In this chapter a brief review of literature has been presented under the different aspects as rainfall analysis, irrigation water requirement, scheduling and crop evapotranspiration, irrigation efficiency, ground water availability for irrigation and optimization models2.
1 Rainfall analysisOut of total cultivated area of India, 70% of area is rainfed which depends on characteristics of monsoon. In the total agriculture income marginal and small farmers constitutes 80% and depends on rainfed farming. During rainy season about 70% rainfall is received from south-west direction. During winter season, about 20% rain is received from north-east direction.Sharma (1986) conducted the probability analysis of 30 years (1965-85) rainfall data at barapani ,shilong.the study indicated that maximum daily rainfall of five years return period is 31 cm with 3 days as the corresponding length of dry spell.Chandra et al.
(1990) analyzed rainfall data of Rampur district of Uttar Pradesh. The analysis had been performed for the period of 20 years (1967-1986) for distribution of months according to normal, abnormal and drought months. The probability analysis had been carried out for 80 per cent, 50 per cent and 20 per cent chances in week, month, season and year. The results may be used for planning of cropping pattern and operation of irrigation scheme of the command area. The weekly and monthly rainfall analysis is also useful for better time of sowing, harvesting and other agricultural programmers. Suresh et al.
(1992) analysed rainfall data recorded at pusa farm by using the Weibull’s method of frequency analysis for predicting weekly rainfall at three probability levels of (10%,50% and 100%). The study included characterstics and variation in rainfall with respect to normal , abnormal and drought months in a years using Weibull’s method and report that at 90% probability level , the expected annual rainfall was below the drought level and during rabi season there was uneven distribution of rainfall.Tripathi et al. (1992) analysed rainfall data of Pantnagar for 25 years using Markov chain model to predict the sequences of wet and dry days. He concluded that weekly probability of particular day in the week showed that wheat planted in the first week of December had a 50% – 70% chances of getting rain at crown root initiation and flowering stages , and a sure chances of rain at jointing and milking stages.Subudhi et. al. (1996) analyzed 28 years rainfall data of phulbani.
they observed that 76% of annual rainfall received from july to October .the frequency analysis for maximum annual rainfall data was done by using Weibull’s technique .they found that kharif season received 839 mm rainfall.Agnihotri et al. (2001) worked out dates of onset of monsoon rainfall and established a relationship between the same and the earliest sowing week at chandigarh. Attempts were also made to relate the amount of seasonal rainfall i.
e. season of july, august and september. The monthly rainfall amount received in that month were separately taken with incidence of onset of monsoon rainfall as independent variable.
It was concluded that date of onset of monsoon rainfall influenced July rainfall and the total seasonal rainfall only. The monthly rainfall amount received in August and september individually were independent of the incidence of onset of monsoon rainfall at Chandigarh.Vogel (2006) The Probability Distribution of Daily Rainfall in the United States. This study has demonstrated that L-moment diagrams and probability plot goodness of fit evaluations provide new insight into the distribution of very long series of daily rainfall.
Though the commonly used 2-parameter Gamma distribution performs fairly well on the basis of traditional goodness-of-fit tests, L-moment diagrams and probability plot correlation coefficient goodness of fit evaluations reveal that very long series of uncensored daily rainfall observations are better approximated by a Pearson-III distribution and importantly, they do not resemble any of the other commonly used distributions.We conclude that for representing uncensored, full record daily rainfall, the 3-parameter Pearson-III distribution performs remarkably well. For cases in which only wet-day precipitation amounts are required, the 4-parameter Kappa distribution 10 should be the distribution of choice when only continuous distributions are considered.
Bhakar et. al. (2007) analyzed weekly rainfall data of ajia for 45 years (1960-2004) for crop planning to increase production. The weekly probable exceedence of rainfall at 20,50and 80% probability level. The annual rainfall at different probability levels (20%, 50% and 80%) was calculated as 1168.6mm ,346.3 mm, 13.
3 mm , respectively.Gouranga-kar et al. (2007) analysed the drought normal and surplus rainfall months in a year using 44 years rainfall (1960-2003) of three undivided districts of western Odisha. The probability of occurrence of moderate drought was found 0.28, 0.43 and 0.29 in Kalahandi, Bolangir and Koraput respectivelyBhakar et.
al. (2011) analyzed the rainfall data of 22 years, from 1987 to 2008 Average annual rainfall was found to be 732.4 mm. Normal, surplus and drought months as well as seasons and years have been presented which may be useful for planning of agriculture and irrigation schemes. The percent probability of Zaid and Kharif seasons to be normal is 81.82 % and 77.27 %, respectively. The probability for Kharif season to be drought is 4.
55 % while that of Zaid season is 9.09 %. Total amount of rainwater available during the Kharif,Rabi and Zaid seasons was found to be in the range of 280.7 – 1100.3 mm, 0.
0 – 128.7mm and 63.7 – 452.
5 mm, respectively. Thus the surplus amount of water during Kharif Season may be stored in water harvesting structures, in order to provide supplemental irrigation for growing of wheat crop in Rabi season or in order to help in recharge of ground water.Mishra et.al. (2013) conducted statistical and probability analysis of 40 years daily rainfall data for the period of 1971-2010 on weekly ,monthly and annual basis.
Rainfall data for the period of 1971-2010 on weekly , monthly and annual basis. Rainfall pattern was also analyzed by using Theissen polygon and Histogram technique. Probability analysis was done using two and three parameter probability distribution function.
Amongst the nine distribution fitted in the Tawa canal command ,log normal distribution and weibull’s distribution was found to be best fit distribution for annual and monthly rainfall respectively.Ray et al. (2016) studied and analyzed the daily rainfall data of 19 years (1997-2015) of Mayurbhanj district of Odisha in order to found out the distribution and characteristics of the rainfall of the area.
The analysis had been done at different probability level (25%, 50%, 70%, 80% and 90%). The statistical analysis as standard deviation and coefficient of variation had been found for all the weeks of the year. Similarly the Seasonal and yearly analysis was also done for the daily rainfall data of Mayurbhanj station. As results the annual average rainfall was about 1808.2 mm with 100 numbers of rainy days. Due to high rainfall in monsoon season it contributes more than 75% of the average annual rainfall, in monsoon season highest weekly rainfall (more than 20 mm) occur 18th to 41st weeks of the year. However, the occurrence of rainfall was almost zero from 1st week of January to 14th week of April and from November 45th week to 52nd week of December.Rajeevan et al.
(2017) developed high resolution (1° × 1° lat./long.) gridded daily rainfall dataset for the Indian region. There were 1803 stations with minimum 90% data availability during the analysis period (1951–2003). For the analysis, they followed the interpolation method proposed by Shepard.
Standard quality-controls were performed before carrying out the interpolation analysis. Comparison with similar global gridded rainfall datasets revealed that the present rainfall analysis is better in accurate representation of spatial rainfall variation. Using this gridded rainfall dataset, an analysis was made to identify the break and active periods during the southwest monsoon season (June–September). Break (active) periods during the monsoon season were identified as those in which the standardized daily rainfall anomaly averaged over Central India (21–27°N, 72–85°E) is less than –1.0 (more than 1.0).
The break periods thus identified for the period 1951–2003 were comparable with those identified by earlier studies. 2.1.1 Prediction of rainfall using Artificial neural network (ANN)Enireddy (2010) Artificial neural network is one of the most widely used supervised techniques of data mining. In this study they used the back propagation neural network model for predicting the rainfall based on humidity, dew point and pressure in the country India. Two-Third of the data was used for training and One-third for testing .
The number of training and testing patterns are 250 training and 120 testing .In the training we obtained 99.79% of accuracy and in Testing we obtained 94.28% of accuracy.
From these results rainfall for the future could be predicted.Kumar et. al. (2012) In this research, possibility of predicting average rainfall over Udupi district of Karnataka has been analyzed through artificial neural network models. In formulating artificial neural network based predictive models three layered network has been constructed. The models under study were different in the number of hidden neurons.
This study implements one of the applications by building training and testing data sets and found the number of hidden neurons in these layers for the best performance.Chaturvedi et. al. (2015) The study materialize training, testing of data set and detecting the hidden neuron in the network. In the research, rainfall prediction in the region of Delhi (India) was analyzed using neural network back propagation algorithm. Three layer model was used for training and studying different attributes of the hidden neurons in the network.
Bisht, Mehta et. al. (2015) In this study they used the back propagation neural network model for predicting the average monthly rainfall of Nainital town based on wet bulb, dry bulb, minimum temperature, maximum temperature and wind speed in Nainital town.
The results showed that the ANN techniques extremely well with the actual data values as compared to the SVM techniques. From these results rainfall for the future could be predicted.2.1.2 Best fit probability distributionSalami (2004) indicated that Gary and Robert in1971 studied the normal, log-normal, square-root-normal and cube-root-normal frequency distributions of meteorological data for Texas. The results of this research shows that precipitation data conform to the square-root-normal distribution, while evaporation and temperature data conform to all of the frequency distributions tested. The evaporation, temperature and precipitation data were further fitted to the Gumbel extreme-value and log-Pearson type III distributions.
The precipitation data fit the log-Pearson type III (LP3) distribution more adequately than the Gumbel distribution, while both the evaporation and temperature data conform very well to Gumbel distribution. Lee (2005) studies the rainfall distribution characteristics of Chia-Nan plain area, by using different statistical analyses such as normal distribution, log-normal distribution, extreme value type I distribution, Pearson type III distribution, and log-Pearson type III distribution. Results showed that the log-Pearson type III distribution performed the best in probability distribution, occupying 50% of the total station number, followed by the log-Normal distribution and Pearson type III distribution, which accounts for 19% and 18% of the total station numbers respectively. Olofintoye et. al. (2009) This study focused at studying the peak daily rainfall distribution characteristics in Nigeria, by using different statistical analyses such as Gumbel, Log-Gumbel, Normal, Log-Normal, Pearson and Log-Pearson distributions. 20 stations having annual rainfall data of fifty-four (54) years were selected to perform frequency analysis. Mathematical equation for the probability distribution functions were established for each station and used to predict peak rainfall, the predicted values were subjected to goodness of fit tests such as chi-square, Fisher’s test, correlation coefficient and coefficient of determination to determine how best the fits were.
The model that satisfies the tests adequately was selected as the best fit model. Results showed that the log-Pearson type III distribution performed the best by occupying 50% of the total station number, while Pearson type III performed second best by occupying 40% of the total stations and lastly by log-Gumbel occupying 10% of the total stations.Lars S.
Hanson and Richard Vogel (2015) this described probability plot correlation coefficient test statistics and L-moment diagrams to examine the complete series and wet-day series of daily precipitation records at 237 U.S. stations.
The analysis indicates that the Pearson Type-III (P3) distribution fits the full record of daily precipitation data remarkably well, while the Kappa (KAP) distribution best describes the observed distribution of wet-day daily rainfall. We also show that the G2 distribution performs poorly in comparison to either the P3 or KAP distributions. 2.2 Irrigation EfficiencyIrrigation efficiency is defined as the ratio between the amount of water used to meet the consumptive use requirement of crop plus that necessary to maintain a favourable salt balance in the crop root zone to the total volume of water diverted, stored or pumped for irrigation.Ahmed et. al.
(1993) studied automatic irrigation scheduling by remote constant tensiometers under arid climatic conditions. They put tensiometers a different places and depths to measure moisture, control and range is set to the start the irrigation and stop. They found 8% increment in average yield and 24% water saving as compared to common scheduling practice.Kasa et al. (2002) conducted study to determine the performance of border irrigation methode, based on some performance indices: application efficiency, storage efficiency and distribution uniformity, with irrigation water loss indicators such as deep percolation ratio and runoff ratio which were determined using the computer program, BORDEV. It was found that the maximum possible attinable efficiency with the present practice was in order of 52% – 58% and the distribution uniformity was in th erange of 84% – 91%showing that the applied depths were somewhat uniformly distributed throughout the border. Nyatuame et. al.
(2013) conducted study to show the effect of scheduling irrigation on the water use efficiency and yield of cabbage. The overall results clearly revealed that in order to obtained optimum cabbage yield and also allocate limited water resources suitably, cabbage should be irrigated daily and in the evening. This means that in order to obtain higher yield in cabbage so as to achieve best weight of the heads, it is important that farmer’s practice daily irrigation since cabbage requires an abundant and well-distributed water supply.Jadhav et. al.
(2014) developed different management strategy to discharged saved water for irrigation in Panchnadi Minor Irrigation Project. There were many losses from lined and unlined canal irrigation system so firstly they determined the conveyance losses under existing situation. Different conveyance losses as evaporation, seepage losses are taken place from the bed and sloping surface of the canal. Seepage loss (98.37%) accounted as major and evaporation loss (0.3%) accounted as minor loss from the canal. The overall results were obtained as overall efficiency from lined section (75%) and unlined section (52%) and unlined field channel (35%) and total loss from the lined (0.184 Mm3), unlined section of canal (0.
61 Mm3) and unlined field channel (0.183 Mm3) respectively. These results showed that converting the unlined canal sections into lined sections improved conveyance efficiency up to 75% and water saved as 0.376 Mm3 and by this water, 43 ha additional area can be irrigated. It was concluded that by adoption of micro irrigation was beneficial because it used small quantities of water as drops or miniature spray through emitters so the output leads to increase in yield and saved water.Elbegawy et.
al. (2014) conducted study in farmers’ fields during 2009/2010–2010/2011to evaluate the water use efficiency and economic viability of sprinkler irrigation system for growing wheat crop. Two field experiments were conducted in the Research and Production Station of the National Research Centre in El-Nubaria El-Behera Governorate.
The water-use characteristics of wheat were studied in the field under sprinkler irrigation system. Treatments consisted of two sprinkler irrigation systems, solid set sprinklers (S1) and hand move laterals (S2), and three irrigation frequencies (IF1: once per week; IF2: twice per week, IF3: three times per week). Under solid set sprinkler irrigation system and irrigation frequency three times per week (IF3) occurred best result of technical side and occurred also, maximum value of net return and there are significant differences. Thokal et. al. (2014) evaluated the performance of AquaCrop model to find out the prop performance by considering deficit and full irrigation condition in region of Maharashtra named Konkan. The study was also done for the yield performance of the crop under different irrigation depths.
For this study the Panchnadi Minor Irrigation. Project was selected. The irrigation efficiency was improved by the application of developed irrigation management and the results were compared with the present performance of the project. The net benefit of the project was found out at 10%, 30% and 50% deficit condition was Rs. 23.
67, 12.20 and 0.74 lakh, respectively. From the results it was concluded that the strategy of 10% deficit condition might be beneficial for the command area.
The water allocation at 10% deficit condition was 0.31Mm3 at the live storage of 1.461Mm3 in the reservoir.
The total running days of the canal was approximately 243 days (31st May to 1st October). Under the existing scenario, the overall efficiency was found for the unlined and lined section of canal was found 52% and 75%. Similarly, 35% was observed for the unlined field section.
The total loss observed as 0.61, 0.184 and 0.183 Mm3 for the unlined, lined and unlined field, respectively.
Pamdaya et. al. (2017) conducted study at national center for agricultural Mechanization-Ilorin.
Data were collected and calculated for coefficient of variation, emission uniformity and distribution uniformity as estimated by Mirriam and Keller (1978).The results shows the micro-irrigation system should be operated at 1.5 to 2.0kg/cm2 under low wind speed condition. Data regarding operating effective radius, average application depth, absolute maximum depth, mean depth, distribution characteristics were also used for the estimation of precipitation pattern. Determining the effect of irrigation system application efficiency on other type of crop and other type of irrigation method other than the ones used in this study were recommended among others. 2.3 Evapotranspiration Estimation ModelsThornthwaite (1948) developed an equation to predict monthly evapotranspiration from mean monthly temperature data.
Blanney and criddle (1948) observed that consumptive use of crops during growing seasons was closely correlated with mean monthly temperature and dry light hours and developed a simplified formula for estimating consumptive use for the arid western regions of the United States.Christiansen and Hargreaves(1969) developed an equation for estimating refrence crop evapotranspiration from USWB “class A” pan evaporimeter and several weather parameters.Jensen et. al.
(1971) have showed that formula developed by them provides a good estimate of potential evapotranpiration when wind speed and humidity data are not available and advection is not severe.Monteith (1981) developed a combination method for estimating evapotranspiration known as the penman-Monteith method. The penman –monteith equation related not only the aerodynamic resistance to sensible heat and vapour transfer, but also surface resistance to vapour transfer too.Doorenbos and Pruitt (1977) presented the revision of most fundamental equation of Blaney and Criddle method.
The modified Penman method was found very suitable and had been adopted in this study as follows: ETcrop = Wn + (1-w) f(u) (ea – ed) … (2.1) where, ETcrop = crop evapotranspiration; WRn = radiation; (1-w) f(u) (ew – ed) = aerodynamic; ea = saturation vapour pressure (m bar) at the mean air temperature; ed = mean actual vapor pressure of the air (m bar) = ea (RHmean/100); Rh = relative humidity; f(u) = a wind related function; (1-w) = a temperature related and elevation related weighing factor for the effect of wind and humidity on ETc; W = a temperature and elevation related weighing factor for the effect of radiation on ETc; Rn = net radiation (Rns – Rnl); Rns = net incoming short wave solar radiation = RA (1-?) 0.25 + 0.5 (n/N)Wane and Nagdeve (2014) estimated the reference evapotranspiration and effective rainfall for Vidarbha region situated in Nagpur district of Maharashtra state by using Cropwat, which is a computer based model. The study period was 2000 to 2009, in which daily meteorological data had been used like rainfall, maximum and minimum temperature, wind speed, relative humidity and sunshine hours. The value of average peak monthly ETo were estimated for April and May as 6.52 and 6.
99 mm day-1 respectively. These high values were obtained due to high temperature during the summer months, whereas in the winter month, the value of average minimum ETo were obtained as 3.06 and 3.22 mm day-1 for month of December and January, respectively. The maximum effective rainfall estimated in July (221.
05mm) which is followed by August (194.76 mm), June (150 mm) and September (137.63 mm) months, respectively. The value of average annual effective rainfall was obtained as 803.45 mm.
2.3.1 Crop coefficient Gonita and Tiwari (2010) estimated actual crop evapotranspiration of wheat crop grown in tarafeni south Main canal (TSMC) irrigation command of west Bengal state in India using remote sensing and GIS techniques. Crop coefficients maps were generated for each month of wheat crop season using the relationship between vegetation indices and crop coefficients (Kc) of wheat for corresponding months.monthly ETo was calculated based on FAO-56, Penman-Monteith method and combined with spatially distributed Kc maps of different months of wheat crop season to generate crop evapotranspiration (ETc) maps each month. The crop water demand of wheat estimated using spatially distributed ETc maps for months of Decmber 2003,January 2004,february2004,March 2004 (1st fortnight ) and maech 2004 (2nd fortnight) were found to be 3.98,8.
14,4.66,2.49 and 1.21 MCM respectively.
Raki et. al. (2010) evaluated the evapotranspiration in the semi-arid region of Tensift al haouz , Marrakech (center of Morocco) using three empirical methods Makkink, Priestley-taylor and Hargreaves.The obtained ETo data were used to estimate crop water requirement (ET) of winter wheat using the crop coefficient (Kc) approach and results were compared with ET measured by the Eddy covariance techniques. The result showed that using the original empirical coefficients and Cmin Hargreaves ,priestley- Taylor and Makkink equatio.ns, respectively, the Hargreaves method agreed fairly well with FAO-PM method at the test site.Ko et.al.
(2010) conducted study to determine growth-stage-specific Kc and crop water use for cotton (Gossypiumhirsutum) and wheat (Tritium astivum) at the Texas Agri life Research field at Uvalde, TX, USA from 2005 to 2008 . Weighing lysimeter was used to measure crop water use and local weather data were used to determine the reference evapotranspiration (ETo). Seven lysimeters , weighing about 14 Mg , consisted of undisturbed 1.5m – 2.
0 m – 2.2m deep soil monoliths. Six lysimeters were located in the center of 1 ha field beneath a linear – move sprinkler system equipped with low energy precision application (LEPA) and a seventh lysimeter was established to measure reference grass (ETo) . crop water requirement s, Kc determination and comparison to existing FAO Kc values were determined over 2 years period on cotton and 3 years period on wheat. Seasonal total amounts of crop water use range from 689 to 830 mm for cotton and from 483 to 505 mm for wheat. The Kc values determined over the growing seasons varied from 0.
2 to 1.5 for cotton and 0.1 to 1.7 for wheat.
Asati (2012) analyzed the 10 years rainfall data for investigation of drought in Brahmapuri, Nagpur. The results of study were used for irrigation planning in that area. Based on investigation was occurred for 10 years period and it was observed that in 1972 the minimum rainfall recoded as 801.9 mm which was experienced as drought year.
The results show that the maximum frequency of drought was recorded in April and May. Based on this comparison the monthly, seasonally and yearly drought data were calculated and then graph had been plotted between rainfall magnitude and return period. 2.4 Groundwater InventoryThe Ground water resources are second major source of irrigation purpose after rainfall. Many studies have been done for evaluation of depth of water table and availability of ground water for irrigation purposes. Here some studies have been given in the respect of ground water availability and its uses.Ali (2015) The technical aspects of the study was rooftop rainwater harvesting which was considered to be catchment areas of College of Nursing, Teerthanker Mahaveer University, Moradabad. Required data such as catchment areas, rainfall data, runoff, groundwater condition etc.
was collected and calculated. Then a recharge pit of suitable capacity and design was constructed. Optimum location of recharge pit was done using the hydrological analysis from the available data. The cost of the project was also calculated.
Bhargava et. al. (1983) performed the field test at U.P.
I.R.I. Roorkee to found out that what part of rainfall entered into the surface of soil to recharge the ground water table, for this analysis the radioisotope technique had been used.
Due to the rainfall alone, the estimation of ground water rise was measured by the tritium tagging method used. For tracing the movement of rainfall through the pores of soil , the two years (1979-1981) analysis had been donein Ganga –sadar interbasin of Bareilly, Buduan and Pilibhit ,the districts of U.P. by the study it was found that about 21.
33% to 26.17% of the rainfall was penetrated into the ground to recharge the ground water table. The following empirical relationship was found out from the study for estimation of ground water which entered into the soil by rainfall to raise the level of water table.Rp=3.
24(R-30)^0.49Where, R= rainfall (cm),Rp= rainfall penetration (cm). Owor et. al.
(2009) described increases in rainfall intensities as a result of global warming may promote rather than restrict groundwater recharge in similar environments of the tropics. Further monitoring and research are required to test the robustness of these findings, but the evidence presented was consistent with recent modeling highlighting the importance of explicitly considering changing rainfall intensities in the assessment of climate change impacts on groundwater recharge.2.5 Optimization modelsNormally linear programming is an optimization technique which is widely used to allocate the limited resources because of the proportionate characteristics of the allocation problems. Maji and Earl (1980) developed optimal reservoir management and crop planning under deterministic and stochastic inflows.Srivastava et.
al. (2002) described a methodology which integrated a genetic algorithm (GA) with a continuous simulation, watershed scale NPS pollution model, annualized agricultural non-point source pollution model to optimize the best management practices on a field by field basis for entire watershed. The optimization analysis was performed to identify BPM that minimized long term water quality degradation and maximized net form to an annual basis.Habab et al. (2003) used linear programming technique to construct the relation between the quantity of water demanded and the economic price of water. Linear regression technique was used to derive water demand function.
Results showed that there was an increase in crop intensity (118 percent), and water demand elasticity (1.7 percent).Saravi et al. (2003) conducted study to suggest and compare different alternatives for optimum utilization of resources in Garmabdasht (subwatershed of Gorgan, Iran), using goal programming as well as socioeconomic and environmental objectives.
Results showed that the proposed pattern using goal programming has acceptable and flexible outputs when compared to other linear preogramming techniques.Itoh et. al. (2003) formulated the crop planning problem as a linear programming problem.
They assumed the profit coefficient for agricultural products aren’t certain values because of influence on future with uncertain (stochastic) values are considered for decision making in agricultural forms.Srinivasa et al. (2004) described the application of Genetic Algorithms (GA) for irrigation planning. The GA technique was used to evolve ef?cient cropping pattern for maximizing bene?ts for an irrigation project in India. Constraints include continuity equation, land and water requirements, crop diversi?cation and restrictions on storage.
Khatri et al. (2005) developed a linear programming model based on conjuctive use planning of surface and ground water for minors 1 to 4 of Waghodia branch of Deo Irrigation Project, Gujrat, India during 1997-98. Model subjected to surface water availability, canal capacity, ground water potential, pumping capacity, drainage requirement, water requirement, management, socio-economic, time integration and non-negetivity constriants..Vivekanandan et. al.
(2008) studied optimization plans of cropping pattern using goal programming approach. They considered irrigation planning and scheduling are essential components of water management in irrigated agriculture. For maximization of net return, protein and calorie values with minimum land and water for barna command area. The factor like amount of net return, utilization of surface and ground water by different plans are considered for the selection of best cropping of GP for optimization of cropping pattern for command area.Raju et.
al. (2012) conducted the study , applicability of multi-objective Differential evolution (MODE) in irrigation planning perspective is demonstrated through a case study of Mahi bajaj Sagar project , Rajasthan, india.it is concluded that selection of suitable parameters is necessary for effective implementation of above methodologies in real –world planning situation.Sadat et.
al. (2015) evaluated rule curves of reservoir operation and compares them for baseline and future periods. The rules are calculated by genetic programming (GP).
Also, the rules extracted are based on the rate of inflow, storage volume, and downstream irrigation network demand. The objective function used is the minimization of the average of squared monthly relative deficiencies in the allocation of water to irrigation demand. The study focused on the reservoir system as well as the downstream irrigation network of Aidoghmoush dam in East Azerbaijan, Iran, under baseline conditions (time interval 1987–2000) and climate change conditions (time interval 2026–2039). To investigate the optimal allocation policy, three operational scenarios are considered: (1) development of current rules under baseline conditions; (2) employment of current rules for future conditions; and (3) development of future rules for future conditions.
Results show that the current allocation policy (resulting from current optimal rules) should be modified under climatic change conditions. Also, the investigation indicated that the application of a future optimal allocation policy under future conditions relative to current rules under current conditions decreases (improves) the root-mean-square error (RMSE) and mean absolute error (MAE) performance criteria approximately 29 and 30%, respectively. In addition, efficiency indicators in the optimal allocation of reservoir water are calculated under climate change (policy used in the third operational scenario) and compared with its corresponding values in baseline conditions.
