Global warming has brought about changes in meteorological variable time series. These changes, accompanied with serious human intervention in nature, has resulted in hydrological changes. So the effects can be tracked down by the trend of the time series. The purpose of this study is to analyze and compare the monthly and annual trends of temperature, precipitation as well as stream flow time series of the Urmia Lake basin, using nonparametric methods. Four non-parametric statistical tests, namely: Mann-Kendall, Theil-Sen, Spearman Rho, and Sen's T tests are investigated throughout the present study. The records of 11 temperature gauging stations, 35 rain gauge stations and 35 hydrometery stations showed significant increasing trends in the basin temperature. In the case of precipitation recordings, it was different, in a way that 8% of the rain stations indicated increasing trend, while the trend being decreased in 14% of the stations. Finally for stream flows, 60% of the hydrometery stations revealed a decreasing trend. A comparison of the methods revealed (at a monthly scale), that Mann-Kendall and Theil-Sen's performance were similar. Furthermore, Sen's T and Spearman Rho methods were detected to be the ones showing the number of stations to undergo minimum and maximum significant trends, respectively. As for annual time scale, Sen's T showed to represent the maximum number of stations to be suspicious of being of non-stationary as regards the trend.