hydrogeology
Rasool Hasan zadeh; Friba Esfandyari; sayyad Asghari saraskanrood; Zahra Miri
Abstract
the object-oriented method in preparing the land use map of Darre Rood catchment area using Landsat 5 and Landsat 8 images in a period of 30 years, from 1990 to 2019 and its effects on changes in Darre rood river discharge it placed. The images were classified into fourteen classes and the changes in ...
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the object-oriented method in preparing the land use map of Darre Rood catchment area using Landsat 5 and Landsat 8 images in a period of 30 years, from 1990 to 2019 and its effects on changes in Darre rood river discharge it placed. The images were classified into fourteen classes and the changes in the area of the classes revealed that the classes of irrigated agriculture, rainfed agriculture, rocky areas, residential areas, gardens and lakes with increased area and barren lands, pastures, forest lands and riverbeds decreased They were. To find out the changes in the river flow trend, SCS method was used which was implemented in SWAT model and according to land use in 1990 and 2019 in SWAT model was determined according to the digital elevation layer of the basin and all the necessary parameters to the model. Which included soil layers and land use changes and climate data were called into the model and two separate scenarios for 1990 and 2019 were used. The results showed that with the change of land use, the amount of CN in the second scenario compared to the first scenario increased by 5% and increased from 02.70 to 5.73, which due to the change in land use in favor of the basin becomes more impermeable to rain. Compared to 1990. Also, due to the increase in the type of vegetation, the amount of deep penetration has decreased from the first scenario to the second scenario from 257.09 to 97.9.
elnaz piroozi; Aghil Madadi; Sayyad Asghari Saraskanroud
Abstract
1-Introduction Rivers are dynamic forms of natural landscapes with different changes at different times and places. The effects of river adjustment caused by the natural factors require much longer span to reveal. However, sometimes the natural factors such as river floods, landslide, or earthquake can ...
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1-Introduction Rivers are dynamic forms of natural landscapes with different changes at different times and places. The effects of river adjustment caused by the natural factors require much longer span to reveal. However, sometimes the natural factors such as river floods, landslide, or earthquake can lead to canal adjustments in a very short time (Chaiwongsaen et al, 2019:153(. In contrast, human activities can have a significant and rapid impact on natural processes and trends, resulting in a short time scale for river adjustments (Rinaldi & Simon, 1998:57). River canal instability plays a major role in erosion, destruction of beaches and riverbanks. This role becomes more significant when the canal and bed of the river is alluvial (Rezaei Moghadam, 2012:33). One of the key issues in studying the erosion and stability of rivers is the initiation of sedimentary particle movement. The motion of sediments occurs if the bed shear stress (available shear stress) induced by the flow exceeds a certain critical value. An alluvial canal, either artificial or natural, persists to deform its boundary by itself while transporting water and sediments. Therefore, erosion and riverbank instability have created major concerns worldwide over the past few decades and significant amount of money have been spent to sustain the riverbanks. Givi-Chay River which is almost 54 kilometers long, is one of the permanent rivers of Ardabil province, Iran; problems of bed and bank erosion are evident in different areas of this river and they damage agricultural lands and adjacent river installations. In addition, a review of the research shows that sufficient studies have not been carried out so far to reveal the stability, erosion and sedimentation process in Givi Chay River.Therefore, this study aimed to analyze and evaluate the erosion stability of Givi Chay River channel. 2-Methodology In this research, the topography map with a scale of 1: 50000, geology map with a scale of 1: 100000, and google earth and Landsat Eight images, including OLI sensor (2019), bedrock maps and the Givi-Chay River area at a scale of 1:2000 hydrological data from two Abegharm stations (upstream of the dam) and Firoozabad (downstream of the dam) and field data are used. In addition, to control the results obtained by quantitative methods, field studies are applied for confirmation and verification. ENVI 5.3, Arc GIS 10.5, Excel, and HECRAS software were also used for image processing and data analysis. The geomorphological parameters of the river and their variations including bending coefficient and central angle were measured. The curvature coefficient is one of the few criteria used in river shape segmentation using s=1/(y.2), i.e., by dividing the valley length by wavelength for each arc, it is calculated. The central angle of the arcs on each of the intervals was calculated using the relation A=180L / Rπ, where A is the central angle, R, of the fitted circle radius.The increased shear stress in the riverbed increases the load of the floor and the scour of the bed, which can affect the riverbanks as erosion, destruction, and rupture of the walls. Direct measurement of shear stress is a difficult task and therefore researchers have developed methods for indirect calculation of shear stress. Existing shear stress (boundary), lateral shear stress, and critical shear stress were calculated by means of equation 1, 2, and 3, respectively: (1) (2) (3) Relative Stability Index Calculation (RBS):Judet has introduced this index as the ratio of critical bed velocity to actual bed velocity. Olsen et al (1997) defined this index as the ratio between the critical shear stress and the shear stress of the sides. Relative stability index (RBS) was obtained using the following equations: (4) (5) (6) 3-Results and Discussion Investigation of the morphology of the intervals shows that in the first, second and fourth intervals the conduit is sinusoidal and in the fourth interval, the pattern is meandering. In addition, according to the results of the study, the first, second and third intervals are developed in a very meandering manner and the fourth interval is just a meandering one. shear stresses in sections 4, 3 (second interval) are more than other sections, and given the direct relationship between shear stress and depth and width of sections, even under current conditions there will be phenomena such as scouring and damaging river bank and rivers. In addition, in terms of critical shear stress, the highest shear stress is in sections 3 and 7. Due to the relative stability values, sections 5 and 7 are stable and other sections are unstable. In the first period, the river flows into a valley bed, and in parts formed by erodible formations and at sections close to the dam, the river width is approximately increased. Therefore, sections 1 and 2, which pass through alluvial terrace sediments, are in unstable condition. In the second interval and immediately after the Givi Dam, the river passes through the valleys overlooking the Givi town, where the width of the bed due to the types of the banks decreases and the riverbed contains coarse sediments covered by broken rocks. In other parts of the city of Givi, erosion conditions prevail and large volumes of flanking material (especially during floods) are eroded and loose flanks lead to the widening of canals and intra-canal ridges, and these sediments are clearly visible in bends, middle islands and marginal lands and steep banks. At sections where the river width is excessive and the slope decreases, the stability factor is almost high (sections 5 and 7). At the beginning of the third period, Firouzabad area is located on path of the flood of the previous interval and by joining Sanghor Chay, the river enters the mountainous part and the coastal areas have deep valleys with steep slopes. Along the river, due to collision with high mountains and rocky outcrops, the alternate route has a meander and river changes are subject to valley changes, and the meandering state is seen throughout the valley. In the fourth period, the river width is reduced and the riverbed is covered with coarse sediments, which extends to Ghezelozan. 4- Conclusion(S) According to the study results, in the plain interval, the main factor affecting the river meandering is the alluvial formation; here, the slope is low and the meanders are inscribed and plain, whereas in the mountainous part, the river changes are subject to valley changes and the meandering state is seen throughout the valley. According to the values of shear stress, the lowest boundary and bank shear stress is in sections 5, 6 and 7 and the highest is in sections 4, 3, 11 and 12. The highest critical shear stress is in sections 3 and 7 and the lowest is in sections 4, 2 and 12. The study of the relative stability of the river shows that the river is more unstable in sections crossing the old and new alluvial terraces, and in sections where the river width is high and the bed slope and flow rate have a decreasing trend, the coefficient of stability is relatively high. The third and fourth intervals are mountainous and semi-mountainous, respectively. In these intervals the river width is small and there is no agricultural land use .Lithologically, most of the third and whole of the fourth period consist of Eocene igneous and pyroclastic formations and they are resistant to erosion and the existing alluviums are the result of transport of water from sediments of other intervals.Therefore, the morphology of the river is affected by lithology and according to field evidence, the interval is stable .But the results of using mathematical and experimental methods have introduced the third and fourth intervals as unstable . Therefore, it can be acknowledged that the methods used in this study apply to the study of stability in rivers and alluvial intervals
Sayyad Asghari Saraskanroud; Rasoul Hassanzadeh; Zahra Miri Atashgah
Volume 6, Issue 19 , September 2019, , Pages 37-56
Abstract
Introduction Land-use studies, using remote sensing techniques, are vital tools for generating rational information for proper decision-making in natural resource management. (Habtamu Teka et al., 2017). Land-use change has the potential to affect land cover and vice versa. ...
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Introduction Land-use studies, using remote sensing techniques, are vital tools for generating rational information for proper decision-making in natural resource management. (Habtamu Teka et al., 2017). Land-use change has the potential to affect land cover and vice versa. Land use change affects the biodiversity and aquatic ecosystems, and change in the watershed affects water quality, resulting in an increased runoff consumption, reduced land use, and evacuated groundwater. Therefore, land-use change information for water selection, planning, monitoring, and management is important in order to meet the change in land use due to the demand for human and welfare requirements without compromising the quality and quantity of water (Ang Kane Hawa, 2017). Methodology Case Study The Aliabad watershed of Horand with the southwest-northeast trend is located at the geographical coordinates of the southeastern part of the eastern part of the eastern province of East Azarbaijan and the southeastern part of Horand. Including the Horand, Majidabad and Yali Yurt mountains, the Eight Shrines, the Cay Thai Castle and Mount Everest, it covers an area of 165278 km2. The Ali Abad River is the main river of this basin, which performs the drainage system of the area and the Dojak and Horand Rivers are the most important branches that join this river. Data and research method The data which was used in this study included Landsat images, which included TM and OLI sensors with track 168 and row 33, with a resolution of 30m between 1992 and 2017. To obtain the amount of water created by new gardens, a pure water irrigation project for Iranian crops, which has become a software called NETWAT was used. Landsat images in the pre-processing stage were used for the atmospheric and radiometric FLAASH corrections. The strongest method of atmospheric correction, and rescale operations were performed on corrected images to ensure accurate numerical calculations. Meanwhile, in the 2017 image, to increase the accuracy of the classification, the method of fusion of multinuclear images with a pancreatic image was made and the spatial resolution up to 15m improved. The processing of images to detect and determine the type of land use in two time intervals was done through supervised classification methods. Of four types of classification, the Maximom likelhood method was chosen due to a better processing and the absence of unknown pixels. Finally, the layer Land use in twelve classes was selected by selecting the number of educational specimens including the first class forests (4181 pixels), second class forests (3958 pixels), garden lands (2665 pixels), first class rangeland (32704 pixels), rangeland grade (30837 pixels), agriculture (7544 pixels), residential land (1911 pixels), shore lands (3257 pixels), blueberries (167 pixels), Bayer lands (2332 pixels), blue areas (131 pixels) and river beds (800 pixels). In addition to the necessity of using large-scale images, field observations and the use of lateral information were necessary to identify some activities at different levels of the earth (Zebiri, 92). Therefore, field observations were also needed to enhance the accuracy of the user classification. To analyze the data, ArcGIS, ENVI were used. After the processing and evaluating the accuracy of the images and examining the results of the classification, there were several ways for assessing the accuracy of the classification. The most common way was the selection of a number of pixels of the specified sample and comparing them with the classification that made these data. The ground truths were called reference data (Alawi Panah, 91: 159-152). The net irrigation project of Iran's products was from the National Project Plan (TOTEK). An optimization of the national consumption of Iranian agricultural water was carried out by the Meteorological Organization of the country and the Ministry of Jihad-e-Agriculture in collaboration with Dr. Amin Alizadeh and his research team. In order to estimate the water consumption of the basin in gardening affairs, based on the method of work, this functionality was constructed using the FAO-Penman-Monteith function and based on this function, the annual consumption of trees was calculated. Finally, the consumption of each tree was estimated as the average annual consumption. Results The verification of the accuracy and results of the classification of images by the Kappa coefficient were performed and the obtained coefficient with the acceptable status showed that the classification of the images was done satisfactorily and the images could be cited for the continuation of the research. In land use maps of the AliAbad River watershed, in each of the periods of 1992 and 2017, the lands were divided into 12 classes. Based on the comparison made between the two maps, land use changes in each of the 12 classes were presented. Based on the results of two time intervals in the Aliabad Chay catchment area, it was determined that the area of the cultivated lands increased by about 5.51 km2 (Table 1). In the field studies, it was concluded that the irrigation Garden lands were built on the basis of ribs by the city's people on the upstream gardens until 1992. The Pearson parametric test showed that there was a significantly positive and strong correlation between the increase in the area of the gardens and the increase in the depth of the wells (0.935). It should be noted that by increasing the area of gardens in the land use, the depth of the wells was increased for the extraction of water from the basin. However, the correlation between rainfall changes and depth of wells (Basin flood) was negative (-0.580). The basis of the net irrigation plan for irrigated crops in Iran, and using the produced function, the annual water demand of these products was calculated in terms of planting area (Table 1 and Table 2). (Table 1) Calculation of the water requirement of cultivated trees in the first six months with the NETWAT software Tree type April May June spring season July August September summer season Apple 8 44 91 143 151 138 103 392 Walnut 10 55 109 184 172 160 119 451 Apricot 13 52 86 151 122 117 53 292 Cherry 8 44 91 143 151 134 89 374 Average 39 115 377 621 596 549 364 1509 (Table 2) Calculation of the annual water requirement of cultivated trees with a built-in function Tree type Annual water requirement (cubic meter per sq. Km) Apple 760000 Walnut 886900 Apricot 490000 Cherry 698000 Average consumption of trees 708725 Total consumption in newly built gardens is 5.15 square kilometers 3,649,934 Discussion and conclusion The study area of this research is one of the important agricultural areas of East Azarbaijan Province, and the Ali Abad Chai River is considered as the only source of water supply in the region. The classification of land use in the region, especially the increase of basin gardens, which is the most important water user in the river, was done by Maximim Likelhood method. The Pearson's parametric test was used in the SPSS software to prove that the basin's decline was caused by variation in the gardens, and the 25 year rainfall variations in the basin and the changes in the gardens were investigated with regard to the depth of the wells. There is was significant relationship between rainfall variations in the basin and the decline of the basin, but there was a significant difference between the changes in the use of gardens and the decline of the basin, and there was a very strong and positive correlation between these two variables. Therefore, it should be acknowledged that in the Aliabad watershed, if the irrigation process of the gardens is not scientific and practical and the management of water storage is not done, the flow of the river in general is disturbed in these seasons. In the warm seasons, permanent changes in geomorphologic and ecological activities will be lost and the negative effects will be felt by the inhabitants of the region.
Ardashir Yousefzadeh; Battol Zeynali; Khalil Valizadeh Kamran; Saayad Asghari Sar Eskanrood
Volume 6, Issue 18 , June 2019, , Pages 181-205
Abstract
Introduction According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and ...
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Introduction According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and the Observational Satellite Observatory. Remote sensing provides a powerful tool for detecting and monitoring soil moisture near the Earth's surface (0 to 5 cm). Also, according to Babaeian research (2015), optical reflection of the soil and thermal emission to Eliit (1979) and Microwave backed by Das researches (2008) is related on soil moisture. Remote sensing techniques based on microwave waves are effective techniques for estimating soil moisture. Surface water levels can be extracted using the NDVI index in Landsat images (Maryam Khosravian et al., 2012, p. 115), and user variations in time series can also be identified. (Malian et al., 1395, p. 49). Due to the limitation of access to radar information, the focus of the study is on the near-visible infrared range and the amount of heat from the surface of the earth is measured from 3.5 to 14 micrometers (Curran, 1985). Soil moisture content with this method requires the estimation of soil surface temperature and vegetation index (Wang & Co, 2009). Vegetation and surface temperature have a complex dependence on soil moisture (Carlson, 1994). According to Gillies et al. (1997), the combination of these two indicators can be used to estimate soil moisture with an acceptable accuracy.In 2017, a model for estimating soil moisture using a visual distance assay was proposed based on the linear physical relationship between soil moisture and the short-range infrared reflection (STR), which is based on the distribution of pixels inside the surface temperature space and the normalized vegetation index (STR-VI) (Sadegi et al., 2017). A trapezoid or triangle model is one of the models used in remote sensing to estimate soil moisture. The study area is the Simineh River basin which is one of the sub basins of Lake Urmia Basin, with an of 3279 km2. Methodology The main data in this study are Landsat 8 satellite imagery. After applying atmospheric and radiometric corrections, the processing of images, between 2016-2017, was done according to the process of view of Figure 1. Figure (1) Research process (Source: Writers) -Thermal-Optical Trapezoid Model (TOTRAM) This model is based on the distribution of pixels in the surface temperature and vegetation cover space that is fitted to estimate soil moisture using a linear equation in space (LST-VI) (Sadegi et al., 2017). Equation (1) -Optical Trapezoid Model (OPTRAM) The base of this model is the insertion of surface temperature to estimate the soil moisture in the visible wavelength range. In this physical model, the linear relationship between soil moisture and infrared reflection is expressed. Equation (2) Result According to the results of this study, the lowest average temperatures of satellite images were respectively -3.23 and 2.12 C in 2015 and 2016, indicating an increase in temperature. In 2017, the highest amount of vegetation density was 0.66. The correlation between the OPTRAM model in 2015 and the STR and NDVI variables, were positive and the correlation indices were respectively 0.709 and 1. These figures for STR and NDVI in 2016 were respectively -0.648 and 1, which indicated a negative correlation between STR and soil moisture; soil moisture decreased with increasing STR and increased with increasing NDVI. And the positive correlation between OPTRAM model and NDVI confirmed it. In 2017, the positive correlation between STR and NDVI with soil moisture were respectively 0.672 and 1. The TOTRAM model in 2015 had a negative correlation with the LST and NDVI indices and they were respectively -0.574 and -1. It indicated low accuracy of this model compared to the OPTRAM model in estimating soil moisture. In 2016, the correlation between LST and NDVI with soil moisture were respectively -0.974 and 0.409. They respectively reached -0.940 -0.787 in 2017. Discussion and Conclusion In this research, due to the limitations of the field information, soil moisture was extracted without the use of ground control points. The comparison of the accuracy of the two models in the region was investigated. The results indicated that soil moisture can be extracted from the STR index with high accuracy, compared to LST index, based on NDVI Triangular space. Due to the low cost and the availability of visible images, radar images were accurately obtained and the correlation between OPTRAM model and soil moisture estimation was confirmed. According to the extraction results, the OPTRAM model can estimate the soil moisture better than the TOTRAM model, due to the fact that it is not influenced by environmental factors and global parameters. According to research results, TOTRAM has two main constraints. First, it cannot be used for a satellite without thermal bonding. Secondly, in addition to soil moisture, the LST depends on environmental factors to be calibrated for each image. To overcome the limitations of the TOTRAM model as well as the empirical visibility of indicators, a new physical trapezoidal model, called OPTRAM, is proposed. It is based on the physical relationship developed between soil moisture and the "reflected infrared reflection" (Sadegi et al., 2015).
Sayyad Asghari Saraskanroud; Zeinab Doltshahi; Mehdi Pourahamad
Volume 3, Issue 9 , March 2017, , Pages 21-41
Abstract
According to the economic and industrial growth and the production of different kinds of compounds and chemical materials which provided by human for their welfare using natural resources and in this direction , they came undesirably materials such as toxic and heavy metals into nature which result in ...
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According to the economic and industrial growth and the production of different kinds of compounds and chemical materials which provided by human for their welfare using natural resources and in this direction , they came undesirably materials such as toxic and heavy metals into nature which result in serious risks and problems for both them and their surrounding environment. The research area of this, study is the city of Khorramabad that is located in west of Iran, central of Lorestan province; In this study, in order to determine the quality of drinking water of studied area, 23 fountains and wells circles related with heavy elements including (chrome, molybdenum, copper, zinc, barium, cobalt, aluminum, lead, cadmium, nickel) during three years (2011-2013) was used. In order to determine the rate of minimum and maximum concentration of elements among studied area’s fountains and wells, Excel software was used. Then, in the software of geographic information system (GIS), The map interpolation method of each heavy elements in each supplier resources of drinking water has produced and amount of each element and parameter and stated amounts by Iran national organization, world Health organization (WHO), America’s environment organization (EPA) have been compared and the rate of pollution of each water resource should be determined. With respect to present data or information, the rate of maximum and minimum of heavy pollutions and elements in each of studied water resources determined. the results showed that the concentration mean of chrome, molybdenum, copper, zinc, cobalt, lead, cadmium and nickel is lower than national standards level, world health organization (WHO) and America’s standard (EPA), but in Motahari’s fountain, the rate of chrome heavy metal is higher than standard level (WHO). The mean of barium element concentration in resources was lower than national standard level and was higher than standard level (WHO) and (EPA) standard in all resources. The concentration of aluminum element is lower than national standard and is higher than standard level (EPA).
