Hydrogeomorphology
Aghil Madadi; sayyad Asghari Saraskanrood; Hossein Hajatpourghaleroodkhany
Abstract
the purpose of this research is to reveal the changes in land use and the destruction of forest cover and its effects on soil erosion in the watershed of Ghaleroodkhan Fuman.In the next step, using the object-oriented classification method and the nearest neighbor algorithm by eCognition software, land ...
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the purpose of this research is to reveal the changes in land use and the destruction of forest cover and its effects on soil erosion in the watershed of Ghaleroodkhan Fuman.In the next step, using the object-oriented classification method and the nearest neighbor algorithm by eCognition software, land use maps were extracted in 1992 and 2023. In the next step, by identifying the effective factors involved in the erosion of the area and preparing the information layers of each criterion in GIS, the valuation and standardization of the layers were done using the fuzzy membership function and the weighting of the criteria, using the CRITIC method. Eventually; The final analysis was done using the MARCOS method. The forest cover of the studied area has experienced a decrease of about 1700 hectares in its area. Also, residential use with an increase of 27.17 square kilometers has changed the most during the 30-year period of study. In addition, the most important land use change in the basin has been the destruction of forests and their conversion to agricultural lands, pastures and settlements, as well as the conversion of agricultural lands to residential areas, and this method of land use change and conversion, along with other natural factors in the area, Such as; The distribution of loose and erodible formations, high slope, adequate rainfall and the abundance of river have played the greatest role in increasing the soil erosion potential of the Ghaleroodkhan basin.
Said Jahanbakhsh ASL; Mohammad Hossein Aalinejad; Vahid Sohraabi
Abstract
1-IntroductionDetermining the temporal change of snowmelt or agriculture water equivalent of snow, predicting flood, and managing the reservoirs of a region is of utmost importance. Some major parts of the western sections of the country are located in the mountainous region and most of the precipitations ...
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1-IntroductionDetermining the temporal change of snowmelt or agriculture water equivalent of snow, predicting flood, and managing the reservoirs of a region is of utmost importance. Some major parts of the western sections of the country are located in the mountainous region and most of the precipitations of this region occur in the form of snow in winter. The runoff resulting from snowmelt has an important role in feeding the rivers of this region and it has a significant share in developing agriculture and the economy.Scientific studies have shown that climate change phenomena have significant effects on precipitations, evaporation, perspiration, runoff, and finally water supply. As the demand increases, climate changes, greatness, frequency, and the damage resulting from extreme weather events, as well as the costs of having access to water increase, as well. Therefore, evaluating the runoff resulting from snowmelt and the effect of climate change seems necessary for managing water resources.2-MethodologyGamasiab basin is located in the northeast part of the Karkheh basin originating from the springs in the vicinity of Nahavand. Its basin has an area almost equal to 11040 square kilometers that have been located in the east part having 47 degrees and 7 minutes to 49 degrees and 10 minutes geographical longitude and from the north part, it has 33 degrees and 48 minutes to 34 degrees and 54 minutes geographical latitude. This basin has an altitude between 1275 to 3680 meters.In this study, snow-related data required for simulation were derived from the daily images of the MODIS sensor. To this end, first, the snow-covered area of the Gamasiab basin was measured during the 2016-2017 water years using the process of satellite images obtained from the MODIS sensor in the google earth engine system. All geometric justifications and calibration processes of images were applied precisely in the mentioned system. In the next step, the output of the GCM model scenarios was utilized for calculating temperature and precipitation changes in future periods. These CMIP5 kind models were under the control of two RCP45 and RCP85 scenarios and were downscaled with LARS-WG statistical model.Moreover, to investigate the uncertainty of models and scenarios, the best models and scenarios were selected for producing temperature and precipitation data of future periods; accordingly, the outputs of the models for future periods (2021-2040) having the basis period of (1980-2010) were compared using statistical indexes of coefficient of determination (R2) and Root Mean Square Error (RMSE). The results were entered into the SRM model as the inputs. In addition, temperature and precipitation data of meteorological station of the studied region as well as the daily discharge of the river flow of hydrometric station of Chehr Bridge (as located in the output part of Gamasiab basin) were used during the statistical period of October 2016 to May 2018. 3-Results and Discussion Using Digital Elevation Model (DEM) of the region and the appendage of Hec-GeoHMS in GIS software, firstly, flow direction map, flow accumulation map, and stream maps were drawn and the output point (hydrometric station of Chehr Bridge) was introduced to the border program of the identified basin and the basin was classified based on the three elevation regions.Producing temperature and precipitation data of future periods requires a long-term statistical period; accordingly, the meteorological station of Kermanshahd was selected since it was in the vicinity of the studied region. To be confident in the ability of the model in producing data in future periods, the calculated data had to be compared with the observed model and data in the studied stations. The capabilities of the LARS-WG model in modeling the mentioned parameters of this station confirmed the observed data. Moreover, the ability of the model in modeling precipitation was very good and acceptable; however, the most modeling error was related to the precipitation in Mars.In the next phase and compared to the basic periods, the mean of changes in average precipitation and temperature was measured in the studied stations during January and Juan of 2015 to 2017(for which simulation had occurred); as an index of changing the climate, this was entered into the SRM model under climate change conditions. During the simulation period (January to Juan), it had been predicted that the precipitation parameter would decrease and the temperature parameter would increase.4-ConclusionThe results of this study indicated that using the MODIS sensor could provide an acceptable estimation of the snow cover level of the Gamasiab basin, which lacked snow gauge data. Moreover, the results of simulation with the SRM model showed that the model could simulate the snow runoff in the studied region. As the main purpose of the study, the effect of temperature and precipitation in future periods was well stated considering the uncertainty of CMP15 series models and scenarios. The results of temperature changes indicated an average increase of 1.8 C. the results of precipitation also indicated an average decrease of more than 5%. However, decreasing precipitation in the cold months of the years had been predicted severely so that the reduction of precipitation in February was of utmost importance for feeding the snow cover and rivers, which had been estimated to be 20%. This happened while increasing precipitation was mainly related to the hot months of the year whose amount was insignificant and didn`t have that much effect on the runoff. Accordingly, due to the increases in temperature and decreases in precipitation in cold seasons, the results of runoff simulation have indicated a 24% reduction for 2016-2017 and a 29% reduction for 2017-2018 water years.
Mohsen Armin; Hadis Valinejad; Vajihe Ghorbannia Kheybari
Abstract
1-Introduction On a national scale, soil erosion in Iran, has an important effect on agricultural production, sedimentation in dam reservoirs, soil degradation and so on. Severe soil erosions and the subsequent high deposition of sediments in dam reservoirs and reduced soil fertility are serious environmental ...
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1-Introduction On a national scale, soil erosion in Iran, has an important effect on agricultural production, sedimentation in dam reservoirs, soil degradation and so on. Severe soil erosions and the subsequent high deposition of sediments in dam reservoirs and reduced soil fertility are serious environmental problems with dangerous economic consequences for the country. The situation of sedimentation in Iranian dams indicate that their design have often focused on civil engineering and structural aspects and no attention has been paid to the issue of erosion and sediment yield in the basin of dams, which makes a large amount of sediment be deposited in many of these dams for many years; this causes a lot of sediment after many years to be deposited in many of these dams` reservoir, as a result of which, the useful life of the dam is greatly reduced. The present study aimed at estimating soil erosion in the Tang-e-Sorkh dam watershed with a total area of 39,000 hectares in the east and south-east of Boyer Ahmad County in Kohgiluyeh and Boyerahmad province using the RUSLE model and the remote sensing (RS) and Geographic Information System (GIS) capabilities in order to plan protective measures in the dam watershed. 2-Methodology Digital altitude, precipitation, physico-chemical properties of soil and satellite imagery data were used to estimate soil losses using RUSLE model in the Tang-e-Sorkh basin. First, the boundary of Tang-e-Sorkh watershed was drawn on a topographic map with a scale of 1: 50,000 in the geographic information system environment. The meteorological stations in and around the watershed were then identified and marked on the map. RUSLE has calculated the average annual soil erosion expected on a sloping land using Equation (1). A=R.K.L.S.C.P (1) Where A is calculated as the average spatial loss of soil and the average time of soil loss per unit area is expressed in terms of units selected for K and the time period selected for R. In practice, these units are usually selected so that A is expressed in tons, per hectare, per year (t ha-1 year-1). R Runoff-rain erosivity factor is expressed in MJ mm ha-1 h-1 year-1, K Soil erodibility factor which is the amount of soil loss per unit area of erosion index for a given soil- is obtained by measuring in a standard plot with a length of 22.1 meters, a slope of 9% and a permanent fallow and is expressed in t ha h ha−1 MJ−1 mm−1. L is the slope length, S is the slope, C is the plant cover management factor and P is the protective measures factor. The parameters L, S, C and P are without units. The layer of parameters of the RUSLE model includes rainfall erosivity (R), soil erodibility (K), slope and length of the hill (LS), vegetation management (C), and soil conservation operations (P) have been prepared in geographic information system environment and after overlayering, the amount of erosion was estimated locally. 3-Results and Discussion The amount of rainfall erosivity was from 179.62 to 327.77 MJ mm ha-1 h-1 year-1. Erodibility factor was from 0.08 to 46.0 t ha h ha−1 MJ−1 mm−1. The minimum and maximum values of slope and hill length were 0.08 and 12.42, respectively. The minimum and maximum values of vegetation management were 0.33 and 0.54, respectively. The minimum and maximum values of soil conservation operations were 0.5 and 1, respectively. The amount of soil erosion in the studied area varied between 0.0033 and more than 100 tons per hectare per year at the pixel level. About 80% of the studied area had an erosion rate of 35 tons per hectare per year, with the highest amount in the western and northeastern parts of the country, which was due to high rainfall erosivity and soil erodibility in the area. 4- Conclusions It can be said that in the current situation of Tang-e-Sorkh watershed, due to the lack of real sediment statistics, the best model for estimating erosion and sediment yield with the aim of introducing soil protection measures at the basin level was RUSLE model. The proposed method and the results of this research can be used as a dam maintenance planning system. The RUSLE model could predict the potential of soil erosion as a cell-by-cell, which was very useful when trying to identify the spatial pattern of current soil losses within a large area. Spatial information systems can be used to separate and inquiry these locations to assess the role of effective variables in the amount of soil erosion potential observed. Regarding the results, decision makers need to manage the risk of soil erosion in the most effective way; and management scenarios can adopt the best ways to improve and rehabilitate the basin based on the priority of different areas of the basin.
Hafez Mirzapour; Ali Haghizadeh; Naser Tahmasebipour; Hossein Zeinivand
Volume 6, Issue 20 , December 2019, , Pages 79-99
Abstract
1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in ...
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1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in the form of a map. Regarding the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, Investigating how landslide changes during time periods are essential for satellite imagery. Since conservation of natural resources requires monitoring and continuous monitoring of an area, Land-use change models are now used to identify and predict land-change trends and land degradation one of the most widely used models in predicting land use change is the Auto-Markov cell model. the aim of present study is to monitor land use changes in the past years and predict changes in the coming years in Badavar-Nurabad watershed in the Lorestan province with an area of 71600 hectares. 2- MethodologyThe Markov chain method analyzes a pair of land cover images and outputs a transition probability matrix, a transition area matrix, and a set of conditional probability images. The transition probability matrix shows the probability that one land-use class will change to the others . The transition area matrix tells the number of pixels that are expected to change from one class to the others over the specified period (Ahadnejad 2010). Automatic cells are models in which adjacent and continuous cells, such as cells that may include a quadrilateral network, change their state or attributes through simple application of simple rules. CA models can be based on cells that are defined in several dimensions. The rules for changing the state of a cell from one mode to another can be either a combination of growth or decrease, such as a change to a developed cell or without development. This change is the source of the change that occurs in the adjacent cell. Neighborhood usually occurs in adjacent cells or in cells that are close together(Ghorbani et al, 2013). In order to detect land use changes in the studied area, TM , ETM+ and OLI satellite images of Landsat were used during three time periods of 1991, 2004 and 2016. After applying geometric and atmospheric corrections to images, the land use map for each year was prepared using the maximum probability method. The Kappa coefficient for the classified images of 1991, 2004 and 2016was 0.81, 0.85 And 0.90 obtained. Then, to model land use changes using the Auto-Markov cell model for 2028 horizons, First, in the Idrisi Selva software using Markov chain, the map was selected as input from the years 1991 and 2004, the 12-year prediction of the changes was considered by 2016 to determine the likelihood of a change in application. Then, using the CA-Markov method, the data from the Markov chain and the map of 2016 were used as input data for the automated-Markov cell method. 3- ResultsAssessment of the match between simulated and actual map of 2016 with 0.97 kappa index showed that this model is an appropriate model for simulating of land use change. The results from monitoring satellite imagery that in 1991 to 2016, the extent of residential areas, land is Dry farming, garden and irrigated farming land added in front of vast pastures, shrubbery and other is reduced. After verifying the model's accuracy, a 2028 map was prepared to predict the changes over the coming years. Well as the results show that the vast pastures of the forecast is reduced in the amount of 659.89hectares and 395.47 hectares will be added to the extent of irrigated farming. 4- Discussion and conclusionThe results of the Auto-Markov cell model showed that if the current trend continues, the size of the ranges will decrease sharply. Comparison of simulated map of 2016 by model and actual map with Kappa index showed that Auto-Markov cell model is a suitable model for predicting land use change and can accurately assess the future status of land use and vegetation to predict. Therefore, it is suggested protective measures and make appropriate management decisions to control non-normative changes continue to apply more than ever.
Mahdi Hasanlou; Meysam Jamshidi; Mohammad Taghi Sattari
Volume 5, Issue 14 , June 2018, , Pages 43-65
Abstract
Introduction
Urmia Lake is located in the North West of Iran and its area between 4750 to 6100 square kilometers at an altitude of 1250 meters above sea level. This lake is a permanent lake in Iran. In fact, Urmia Lake is one of the lowest parts of the catchment area North West of Iran. The total surface ...
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Introduction
Urmia Lake is located in the North West of Iran and its area between 4750 to 6100 square kilometers at an altitude of 1250 meters above sea level. This lake is a permanent lake in Iran. In fact, Urmia Lake is one of the lowest parts of the catchment area North West of Iran. The total surface area of Urmia Lake is 51,876 km square, which is 3.15% of the total area of Iran and 7% of all the water’s surface in the country. The depth varies between 6 and 16 meters, the length of the lake is 50 km and its width varies between 128 km to 140 km. In the catchment area of the lake, there is the main river with annual input about 2 billion cubic meters. Annual rainfall in the catchment area is variable between 200 and 300 mm. Air temperature the area around the lake in winter to 20°C and 40°C in summer increases. Urmia Lake is important in terms of economically, transport, exploitation of the mineral wealth of biodiversity, mitigating climate, and tourism. This unique Lake addition to the previous is habitat for kind of native artemia its name is urumiana artemia that this artemia is unique to this lake. Also, Urmia Lake is the world's second largest habitat for Artemia. According to the research, the main elements in the Urmia Lake include Cl-, Na +, Ca2+, Mg2+, HCo3-, K+, Li, So42- and F.
Methodology
In this study, newly launched Landsat series (Landsat-8) was used for monitoring Urmia Lake salinity and retrieving the salinity map. By incorporating the Landsat-8 datasets, this study determined the salinity changes and created a model to estimate the salinity in Urmia Lake with processing Landsat-8 satellite images as a result; we can obtain salinity map regularly without ground operations. We can also monitor the health of the habitat in terms of salinity and examine the impact of increasing salinity on the plants, animals, and ecosystems of the region. This study applied remote sensing techniques to develop a salinity prediction model for Urmia Lake. In this study, we use Landsat-8 satellite images radiances of Urmia Lake and some salinity indices and in-situ data so we have 17 features to make water surface salinity model with support vector regression (SVR) with all features. After that, we use two algorithms; GA and SFS for selecting suitable features and make models with those features.
Result
Results with all features model show RMSE=24.55 and R2=41% and result with GA feature selection model shows RMSE=21.97 and R2=54% and results with SFS feature selection model shows RMSE=21.93 and R2=53%.
Discussion and Conclusion
Satellite images show that from 1995 to 2003, the lake water surface dropped and proportionate to the dropping water salinity increased to 220 to 300 grams per liter. Also although Artemia is resistant to salt, appropriate salinity is below 100 grams per liter. When water salt is more than 100 grams per liter contents of his tiny body lost and die. Now because of reduction in salinity, the lake has arrived at about 300 grams per liter. Dissolved salt in water has a direct effect on the electrical conductivity of water. In this regard, incorporating high spatial resolution satellite like Landsat-8 images is inevitable. Also, the proposed modeling methods show these changes in multi-data and in widespread Urmia Lake very well.
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.
Bakhtiar Feizizadeh
Volume 4, Issue 11 , September 2017, , Pages 21-38
Abstract
Introduction
The modification of the Earth’s terrestrial surface by human activities is commonly known as the land use/land cover change (LULCC) around the globe. Although the modification of the land by humans to obtain livelihoods and other essentials has been a common practice for thousands ...
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Introduction
The modification of the Earth’s terrestrial surface by human activities is commonly known as the land use/land cover change (LULCC) around the globe. Although the modification of the land by humans to obtain livelihoods and other essentials has been a common practice for thousands of years, the extent, intensity, and rate of LULCC are far greater now than they were in the past. These changes are driving forces for local, regional, and global level unprecedented changes in the ecosystems and environmental processes. The empirical studies conducted by researchers from diverse disciplines have found that changes in the land use/land cover is a key to many diverse applications such as agriculture, environment, ecology, forestry, geology, and hydrology.
Satellite Remote Sensing and GIS are the most common methods for the quantification, mapping, and detection of the patterns of the LULCC, because of their accurate geo-referencing procedures, digital formats suitable for computer processing, and repetitive data acquisition. Technically speaking, the remote sensing based digital satellite images have a high capability for natural resources' management operations. Land use/land cover change detection is considered as one of the most important applications in the domain of the remote sensing satellite images. Related to this applicability, it will be possible to apply multi-temporal satellite images for the detection of the land use change. Based on the results obtained from the change detection operation and modeling of the further land use changes, one will be capable to makes better decision for natural resources' management. Based on this statement, the main objective of this research is to represent the applicability of the satellite images for the detection of the land use changes, particularly on the upper areas of the Allavian dam of the Sofi-chai basin.
Dataset and methods
The study area was the upper area of the Allavian Dam in Maragheh. The research was carried out based on the digital interpretations of the Landsat images (ETM+ and TM) of the years 1989, 2000, 2002, and 2015. Based on these images, the land use changes of this region were separately detected for 3 periods. It should be noted that the widely practiced operations such as image preprocessing, classification, and post processing with those related techniques were considered in this study. Indeed, it is widely known that preprocessing before the the change detection phenomenon is very important in order to establish a more direct relationship between the acquired data and the biophysical phenomena. Accordingly, atmospheric and geometric correction were applied as the first step on satellite images. In doing so, the LSLC classes were determined based on the spatial resolution of the satellite images. Then, image enhancement methods were applied to detect each LULC class on the satellite image. Next, GPS based training data was collected in the field operation and integrated with the satellite images. In addition, the supervised maximum likelihood was applied to derive LULC map for each year. The validation step was also part of this section for the accuracy assessment based on kappa coefficient and error matrix.
Results and Conclusion
After developing LULC maps, the results were transformed into GIS environment for the following steps and GIS analysis. The results indicated a significant changes in LULC of the study area. They also indicated that orchards cover had increased throughout the study periods but rich range lands widely converted into poor range lands because of losing the significant canopy of the native plants. Increasing the trend of the orchards area may be in relation with the population growth and this factor can be affected by ( have an effect on) range land degrading. The water supply out of Allavian dam might be another reason for increasing the orchard’s area. The results also acknowledged the capability of the remote sensing for the LULC and change detection analysis. The results of this research are of great importance for decision making authorities in governmental departments such as the ministry of agriculture and natural resources for the purposes of planning and decision making.
Abbass Malian; Ali Mohammadi; Abbass Alimohammadi; Jalal Valiallahi
Volume 3, Issue 9 , March 2017, , Pages 43-62
Abstract
Gradual drying of Urmia Lake has become a national and international challenge. In recent decades, unsustainable agricultural and industrial development together with uncontrolled exploitation of aquifers are major causes of Urmia Lake drying. In this study, the change detection and monitoring of Urmia ...
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Gradual drying of Urmia Lake has become a national and international challenge. In recent decades, unsustainable agricultural and industrial development together with uncontrolled exploitation of aquifers are major causes of Urmia Lake drying. In this study, the change detection and monitoring of Urmia Lake and its environment during a period of 60 years has been conducted by integrating geospatial information system, remote sensing and photogrammetry. To achieve this objective, aerial photogrammetric data of the region captured in 1955 and the oldest topographic map of Urmia Lake area, digital elevation model data (DEM) of the study area, collected information about water wells around the west part of the Lake, water quality data and multi temporal satellite imageries of Landsat 5 TM, Landsat 7 ETM + and Landsat 8 OLI were used. Study is performed within a period from 1955 to 2014. Twelve different images at different epochs were processed. The results show that the area of the environment surrounding Urmia Lake has been extensively changed in recent years. In other words, the Lake area of about 451,800 hectares in 1955 has been affected by various factors and decreased to 89,730 hectare in 2014. The research results also indicated that the largest change in Urmia Lake environment has occurred in its southern part. Moreover, regression of the extracted information applied to the coastal zone of the Lake showed that the lowering rate of the lake water level is directly related to the expansion of agricultural lands around the Lake and inversely dependent to the electrical conductivity (EC) of Lake water. These fluctuations can be important implications for environmental, economical and social problems. If the current trend of Urmia Lake and its environmental changes remains as it currently is, it can be predicted that Urmia Lake will be completely dried and its surrounding area will wholly convert to salty lands by 2033.
Saeed Jahanbakhsh Asl; Yaghoub Dinpajouh; Mohammad Hossein Alinejad
Volume 2, Issue 5 , January 2017, , Pages 101-117
Abstract
Saeed Jahanbakhsh Asl[1] Yaghoub Dinpajouh[2] Mohammad Hossein Aalinejad[3]* Abstract In this study for the purpose of simulation of runoff originated from snowmelt in Shahrchay River basin two models namely SRM and HEC-HMS were used. For this purpose, entering the snow cover data, meteorological ...
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Saeed Jahanbakhsh Asl[1] Yaghoub Dinpajouh[2] Mohammad Hossein Aalinejad[3]* Abstract In this study for the purpose of simulation of runoff originated from snowmelt in Shahrchay River basin two models namely SRM and HEC-HMS were used. For this purpose, entering the snow cover data, meteorological variables and other needed parameters an input to SRM model the runoff from snowmelt was simulate. In HEC-HMS model, after derivation of watershed model using the HEC-GEOHMS software in GIS environment and derivation of meteorology model and entering the required parameters such as water losses, flow routing and snow melt was simulated. The coefficient of determination (R2) of SRM model was 0.9 and the percentage of volumetric error (DV) was equal to 1.96. On the other hand, the HEC-HMS model simulated snowmelt runoff was satisfactory (but less than SRM), so that the R2 and DV obtained for this model were as equal to 0.85 and 2.1%, respectively. Utilization of air temperature and precipitation data (neglecting the high accuracy of SRM) can be considered as advantages of HEC-HMS. In contrast, in SRM model in the snow covered area of region apart from the mentioned parameters satellite images of the region is also required. Results indicated that the total volume of runoff in the study area was 129.9×106 cubic meters for SRM, whereas it was 129.6×106 cubic meters for HEC-HMS. Comparing these values with that of the observed (i.e.134.4×106 m3) it can be concluded that SRM model performance is relatively better than the HEC-HMS. [1]- Professor, Department of Meteorological, the University of Tabriz. [2]- Associate Professor of Water Engineering, the University of Tabriz. [3]- M.A. student in Meteorological, the University of Tabriz (Corresponding author), Email:aalineghad63@yahoo.com.
Ahmad Nohegar; Majid Khazaei; Rasoul Mahdavi Najafabadi; Abdolrasoul Telvari
Volume 3, Issue 8 , December 2016, , Pages 161-181
Abstract
Received: 2015.03.15 Accepted: 2016.10.15 Ahmad Nohegar[1]* Majid Khazaei[2] Rasoul Mahdavi Najafabadi[3] Abdolrasoul Telvari[4] Abstract River bank erosion is one of the major sources of sediment for many rivers around the world. The aim of this study was to identify erodible riches in Bashar river ...
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Received: 2015.03.15 Accepted: 2016.10.15 Ahmad Nohegar[1]* Majid Khazaei[2] Rasoul Mahdavi Najafabadi[3] Abdolrasoul Telvari[4] Abstract River bank erosion is one of the major sources of sediment for many rivers around the world. The aim of this study was to identify erodible riches in Bashar river because the role of this river's in development of Kohgiluyeh and Boyerahmad province. In this study, using Satellite imagery, aerial photos and field survey from 1975 to 2015, the riverbank migration patterns for period of 40 years were investigated. After creating database of recorded data Bashar river, bank lines in different years digitized. Then, by comparing successive changes in bank line position, patterns of erosion and accretion was determined. Due to dischargr and Geological factores effectes on river pattern, these factors were evaluated. The Result of for identify erodible riches in Bashar river using GPS and field surveys, verified. The area of bank erosion and accretion by comparing sequential changes in banklines position determined. For short-term analysis, the migration rate from one any image to the next image is estimated. For long-term analysis, the migration rates are based on the difference between the 1975 image as the reference, and subsequent images. The results indicate that the erosion– accretion patterns in the sub aerial and subaqueous areas of the beshar river have changed significantly since 1994. For the short-term analysis, the highest erosion and accretion amounts are 4.7 and 7 ha from 1975-1984 and 1984-1990, respectively. the lowest erosion and accretion amounts are 2.6 and 2.3 ha from 2008-2011, respectively. The highest rate of bank erosion in different reaches equal to 9.6 and 4.6 ha/y in reachs of fifth and second bserverd. Also rate of bank accretion in fifth and second reachs equal to 9.6 and 4.6 ha/y observerd. the lowest erosion and accretion rate in reach of seven equaled 11.7 and 6.4 ha/y accounted, respectively. The correlation coefficient the between erosion and discharge and annual discharge averagelly for the short-term analysis observed 0.54 and 0.44, respectively. [1]- Professor, Faculty of Environment, University of Tehran (Corresponding Autor), Emial:nohegar@ut.ac.ir. [2]- Ph.D Student Watershed Management Engineering, Hormozgan University. [3]- Assistant Professor, Department of Rangeland & Watershed Management, Hormozgan University. [4]- Science and Research Branch Islamic Azad University (Ahwaz Branch).
