h. ramezani etedali

Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.

The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.

The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.

Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.

Evaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors on the performance and efficiency of plant water consumption by spending less time and money. Since the most important agricultural product in Iran is wheat, so proper management of wheat fields has an important role in food security and sustainable agriculture in the country. The main source of food for the people in Iran is wheat and its products, and any action to increase the yield of wheat is necessary due to limited water and soil resources. Evapotranspiration is a complex and non-linear process and depends on various climatic factors such as temperature, humidity, wind speed, radiation, type and stage of plant growth. Therefore, in the present study, by using daily meteorological data of Urmia, Rasht, Qazvin, Mashhad and Yazd stations, the average daily evapotranspiration values based on the results of the FAO-Penman-Monteith method are modeled and the accuracy of the two methods temperature method (Hargreaves-Samani and Blaney-Criddle) and three radiation methods (Priestley-Taylor, Turc and Makkink) were compared with FAO-56 for wheat.

The present study was conducted to evaluate the accuracy and efficiency of the AquaCrop model in simulation of evapotranspiration and biomass, using different methods for estimation reference evapotranspiration in five stations (Urmia, Qazvin, Rasht, Yazd and Mashhad). Four different climates (arid, semi-arid, humid and semi-humid) were considered in Iran for wheat production. The equations used to estimate the reference evapotranspiration in this study are: Hargreaves-Samani (H.S), Blaney-Criddle (B.C), Priestley-Taylor (P.T), Turc (T) and Makkink (Mak). Then, the results were compared with the data of the mentioned stations for wheat by error statistical criteria including: explanation coefficient (R2), normal root mean square error (NRMSE) and Nash-Sutcliffe index (N.S).

The value of the explanation coefficient (R2) of simulation ET and biomass in the Blaney-Criddle method is close to one, which shows a good correlation between the data. The NRMSE and Nash-Sutcliffe values for both parameters and the five stations are in the range of 0-20 and close to one, respectively, which indicates the AquaCrop model's ability to simulate ET and biomass. On the other hand, the value of R2 in the Hargreaves-Samani method for biomass close to one, NRMSE in the range of 0-10 and Nash-Sutcliffe index is more than 0.5, which indicates a good simulation. The NRMSE index in the evaluation of ET and biomass wheat is excellent for the Blaney-Criddle method and about Hargreaves-Samani for ET is poor and for the biomass is excellent.The Turc method with NRMSE in the range of 0-30, explanation coefficient close to or equal to one and a Nash-Sutcliffe index of one or close to one can be used to simulate ET and biomass at all five stations. Also, for biomass simulation, Priestley-Taylor and Makkink methods have acceptable statistical values in all five stations.Based on the value of explanation coefficient (R2) of estimation ET and biomass wheat for radiation methods, the correlation between the data in all three radiation methods is high. Percentage of NRMSE index of Makkink method for wheat in ET evaluation in Qazvin station is poor category and in Urmia and Rasht is good and in Mashhad and Yazd is moderate and about biomass in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd) is excellent category, the error percentage of Priestley-Taylor method for wheat in ET evaluation in Yazd station is good and the rest of the stations is poor, about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd). The error rate of Turc method for wheat in ET evaluation in Urmia, Rasht and Mashhad stations is good and in Qazvin and Yazd is poor and about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd).

According to the results obtained using Blaney-Criddle method with R2 value close to one, NRMSE in the range of 0-20% (excellent to good) and Nash-Sutcliffe index close to one and Turc method with R2 value close to one, NRMSE in the range of 0-10% (excellent) and Nash-Sutcliffe index close to one was showed a good accuracy of AquaCrop model in simulation of evapotranspiration and biomass with these methods of estimation of evapotranspiration compared to other methods.

Studying the effects of drought in mountainous areas is facing problems due to the inappropriate distribution of stations, the lack of long-term data, and areas lacking statistics. Therefore, the main objective of this research was to investigate the drought indices of Kurdistan province using TRMM satellite data and ECMWF dataset, as well as to evaluate their accuracy against the data of land stations in Kurdistan province. First, ECMWF precipitation data for the 2000-2020 period and TRMM precipitation data for the 2000-2019 period were obtained and evaluated using RMSE, MBE, and correlation coefficient statistics. Spearman's correlation coefficient showed a significant relationship between the TRMM satellite precipitation data and the ECMWF dataset with ground stations at the 5% level, and the value of this coefficient was between 0.95-0.85. According to the results, it can be acknowledged that the TRMM satellite rainfall and ECMWF dataset in the monthly time scale had proper accuracy at the Kurdistan province level. Therefore, these two sources were used to examine the drought indices. SPI, SPEI, and ZSI drought indices were calculated in different monthly periods (1-48), PNI in different monthly, seasonal, and annual periods in Kurdistan province (Saqqez, Qorveh, Bijar, Sanandaj stations). Spearman's correlation coefficient indicated a significant relationship at the 5% level between the SPI, ZSI, PNI, and SPEI index of the ECMWF dataset with ground stations. The results of the SPI index showed that the lowest RMSE value for the TRMM satellite at the Saqqez station and the three months was equal to 0.45, and for the ECMWF dataset at the Sanandaj station and the 24 months was equal to 0.35.

The rise in water demand and reduction of water quality and soil in irrigating areas, especially in dry and semi-arid areas of the world, have turned into one of the most crucial challenges for water and soil engineering in recent years. This issue leads us toward optimal quantitative and qualitative management of these valuable resources aimed at achieving economic performance and water productivity. The periodic evaporation and transpiration of the plant in the conditions of simultaneous water and salinity stress are known as one of the most important factors in the qualitative and quantitative growth of the plant yield. Applying mathematical models that simulate the relationship between field variables and yield can be seen as a useful tool in water and soil management issues in such a situation, which has the potential to ensure optimal use of the water and soil resources of any country by providing the plant's water needs and preventing its further loss.

A factorial experiment was performed in 2019 based on completely randomized blocks design with three replications in plots with an area of 9 square meters at the agricultural and animal husbandry farm of Aliabad Fashafuyeh, located in Qom province to examine the simultaneous effect of different levels of water stress and salinity on the periodic evaporation-transpiration and fresh yield of the single cross 704 forage corn cultivar. The applied treatments included the irrigation water salinity at three electrical conductivity levels of 1.8 (S0), 5.2 (S1), and 8.6 (S2) deci Siemens/meter (dS/m), which were prepared by mixing saline well water of the region with fresh (drinking) water and three water stress levels of 100% (W0), 75% (W1), and 50% (W2) of the plant's water requirement. The depth of soil moisture in the corn plant root zone was measured by the TDR device at five depths of 7.5, 12, 20, 40, and 60 cm during different growth stages of the plant using pairs of 7.5, 12, and 20 cm stainless steel electrodes.

The simultaneous water and salinity stresses, which led to the reduced amount of periodic evaporation-transpiration of the yield compared to ideal conditions (without stress), were simulated by additive and multiplicative models. The results suggested a decrease in the evaporation and transpiration with the increased simultaneous water and salinity stresses so that the amount of total evaporation-transpiration in different treatments was measured to be between 692.7 and 344.9 mm and the fresh yield was estimated between 50.4 and 3.2 tons per hectare. Also, the highest amount of periodic evaporation and transpiration in all treatments was found to occur in the development and intermediate stages, and the relative fresh yield in the W0S0 to W2S2 treatments was calculated between 66% and 100%. The results of modeling the relative yield of the crop based on the amounts of relative evaporation and transpiration of corn in different growth stages and under the different treatments of water stress and salinity, indicated that Singh's additive model and Rao's multiplicative model were appropriate, while the Minhas model was recognized to be inappropriate in this estimation.

The research results suggested the significant impact of water stress and salinity at least at the 95% level on evaporation and transpiration and the corn yield. Moreover, the effect of the sensitivity of different growth stages of the plant on the reduction of evaporation and transpiration of corn varies so that in the three treatment groups W0, W1, and W2, the highest average decrease in slope was related to the final stage (13.6%) followed by the middle stage with an average decrease of 8.4% compared to the control treatment. Therefore, the highest decrease rate in evaporation-transpiration slope has been observed in these two growth stages due to the beginning of flowering, fruit formation, and physiological ripening of seeds. These results come from the lack of sufficient water storage and increased salinity of irrigation water in the soil. Water stresses and salinity will reduce water absorption and evaporation-transpiration, and ultimately, reduce crop production due to the decreased amount and potential of water in the soil. Another finding to be mentioned is the priority of water stress compared to salinity stress in reducing evaporation and transpiration and production yield. Also, by managing water and salinity stresses in the critical stages of plant growth (especially the middle stage), which is the time of flowering and the beginning and completion of the maize production process, a significant reduction in the crop can be somewhat prevented.

Water resources management in arid and semi-arid regions is very important specially, in agricultural sector.  The major share of water use is daily consumption by humans for drinking, washing and cooking. Furthermore, population growth increases agricultural production demand, and this highlights the role of water resources management in the agricultural sector. The 1950’s studies showed 12 countries with a population of 20 million experienced water shortage. Virtual water is the volume of water which is consumed for a production from the beginning stage to the end. Scientists have shown that 96% of water footprints are related to crops, livestock and horticultural productions and only 4% it consumed as domestic water. Water balance data in Qom province shows that 90% of water resources are using in the agricultural sector. Investigation of water footprints in the agricultural sector is highly beneficial to improve water resources management in arid and semi-arid regions such as Qom.

The research was conducted to find out the production and cultivation water needs in the agricultural sector for 10 years, via calculating the gray, blue and green water footprints using Mekonnen and Hoekstra models. In the livestock sector, water footprint’s information such as the number of livestock and poultry, production of red meat, chicken meat, egg and milk were also determined using the Mekonnen and Hoekstra. The water footprint in fertilizer was calculated using a questionnaire survey. Excel and SAS apps were used to analyze the collected data for all three study sections.

The results showed that the water footprint in wheat, barley, cotton, onion, tomato, melon, watermelon, alfalfa, and corn were 3018, 2882, 10960, 1463, 1525, 960, 2504, 1683 and 416 m3/ton, respectively. The low irrigation efficiency led to a very high amount of white water footprint in the productions. Green water footprint was very low due to the lack of rainfall. In the livestock sector, the water footprint in red meat and milk were 39 m3/kg and 2.42 m3/lit, respectively which were much more than the global average. In the poultry sector, the water footprint in chicken meat and egg were 7.4 and 4.34 m3/kg, respectively, that were very high compared to the global average. The water footprint in fertilizer for wheat, barley, cotton and alfalfa productions were 2.62, 1.19, 1.07 and 2.54 m3/kg and this amount was higher under nitrogen fertilizer. The average virtual water footprint for chicken meat production in Qom province was 7.4 m3/kg. This amount in the world, USA, India, Russia, Mexico and the Netherlands is equal to 3.92, 2.39, 7.74, 5.76, 5.01 and 2.22 m3/kg respectively. In Netherlands, less water is in use in the agricultural sector than the other countries. In this country, the virtual water footprint in chicken meat is in the best position. India has the highest water consumption in poultry breeding with a consumption of 7.74 m3/kg. The average virtual water footprint in Iran for egg production is 4.34 m3/kg , while the average virtual water footprint for egg production in the world, USA, India, Russia, Mexico and the Netherlands is 3.34, 1.51, 7.53, 4.92, 4.28, and 1.4 m3/kg, respectively. India consumes the most water in the production of eggs such as chicken with a quantity of 7.53 m3/kg and the Netherlands has the least consumption with a value of 1.4 m3/kg .

The concept of virtual water footprint in each region reduces the pressure on water resources. For better management in agricultural regions, it is possible to prevent the cultivation of high water demand crops. The most common cause of high water footprint in livestock and poultry is nutrition, so, internationally food import can be a good solution. Industrialization of poultry can also reduce water footprint. The implementation of this research can be a useful clue to the sustainable control and management of water resources and achieving an optimal cultivation pattern in our country and all provinces facing limited water resources.

Supplying human and animal nutritional needs requires suitable use of water resources. Due to the decrease of fresh water resources for agriculture, saline water resources cannot be ignored. Increasing water salinity reduces the water absorption by plant, due to decreasing the water potential. On the other hand, soil infertility (such as nitrogen deficiency) decreases the evapotranspiration and crop yield. The present study was to increase the water and nitrogen fertilizer use efficiency of maize, under salinity stress condition. This was done by managing the consumption of saline water and nitrogen fertilizer. In this research, irrigation requirement was determined proportional to the plant evapotranspiration to avoid excessive saline water use.

In this research, two treatments of water salinity and nitrogen deficiency in four levels and three replications were implemented as a factorial experiment in a randomized complete block design. The studied plant was maize (S. C. 704 cultivar) sown in plots with dimensions of 3 × 3 meters and 1.5 meters distance. In this research, fertility stress was in the form of nitrogen fertilizer consumption and at four levels. Treatments of ، ، and consisted of consumption of 100, 75, 50 and 25% of nitrogen fertilizer, respectively. Salinity stress has been applied by irrigation of the plant with saline water. Water salinity treatments were selected based on the yield potential of maize, at four levels of 100, 90, 75 and 50%. According to the above four performance levels, treatments of ، ، and included irrigation water with electric conductivity of 0.5, 1.2, 3.5 and 7.5 (dS/m), respectively. The soil moisture content was measured at the depth of root development during the interval between two irrigations. Daily maize evapotranspiration was measured by the volumetric balance of water at the depth of root development. The stomata resistance of maize leaf was measured by the AP4 porometer device between two irrigations interval. Variance analysis and mean comparison of data were done by SPSS software and Duncan's multiple range test, respectively.

Water use efficiency In this research, the evapotranspiration and dry matter yield of maize decreased under salinity stress and nitrogen deficiency treatments. This seems to be caused by the water potential decrease (due to salinity stress) and the nitrogen deficit in the soil. Under these conditions, optimum use of water and fertilizer increased water use efficiency. At first without water and fertilizer management, water use efficiency in different treatments (to), ranged from 2.74 to 4.4 kg/ (in 2017) and from 2.57 to 4.35 kg/ (in 2018). With suitable management of irrigation, water use efficiency, however, increased in stress treatments and approached to optimum treatment. The range of water use efficiency was from 4.2 to 4.4 kg/ (in 2017) and from 4.15 to 4.32 kg/ (in 2018). The reason for this was the management of irrigation volume based on actual evapotranspiration in stress treatments. On the other hand, increasing soil nitrogen was an appropriate strategy to increase water use efficiency. But in high salinity stress, despite the optimum use of water and fertilizer, it was not possible to achieve optimal water use efficiency. This is explainable by the harmful effect of salinity on the reduction of nutrient uptake (especially nitrogen) by the plant. Nitrogen use efficiency Soil nitrogen deficiency and increasing water salinity reduced nitrogen use efficiency. In different stress treatments, nitrogen use efficiency ranged from 3.34 to 5.11 kg/kg (in 2017) and from 3.06 to 5 kg/kg (in 2018). The results showed the destructive effect of salinity on nitrogen uptake by the plant. Under these conditions, the ions in the soil (especially the sodium and calcium) caused the plant to be unable to absorb nitrogen from the soil. Therefore, the production of plant matter was reduced. The results showed that proper management of nitrogen can increase nitrogen use efficiency under salinity stress. At high salinity levels, the nitrogen fertilizer was not, however, absorbed by the plant and accumulated in the soil.

The results showed that water use management could increase the water use efficiency under stress treatments, by controlling evapotranspiration. On the other hand, soil fertility increased nitrogen fertilizer use efficiency under salinity stress. Among all treatments, had optimum water and nitrogen use efficiency. Overall, the volume of water used in the field should be adjusted to the actual requirement of the plant to prevent excessive consumption under salinity stress. In addition, increasing soil nitrogen, rather than more irrigation water, appears to be a suitable strategy to increase crop yield.

Estimating the ecological water footprint and the virtual water trade in different agricultural crops in arid and semi-arid regions can help better manage the limited water resources.This research calculated temporal and spatial ecological water footprint of rain-fed and irrigated almond production in national and provincial scale using during 2008 to 2014. The results show that annual average water footprint in rainfed almond is 9.2 m3/kg, which the share of green and grey water is 72% and 28%, respectively which Ilam and Kohgiloyeh & Boyerahmad have a largest share in green water footprint with 91% and 90%, respectively. In adition to, in irrigated almond, the annual average water footprint is 11.4 m3/kg, which the share of green, blue and grey water is 0.19%, 71% and 10%, respectively. Sistan & Balouchestan, Khuzestana and Hormozgan have the highest share in blue water footprint. The total volume of water footprint of rain-fed and irrigated almond production is 1923 and 8242 MCM, respectively. Also, results show that about 92 percent of the total volume virtual water (equivalent to 9343 MCM per year) in almond production, has been exported to other countries through the virtual water trade.

Regarding to high importance of water as an economic input in agriculture and limitation of common irrigation efficiency evaluation methods, evaluation of irrigation systems with economic indices has high priority. The general goal of this research was study of usual irrigation systems in Qazvin area in the base of economic efficiency indices. In the field of economic efficiency, there is no comprehensive research and there is an undeniable necessity for research in modern irrigation systems development in comparison with traditional irrigation systems. This study can help us in planning of modern irrigation systems development. Focus of this study was on evaluation of 42 modern irrigation systems. Among the sprinkler irrigation systems, average of benefit–cost ratio was 1.4 and this index was 2.94 for drip irrigation systems. Hence, both of these systems are beneficial in the area. Drip irrigation systems are more profitable than sprinkler irrigation systems. Average of benefit per drop for crops products such as wheat, corn, maize, barley and alfalfa were 8333.6, 4854.6, 2486, 6685.8 and 4404.5 Rial per cubic meter, respectively. Also, average of benefit per drop for products such as peach nectarine, grape, apple, pear in drip irrigation system was 34716, 34574, 14975.7, 26503 and 31945 Rial per cubic meter, respectively. Wheat and peach had highest values of benefit per drop index among the studied crops fruits. In this study, 10 sprinkler irrigation systems were evaluated technically and average values of Coefficient of Uniformity (CU), Distribution Uniformity (DU), Application Efficiency of Low Quarter (AELQ) and Potential Efficiency of Low Quarter (PELQ) indices was 76.8, 64.2, 55.2 and 65.3 %, respectively. Results of 5 drip irrigation systems evaluation showed that Efficiency of Uniformity (EU), Application Efficiency of Low Quarter (AELQ) and Potential Efficiency of Low Quarter (PELQ) indices was 65.26, 51.32 and 57.8 %, respectively. Results of this research, showed that In both systems, the benefit–cost ratio increases by increasing the field area. Also, there is high potential for economic water productivity improving in the studied fields. Furthermore, Irrigation efficiency assessment showed the weak condition of irrigation systems operations in the study area.

In arid and semi-arid areas during fallow seasons, saline groundwater movement toward soil surface not only leads to soil moisture loss, but also leads to accumulation of salts in the soil salinity. In this study in order to examine the effects of mulch on soil moisture and salinity in fallow season, two laboratory model with 1×1×0.5 m which underground saline water was injected by using subsurface drainage to its soil was used. Part of soil surface was covered with wheat straw. During the 122-day simulation period, moisture and salinity were measured with HH2 sensors in three locations and five depths (10, 20, 30, 40 and 50 cm), soil samples were taken from the same locations and extraction saturate was measured. The Duncan’s test and t test are used for mean comparisons. Salinity of 0-20 cm depth in the control treatment has increased two times compared to the mulch treatment. In the final twenty days, humidity of 0-10 cm depths of mulch treatment was more than control and this difference was significant at the 5% level. So using of mulch in fallow season with the high groundwater table, it prevents the salinity of the soil surface layer.

Drainage water created from irrigation and drainage projects in south of Khuzestan province are saline and its disposal to rivers such as Karoon river is faced with certain constraints. One of drainage water disposal strategies is using evaporation ponds, in which the most important outlet is evaporation. Evaporation rate of saline water of these ponds is lower than fresh water. In this study the evaporation rate of implicit ponds for drainage water control in Mirza Kuchak Khan Project was estimated by adjusting the saturation vapor pressure equation of saline water with regard to pure water and by using of Penman equation. Finally, using the evaporation and the annual drain water volume, the required area of evaporation ponds was determined. By evaporation of water from ponds the salinity of water increases. This increase of salinity continues until the salt saturation threshold is reached, and then salts precipitate. The most common salt in drainage water of Khuzestan province is NaCl which has a saturation threshold of 300 g/lit. The results showed that the average annual evaporation of water with 300 g/lit salt is 1903 mm. The drainage water volume produced from Mirza Kuchak Khan Project (12,000 ha area), requires evaporation ponds as big as 7740 ha. This study showed that disposal of the drainage water of sugarcane projects is impossible if only evaporation ponds are used, and therefore, other management options should be considered for reducing the volume and salinity of drainage water.