Journal of Spatial Analysis

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Drought is one of the most important challenges faced by pastoralists around the world. This phenomenon can have significant negative effects on livestock health, production, and livelihoods. However, pastoralists can adapt to drought and reduce its negative effects by adopting various strategies. Semi-nomadic people in Darbandi, Kalat-Naderi County, have been facing drought since 2007 due to their livestock farming. Since livestock farming has profound impacts on the lifestyle and livelihoods of these communities, the present study examines their experience in facing drought and identifies their management strategies in these conditions. The research method is qualitative and the research tool is in-depth interviews with 20 semi-nomadic people in Darbandi, Kalat-Naderi. Sampling was purposeful and carried out until theoretical saturation was reached to ensure that a wide range of perspectives and experiences were collected. The data from the interviews were analyzed using a qualitative grounded theory approach to extract key patterns and concepts. According to the findings, the semi-nomadic Darbandi people of Kalat County have adopted various strategies in the face of drought, which are classified into four categories: rangeland and grazing management strategies, livestock nutrition management, water consumption management, and livelihood diversification. These results can be used as a basis for formulating better policies in the field of crisis management and rural development. Also, these results can be used for more effective planning to reduce the vulnerability of nomads to drought.

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The occurrence of persistent rainfall, resulting from the integration of multi-scale cyclonic systems, is prone to producing heavy to severe precipitation. Therefore, it is highly significant due to its potential to cause disasters such as floods and landslides, as well as its importance in water resource management for agricultural purposes. In this study, persistent rainfall refers to rainfall events lasting at least three consecutive days with a total precipitation of more than 1 millimeter. The aim of this research is to identify and classify these types of rainfall for the western region of Iran over a 30-year statistical period (1993 to 2022) for the first time using the Lamb-Jenkinson method. In this method, the central coordinates of the study area are used as the reference point in the calculations. Sixteen additional points are also determined around the reference point. With the availability of instantaneous standard sea-level pressure data for these points, it becomes possible to calculate the values of geostrophic wind and vorticity. By comparing these two quantities, the types of weather patterns in the L-J method—which include four types: directional, cyclonic, hybrid, and undefined—are identified and categorized. Disregarding the undefined category, it was ultimately determined that the provinces of Kermanshah, Kurdistan, Hamedan, Lorestan, and Ilam each had 50, 50, 50, 40, and 39 occurrences of the directional state, respectively. Moreover, the frequencies of the cyclonic state for these provinces were 69, 94, 65, 66, and 38, respectively, with cyclonic rotation percentages of 100%, 98%, 97%, 95%, and 97%, respectively. As for the hybrid state, the frequencies obtained for each province were 49, 53, 43, 41, and 38, respectively.
 

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The vulnerability of rural settlements to environmental hazards necessitates attention to local resilience. The aim of this study was to prioritize the factors affecting the physical-environmental and institutional resilience of Divshal rural district. The approach of the present study is descriptive-analytical and survey. Data collection was done in two forms: documentary and field (expert questionnaire). The statistical population of the study is 16 Rural managers of Divshal rural district. Using DEMATEL and ANP techniques, the obtained data were analyzed. According to the results of the ANP method, the sub-criteria of respecting the privacy of roads and preserving indigenous and cultural identity in new constructions have the greatest impact on the resilience of the study area. Also in the quality index of passages, sub-criteria of observing the privacy of passages; In the index of quality of access to services and infrastructure, quality of access to welfare and administrative services; In the shelter index, the existence of a strong public shelter in critical situations; In the index of quality of housing and constructions, preservation of indigenous and cultural identity in new constructions; In the environmental quality index, waste collection and disposal; In land use quality index, proper location of land uses; In the context of institutions, the existence of administrative organizations and institutions to help people; And in the Index of Institutional Relations, the cooperation of institutions in facilitating laws and giving credit to the people has the greatest effect on physical-environmental and institutional resilience.

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Introduction: Assessing the vulnerability and pollution of the aquifer is necessary for the management, development and allocation of land use, quality monitoring, prevention and protection of groundwater pollution. The purpose of this research is to identify and analyze the qualitative vulnerability of the Mashhad plain aquifer in order to monitor and manage underground water resources and prevent its future pollution.
Methodology: Mashhad plain is located in the northeast of Iran between Binaloud and Hezarmasjed mountains and in the watershed of the Kasfroud river, and its area is 2527 square kilometers. In this research, the vulnerability of the Mashhad Plain aquifer was evaluated with DRASTIC and SI models, and ArcGIS was used to analyze the parameters and prepare the vulnerability map. DRASTIC model is one of the overlap and index methods. In this method, the seven measurable parameters for the hydrogeological system include the depth of the groundwater level(D), net recharge(R), aquifer environment(A), soil environment(S), topography(T), Impact of the unsaturated Zone(I) and hydraulic conductivity(C) is used. The ratings for the sub-layers of each criterion vary from one to ten depending on their impact on the vulnerability potential. In SI method, five parameters of groundwater depth(D), net recharge (R), aquifer lithology(A), topography(T) and landuse(LU) are used for aquifer vulnerability. After preparing the SI model layers and weighting each of the layer classes using the functions available in the ArcGIS, the sensitivity index is obtained from the weighted sum of the mentioned parameters.
Conclusion: Study area is divided into four zones with very low vulnerability(21.85%), low(32.09%), medium to low(31.05%) and medium to high vulnerability(14.59%). Also, based on the results of the SI model, the study area is divided into five areas with very low vulnerability(0.4%), low(24.63%), medium to low(23.98%), medium to high(18.71%) and high vulnerability(32.25%). In general, the vulnerability of the aquifer increases from the southeast to the northwest.For verification, statistical method and calculation of correlation coefficient between vulnerability maps and TDS layer was used in TerrSet software and the results showed that both DRASTIC and SI models have high accuracy in zoning the vulnerability of Mashhad plain aquifer, so that the correlation coefficient of vulnerability maps with index The quality of TDS in Drastic model is (0.996) and in SI model (0.995); Therefore, the results of the following research can be used in environmental assessments and analysis of various pollutions and can be used as a basis for management decisions.

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Reducing the emissions caused by deforestation and forest degradation REDD is a strategy to moderate climate change, which is used to reduce the intensity of deforestation and greenhouse gas emissions in developing countries. In the last few decades, drastic changes in land use have caused a significant decrease in Hyrkan forests located in Mazandaran province. For this purpose, the aim of this study is to investigate the changes in land use and its prediction for the year 2050 using the Markov chain and the REDD project to reduce carbon dioxide emissions for the cities of Nowshahr and Chalus. Using the images of TM and ETM⁺ sensors of Landsat satellite, a land use map has been prepared in three time periods related to the years 1989, 2000 and 2021. Maximum likelihood method was used to classify images from supervised classification. From the error matrix, the Kappa coefficient in this evaluation was equal to 0.83 for 1989, 0.81 for 2000, and 0.92 for 2021. The results show that the forest cover decreases in 2050. In contrast, the area of ​​range land, city, barren land, agriculture and wetland will increase. Based on the goals of the REDD project, the amount of carbon dioxide emissions was calculated until 2050. If the REDD project is not implemented, a large area of ​​forest cover will be destroyed and a lot of carbon dioxide is released. The amount of carbon dioxide in the project area in 2021 is 49,681 tons and will reach 806,732 tons by 2051, and with the implementation of the REDD project in the region, this amount of gas can be increased to the equivalent of 402,321 tons. 404411 tons of carbon dioxide was prevented from entering the upper atmosphere of the earth. Examining changes using satellite images can help managers and planners to make more informed decisions.
 

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Objective: Investigating the occurrence of land subsidence in the country and the extent to which rural settlements are exposed to the phenomenon of land subsidence.
Methods: The present study was based on library surveys and studies, field observations and impressions. Using information received from the National Mapping Organization, a map of the country's subsidence zones and the degree of risk of each zone, including very low, low, medium, high and very high risk, was drawn, and the aforementioned maps were compared with the location of the villages.
Results: According to the analysis of the available data, 302 villages are at very high risk, 768 villages are at high risk, 834 villages are in the medium risk zone, and 573 villages are in the low risk zone. In terms of percentage weight, about 4 percent of the country's villages are at medium to very high risk of subsidence, of which 1,904 villages are at medium to very high risk, and 573 villages are at low risk.
Conclusions: The highest provincial distribution of villages at risk of subsidence in the country with a very high degree is in the provinces of Alborz, Tehran, Khorasan Razavi, Qazvin, Kerman, Golestan and Hamedan, and the highest provincial distribution with a high degree is in the provinces of West Azerbaijan, Isfahan, Alborz, Tehran, Khorasan Razavi, Semnan, Qazvin, Kerman, Golestan, East Azerbaijan, Hamedan and Yazd. Also, the highest provincial distribution of villages at risk of medium-level subsidence is in the provinces of East and West Azerbaijan, Isfahan, Alborz, Tehran, Semnan, Qazvin, Kerman, Golestan, Mazandaran, Markazi, Hormozgan, Hamedan, and Yazd.

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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: 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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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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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: 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
 

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Objective: A major part of rural risk, such as production risks, economic risks, and severe climate changes, is related to agricultural risks, which have significant negative impacts on the agricultural sector. This study aimed to investigate the effects of risks and hazards of the rural ecosystem on the development of various types of rural tourism in the rural tourism area of ​​Mahmoudabad County, located in Mazandaran Province, in order to examine the development of various types of rural tourism as a strategy for resilience and adaptation of villagers, as well as a preventive strategy in villages against economic, environmental, and social shocks, as well as reducing vulnerability and diversifying the rural economy.
Methods: The research method is applied in terms of purpose and descriptive (non-experimental) and correlational (variance matrix analysis) in terms of data collection method with the aim of showing the relationship between variables. The study population included all 88 villages in Mahmudabad city. The data collection tool was a researcher-made questionnaire and the respondents were the villagers. The structural equation modeling (SEM) method based on Smart-PLS was used to analyze the data.
Results:
The results of the research on prioritizing dimensions and hazard and risk items for rural ecosystem assessment show that economic, environmental, and social risks were ranked in priorities one to three, respectively. Based on the path coefficient (pc), only the direct effect of environmental pressure (with a path coefficient of 0.338 and a T-value of 2.467) was significant. About 30 percent of the changes in the development of tourism types are explained by the proposed model with the direct effect of environmental hazards and the indirect effect of economic hazards and social hazards.

Conclusions: As a general conclusion, the types of rural tourism in the region should be given serious attention as opportunity driven entrepreneurship and necessity driven entrepreneurship. In this regard, recognizing ecological values ​​through education and long-term propaganda for the sustainable development of rural livelihoods with emphasis on the development of green tourism, ecotourism, and agrotourism is recommended

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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.