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Go to Editorial ManagerData sets of highest monthly rainfall for the period (1887-1958) are used for evaluating the proper theoretical statistical distribution of extreme monthly rainfall in Baghdad city. The frequency analyses and most statistical test were done using a commercial version of HYFRAN. Five distributions are used in this research, which are: - Normal, Pearson Type III, Lognormal, 3-parameter lognormal and Gumbel. Estimation of theoretical distribution is achieved by using maximum likelihood method and adequacy test is carried out using chi-square test. Lognormal, 3-parameter lognormal, and Gumbel distributions seem to be suitable for representing of maximum monthly rainfall in the study area.
Since the 1970s, rainwater harvesting has gained more attention, specifically in semi-arid and arid areas. It is essential to take into account how much water can be collected from a single catchment site. Rainfall that has been harvested provides an alternative source of water in the northern region of Iraq. Numerous scholars have developed and executed a range of strategies and guidelines to choose appropriate locations and methods for rainwater harvesting (RWH). However, choosing the optimal method or set of rules for the choice of site is challenging. This study's primary goal was to evaluate previous research regarding the selection of appropriate RWH locations in northern Iraq by assembling a list of the most important techniques and guidelines that evolved over the previous thirty years. The primary factors considered in the process of choosing acceptable locations for RWH were soil type, slope, land use/cover, rainfall, and runoff. A literature review for RWH indicated that these criteria were chosen more frequently and significantly, and the opinions of experts should be used to establish the weight of each criterion. The majority of studies select RHW sites using geographic information systems, hydrological models, and multi-criteria analysis.
The land surface erosion is controlled by multifarious of different parameters, such as slope, soil physical properties (texture, structure, permeability, etc.), rainfall, runoff, and crop cover. However, it is impossible to develop precise simplest mathematical model that can predict the values of land surface soil erosion due to the behavior of controlled parameters. This paper presents the Neural Networks Model for assessing land surface soil erosion as amass per unit area per unit of time. The model derives from the analysis data obtained from available literature and was formulated as linear regression model and back propagation algorithm neural model. Both models were built by correlating firstly five watersheds variables with land surface erosion and secondly ten watershed variables with land surface erosion. The coefficients for independent variables were highly significant for both models. The case of correlating 10- watershed variables with land surface erosion gives R=0.978 & 0.976 for both models which is higher than that for 5- watershed variables. The mean absolute relative error (MARE%) is another procedure that used in order to evaluate the accuracy of the model and The average error % is 0.025 for (5) variables and 0.0064 for (10) variables. Both the supporting practices (P) and the slope length and slope steepness (LS) coefficients have a marked effect on the amount of land surface erosion in the case of 5- watershed variables. The amount of land surface erosion show a high level of sensitivity to the content of fine sand% in soil (FS) watershed variables on The amount of land surface soil erosion.