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One of the most important stages in climate-change-impact studies is uncertainty analysis, due to its great effect on both predictions and decision-making. This study presents a procedure that characterizes the changes of climatic variables for the period 2011-2040 under representative concentration pathway (RCP) scenarios and then quantifies the uncertainty linked with the downscaling process using a bootstrapping method at 95% confidence intervals in one of the most vulnerable basins, the "Karaj-Jajrud" located in the South Alborz Range, Iran. The results show that there is a consistent warming in mean air temperature time-series with different magnitudes for all the RCP scenarios in the region for 2011-2040, whereas the results indicate decreasing precipitation compared with the baseline period for all RCP scenarios in the study area. Analysing the impacts of the downscaling process uncertainty on the prediction results shows that the contribution of this uncertainty source to the prediction uncertainty is relatively high, as about 30% of the downscaled temperature and precipitation data fall inside the 95% simulation confidence intervals. Furthermore, precipitation-series uncertainty is more than the air temperature series. Climate change assessments and their uncertainty analysis can help managers to enhance preparedness and adaptation strategies in order to mitigate the consequences of natural hazards. More investigations can be done by adopting more general circulation models and other downscaling methods to compare the uncertainty that arises from each uncertainty source.

Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran.

Analysis of the trend of climatic records is necessary for better climate modelling and subsequently adopting effective planning and management strategies. In this research, the Southern Alborz Range, Iran was selected to analyse the trends and stationarity of hydro‐climatic time series. The Mann–Kendall (M‐K) classic test was considered to identify the monotonic trend, while trend free pre‐whitening approach was applied for eliminating serial correlation from the time series. Meanwhile, time series stationarity was tested by Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. The results indicated both increasing and decreasing trends for precipitation, mean air temperature and pan evaporation in annual time‐scale which were insignificant in most of the stations. The western parts of the area illustrated the highest positive trend (2.018 mm⋅year–1) in annual precipitation; while the south of the study region showed the highest negative trend (−0.999 mm⋅year–1). The highest warming trend belongs to the northern parts of the region (0.035°C⋅year–1); while the western parts show the highest negative trend in temperature variable (–0.07°C⋅year–1); on the other hand, in the entire of the region, an insignificant decreasing trend is observed in most of the evaporation records. Non‐stationarity tested with the KPSS suggested annual time series to be significantly stationary after de‐trending and removing lag‐1 serial correlation. The findings of this investigation may provide important information about the climate of the region and consequently help to implement better management and planning strategies for scarce water resources of the region.

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