Parivash Toufani; Ahmad Fakheri fard; Abolfazl Mosaedi; AmirAhmad Dehghani
Abstract
Prediction of Precipitation is very important. Regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. Wavelet is one of the novel and very effective methods in time ...
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Prediction of Precipitation is very important. Regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. Wavelet is one of the novel and very effective methods in time series and signals analyzing, that has been considered in the field of hydrology in recent years. In this research, precipitation signal has been decomposed via selected mother wavelet, and then the resulted data are used by fitting direct equations and nero-wavelet hybrid in order to anticipate the precipitation. The mentioned method was applied in Zarringol station (Iran) to predict monthly precipitation since 1975-76 until 2007-2008 for the period of 33 years. The results showed that, by decomposing signal via wavelet, the correlation among observed and calculated data were significantly increased, and the precision of prediction was improved. So that in direct method the value of R2 is equal to 0.74 and in nero-wavelet hybrid in the best case and for 4 level decomposition the value is equal to 0.95. This shows the capability of wavelet in simplifying of signal and intensification of accuracy random data in prediction of precipitation. Moreover, the meaningless F test, verifies the mentioned object. Keywords: Precipitation, prediction, Signal, Wavelet theory, Nero-wavelet hybrid, Zarringol. Prediction of Precipitation is very important. Regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. Wavelet is one of the novel and very effective methods in time series and signals analyzing, that has been considered in the field of hydrology in recent years. In this research, precipitation signal has been decomposed via selected mother wavelet, and then the resulted data are used by fitting direct equations and nero-wavelet hybrid in order to anticipate the precipitation. The mentioned method was applied in Zarringol station (Iran) to predict monthly precipitation since 1975-76 until 2007-2008 for the period of 33 years. The results showed that, by decomposing signal via wavelet, the correlation among observed and calculated data were significantly increased, and the precision of prediction was improved. So that in direct method the value of R2 is equal to 0.74 and in nero-wavelet hybrid in the best case and for 4 level decomposition the value is equal to 0.95. This shows the capability of wavelet in simplifying of signal and intensification of accuracy random data in prediction of precipitation. Moreover, the meaningless F test, verifies the mentioned object.
samaneh Mohammadi Moghaddam; Abolfazl Mosaedi; Mohammad Jankju; Mansour Mesdaghi
Abstract
Although precipitation is the most important factor which effects rangeland production, but there is little information on the relationship between production and the interactions of climatic factors and specially drought indices. .In this research, the relation between production and climatic ...
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Although precipitation is the most important factor which effects rangeland production, but there is little information on the relationship between production and the interactions of climatic factors and specially drought indices. .In this research, the relation between production and climatic factors of rainfall, temperature, evapo- transpiration, and as well as drought indices of Standardized Precipitation Index )SPI) and Reconnaissance Drought Index (RDI) were investigated in Noudoshan Rangelands.Then the data with 33 variables were generated for different time periods of one to four months based the years of available production data. PCA was employed to decrease the number of variable and based on further component analysis, some variable were selected. To find the relation between production and climatic factors, regression analysis was used. Finally, the model with least IPE was selected as preferred model. By comparison equations based on rainfall, temperature, evapo- transpiration, and drought indices, the model resulted from RDI, selected as preferred range production estimator (R=0.969, MARE=0.111).
محمد قبایی سوق; Abolfazl Mosaedi
Abstract
Reconnaissance Drought Index (RDI) is based on fitting a Log-normal distribution to the ratio of precipitation to evapotranspiration (ETo) values in selected periods. In this index value of ETo were calculated based on mean temperature by Thorenth-Waite (Th) method. Th method, may underestimated ETo ...
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Reconnaissance Drought Index (RDI) is based on fitting a Log-normal distribution to the ratio of precipitation to evapotranspiration (ETo) values in selected periods. In this index value of ETo were calculated based on mean temperature by Thorenth-Waite (Th) method. Th method, may underestimated ETo values comparing to the actual in arid and semi arid regions. The log-normal distribution may not be fitted to the ratio of precipitation to ETo values of some regions. In order to investigate the effects of these two limitations on drought situations' changes, meteorological parameters have been used during 50 years period at 8 Synoptic Stations of Iran. In the first step, the values of RDI(Th) for any stations during the mentioned time were calculated. Then, ETo values were calculated from best fitted empirical equation in any situation of lack of parameters. Subsequently RDI(select) index were established. The Kolmogorov–Smirnov (KS) test is used to assess the goodness of fitting most appropriate distribution function to the ratio of precipitation to ETo values. Then, according to equi-probability transformation the values of RDI(Th) were modified to *RDI(Th). The occurrence of different classes of drought according to RDI(select) and/or *RDI(Th) comparing to RDI(Th) showed the elimination of any mentioned limitations may leads to changing the amount of occurrence of any drought classes in RDI(Th). Hence, The RDI(Th) modified to *RDI(select) by estimating ETo values from selected method and applying appropriate distribution function to the ratio of precipitation to ETo values.
M. Nabizadeh; A. Mosaedi; A. A. Dehghani
Abstract
River flow forecasting for a region has a special and important role for optimal allocation of water resources. In this research, for forecasting river flow process, Fuzzy Inference System (FIS) is used. Three parameters including precipitation, temperature and daily discharge are used for forecasting ...
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River flow forecasting for a region has a special and important role for optimal allocation of water resources. In this research, for forecasting river flow process, Fuzzy Inference System (FIS) is used. Three parameters including precipitation, temperature and daily discharge are used for forecasting of daily river flow of Lighvan River located in Lighvanchai watershed. For the initial preprocessing, the randomness of data was examined by return points test. Then, for determination of the optimum lags for input parameters, correlogram of data was considered. Finally to investigate the effects of temperature on river flow forecasting, the process were done for any months separately. Assessments of prediction by using various criteria such as Nash-Sutcliff coefficient showed that FIS model had high precision (CNS=0.9976) and low error (RMSE=0.0113) in prediction which shows that the FIS model can be employed successfully in river flow forecasting. Final assessment of the results was also revealed the effects of temperature on prediction in some months (April and December).
