Seyed Hamidreza Sadeghi; Shirkouh Ebrahimi Mohammadi; Kamran Chapi
Abstract
The behavior of suspended sediment during flood events is not only a function of energy conditions, i.e. sediment is stored at low flow and transported under high flow conditions, but also is related to the variations in sediment supply and sediment depletion. These changes in sediment availability result ...
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The behavior of suspended sediment during flood events is not only a function of energy conditions, i.e. sediment is stored at low flow and transported under high flow conditions, but also is related to the variations in sediment supply and sediment depletion. These changes in sediment availability result in so-called hysteresis effects. Therefore, Hysteresis pattern analysis is of great importance in sediment studies in the watersheds. However, their analyses has been rarely considered. In this study, based on the discharge and sediment concentration data collected from 8 storm events occurred during March 2 011 to April 2012, event suspended sediment dynamics of 7 tributaries of the Lake Zarivar watershed was investigated using hysteresis patterns. Based on the fact that all sampling points were not active in all events, about 46 hysteresis patterns were obtained. The analysis of results showed that 16, 13, 11, and 6 events had clockwise, irregular, complex and counterclockwise patterns, respectively. Small tributaries of the Zarivar lake watershed showed the rapid responses to the variation of storm intensity and the most hydrographs of different storms were multi peak discharges and consequently high suspended sediment variations led to different hysteresis patterns. The diversity of patterns suggested that the detailed processes of sediment transport were not only complicated during one event but also varied from event to event. The reasonable and statistically significant relationship (p<0.05) between suspended sediment yield and peak discharge of each sampling point indicated that the data from all events may be statistically well described by a simple regression equation, regardless of different inter and intra-storm variations of the suspended sediment.
Maryam Khosravi; Ali Salajegheh; Mohammad Mahdavi; Mohsen Mohseni Saravi
Abstract
It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan ...
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It is necessary to use empirical models for estimating of instantaneous peak discharge because of deficit of gauging stations in the country. Hence, at present study, two models including Artificial Neural Networks and nonlinear multivariate regression were used to predict peak discharge in Taleghan watershed. Maximum daily mean discharge and corresponding daily rainfall, one day antecedent and five days antecedent rainfall, sum of five days antecedent rainfall and monthly mean temperature were extracted in Gatehdeh, Mehran, Alizan, Joestan and Gelinak hydrological units and entered into neural network model (from upstream to downstream, respectively). The feed forward network was used with one hidden layer and back-propagation algorithm. Then, the models were trained, validated and tested in three stages. The observed and estimated peak discharges of the models were compared based on RMSE and r. The results showed that neural network has better performance than nonlinear multivariate regression.
