فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 126 (زمستان 1402)

Aridity and drought are inseparable features of every climate. But in arid and semi-arid climates, tarsal is an ideal opportunity to restore or compensate for the lack of water in the region. The southern part of Iran has a dry climate despite access to the huge moisture resources of the warm southern seas. To conduct the research, first, the daily rainfall data of all the synoptic stations of the southern provinces of the country, which had complete statistics in the 33-year statistical period (1986-2019), were extracted. Then, using ZCI, ZSI, and SPI indicators, droughts and droughts were identified. Finally, the years that were in extreme poverty in all three above indicators were selected as samples. In the next step, the data of specific humidity, orbital and meridional wind, geopotential height and omega for all atmospheric levels in the lower and middle layers of the Verdosphere were received from the NCEP/NCAR site for all rainy days. Examining the maps of subsurface levels of the Verdspehr (sea, 1000 and 925 hectopascals) showed that three main systems control the pattern of the subsurface layer of the Verdspehr. The Siberian, Tibetan and Mohajer high-pressure fronts spread over the warm waters of the Oman and Arabian seas from 3 to 7 days before the start of the system's rainfall and spread the necessary moisture into the Sudanese system. It plays a very important role in determining the entry path of the system, the expansion pattern of the Mediterranean trough and the duration of the activity of the precipitation system in the middle layer of the Arabian Wardspehr

Among the cyclones that affect the sometimes-widespread rainfall in Iran are the merging systems of the Mediterranean and Red Seas. Therefore, it is very important to obtain the changes in the intensity of the geopotential height and the geopotential height shift of the Mediterranean-Red Sea convection patterns as one of the factors of the manifestations of these gyres, as well as the precipitation in some areas of Iran. To carry out this research, the data of geopotential height level of 1000 hectopascals related to the European Center for Medium-term Atmospheric Forecasting and ERA-Entrim version were used as a six-hour observation during the period of 1979-2018. To investigate the presence of jumps and fluctuations in the intensity of the Mediterranean-Red Sea cyclone centers during the statistical period, the Alexanderson index, known as the Standard Normal Homogeneity Test (SNHT) index, was used. A non-parametric chi-square statistic was exerted to verify and investigate the significance of the trend between geopotential height data and geopotential height tilt data. The parametric linear regression method was used to analyze and model the long-term trend. The findings of the present research indicate the increase of geopotential height in the place of the formation of the circulation centers of the Mediterranean Sea, as well as the decrease of the pressure gradient in the average annual values, which will probably lead to a decrease in instability and precipitation in the affected areas. The geopotential height shift data of the Mediterranean Sea had a significant jump in 1996, which divided the time series into two periods before and after the jump. The results indicate an upward trend in these two time periods, but the second period, with a gentler slope, has increased compared to the previous period

Having sufficient information about the operation area is one of the most important and key success factors in planning, directing and successfully implementing operations, and this issue is of double importance, especially regarding the weather conditions of the area, in water and soil operations. In order to conduct research and identify the weather types of Abu Musa Island, in order to plan and implement hydrological operations, the data related to 18 weather variables in a period of 30 years (1991-2021) as well as the average and maximum strength of the sea in one day have been used. In order to statistically analyze the data and obtain the statistical characteristics of each weather type, MATLAB software was used and to identify the weather types, weather elements were subjected to cluster analysis by integration method. After conducting trial and error operations to select the number of air brigade groups, four air brigades were finally identified for this island. Hierarchical analysis has been used to determine the most suitable and worst type of weather. The results showed that based on the pairwise comparison of weather elements to determine the priority of the air force to carry out soil dredging operations, the elements of wind speed and horizontal visibility were assigned the highest weight and the most effective weather elements in soil dredging operations were identified. After forming the decision matrix, the weight of each of the specific weather types and the moderate weather type with the maximum sea power with a weight of 0.343 with the activity period in the late winter and early spring season was estimated as the worst weather type for carrying out dredging operations in Abu Musi Island

Desertification is one of the factors in the destruction of natural ecosystems in arid regions of the world. Knowing the areas exposed to desertification is very important to combat this phenomenon. Remote sensing is a practical tool for evaluating and monitoring land degradation and desertification. The current research aims at the desertification intensity evaluation in Bandar Mahshahr County based on the spectral indices derived from satellite images. To begin with, utilized the ENVI software to extract several indices, such as NDVI, SAVI, RVI, TGSI, and Albedo, from the satellite image captured by the Landsat 8 OLI in the region. Then, Linear regression was utilized to determine correlations of spectral indices in the region, and the desertification intensity in the region was classified. The results showed that the correlation coefficient between NDVI and Albedo indices was -0.83, between SAVI and Albedo indices was -0.78, and between RVI and Albedo indices was -0.77. The correlation coefficient between TGSI and Albedo indices was 0.86. The higher correlation between TGSI and Albedo indicates that the Albedo-TGSI model is more appropriate for evaluating the desertification intensity in the region. The desertification map of the Albedo-TGSI model showed that the areas with less desertification intensity are located mainly in the northern and eastern parts, and the areas with more desertification intensity were situated in the southern and southwestern parts of the region

The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion

The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion

The aim of the paper is to assess the morphological characteristics, mobility, and distribution of sediment particle size as maturity indicators of young dunes. The results indicated that according to the Equivalent Sand Thickness (EST) parameter and the wind direction variability parameter, the morphology of the sand dunes was determined as linear. The climatic index of sand dune mobility using meteorological data of Qom, Kashan, and Ardestan synoptic stations in a 27-year period showed that the sand mobility index (M) for sand dunes is 210, which is in the range of fully active dunes. The Grain Size Distribution and the scatterplots diagrams of sorting, skewness, and kurtosis versus the mean size of particles in differentiating the types of sand dunes showed that the relationship of sorting and skewness versus the mean size is effective in showing the dune's mobility. Sediment grain size parameters can be used as an indicator for transport environment and spatial changes. The studies of this research showed that based on grain size characteristics and climatic index, the sand dunes are the simple linear type with full activity. The transport environment in the dune sediments of the upwind sector is more energetic than the dune sediments of the downwind sector