Mojtaba Yamani; Abolghasem Goorabi; Shirin Mohammadkhan; Hamid Ganjaeian
Volume 4, Issue 12 , December 2017, Pages 1-23
Abstract
Introduction Qorveh, one of the cities of Kordestan province, has high amount of precipitation and, consequently, a significant resource of water. Geological surveys, physiography and hydrology, and in particular field studies, have revealed rivers' significant role and influence on the formation ...
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Introduction Qorveh, one of the cities of Kordestan province, has high amount of precipitation and, consequently, a significant resource of water. Geological surveys, physiography and hydrology, and in particular field studies, have revealed rivers' significant role and influence on the formation of human activities and settlements. Unfortunately, much of the locally carried out planning has ignored hydro-geomorphological factors. In addition, population growth and the growth in residential and industrial areas have caused uneven progress of the residential areas towards the riverside which has, consequently, changed the natural shape of the river such as its width, length, slope, sediment, and the like. These problems shows the importance and necessity of thorough hydro-geomorphological studies. Therefore, the main objective of this study was to investigate the hydro-geomorphological status of the Shoor River's basin and evaluate the its lands' capabilities to develop urban areas, determine the optimum location based on hydro-geomorphological criteria, minimize the undesirable impacts of inhabitants of cities and villages on the highlands, especially the catchment areas, and minimize the harmful effects of the environmental hazards on agricultural products and inhabitants. Materials and methods The study is based on field, library, and software methods. Firstly, using the topographic maps, the study area of the basin was specified. In this study, 8 factors of lithology, faults, slope, aspect, elevation, distance from the river, vegetation, and land use were used in order to evaluate and zone suitable areas for urban development of Qorveh. After the preparation of the information layers, the coefficients and their values were estimated by the use of ANP model. In this model, like AHP, the measurement of the amount of the relative importance is done by pairwise comparisons with the help of the range of 1 to 9. Number 1 represents the equal importance of two factors and 9 represents the extreme importance of one factor over the other. To this end, for each model, a total of 15 questionnaires were distributed among specialists (5 Land use planning, 5 urban planning, and 5 geomorphology specialists) to rate each of the factors. After analyzing the questionnaires, to calculate the final weights of each criteria and subcriteria (according to the internal relations), Super Decisions software was used for the ANP model. Then, indicators and variables affecting urban development, using the Fuzzy function, were sub-Fuzzed and all layers of the study were standardized and compared. The coefficients were transmitted to the GIS polygonal descriptive databases to make them more quantitative and comparable. Finally, the final maps were made by overlaying the information layers maps. Discussion and results In the present study, to locate suitable areas for urban development, 8 factors were used and the classification was done. On this basis, the suitable areas for future development of the city have a slope of less than 11 %, the lithology persistent for building, low elevation, barren land use, and the changeable vegetation. In addition, they are distant from the fault lines and they are near the riverside. Additionally, the map of each criteria was prepared and the final map was obtained by combining the information layers, based on the weight obtained through ANP. Conclusion The ANP Model, because of having features such as simplicity, flexibility, simultaneous use of qualitative and quantitative criteria and final ranking of the options, can provide a suitable framework for analyzing the issue and determining the final ranking of the options. However, since allocating evaluation weights cannot provide enough reliability, the use of fuzzy logic, for more integration between layers and fixing possible errors, is needed. Finally, the study area was divided into five regions in terms of available potential and the ability for the purposes of the urban development. According to the criteria which was considered in this zoning, it can be said that, areas with the highest score, are located in ideal areas and away from danger. However, it should be noted that the significant presence of the geomorphologic phenomena, such as Bader and Parishan mounts in the southern parts of the study area, as well as the existence of faults in the southwest and southeast of the area have caused the basin to be in a very inappropriate class in relation to the development of the space. The results of this study indicate that the Shoor River's basin has a high potential for spatial development and urban planning in the future.
پژوهشی
Mahmood Khosravi; Taghi Tavousi; Kohzad Raeespour; Mahboobeh Omidi Ghaleh mohammadi
Volume 4, Issue 12 , December 2017, Pages 25-44
Abstract
Extent Abstract Introduction In some parts of Iran, especially in its highlands, the predominant precipitation is snow. The large part of the snow cover is located in the mountainous and impassable areas. Consequently, it is almost impossible to study and investigate the snow point using traditional ...
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Extent Abstract Introduction In some parts of Iran, especially in its highlands, the predominant precipitation is snow. The large part of the snow cover is located in the mountainous and impassable areas. Consequently, it is almost impossible to study and investigate the snow point using traditional methods and snowflake stations. Chaharmahal-Bakhtiari province is one of the snowiest areas of Iran, and snowfall has a great role in the status of the water resources supplying the water of its central and southern regions, especially the Karun and Zayandeh Rood Rivers. Methodology Regarding the role and importance of Mount Zardkouh heights and its rivers in the region, the purpose of this study was to investigate the changes in the snow cover levels in Mount Zardukh altitudes. Therefore, remote sensing data, due to its provision of better results, is used with the aim of obtaining detailed information on snow cover. Today, remote sensing technology and revolutionary satellite imagery are created in the field of snow cover study so that wide-area snow measurements are dramatically more accurate over time. The occurrence of the recent droughts, the severe decrease of water resources, and the role and importance of snowfall in the supply of groundwater resources in mountainous areas needs to maximize the use of available resources by making the necessary arrangements. Discussion The process of these changes was measured using landsat satellite data, TM and ETM + sensors. In addition, the ndsi index was used to analyze the changes in the snow cover level of April (Farvardin) and September (Shahrivar), which were the peak months of the snow cover. The peak time of the snow cover melting in the region, Zardkouh Bakhtiari heights, during 1991, 2003, and 2011 (time spans of approximately 10 years) was also investigated to study the changes in the snow cover levels. Pre-processing steps including examining changes in the snow cover levels using the normalized differential snow index (NDSI) and corrections (radiometric, geometric, etc.), processing, classification, and after classification on the selected images using the ENVI software were taken. The NDSI index was applied based on the maximum snow cover per pixel of images (April & September). Conclusion Finally, the values, or maps, derived from the above indicators were classified into two classes of snow cover and snowless surfaces. After this classification, the areas of both classes were summed up for the investigation of the changes in snow cover and snowless cover during the studied years. The results showed that while the amount of the snow cover level in April 1991 was 1758.07 km2, it became 1128.43 km2 in April 2003. In other words, there was a decrease of 529.64 km2 between the years 1991 and 2003. In addition, it was 979.83 km2 in April 2011 and there was a decrease of 778.24 km2, compared to 1991. Moreover, while it was 802.86 km2 in September 1991, it became 615.83 km2 in September 2003. In other words, there was a decrease of 187.06 km2 between September 1991 and September 2003. In addition, it was 601.83 km2 in September 2011 and there was a decrease of 201.03, compared to September 1991.
پژوهشی
Ali Haghizadeh; Arman Kiani; Milad Kiani
Volume 4, Issue 12 , December 2017, Pages 45-66
Abstract
Introduction One form of precipitation is snow. Due to the long-lasting process of its transformation into runoff, it is different from other ingredients of the water budget. In most permanent rivers whose basins are covered with snow, it plays a little role in water resources' studies. This case study ...
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Introduction One form of precipitation is snow. Due to the long-lasting process of its transformation into runoff, it is different from other ingredients of the water budget. In most permanent rivers whose basins are covered with snow, it plays a little role in water resources' studies. This case study is the Gush Bala mountain watershed, which is located in the eastern part of Mashhad in Khorasan-Razavi province. Materials and methods In this study, 11 measured samples were used to map the depth and density of snow. Using Minitab software, the normality of the gathered data of snow was assessed through Kolmogorov-Smirnov test. The probability of more than 0.05 was considered as a criteria for the normality of the distribution of the data. If it does not have a normal distribution, they are normalized, through using modified shapes, in regard to their skewness. After testing the normality, the point data was transformed to regional data through Geo-Statistics such as Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Kriging, and Cokriging. Geo-statistical estimation consists of two phases. Its first phase involves identifying and modeling spatial structure that can be studied by means of half-changing facade and estimation which can be the best linear unbiased estimation. The mutual assessment method was utilized to choose the most appropriate Geo-Statistical method. In this method, for each step, one observed point was crossed out and its value was estimated. After that, the estimated value was compared with the observed one. Results and discussion The most vital criterion was Root Mean Square Error (RMSE).The comparison of the RMSE of different methods like IDW, RBF, Kriging, and Cokriging showed that the most and the least values were for simple cokriging and simple kiriging methods whose values were respectively 0.518 and 0.023. Therefore, the simple kriging revealed better results than the other methods. Overall, less values of RMSE led to a better performance of a spatial semi-variogram for depth and density. Because the values of RMSE for 11 functions including Circular, Spherical, Tetra spherical, Penta spherical, Exponential, Gaussian, Quadratic Rational, Hole Effect, K-Bessel, J-Bessel, and Stable for simple Cokriging for depth and simple kriging for density were respectively 0.518 and 0.023, the interpretation Variogram for 11 functions was needed in the case of simple cokriging for depth and simple kriging for density. The criteria which were used were Nugget and Nugget to sill ratio. Generraly, if they are less, their results are better super structure. The value with simple cokriging method for the depth of the snow for J-Bessel and sill ratio were respectively 0.795 and 0.95 which experienced better super structure. The value with simple kriging method for the density of the snow for J-Bessel and sill ratio were respectively 0.806 and 0.9 which showed an optimum method compared to other methods. All values that are obtained from interpolating kriging and cokriging methods must be evaluated with variogram structure, especially Nugget and Nugget to sill ratio. If the values of Nugget and Nugget to sill ratio increases, the predictability of the variogram decreases. In the variogram which was related to the depth and density data, the piece effect showed high levels. Furthermore, the ratio of nugget to sill was more than 0.75, which revealed a weak super structure between values in various distances. These findings demonstrated the heterogeneity of the data. On the other hand, considerable oscillations between nugget in depth data and density was shown on high values for nugget. Conclusion The results of the predictions assessment done with simple cokriging for depth and simple kriging for density showed the higher accuracy of the aforementioned methods to other methods. One reason for this high accuracy can be ascribed to the influence of these parameters. In general, when the environment is more homogeneous, the Mardr small scale's results will be better than conventional statistical analysis. Thus, it seems that the sampling method with homogeneous units using satellite and aerial images could result in the homogeneity of the data. In addition, as a result, the spatial vacillations of the data went down and the ability of Geo-Statistics to predict and estimate will improve.
پژوهشی
Ahmad Nohegar; Mohamad Kazemi; S.Javad Ahmadi
Volume 4, Issue 12 , December 2017, Pages 67-87
Abstract
Extent Abstract Introduction Considering the impact of accelerated rates of sediment yield and soil erosion on catchments, which results from land clearance and poor land management (Palazón et al., 2015) including soil degradation, environmental pollution, and sedimentation in dam reservoirs, ...
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Extent Abstract Introduction Considering the impact of accelerated rates of sediment yield and soil erosion on catchments, which results from land clearance and poor land management (Palazón et al., 2015) including soil degradation, environmental pollution, and sedimentation in dam reservoirs, the reduction of the sedimentation is required to implement appropriate methods of sediment control and soil conservation in the critical areas of sediment resources in the catchment (Patrick et al, 2015). In addition, the recognition and identification of the relative importance of the sediment resources and their contribution (Chen et al., 2016) to sedimentation are necessary for identifying appropriate methods and proper implementation of soil conservation programs. The sediment finger printing is a direct approach to identify the relative contribution of each source and provides a direct approach for quantifying sources of sediment. A fingerprint of sediment sources is obtained using radionuclides, tracer metals, or other sediment properties, which enables the determination of the relative source contributions (Motha et al., 2004). Including the erosion and sediment, the important thing is to choose a model or method to estimate the actual loss or erosion of soil and the contribution of each source to its value. There are few studies estimating the level of GOF and ME on sediment fingerprinting approach to determine the relative contribution of each of the resources. GOF and ME allow making better informed decisions on sediment management (Minella et al., 2008) and can reliably determine mixing the contribution of each sediment source in mixing models (Collins et al., 2010). Methodology The study area, Tange Bostank catchment, covers an area of 81.73 km2 and is located at about 80 km far from the Northwest of Shiraz, at the geographical location of 52° 03' 43'' to 52° 13' 36'' in the East and 30° 16' 33'' to 30° 25' 18'' in the North. Geological formations maps were provided as Razak, Kashkan, Bakhtiari, Quaternary, PabdehGurpi, and Asmari formations using SFF method. Land use maps were also provided as rangelands, forests, gardens, and irrigations using ML method with Landsat satellite image 8 of OLI sensor. Discriminating sediment sources to confirm the discrimination of the potential sediment sources was done in two steps. The first step was based on the use of the Kruskal–Wallis H-test to discriminate the potential sources by the fingerprint properties. In the second step, stepwise multivariate discriminant function analysis (DFA) was used to identify the optimum combination of the tracers passing the Kruskal–Wallis H-test and to maximize discriminating between the potential sources. The multivariate mixing model (Walling, 2005) involves minimizing the sum of the squares of the residuals between predicted tracer values for each source in sediment samples and the observed values. The sediment source apportionment involved a comparison of the results obtained using several multivariate mixing models. Using an optimization source proportion minimizes the errors in mixing models. We minimized the sum of the squares of the relative errors (R) in the objective functions( Eq.s 1-5). Eq.1: Eq. 2: Eq. 3: Eq. 4: Eq. 5: where: ci = concentration of fingerprint property (i) in sediment samples; Sij = concentration of fingerprint property (i) in source category (j); X j = percentage contribution from source category (j); Z j = particle size correction factor for source category (j); Oj = organic matter content correction factor for source category (j); Wi = tracer discriminatory weighting or tracer specific weighting; SVji = weighting representing the within-source variability of fingerprint property (i) in source category (j); VARij = variance of the measured values of tracer in source area j; mj = the total number of samples for an individual source; n = number of fingerprint properties; m = number of sediment source categories. Genetic Algorithm optimization (GA) was employed to find the optimal source sediments contribution. In addition, goodness of fit (GOF) equation and Mean Error (ME) were used to determine the results of each of the mixing models (Eq.6 and Eq.7) Eq.6: Discussion Soil erosion and sediment yield are the most destructive phenomena that cause a lot of damages in different regions. However, in order to combat them, it is needed to be aware of the sediment sources location in the region. Sediment fingerprinting technique, based on geochemical tracers, organic and isotopic ratios, and various mixing models, is used in the recognition of the contribution of the different sediment sources in an area. In this study, the optimum combination of organic and rare tracers was used to separate the different sources. In addition, to determine the contribution of this erosion and sediment yield resource, Collins, Collins modified, Motha, Landwehr and Slattery models associated with genetic algorithm optimization were used. The results of the discriminant analysis showed Compounds of C, Cu Si, and Ti as tracers for land uses and four tracers (Nd143/144, Cu, Si,Ti) to discriminate between geology formation’s source categories. To determine the best model, GOF and ME indexes were used. Tables1-4 render the results of applying the ME and GOF indices to select the best models in formation and land use units. The M Collins and Collins mixing models with GOF and ME indices of 99.95%, 99.996% and 99.16%, 99.977% were respectively selected as the best models in land use and formation units. According to ME and GOF results, the calculated relative contributions of the range lands and the Asmari formation with 65% and 56.5% were the highest. Moreover, sedimentation rates of sub basins number 6 and 5 with 59.11% and 58.7% were very important in the management of the soil conservation (the highest proportion in sediment and erosion basins) and sub basins number 31 with 7.54% were not important in the management of the soil conservation (minimal role in Sediment yield of Tange Bostanak watershed). Conclusion Soil erosion and sediment yield are the most destructive phenomena that cause a lot of damages in different regions. However, in order to combat them, it is needed to be aware of the sediment sources location in the region. Sediment fingerprinting technique, based on geochemical tracers, organic and isotopic ratios, and various mixing models, is used in the recognition of the contribution of the different sediment sources in an area. In this study, the optimum combination of organic and rare tracers was used to separate the different sources. In addition, to determine the contribution of this erosion and sediment yield resource, Collins, Collins modified, Motha, Landwehr and Slattery models associated with genetic algorithm optimization were used. The results of the discriminant analysis showed Compounds of C, Cu Si, and Ti as tracers for land uses and four tracers (Nd143/144, Cu, Si,Ti) to discriminate between geology formation’s source categories. The M Collins and Collins mixing models with GOF and ME indices of 99.95%, 99.996% and 99.16%, 99.977% were respectively selected as the best models in land use and formation units. According to ME and GOF results, the calculated relative contributions of the range lands and the Asmari formation with 65% and 56.5% were the highest. Moreover, sedimentation rates of sub basins number 6 and 5 with 59.11% and 58.7% were very important in the management of the soil conservation (the highest proportion in sediment and erosion basins) and sub basins number 31 with 7.54% were not important in the management of the soil conservation (minimal role in Sediment yield of Tange Bostanak watershed).
پژوهشی
Samaneh Poormohammadi; Mohammad Taghi Dastorani; Alireza Massah Bavani; Hadi Jafari
Volume 4, Issue 12 , December 2017, Pages 89-110
Abstract
Extended Abstract Introduction Climate change has a huge impact on all aspects of human life. Some of its impacts can be reduction in the surface and ground water resources of the country, changing the amount, timing, and type of precipitation, and influencing water quality. It can also lead to the increased ...
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Extended Abstract Introduction Climate change has a huge impact on all aspects of human life. Some of its impacts can be reduction in the surface and ground water resources of the country, changing the amount, timing, and type of precipitation, and influencing water quality. It can also lead to the increased droughts, increased demand for water, changes in the management of water resources, sea level rise and its complications, and extreme maximum and minimum temperatures. The aim of the present study was to evaluate the impact of the climate change on rainfall and minimum and maximum temperatures using 15 atmospheric general circulation models under two scenarios, including A1B and B1, between the years 2011-2039. Methodology For this purpose, through the use of beta statistical distribution of rainfall changes and based on the probability of 20, 50 and 80%, the minimum and maximum temperatures, were calculated from 15 general circulation models. Standard errors, absolute errors, and Nash-Sutcliff coefficients were determined for simulated data on the base and the upcoming periods. Next, of the 15 climatic models, the minimum temperature changes (ΔTmin), the maximum temperature changes (ΔTmax), and rainfall variation ratios (ΔP) for A1B and B1 scenarios for 12 months were extracted from the Lars model. The introduction of the climatic scenarios in the family scenarios of the A1 group, a rapidly growing economy and the growth of the population that will peak in the mid-21st century and decline thereafter introduces new and more efficient technologies. In this family, economic issues are more emphasized and opinions are rather global rather than regional. Three different subgroups for group A1 are based on the type of technology used in the 21st century, the intensification of the use of the fossil fuels (A1FI), the use of non-phosphate energy sources (A1T), and the use of fossil and non-fossil sources in a balanced manner (A1B). Results and discussion The results showed that, with the probability of 20 to 80% and under both A1B and B1 scenarios, the minimum and maximum temperatures are rising and the rain is falling. In addition, the increase in the minimum and maximum temperatures under A1B was more than that of the B1 scenario, but the reduction in the precipitation under B1 was more than A1B. The results also showed 19 to 22% decrease in precipitation, minimum temperature of 13 to 20%, and a maximum temperature of 2.4 to 6.4% compared to the baseline of the Tuyserkan catchment. In Table 1, the percentage change in climatic parameters under the influences of A1B and B1 scenarios and in relation to the base curriculum is presented. Under A1B scenario, and with the probability of the occurrence of 80%, there is 19.1% decrease in precipitation, 4.6% increase in maximum temperature, and 20% increase in minimum temperature in future periods. In addition, under B1 scenario and with the probability of occurrence of 80%, there is 22% decrease in precipitation, 13% increase in minimum temperature, and 4.2% increase in maximum temperature. Table (1) Assessment of the percentage change in climate parameters relative to the base curve Scenarios Probability of occurrence Precipitation (%) T_max (%) T_min(%) A1B 20 -3.8 1 7.2 50 -13.7 2.8 14.5 80 -19.7 4.6 20 B1 20 1.5 -0.9 7 50 -20 1.5 7.7 80 -22 4.2 13 Conclusion Generally, it can be argued that the climate change in future periods will increase the minimum and maximum temperatures and reduce the rainfall in Tuyserkan Plain. Consequently, these changes in temperature and precipitation will affect plain water resources. The most important of change is the change in the seasonal precipitation pattern and temperature rise in cold seasons. These changes will also have a significant impact on the region's cropping pattern, as the dryland cultivation is limited, due to the reduced rainfall, and its time will vary with time variations.
پژوهشی
Reza Ghazavi; Majid Ramezani
Volume 4, Issue 12 , December 2017, Pages 111-129
Abstract
Extend Abstract Introduction Groundwater is one of the most important resources of fresh water in the world, especially in arid and semi-arid areas. In these areas, the demand for groundwater has increased due to the decline of rainfall, population growth, and industrialization, while its quality has ...
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Extend Abstract Introduction Groundwater is one of the most important resources of fresh water in the world, especially in arid and semi-arid areas. In these areas, the demand for groundwater has increased due to the decline of rainfall, population growth, and industrialization, while its quality has declined via industrial and urban contamination. The removal of the groundwater pollution is very costly and time-consuming. Consequently, the prevention of the groundwater contamination is the best way for groundwater protection. The main aim of this study was to investigate the trend of groundwater quality and quantity changes in the Rafsanjan plain in relation to the groundwater discharge and rainfall change. Methodology The study area is the Rafsanjan plain with an area of 5459.36 km2 (with an altitude of 45°, 30¢ to 56°, 30¢ and latitude of 29°, 59¢ to 29°, 15¢). In this study, the essential maps including topography, drainage, piezometric wells location, and groundwater quality and quantity maps were created using GIS10.1. The groundwater level in 80 pizometric wells and the groundwater quality in 50 wells were investigated and analyzed for a period of 10 years (2002-2012). The groundwater unit hydrograph and rainfall pattern were compared to indicate the impacts of rainfall variability and the groundwater over-extraction on the groundwater level variation. Water quality maps were created using Vilcox method. Based on kriging interpolation method, the quantitative and qualitative maps of the study area were prepared using geographic information system (GIS). Results The groundwater hydrograph of the study plain indicated that the groundwater level declined continuously. As during the past 10 years, the groundwater decline was 8 m, so the annual groundwater decline in the study plain was 0.8 m. comparing the groundwater level of 2002 and 2012 via piezometric wells indicated a significant decline of the groundwater level. In 2002, for 81% of the study plain, the groundwater level was between 30-90 m, while it declined to 68% in 2012. The maximum groundwater decline was related to the area where groundwater level in 2002 was between 30 and 60 m. The area where the groundwater level was between 90 to 120 m, it increased from 683.8 km2 in 2002 to 999.7 km2 in 2012. Also the area where groundwater level was more than 120 m, it increased by 5.3%. A significant relationship was observed between the groundwater level and the volume of the groundwater extraction in 10 years of the study (R2 = 0.6). However, no significant relationship was observed between the groundwater level and the average rainfall between 2002 and 2012 (R2 = 0.04). These results indicated that the impact of the groundwater extraction on the groundwater level decline was more important than the rainfall change. In this study, Wilcox method was used for the investigation of the variability of the groundwater quality. Based on Electric conductivity (EC) and Sodium absorption rate (SAR) in Wilcox method, 16 classes of groundwater quality should be investigated. According to these results, in 10 years of the study period, the number of wells located in C3S2 and C4S2 classes of groundwater quality declined by 2 and 4% respectively. The number of wells located in C4S4 increased from 33% in 2002 to 38% in 2012. Cumulative discharge of all study wells decreased from 610 liter per second to 469 liter per second. The maximum decline was related to C4S3 and C4S2 groups. Discussion The results of this study indicated that the groundwater quality and level declined in the study area. According to the results of the water quality maps, the area of the aquifer with groundwater quality located in C3S2 and C4S2 respectively decreased by 6 and 1.4 %, while the area of the aquifer with groundwater quality located in C4S4 increased by 4.5 percent. The study of the piezometric wells with a depth of 30 m and less indicated that 15% of these wells dried between 2002 and 2012 due to groundwater level declination. The water quality of the profound wells (with a depth of 31 to 200m) decreased by 8.5%. These results indicate that the groundwater quality decreases with increasing of the groundwater level. Conclusion According to these results, the groundwater decline due to the rainfall decline, and the role of the groundwater abstraction in the agricultural area were more important than the rainfall deficits. The qualitative and quantitative maps of groundwater were also prepared via kriging interpolation method and GIS. Based on these results, it can be suggested that rainfall decline leads to the decline of groundwater, but excessive removal of groundwater resources in agricultural lands is a major factor that should reduce the quality of the groundwater in the study area.
پژوهشی
Abolghasem Amir Ahmadi
Volume 4, Issue 12 , December 2017, Pages 131-152
Abstract
Extent Abstract
Introduction
Gully erosion is a major problem for natural resource management, leading to land degradation and economic losses worldwide. Determining the threshold for research on Geomorphology and natural ecosystems is important for many scholars. Land managers and specialists knowledge ...
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Extent Abstract
Introduction
Gully erosion is a major problem for natural resource management, leading to land degradation and economic losses worldwide. Determining the threshold for research on Geomorphology and natural ecosystems is important for many scholars. Land managers and specialists knowledge about factors affecting the growth of gully enables them to control them and predict their growth rate under similar conditions in other ecosystems. In the study area, this type of erosion has caused many lands to be destroyed, and with runoff and flood runoff, there is a significant amount of sediment that leads to unutilized land. It seems that examining these factors and determining their thresholds will help determine control strategies and more successful implementation of water and soil conservation projects. The purpose of this study was to determine the threshold of effective factors in the longitudinal growth of gullies using data mining techniques in Sanganeh Kalat watershed in the northern part of Khorasan Razavi province.
Methodology
Initially, the location of 23 gullies was recorded using the Global Positioning System (Garmin 76CSX) and the distribution map of the gullies in the study area. Then, the Soil gravel, bare soil, cover, litter in heads of the selected gully were measured.
For this purpose, 15 plots were placed in one square meter and their means and the previously mentioned parameters were determined. In order to measure the physical and chemical properties of the soil, a soil sample was taken from a point at the head of each gully. After they were transferred to the erosion and sedimentation laboratory, the electrical conductivity (ECe), PH, OM, SAR, Clay, Silt and the Sand were measured. Also, the permeability at the top site of head of each gully was calculated using double cylinders. In addition, the amount of water penetration of the soil was calculated. Finally, using the data mining technique (K-Means Clustering and CART Decision Tree), the threshold of the factors influencing the longitudinal growth of gully in the study area was determined.
Discussion
Of the total of 23 gullies studied in this study, the accuracy of the estimation based on the parameters influencing the longitudinal extension of the gullies in the final model were measured and were respectively 100% and 85% for the educational and experimental data sets. The interpretation of the rules extracted from the decision tree of the CART, based on the clustering of the length of the gullies, is as follows:
- The results of the analysis of the CART decision tree algorithm show that when the width of the gullies is 275.32, the SAR is 0.147, the gullies headcuts slope is 1.39, and the percentage of silt would increase from 37.12, long-length gullies (cluster 1) are created.
- In the formation of mid-range gullies, when the ratio of girder width is greater than 198.84, the SAR is less than or equal to 0.174, and the gradient of the gully headcuts slope is less than 0.73, the average length gullies (cluster 2) are created.
- When the width of the gullies is from 108.77 m, the SAR is less than or equal to 0.174, and the gullies headcuts slope is smaller or equal to 0.481, gullies of low length (101.35 to 163.23 m) are created.
Conclusion
The results of the decision tree of CART based on the length of gullies clustering showed that the most important factors affecting the longitudinal expansion of gullies in the study area were gully width, SAR, gully headcuts slope and silt percentage.As a result, the main factor in the longitudinal expansion of gullies is the surface runoff. The second factor is the soil erosion sensitivity in the study area. The main reason for this is the poor vegetation and low soil permeability. In addition, the texture of the soil is another factor that overwhelms the longitudinal extension of the gullies. The prevalence of the amount of silt in the soil texture is due to the lack of adhesion, waste, and the transfer of more sediment, resulting in the longitudinal extension of the gullies.
پژوهشی
Amir Hossein Halabian; shamsolah Asgari
Volume 4, Issue 12 , December 2017, Pages 153-177
Abstract
Extent Abstract Introduction One way to decrease flood damage is to zone the potential of flood in watersheds. In other words, separating the flooding areas and determining the effective factors in flooding can play a special role in preparing a suitable medium and long term policy making for optimal ...
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Extent Abstract Introduction One way to decrease flood damage is to zone the potential of flood in watersheds. In other words, separating the flooding areas and determining the effective factors in flooding can play a special role in preparing a suitable medium and long term policy making for optimal exploitation of lands. Some of the studies which were based on zoning the potential of flooding worldwide and Iran includes Hawkins (1979), James et al. (1980), Bales et al. (1981), Enayat Rasoul et al. (1994), Suwanwerakamator (1994), Singh (1997), Francisco et al. (1998), Stephen (2002), Sinnakaudan et al. (2003), Sanyal and Lu (2004), Levy (2005), Meyer et al. (2009), Cook et al. (2009), Qin et al. (2011), Bakhtyari Kia et al. (2011), Al-Ghamdi et al. (2012), Ismail et al. (2013), Demir and Kisi (2016); in Iran: Qaemi and Morid (1375), Qanavati and Farajzadeh (1379), Abdi and Rasouli (1380), Omidvar et al. (1389), Malekian et al. (1391), Lajevardi et al. (1392), Nasrinnejad et al. (1393). In this research, Mishkhas watershed was studied in terms of flooding potential using multivariate statistical methods of factor and cluster analysis and geographical information system (GIS). Finally, the watershedflooding map was drawn in three classes of low, moderate and high. Zoning the flooding potential in this watershed can help reduce the damage caused by this natural hazard. Such studies can be a basis for future planning of regional and local developments. Methodology One of the appropriate criteria for understanding the potential of flooding in basins is classifying them according to geometry, physiography, permeability, and climatic criteria. In this study, the topography maps of geographic organization (1:50000), and the geological map of vegetation (1:250000), land use, soil maps of Ilam province (1392), precipitation data, and multivariate statistical methods of factor and cluster analysis have been used. In this research, Mishkhas watershed was divided into 12 sub-watersheds and their flooding intensity was classified into 3 classes. According to the aim of the research, the maximum instant debit, daily precipitation, and the date of watershed floods during the statistical period were selected. In addition, the effective criteria in watershed flooding was calculated using ArcGIS software including geometry, physiography, permeability, and climatic parameters for Mishkhas sub-watersheds. Then, they were analyzed using factor analysis and 28 parameters were summarized in the form of 5 main factors (form, stream, slope, drainage, and runoff). Finally, the intensity of the sub-watersheds' flooding were c 3 high, moderate, and low classes according to the mentioned criteria. Discussion In this research, the total criteria which were used were operating by a type R factor analysis. The results of this research decreased 28 initial criteria to 5 superior factors including (1) form, (2) stream, (3) slope, (4) drainage, and (5) runoff. According to calculations done on the criteria in the first factor, sub-watersheds 1, 2, 3, 5, 9, 11, and 12 with the highest flooding, sub-watersheds 6, 8, 10 with moderate flooding and sub-watersheds 4 and 7 with the lowest flooding intensity were identified. The first factor indicated the reverse relationship between the watershed's form and flooding intensity. That is, the more its length and area, the less its flooding intensity. In the second factor (stream) it was specified that sub-watersheds 1, 10, 3, and 12 have high flooding, sub-watersheds 7, 8, 5, 2, and 9 have moderate flooding, and sub-watersheds 11, 4, and 6 have low flooding. It was also indicated a reverse relationship between stream density and flooding intensity. In the third factor (slope), it was specified that sub-watersheds 6, 5, 1, 9, 10, and 11 have high flooding intensity, sub-watersheds 2, 4, and 8 have moderate flooding intensity, and sub-watersheds 7 and 12 have low flooding intensity. The sub-watersheds with high flooding intensity are located in northeastern and eastern parts of the basin which are mostly mountainous and have high height difference and slope. Sub-watersheds with low flooding intensity have little height difference, low slope, and relatively suitable vegetation. The calculations done on the fourth factor (drainage) indicated that sub-watersheds 12, 7, 4, and 2 have high flooding intensity, sub-watersheds 5, 6, 10, and 11 have moderate flooding predisposition, and sub-watersheds 1, 3, 8, and 9 have low flooding predisposition. Sub-watersheds with high flooding have been operated as the main drain of watershed. The results indicated that 33% of sub-watersheds have high flooding in terms of drainage factor. According to calculations done on the fifth factor (runoff) sub-watersheds 12, 3, 4, and 5 have high flooding intensity, sub-watersheds 1, 2, 6, and 8 have moderate flooding intensity and, sub-watersheds 7, 9, 10, and 11 have low flooding intensity. According to the factor's score, Mishkhas watershed is divided into three high, moderate, and low flooding classes and the zoning map of sub-watersheds' flooding intensity has been prepared. Conclusion In this research, factor analysis and cluster analysis were used for studying the flooding intensity of Mishkhas watershed and the role of sub-watersheds in flooding of this area. According to factor analysis results, 28 initial criteria reduced to 5 factors including form, stream, slope, drainage, and runoff. Analyzing the factors indicated that sub-watersheds 3, 5, 8, and 9 in form factor, sub-watersheds 1, 6, and 11 in slope factor, sub-watersheds 2 and 7 in drainage factor, and sub-watersheds 4, 12, and 10 in runoff factor have extra flood hazard intensity. Sub-watersheds were divided into 3 groups including high, moderate, and low flood producing based on the similarity of flooding intensity, erosion, vegetation, and human activities. For separating the sub-watersheds in homogenous groups, three homogenous groups were identified after data standardization by a standard model and applying Euclidean distance and Ward method. The first group's sub-watersheds 1, 2, 3, 4, 5, and 6 have high power to produce run off because of having high height and slope, low vegetation and permeability, and high flooding capacity. The second group's sub-watersheds 7, 8, 11, and 12 have high power to produce runoff, because of high slope, low vegetation, high height, low permeability, and high flooding power. In the third group's sub-watersheds 9 and 10, the flow was decreased because of decreasing the slope and increasing the permeability, so they indicated lower power to produce runoff. In fact, sub-watersheds play fundamental roles in flooding of this watershed that affect large downstream agricultural lands.
