جستجوی مقالات مرتبط با کلیدواژه « رواناب روزانه » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

River flow forecasting plays an important role in planning, management and operation of water resources. To achieve this goal and according to the phenomenon of global warming, it is necessary to simulate the daily time series of rainfall and runoff for future periods. Therefore, it is important to survey the detection of climate change event and its impact on precipitation and runoff in the basin. In the first step of this research, using Mann-Kendall trend statistical test, climate change event in the Pashakola Babol basin in Mazandaran province is confirmed. The results of the survey on 36 years daily mean temperature data, reflect the increasing trend in average temperature in the basin. In the second step, the LARS-WG model under general circulation models HadCM3 and A2 scenario is used to generate the daily rainfall time series in the future period. In order to rainfall data, minimum temperature, maximum temperature and sunshine hours is entered in the model for 12-year base period (2004-2015) and Then daily rainfall time series in the basin is predicted for the next 10-year period (2016-2025). In the third step, the artificial neural network model is used to simulate the process of rainfall - runoff in the climate change condition and to generate the daily runoff time series in the future period. Finally, in order to enhance the capability of the artificial neural network model in predicting the daily runoff, besides the predicted daily precipitation data, the rainfall and runoff data one day before as effective factors on the current day runoff is also entered in the model and using rainfall and runoff neurons. Correlation coefficient was obtained equal to 0.8. This correlation coefficient is significant at 1% and show the ability of model to simulate rainfall–runoff process.

Rainfall- runoff modeling is one of the key steps of hydrology. Among rainfall- runoff models, the conceptual models are frequently used. However, calibration of the conceptual rainfall-runoff models has been a challenge for hydrologists for decades. In this study, automatic calibration of ARNO conceptual rainfallrunoff model is developed using genetic algorithm. This model is in the class of continuous semi distributed models and has been successfully used in different parts of the world. Using the employed automatic calibration tool, the calibration step of the model can be performed rapidly and simply for a given basin, without requiring an extensive knowledge of the model structure and parameters. The model was calibrated automatically for rainfall-runoff simulation of a basin in southwest Iran. The calibration results for 5 years daily observed data showed that the values of efficiency coefficient and squared correlation coefficient was 0.80 and 0.82, respectively and the validation results for 4 years daily observed data showed that the values of efficiency coefficient and squared correlation coefficient was 0.82 and 0.83, respectively. The results show that this model in conjunction with employed automatic calibration method can successfully be used for daily rainfall- runoff simulation.