Journal of Spatial Analysis

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Drought is one of the most significant natural hazards, characterized by complex spatiotemporal patterns. This study analyzes the structural and spatial characteristics of droughts in Iran across three temporal scales: annual, seasonal, and monthly. To achieve this, the intensity and extent of droughts were calculated using the RAI index and ERA5 monthly precipitation data over 42 years (1979–2021). Initially, the spatial distribution and directional trends of the drought centroid were examined, and its spatial variations over the years were analyzed. Additionally, the relationship between the location of the drought centroid and its extent was investigated. The results revealed that during the cold season, the drought centroid is primarily concentrated in central Iran, while in the warm season, it shifts toward the northwest, the Caspian Sea coast, and the southeastern regions of the country. The distribution pattern of droughts at all scales predominantly follows a northwest-to-southeast trajectory. Furthermore, shifts in the drought centroid toward the northeast, east, southeast, and south were observed to coincide with an increase in drought extent, whereas shifts toward the north, northwest, and west were associated with a reduction in drought extent. Overall, the findings of this study demonstrate a direct relationship between the location of the drought centroid and changes in drought extent, despite the fact that droughts in Iran lack consistent and predictable spatiotemporal patterns
 

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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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subtropical high pressure (STHP) and Mediterranean cyclone are among the most important synoptic systems affecting Iran's climate. In this study, the effect of the high altitude location of the sthp on the Mediterranean gyres during the droughts and wetness of Iran during 1979 to 2020 was analyzed. In this regard, two datasets were used. Station data were used to identify drought and wetness periods, and ECMWF-ERA5 grid data was used to identify the location of high pressure in the subtropical region. The results showed that STHP with 3 anticyclone cells (ridge) affects the position of atmospheric waves affecting Iran's rainfall. The STHP system, especially the Arabian Subtropical anticyclone (ASA) and North Africa, play a more important role in the location of the cyclone affecting Iran's rainfall, so that widespread droughts with the expansion of the ASA to the west and its integration with the African anticyclone, the lack of expansion of the Mediterranean trough to the sea Redness and reduction of Sudan low and Mediterranean integration systems occur. With the eastward movement of the ASA over the Arabian Sea and the northern Indian Ocean, the Mediterranean trough deepens and the amount of waves and consequently the rainfall of the country increases. Therefore, the eastward expansion of the Arabian Peninsula and the strengthening of the North African Ridge provide the conditions for the expansion of the Mediterranean Sea. Whenever the ASA is located in its easternmost position on the Oman Sea and the Arabian Sea, it will lead to the advection of moisture for Iran through the access to the large areas of southern water and eventually rainfall. The main cause of the occurrence of drought and wetness in Iran is the spatial variations of atmospheric waves due to the spatial variations in the ASA.

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The ENSO weather phenomenon, including El Niño and La Niña phases, has significant effects on precipitation, temperature, and drought patterns in different regions of the world. This study aimed to investigate the relationship between ENSO indices such as MEI, SOI, and NINO oscillations with drought indices (TCI, VCI, VHI, and SPI) in the provinces of Guilan, Golestan, and Mazandaran from 2013 to 2022. Satellite data of NDVI, LST, and precipitation were extracted from Google Earth Engine to calculate drought indices, and ENSO data were obtained from the NOAA website for correlation analysis. The results showed that the MEI index had a positive and significant correlation with SPI and showed a decrease in drought with an increase in precipitation, but had a weak relationship with other drought indices. The SOI index showed a negative and significant correlation with SPI, indicating the effect of La Niña on increasing drought, especially in Golestan province. The El Niño indices were positively correlated with SPI in the northern provinces of Iran, confirming the effect of reducing drought and increasing precipitation. During the El Niño phase, the northern regions of the studied provinces experienced an increase in temperature and the south of Mazandaran province experienced subzero temperatures, while during the La Niña phase, the temperature increased and the northern regions of the studied provinces experienced higher temperatures. Vegetation was denser in the south of Golestan province and the east of Mazandaran province during the El Niño phase, but it decreased during the La Niña phase. The SPI index showed that drought during the El Niño phase was more widespread and severe in the western half of the studied provinces and more extensive and severe during the La Niña phase. The VHI index showed better vegetation health in El Niño, especially in Gilan and Mazandaran provinces, and decreased health in La Niña phase, especially in Mazandaran and Golestan provinces.

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Abstract
Objective:The Kabul Basin is one of the most vulnerable regions in Afghanistan due to the frequency of heavy rainfalls and devastating floods. This study aims to identify heavy rainfall events (above 20 mm) and analyze their synoptic mechanisms, focusing on their causes and patterns.
Methods:The study uses a descriptive-analytical approach based on daily rainfall data from 18 hydrometeorological stations in the Kabul Basin over the statistical period of 2008 to 2022. Heavy and flooding rainfall events were identified using the environmental-circulation method. Cluster analysis was conducted using Ward’s hierarchical clustering technique, and GrADS software was employed to extract and interpret synoptic maps.
Results:The analysis revealed three main synoptic circulation patterns responsible for heavy rainfalls in the basin. Three representative days were selected for detailed analysis: March 23, 2009 (31 mm rainfall at Qala-e-Malak), March 17, 2014 (59 mm at Bagh-e-Umumi), and February 5, 2017 (60 mm at Qala-e-Malak). These events were associated with Mediterranean troughs, cold Siberian air intrusions, and Indian anticyclone influence, which collectively intensified rainfall. The findings suggest that these systems can be monitored in advance for early warning.
Conclusions:Heavy and flooding rainfalls in the Kabul Basin are strongly influenced by specific synoptic systems and atmospheric interactions. Recognizing these patterns enables early detection of risk and can improve the efficiency of disaster preparedness, water resource management, and regional warning systems. This study provides valuable insight for reducing vulnerabilities and mitigating the impacts of extreme weather events in the region.

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Climate change stands out as one of the most pressing environmental challenges of the modern era, exerting profound impacts on aquatic ecosystems—particularly wetlands. This study investigates the influence of climate change on three wetlands in Chaldoran County, West Azerbaijan Province—Pir-Ahmadkandi, Naver, and Zavieh-ye Sofla—spanning the period from 1984 to 2023. To achieve this, climate data were obtained from the TerraClimate database and CMIP6 model outputs under four emission scenarios. Landsat and Sentinel-2 satellite imagery, along with JRC/GSW data, were processed to evaluate changes in wetland surface areas. Annual wetland extents were extracted and compared against climatic parameters (temperature, precipitation, actual evapotranspiration, and snow water equivalent) using time-series analysis, Pearson correlation, and multivariate regression. Additionally, the Delta Method was employed for downscaled climate data to project possible trends over the next 20 years.
The results indicate that rising temperatures and evapotranspiration constitute the primary drivers of wetland shrinkage. Pir-Ahmadkandi and Naver have lost over 27% and around 20% of their surface area, respectively, whereas Zavieh-ye Sofla exhibits an irregular, seasonal reduction due to human interventions and agricultural runoff. Projections suggest that wetland surfaces—especially in Pir-Ahmadkandi and Naver—will continue to decline, potentially exacerbating drought conditions, diminishing biodiversity, and reducing water quality. These findings underscore the necessity of implementing sustainable water resource policies, controlling evaporation, and incorporating human impact assessments into conservation measures. Moreover, harnessing advanced hydrological modeling techniques and integrating remote sensing data with machine learning approaches may offer more effective strategies for safeguarding these vital wetland ecosystems.
 

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Objective: During a landscape, it is not facile to discriminate land parts that have dissimilar amounts and types of vegetation. Plant Ecological Units (PEUs) are known as management units and are a reflection of the management actions and natural disturbances in the region. This research aims to fuse different resolutions of satellite images to increase the PEUs classification accuracy.
Methods: For this purpose, the Marjan-Borujen watershed in Chaharmahal va Bakhtiari province was selected. After field monitoring and surveys, four dominant PEUs groups were identified in the study area. In this study, bands from the Landsat_8 satellite images with 30 m  spatial resolution (bands 7_2) and a 15 m panchromatic band (band 8) were used, as well as the Sentinel_2 satellite images including panchromatic bands (8, 4, 2, 3) with 10 m spatial resolution. First step, using the Landsat panchromatic band, the 30-m bands were upgraded to 15 m through the pen-sharpening process; so the 15 m  data set was prepared from the Landsat_8 satellite. Then, to increase the spatial resolution of the 15-meter data set to 10 m, the Sentinel_2 panchromatic bands were used. In this way, the Sentinel_2 panchromatic bands were geometrically matched with the Landsat_8 15 m data set, and the Co-Registration process was performed with the minimum RMSE(0.05). Finally,  two data sets (2 to 8 bands) of the Landsat_8 satellite images with 15 m and 10 m spatial resolution, the PEUs classification maps were prepared using the RF classification algorithm, and the maps' accuracy was displayed as an error matrix.
Results: The results show that increasing the spatial resolution significantly enhances the accuracy of PEUs classification maps. The 15 m set shows an overall classification map accuracy of 66%, while increasing the spatial resolution to 10 m enhances the overall accuracy to 82%. As well as, the error matrix results show that the classification map procured from the 10 m set, all four PEUs groups have improved the producer accuracy, user accuracy, and kappa agreement index. So, in this map, PEU 2 and PEU 3 have the highest kappa agreement coefficient (83 percent).
Conclusions: This study shows that using the Gram-Schmidt fusion algorithm and consequently increasing the spatial resolution of Landsat 8 images from 30 m to 10 m reduces mixed pixels and increases pure pixels, which in turn improves the quality of PEU classification maps.
 

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Objective: Land use/land cover (LULC) changes, as one of the main anthropogenic drivers, significantly influence runoff patterns and intensify flood hazards. This study aims to assess the impact of land use changes on flood hazard zonation over the period 2015 to 2024 in the Samian watershed, located in Ardabil Province, Iran.
Methodology: Satellite imagery from Landsat 7, Landsat 8, and Sentinel-2 was utilized to extract land use maps for the years 2015 and 2024 using the Google Earth Engine platform. LULC classification was performed using the Classification and Regression Trees (CART) algorithm. Subsequently, the Modified Flash Flood Potential Index (MFFPI) model was applied by integrating key environmental layers, including slope, flow accumulation, land use, geology, curvature, and soil texture, within the ArcMap environment to generate flood hazard zonation maps.
Findings: The results indicated substantial LULC changes between 2015 and 2024, including an 18.47% increase in irrigated agricultural lands, a 9.38% increase in residential areas, and a 25.85% rise in sparse rangelands. In contrast, dry farming lands decreased by 25.21%, dense rangelands by 9.14%, and snow-covered areas by 98.61%. These changes have led to a notable expansion of high-risk flood zones. The LULC classification achieved a high overall accuracy and Kappa coefficient exceeding 0.98, indicating reliable results.
Conclusion: The expansion of impervious surfaces and reduction in natural vegetation cover have increased surface runoff and, consequently, the extent of high-risk flood-prone areas. The MFFPI model, by incorporating both environmental and anthropogenic factors, proved to be an effective tool for flood hazard prediction and management.
 

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Objective: This study aims to analyze greenhouse gas variations across Iran and to identify the gases that exert the greatest influence on their overall dynamics. The findings enhance understanding of atmospheric pollution patterns and support the development of effective mitigation strategies. These results provide a scientific basis for climate-change mitigation planning in Iran. The study relies on satellite-based remote sensing datasets.
Methods: This study analyzes the temporal and spatial variations of major greenhouse gases including carbon monoxide, nitrogen dioxide, ozone, water vapor, and methane across Iran from 2019 to 2024. Sentinel-5P satellite data were extracted via the Google Earth Engine platform, and after filtering and removing low-quality observations, the data were standardized using the Z-Score method to enhance comparability and correlation analysis. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify dominant variation patterns. Temporal and spatial trends were then quantified using complementary statistical techniques.
Results:
Methane exhibited a consistent increasing trend from late 2021 through 2024 and accounted for the largest share of total variance (R² = 0.87), likely reflecting intensified anthropogenic activities and regional climatic shifts. CO, NO₂, and O₃ were mainly affected by seasonal fluctuations and nonlinear factors, and no clear long-term increasing or decreasing trends were observed. Water vapor showed a direct relationship with temperature variations, water sources, and atmospheric patterns, with its lowest concentrations recorded during the cold months and increases observed in the warm months. PCA analysis indicated that the first two principal components explained more than 70% of the total data variance, with CH₄, O₃, and NO₂ contributing the most to the overall variations.
Conclusions: The study results indicated that greenhouse gas variations in Iran are simultaneously influenced by natural factors and human activities. The combination of satellite data, statistical analysis, and PCA enabled a precise assessment of the temporal and spatial trends of greenhouse gases, providing valuable information for planning pollutant reduction and developing strategies to combat climate change.



 

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Land use is one of the most important aspects of studying natural resources management and reviewing environmental changes, and studying it is also very important in understanding the microclimate of urban areas. Therefore, according to the importance of the topic in this research, the spatial pattern of land use changes and surface temperature in Bukan city in the statistical period of 1990-2020 using Landsat satellite images and sensors (OLI-TIRS, ETM+, TM) and the separate window algorithm. was evaluated. The results showed that the land use of the area has changed a lot during the period under review, so that the residential use has increased and the agricultural use has decreased. The results of the survey of the earth's surface temperature also showed that in 1990, the highest temperature was related to pasture areas and barren lands with a temperature between 32 and 40 degrees Celsius and the lowest temperature was related to areas with dense vegetation with a temperature between 15 and 20 degrees. It is Celsius. The temperature in residential and urban areas varies between 28 and 31 degrees Celsius. In 2020, the average temperature of pasture use was 35 degrees Celsius, residential use was 30 degrees Celsius, and garden and agricultural land was 14 and 24 degrees Celsius, which, apart from pasture use, which did not change significantly, other studied uses increased. They show a temperature of 2 to 4 degrees Celsius compared to 1990. The examination of the temperature in relation to the land use changes showed that there is a high correlation between the land cover and the surface temperature of the land, so that in some of the sampled places, it showed that the change in the use of gardens Residential use or pasture has caused an increase of 15 to 20 degrees Celsius in the temperature of the earth's surface in these areas. Based on the results of land use and overall vegetation, it has an indirect and strong relationship with the surface temperature of the earth, and with the increase in the area of residential and barren lands and the decrease of vegetation and agricultural lands, the surface temperature of the earth will increase.

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Objective: in recent decades, population growth, urbanization development, and change in land use have led flooding as one of the most destructive natural disasters in the world. Therefore, our goal is to identify flood areas and the synoptic patterns that lead to it, which are among the most important issues in preventing and reducing the effects of flooding and dealing with it.
Methods: In this study, in order to prepare a map of flooded areas, the extent of the floodwater that occurred in June (2024) in Ardabil province, were processed SAR radar images before and after the flood. Then, to identify synoptic patterns, daily maps of geopotential height at 500 hectopascals, sea level pressure at 1000 hectopascals, omega pressure at 500 hectopascals, and relative humidity at 700 hectopascals with a spatial resolution of 2.5 degrees in 2.5 degrees latitude were received and analyzed from the National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) of the United States.
Results: The flood area study indicated that in the studied province, Bilehsavar city with an area of 593 hectares, Parsabad city with 505 hectares, Meshkin-shahr with 245 hectares, and Germi city with 192 hectares were flooded due to the waterlog. The analysis of the flood zones also showed that the largest volume of flood entering Ardabil Province during the studied period was related to the northern cities of the province, where the provision of all moisture conditions and instability at the full depth of the troposphere layer led to the occurrence of heavy flood-causing rainfall in these areas.
Conclusions: The results of this study indicate that the use of radar data, due to its outstanding capabilities, is a useful tool in detecting and continuously monitoring of floods. Therefore, by detecting flood-prone areas and synoptic conditions that produce floods, executive managers can make the best decisions to deal with possible future floods.
 

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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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This study employs the Analytic Hierarchy Process (AHP) to assess the vulnerability and resilience of the urban water distribution network in the Feyzabad and Baharan districts of Sanandaj against the parameter of Peak Ground Velocity (PGV). The main objective is to identify the key factors influencing network vulnerability and to propose strategies for enhancing the resilience of this critical infrastructure. PGV values were derived based on data from 40 faults longer than 10 km within a 70 km radius of the city, using empirical attenuation relationships. Geological, geomorphological, soil type, and pipe diameter and material data were collected from reliable local sources.In the AHP model, the main criteria including PGV, geology, soil, pipe material, and pipe diameter were integrated with weights of 0.460, 0.112, 0.243, and 0.182, respectively, and vulnerability maps of the network were generated. Results showed that PGV values across the city range between 35 and 39 cm/s. In Feyzabad, lower PGV values combined with thick steel pipes and Quaternary alluvial soils resulted in 81% of the network falling into the low-vulnerability class and only 2.1% into the high-vulnerability class. Conversely, in Baharan, higher PGV values (39 cm/s), combined with small-diameter asbestos pipes and shale bedrock, placed 34% of the network in the very high-vulnerability class.

                               

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Objective: Over the past two decades, land subsidence has emerged as a significant geomorphological hazard and one of the most critical environmental crises in Iran, causing irreversible damage to many plains each year. Among its primary current causes is the excessive and unregulated extraction of groundwater. The Eshtehard Plain, recognized as one of the industrial and agricultural hubs of Alborz province, is no exception. Due to severe groundwater depletion, it has been officially declared a critical zone by the Ministry of Energy. The objective of this study is to model the risk of land subsidence in this plain using the Random Forest algorithm and to analyze the contributing factors influencing its occurrence
Methods: In this study, twelve independent spatial layers were utilized, including: digital elevation model (DEM), distance to rivers, distance to qanats, distance to wells, distance to faults, groundwater depth, drainage density, soil type, lithology, land use, topographic wetness index (TWI), and solar radiation. The dependent layer consisted of subsidence zones. The Random Forest model was implemented in the R software environment. Two key importance measures—Mean Decrease Accuracy and Mean Decrease Gini—were employed to rank, assess the significance of, and assign weights to the contributing factors of land subsidence. Finally, model performance was evaluated using three complementary metrics: Accuracy, Kappa, and AUCResults: The results demonstrated that the Random Forest model achieved high accuracy in classifying land subsidence risk. Model evaluation showed strong performance with an overall accuracy of 0.963, a Kappa coefficient of 0.611, and an AUC value of 0.955, indicating that the model is highly effective for spatial risk zoning of land subsidence. The most influential variables in subsidence occurrence were identified as groundwater depth, distance to wells, geology, and land use. Furthermore, more than 65% of the study area was categorized as high-risk and very high-risk, reflecting the critical condition of the Eshtehard Plain. Notably, the share of urban land use has shown a steady increase from 2011 to 2023, with a significant spike in 2023, where increased population concentration has placed additional pressure on groundwater resources, leading to an intensification of subsidence in affected areas
Conclusions: The Random Forest algorithm successfully modeled the spatial distribution of land subsidence risk with high accuracy. This method can serve as an effective tool for informed decision-making in groundwater resource management, sustainable development planning, and hazard mitigation in similar regions.

 

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Dust storms rank among the most significant natural hazards in the world’s arid and semi-arid regions, inflicting irreparable damage across multiple sectors each year. Given the rising frequency of dust storms in Kerman Province and other desert and arid areas of Iran, it is imperative to undertake a study aimed at identifying the synoptic patterns that precipitate dust events and at determining their source regions as well as their transport and dispersion pathways. In this research, the conditions and origins of dust storm formation over the 2000–2023 period were examined using synoptic and remote-sensing methods. The HYSPLIT model was applied to track airflow trajectories, and factor analysis together with cluster analysis were used to identify the synoptic patterns responsible for dust generation. Finally, the principal source regions of dust were delineated.
The results revealed that 63% of the province’s dust storms originate from domestic sources, whereas 37% originate from other areas. Three main atmospheric patterns were identified as drivers of dust activity in Kerman Province:
1. The co-advection of simultaneous low-pressure and high-pressure systems;
2. A lower-tropospheric cutoff low pressure in conjunction with the Siberian high;
3. A pressure-gradient regime featuring a core of elevated wind speeds.
Modeling of transport and dispersion pathways indicated that 60% of externally sourced dust is advected from the Arabian Peninsula, while 55% of dust emitted disperses southward, impacting the Makran coast and the Sea of Oman. Analysis of source regions further showed that the desert areas of Saudi Arabia, Iraq, Syria, and Jordan, as well as those of North Africa, together with internal sources such as the dried Jazmourian wetland, the Lut Desert, the Hamun region, and the Tabas Desert, contribute most substantially to the dust events observed in Kerman Province.
 

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Objective: “This study aims to investigate the drivers of ecological rupture in the Sultanieh Grassland, one of Iran’s most valuable natural ecosystems, which has experienced severe degradation over the past two decades. The research seeks to identify and prioritize the relative contributions of climatic, hydrological, and anthropogenic factors in triggering systemic instability and to assess whether the ecosystem has crossed a critical threshold toward irreversible collapse.
Methods: An integrated analytical framework was employed, combining multi-source datasets from 2000 to 2021. Remote sensing indicators—including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Soil Moisture (SM)—were derived from MODIS and Landsat imagery. Hydroclimatic time series (temperature, precipitation, potential evapotranspiration [PET], and groundwater levels) were analyzed alongside demographic statistics and land use/land cover (LULC) changes. A multi-criteria weighting approach, grounded in catastrophe theory, was applied to objectively quantify the relative influence of key drivers while minimizing subjective bias in decision-making.
Results: The analysis reveals a 15% decline in effective precipitation, a 1°C increase in mean annual temperature, and a groundwater table drop exceeding 30 meters over the study period. These environmental stresses were compounded by a fourfold population growth and a doubling of per capita water consumption. Consequently, vegetation cover declined persistently, with NDVI decreasing from 0.2817 in 2004 to 0.1701 in 2021, while barren lands expanded significantly. Within the catastrophe theory framework, three primary drivers—groundwater depletion, vegetation loss, and population–water pressure—were identified as collectively responsible for 50% of the system’s destabilization. The evidence confirms a transition from a stable ecological state to a dissipative, degraded phase.
Conclusions: The Sultanieh Grassland has likely crossed a critical ecological threshold due to the synergistic intensification of anthropogenic and climatic pressures within a geomorphologically and hydrologically vulnerable setting. Without immediate intervention—including sustainable groundwater management, strict control of urban expansion, and active restoration of hydrological equilibrium—the ecosystem faces irreversible transformation into an active source of dust emissions and desertification. This study underscores the urgency of science-based policy actions to prevent the total collapse of this irreplaceable natural and cultural heritage site.

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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.

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Subsidence is one of the most important natural hazards that has affected many plains of the country in recent years. Eyvanakey plain in Semnan province is among the plains that have faced this danger. Due to the importance of the subject, in this research, the evaluation of the subsidence risk and the estimation of the subsidence rate in this plain have been done. In this research, Sentinel 1 radar images, Landsat satellite images and SRTM 30 meters high digital layer are used as the most important research data. The most important research tools are GMT, ArcGIS and Super Decisions. Also, Fuzzy-ANP logic and SBAS time series models have been used in this research. This research has been done in two stages, in the first stage, the assessment of the subsidence risk and in the second stage, the estimation of the rate of subsidence in Eyvanakey Plain. Based on the results, 251 square kilometers of the study area (equivalent to 58.5% of the area) has a high and very high risk of subsidence, which mainly corresponds to the southern areas of the Eyvanakey Plain. Also, the results of the SBAS time series method have shown that the Eyvanakey plain has subsided between 28 and 533 mm during a period of 6 years. Considering that, the high risk class has the highest amount of subsidence in the study area, so it can be said that there is a strong relationship between the subsidence risk classes with radar images and the accuracy of the results of the subsidence risk classes is confirmed.
 

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introduction: Resilience is the key to urban sustainability, and by reducing vulnerability, it creates a sustainable environment for cities and plays a fundamental role in reducing urban vulnerability to environmental hazards.
Methods: The present research method is descriptive-analytical and applied in terms of purpose. The aim is to measure the resilience level of District 2 of Karaj and identify its strengths and weaknesses in facing environmental hazards. The research combines data from statistical documents, written reports, and questionnaire data obtained from surveys. The statistical sample consists of 30 experts related to crisis management in District 2 of Karaj, selected through the snowball sampling method. Data analysis was performed using mean statistics, standard deviation, and factor analysis.
Results: The results indicate that enhancing the resilience of this area through improving the quality of physical infrastructure, better construction management, adherence to architectural standards, and urban planning principles can reduce vulnerability and create a sustainable and resilient environment. Additionally, the evaluation of building sustainability shows that the average index in District 2 was 4.45. The average indices for spatial organization were 4.43, geographical characteristics were 4.57, and infrastructure sustainability was 4.8. This indicates a favorable status of resilience and sustainability indices in District 2 of Karaj.
Conclusions: Factor loadings show that the coefficients are above 0.6, confirming the validity of the resilience indices. Therefore, the urban resilience and sustainability of the region can be evaluated as favorable to highly favorable.
 

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Objective: Chaharmahal and Bakhtiari Province, due to its mountainous location and exposure to Mediterranean and Sudanese synoptic systems, has experienced intense rainfall events and considerable hydrological fluctuations in recent years. These conditions have often led to flash floods and posed serious threats to regional water resources. Accordingly, this study aimed to analyze rainfall intensities, estimate their values for different return periods, and construct Intensity–Duration–Frequency (IDF) curves as well as spatial distribution maps for four synoptic stations: Kouhrang, Farsan, Shahr-e-Kord, and Borujen.
Methods: Precipitation data over a 20-year period (2000–2020) were collected, and rainfall intensities were calculated for durations ranging from 15 to 1440 minutes. Maximum rainfall intensities corresponding to return periods of 2, 5, 10, 25, 50, 100, and 200 years were then estimated using several statistical distributions, including Gumbel, Normal, Pearson type V, and Weibull. Goodness-of-fit tests were applied to identify the most suitable distribution. In addition, spatial interpolation methods within a GIS environment were employed to illustrate spatial patterns of rainfall intensity across the province.
Findings: Results indicated that the Gumbel distribution provided the best fit to the observed data. It was also revealed that rainfall intensity decreases with increasing duration, while it increases with longer return periods. Spatial analyses showed that the highest intensities occur in the northwestern mountainous areas, particularly at Kouhrang station, and gradually decrease toward the southern and eastern parts of the province.
Conclusion: The findings confirm that statistical distributions—particularly the Gumbel model—enable accurate modeling of extreme rainfall events in Chaharmahal and Bakhtiari Province. Moreover, the spatial variability of rainfall intensity highlights the necessity of incorporating such patterns into hydrological infrastructure design, flood management, and water resource planning.
Keywords: Intensity–Duration–Frequency (IDF), Convective Rainfall, IDF Curves, Spatial Distribution, Chaharmahal and Bakhtiari