Shahla Tavangar; Hamidreza Moradi; Alireza Massah Bavani; Mahmood Azari
Abstract
Climate is a complex system that changing mostly due to increased greenhouse gases and global warming, leading to intensification of change in climatic factors such as precipitation amount and intensity of extreme precipitation events. In effect of climate change in the future, change amount and volume ...
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Climate is a complex system that changing mostly due to increased greenhouse gases and global warming, leading to intensification of change in climatic factors such as precipitation amount and intensity of extreme precipitation events. In effect of climate change in the future, change amount and volume of the soil erosion is expected which the most important sensitive factor will be the rain fall erosivity. The aim of this study was to determine the effect of climate change on rainfall erosivity factor. For this purpose the HadCM3 model from A1B scenario was used and downscaling with LARS-WG model was used. So monitoring of rainfall erosivity factor for three periods of 2011-2030, 2045-2065, and 2080-2099 in north of Iran was simulated. Results show that rainfall erosivity factor in Sangdeh, Babol, Korkorsar, Anzali, Behshar and Gorgan stations will be increasing during the 2011-2030 period but for stations in Babolsar, Hashtpar, Rasht and Gorgan in the period 2045-2065 and 2080-2099 decreased. According of calculations, maximum changes of the rainfall erosivity factor in future will be occurring during the 2011-2030 and it’s minimum will be occurring period the 2080-2099. So largest rainfall erosivity factor was simulated about 42.6 MJ mm ha-1 h-1 for Hashtpar station during the 2011 to 2030 period. The obtained results show that the erosivity factor increase will be during the current century in the north of Iran.
Bagher Ghermezcheshmeh; Aliakbar Rasuli; Majid Rezaei-Banafsheh; Alireza Massah; Alimohammad Khorshiddoost
Abstract
In the statistical downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers ...
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In the statistical downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers from be uncertainty. The outputs of downscaling models are used as inputs to agriculture and water resources models; therefore, identifying the models inputs’ error or uncertainty is essential to realize the reliability of obtained results. In this research, an attempt is made to investigate the uncertainty of Artificial Neural Network (ANN) as a downscaling model in a case study in the northwest of Iran. For this purpose, precipitation, minimum and maximum temperature variables were used in the designed ANN model, and the NCEP data was employed for its calibration and validation. The HadCM3 was the selected AOGCM in this study. Observed daily time series were gathered at all stations in the study period and on the basis of bootstrap method the 99% confidence interval was calculated for all the variables. In the next step, the simulated (downscaled) mean and variance of the variables by the ANN model, compared to the calculated confidence interval. To compare the results, the criterion of the number of station-month was used. The results showed that the average maximum temperature at 14 station-months were within the confidence interval. The results of monthly analysis showed that the accuracy of ANN model in summer was low and its uncertainty is more than the other seasons. In the simulation of minimum temperature based on this criterion, 18 station-months were within the confidence interval. The accuracy of ANN to estimate the minimum temperature in summer was low with high uncertainty in almost all the stations. Moreover, in June and August in any of the stations estimated values were not within the confidence interval. Due to the high variability of rainfall in relation to temperature, confidence range was very high, and in some stations was more than 50% of average monthly precipitation. Because of the high confidence rang of precipitation, in 53 Stations-month cases were within the confidence interval.
