پرویز حقیقت جو

Quinoa (Chenopodium quinoa) has been introduced as one of the crops to ensure food security in the world, which can tolerate water and drought stress. However, the cultivation development of quinoa under water and salinity stress in Khuzestan province, Iran, should be based on determining the limits of irrigation water and determining its tolerance to salinity. To achieve this goal, the present research was conducted in a research farm located in Baghmalek city, in the east of Khuzestan province, Iran, at the latitude of 31° 41’ N and longitude of 49° 51’ E during 2022-2023. In this research, the quinoa was grown under pulse drip irrigation. Irrigation water was supplied between 60% and 100% of the water requirement (code -1 to +1) in different plots. Applying salinity treatment with two water sources with salinities of 0.5 and 0.6 dS.m-1. Thus, in the absence of salinity stress (code +1), pulse irrigation was done with three fresh water pulses. In the conditions of full salinity stress (code -1), pulse irrigation was done with three pulses of saline water. Response-surface method was used to determine the optimal amounts of these parameters. The results showed that in the optimal state (Treatment of 60% irrigation and pulse irrigation in the form of fresh water-salt water-fresh water), the dry weight of fodder was equal to 6845.7 and the wet weight of fodder was equal to 24827.9 kg.ha-1. In addition, the percentage of fiber and soluble sugars of fodder reached 0.15 and 10.4%, respectively. It is worth mentioning, the optimal amount of irrigation water was equal to 60% of the water requirement and the pulse method code was equal to zero. Therefore, it is suggested to reach the optimal quantitative and qualitative parameters of quinoa fodder, the pulse irrigation method is done in the form of fresh water-salt water-fresh water and by providing 60% of the water requirement of the quinoa.

The presence of salts in irrigation water and high evapotranspiration rate in Sistan and Baluchestan cause the accumulation of salt in the soil and as a result increases the osmotic force in soil. One method for crop irrigation planning is the use of Idso crop water stress index (CWSI) and Moran water deficit index (WDI). In order to investigate the effect of irrigation water salinity on CWSI and WDI and also wheat irrigation planning, an experiment was performed with three different irrigation water quality in Iranshahr, through the 1398-99 crop year. The experiment was performed as a randomized complete block design with 4 replications and 3 treatments including (1) irrigation water with salinity of 0.7, (2) irrigation water with salinity of 2.5 and (3) irrigation water with salinity of 5.2 dSm-1. The results showed that irrigation water with salinity of 0.7 and 2.5 dSm-1 have no significant difference in terms of yield and water use efficiency. However, the use of irrigation water with a salinity of 5.2 dSm-1 caused a significant reduction in yield and water use efficiency at a 1% level. Moreover, the salinity of irrigation water increased the upper baseline in the Idso diagram and the upper side of the proposed trapezoid of Moran, but it had no effect on the baseline (plant transpiration limit under standard conditions). As a result, according to the definition of CWSI and WDI indices, these two indices decreased by 19% and 22%, respectively compared to the control treatment. The average optimal CWSI and WDI in the first (non-saline) treatment were 0.39 and 0.38, respectively, and they were 0.33 and 0.32 in the very saline treatment, respectively. This showed a decreasing trend of irrigation frequency with increasing salinity of irrigation water.

One of the important indicators in determining water use efficiency for agricultural productions is the index of water consumption efficiency. This study was conducted to determine the water use efficiency of pistachio in Sirjan city located in the southwest of Kerman province in 2015. To compare bubbler and subsurface drip irrigation methods, 27 pistachio orchards with an area of ​​1373 hectares were selected to determine water use efficiency, of which four orchards were equipped with the bubbler system and the rest were equipped with subsurface system. Bubbler discharge, irrigation time, area of ​​each irrigation plot and crop yield were studied. By measuring the volume of irrigation water after irrigating the field and the amount of water wasted along the route, the average gross volume of applied irrigation water was calculated. After harvesting, the average yield of pistachio per hectare was calculated. The results of this study showed that the subsurface drip irrigation method compared to bubbler irrigation method, not only significantly increases water use efficiency but also increases crop yield by 34%. Therefore, according to the results and also the current water shortage conditions in Sirjan city, the use of subsurface drip irrigation method is more appropriate than bubbler irrigation to irrigate pistachio orchards.

Anthropogenic water scarcity is among global concerns which threats the sustainability of the world in future. Overexploiting limited blue water resources for supplying human’s growing food demand is the main root of such a scarcity. Being still in its infancy, water footprint (WF) benchmarking is a proper method to determine the inefficient fraction of water consumption. Hence, we assessed if achieving benchmark levels can limit agricultural WF to its sustainable cap in Khuzestan province, which is the agricultural backbone of Iran.

The research consists of three stages including WF accounting, and sustainability and efficiency assessment. First, consumptive and degradative WFs were accounted for 32 in counties of Khuzestan province over the period 1986-2016. Green and blue WFs were accounted by dividing green and blue evapotranspiration by crop’s yield, respectively. The AquaCrop model was used to simulate daily evapotranspiration, and then, seasonal values were estimated by aggregating daily values. Grey WF was estimated for the applied nitrogen fertilizer in croplands. In the next stage, the sustainability status of water consumption pattern was assessed by dividing overall blue WF by blue water availability. The latter was estimated by subtracting environmental flow requirements from natural runoff. Thereafter, WF benchmarks were extracted for 10th, 25th, and 50th level of crop production, and based on which, the inefficient fractions of blue WFs were estimated for individual crops. Finally, inefficient blue WFs were compared with unsustainable ones to know if achieving benchmark levels can limit agricultural blue water consumption to its sustainable caps in the study area. A correlation assessment was also done to determine the main roots of inefficiency in Khuzestan province.

Along with a 63% increase in crop production and 53 increase in unit blue WF, the overall blue WF increased by 80% over the study period. Such an increasing pattern caused groundwater overexploitation with an annual average rate of 12 million m3 y-1. The contribution of green WF in consumptive WF decreased from 60% in 1986 to 48% in 2016. Overall grey WF, however, followed a decreasing trend, with an average annual decreasing slope of 140 m3 ha-1 y-1, which mainly occurred by a considerable reduction in cash crop’s grey WF. In 2016, a total of 0.98 billion m3 of blue water was consumed unsustainably, which was roughly 85% higher than one in 1986. The number of countries with unsustainable water consumption also increased from 2 counties in 1986, to 9 counties in 2016. Stable crops had the largest contribution in unsustainable WF. Based on the results of efficiency assessment, and considering WF benchmarks developed at 25th of crop production level, inefficient blue WF varies in the range of 0.26 to 606 million m3 y-1 in different counties in 2016. Compared with 1986, the inefficiency increased by 10-3860 m3 ha-1 in 16 counties in 2016. While achieving benchmark levels can reduce blue WF to its sustainable level at provincial scale, unsustainable blue WFs in Andimeshk and Ramshir counties are still 6.8 and 340.9 million m3 higher than the inefficient blue WFs, respectively. The correlation assessment showed that inefficiency increase along with any increase in added value by agricultural production and/or export, unit blue WF, blue water scarcity level, and access to groundwater consumption; while it decreases with any increase in the number of employees per ha, crop yield, and cash crop’s harvested area.

WF assessment showed that crop production in Khuzestan province is done at the cost of deteriorating environment, which threats the stability of agricultural system in near future. While achieving benchmark levels helps with being limited to sustainable WF caps through removing inefficient blue WF fractions, there are still the risk of unsustainability in some regions, which indicates the importance of high-resolution assessments for determining hotspots. Hence, further research is required to determine temporal hotspots as well, in order to implement a proper sustainable development plans.

Virtual water in Tony Allen's definition is the amount of water that consumes during agricultural or industrial products and services (Sadek, 2011; Mousavi et al., 2009; Mohammadi, 2012; Agahi et al., 2011; Mehdi Zadeh, 2014; Mokhtari, 2013). The population growth, water crisis, global warming increases the challenges of food security. Therefore, it is essential to consider enhancing water productivity as well as reducing virtual water. Livestock is a kind of activities that consume a considerable amount of water. Some previous studies have been done to the estimation of virtual water in the livestock industry in India (Brindha 2017) and in Saudi Arabia (Shakhawat Chowdhury et al., 2017) in recent years. Moreover, Ibidhi and Salem (2020) reported the water footprint of livestock products strongly related to the water footprint of forage and other kinds of livestock nutrition. Although Iran located in an arid region and the Khorasan Razavi province has an active livestock industry, there were not any surveys in the virtual water trend of live stocks in Khorasan Razavi province. Accordingly, in this study, the virtual water of the purebred, hybrid and native cows has estimated in each city of Khorasan Razavi province.

The virtual water content of livestock products was calculated based on Chapagin and Hokestra (2003). At first, the water requirement of purebred, native and hybrid cows was calculated with CROPWAT software for three consecutive years from 2015 to 2017in 28 cities of Khorasan Razavi province. Then the virtual water content of live cows was calculated. Finally, the virtual water of milk and meat was calculated separately for each city.

The results showed that the highest values of milk’s virtual water, related to hybrid dairy cows with 1590 cubic meters per ton (m3 ton-1) in in Gonabad and Bajestan cities and the lowest one related to native dairy cows with 309 m3 ton-1 in Mashhad. Moreover, the highest amount of virtual water of meat production belonged to hybrid dairy cows with 52350 m3 ton-1 in Gonabad and lowest was for purebred 9232 m3 ton-1 in Mashhad. Therefore, it would be recommended to produce milk from native cows and meat from purebred. Compared to world average values, virtual water of native cow’s milk in Mashhad was 68.8% lower while in Gonabad it was 60.6% higher. Therefore, it could be recommended to produce milk from native cows and meat from purebred.

Livestock feed compositions are one of the most influential factors on the total amount of virtual water in livestock products. In accordance with the previous studies, it could be suggested, the most milk should be produced in industrial conditions as well as should produce in cities which have advantages in producing fodder, based on virtual water aspects. It also might suggest changing the cultivation patterns of the province with less water consumption such as clover, fodder peas, fodder beet and sorghum. Other influential factors in the total amount of virtual water are cattle breeds and the value of milking. Virtual water for milk and meat productions in Gonabad city had the highest values in all three types of livestock and was the lowest for Mashhad and Quchan cities, so it might be better to grow more dairy cattle in Mashhad as well as to decrease the livestock productions and development in Gonabad and similar areas. Calculating the virtual water trends for the whole country also are deeply recommended in order to achieve a better vision for water resource management. Keywords: Virtual water, Khorasan Razavi province, Animal feed, Milk and meat, CROPWAT.

Virtual water can be considered as a solution for the water crisis in arid countries which they have water scarcity. Meat products utilize far more virtual water than plant products. Therefore, estimating the virtual water of livestock products and revealing its information can play an important role in adopting proper management policies to reduce the pressure on water resources. The aim of this study was to determine the virtual water content of boneless meat and the meat without bones. In order for that, virtual water of livestock products of 28 cities located in Khorasan Razavi province was calculated for years 1394, 1395 and 1396. In this study, by calculating the water need of animal feed with CROPWAT software, the amount of virtual water of livestock products was estimated. Calculations revealed the highest and the lowest "virtual water of cow with bones" among all three types of purebred, native and hybrid cows in Bajestan (30523 ) and Quchan (4956 ), respectively and the highest and the lowest "virtual water content of boneless cow" is among the three types of cows in Gonabad (57917 ) and Quchan (7894 ). The highest and the lowest "virtual water of boneless mutton” are in Gonabad (5643 ) and Mashhad (763 ), respectively, and the highest and the lowest "virtual water content of mutton without bones" are in Sabzevar (6446 ) and Mashhad (864 ).

Due to the recent water shortages and droughts, the importance of water productivity is becoming more apparent. The main objective of this study is to examine water productivity of wheat according to its water requirement and proper irrigation management. This research was performed in Orzouieh city of Kerman province located in an arid region. In this study by using meteorological data of the region, wheat evapotranspiration was calculated by CROPWAT. Then, potential evapotranspiration was also calculated by Hargreaves-Samani and Jensen-Haise methods to verify the values resulted by CROPWAT package. Irrigation Hydromodule was calculated too. The Volume of water consumed by wheat per hectare during growing season was also calculated using irrigation hydromodule. For performing this study, three wheat farms with proper irrigation management were selected. For calculating physical and economical productivity of wheat farms in the region, one of the farms was examined. The results of this study showed that the physical productivity (CPD) is equal to 1.6 kg/m3 and economical productivity (NBPD) is equal to 13000 Rls./m3. The calculated productivities demonstrate that supplying actual water requirement of wheat along with proper management of the fields, results high economical efficiency.

ABSTRACT In order to investigate the yield and water use efficiency of wheat (Chamran 2), rapeseed (Hyola 50), potato (Sante) and corn (704) under different levels of irrigation, the present research was carried out in two years 2017-2018 and 2018-2019 as complete randomized block design with four replications at Agricultural Research Center of Jiroft. Irrigation levels were 50, 75 and 100 % of the crop's water requirement. The Yield, biomass, harvest index and water use efficiency were investigated for the proposed crops. The results showed that the effect of irrigation levels was significant during two years on grain yield, biomass and water use efficiency at 1% level and on harvest index at 5% level. By increasing irrigation water, both grain yield and biomass were increased significantly in all crops. Potato and rapeseed had the highest (0.65-0.74) and the lowest (0.20-0.25) water index, respectively. On the other hand, potato and rapeseed showed the highest (10.68-22.76 kg/m3) and the lowest (1.29-1.84 kg/m3) water use efficiency, respectively. The 75% irrigation level is recommended due to lower decrease in yield and higher increase in water use efficiency. In general, according to the results of this study, the highest water use efficiency was observed in potato, wheat, maize and rapeseed plants, respectively. Overall potato and maize rotation is recommended due to higher water use efficiency in Jiroft.

In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were   then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station.

The objective of this research is to compare the performance of linear time series models of Box-Jenkins and IHACRES, multilayer perceptron ANN and hybrid ARMA-ANN in order to simulate and predict the daily discharge of Maroun River. For this purpose, daily discharge data of (1991-2006) were used for calibration and data of (2007-2017) were used for verification of the models. Schwartz (SBC) and Akaike information criterion (AIC) were used to select the best model. Different scenarios, learning algorithms and transfer functions with various neuron structures were used to develop the ANN model. The first scenario with less parameters and delay time was selected as the best ANN model in prediction of daily flow rate. Evaluation indices showed that the conceptual model performance in verification stage was better than that in calibration stage. Also, the 4th order moving average model with R2=0.61 had the weakest performance as compared to the other Box-Jenkins models. Evaluation indices indicating a relative promotion for ARMA-ANN hybrid model as compared to the other proposed models.  As, ARMA-ANN hybrid model obtained the highest  R2=0.86 and Nash-Satcliffe coefficient equal to 0.81. The results prove the ability of ARMA-ANN hybrid model for simulation and prediction of daily discharge, as compared with other models.

To study the effects of different levels of vermicompost and humic acid on the seed macro-elements uptake, seed yield, and essential oil of cumin (Cuminum cyminum L.), an experiment was conducted in a field located in Mobarakeh city as factorial based on a randomized complete block design with three replications during the growing season 2016-2017. Experimental treatments included vermicompost at three levels (0, 5 and 10 ton ha-1) and foliar application of humic acid at four levels (0, 2, 4 and 6 lit ha-1). The results showed that the application of 10 ton ha-1vermicompost increased the essential oil yield and seed phosphorus content by 86.7 and 33% as compared to control, respectively. The foliar application of 4 lit ha-1 humic acid along with 0, 5, and 10 ton ha-1vermicompost application increased the seed potassium content by 27.8, 9.6, and 9.2% as compared to control, respectively. The highest percentage of seed nitrogen was observed in the combined application of humic acid at 4 lit ha-1 and vermicompost at 10 ton ha-1, which was 11.7% more than control. The foliar spray of 4 and 6 lit ha-1 humic acid at 10 ton ha-1 vermicompost increased seed yield by 11.1 and 9.9% as compared to control, respectively. The combined use of humic acid and vermicompost could meet the nutritional needs of cumin and could be considered as an effective step towards achieving sustainable agriculture goals while increasing quantitative and qualitative performance.

In the present study, considering the importance of wind speed forecasting, one of the important factors especially in the discussion of evaporation in watershed, is the construction of water structures and dams ponds so For this purpose we use the SARIMA linear time series models and non-linear SETAR linear time series models to predict wind speed of Ahwaz synoptic station with a statistical period of 22 years 1988-2002. Correctly fitted Sarima models were selected based on self-correlation and partial correlation functions and independent residue test (Logon-Box). The SETAR models were also used to assess the adequacy of the remaining models of the model. The results of the evaluation of the matched models showed that the SETAR model (2; 6.2) with the coefficient of explanation of 0.74 compared to the SARIMA linear model had a precision It is acceptable to predict the monthly wind speed of Ahwaz synoptic station.

In the present research, the SARIMA seasonal time series, Holt-Winters, bi-linear and Self-Exciting Threshold Auto-Regressive models were used to predict the monthly inflow to the Maroun dam reservoir. To this end, the 34-year data of the Idanak hydrometric station located in Khuzestan province of Iran between the years 1982 and 2015 have been used. The logarithmic transformation was used to normalize the monthly discharge data of the Idanak hydrometric station. differencing technique was used to eliminate the seasonal component of the monthly data. The independence test of the model residuals (Ljung-Box or porte-manteau), the autocorrelation and partial autocorrelation functions were used to check the validity (quality of fitting) of these models. SARIMA models (1.0.1) * (2.0.2) 12, BL (2.1.1.1) and SETAR (2; 7.3) were chosen as the best models with the minimum values of Akaike and Schwartz criteria. The results of the evaluation of fitted models showed that the BL model with the values of the coefficient of determination and root mean square error which are 0.81 and 14.80 m3/s, respectively, has an acceptable accuracy to predict the monthly flow to the Maroun River. It was also found that by increasing the non-seasonal rank degree in SARIMA models, the model validity and performance are weakened to predict monthly flow. The results showed that the Holt-Winters model with the values of the coefficient of determination and root mean square error which are 0.56 and 10 m3/s, respectively, has the weakest performance to predict the monthly flow to the Maroun dam reservoir.

Prediction of runoff in order to effective operation of flood control reservoirs and earth flood walls is essential. Predictions also make possible emergency operation of reservoirs by estimating time of floods occurrence and expected damages. Predictions are based on recent meteorological and hydrological conditions of the basin and may include future conditions. Although, most of applications are for flood prediction, these may support water supply, hydroelectric requirements, environmental needs and other requirements for operation. Thus, various complex relations and models such as conceptual rainfall-runoff, linear time series, and hybrid models are developed, but in most cases the calculated values obtained by the models are significantly different. The objective of this research is assessment of precision of HMS-SMA conceptual model and bilinear model in prediction of daily runoff of Maroun basin located in Khouzestan province of Iran. The distinction between the current study and previous studies is that the comparison of the HEC-HMS and bilinear time series models has not been considered so far to predict daily runoff in Iran.

In the current study, daily discharge data of Maroun river within 17 years (1995-2011) at Idenak hydrometric station located in Maroun basin were analyzed. Maroun basin to upstream of Maroun dam according to topography and location of hydrometric stations was divided to four sub-basins and each of the sub-basins were introduced to HMS-SMA conceptual model independently. Amongst different rainfall-runoff models in HEC-HMS conceptual model, Clark’s unit hydrograph was used because of its applicability and acceptable performance in large basins. Furthermore, according to literature review and suggestions, SMA model of linear reservoir base flow was used for estimating of base flow. Flood routing in various reaches were performed by Muskingum method. Additionally, due to possibility of snowfall in the basin, for modeling of snow melt, index temperature method was used. Bilinear models were introduced by Granger and Anderson. In fact, bilinear models are extension of second order Taylor series. Considering to non-persistence of mean and variance of time series of discharge of Idenak station, the time series transformed to a persistent one by differencing, Box-Cox and square root transformation.

Considering to various hydrograph shapes in verification and calibration processes, it is evident that HMS-SMA model has a good precision in estimating low flows compare to high flows. The mean and variance of the time series transformed to a persistent one by second order differencing. Then various bilinear models with various orders were fitted to time series data. Verification of the fitted bilinear models to daily discharges of Idenak station was achieved by portemanteau statistic. Finally, bilinear model in the form of BL(2,2,1,1) with the least Akaike criterion was selected as the best model and was applied for comparing to the predicted daily discharges by the HMS-SMA model.

Summing up assessment criteria of the HMS-SMA and bilinear models show that bilinear model in the form of BL(2,2,1,1) with a coefficient of determination, sum of residual errors and mean square root of errors equal to 0.91, 8.9, and 17.8 respectively, is the best model with high precision in modeling and prediction of daily discharges of Maroun basin compare to HMS-SMA model. Furthermore, it is concluded that by increasing order of moving average in bilinear models, their ability for predicting daily discharges decreases.

Extraordinary withdrawal of groundwater resources has caused severe damages to aquifers, and the country's plains are facing subsidence due to this situation. In this regard, following the encouragement of the government by offering financial supports, many of farmers have volunteered to run pressurized irrigation systems. Therefore, gathering proper quantitative and qualitative data of water resources is necessary for sustainable agriculture. In this study, water quality of 103 agricultural wells in Sarab plain, which owners are applying for the operation of pressurized irrigation system, was investigated. To determine the water quality conditions of these wells, the magnesium absorption ratio (MAR), permeability index (PI), salinity potential (PS), Kelley's Ratio (KR), sodium absorption ratio (SAR) and soluble sodium percentage (SSP) indices were evaluated along with some other chemical parameters in the studied plain. The EC, SAR, MAR, PS and chloride indices indicated a decline in water quality from east to west and from south to north of plain. Approximately, water in 50% of wells were in the C3S1 class and the average KR of all wells were in a suitable condition with the value of 0.46. Except for one case with the value of 15.52, the remaining wells water had non-alkaline water with the SAR values less than 15. Only 17% of wells water had SSP values over 40% and they were not suitable based on this indicator. Most of the samples were located below the standard limit of 50% for MAR. Finally, in this study condition the application of sprinkler and tape irrigation systemsfor 90% of wells is recommended and for 10% of the rest of wells gated the pipes irrigation system may be adapted.

The prediction of stream-flow have incessantly attracted water engineer studies with due regard to its importance in the design of water structures, water intake from the rivers, planning and operation of reservoirs of dams, and erosion and sedimentation control measures in rivers. In this research, HEC-HMS model was used in prediction of daily stream-flow in the Maroun Basin, evaluating the ability and precision of this model in predicting stream-flow. For this purpose, continuous simulation of daily rainfall-runoff was performed before the prediction period using soil moisture accounting algorithm (SMA). In order to calibrate and verify the HEC-HMS model, daily stream-flow data from 1995 to 2006 and from 2007 to 2011 at the Idenak hydrometric Station were respectively used. Then, daily stream-flow at Idenak Station was predicted from 2011 to 2012. The results showed that the goodness of fit tests including Nash-Sutcliffe efficiency index, coefficient of determination and root mean square error in the runoff prediction process at Idenak station are equal to 0.56, 0.67 and 32.2 m3/s, respectively. Therefore, the HEC-HMS model has acceptable ability and precision in predicting runoff.

Based on a 30-year available data during 1980-2010, water saving potentials consumed in the irrigated agriculture of Khuzestan province was investigated. To this end and based on FAO classification system, all agricultural and horticultural crops were divided into 8 groups of cereals, vegetables, legumes, oil crops, sugar crops, root and tubers, nuts and fruits. Then, different management scenarios were defines by combining three planting dates of on time cultivation (local date), early and late plating dates, and furrow, sprinkler and drip irrigation systems, and four levels of supplying crop water demand (100, 85, 70 and 55 percent). The best scenario and cropping pattern were determined based on water use efficiency and irrigation water productivity indices, and then, the corresponding indices under the selected scenarios were considered as the benchmark levels. The highest values of water use efficiencies were obtained for sugar crops had the highest water use efficiencies due to their high crop productivity, root and fiber due to their low water requirement, and vegetables due to their low water requirement and high yield. While taking the fifth place regarding water use efficiency, cereals were the largest fresh water consumer in the province due contributing more than 70% in total irrigated harvested lands. Substituting one hectare of irrigated lands under cereals with root and tuber productions, results in an average 3.8 thousand cub meter (63%) saving in net crop water consumption. substituting surface irrigation system with drip irrigation system led to an increase in water sue efficiency in sugar crops, legumes and oil, while such substitution caused a reduction in the amount of produced crop per unit consumed water. A 15% water stress results in a negligible change in water use efficiency due to maintaining crop water requirement at the favorable rates and reducing crop yield by only 1.4-8.3%. Increasing the water stress level up to 30% result in a maximum reduction of 5.6% in crop production per net consumed water. Regardless of the irrigation systems and water stress levels, early planting will increase water use efficiency up to 20%. Hence, a mild water stress and adjusting planting date as well as prioritizing the cultivation of agricultural and horticultural crops based on their water use efficiency could be considered as favorable management scenarios for alleviating pressure on blue water resources in Khuzestan province.

The necessity of existence of a model is very important for suitable planning along with management of ground water resources and with regard to water scarcity and decline aquifers. Because of various factors in the amount of changes the depth of the aquifer can be effective. in this article by using association rules, dependency between aquifer depth changes in irrigation network of Qazvin city and human and natural factors affecting on it, was investigated. for the years 2001 to 2015 using Apriori algorithm. to evaluate accuracy of the rules used three indicator such as lift, support percent and confidence percent.The results show that the maximum positive correlation has been between two sets of the Antecedent value, consisting high percent of relative humidity and low volume of water in irrigation network and withdraw of agricultural water, and Consequent, rising of aquifer depth more than one meter. With 4.8 percent of support it can be stated that effective factors on aquifer fluctuations are low precipitation, high volume of water in irrigation network and high crop water requirements occurred simultaneously with decline of 0.3 to one meter. In cases that the temperature was high, air humidity and precipitation were low, the decline has been 0.3 to one meter with 66 percent of confidence. Furthermore, due to the derived association rules by increasing withdrawal of water from the well, air temperature, volume of water in irrigation network and crop water requirements, causes aquifer drops and On the other hand, increasing air humidity and precipitation causes a slump in aquifer decline.

Due to importance of predicting groundwater table fluctuations as an important and key factor in agricultural activities, the main aim of this study is finding the model and effective factors in predicting groundwater table fluctuations by using CART tree algorithm in data mining package of "SPSS modeler 18.0 IBM", in Qazvin irrigating area during 15 years from 2001 to 2015. Input variables to the model were the degree of effectiveness of each piezometer, net water requirements, the amount of consumption in each piezometer, rainfall depth, inflow and outflow from Taleghan reservoir, cropping area, and inflow to the irrigation system, the model output as objective function is groundwater table fluctuations. The predictability of the model was determined by the criteria such as correlation coefficient and mean absolute error. The results showed that the performance of the CART algorithm in predicting water table fluctuations inside the irrigation system is better than the outside of it. Furthermore, the results show that the most important parameter effective in predicting groundwater table fluctuations inside of the irrigation system is inflow to reservoir.

The purpose of this research is the role of deficit irrigation in compromising environmental – economical goals of cropping pattern in Fars Province.
This technique is considered as a strategy to increase profitability. However, in addition to profitability goal, many studies have focused to optimize water and fertilizers consumption as environmental objectives. Current study investigates the rule of deficit irrigation in a model that compromises the economic and environmental objectives. Fasa city in Fars province were selected as the study area since placed in warm climate field and affected by water stress. A Fuzzy nonlinear programming model were used to compromise the goals of maximum gross margin and minimum chemical fertilizer and water consumption for a representative farm in study area. This model was subjected to yield response functions to water and other constraints.
Findings: The results showed that the representative farm is inefficient to select cropping pattern in comparison to compromised goals model. This comparison also revealed there is a the possibility for increasing income of representative farm by applying full irrigation to gain potential yield and reducing cultivation area for considering the available water restrictions.
Discussion and Although, increasing of gross margin is conflicted by decreasing water consumption and partly by reducing fertilizer use, but generalizing the results of representative farm to the whole city showed that implementation of this model will add the revenues about 94 billion Rials in total area. While the positive environmental outcomes such as reducing about 4.6 million cubic meters of water and 7,246 tons of chemical fertilizer would be the other results of implementing this model.

Due to population growth and agricultural development, predicting aquifer conditions and also determining factors effecting its performance are very important. In this research study, three algorithms of data mining package, i.e. IBM SPSS Modeler were applied for detecting models effective on predicting an aquifer conditions. Furthermore, human and natural factors were considered as input variables effective on predicting Qazvin plain aquifer conditions during 2001 to 2015. The accuracy of CHAID, CART, and CVM algorithms were 0.67, 0.75, and 0.74, respectively. The values obtained for different indices including TPR, TNR, ACC, FPR, FNR, FM, GM, and ER for CART decision tree model showed that the performance of the mentioned algorithm was better comparing to other algorithms. In addition, the results based on CART decision tree model show that the most important factors affecting water table fluctuations were air temperature, evapotranspiration and agricultural water demand. Thus, the developed model lead to predict water table depth in an aquifer and optimum management of the water resources.

Accurate estimation as one of the important elements of the hydrological cycle of evaporation play an important role in the development and management of water resources plays countries facing a water crisis. so far methods, and many empirical formulas in estimating the nonlinear process evaporation of the basin, which provide high accuracy and as well as access to all the input parameters problem or measure they need a lot of time and money.The aim of this study was to compare of ability of Gene expression programming and neuro-fuzzy methods for estimation of evaporation in South Khorasan province. For this purpose daily data collected from occurs six synoptic stations during years 1990-2010. Input parameters are daily mean temperature, relative humidity, max and min temperature, wind speed and sun shine. Finally, to evaluate of models and compare them criteria such as coefficient of determination (R2) and Mean Bias Error (MBE) and Root Mean Square Error (RMSE) and MBE is used. Compression of result in test period showed the GEP Model has better performance than the neuro-fuzzy model to estimate the daily evaporation. The best result of GEP is R2=0.79¡ RMSE= 1.44 and MBE=0.35 in Boshroie station and worst is R2=0.7¡ RMSE= 2.6 and MBE=1.2 in Birgand station. Also the results showed the main factor in estimation of evaporation is mean temperature in all station except that mine temperature is impact parameter.

Estimation of sediment concentration in rivers is very important for water resource projects planning and managements. Various models have been developed so far to identify the relation between discharge and sediment load. One of the most common methods for estimating sediment of rivers is sediment rating curve. For better estimation of the amount of sediment based on the sediment curve rating equation¡ it is possible to optimize its coefficients. One of the methods used for optimizing the coefficients of the sediment curve rating equation is taking advantage of meta-heuristic algorithms. Nowadays¡ optimization algorithms are used regularly for solving complex and non-linear problem. The main objective of this research is the use of genetic algorithms and particle swarm to optimize the relationship between discharge and sediment discharge in Kohak station on the Sistan River and comparison the results of these models with sediment rating curve. For the calculation of sediment discharge by the models initially necessary statistics and information including flow discharge and sediment concentrations have been measured since 1981-2011 in the stations are collected. Genetic Algorithm(GA) and Particle Swarm Algorithm(PSO) models were coded in MATLAB. After the models were trained with 70% to 30% of the data at both stations were tested. Evaluation parameters efficiency such as coefficient of determination (R2)¡ root mean square error (RMSE) and Nash-Sutcliffe coefficients (CE) are used in evaluating the models. The results showed that the genetic algorithm with 33484.47 values at Kohak station has lowest root mean square error in all models. After genetic algorithm¡ Particle swarm algorithm with 34754.31 values has lowest values of the objective function.

The knowledge about spatial variability of precipitation is a key issue for regionalization in hydro-climatic studies. Measurements of meteorological parameters by the traditional methods require a dense rain gauge network. But, due to the topography and cost problems, it is not possible to create such a network in practice. In these cases the spatial distribution pattern of precipitation can be produced using different methods of interpolation. Interpolation could be done only based on the data of the main variable (i.e. through univariate methods) or on the information obtained from both the main and one or more auxiliary variables (i.e. through multivariate methods). The classical interpolation methods such as arithmetic mean and linear regression (LR) methods are independent of the spatial relationship between observations, while geostatistical methods (such as kriging) use the spatial correlation between observations in the estimation processes (Isaaks and Srivastava, 1989). The previous studies showed that the choice of interpolation method depends on data type, desired accuracy, area of interest, computation capacity, and the spatial scale used. Hence, different interpolation methods, including geostatistical methods (OK, SK, Sklm, KED, UK and COK), univariate deterministic methods (IDW, LPI, GPI and RBF) and linear regression (LR) were compared to estimate monthly and annual precipitation in Sistan and Baluchestan Province. The auxiliary variables used in the multivariate approaches were DEM, distance to Sea and spatial coordinates. Study area Sistan and Baluchistan Province is located in southeast of Iran and covers an area of 181471 km2. It is located between the latitudes 25˚03ʹand 31˚27ʹN and the longitudes 58˚50ʹ and 63˚21ʹE. The precipitation data collected from 50 precipitation stations over the same period of 25 years (1988-2012) were used in this study. Interpolation methods Detailed description of geostatistical interpolation methods used in this study including OK, SK, Sklm, KED, UK and COK are provided in the variety of resources, such as Goovaerts (1997) and Deutsch and Journel (1998). In geostatistics the most important tool for investigating the spatial correlation between observations is the semivariogram. In practice, experimental semivariogram is calculated from the following equation: (1) where is the experimental semivariogram, N(h) is the total number of data pairs of observations separated by a distance h, Z(ui) and Z(ui+ h) are the observed values of the variable Z in locations ui and ui +h, respectively. After calculating experimental semivariogram, the most appropriate theoretical model is fitted to the data. Unknown values are estimated using the semivariogram model and a geostatistics estimator. Comparison method and evaluation criteria To assess the accuracy of interpolation methods and the best method for estimating precipitation, cross-validation technique is used (Isaaks and Srivastava, 1989). Evaluation criteria are including the Root Mean Square Error (RMSE) and the Mean Bias Error (MBE). Statistical analysis showed a high coefficient of variation of precipitation in August, September and July. Kolmogorov-Smirnov test showed that precipitation data are normally distributed over the study area. The precipitation semivariogram was considered isotropic as a little change was seen for different directions. Results of autocorrelation analysis showed a high spatial correlation of precipitation in all periods (except for January and February) with a spherical semivarioram model. This confirms the results of previous studies (Lloyd, 2005; Haberlandt, 2007; Mair and Fares, 2010). The maximum sill was observed for months January, February and March with a higher amount of mean and variance. The maximum radius of influence was seen for January (511 km) followed by May (205 km). The performance of UK was evaluated using the trend function of the first and the second order polynomial. The evaluation results indicate that the first order polynomial is the more accurate one. The cross validation results showed that the best method for precipitation estimation was linear regression (precipitation versus elevation) for April, KED for May, UK for June and September, RBF for July, August, October, December, January, February and annual precipitation and SK for November and March. The LPI and GPI methods did not perform well in any of the time periods. This could be possibly due to large changes in surface topography of province. RBF method had the highest accuracy in most of the periods. The estimated values in this method are based on a mathematical function that minimizes total curvature of the surface, generating quite smooth surfaces (Zandi et al., 2011). Geostatistical methods had the highest accuracy for other periods. One of the reasons for good performance of geostatistical methods may be due to the low density of the meteorological stations. It confirms other researchers’ results (Creutin and Obled, 1982; Goovaerts, 2000). The use of elevation as covariate has improved the estimation results only for April and May. However, the distance to Sea did not improve the estimation results in any cases. The reasons for little improvement of the precipitation estimation through the multivariate methods could be due to the complex topography, low density of meteorological stations, and low correlation between precipitation and covariates. Geostatistical interpolation methods, in deterministic and linear regression methods, were evaluated for precipitation data in Sistan and Balouchestan province. According to the results of cross-validation, linear regression (elevation- precipitation) for April, geostatistical methods for May, June, September, December and March and RBF method for other periods had the highest accuracy. According to the estimation error maps produced by the geostatistical methods, the highest estimation errors were seen in the area with a low density of stations and the boundaries of the province. These areas are recommended for developing the meteorological network in the future. Also, due to the variability of climate, distance from Oman Sea and changes in the surface topography for the precipitation stations, we recommend that the province is divided into more homogeneous regions and the proposed approaches are investigated in each section, separately.

Water shortage in the eastward region of Mazandaran and Golestan provinces is such as problem that water managers is scrambling to try to solve that. According to the focus of the Mazandaran rivers in western and central part of the province، one way to solve this water shortage problem is transfer surplus water of the central and western parts to the east of Mazandaran and Golestan provinces. The purpose of this study is evaluating of the impact of construction of Haraz dam and Chalos across channel on present supply water in Haraz basin and finaly estimating of transferable volume of water from Haraz basin to eastern regions of Mazandaran and Golestan provinces that have been evaluated in terms of three scenarios in dry، normal and wet conditions. For this purpose was used RIBASIM model due to its comprehensiveness. In order to evaluate the hydrological performance of the prepared model، was used the determining coefficient parameter (R2). R2 is 0. 87 in this study that is the indication of acceptable performance of the model. Results showed that، the volume of transferable water in the best and the worst weather conditions (wet and dry conditions) is approximately 531 and 378. 25 million cubic meters، respectively.