Mohammadreza Khazaei; Ahmad Sharafati; Hadis Khazaei
Abstract
One of the most important impacts of climate change is the change of snowfall regime, especially extreme snowfalls in the future. Extreme snowfalls will be affected by increasing in extreme rainfalls and temperature oppositely in the future climate while the trend of changes is not clear. In this paper, ...
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One of the most important impacts of climate change is the change of snowfall regime, especially extreme snowfalls in the future. Extreme snowfalls will be affected by increasing in extreme rainfalls and temperature oppositely in the future climate while the trend of changes is not clear. In this paper, climate change impact on extreme daily rainfalls in the Mehrabad station of Tehran is assessed. Future daily precipitation and temperature projections of the CGCM3 model under B1, A2, and A1B emission scenarios are downscaled using IWG stochastic model. Snowfall is simulated using temperature threshold criteria, and climate change impact is assessed on snowfall in 2036-65 period. Results of the validation tests showed that the IWG model have reproduced a broad range of the temperature and precipitation statistics. Also, snowfall statistics specially the annual maximum daily snowfall distribution are well reproduced. The climate change impact assessment results showed that under various emission scenarios, despite increasing in extreme precipitation in the future in the Tehran Mehrabad station, annual maxima daily snowfalls would greatly decrease. So that, maximum daily snowfall with return period of two years would decrease more than 50% under all considered scenarios.
Ahmad Sharafati
Abstract
Rainfall runoff models are used mostly in simulation of flood events. Also, calibration of rainfall runoff model parameters is an important and challenging issue in flood simulation. Due to random characteristic of these parameters, the deterministic optimization is not a suitable approach for calibration ...
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Rainfall runoff models are used mostly in simulation of flood events. Also, calibration of rainfall runoff model parameters is an important and challenging issue in flood simulation. Due to random characteristic of these parameters, the deterministic optimization is not a suitable approach for calibration of rainfall runoff model. So, in this study, the SUFI (Sequential Uncertainty Fitting) algorithm is used as a stochastic approach and the optimized range of parameters were extracted. The obtain results shown, in calibration step, the correlation coefficient between observed hydrographs (three events) and the best generated hydrographs were more than 0.9 and also, the average difference between observed hydrographs and the best generated hydrographs were less than 5 percent. Furthermore, in validation step, the correlation coefficient between observed hydrograph and the best generated hydrograph was 0.99 and also, the average difference between observed hydrograph and the best generated hydrograph was 11 percent. So, the SUFI algorithm is a suitable approach in stochastic calibration of HEC-1 model.
