جستجوی مقالات مرتبط با کلیدواژه « تغییرات مکانی » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

The decrease in global biodiversity caused by human activities and the continuous expansion of man-made land use has deepened the need better to understand the relationship between biodiversity and human disturbance. By knowing and examining the process of disturbance changes over time, it is possible to identify the factors that cause it and make appropriate decisions to reduce disturbances. Meanwhile, the application of landscape ecology in various fields of urban planning shows the ability to analyze and quantitatively express the results of human-environment interaction. Gaining comprehensive knowledge of the behavior of this type of change is necessary to achieve systematic management and efforts to ensure the sustainability of ecosystems. In addition, identifying the main processes and structural factors in creating key changes in the landscape provides a proper understanding of the behavior and changeability of the landscape. Ecological disturbances can be caused naturally (fires, floods, avalanches, hurricanes, or volcanoes) or by human activities (road construction, pollution, land use, or mining) and can even completely change a habitat. One of the needs of planning for effective and sustainable management and protection is a better understanding and awareness of the process of spatial changes of this type of disturbance. The timely identification and monitoring of ecosystem responses to disturbance stimuli is an important step in ensuring the sustainable use of natural resources and integrated watershed management. Based on this, the current research aims to identify disturbance drivers, develop multifunctional disturbance indicators, and analyze their spatial patterns in the Samian Watershed located in Ardabil Province.

Materials and Methods  :

In the present study, according to the literature review of the watershed and research related to disturbance indicators, a total of 29 disturbance drivers were identified. Then, Spearman's test was used to identify and remove disturbance indicators with significant correlation (p-value<0.05). Finally, using the weighted sum of disturbance drivers, four multifunctional indices based on land use (DI-LU) (with criteria of agricultural area, range area, total agricultural and residential, dry farming area, irrigation area, urban area, urban 600 m buffer, urban-agricultural 600 m buffer, ndvi and road density), demographic (DI-DG) (with criteria of population density, rural household density, density of 10-year working people, density of people working in agriculture, density of people working in the industry, density of people working in services and residential density), sources of pollution (DI-CR) (with criteria of soil erosion, special sediment, LQ spatial coefficient in the agricultural, LQ spatial coefficient in the industry, river distance from residentional areas, density of mining operations, density of industrial centers, pol1utant density) and hydrology (DI-HR) with criteria (with criteria of slope, reservoirs and wetlands area, dam storage, dam density) and a total disturbance index (TDI) were developed.

In this research, the multi-functional indicators of disturbance in 27 Samian sub-watersheds were calculated and zoned based on identified disturbance drivers. The results of equal weighting showed that the variables used in the calculation of disturbance indices based on the first to fourth groups were assigned weights of 0.13, 0.50, 0.17, and 0.33, respectively. The results of zoning showed that the maximum value of disturbance index based on land use (DI-LU) related to sub-watershed 27 with a value of 0.77 in the high class, demographic (DI-DG) related to sub-watershed 27 with a value of 0.89 in the class very high, pollution sources (DI-CR) related to watershed 15 with a value of 0.61 were placed in the high category and hydrology (DI-HR) related to watershed 25 with a value of 0.82 were placed in the very high disturbance category. The average of the first to fourth multifunctional indices were 0.46±0.16, 0.40±0.16, 0.38±0.09, 0.52±0.15 and 0.44±0.08 respectively. The results of the total disturbance index showed that the mean and standard deviation of the total disturbance index (TDI) are equal to 0.44 and 0.08. This confusion index was categorized into three categories: low, medium, and high. Besides, a significant area of the entire Samian watershed was placed in the middle class (0.41-0.60).

In recent decades, natural ecosystems have experienced unprecedented negative disturbances due to global climate warming, extreme weather events, excessive human exploitation of resources, and damage to the environment. In the long run, this has led to a series of complex problems that will threaten the survival and sustainable development of society. Accordingly, the regional identification of disturbance drivers is very important to reducing the conflicts between socioeconomic development and environmental protection. In this context, according to the zoning results, most of the sub-watersheds were placed in the middle class based on the fourth group, which indicates the importance of considering factors such as slope, dam storage, and dam density. From the general point of view, the Samian watershed has a medium, low, low, and medium class, respectively, based on the disturbance indicators of the first to fourth groups. In general, the central and northern parts of the watershed have higher disturbances than other parts. The results of the spatial analysis also showed that the central parts of the Samian watershed are affected by more disturbance. The results of the present research are used in the diagnosis of the dynamics of disturbances to understand the interactions of a watershed system and develop the strategies required for sustainable management.

The aim of this study was to evaluate the relative resilience of the Samian watershed located in Ardabil province using five indicators including specific erosion and sediment, drought, flood potential, climate, and socio-economic in different weighting conditions. At the equal weighting, the average relative resilience index of Samian Watershed was 0.53, which indicates the average relative resilience. Considering the weight of each indicator at the same level in 27 different sub-watersheds of Samian, sub-watershed 8 located in the east with priority 1 had the highest relative resilience. Considering the equal weight and with emphasis on climate, flood potential and socio-economic indicators, sub-watershed 27 located in the central part of Samian Watershed was at the lowest level of relative resilience and was given priority 27 and sub-watershed 8 has been recognized as the most resilient sub-watershed. Sub-watershed 27 has been permanently in utilization due to the use of rainfed cultivation. Emphasizing erosion and sediment, and drought indicators, respectively sub-watersheds 25 and 23 located in the center of Samian Watershed were ranked in 27 priority.

Groundwater is the main source of fresh water in arid and semi-arid regions, which make them resilient against lack of rainfall. Considering the great impact of groundwater quality on water and soil health, its evaluation is important. In this study, the assessment of the quality of underground water resources of the Shamil plain aquifer was carried out in Hajiabad county of Hormozgan province using the groundwater quality index (GQI) in the GIS environment. In this study, the results of the analysis of ten chemical parameters of calcium, sodium, magnesium, potassium, sulfate, bicarbonate, chloride, electrical conductivity, total dissolved solids and total hardness were used in 9 wells between 2017 and 2023. Then, using the geographic information system (GIS), the concentration map of the variables was first drawn using the IDW (interpolation) method, and based on the WHO standard, a normal and rank map was prepared, and by extracting the weight of each component from the rank map, a qualitative index map was prepared. Also, the time analysis was analyzed by Mann-Kendall and Pettit trend analysis test in two wet and dry periods of the year. Based on the results of time analysis with Mann-Kendall, all the physicochemical variables of underground water have a decreasing trend in two periods and GQI has an increasing trend, and the time of change in most of them was reported by Petit method, 2013. Also, based on the results of spatial analysis, all the ten investigated variables, except for bicarbonate and potassium, have similar changes in the plain. The group of variables with similar spatial changes on the level of the plain has the lowest concentration in the southern parts of the plain, and its concentration gradually increases in the northern parts of the plain. During the studied period, the groundwater quality index (GQI) was between 96 and 97 and it is placed in the appropriate category based on the WHO standard.

In recent years, the world has faced many challenges. Among the natural phenomena, the flood effect has attracted a lot of attention due to its adverse effects. More than half of the destruction and damage caused by global flooding occurs in the Asian continent, which causes loss of life, damage to infrastructure, and creates conditions of panic in communities. Increasing resilience is critical for urban systems to cope with and minimize the risks of flooding. This issue is certainly more important in Iran because the increase in the frequency of floods is a significant concern for many regions of this country. In Iran, despite the existence of much research, this issue has not yet been fully covered by the scientific community. Therefore, the current research is planned with the aim of measuring flood vulnerability in the 26 watersheds of Ardabil Province based on 19 criteria and three different factors. To calculate the criteria, the meteorological and hydrological data, shape files, accessible GIS maps, statistical yearbooks, and previous studies were used as well. In addition, Shannon's entropy was used for criteria weighting and the VIKOR method was used for watershed ranking based on the exposure, sensitivity, resilience, and flood vulnerability. The results showed that Mashiran, Firozabad, and Gilandeh watersheds in terms of exposure factor, and Hir, Barogh, and Lai watersheds in terms of sensitivity factor had the highest ranks. Moreover, in terms of resilience factor, Polesoltani, Hajahmedkandi, and Barogh watersheds are highly resilient against floods respectively with ranks of 1 to 3. The results of flood vulnerability ranking also showed that Doost Bigloo, Mashiran, and Samian watersheds are the most vulnerable with ranks 1, 2, and 3.

As natural land features, landscapes ranging in size from large plains and mountains to small hills are regarded as morphological formations characteristic of the earth's surface. The objective of the present study was to determine the physiographic unit boundaries in Hormozabad, Rafsanjan, using the ordinary kriging estimator. For this purpose, 77 observation points at distances of 500 m from each other were selected over a regular sampling grid and soil samples were collected from depths of 0–40, 40–80, and 80–120 cm at the specified locations. Similar to the findings reported elsewhere and based on model efficiency estimates, the results obtained showed that ordinary kriging served as a good estimator in zoning the properties of the study area. Additionally, geostatistical results indicated that a sampling distance of 1800 m could be used as a criterion for future surveys in areas similar to the present study one. Comparison of the kriging and topographic maps of the area demonstrated that the kriging estimator used was superior to topographic maps in representing physiographic units. This was evidenced by the more accurate unit boundaries detected by the estimator despite the high variations in soil properties depicted in the respective variograms for intervals above 1800 meters. It is, therefore, recommended that modern techniques such as geostatistics should be employed for detailed soil studies as required for preparing more adequately detained maps with precise data.

Particulate matters (PM) has a negative effect on human health especially in industrial and urban areas due to their great potential of reaching the furthest parts of human lungs. Therefore, it is essential to conduct continuous monitoring of PM in order to assess their spatial and temporal trend in ambient air. This study aimed to assess the spatial and seasonal variations of PM2.5, PM10, total suspended particles (TSP) concentration and evaluating their correlations with meteorological parameters in surrounding regions of Isfahan city, Iran. PM2.5, PM10 and TSP concentrations (24-hours) were measured by gravimetry method with low volume air sampler for one-year period (July 2015-July 2016) at three stations located on western, eastern and southern part of Isfahan. The overall daily mean concentrations of PM2.5, PM10 and TSP were 73, 101 and 134 µg/m3 in east; 50, 86 and 103 µgr/m3 in the south; and 23, 45 and 53 µgr/m3 in west stations, respectively. This study identified that PM2.5 concentrations were above the US Environmental Protection Agency (EPA) guideline at all stations. The daily mean concentration of PM2.5, PM10, and TSP showed a negative correlation with humidity, while a positive correlation was observed between daily average temperature and all pollutant concentrations. A weak positive correlation was observed in case of wind speed. This study found that the meteorological condition has the highest impact on fluctuating PM2.5 concentration over the monitoring period. Pollution rose analysis indicated that westerly and north-westerly wind increased PM2.5 concentration at all sites.

Flood as a rapid and destructive event causes great human and economic losses in Iran and around the world, annually. This study aimed to assess the temporal trend and spatial changes of flood coefficient of Ardabil Province rivers using daily discharge of 22 hydrometric stations over a 22-year (1991 to 2011) of recorded data. The flood coefficient amount was calculated after subtracting the defined base flow resulted from the local minima technique. The non-parametric Mann-Kendall test was used to detect the temporal tend in the time series values. The recorded data length were divided into 5-year periods and the variations of changes of flow rate in different periods were analyzed. The significant differences were determined by using the Kruskal-Wallis statistical test. The results showed that changes of flood coefficient was significant (p<0.05) between different periods. The spatial changes of flood coefficient variation coefficient showed a similar spatial pattern in different studied periods and the highest variability were defined at North-East and West parts of the study area. The highest rate of flood coefficient was observed in Akbardavood station with the value of 0.8 in the fourth sub-period. According to the results of trend analysis, there is an increasing significant temporal trend in 6 stations, and one station had decreasing trend, while other stations had increasing trend with no statistical significance. The increasing trend of flood coefficient can be related to rainfall variations and land use change along with intensified use of extensive rangelands areas which increases the potential runoff and surface flow resulting increase in the flood coefficient in the studied statistics.

It is predicted that the water scarcity will rise in the coming years due to a population growth, increased pollution sources, climate change and occurrence of droughts can ultimately affect the amount of available water resources. A global cooperation and foresight and joint planning of water resources is necessary to resolve the water crisis. In recent years, development and designing new multidimensional and comprehensive indices seems to be the most important issue for assessment of the current condition and predicting the future trend of surface and ground water resources changes. Towards this attempt, the Water Poverty Index (WPI) is recommended as a multi-dimensional indicator to assess the availability of surface water resources. Some applications of the WPI is highlighted at different countries and also its calculation steps is demonstrated through different criteria. According to the results, the WPI is derived based on the weighted combination of five components in different studies. Main WPI components are: resources (availability, seasonal and inter-annual variability and water quality), access (accessibility of water for effective use), capacity (managing water resources based on socio-economic variables), use (level of water use by different sectors), and environment (environmental impact of water management on ecological integrity). According to multidimensional nature of Water Poverty Index along with considering all affecting factors on water scarcity and availability behind social and economic characteristics can be a useful tool in prioritizing critical areas can be considered as an effective step in optimal planning of water resources. Water Poverty Index is a combination of influencing factors on water stress and scarcity, which provide a basis to prioritize and specifying management options for different watersheds. It should be noted that determination and analysis of water scarcity in different areas is depend on the condition of water resources, data availability and the employed sub-criteria in the calculation of water poverty index.

The aim of present study was performed for study of spatial variation of Artemisia aucheri yield, density and canopy cover and its spatial relationship with soil properties and topographical factors in Baladeh semi-steppe rangelands, Noor. For this reason, 132 sample points were carried out within the form of a network of 550 × 600 m with the systematic method by the plots in 50 m through transects. Soil samples were taken in depth of 0 to 20 cm and the samples elevation, latitude and longitude were registered by GPS device and the slope percent was measured using a slope meter. To determine the yield, density and canopy cover percentage of Artemisia aucheri was used plots of 4 m2. The data normality was determined using Kolmogorov–Smirnov test and correlation of data using Pearson correlation coefficient using SPSS software. To perform of geostatictics analyses and provide of spatial distribution map were used GS and Arc/GIS softwares. According to spatial correlation analysis, among soil properties and topographical factors, soil organic matter and clay showed, a more strong space structure with the study species, Interpolation and obtained maps for spatial distribution of Artemisia aucheri was performed using Kriging, Cokriging and inverse distance weighting methods. In this study, except density, yield and canopy cover of Artemisia aucheri showed a weak space structure using Kriging. This result shows that this method were not validated for spatial variability of yield and canopy cover of Artemisia aucheri but were obtained a strong space structure using Cokriging methods for studying variables.

Water resources management especially for groundwater resources is a very important task in arid and semi-arid regions. Over last decades, because of various natural and anthropogenic factors, critical condition and declined groundwater level have been reported in most regions of the country. With regard to importance of this issue variation pattern of groundwater level in Garmsar plain was assessed using the best geostatistics estimator method over 2002-2011 period.‌‌ Among the various interpolation methods including Kriging and inverse distance weighting (IDW) with power 1-3, Kriging method was determined as the best interpolation method based on RMSE criterion. Results showed that spatial zoning maps of groundwater level in this period indicates intense depletion of groundwater level in the northwest of the region. Results also show that the average level of groundwater in the whole plain has decreased 3.5 m because of decreased rainfall and over-pumping of groundwater for agricultural uses in the period.

Geostatistic has both interpretation (variogram) and estimation (i.e. Kriging) tools for studying spatial variability of soil properties. The analysis of dependence is based on the structure of the semivariogram, which demonstrates the existence of spatial dependence is increasingly used in the assessment of spatial variability in agronomy. In this study, determined spatial variability of soil chemical and physical properties includes, texture, pH, soil organic Carbon, CaCO3, total nitrogen, C:N ratio and Electrical conductivity and was surveyed by using of geostatistics. Results showed that all of the studied properties except EC and total N, showed structural spatial variations. Ordinary Kriging as the best estimator used to interpolation of properties. According to the map and variogram analysis showed that the amount of organic carbon and carbon to nitrogen ratio affected by land use type and management practice such as irrigation and surface plowing of afforest area. Variation of The other properties was affected by soil natural condition effects. These results suggest that Kriged maps can be used for planning and Management Park.

Study of spatial variability of runoff and soil loss and the understading of their distribution is vital for strategic planing in water and soil conservation. In this study spatial variability of runoff and soil loss were investigated in summer rangeland of Nesho in the Mazandaran province. For To do this، 110 runoff and sediment yield samples were collected by a systematic sampling strategy on a regular grid with a dimension of 30×30 m using simulated rainfall with a 2 mm/min intensity and 11-minute duration on 0. 09 m2 plots. All the samples were transported to the laboratory، and their characteristics including sediment yield and concentration were analyzed. After data normalization، classical statistical analysis was used to describe runoff and soil loss properties. Geo-statistical analysis was also applied to illustrate the spatial correlation of runoff and soil loss so that their spatial distribution was finally mapped using the kriging technique. The highest and lowest coefficient of variation was related to sediment yield and time to runoff generation with 93. 75 and 28. 43 percent، respectively. Fitted models for runoff and sediment characteristics were spherical and exponential models، respectively. Based on the results of variography، runoff volume and sediment concentration with 1010. 90 and 831 meters showed to respectively have the highest and lowest impacts on runoff and sediment characteristics. Given the strong spatial dependancy of these variables، it can be stated that kriging method has high cabability in estimating these variables at unsampled points as well as their zoning.

The amount of sediment load significantly changes along a river due to environmental effects. This variability should be considered to estimate the amount of sediment load. In this research، spatial and seasonal variation of sediment load along of Gorganroud river were evaluated based on the information of 7 hydrometry stations sited in main branch of the river namely، Tamar، Hajighoshn، Gonbad، Voshmgir، Aghghala and Basirabad. Since the amount of sediment load is directly correlated with water discharge، for assuming the effect of water discharge، linear form of sediment rating curve is used where the linear form is produced by the logarithm transformation. Based on this linear relationship with possibly different slopes، the generalized analysis of covariance with logarithm of discharge as its covariate was used to compare the logarithm of sediment load as its response. In order to precisely estimate the amount of sediment load and adequately modify the sediment rating curve، the spatial comparison was drawn separately at low، middle and high rate of water discharge. Spatial evaluation demonstrated an increase in sediment load from Tamer station to Basirabad but there are some distinctive areas between mentioned stations with noticeably decreasing (80% in average) in their sediment load mainly due to reservoir building. For seasonal evaluation of sediment load، the percentage of sediment yield at different seasons was compared. This evaluation revealed that spring with at least 50% and at most 70% of total suspended sediment discharge has the highest amount of suspended sediment yield in this river.

This study was performed in order to determine spatial variations of drought in Kerman province by Standard Precipitation Index (SPI) and comparing interpolation methods of Kriging and inverse distance weight with powers of 1 to 5, over a thirty-year period (1970-71 to 2000-2001). According to the results during the study period (10-year intervals), maximum and minimum average amounts of SPI were respectively recorded for 1990-91 and 2000-2001. In all studied years, exponential model was determined to be the best variogram fitted to the spatial structure of data and the effective range of each station was a distance of almost 18.03 kilometers for all years except 1990-91 with an effective range of 5, 02 km. Cross validation techniques introduced Kriging as the best interpolation method for all years except 1990-91 in which IDW to the power of 5 was the best interpolation method. Spatial zoning map of SPI showed that moderate drought was recorded for southern parts of Kerman province such as Anbarabad and Jiroft in 1970-71 while in other parts of the province slight drought was observed except Rafsanjan. In 1980-81, slight drought was observed for most parts of the province while in Bam, Bardsir and east of Jiroft slight moisture was observed. In 1990-91, there was a slight drought was recorded for Kerman, Baft, Kahnooj and east of Jiroft. In 2000-2001, all parts of the province were at risk of drought as in the southern half of the province (Baft, Anbarabad and Kahnoj) moderate drought and in the northern half of the province(Kerman, Rafsanjan and Bardsir) slight drought were observed. Error maps showed considerable accuracy of interpolation, especially near the stations.

Due to the variation of ground water properties in temporal and spatial scale, these properties can not be assumed constant over time and space. This research was conducted to assess spatial and temporal variations of ground water table in Kerman plain during a 10-year period (1375-1385) by using the best geostatistical estimator. Firstly, available statistical resources related to the ground water table of this plain (including 67 wells) were collected. After controlling for quality, accuracy and normality of the data, different interpolation techniques were used including kriging (with and without logarithm), cokriging, inverse distance (with exponents of 1 to 5). The best technique was selected based upon MAE and MBE criteria and provided spatial zonation maps in the beginning and end of the period. Spatial zonning maps were prepared at the beginning and end of the study and ultimately, iso-falling map was provided in Arc GIS 9.1 package. Our findings showed that Gaussian variogram model was the best for spatial structure of these data and inverse distance method with exponents of 5 and 4 were the best interpolation methods in the beginning and end of the period, respectively. Zonation maps showed a falling trend of the ground water level in majority of the plain. Maximum falling data (32 meters), was recorded respectively for the northern outlet of the plain with an effective range of 17 and 62 pizometers and western outlet with an effective range of 8 pizometers. Also, the results showed an upward trend of water, as 7 meters, in effective range of some wells with 50 and 52 pizometers in Kerman city due to being located in sewage and waste water zone of the city.

Autographic rain-gauge networks are designed to provide the density and distribution of stations so that characteristics of the rainfall events both in time and over the region can be determined with sufficient precision. Development and application of scientific methods for network design is probably one of the most complex issues of hydrology. In this study, annual rainfall data for rain-gauge stations of khorasan province consisting of Northern, Razavi and Southern Khorasan were collected from Khorasan Regionl Water authority and geostatistical analysis was made for finding the spatial correlation of them. Finally, some additional rain-gauge stations were suggested based on errors of kriging method. Geostatistical studies in Razavi Khorasan province show that adding 15 rain-gauges, decreases coefficient of variation (CV) values from 14% to 17% in vast central parts and decreases approximately 7% in western parts. Also, the results of mentioned analysis of whole provinces including North, South and Razavi Khorasan show that 15 added rain-gauges, decreased CV values about 8% to 9% just in central northern and eastern parts and approximately 23% in Western parts.

During precipitation event, an area of land is prone to rainfall, but precipitation measuring is only accomplished in few points. So estimating of precipitation in other points can be done in several methods such as: correlation method, normal ratio method, and representative stations and inverse distance method. According to the principle of existing spatial structure among point rainfalls, the less distance between tow points, the less difference in precipitation received by them. The purpose of this research is to find the relationship between distance of two points and depth of precipitation. Then it is possible to estimate the precipitation in points without rain gauges via this relationship. To display this relationship existing rain gauge data in Daryacheh Namak Watershed were used. In this research distances among any rain gauge from others are calculated. Then variations between their monthly precipitations are calculated. Maximum and minimum of the variations are upper and lower limits of variations respectively. The best curve is fitted between variation limits and distance between two points. These equations are calculated at 3 levels of likelihood (80, 90, and 95%) and results are displayed in curves and equations. Using results of this research, it is possible to estimate the depths of precipitation in pointes without gauges by having the data of other rain gauges and the distance from the points to rain gauges. For example: if monthly precipitation in a rain gauge station is "X mm" and other point is "L km" far from this station, this point can receive precipitation between "X + Y1" to "X + Y2". In which "Y1" and "Y2" are the upper and lower limits of difference respectively. By calculating the average the upper and lower limits the monthly precipitation of that point can be estimated. To promote the likelihood, estimation precipitation in the point is possible by many rain gauges data and then calculating the average of results.