Journal of Spatial Analysis

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Landslides, as one of the most dangerous natural hazards in mountainous regions, continuously threaten human infrastructure, especially roads and transportation routes. Their occurrence often results in significant loss of life and property, making it crucial to study and assess landslide hazards for effective zoning. The purpose of this research is to zone the landslide hazard along the Masal to Gilvan road using a neural network algorithm. The neural network algorithm is recognized as one of the most effective machine learning models, capable of solving complex problems in prediction and classification despite its simplicity. For this zoning analysis, nine influencing factors were considered: (1) geology, (2) vegetation cover, (3) slope, (4) land use, (5) distance from the road, (6) slope aspect, (7) elevation, (8) distance from fault lines, and (9) distance from rivers. The data were prepared, preprocessed, and then entered into MATLAB 2018. A neural network model was designed and implemented with 9 input neurons, 8 hidden neurons, and 1 output neuron. The results indicated that the four most influential factors, ranked by weight, were: slope (0.24), vegetation cover (0.17), distance from fault lines (0.14), and geology (0.11). Final validation using the ROC curve showed that the AUC values were 0.854 for the training phase and 0.971 for the testing phase, both of which reflect highly favorable results. The error rate was found to be very low.
 

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Objective: The main goal of this research is to identify and analyze the seasonality of the most widespread Torrential rains in Iran during the years 1940 to 2023.
Methods: To achieve this goal, precipitation data was obtained from the ECMWF database with a spatial resolution of 0.25 by 0.25 degrees of arc for the Iranian region during the study period. The next step was to calculate the threshold of torrential precipitation for each cell seasonally using the 95th percentile, and days with torrential precipitation were identified. By applying the condition of the highest spatial spread of the 95th percentile, the days with the most widespread precipitation above the threshold were identified for each season. Finally, the prevailing atmospheric conditions were examined.
Results: Analysis  shows that the highest precipitation of 146.85 mm occurs in winter and the lowest of 85 mm occurs in summer. The highest spatial coverage of total precipitation occurs in spring (41.9), winter (40.69), autumn (32.55) and summer (16.84), respectively.The analysis of sea level pressure indicates that during widespread precipitation in the summer, a low-pressure belt extended from the westernmost to the easternmost regions of the upper atmosphere map, encompassing Iran. In contrast, during other seasons, a high-pressure belt was present in the same area. At the 500 hectopascal level in summer, a closed high-pressure dynamic cell was observed over Iran, while at the 850 hectopascal level, two low-pressure centers over Saudi Arabia and Pakistan intensified instability over Iran. Consequently, it is evident that at lower levels, the conditions for atmospheric precipitation were stable, and even the omega level at 500 hectopascals over Iran on that day indicated a weak upward movement of air. However, in other seasons, a trough consistently positioned over western Iran, with active band patterns in spring and winter, facilitated the slowing and diversion of currents toward moisture sources, thereby enabling the transfer of more moisture than normal conditions to Iran. The precipitation study revealed that, except for the summer season, wind dominated over Iran. The presence of wind intensified instability at lower levels. A study of the Atmospheric River reveals that during widespread rainfall across all seasons, the Atmospheric River, which originates from the Red Sea and the Persian Gulf, has consistently been present. However, in the fall and winter seasons, a branch from the Mediterranean Sea also contributes, resulting in increased rainfall.

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This study investigates the spatio-temporal dynamics of evapotranspiration (ET) and its modulation by biophysical variables and land use/land cover (LULC) changes in the Karun River Basin, southwestern Iran, from 2000 to 2023. The basin, spanning 67,257 km² and characterized by diverse topography, experiences significant annual water loss (72% of 413 billion m³ national precipitation) due to ET, leading to salt and sediment accumulation. Data from MODIS products (MCD12Q1, MOD13A1, MCD43A3, MOD11A2, MOD16A3, CHIRPS) provided land cover, NDVI, albedo, LST, precipitation, and ET at 500-meter resolution, supplemented by Landsat imagery (30-meter resolution) for validation. Multiple regression and Geographically Weighted Regression (GWR) analyses revealed a 39.5% ET increase (31.48 to 43.92 mm/year), a 32.78% NDVI rise (0.18 to 0.239), and a 16.35% LST decrease (33.52°C to 28.05°C), correlated with a 6.90% agricultural decline (6,939,225 to 6,460,335 ha), a 6.94% rangeland increase (3,840,375 to 4,106,780 ha), and a 42.76% forest expansion (156,000 to 222,700 ha). GWR (AdjR² > 0.97, peak 0.9887 in 2010) identified spatial non-stationarity, with overprediction in mountainous northeast regions and underprediction in agricultural southwest plains, reflecting LULC influences. Landsat-derived false color composites and classifications (overall accuracy 85–90%, Kappa 0.85–0.90) validated a 2,477 km² forest loss to high-ET rangelands/agriculture, driving warm-season ET elevation. Results emphasize the need for integrated hydrological models incorporating irrigation data and high-resolution analyses to enhance sustainable water management in this water-stressed region.

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In this research, the most important synoptic patterns of widespread snowfall in the western half of Iran have been investigated. For this purpose, the data of current weather code and snow depth of 36 synoptic stations during the statistical period of 1371-1400, for the months of October to March, were received from the Meteorological Organization of the country. In order to investigate wide snowfalls, the days when more than 70% of the studied area saw snowfall at the same time were extracted as a wide day. In order to perform synoptic-dynamic analysis of wide snowfalls in the western half of Iran, the classification method using cluster analysis was used and maps of representative days were drawn, including atmospheric temperature, moisture flux, geopotential height, tovai, front formation, jet stream, omega index, and orbital and meridian wind data. Trend analysis was also performed using the Mann-Kendall test.  The results showed that 4 models justify the widespread snowfall in the studied area in the best way. According to the results in all the models, at sea level, the collision of cold and dry air of northern latitudes with warm and humid air of southern latitudes has caused the formation of frontal fields in the western half of Iran. At the level of 500 hectopascals, the intensification of the meridional currents in the western winds caused the creation of closed centers and as a result the flow changed in the direction of the westerly winds, and the location of the western half of Iran in the east of Naveh Al-Aghti and Sardchal has provided the necessary conditions for air to rise. Also, there was no trend in the number of snow days in the western half of Iran at the significant levels tested. But; The number of snow days has been decreasing over time. In general, it can be concluded that due to the warming of the earth and climate change, the number of snowy days has decreased and these changes have led to a significant shortening of the snow season.