JWSS - Journal of Water and Soil Science

The purpose of this research was to investigate the trend of annual changes in Yazd station's meteorological parameters including minimum and maximum average daily temperature and average daily precipitation (1961-2005), as well as the predicted annual mean of these parameters in the three upcoming thirty years of the 2040s, 2070s and 2100s, by the SDSM model, under RCP2.6, RCP4.5, RCP8.5, A2, and B2 scenarios. Accordingly, by using the coefficient of determination and the MAE, R², RMSE indicators, we evaluated the data generated by the SDSM model in comparison with the observed data in the base period. The lowest value of R² based on the calibration and validation of the mean values of observed and simulated SRES was obtained for precipitation (86 and 80%). In terms of the R² evaluation index, the accuracy of the small-scaled results of the minimum and maximum average temperature values was more than that of the average precipitation; however, in terms of the MAE and RMSE evaluation indicators, the accuracy of the small-scaled results of the average precipitation was higher than that of the minimum and maximum average temperature values. Subsequently, HadCM3 large-scale climatological data was used to predict the future periods (2010-2100). The results indicated that the temperature was raised in all months and seasons and the precipitation was decreasing in most of them, thereby confirming that the climate was changing in the studied region.
 

In the present study, the spatial and temporal changes of climate variables such as pan evaporation (Ep), temperature (T), relative humidity (RH), sunshine duration (SD), wind speed (W) and precipitation (P), as well as their relationship with altitude, were investigated. For this purpose, 68 meteorological stations with 30 years of data (1987-2016) throughout Iran on both seasonal and annual time scales were selected. Trend analysis of climate variables showed that over the past 30 years, most areas of Iran have become warmer and drier although all trends have not been significant. Investigation of the relationship between the trend slope of climate variables and altitude illustrated that there was no significant relationship between them during the study period on the annual time scale (p>0.1). However, in winter, the rate of increase in T (minimum, maximum and mean temperatures) and SD (p<0.1), as well as the rate of decrease in P (p<0.01), was significantly enhanced by increasing the altitude. The increase in mean and maximum T (p<0.1) and SD rates (p<0.001) in summer were significantly lower in the highlands than in the lowlands. In autumn, the trend slopes of minimum and mean T (p<0.05) were negatively correlated with altitude; in addition, the rates of increase in P and RH (p<0.05) in the highlands demonstrated a sharper increase. It seems, therefore, that most changes in climate variables have occurred in both autumn and winter. The results also showed that in winter, the highest rates of increase in Ts were related to the altitude of 1500-2000 m; however, the highest decrease in P belonged to the altitude of 2000-2500 m. In autumn, the highest rates of decrease in minimum and mean Ts had occurred in the altitude of 2000-2500 m; as well, he highest rate of increase in P was observed in the altitudes of both 0-500 m and 2000-2500 m.

Precipitation is one of the most significant climatic parameters; its distribution and values in different areas is the result of complex linear and nonlinear relationships between atmospheric elements-climatic processes and the spatial structure of the earth's surface environment. Classification of data and placing them in small and homogeneous zones can be effective in improving the understanding of these complex relationships and their results. In the present study, zoning and analyzing the distribution of rainfall in Iran concerning environmental factors was performed using the annual precipitation data of 3423 synoptic, climatological, and gauge stations in the country during the period from 1961 to 2015 and the altitude, slope, aspect, and station density data. After standardization and preparation of the data matrix, the optimal number of clusters was determined and the data set was entered into the neural-fuzzy network model (ANFIS-FCM). The results showed that the values of R²  and MAE  indices were 0.76 and 0.23, respectively which indicate the appropriate accuracy of the model. It was also found that in the four output zones of the model, environmental factors have a high impact on the spatial distribution of precipitation. In the first and third zones, the combination of high altitude and slope factors along with geographical proximity to precipitation systems has caused the average annual rainfall in these zones to be 318 and 181 mm, respectively. The mean annual rainfall has decreased to about 100 mm by the weakening of the role of environmental factors in the second and fourth clusters.

Identifying the behavior of precipitation is one of the most important planning principles related to water resources. In this research, an attempt was made to analyze the trend of time changes in extreme rainfall profiles of the country by using the daily rainfall data of 3423 synoptic, climatology, and rain gauge stations for the period from 1970 to 2016 and by performing interpolation using the kriging method. Then, using percentile profiles (percentile less than 10, less than 25, 25 to 75, 75 to 90, and above 90) and regression analysis, changes in the frequency of member days of each of the percentile methods over time were calculated and mapped. The results showed that during the studied period, 86.6% of cells associated with days with the tenth percentile or less in the country had an increasing trend. On the other hand, the pixels associated with days with the 90th percentile and more have shown an increasing trend. Considering that the pixels with the 25th, 25th-75th percentiles (normal), and 75th percentile have shown a decreasing trend in terms of the number of days in their group, it can be concluded that the country's rainfall conditions and the days with rainfall are towards the limit values has moved and the possibility of drought or destructive floods has increased in the country.