JWSS - Journal of Water and Soil Science

One of the major issues in the contemporary world is climate change. The behavior and characteristics of parameters affecting climate change can cause them to be seen and hidden. As one of the effective ways to detect overt and covert behaviors for periodic climatic data series, Spectral analysis can be used. It is the analysis of each of the wavelengths series, making this behavior clear. Accordingly, the present study was an attempt to use the method of spectral analysis, data cycles in the minimum temperature, maximum temperature and precipitation in Ramsar station (located in the western regions of Mazandaran province) an nd Babolsar (located in the central parts of this province) in a period from1961 to 2014. For this purpose, temperature and precipitation data were obtained from these stations; MATLAB software environment and the environment for the software were logged for each of the variable in the stations. The results revealed that the minimum temperature at both stations had significant cycles, with the return period being 2 to 5 years; Remote Link could be fit into the cycle parameters such as NAO, AO and ENSO. Analysis of the period gram showed cycles 8 and 5/13-year-old and 5-year-old period in Ramsar and Babolsar. During the rainy cycles, the difference between the two stations and the difference in the geographical position affected systems, and rain accounted for the difference in speed dual-zone climate indicator for Remote Link.

In the vast majority parts of the Earth, a prospect now visible is the mostly synthetic thinking and fabrication by the human hand. Collision and impact of humans on the natural environment in the short and long-term courses for obvious geographical features have changed a variety of spaces. One of the consequences of human impact on the natural environment during the current period is the phenomenon of climate change. One of the climatic parameters that plays an important role in agriculture, energy, urban, tourism and road transport is the minimum temperature. In this study, an attempt was made using the minimum temperature data from 5 meteorological stations in the West Mazandaran province, as well as HADCM3 model data, to show how to change this parameter in the future periods based on simulation by the SDSM model. Accordingly, after selection of the suitable climate variables and model calibration, the accuracy of the created model in the base period was evaluated; after ensuring the sufficient accuracy of the model according to A2 and B2 scenario, data minimum temperature in 2100 was simulated. Based on the simulation results showed that the values of minimum temperature in the region over the coming years would increase. This parameter was such that the average seasonal periods 2016 to 2039, 2040 to 2069 and 2070 to 2099, as compared to the baseline period would increase, on average, by 1.8, 3.5 and 6 percent. The largest increases in the minimum temperature in the western and southern parts of the region could occur. It was also found that unlike other months of the year, the minimum temperature in January would be a decreasing trend.

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.