Journal of Spatial Analysis Environmental Hazards

The effect of subtropical high pressure on the position of Mediterranean cyclones and the occurrence of droughts and widespread wetness in Iran.

nabi mirzaei

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.

Impacts of Climate Change on Wetland Hydrology: A Case Study with Global Implications

Behzad Rayegani

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.

Assessment of Land Use/Land Cover Change Impact on Flood Hazard Zonation in the Samian Watershed"

Sayyad Asghari Saraskanroud

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.

Increasing the Plant Ecological Units monitoring accuracy with multi-sensor data fusion: a new approach in environmental hazards management

Ataollah Ebrahimi

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.

Analysis of Synoptic Patterns in Heavy and Flooding Rainfalls in the Kabul Basin

Kamal Omidvar

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.

Analysis and Assessment of Greenhouse Gas Emissions in Iran

Vahid Safarian

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.

Analysis of the Relationship between Wet and Dry Seasons in Northern Provinces of Iran with some Teleconnection Indices

Bromand Salahi

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.