and Şen, Z.: Triple diagram model of level fluctuations in Lake Van, Turkey, Hydrol. Earth Syst. Sci., 7, 235–244, 2003. Huang, W. C. and Yang, F. T.: Streamflow estimation by using Kriging, Water Resour. Res., 34(6), 1599–1608, 1998. Krige, D. G.: A statistical approach to some basic mine valuation problems on the Witwatersrand, J. Chem. Metall. Min. Soc. S. Afr., 52, 119-139, 1951. Matheron, G.: Principles of geostastistics, Econ. Geol., 58, 1246–1266, 1963. Matheron, G.: The theory of regionalized variables and Its applications, Ecole de Mines, Fontainbleau, France, 1971. Matias, J. M., Vaamondel, A., Taboada, J., and Gonzalez-Manteiga, W.: Comparison of Kriging and neural networks with application to the exploitation of a slate mine, Math. Geol., 36(4), 463–486, 2004. Peng, C. S. and Buras, N.: Practical estimation of inflows into multireservoir system, J. Water Res. Pl.-ASCE, 126(5), 331–334, 2000. Öztürk, C. A. and Nasuf, E.: Geostatistical assessment of rock zones for tunneling, Tunnelling and Underground Space Technology, 17(3) , 275–285, 2002. Şahin, A. D. and Şen, Z.: A new spatial prediction model and its application to wind records, Theor. Appl. Climatol., 79(1–2), 45–54, 2004. Şen, Z. and Habib, Z. Z.: Point cumulative semivariogram of areal precipitation in mountainous regions, J. Hydrol., 205, 81–91, 1998. Şen, Z.: Cumulative semivariogram model of regionalized variable, Math. Geol., 21, 891–903, 1989.