zohreh khorsandi; Mohammad Mahdavi; Ali Salajeghe; Saeid Eslamian
Abstract
Quantification of urban hydrologic response of catchments to rain fall is one of the most importantissues in urban hydrology. Despite its importance, there is scant information by means of whichrequired data can be obtained for quantifying hydrologic response. In this study, urban database wasdeveloped ...
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Quantification of urban hydrologic response of catchments to rain fall is one of the most importantissues in urban hydrology. Despite its importance, there is scant information by means of whichrequired data can be obtained for quantifying hydrologic response. In this study, urban database wasdeveloped for a part of Baharestan City in Isfahan Province and using the information, the urbanunit hydrograph was determined through URBS-UH model for two catchments of Baharestan. Peakof hydrograph of the first and second catchment was estimated 0.0727m3/s and 0.096, respectively.Flood hydrograph of some rain occurred previously in Baharestan was determined through the unithydrograph. Peak discharge of flood was also measured and the efficiency of the developed modelwas examined based on the peak information. Nash–Sutcliffe coefficient of the first and the secondcatchment were estimated 0.89 and 0.79, respectively. The developed model showed good to verygood performance in the pilot area.
Ommolbanin Bazrafshan; Ali Salajegheh; Ahmad Fatehi; Abolghasem Mahdavi; Javad Bazrafshan; Somayeh Hejabi
Abstract
Drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. This paper presents the performances of autoregressive integrated moving average (ARIMA), Direct multi-step neural network (DMSNN), ...
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Drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. This paper presents the performances of autoregressive integrated moving average (ARIMA), Direct multi-step neural network (DMSNN), Recursive multi-step neural network (RMSNN), Hybrid stochastic neural network of directive approach (HSNNDM) and Hybrid stochastic neural network of recursive approach(HSNNRM) with time scale monthly and seasonally for hydrology drought forecasting and SDI selected as predictor in the Karkheh river basin. The results shown performances of HNNDA was found to forecast hydrological drought with greater accuracy for SDI forecasting, so performances model in monthly scale was greater accuracy to seasonality scale.
Asghar Zare Chahouki; Ali Salajegheh; Mohammad Mahdavi; Sharam Khalighi; Said Asadi
Abstract
A flow-duration curve (FDC) illustrates the relationship between the frequency and magnitude of streamflow. Applications of FDC are of interest for many hydrological problems related to hydropower generation, river and reservoir sedimentation, water quality assessment, water-use assessment, water allocation ...
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A flow-duration curve (FDC) illustrates the relationship between the frequency and magnitude of streamflow. Applications of FDC are of interest for many hydrological problems related to hydropower generation, river and reservoir sedimentation, water quality assessment, water-use assessment, water allocation and habitat suitability. This study was carried out in 11 selected watersheds with common characteristics such as the 20 years period, the minimum land use change and similar annual water volume through all watersheds in 3 province of: Yazd, Semnan and Markazi which are located in central zone of Iran to regional flow duration curve. It was extracted Q5, Q10, Q20, Q30, Q40, Q50, Q60, Q70, Q80 and Q90 from 11 Hygrometric stations as a dependent variable were derived from flow duration curve. The flow duration curve is regionalized by using morphoclimatic characteristics of the drainage basin. Using multiple regression techniques, the geographic variation of each parameter of the best fitted flow duration model is explained in terms of the drainage area, length of longest flow, Stream slope, mean annual areal precipitation, course from the divide of the basin to the site of interest. The regionalized nonlinear regression equations are successfully used to flow duration curves at other locations within the hydrologically homogeneous regions of center of Iran. A cross-validation Nash–Sutcliffe Efficiency procedure was used to evaluate best fitting of the regional model in ungauged watershed.
