Do you want to publish a course? Click here

The study shows the factors affecting rain precipitation, general rate and annual, monthly and daily changes by calculating the standard deviation and the annual fluctuation. The standard deviation from the general average shows large values in th e stations located in the north of the study area. Stations in the center and south, because of the nature of the dry climate, as well as that the increase in the number of rainy days does not necessarily mean an increase in the amount of precipitation, and a difference in the amount of rainfall from one station to another because of the difference in climatic factors affecting them .
The aim of this study is to determine the best probability distribution of annual, monthly, annual one day maximum precipitation for stations in Aleppo Governorate by using nw2 test, then estimating annual, monthly, one day maximum precipitation to various return periods according to the best probability distribution, and using Chow’s general frequency formula.
Raising number of researches dealt with precipitation properties especially after the recent advances in measurement techniques and devices. It is becoming essential to reach a common understanding of rain event when addressing the relation of rain p roperties with different climate patterns and its influence on variety of human activities. The aim of this research is to present a suitable rain event definition that would serve future research in this field. Data was acquired in Freising south of Germany in the summer of 2009. Four event definitions were generated then compared according to rain properties obtained by the disdrometer and the rain gauge, These properties included event count per definition, mean event duration, mean event rain intensity, mean event rain amount, total rain amount and total number of drops. One definition proved to be more suitable than the others exploiting the disdrometer precession.
Accurately modeling rainfall-runoff (R-R) transform remains a challenging task despite that a wide range of modeling, either knowledge-driven or data-driven. knowledge-driven models need a large amount of parameters, so it suffers from plenty numbers of parameters, for this reason the hydrologists start looking for a simple modeling methods, that need a few parameters such as data _driven methods, so The present study amis to use artificial neural network, which is one type of this methods for modeling the relationship between rainfall and runoff in Alkabeer Aljanonbee river catchment in Tartous City. Elman Neural Network is depended on for prediction of runoff by testing twenty four models have different architectures. So all models have been tested by using different numbers of neurons in the hidden layer, by using nntool book, which is available in the Matlab program. The results of the research verify that the model which has each of temperature, relative humidity, evaporation and rainfall in the input layer with time delay equal to three days (0:-3), in addation to preveous value of runoff (-1:-3), gives a best performance for used data with mean square error equal to 2.8*10^-5, and correlation coefficient 0.96. So it has been reached that Elman network technology gives a good results in modeling the relation rainfall_runoff So it could be a good alternative instead of traditional approaches.
The research has been conducted in different sites in lattakia, Alhamra and alsabahia villages of Rabea and Aen alzarqa of Mashqeta. 14 cases of precipitation was marked which caused runoff and water erosion of soil during the research (seasonal ra infall in 2011/2012). Samples of rain water of every precipitation case were collected. by using rain gauges that were installed in the research sites. After that, samples were moved to the laboratory. The pH was determined after that the samples were analyzed by using an ion chromatography device(IC).
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا