Journal of Spatial Analysis

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Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.
 

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Flash floods are one of the most dangerous natural events and often cause loss of life and damage to infrastructure and the environment. This research investigated the occurrence of the most intense continuous monthly floods (October-March) from 1989 to 2021. Precipitation data from 115 synoptic stations were selected. Then, the total rainfall of 1 to 9 days was sorted according to intensity. Using Minitab statistical software and the Andersen-Darling index, heavy rains were extracted based on the 95th percentile. Then, based on the criteria of the highest and lowest number of rainy days, the highest and lowest accumulated rainfall, the wettest and driest months were determined. Considering the three criteria of intensity, continuity, and rainfall coverage, the strongest storms in the wettest months were selected. The data used for synoptic analysis include the average sea level pressure data, the height and vertical component of the wind at 500 hPa, the wind and humidity field specific to the pressure levels 925, 850, and 700 hPa, and the horizontal moisture flux values specific to the pressure level 925, 850 and 700 hPa. The probability of the occurrence of atmospheric rivers was identified by the moisture flux extracted from the specific, meridional, and meridional wind components. The results showed that the storms of October 27-31, 2015, November 5-7, 1994, December 12-16, 1991, January 11-15, 2004, February 3-9, 1993, and March 13-15, 1996 were the strongest in the wettest months. During the storms of October, November, February, and March, moisture has been transported from the southwest of the Red Sea by atmospheric rivers to the western, southwestern, southern, and southeastern regions of Iran.
 

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Coastal areas are constantly changing physically and ecologically, depending on natural and human factors. The natural causes of coastline changes are assessed in three ways: short-term changes including the effects of up and down currents, long-term changes including climate change, periodic storms and waves, and accidental changes including sudden natural events. Today, coastal tourism is considered as one of the important factors in the development of coastal areas. In this regard, the Caspian Sea, with many tourist attractions such as lush forests, accessible foothills and mountains, historical monuments and appropriate welfare facilities, benefits from the sea and beaches. The coastal area of Babolsar City, due to its many facilities and capabilities to attract tourists, much of which is due to natural and environmental attractions, every year, hosts a large number of tourists who come to this area to take advantage of its facilities and attractions, including the beautiful beach and very beautiful forests. This coastal area because its dynamic nature, is exposed to permanent erosion and variability due to processes such as river, wind, tectonic, wave and tide and marine transgression-regression in the area causes the destruction of coastal facilities and recreational places. Therefore, any planning to change the land use and construction in this coastal area should be considered in terms of the sea water fluctuation impacts on the shoreline position. Coastal environmental degradation as a result of Caspian Sea water level fluctuation are very probable and human behaviors in non-optimal choice of the land use locate intensify these losses. Coastal tourism, as one of the coastal land uses is heavily influenced by fluctuations in sea level in both marine transgression-regression statuses.
 

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Nowadays, deep learning as a branch of artificial intelligence acts as an alternative for human with hopeful outcomes. Open Street Map as the biggest open source data is used as a complementary data sources for spatial projects. It is notable that is some advanced counties the accuracy of VGI data is higher than governmental official data. This research aims to use artificial intelligence to produce and subsequently promote completeness of OSM data. Res_UNet architecture was utilized to train landuse categories to the network. The result shows that IoU metric is about 83 percent that implies a high accuracy paradigm. Then, united-based method was used to calculated completeness of OSM data. The unit-based results show that completeness of building blocks, forest, fruits garden and agriculture land are: 3.6, 9.7, 90.4 and 81.88 respectively. It shows the low volunteer  participation rate to produce OSM data. On the other side the high accuracy achieved by deep learning leads us to complete OSM data by artificial intelligence instead of human prepared data. The advantage of using machine rather than human may be utilized in undeveloped countries or low density population regions as well as inaccessible areas.
 

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This study aimed to systematically explain the potential of the landslide occurrence to provide a prediction model of the possibility of this phenomenon in the Yamchi catchment in Ardebil province. In this regard, both approaches of discrete and continuous variables were used by means of overlay and logistic regression, respectively. Independent variables included elevation, slope, aspect, lithology, annual rainfall, roughness, general curvature, topographic wetness index, vegetation index, distance to fault, distance to stream and distance to road. The results, firstly, revealed the areas with high landslide potential by the matching layers of independent variables with the landslide layer in the geographical information system (GIS). These areas were in the middle elevation, high slopes, northern slope, high roughness, erodible formations, high rainfall, medium vegetation, surroundings of faults and rivers. Secondly, the results of the logistics regression model by providing a prediction equation of probability of landslide occurrence showed that the resulting model with pseudo r² and ROC 0.22 and 0.86, respectively, had good power and efficiency to predict landslide through the catchment. In addition, the resulting beta coefficients for independent variables indicated that the importance of the variables was as follows: vegetation index distance to road, rain, lithology, distance to fault, elevation, topographic wetness index, roughness index, aspect, slope, and distance to river. In the end, the need to pay serious attention to the supporting and protection of vegetation cover of the mid -range and upstream of the catchment was determined because of unstable geomorphic conditions of these areas.
 

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The process of erosion and sedimentation on river banks is often accelerated by natural events and human activities, which lead to natural hazards. This process has also faced serious risks in Jajrud river, urban and rural settlements, agricultural lands, construction structures around the river. The Jajroud River has important importance and effects in securing agricultural water rights, feeding underground aquifers, providing drinking water in the region and the green and natural spaces of Fashm, Migun, Lavasanat and downstream, which has been subject to many changes in recent years due to direct and indirect human interference. including the expansion of urban and rural construction, river engineering operations and wall construction along the river, encroachment on the river boundary and changing the course of the river channels, the use of non-native vegetation on the banks of the river, and as a result, the eco-hydro geomorphological conditions of the river have become unstable. The phenomenon of erosion and sedimentation in Jajroud river has brought negative consequences such as changing the bed, accumulation and settling of large amounts of sediment and overflowing of canal water and as a result the risk of flooding.
 

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Evapotranspiration is one of the main elements of hydrologic cycle. Accurate determination of reference crop potential evapotranspiration (ET₀) is crucial in efficient use of water in irrigation practices. ET₀ can be measured directly by lysimeters or estimated indirectly by many different empirical methods. Direct measurement is cumbersome, needs for more time and costly. Therefore, many investigators used empirical methods instead of direct measurements to estimate ET₀. Nowadays, the FAO-56 Penman Monteith (PMF56) method is known a bench mark for comparing the other empirical methods. For example, in the works of Zare Abyaneh et al. (2016), Biazar et al. (2019), Dinpashoh et al. (2021) and Dinpashoh et al. (2011) PMF56 method was used to estimate ET₀ and comparing the outputs of other empirical methods. Many researchers analyzed trends in ET₀ time series in different sites around the Earth. Among them it can be referred to the works of Sabziparvar et al. (2008), Babamiri & Dinpashoh (2015), Dinpashoh et al. (2021), Dinpashoh  (2026) and Tabari et al. (2013). ET₀ can be affected by many different climatic factors such as maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH), wind speed, and actual sunshine hours. Factor analysis (FA) is a multivariate method that reduces data dimensionality. In general, climatic variables have high correlation with each other. On the other hand, these variables affect ET₀. The FA can be used to reduce data dimensionality in which correlated variables converted to few uncorrelated factors.
 

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Gully erosion is one of the advanced forms of soil erosion, which needs to be analyzed and identified in order to protect the soil. In this research, according to the complex system of factors influencing the creation of ditch erosion, 23 factors were analyzed in the two famous Dempster-Schiffer models and the entropy model, and using Google Earth images and field visits, 331 ditch points were identified, recorded, and a ditch distribution map was prepared. Spatial data of gully erosion distribution were divided into two random training (70%) and experimental (30%) groups. In this research, two indicators of tolerance coefficient and variance inflation factor were used to check the collinearity test, and as a result, two indicators of waterway density and relative humidity index were removed and 21 factors were used in the modeling process. The output results of the layers, weighting and classification and integration in two Dempster-Schiffer and entropy models are the extraction of the zoning map of the gully's erodibility sensitivity. and 30% of the calibration and validation of the models, the area under the ROC system performance characteristic curve and the area under the AUC diagram of the Dempster-Schiffer model with an explanation factor of 0.934 and the maximum entropy model with an explanation factor of 0.936, both models have an acceptable percentage of the area under the curve were that this issue shows the high performance of both models in the region. Among other results of statistical analysis, the prioritization of the impact of 21 factors in causing ditch erosion in the region was determined. The scientific results of the research can be promoted and taught, and from the practical point of view, the relevant executive body to control ditch erosion can take the necessary measures using the results of this research.
 

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Developing roads and constructing new highway are urban policices contributing to solve transportation problems in cities. These projects often being passed through urban fabrics, so it is nessessery to buy and demolish buildings from their owners including individuals or governmental and public institutions to imply the projects. However, acquiring land is not an easy task and completing these projects may hit with long-term delays. This paper aimed to analyze the impacts of delaying in constructing Shoosh Highway in Tehran. The investigated impacts originate from land acquisition problems. The research data was obtained from many sources including documents and research reports, a survey, and interviews with Tehran Municipality managers. The One Sample T-Test in SPSS software was performed to analyze the data obtained form the survey. Findings indicate that the residents are often dissatisfied with the project because since the beginning of the project, social security decreased a lot and people are less likely to respect citizens' rights than before, recreational sites are often demolished, the value of residential buildings slowed down significantly, living costs incresed, and businesses were stagnant. In conclusion, if urban highways are not contributing to proper planning and site selection, they will disrupt the physical, social, and economic structures of urban neighborhoods and cause to many environmental problemes including air pollution. To avoid these adverse outcomes, it should be thought in advance about sufficient financial resources and possible practical methods to acquire land for projects. These consequences are reduced by studying and managing the risk of projects.
 

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Forest fire in many ecosystems is a natural phenomenon, but also a serious and dangerous threat with environmental, ecological, and physical effects. Therefore, this research investigated the risk areas of fire in Zagros forests identification to evaluate the changes in the time series of deals with a potential fire hazard. To achieve this goal fuzzy layers of analysis network process and order weighted average method were used regularly. For this purpose, fire Zagros forests using satellite images Landsat and MODIS Lordegan city in the period between 2000, 2007, and 2014 and the factors affecting fire are examined. The high-risk areas based on classification utility area and the number of zones were identified as fire-prone areas. In the analytical network process procedure, the largest weighs were assigned to the distance from residential areas and roads, GVMI index, and maximum daily air temperature factors which were 0.209, 0.198, 0.09, and 0.0716, respectively. Time series analysis map showing the extent of critical areas from 2000 to 2014 decreased by investigating the factors affecting fire occurrence in critical areas, distance for roads and residential areas, slope, aspect, GVMI index, and NDVI and maximum temperatures have the greatest impact were on fire. The low-risk scenario and a small amount of compensation with the ROC higher than 0.7 as the best model was the estimated risk of forest fires. The preparation of a map of areas susceptible to fire, as well as analyzing and analyzing the time series of factors affecting the fire in different years, is an effective step in helping forest managers to plan and implement preventive operations in high-risk areas.
 

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Abstract
Dust storm is a complex process that it was affected by relation between earth-atmophere system and point of veiw climatologist and meteorologist that they assessing atmospheric and climatic change, in general of world veiw, monitoring from dust cover is a need structures.
The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008. Finally, the aim has analyzed using Arc GIS and ENVI softwares and NDDI index.
According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that extent of case study have not a equal system aspect quantity of occuring from dust phenomenon and how is it trend. The number of dust days increase from north toward south and sites located in northen proprotion of studied area have experienced a lower dust events. While, maximum hotspots are occuring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principle offspring of dust input has been out of country boundaries and arrived far way area. On based resultes obtined on satellite images using NDDI index also idicate that maximun of intense cover dust is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, thrid and forth of July. But, the lowest rate of index situated in extent far such as: Eastern Azarbayjan, Western Azarbayjan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust core was located in the southwestern (mostly ahvaz). The long-term result was consistent with the use of NDDI index and the daily average of NDDI index in the whole study area indicated the hotspot areas (Ilam, Ahvaz, Omidieh, Bushehr and Shiraz) during the first to fourth days July 2008. However, in the region has reduced the level of dust cover when a wet and cloudy synoptic system pass over the central and northwestern parts of the study area.

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Iran is a country with low rainfall and high-intensity rainfall that is affected by various synoptic systems, the most important of these systems is Sudan low pressure, Therefore, recognizing the low pressures of the Sudan region is of particular importance, The purpose of this study is to gather a complete and comprehensive knowledge of the set of studies conducted about this low pressure, structure and formation and its effects on the surrounding climate. The present study was conducted using the library method and a search for authoritative scientific and research sources in connection with research on low pressure in Sudan and no data processing was performed in it. Thus, it has studied and analyzed the temporal and spatial changes of Sudan's low pressure over several years and its effect on the climate of the surrounding areas, especially Iran. In general, the results of this study can be divided into several categories, including studies on the recognition and study of Sudan low pressure, its structure and formation over time, pressure patterns affecting it at different atmospheric levels, and its effects on the climate of surrounding areas, especially Iran. Has been studied, The effect of this low pressure on seasonal and spring rainfall in Iran, snow and hail, floods, thunderstorms and also the effect of remote connection patterns on this low-pressure system have been studied, and finally, the analysis of these findings has been studied. It can be concluded that the Sudanese low-pressure system is a Low-pressure reverse in the region of Northeast Africa and southwest of the Middle East, which is strengthened and displaced in the upper levels of the Mediterranean and Subtropical jet stream and in the lower surface moisture injection from the Arabian Sea and Oman through high pressure. Saudi Arabia is inwardly the cause of severe instability in Iran and a major cause of heavy rainfall in various parts of the country.

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In order to plan, manage and exploit water and land resources, awareness of the spatial variability of resources, as well as understanding the response behavior of the watershed in order to model physical processes, has an identical significant role. Due to its location in arid and semi-arid areas, special climatic and geomorphological conditions, Qom-Roud basin is prone to flash floods. Due to the lack of hydrometric and topographical data with high accuracy in the basin, the use of hydraulic models does not lead to accurate results of the hydraulic characteristics of floods. In such a situation, the methods based on the geomorphological features of the basin can be advantageous. In this article, Variable flood stage method (VFS) method is used, which combines the hydraulic characteristics of the river with the geomorphic characteristics of the basin in order to estimate the water depth in the river caused by floods with different return periods. The water depth was investigated for different return periods of two, five, ten, twenty-five, fifty and hundred years. In each period, the highest water depth was in the parts near the outlet and the lowest water depth was in the upstream parts of the river. The research illustrations there is a direct relationship between the depth of water and the area of the sub-basin. The results of this research can be used for basins without hydrometric and topographic statistics with high accuracy in order to estimate the peak speed and flood depth.
 

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Assessment of the measurement statistics of model accuracy and the appropriate
use of them (Case study: Interpolation of Precipitation in Fars province)
Abstract
In many scientific researches, error measurement statistics are often used without taking notices into account
when selecting a model or method for the spatial analysis of environmental hazards. In order to assess the
accuracy of precipitation interpolation methods in Fars province, the performance of widely used error
measurement statistics and some comments were implemented. Spatial interpolation of precipitation was
accomplished using inverse distance weighting, kriging, co-kriging, and radial basis functions methods with 161
weather stations (22 synoptic and 139 rain gauge stations) for 2018 as a rainy year. The results of MBE statistic
evaluation indicated that the researcher may have chosen the incorrect interpolation method in certain cases
where the sum of the positive and negative values became zero. In addition, this statistic is limited to indicating
overestimation or underestimation and should not be used for assessing accuracy or selecting interpolation
techniques. Regarding the coefficient of determination (r 2 ), the results revealed that due to the lack of
compatibility in the magnitude of the range of this coefficient (0 to 1) with error values (100 to 400 mm for the
interpolation of precipitation in Fars province), its use in evaluation of the accuracy of a method is not
recommended. In terms of NRMSE, the results showed that samples with a small number of observations (n=3),
its value increased excessively (NRMSE=0.35) when compared to samples with a bigger number of data (n=20,
NRMSE=0.097). Therefore, it is not advised to use this statistic. In conclusion, since MAE and RMSE statistics
provide a more realistic error value, it is advised to use them for assessing the accuracy of interpolation
methods.
Keywords: Precipitation, Error evaluation statistics, Interpolation methods, Fars province