Given the vital importance of water in human life, recognizing the EFFECTIVE use of RAINFALL and crop water requirement and economic planning are very important. Identifying and applying appropriate method for estimating EFFECTIVE RAINFALL, especially in rainfed is highly important. The current study aims to determine the most suitable experimental method for estimating EFFECTIVE RAINFALL for sowing Wheat, Barley, Peas and Lentils in high RAINFALL zones (Rasht in Gilan) as compared to low RAINFALL zones (Daran in Isfahan). In this study, five different experimental methods are presented in order to determine the EFFECTIVE RAINFALL including: the Soil Conservation Service (SCS), reliable method, empirical method, United States Department of Agriculture (USDA), and percentage method. The results show that all methods used for determination of EFFECTIVE RAINFALL in Rasht are useful. However, these methods require to be pre-calibrated in order to determine the EFFECTIVE RAINFALL in Daran identified as a low RAINFALL area. Based on the obtained results, as regards prioritization of using the methods under study in these areas, SCS and USDA methods are proposed for Rasht, while the USDA and percentage methods are proposed for Daran. Since the amount and duration of RAINFALL is not controllable, EFFECTIVE strategies can be applied to increase precipitation efficiency and consequently EFFECTIVE precipitation; these strategies include: reduction of surface runoff, water storage for times of low RAINFALL, reduced water depth penetration, and planning for the cultivation of plants that are consistent with the precipitation regime.

The conversion of RAINFALL to runoff in basins includes nonlinear relations between the complex interactions of various hydrological processes. In this study, without considering of predetermined structure, relationship between input and output system was derived individually from the nature of the data recorded. Also, the phase difference occurred between RAINFALL and runoff signals using cross-wavelet transform analysis. Then phase difference diagram was plotted for the single and compound events of Lighvan basin. By applying these phases at the time of the RAINFALL signals, all errors resulting from considering average losses in basin was minimized that in this study was introduced as "minimum error time position"(METP). Also, discharge forecasting for basin was carried out by Kalman filter model and optimization in Lighvan basin state space, using calibration step events. By applying this phase difference to EFFECTIVE RAINFALL components, the error resulting from the considering of average infiltration losses and φ index decreases to the minimum. Also, using the linear programming optimization method, the unit hydrograph for Lighvan basin was used as Kalman filter measurement model. The results showed that by applying the phases difference between RAINFALL and runoff signals and integration with Kalman filtering and linear programming (KF-LP-CW), the corrected Nash-Sutcliff coefficient were obtained 0. 94 in both Calibration and verification steps. These values were obtained 0. 63 and 0. 68 in calibration and validation steps respectively, as compared to the state that phase difference was not applied (KF-LP method). Therefore, in this study, significant improvement was observed with the application of phase differences in EFFECTIVE precipitation components.

EFFECTIVE RAINFALL is one of the processes of the hydrologic cycle that has been studied very extensively. The various models proposed for description of this process are usually applicable to small plots, subjected to RAINFALL intensities that are higher than the infiltration capacity of the plots. In this paper we try to introduce a model which presented by Diskin and Nazimov. The model presented here in is not restricted to infiltration capacity only. It can also be used for computing the variations in the infiltration capacity rate and the production of RAINFALL excess for any given RAINFALL hyetograph and a specified value of the initial moisture content of the upper soil layer. The model comprises two elements, a linear reservoir and a regulating element. The definitions of the two elements require three parameters: minimum and maximum rate of infiltration capacity (fc, fo) and the maximum value of moisture storage in upper soil layer. In order to calculate the amount of RAINFALL excess, the model used in Kechik watershed, Golestan Province. The results show that the value of the RAINFALL excess during a selected RAINFALL was about 55.9 mm.

For the past several years, the severity and frequency of extreme events like flooding or prolonged droughts seem to be on the rise, globally. Precipitation is the most vital element of climate which almost influences many aspects. The effect of precipitation on different human activities including farming, industry and services is obvious (directly, indirectly, or with intermediate). In this regard, the relation between precipitation and farming, especially dry farming, is considerable and somehow distinct from other activities. It is clear that the most natural kind of using precipitation at farming section is dry farming. Identification of RAINFALL events are important in designing the related water structures, farming, weather modifying, policy making and planning and also in monitoring climate change. Methodology identification of trend or persistence in the RAINFALL series is essential to present the hydrological information in a condensed form for decision making and planning in water resources of any region. EFFECTIVE RAINFALL (ER) is defined as that portion of RAINFALL which is useful directly and/or indirectly for crop production at the site where it falls. EFFECTIVE RAINFALL is influenced by the factors such as quantity and intensity of RAINFALL, evapotranspiration (ET) and deep percolation losses, and irrigation management practices. Estimating the EFFECTIVE RAINFALL is extremely useful for operational planning and management issues. This paper exemplifies a study involving non-parametric statistical method of Mann-Kendall test for identification of trends in annual RAINFALL series in Iran. The nonparametric Kendall test was applied to find trends in a number of climatic and hydrologic variables. This test was selected because it can handle non-normality, censoring, or data reported as values "less than", missing values or seasonality and because it has a high asymptotic efficiency. Mann Kendall nonparametric test is widely used for the analysis of trends in meteorological and hydrological series. One of the advantages of this method is its applicability for a time series distribution, which does not follow a typical statistical distribution. This method of analysis adopts two parameters such as the Kendall statistic, S and the normalized test statistic Zs, which are used to determine the nature and level of the significance of trends exhibited by the variables. Generally, a positive value of S is an indication of an upward trend, while a negative value indicates a downward trend. Also, the value of Zs-greater than 1. 96 at a selected confidence limit of 95%-shows that the trends can be interpreted as statistically significant or otherwise. The data must be serially independent in the case of the non-parametric tests. Based on the performed studies, the existence of serial correlation will increase the probability for significant trend detection. This leads to a disproportionate rejection of the null hypothesis of non-trend, whereas the null hypothesis is actually true. Ministry of Energy Khuzestan Water and Power Authority Therefore, the influence of serial correlation must be eliminated. In this regard, different methods such as pre-whitening, variance correction, and TFPW have been proposed. The TFPW procedure presented here provides a better assessment of the significance of the trends for serially correlated data than the other approaches and several researchers have used this procedure. The trend changing of total and EFFECTIVE RAINFALL of the entire country of Iran on three scales (annual, monthly and seasonally) was analyzed using statistical tests. The data of 33 stations was used in the study area. In this analysis, the trend changes of total and EFFECTIVE precipitation in the period of 40 years (1971-2010) and (1961-2000) and a 50-year period (1961-2010) were studied. The slope of the linear trend of data was estimated by TSA and then, if there was a correlation between data, the autocorrelation coefficient between data removed using TFPW method, and the time series of precipitation pre-whited. Then, the trend channeling of total and EFFECTIVE RAINFALL time series was analyzed, using the Mann-Kendall test. The results showed that the median of EFFECTIVE RAINFALL in the selected stations were negative and therefore they had decreasing trend. Also, the median of total RAINFALL in the selected stations for a period of 40 years (1961-2000) was approximately equal to zero and for the 40-year period (1971-2010) the trend was significant at the level of 10%. While, for the period of 50 years (1961-2010) the median was negative, but not significant.

The amounts of consumptive water requirements (ETC) for premature and serotinous varieties of rice were calculated at ten stations in the Mazandaran Plain, based upon evapotranspiration amounts (ETO) and plant growth coefficients (KC). Then the EFFECTIVE RAINFALL amounts (ER) and the groundwater amounts, absorbed by plants’ roots (GWC), were calculated according to the most suitable methods. The net irrigation requirement (IR) is defined as the subtraction of ER and GW amounts from ETC amounts. The results of the study showed that the ETC and IR amounts are more in the stern part of the plain than the western part; wereas ER amounts are more in the western part. ER has more proportion of ETC in the western part; wereas this proportion is less in the east. ER amounts are much less than IR amounts in the whole region.

Given the present water crisis and since more than 94 percent of the country's water is spent in the agriculture sector, it seems logical not only to mechanize the irrigation systems, but also to re-estimate the plants' actual water requirements. This becomes possible by predicting the amount of snow and RAINFALL in the plants' growth seasons. In designing irrigation systems, it must be taken into account that the total amount of RAINFALL is not usable for the plant, and that a portion of this rain and is used as runoff and a part of it penetrates deep into the soil, leaving the plant with only a part of the rain water for its water needs. It is this portion of water that is EFFECTIVE in the plants' growth. In the present study, the best regression analysis model was obtained using field data including RAINFALL, evapotranspiration and EFFECTIVE rain, and the results were compared with the output of neural networks. Results of the study showed that natural mathematical models (neural networks) have a higher degree of accuracy compared to the pure mathematical models (regression models). As a result, use of the networks in predicting the EFFECTIVE rain not only decreases water consumption expenses, but also prevents the imposition of water stress on plants and the reduction of agricultural production.

Isfahan district has been faced with limited water resources in recent years because of its special geographical location and highly dependent on the Zayandeh-rood River and groundwater for various uses, including agriculture. Meanwhile, according to available statistics, more than 90% of the country's water consumptions are allocated to the agricultural sector. Calculating EFFECTIVE RAINFALL, especially in arid and semi-arid regions that face with limited water resources is very important. In this study, EFFECTIVE RAINFALL in wheat cultivation in the 2015-2016 crop year was estimated by direct (field) method of Ramdas and obtained results from six empirical methods of Renfro, US Bureau of Reclamation (USBR), Evapotranspiration to precipitation ratio, US Soil Conservation Organization (SCS) method, FAO "(FAO/AGLW)", and percentage were compared with Ramdas technique by root-mean-square error (RMSE) tests, normalized root-mean-square error (NRMSE) and the mean absolute error (MAE). The results indicated that the ratio of evapotranspiration to precipitation and the method of the US Soil Conservation Organization (SCS) with RMSE and NRMSE 0. 31 and 0. 7, MAE 0. 11 and 0. 45 respectively could be recommended as empirical approaches in Kabootarabad region (Isfahan district).

The EFFECTIVE RAINFALL amounts (ER) as a part of the irrigation requirement were estimated for the premature and serotinous varieties of rice in the Mazandaran Plain, using different methods. Finally the "Dependable Rain" method was selected for the estimation. Comparison of the maps reveals that the ER amounts are more in the western part of the plain than the eastern part; consequently, the net irrigation requirement is low in the western part. Since knowing the minimum and maximum values of the ER with specific confidence, helps the planners in making different decisions, the ER amounts were estimated at 90, 95 and 99 percent confidence intervals. The related maps show that the confidence for ER amounts is low for both premature and serotinous varieties in the eastern part, while the ER amounts are almost 50 millimeters more for serotinous variety than premature variety at different confidence intervals in the whole region. Also, the maps of return periods show that the ER amounts are higher in the western and central parts than the eastern part and that the accessibility of ER varies from lower than 80 to more than 420 millimeters in the growing season in terms of various return periods and different parts of the region.  

Climate change through increased water demand, especially in agricultural sector, is the main challenge facing water resources management. Wheat is one of the staple and strategic crop throughout the world, particularly in Iran. In this study, historical observations from 1985-2005 were used to simulate the effects of climate change on the winter wheat water requirement across Gorgan plain. SDSM4. 2 and CanESM2 models were used to downscale winter wheat water requirement under three concentration pathway scenarios; RCP2. 6, RCP4. 5, and RCP8. 5 in four periods (2020-2039, 2040-2059, 2060-2070, and 2080-2099). The Hargreaves-Samani (HS) model with less input variables in comparison with FAO-Penman-Monteith (PMF-56), was used for downscaling water requirement. The results of RCP2. 6 scenario showed that the maximum and minimum annual water requirement of winter wheat are 403 and 286 mm in 2040-2059 and 2020-2039 periods, respectively. These values for RCP4. 5 scenario were predicted to be 361 and 336 mm in 2020-2039 and 2040-2059 periods, respectively. For RCP8. 5 scenario, they were predicted to be 336 and 199 mm in 2020-2039 and 2060-2079 periods, respectively. The growing period will be reduced in all three proposed scenarios and the reduction rate in RCP8. 5 scenario is more than that in RCP4. 5 scenario. According to the results, climate change is generally expected to reduce agricultural water consumption in Gorgan plain by reducing the cumulative winter wheat water requirement.