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Abstract
Stochastic climate generators are used in many studies such as application of hydrologic, environmental management and assessment of agriculture risk. These studies require for assessment of risk to long term series from meteorological data. Considering of climate data limitation at large area in Iran ...
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Stochastic climate generators are used in many studies such as application of hydrologic, environmental management and assessment of agriculture risk. These studies require for assessment of risk to long term series from meteorological data. Considering of climate data limitation at large area in Iran and short term of data, it is necessary using of climate generator and evaluation of precision and accuracy. Therefore in this study have been evaluated the efficiency of three generators namely CLIGEN, ClimGem and LARS-WG in sangeneh and Zidasht station with different climate condition. Statistical test of t (t paired) have been used to compare the differences between observed and production weather data such as yearly and monthly precipitation amount, yearly number of wet day, yearly average of max. and min. temperature. The obtained results show that CLIGEN generator have the better efficiency than two others generator in two stations and five considered variables. ClimGen haven't had the good efficiency in two stations. Also, LARS-WG generator have had the good efficiency in Zidasht station, but it's efficiency have had the less efficiency for product of temperature variables in Snganeh station. Generally, the obtained results show that the efficiency of these generators is better in mild climate than arid and semi-arid climate.
Ali Talebi; Shahrbanoo Abbasi Jondani
Abstract
WEPP model needs a great deal of input data. Identifying the model’s sensitive parameters andtheir prioritization increases the accuracy and efficiency of the model. On the other hand, WEPPmodel can simulate processes affecting on runoff, erosion and sediment throughout the year. Thus,model sensitivity ...
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WEPP model needs a great deal of input data. Identifying the model’s sensitive parameters andtheir prioritization increases the accuracy and efficiency of the model. On the other hand, WEPPmodel can simulate processes affecting on runoff, erosion and sediment throughout the year. Thus,model sensitivity must vary based on the storm occurrence time and parameters value in differentsections of the year. To prove this assumption, two spring and autumn storm events related to 2008were selected and sensitivity analysis of the WEPP model was done in three plots with differentconditions in Sanganeh watershed. For sensitivity analysis, the OAT method was used andsensitivity degree of parameters was calculated. Obtained results show that the rate of sand is themost sensitive parameter of WEPP model. This parameter was followed by other parameters likeclay percent, effective hydraulic conductivity, height and intensity of rainfall, day degree ofgrowing, growing season and percent of growing season when leaf area index decreases. Mostvariations are observed in prioritization of sensitive parameter in the plant/ management file. Inmost cases, sensitivity degree of these parameters in autumn event comparing to the spring eventhas significantly reduced in all plots. In general, obtained results show that the rate of sensitivity ofthe WEPP model to different parameters varies during the time. Hence, for using this complexmodel in regions with data limitation, the user must be aware to this issue that regarding storm time,which parameter is more sensitive in the pilot area and need to be carefully measured in the field.
Sharbanoo Abbasi Jondani; Ali Fathzadeh
Abstract
Snow is one of the main components of hydrological cycle in most of mountainous basins. Since collecting the snow data (e.g. snow water equivalent data) is very difficult and time consuming, some effort is necessary to develop methods to estimate spatially variation of snow depth distribution. In the ...
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Snow is one of the main components of hydrological cycle in most of mountainous basins. Since collecting the snow data (e.g. snow water equivalent data) is very difficult and time consuming, some effort is necessary to develop methods to estimate spatially variation of snow depth distribution. In the present study, the at-site SWE data of 14 stations located in the west of Isfahan providence for the period 1989-2010 were spatialized applying four methods composing the Kriging, the Co-Kriging, the Radial Basis Functions (RBF) and the Inverse Distance Weighting (IDW). In order to reach this purpose, first, the normality of data was checked using the Kolmogorov – Smirnov test. The homogeneity, the stability and the trend of data were tested employing the semivariogram approach. Then the appropriate data of each year was entered into the ArcGIS 9.3 to conduct the methods. Finally, the best method for spatializing the SWE data was selected based on the RMSE values. The results showed that the RBF method provided the best results for most of the years. Furthermore, it was found that the amount of SWE reduced from the south and west to the north and east of the basin.