Fariba Esmaeili; Mehdi Vatakhah; Vahid Moosavi
Abstract
The current study evaluates the effectiveness of Clark's Instantaneous Unit Hydrograph (IUH) model from the accuracy of different Digital Elevation Models (DEMs) including TOPO, ALOS PALSAR, ASTER, SRTM and GTOPO in the Amameh watershed. For this purpose, at first, 34 rainfall-runoff events were selected. ...
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The current study evaluates the effectiveness of Clark's Instantaneous Unit Hydrograph (IUH) model from the accuracy of different Digital Elevation Models (DEMs) including TOPO, ALOS PALSAR, ASTER, SRTM and GTOPO in the Amameh watershed. For this purpose, at first, 34 rainfall-runoff events were selected. Also, the drainage network, the length and slope of the main river in each of the five DEMs were calculated using Arc GIS software. Then, the 30-minute isochrone map of the watershed was extracted using the spatial distribution of travel time method. Finally, the dimensions of Clark's IUH were estimated for each rainfall-runoff event and DEM. The results showed that with the decrease in the length of the main river following the decrease in DEM spatial resolution, the number of isochrone has been decreased, so that TOPO DEM has estimated the largest number of isochrone with the largest estimate of the length of the main river. The average percentage of the Relative Error (RE) of the runoff volume was estimated as 22.92, 26.68, 27.7, 32.15 and 35.66% respectively for the aforementioned DEMs. Regarding peak flow estimation, there is a significant difference between the average RE values in different DEMs. So that the lowest average value of the RE is related to TOPO DEM with a value of 31.7%. On the other hand, the Root Mean Square Error (RMSE) values also show that TOPO DEM has the lowest RMSE value (3.39 m3) compared to other DEMs. In general, it can be said that the use of TOPO DEM in Clark's IUH model will provide acceptable results.
Mehdi Vatakhah; Mohammad Tavosi
Abstract
Today, the use of surface water to meet various human requirements such as drinking, agriculture and industry has endangered the health of this river ecosystem and its role in the natural system. The minimum environmental flow in the river provides a safe level of protection for the water-dependent environment. ...
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Today, the use of surface water to meet various human requirements such as drinking, agriculture and industry has endangered the health of this river ecosystem and its role in the natural system. The minimum environmental flow in the river provides a safe level of protection for the water-dependent environment. In the present study, the environmental flow requirement of three stations of Cheshmeh Langan River located in Isfahan province were calculated by two hydraulic methods: slope and maximum curvature of wetted perimeter. The results showed that both slope and curvature methods estimate the same minimum environmental flow requirement. So that the difference between the two methods in S1 station was about 0.07m3/s. According to the discharge measured at station S1, minimum environmental flow requirement is provided in all months except December. While the measured discharge at S2 and S4 stations cannot provide the minimum environmental flow requirement due to the impact of the dam and water transfer in any of the months. The flow of 0.39 m3/s for the first station (S1) and 1.44 an 1.68 m3/s for the second and third stations, respectively has been proposed as the minimum environmental flow requirement of Cheshmeh Langan River. The results of this study showed that the environmental flow requirement can be estimated using hydraulic method in data scarce.
Eisa Gholami; Mehdi Vatakhah; Seyed Jalil Alavi
Abstract
Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine ...
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Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine (SVM), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) for preparing the flood susceptibility mapping in Guilan province, Iran. For this purpose, slope, aspect, plan curvature, elevation, distance from the river, drainage density, geology, land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) layers were derived in GIS (ArcGIS and SAGA-GIS). Using 220 flood locations, 70% and 30% out of total flood locations were then used to calibrate and to validate the performance of the models, respectively. The evaluation results of the models accuracy using the area under the curve (AUC) and Kappa indices showed that in terms of AUC, the SVM with 0.835 and the GAM with 0.827, and the GLM with of 0.79 performed very good and good classes, respectively. In terms of Kappa index, the SVM with 0.58, GAM with 0.53 and GLM with 0.48 are performed good and acceptable classes, respectively. Therefore, based on the mentioned indices, the SVM superior to other two models for identifying the flood susceptibility areas.
Alireza Daneshi; mehdi vafakhah; Mostafa Panahi
Abstract
Due to problems of Urmia Lake, several strategies have been proposed by professionals to restore it. But it should be noted that the implementation of each plan and project within the watershed requires the participation ofstakeholders and farmers within that watershed. Due to the lack of attention to ...
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Due to problems of Urmia Lake, several strategies have been proposed by professionals to restore it. But it should be noted that the implementation of each plan and project within the watershed requires the participation ofstakeholders and farmers within that watershed. Due to the lack of attention to stakeholders, management of many natural resources and development projects has failed. Therefore, public acceptance and participation of stakeholders including those users who are engaged with farming and gardening was explored in some proposed approaches for restoring Urmia Lake. The study area was Siminehroud watershed as one of the largest Urmia Lake sub watersheds. Research instrument was a questionnaire which was filled by data obtained from interview with stakeholders and achieved results were analyzed using SPSS 17 software. The results showed that shift in irrigation system from traditional type to pressure irrigation system with 91.45% approval rating can be considered as the most successful strategy among poposed strategies. Compensation payment initiative specific to farmers for non-utilization of owned farming lands had the appropriate acceptance with 53.55%. Also 50.25% of stakeholders showed their inclination to the use of species of low water demands instead of those with high water demand providing that government support such scheme. Generally, it was revealed that increasing water charge anticipating subsequent reduction of water consumption will not be effective. Consequently, it can be stated firmly that irrigation system change must be put in executive priority and next priorities will be compensation payment to farmers and change in farming pattern.
Seyed Hamid Reza Sadeghi; Mohsen Zabihi; Mehdi Vafakh
Abstract
Regarding the undeniable role of rainfall erositivity factor in initiating water erosion, studying its different aspects is important in optimal soil and water resources management. It is taken in to account in many soil erosion estimation models which are used for soil and water conservation. However, ...
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Regarding the undeniable role of rainfall erositivity factor in initiating water erosion, studying its different aspects is important in optimal soil and water resources management. It is taken in to account in many soil erosion estimation models which are used for soil and water conservation. However, the impact of data length, study span, and the number of stations on variability of rainfall erosivity factor has been understudied. The present study therefore is an attempt to investigate the temporal variation of Wischmeier and Smith’s rainfall erosivity factor at different time scales and also the effect of data length, study span, and number of stations upon rainfall erosivity factor is scrutinized. Accordingly, the results of the present study with 70 stations, data span of 20 years and different study periods were compared with those obtained for another study with 18 stations and 23 years of data span. Rainfall erosivity factor of over 12,000 storm events was calculated in present study and mean values for different time scales were compared using t-Test. Results showed that the maximum and the minimum values of monthly rainfall erosivity factor in the country were different from each other. Besides, the results of t-Test showed significant difference between the calculated values of rainfall erosivity factor in some months (p<0.05) and seasons (p<0.05). Nonetheless, the difference between annual rainfall erosivity factor was not significant (p<0.05).
Abbas Gholami; Kaka Shahedi; Mahmud Habibneghad; Mahdi Vafakhah; Karim Solymani
Abstract
Present study is aimed at forecasting and comparison of future climate change by using GCM model (General circulation model) under different climate scenarios in Talar watershed of Mazandaran province. Regarding the data of existing stations, to study the climate change phenomenon in Talar watershed, ...
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Present study is aimed at forecasting and comparison of future climate change by using GCM model (General circulation model) under different climate scenarios in Talar watershed of Mazandaran province. Regarding the data of existing stations, to study the climate change phenomenon in Talar watershed, the LARS-WG5 model and 3 climate scenarios i.e. A1B, A2, B1, each in three emission series i.e. 2011-2026, 2046-2061, and 2080-2095, that were extracted from Gharakhayl regional synoptic stations in Quaemshahr, were used and the base year was considered 1992-2007( for a 15- year duration). Since this model is one of the most authentic statistical downscaling methods and its data is produced in three phases of calibration, evaluation and development of meteorological data, it was applied for research in present study. According to the findings, the most precipitaion changes occurred in May and October and the most severe reductive changes occurred in 2080-2095, the result of which warns about seasonal floods in rainy months and drought or water shortage in dry months in the relevant watershed study area. Besides, in future in June, July, August, and September temperature increase will be experienced but in January and February, the minimum simulated tempreture mean will be observed.
Yosef Nabipour; Mahadi Vatakhah
Abstract
Rainfall spatial analysis methods are very helpful since there are not enough rainfall gauge stations and watersheds are scattered in large extent. There are many different methods for estimating average precipitation such as; arithmetic method and Thiessen polygon. However, the arrangement and location ...
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Rainfall spatial analysis methods are very helpful since there are not enough rainfall gauge stations and watersheds are scattered in large extent. There are many different methods for estimating average precipitation such as; arithmetic method and Thiessen polygon. However, the arrangement and location of data and their correlations are not considered by classic methods. Thus, geostatistical techniques are applied instead. In the present article, 22 meteorological stations from within and around the basin with data collection period of 30 years were selected for the analysis. The geostatistic analysis methods including ordinary kriging, simple cokriging, ordinary cokriging, standardized ordinary kriging, moving average using inverse distance with powers of 1 to 5 were applied for spatial analysis of annual, monthly and 24 hourly maximum rainfall data in Hajighoshan watershed located in northeast of Iran. For this reason, rainfall data were fitted to different methods and compared using cross validation by removes rainfall values of each station, one at a time, and predicts the associated data value. The results of geostatistic analysis showed that ordinary kriging is the best method with MBE=34.26 for annual rainfall while moving average using inverse distance with power of 5 is the best method for monthly and 24 hourly maximum rainfall. According to the results obtained through analysis of variogram model, gaussian model are supposed as the best models for annual, monthly and 24 hourly maximum rainfall data.
Sadegh Tali-Khoshk; Mohsen Mohseni Saravi; Mahadi Vatakhah; Shahram Khalighi-Sigarodi
Abstract
Because of insufficient factors including facilities, budget, human resources as well as time watershed operation is not applicable throughout the basin. As a result, watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as ...
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Because of insufficient factors including facilities, budget, human resources as well as time watershed operation is not applicable throughout the basin. As a result, watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as destruction, degradation; physical and financial damage and also flooding are considerably high. In addition, due to hydrometric stations, defects or the lack of stations in some areas, some efforts have been made experts recently to assess and consequently introduce some novel and reliable methods for prioritizing on the basis of current data obtained from sub-basins features of different geographical regions. In current study, the utilization possibilities of neuro-fuzzy technique and SCS in HEC-HMS model that have different potential to examine a wide range of advantageous and disadvantageous in making various decisions were studied. To determine the prediction accuracy of these methods, the rate of flow and sediment output of Taleghan sub-basins were taken over one year. The results of these methods were then compared with the maximum two-year return period flow observations. Our results revealed that in making prioritization, neuro-fuzzy as compared with the SCS method can produce the best prediction as long as the coefficients of errors, efficiency compared to the observational data and predictions are taken into account.
Mehdi Vatakhah; Mohsen Mohsenisaravi; Hasan Ahmadi
Abstract
Land use optimization is one of the suitable methods for soil conservation. The present research with objective of land use optimization for soil erosion minimization and pure income maximization was conducted in the part of Taleghan Watershed which comprises 80427.23 ha in area. To achieve this propose, ...
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Land use optimization is one of the suitable methods for soil conservation. The present research with objective of land use optimization for soil erosion minimization and pure income maximization was conducted in the part of Taleghan Watershed which comprises 80427.23 ha in area. To achieve this propose, area, erosion rate and net income value of each land uses according to requiring standards of each land uses area was extracted. Then, limitations and two objective functions were determined and optimization problem was solved by using ADBASE software. The results revealed that the optimization decrease 6.99 percent of erosion rate(6.28 ton/ha/year to 5.84 ton/ha/year) and 4.65 percent of pure income(118174.38 to 112681.02 million Rials). Also, the results proposed that drylands farming are changed to garden and orchard and rangelands.
Mahadi Vatakhah; Hamzeh Saidian
Abstract
Erosion and sediment movement phenomena are one of the most complex issues in management of rivers drainage areas that in water projects are very important. That its measurement wants high time and cost. Issue of surface runoff in river basin is a complex issue that human knowledge and understanding ...
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Erosion and sediment movement phenomena are one of the most complex issues in management of rivers drainage areas that in water projects are very important. That its measurement wants high time and cost. Issue of surface runoff in river basin is a complex issue that human knowledge and understanding about its physical laws a viewpoint of some mathematical formulas is limited. In this study to investigate modeling runoff and sediment production in different land uses of Aaghajari formation deposits, part of Margha watershed in Izeh city with area 1609 hectares was selected. In this study, some soil physical and chemical characteristics such as percentage of sand very fine, sand, clay, silt, pH, electrical conductivity, moisture, calcium carbonate and soil salinity in different land uses of Aghajari formation were used. Then the rain simulator in 7 point and with three replicated in different intensities 0.75, 1 and 1.25 mm in minute in three land use range, residential areas and agricultural lands, were used the amount of runoff and sediment. And the same of number were sampled in 0-20 cm in soil layer. In totally, 126 times sampling runoff and sediment were done. And 189 soil experiments were done. In order to perform all statistical analysis were used 11.5 SPSS and EXCEL and MATLAB 2008 software. The results showed that multi regression analysis in conditions with high input and little output data shows more favorable results than neural network. And in high intensities owing to data homogeny, neural network operation than to low precipitation intensities is better. But in multi regression in high and low precipitation intensities showed acceptable operation. The average of relative error in three land uses in sediment production in precipitation intensity 0.75 mm in minute were in multi regression 7.2 percent and root mean square error 0.06. And in neural network in same precipitation intensity the average of relative error 146/9 percent and root mean square error 0.41 were. The average of relative error in three land uses in sediment production in precipitation intensity 1 mm in minute were in multi regression 8.5 percent and root mean square error 0.19. And in neural network in same precipitation intensity the average of relative error 96.36 percent and root mean square error 0.85 were. The average of relative error in three land uses in sediment production in precipitation intensity 1.25 mm in minute were in multi regression 1.8 percent and root mean square error 0.38. And in neural network in same precipitation intensity were the average of relative error 37/6 percent and root mean square error 0.73.
Zakariya Asadolahi; Mahdi Vafakhah; Seyed Hamidreza Sadeghi
Abstract
Todays, dynamic models are supposed as the most important tools in erosion and sediment phenomenadue to their complexities and existence of many affecting factors. Towards, the present study wasconducted in the Kojour watershed for daily sediment modeling using daily rainfall, discharge andsediment during ...
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Todays, dynamic models are supposed as the most important tools in erosion and sediment phenomenadue to their complexities and existence of many affecting factors. Towards, the present study wasconducted in the Kojour watershed for daily sediment modeling using daily rainfall, discharge andsediment during 2007 to 2010. The modeling process was carried out all data and the monthly andseasonally classification data in linear and nonlinear models. The results indicated that daily linear andnon-linear models did not indicate a suitable model. The monthly and seasonally classification of thedata led to achievement of better models with determination coefficient significant at 5 percent leveland relative error less than 40 percent as compared with those obtained from no classification. It wasalso found out that daily sediment of Kojour watershed was affected by discharge occurred event dayand before four days. The discharge occurred event day is the most effective factor in 80% selectedmodels in the study watershed. The nonlinear models were better estimation than linear models inJuly, September, December and March and autumn but linear models were better than nonlinearmodels in other months and seasons.
