Jahantab Khosrovanian; Majid Onagh; Masud Guderzi; Seyyedasadollah Hejazi
Volume 19, Issue 53 , September 2015, , Pages 93-115
Abstract
Abstract
A stochastic weather generator can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which could incorporate changes both in mean climate and in climate variability as well. In this paper, LARS-WG model was used to downscale ...
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Abstract
A stochastic weather generator can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which could incorporate changes both in mean climate and in climate variability as well. In this paper, LARS-WG model was used to downscale GCM outputs and then tp assess the performance for generated daily data of precipitation, minimum and maximum temperature and sunshine hours. Study area was Ghare-su basin in Gorgan and the station is called Gorgan synoptic station. The first step was running the model for the 1970-1999 periods. Then mean of observation and synthetic data were compared. T-test was used in the 99% significance level, and the difference between observation and synthetic data was not significant. Finally monthly mean of observation and synthetic data were compared using statistical parameters such as NA, RMSE & MAE. As a final result, it was found that performance of model was appropriate for generating daily above-listed data in Ghare-su basin. Thus, it was possible to predict the climatic parameters from GCM output using LARS-WG model. Also minimum and maximum temperatures had the highest and sunshine hours involved the lowest correlation. After ensuring performance of model to simulate above-mentioned parameters, this model used to predict future trends (in 2011-2030 and 2080-2099) with A2, A1B and B1 scenarios of the HadCM3 model was. Results showed that future temperature would increase 0.56-4.04 degrees centigrade while precipitation would increase 10.28-23.71%.
Majid Hosseini; Mohammad Ghafouri; Mahmoodreza Tabatabaee; Masood Godarzi; seyed asadollah Hejazi
Volume 17, Issue 45 , November 2013, , Pages 27-41
Abstract
One of the main concerns in recent years with regard to climate change and global warming is how to efficiently manage the water resources of the world. Insufficient or unavailable hydroclimatological data further aggravate the difficulty of good water management. Hence the use of hydrologic and hydraulic ...
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One of the main concerns in recent years with regard to climate change and global warming is how to efficiently manage the water resources of the world. Insufficient or unavailable hydroclimatological data further aggravate the difficulty of good water management. Hence the use of hydrologic and hydraulic models is a possible solution to ease the job of the water managers. In this research, Soil and Water Assessment Tools (SWAT) are used to predict and validate the discharge in Taleghan Watershed of Iran. The inputs required include soil, land use and DEM layers with hydroclimatological data. Statistical methods were used for calibration and validation of the SWAT model. The results indicate that the observed and predicted discharge have the least mean absolute relative error both in the annual and monthly periods. From the SPSS analysis, these values were found to be not significant at 95% probability for the annual and monthly discharges for the calibration and validation periods. The study illustrates the usefulness of the SWAT Model in predicting runoff components in a watershed. The annual results in Taleghan catchments during 1987 and 2007 indicate an increasing 7.3% surface runoff and decreasing 11.3% and 11% interflow and groundwater flow respectively.
