Journal of Spatial Analysis

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Explanation of the subject: The annual drought phenomenon, by affecting economic, social and environmental issues, leads to the vulnerability of urban and rural households and the instability of their livelihoods. Yazd is one of the provinces with drought. Consecutive droughts in the province necessitate integrated management and community adaptation in times of drought.
Method: Taking into account the length of the statistical period of 20 years and to obtain the results with a high level of confidence, the main data of the census documents that have been compiled for the development of cities and villages have been used. By analyzing the main components of several factors, it was selected as the main components. By calculating the standard precipitation index in the arid region, the driest year was determined and by calculating the weighted average of their correlation index with the main components of socio-economic and ecological environment based on appropriate statistical inference. At the end of the year, the effect of drought on different dimensions was presented by step-by-step linear regression, analysis and communication between them to adapt and resilience of individuals in society.
 According to the general results, one of the most important economic and dry economic losses is the annual income of the villagers, which can be due to the decrease in the area under cultivation and production of the main agricultural products. In the social sector, people with knowledge and awareness should increase their adaptive capacity to the occurrence of drought, in order to reduce the vulnerability of social issues to the phenomenon of drought. The results show that unemployment insurance has increased following the drought. The main reason for this is the unemployment of farmers affected by drought, so changing jobs along with temporary migration or the production of handicrafts, etc. can increase the relative income of households at the time of occurrence and prevent unemployment in these conditions. Increasing unemployment will cause other social harms such as poverty, declining health, increasing disease, and reducing judicial and social security. According to the results, one of the components that has established a high standard of rainfall during the drought year is the theft of livestock, which shows a decrease in the social security of the community. People in the study community increase their adaptability to the annual drought by increasing breeding work, such as rangeland improvement, rainfall collection, biological improvement, afforestation, and irrigation reform.

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Spatial analysis of natural resilience in border areas
Case study: Zahak county
problem statement
Occurrence of natural disasters such as drought, floods and earthquakes in geographical areas, especially in rural areas, often have devastating effects. Hence, resilience has become doubly important, especially in special areas that are of special importance and sensitivity. On the other hand, border areas have a special place in policy-making and planning is important in this areas. One of these areas is Zahak county in Sistan and Balochestan province, which due to the instability and reduction of the inflow of river water resources, as well as climatic conditions and drought in combination with other factors, the traditional employment opportunities of the often rural population face serious challenges and therefore the county5b is deprived. Increasingly, the sustainability of livelihoods is facing problems. The question is, how do the spatial zones and the villages located in these zones react to the change of internal and external natural factors? Which areas and villages are more resilient?
 
Method of research
This article deals with the spatial analysis of environmental resilience in Zahak county and its purpose is to investigate the differences in resilience in different areas of the county. The general approach to the study is integrated and descriptive-analytical in terms of method. Data were collected using documentary and field methods with observation tools and questionnaires and findings of a specialized panel. The statistical population of this research is the villages of more than 20 households in Zahak city that have had governor of a rural district or village council.
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Description and interpretation of results
The villages of Zahak county are threatened by the threat of these resources due to their dependence on natural resources. The results show that none of the defined geographical areas in the rural area is sustainable and three rural areas are semi-sustainable and one rural is unstable. Assessment of sustainability in micro zones also shows that naturally unstable villages are often sparsely populated, which means that activity has not developed either. After qualitative and quantitative evaluation of various natural and environmental indicators in the county and their impact on the resilience of places and settlements in the county, settlements and places in terms of resilience were classified into three levels of high, medium and low resilience. In total, 46.7% of settlements and places are at high level of resilience, 37% at medium level and 16.3% at low level of resilience. After matching the settlements and places with the geographical area of ​​the village, three of the four geographical areas are in transition and one is unstable. This study shows that the resilience of individual villages cannot perform well when it is located in areas surrounded by villages with low resilience and the whole area becomes unstable. Thus, in special areas such as Zahak county, crisis management should focus on providing natural resources and preventing vulnerability to natural crises, and it is expected that with natural stability, housing and activity will be sustainable.
 
Key word
Resilience, special areas, Zahak county, border areas, geographic zoning
 

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 Explain the Processes of Modernization on the Spatial Mismatch in Urban Neighborhoods
(The case of, Region 4 of Tehran Municipality)
 
Modernization processes and modern urban planning policies have had significant effects and consequences on the spatial transforms of cities in the world and Iran. Among that processes, we can mention the growing gap between social groups and urban spaces based on a number of contexts and mechanisms that, from the late 1960s onwards, have been conceptualized and measured experimentally under what is called the “spatial mismatch hypothesis”. The basic methodology for estimating the state of spatial mismatch in cities or urban regions is based on the logic of “spatial segregation” between social groups and land uses simultaneously; Because based on the spatial mismatch hypothesis, it is not possible to explain the segregation mechanisms between social groups in the city without considering its relation with segregation mechanisms in urban spaces or land uses, and vice versa. Based on such methodological logic, the present paper has assessed the state of spatial (mis)match in Region 4 of Tehran Municipality. The method of data collection was in the form of libraries and data available in the Statistics Center (General Census of Population and Housing in 2016 and at the level of demographic blocks of the region), Road and Urban Development Organization, Municipality of Region 4. Variables used to analyze the spatial mismatch in the region
The level of education, employment in study abroad and inside the country, employment and unemployment status, level of housing infrastructure, type of housing ownership, changes in land use pattern and the amount of daily commutes in the study area.
 Findings obtained based on the defined variables and techniques used in Segragation Analyzer and ArcGIS software show that the state of spatial mismatch in this urban region (like many other cases in cities around the world) is high, but its intensity is higher in terms of job and literacy of social groups in relation to the state of activity and residential land uses. Relying on such findings, some strategies and policies have been proposed to reduce the state of spatial mismatch in Region 4, and to contribute to a more even and equitable distribution of development in this region and hence reduce poverty among the lower classes.
 
Keyword:
Urban modernization, spatial mismatch hypothesis, socio-spatial segregation, Region 4 of Tehran Municipality
 

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Regional leveling of drought vulnerability in Golestan province
Narges Kefayati*¹-  Khalil Ghorbani²- Gholamhossein Abdollahzadeh ³-
 
1- PhD student of irrigation and drainage, Department of Water Engineering, College  Of Water   Engineering, Gorgan University of Agricultural Sciences and Natural Resources,Gorgan,Iran. (Corresponding Author)*
2- Associated Professor, Department of Water Engineering, College  Of Water   Engineering, Gorgan University of Agricultural Sciences and Natural Resourcesm, Gorgan, Iran.
3- Associated Professor, Department of Agricultural Promotion and Training, Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources
 
Abstract
Drought is one of the natural phenomena that causes a lot of damage to human life and natural ecosystems. In general, drought is a lack of rainfall compared to normal or what is expected, when it is longer than a season or a period of time and is insufficient to meet the needs. Drought causes damage to the agricultural sector. The vulnerability of the agricultural sector in each region depends on three factors: the degree of drought exposure, the degree of sensitivity to drought and the capacity to adapt to drought. A review of previous studies indicates the diversity of indicators and methods used to assess vulnerability, which indicates the importance of the issue. Institutions responsible for agricultural management can only manage drought properly if they have the appropriate tools to measure the vulnerability of the agricultural sector to drought. Therefore, the first step in drought studies is to identify vulnerable areas and assess the vulnerability of areas. Vulnerability measurement in geographical dimensions and measurement of indicators by main vulnerability components have received less attention. Based on this, the present study has investigated drought vulnerability in Golestan by scientific method and by combining the three mentioned components and has compared the exposure situation, sensitivity level and level of drought adaptation capacity among the cities of Golestan province. Golestan province as one of the important agricultural hubs is highly dependent on the amount of annual rainfall. Due to fluctuations in rainfall and drought in some parts of the province, there have been 4 outbreaks and as a result, 7-12 and 10 days of drought have occurred, which has caused severe damage to the livelihood of farming families. Therefore, the aim of the present study was to compare drought vulnerability among cities in Golestan province by three components (exposure, sensitivity and adaptation). First, by reviewing the sources, the effective indicators on drought vulnerability are identified separately by the three components and judged by experts (faculty members of water engineering, agriculture and plant breeding, agricultural extension and education, and agricultural economics and experts of water engineers). 55 appropriate indicators in three main dimensions of vulnerability, namely: a) exposure (14 indicators), b) sensitivity (26 indicators) and c) compatibility (17 indicators) were developed and data related to the indicators were collected. The weights of the indices were extracted by Shannon entropy model and by the TOPSIS method the combined index was compiled separately into three vulnerability components. The final result of the combined index was combined with the GIS layers of the cities of Golestan province, and the level of vulnerability of the cities was determined separately for the desired components. The results showed that in terms of exposure to Bandar-e-Gaz, Bandar-e-Turkmen and Aq Qala are in the first to third ranks, respectively, and are exposed to drought. Azadshahr, Galikesh and Bandar-e-Turkmen counties are in the first to third ranks with the highest sensitivity to drought, respectively. The cities of Gomishan, Galikesh and Maravah Tappeh are the most adapted to drought, respectively. Finally, the results of calculating the total vulnerability index showed that the cities of Marwah Tappeh and Bandar-e-Turkmen are the most vulnerable areas to drought in Golestan province. The findings of this study showed that rainy areas can be more exposed to drought at the same time than other areas and there is no direct relationship between rainfall and drought exposure. This confirms the findings of other studies such as Kramker et al. And O'Brien et al. On the other hand, the findings of this study showed that there is no direct relationship between rainfall and vulnerability to drought and the most  rainy areas of a region at the same time can be the most vulnerable to drought. This is in line with the findings of Tanzler et al. And Salvati et al. On the relationship between rainfall and drought vulnerability. Due to the fact that the rainy areas of this province are more exposed to drought than other areas and farmers in these areas have shown a higher degree of sensitivity to drought and are more vulnerable to drought than other areas, it is recommended Measures should be taken to reduce the sensitivity and increase the adaptation capacity of farmers in these areas.
 
Keywords: Drought, Vulnerability, Exposure, Sensitivity, Compatibility, Regional Leveling

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Zoning map Vulnerability of Flood Spreading areas
(Case study: Musian Flood spreading station in Ilam province)
 
 
 
Introduction
One of the flood plain hazards is a change in the pattern of surface flows due to natural factors or human activities. Changes in the stream pattern are the changes that occur due to the surface stream patterns in terms of the shape of the drains, drainage form and quantitative morphological indices of the basin. These changes ,by formation of flood, submersibility, erosion, longitudinal and transverse displacements of rivers and streams, environmental degradation, etc., have a great deal of risk and harm to residents of the land adjacent to the watersheds, including the demolition of residential buildings,  valuable agriculture lands, facilities, river structures, buildings and relation routes, etc. There are several watersheds in the Musian Plain Basin that regularly change the direction of surface streams and, while displacing large volumes of sediments of erosion-sensitive structures, degrades crops, rural dwellings, connection paths, facilities, Irrigation canals obstruction, water supply and a lot of financial and physical damage to the residents of the region. Therefore, in order to solve these problems, in 1997, the Dehloran flood spreading plan was carried out at a level of 5000 hectares from the Basin of Musian Plain. Although some of the changes in the dynamics of the region, such as stream pattern, flood control, supllying groundwater aquifers, etc., have been caused by the implementation of this plan, but the problem of the concentration of watersheds behind the embankments composed of sensitive formations ,and the release of these areas will have many financial and even physical losses. Therefore, with the implementation of this research, it is attempted to identify the domain and risks that threaten the lowlands and to identify the appropriate measures to prevent them from happening with the zoning and inspection of the vulnerable areas of the Musain Plain.
 
 
Methodology
This study was conducted in five stages to prepare a vulnerability map of the flood spreading area of ​​Mosian plain. First, the implementation phases of the flood distribution plan were separated. In the second stage, information layers of effective factors in changing the flow pattern and concentration of surface currents behind the flood spreading structures were prepared. These layers included elevation, slope, and direction classes, which were prepared based on the Digital Elevation Model (DEM) extracted from the 1: 50,000 topographic maps of the Armed Forces Geographical Organization, as well as the layers of geological formations and land use changes. The lands were prepared based on the maps of the Geological Survey of Iran and the processing of Landsat satellite images of eight OLI sensors in 2013, respectively, by the method of determining educational samples. In the third stage, each class of effective factors in changing the flow pattern (mentioned layers) was given a score based on the range of zero to 10. The basis of the scores of the classes of each factor was according to the number of classes and the average of the total classes of that factor. The fourth stage in the GIS environment was created by combining the weight layers created, the vulnerability layer of the study area (quantitative map of vulnerability areas) of the basin. Then, by analyzing the vulnerability layer (filtering), the pixels and small units were removed or merged into larger units. The last (fifth) step was to classify the quantitative layer and then extract the qualitative map of the vulnerability zoning according to the range of scores based on the five very low, low, medium, severe and very severe classes. A summary of the research steps is shown in the form of a diagram.
 
Results and Discussion
The results showed that the most important threat and danger factor is the concentration of waterways behind erosion-sensitive embankments. Also, the study area in terms of vulnerability includes three classes with medium risk, high and very high and covers 16, 62 and 22% of the area, respectively. Flood and upland Spreading areas, risk areas and lowland lands are the most vulnerable parts of the basin in terms of floods and sedimentary deposits.
 
Conclusion
Based on the results obtained by combining the information layersof the factors influencing the stream pattern change, the zoning map of vulnerable areas of the region was created in 5 classes. Except for very few and very small classes that are not present in the region, there are other cases at the basin level:
Medium class:Includes about 16% of the basin. The existing watersheds in this part are ranked 1th class, and some of them are entering the rivers of Dojraj and Chiqab in the eastern and western parts. The formations of this part are often Bakhtyari and limitedly Aghajari. The floors have a height of 100 to 400 meters and the gradient is from 0-2 percent to 20 percent.
Medium class: About 62% of the basin level. The watersheds that flow in this section are in 1to 5 class. The formations of this part are often alluvial and bakhtiari of lahbori sections. It has a height of less than 100 meters to 300 meters and a gradient of 2-0 percent to 20 percent.
very intense: it covers about 22% of the basin's surface. The existing watersheds are of of class 2 and 3. The formations of this part are often alluvial and bakhtiari of lahbori sections. They have height classes of 100 to 300 meters and the gradient is 5-2 percent and is limited to 5 to 10 percent in the slopes.
 
Keywords: Vulnerability, Aquifer, zoning, Satellite imagery, Environmental hazards, Musian

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River channel changes, bank erosion and sedimentation are the natural processes of the alluvial rivers that destroy the agricultural land and damage to human installations around the river. In the present study, the CAESAR model was used to assess the changes of the Kaleibar Chai River in order to measure the variation of 3 km of its main channel.CAESAR is a cellular automata model for river system evolution. CAESAR  is a cellular model  that uses a regular mesh of grid cells to represent the river catchment studied. Every cell has properties of elevation, water discharge and depth, vegetation cover, depth to bedrock and grain  size.  It  is  based  upon  the  cellular  automaton  concept,  whereby  the repeated  iteration of a series of  rules on each of  these cells determines  the behaviour of the whole system. CAESAR has a set of rules for a hydrological model, hydraulic model (flow routing), fluvial erosion and deposition and slope erosion  and  deposition.  For  every  model  iteration,  cell  properties  (e.g. elevation) are updated according to the rules, and the interaction between an individual cell and its neighbours. For example, the amount of fluvial erosion in a cell may depend upon the depth of water in the cell and the slope between that cell and its neighbours.
For modeling, the input data such as topography (DEM), daily discharge (year 2012) and sediment grain size were prepared and then channel modifications were simulated. Channel changes were identified before and after the simulation by plotting profiles of each cross-sections and were analyzed sensitive to erosion and sedimentation.Six cross-sections were selected before and after simulation. Results showed that the channel geometry has changed. The width and depth and form of the channel have changed. And only the mean depth of the channels was changed in sections 1, 2, 6 and 4. The erosion was dominated in the cross- sections 1, 2, and 3 (the initial part of the main channel). Then the sedimentation was dominated in the cross- sections 4, 5 and 6.


 

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One of the causes of environmental hazards is the change in the pattern of surface water flow in floodplains following the construction of flood Spreading networks. The purpose of this study is to prepare a zoning map of vulnerable areas of the flood Spreading station of Musian plain  in Ilam province after the implementation of the aquifer project in this plain. To prepare this map, five factors influencing the change in flow pattern including elevation, slope, flow direction, geological formations, and landuse change were examined. Then, in the GIS environment, each class of the mentioned factors was given a score of zero to 10 based on the range and the corresponding weight layers were created. Then, by combining the created weight layers, the vulnerability zoning map of the area was created based on 5 classes: very low, low, medium, high and very high. The results showed that the most important threat and danger factor is the concentration of waterways behind erosion-sensitive embankments. Also, the study area in terms of vulnerability includes three classes with medium risk, high and very high and covers 16, 62 and 22% of the area, respectively. Flood and upland Spreading areas, risk areas and lowland lands are the most vulnerable parts of the basin in terms of floods and sedimentary deposits.

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Air pollution, as one of the most important environmental hazards in urban areas, is closely related to weather conditions. Today, pollution in metropolitan areas has become an important issue that requires the study and presentation of practical solutions to improve living conditions in this area. Therefore, understanding the relationship between synoptic systems and air pollutants helps a lot in how to solve environmental problems and future planning. Therefore, in this study, compression algorithms of carbon monoxide emission and transfer from domestic and foreign sources were analyzed. For this purpose, GEOS-5 / GMAO / NASA satellite images were used. The results showed that the highest amount of pollution from the seasonal point of view is related to the cold and early morning seasons and the lowest is related to the early afternoon and hot season of the year. And Khuzestan are densely populated carbon monoxide cores. Low pressures of the eastern Mediterranean play an important role in reducing pollutants in the southwest of the country and in the south of the country, under the influence of atmospheric currents from the topographic cut of Bandar Abbas, air streams polluted with carbon monoxide are able to penetrate into the interior to the southern half of Kerman. Increased by low pressure systems in Afghanistan and Pakistan. The Zagros Mountains also play an important role in preventing the entry of pollutants produced by western neighbors into Iran. In summer, Iran is polluted by carbon monoxide carriers by monsoon currents from central and southern Africa to Iran and has caused a lot of pollution

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Investigation of Temperature and Precipitation Changes in the Seymarreh Basin by Using CMIP5 Series Climate Models
 
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R²) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.            
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km². Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R²) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
 

• : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.

 
 

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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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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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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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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: 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: 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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Abastract
Introduction
Road accidents is the outcome of driver behavior, road condition, vehicle status, and environmental factors. Therefore, identification and assessment of effective parameters on road accidents can be considered as an appropriate way to reduce the accident events, driving violations and increase the road safety. Determining the effects of meteorological factors on the road accident events has gained more attention in recent years.
  The The main objective of this study was to investigate the relationship between the number of road accidents and the meteorological variables in the intercity road of Grmi-Ardabil in the Barzand route.
Methodology:
In this regard, the effects of climatic factors (including rainfall amount, the minimum absolute temperature, and the number of frost days) on the frequency of perilous events were analyzed. The data of accident events (in recent 4 years) were obtained from the trooper department of Ardabil Province along with the meteorological parameters of Germi station through a 11-year period. The statistical tests were performed using R programming software through statistical analysis.
Findings and Discussion:
The results showed that the majority of accidents were occurred in winter season which is in consistent with the frequency of frost days and also corresponded to the absolute minimum temperature. According to the results, the highest significant positive correlation at (R²= 0.43) was observed between the number of injured people and frost days. In addition, the relationship between the absolute minimum temperature and the number of were identified as significant negative correlation.
Conclusion:
As a concluding remark, the poor road conditions caused by climate element can be considered increasing the frequency of accident events. Accordingly, the proper strategies related to behavior change could be
considered in setting the rules and regulations to reduce the accidents and the number of injuries.

Keywords: Climatic hazards, Correlation analysis, Frost days, Minimum absolute temperature, Germi-Ardabil road


 

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Introduction
Land use reflects the interactive characteristics of humans and the environment and describes how human exploitation works for one or more targets on the ground. Land use is usually defined on the basis of human use of the land, with an emphasis on the functional role of land in economic activities. Land use, which is associated with human activity, is undergoing change over time. Land use information and land cover are important for activities such as mapping and land management. Over time, land cover patterns and, consequently, land use change, and the human factor can play a major role in this process. Today, satellite-based measurements with geographic information systems are increasingly being used to identify and analyze land-use change and land cover. With regard to the problems of changes and transformations in the studied area, remote sensing can allow managers to categorize images and evaluate land use changes, in addition to saving time and costs, which allows planners to make plans based on changes, more resources are lost. To be prevented.

Materials & Methods
In order to classify and detect the marginal land of the river, TM and OLI image images were selected for a specific month (August, August) for the years 1987 and 2017. The purpose of this study was to investigate the changes occurring in the studied area with an emphasis on agricultural lands. To do this, the images before processing in the ENVI software took radiometric, atmospheric and geometric corrections on them. After that, the main components of the river route were extracted. Five basic algorithms were used to classify the base pixel, but eCognition software was used to classify the object. Supervised classification identifies homogeneous regions with examples of land use and land cover, in which pixels are assigned in known information classes. Education is a process that determines the criteria for these patterns. Learning output is a set of spectral signatures of proposed classes. The first step in object-oriented classification is the segmentation of the image and the creation of distinct objects, consisting of homogeneous pixels. The main purpose of image segmentation is to combine pixels or small objects to create large image objects based on the spectral and spatial characteristics of the image. In order to evaluate the accuracy and compare the resulting maps, the overall accuracy and Kappa coefficient are used. When the sampling of pixels is done as a spectral or informational class pattern, the evaluation of the spectral reflection of classes and their resolution can also be done. An algorithm with the highest accuracy and accuracy will be the basis for the detection. Detection of changes, which leads to a two-way matrix and shows variations of the main types of land use in the study area, was carried out in this study. Pixel-based cross-tabulation analysis on pixels facilitates the determination of the conversion value from a specific user class to another user category and areas associated with these changes over the given time period.

Results & Discussion
The results showed that the object-oriented method is more accurate than the base pixel algorithms for providing user-defined maps. The amount of accuracy in the method based on object-oriented classification depends largely on choosing the appropriate parameters for classification, defining the rules, and applying the appropriate algorithm to obtain the degree of membership. The Kappa coefficient for each image is approximately 0.90. So these maps are the basis for the discovery of change. According to the results, the agricultural and residential lands have been increased and this increase has been accompanied by a decrease in rangelands. A general overview of this 30-year period shows that the arable and dry farming, respectively, increased by 2418.79 and 719.61 hectares and the rangelands had a decrease of 2848.86 hectares. However, the residential class and human effects show an increase of 428.88 hectares or a growth of 178.87%, which indicates the importance of agriculture in the studied area.

Conclusion
Identifying and discovering land cover changes can help planners and planners identify effective factors in land use change and land cover, and have a useful planning to control them. For this reason, maps are needed with precision and speed, and object-oriented processing methods make this possible with very high precision. The results of this study, in addition to proving the precision and efficiency of object-oriented processing in land cover estimation, between 1987 and 2017, have witnessed a decrease in the area of rangeland lands and, on the other hand, agricultural and residential lands, which is indicative of the overall trend Destruction in the area through the replacement of pastures by other uses such as rainfed farming.

Keywords: Land Use, Gamasiab, Object Oriented, Pixel Base, Kappa Coefficient
 

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Introduction
Geographical situation of Iran is a place for interacting many physical and human processes which lead to specific precipitation climatology in the country. The month to month variation of precipitation is one of  the features which the precipitation climatology may reflect due to tempo - spatial characteristics. In fact, monthly distribution of precipitation is one of precipitation normal features building up the climate structure. In order to recognize this fundamental characteristic three following questions have been raised:
1) Have the month to month distribution of precipitation changed over recent four decades?
2) How is the pattern of relationship of month to month distribution of precipitation and spatio - topographical variables?
3) Is it possible to find a spatial pattern for decadal changes of precipitation of month to month distribution?

Data and Methods
In order to find a responses for the abovementioned questions the distribution of month to month precipitation and its decadal changes was considered by adopting coefficients of variations (CV) for 46 years (1970-2016)  and using the third version of Asfazari dataset. The relationship of precipitation data and spatio-topographical variables calculated based on regression techniques. Moreover, the spatial pattern considered by using cluster analysis.  The CV calculated as follow:

here ،،  are ith raw's and jth column's CV, standard deviation, and monthly mean, respectively.
CV and its relationships with spatio-topographical variables were calculated in two temporal scale, for whole the under investigation period (1970-2016) and in decadal period for four decades (1977-1986, 1987-1996, 1997-2006, 2007-2016).
Discussion
 The results of current study proved that the month to month different in precipitation amounts have had spatial variations, whilst the temporal trends is not statistically significant. In addition, the minimum, maximum, and consequently, the range of values also the averages have not experienced significantly changes. However, the region experiencing the same values of precipitation illustrated oscillatory behavior. Accordingly, the decadal variations have happened in different areas. Although the there have been statistically significant relationships between monthly CV and spatio - topographical factors, the correlations were low. Based on cluster analysis, we found 5 regions according to CV and its anomalies in compares with normal CV for all under investigation period. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns.
Results
Precipitation is known a chiastic and complicated climate element. One of chiastic behaviors which precipitation shows in its different time - scale behavior is its month to month distribution among a given year. In current research the decadal variation of  above-mentioned behavior among recent four decades and the variation of its relationships and the spatio - topographical features , as parts of climate structure of the country, have investigated in details. 
Our finding illustrated that the month to month different in precipitation amounts have had tempo - spatial variations, whilst the temporal long - term trends is not statistically significant. Moreover, the values of minimum, maximum, and consequently, the range of month to month CV also the decadal averages have not experienced significantly changes over four under study decades. However, the region experiencing the same values of precipitation depicted oscillatory behavior. consequently, the decadal variations have happened in different areas. Although there have been statistically significant relationships between monthly CV and spatio - topographical variables, the correlations were not considerably high. Based on cluster analysis technique, we found 5 regions according to CV and its anomalies in compares to normal CV for all under study decades. These regions generally follow the latitudes from 32 N toward northern latitudes, whilst the region in the south of 32 N generally follow the longitude patterns.

KeyWords: Iran precipitation, Month to month changes in precipitation, Inter annual variation of precipitation, Precipitation anomaly, Spatial analysis of precipitation