Verifying precipitation data of TAMAB and meteorology institute in Urmia basin

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Navid Ghajarnia ¹ , Abdolmajid Liaghat ² , Peyman Daneshkar Arasteh ³

1 - Ph.D Student, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
2 - Professor, Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
3 - Assistant Professor, Department of Water Engineering, Faculty of Engineering and technology, Imam Khomeini International University, Ghazvin, Iran

Received: 2013-10-09 Accepted : 2015-02-02 Published : 2014-12-21

Keywords: homogeneity test, Rainfall Time series, Urmia basin, XLSTAT (2013) software,

Abstract :

Water resources management, forecasting, and decision making require reliable estimates of precipitation. Therefore, a normal and an inevitable part of any hydrological or water management project before starting the research is analyzing the accuracy of time series. For this purpose by using statistical tests, the time series are analyzed heterogeneous and improbable fluctuations of data and if needed are corrected or omitted. Although usually passing the statistical tests, scientifically verifies the efficiency of the data, more precise verifications on the data may lead to different results. Therefore, in this study by choosing precipitation data of Urmia basin as a case study, final results of some statistical tests on the efficiency of the data are analyzed. Careful and precise analysis of the time series especially in comparison with neighboring stations shows that full reliance on the statistical tests alone is not enough for analyzing the efficiency of the time series and the results of these tests may mislead users on the true condition and efficiency of the data. Based on the results of this study, only 2.4 percent of the data need correction or must be omitted; nevertheless, more precise analysis through data shows that about 12.6 percent of the data are completely unsuitable and must be omitted.