Trend Analysis of Temperature, Precipitation, and Relative Humidity Changes in Iran

Introduction In the pursuit of detecting the trend and the shift in trend in hydro-meteorological variables, various statistical methods have been developed and used over the years. Of the two methods commonly used (parametric and non-parametric), the non-parametric method has been favored over parametric methods. Long term trend analysis can reveal the beginning of the trend year, trend changes over time, and abrupt trend detection in a time-series. It is expected that the findings of this study will bring about more insights on understanding the regional hydrologic behavior over the last several decades in Iran.   Methodology This paper analyzes the behavior of annual and seasonal temperatures, precipitation, and relative humidity. Datasets of 37 stations in Iran were analyzed from 1961 to 2010. The  pre-whitening technique was used to eliminate the effect of the autocorrelation of data series. The Mann–Kendall (MK) test and the Sen's slope estimator were applied to quantify the significance of the trend and the magnitude of the trend at a 95% confidence level, respectively. The surface interpolation technique was used to prepare a spatial temperature, precipitation, and relative humidity data map over Iran from the point data measuring stations within the ArcGIS framework. For spatial distribution of trends in maps, contours are generated using an inverse-distance-weighted (IDW) algorithm.