Journal of Irrigation and Water Engineering
Year:2014 | Volume:4 | Issue:14|Papers Count:9

Climate change especially global warming is the most problem in the 21st century. So investigation variability trend this problem is very important in global, regional and local scale. Newadays numerous general circulation models (GCMs) have been designed to predicat future climat. An outstanding issu of output for regional and local applications is coarse spitial resoluation. To produce accurate predications of future climate variables at the regional and local scale various methods are suggested. Despit many studies this case, ufortunately, there is not a standard method for a specific rogion. Thus it is necessary that accurate predications of these methods are evaluated befor applaying in a certain region. One of th most widspread methods is Statistical DownScaling Model (SDSM). In this research efficiency of SDSM model is evaluated to simulate temperture indexes in Kerman station, instance arid and semi- arid regions. Hence, SDSM is calibrated and validated by using kerman station observ tempertur and national center enviromental predication data. We used mean absolute criterium to evaluate model. After obtaining confidence simulation accuracy. Temperture indexes (mean, absoluate maximmum and minimm temperture) are simulated by using two GCMs (CGCMand HadCM3 under A2 and B2 scenarios) until 2100-year.The result of this study is shown that SDSM model has suitably to simulate temperture indexes also using HadCM3 model data is beter than that of CGCM model. Increasing mean annual temperture on base HadCM3 model in (2010-2039), (2040-2069) and (2070-2100) periods relation to base period (1961-1990) is respectively 1.5, 2.8 and 4.5 degree of centigrade in Kerman station.

Today, conservation and protection of water resources and optimum utilization and equitable economy and it is a global issue And therefore the century of water as a universal human challenge is to learn. Water is the primary factor limiting agricultural production, thus increasing the need for optimum utilization of water resources, especially in Agricultural use, which comprises the bulk of the country's water resources is felt. This paper presents an algorithm for performance evaluation Zayandehrood Dam with emphasis on the centrality of water. To achieve the objectives of this study used modeling method reservoir operation. WEAP model to be acceptable The simulation of water resources systems. Thus collected after the completion of the required information, file input Formed WEAP model is calibrated for a period. Then the optimal exploitation policies Reservoir model in various scenarios of water use in the basin, and in each case the WEAP model was implemented and results. Results are evaluated. In this study, 5 scenarios were evaluated four scenarios that reduce crop acreage Macro and three scenarios and two scenarios of growth required to reduce Nyazaby drinking water industry, and the combination of these two scenarios. scenarios SC2 inter strongest consider Insurance and Insurance Water needs and the maximum volume of water stored in the reservoir during the year better than the other scenarios for reservoir. We carry in our drought.

To predict the value of the streamflow, usually two methods of the Process-driven methods and Data– driven methods is used. Including Data – driven methods in river flow forecasting is artificial neural network, regression, time series and fuzzy logic. In this study, performance of another method empirical orthogonal functions than artificial neural network and fuzzy inference system to predict monthly inflow Latyan Dam reservoir was evaluated. Five models of ANN and ANFIS are similar and depends on rainfall, temperature and streamflow. Whereas five models of EOF depends on only streamflow in Latian station and Adjacent stations. Initial for all models the best combination identified, then statistical parameters of CE, MAPE, RMSE, CORR in the best combinations of all models were compared. The results showed that the fuzzy inference system, a better performance than the other way round.

Management of watershed basins of the country as one of the most important ways by optimal use of water resources is considered. In this regard, it is believed that a simulation of hydrological phenomenon of basin seems to be the way is the optimal solution. In this research, SWAT model, for the simulation of the hydrological basin of Firoozabad in Fars Province were used. The first statistical runoff during 1994-2010 simulated by SWAT model. Then the results by using discharge measurement of at Dehrood station and method of SUFI-2 in SWAT-CUP software, sensitivity analysis, calibration, validation and uncertainty analysis were done. The years 1998-2006 model years of calibration and 2010-2007 for validation were used. Ability of the SWAT model to simulate runoff modeling by using parameters P-factor, d-factor, Nash-Sutcliffe (NS) factor, coefficient of determination (R2) and the objective functionf was studied. The results showed that the SWAT model is a suitable simulation tool in discharge simulation of river.

Erosion and sedimentation as a serious phenomenon in Iran and the diversity of its affecting factors caused empirical methods be applied which were primarily developed for the other regions of the world. These methods were developed based on the number of affecting on the above mentioned phenomenon. Soil erosion causes soil fertility reduction; sedimentation in waterways, irrigation canals and rivers; reduction of the capacity of reservoirs and their life long; increase in flood; environment pollution and blocking roads. The soil as one of the most national capital of each country must be taken concrete actions to protect and better use of it. In order to prevent such a destructive process measures are planned to correct the situation by determining the amount of erosion and sedimentation. In this study, MPSIAC model in GIS environment was applied to estimate the amount of erosion and sedimentation of Kasilian basin. After logging information Layers and mixing layer in GIS, 36 work units were segregated and after the fifth class erosional, the value of sedimentation and erosion in each work units are separately estimated. The total sediment and erosion, respectively, were equal to 43693 tonnes per year and 1747722 tons per year.

Flood has destructive effects on the social, economic and environmentaround world and flood management is able to decrease destructive effect of a flood. Because of the multi-dimensional impacts of floods, application of multi criteria decision making as a decision tool will be useful in flood management. In this article, analytical hierarchy process (AHP) and evaluation and mixed criteria (EVAMIX) models are used for the ranking of seven flood management measures in Gorganrood River flood management project, including non-project condition, Golestan reservoir management, levee construction, construction of diversion channel, flood forecasting and warning system, flood insurance, and flood warning system and flood insurance integration. Eleven criteria including expected average number of casualties, recovery rate, gradual rate, expected annual damage, safety feeling, employment rate, public participation, landscape protection, wildlife habitat conservation, water quality conservation and technical feasibility and performance were used for evaluation of the measures. Ranking results shows that these two models were contradict just in first and second rank, So that, the AHP allocated Golestan reservoir measure in first place and EVAMIX preferred flood warning system and flood insurance integration measure. But the other ranks were the same in both of the models. Thus, the models didn't rank measures so differently, but nomination of the best measure was different to each other.

Rainfall has been one of the most important agents in water cycle which has an effective rule in each region characters measurement. Prediction of month scale rainfall is important for main goals as torrent estimating, drought, run-off, sediment, irrigation programming and also manage the drainage basins. Rainfall measure prediction in each area mediated by punctual data measured of humidity, temperature, barograph manometers, wind speed and etc. the limitations such as unavailable enough data about rainfall measure on a scale of time and location and also complicated boundaries among meteorology agents related to rainfall, caused to inexact and non trustable amount based on unusual manners. In this research, firstly the description of different concepts of meteorology parameters on month scale in Ahar and Jolfa regions, EAST AZARBAIJAN, have been explained in which the entrance Artificial Neural Networks, Genetic Programming and M5 tree model have been defined too. Then, the best concept has been chosen for each model according to both R and RMSE statistics. In Ahar station Genetic Programming approach with (R=0.88) and (RMSE=3.32), also in Jolfa station Genetic Programming approach with (R=0.87) and (RMSE=3.79) presented the best results. The conclusion determined that each mentioned approaches presents the comparatively exact result for rainfall prediction in region but due to having simple liner models and understandable with M5 tree model, this approach would be considerate as an efficient application and substitutes for rainfall measurement.

Now, water is the most important and the most limiting factor in agricultural production sector. Certainly, different countries which located in arid and semi-arid region and are faced with water shortage must be applied specially strategies for the suitable and correct water use. In this research two-year experiments from year 2009 -2010 and 2010-2011 was conducted to find the effect of time and value of three supplementary irrigation procedure including without supplementary irrigation, once irrigation at flowering and twice irrigation at flowering and seedling stages on three wheat cultivars in the presence of constant groundwater table with 80cm depth in three replicate. Experiments performed at Razi University lysimeteric research station No1. Results showed no significant difference (P<0.05) between without and once supplementary irrigation treatment was happened. Totally, groundwater contribution under different supplementary irrigation was varied from 57.1% to 71.0% during two years of experiments for different wheat Cultivars. Also, the outcome of this study indicate that, by applying timely and optimal supplementary irrigation in the presence of a shallow water table (at a constant depth of 80 cm) in a semi-arid climate with an limited average annual rainfall, enables to compensate the total water requirement of winter wheat during the growing season. The results on this investigation showed that during wheat cultivation, existing shallow groundwater in different western, north western, north and southern parts of Iran can compensate some parts of wheat water requirement. Therefore a huge volume of surface water for wheat irrigation can be saved and more land irrigation, and job creation will be possible.

According to the importance of river flow forecasting in water resources management, various methods are considered to model the flow in rivers. For the propose of minimizing the flood or drought hazard from the view point of management. Having nonlinear features and multiple time scales, the time series of daily flow were considered to be analised using artificial neural network (ANN) and wavelet hybrid procedures. For this purpose the original time series for 35 years was decomposed to 11 multi-frequency subseries by wavelet transform and then in order to predict the flow of future 1, 2, 3, and 4 days, this sub series was entered as input data to ANN model. The results of the Application modeling of wavelet- ANN with the results of modeling of ANN is compared, and it was observed that method of wavelet-neural networks has a higher forecast accuracy than method of ANN and also forecast accuracy in both models with increasing number of delays in the output neurons is reduced, and it was observed that in predicte by wavelet-neural networks were used from Haar wavelet and Meyer wavelet that results the simulation of Meyer wavelet were more accurate than Haar wavelet.