جستجوی مقالات مرتبط با کلیدواژه « swap model » در نشریات گروه « آب و خاک »

Potato is the fifth largest food source in the world, which is widely cultivated in Iran. Due to the sensitivity of this crop to the amount of irrigation water, it is necessary to investigate the effect of different amounts of irrigation on it. For this purpose, SWAP model was used as a powerful tool for potato simulation. At first, to evaluate this model, biennial data (1998-1999) collected from a research station in ChaharTakhteh, Shahrekord were used. These data include five levels of water stress (E0, E1, E2, E3 and E4, respectively, indicating 100, 85, 70, 50 and 30% water supply) and three stress application times (T1, T2, and T3 indicating 50, 100, and 150 days after sowing, respectively). NRMSE values for yield and water use efficiency showed that SWAP model in both calibration and validation were excellent (NRMSE < 0.1). The error of this model for simulating yield in the calibration and validation stages was equal to 1.42 ton.ha-1 (4.6 percent) and 2.36 ton.ha-1 (7.2 percent), respectively. The error value of this model for water consumption efficiency in the mentioned two stages was 0.04 kg.m-3 (4.9%) and 0.33 kg.m-3 (7.7%), respectively. The efficiency of the SWAP for potato simulation during calibration and validation was acceptable. Potato irrigation planning using SWAP showed that to achieve maximum yield, the irrigation water depth in the first growing period is equal to 750 mm and irrigation depths are 900 mm in subsequent periods.

In recent decades, several models have been developed to simulate farm water management. The main focus of most researchers is obtaining more products per unit of water consumed. Water productivity defined as crop yield per unit of water consumption. Due to limitation of water resources and its optimal consumption in order to save water and increase its productivity, this study was conducted to simulate water productivity indices of rice (Hashemi caltiver) using SWAP model in paddy soils at field scale. For this purpose, two closed-ended lysimeters were used to measure the actual evapotranspiration. The quantity of evapotranspiration was measured daily and water productivity based on irrigation water (WPir) and evapotranspiration (WPET) was calculated and compared with the simulated values afterward. The obtained results indicated that the SWAP model had a high accuracy for estimating amount of yield (R2=0.90 and RMSE=648.73) and the amount of water consumed in the evapotranspiration process (R2=0.89 and RMSE=164.07). Using the calibrated SWAP model, the water productivity indices from division of yield on evapotranspiration (WPET) and yield on irrigation water (WPir) in the studied farm was estimated amount of 0.553 and 0.876 kg/m3, respectively. Also, results showed that with increasing irrigation efficiency, elimination of deep percolation and reduction of evaporation, water use efficiency increases by 30%. In general, for optimal management of paddy soils in field scale and considering the optimal yield at harvest time, serious attention must be paid to water efficiency to come up with most effective ways to deal with water crisis and to increase the quantity and quality of rice production.

Agro-hydrological models play an important role in water resource management. However, their predictions always suffer from various sources of uncertainty, including model structures, parameters, and input and output data. Model structural uncertainty is caused by the fact that the model cannot perfectly represent the natural processes involved in the studied system. Parameter uncertainty indicates that many model parameters are not directly measurable or can only be obtained with unknown errors. Measurement uncertainty in input and output data is due to unknown measurement errors and incommensurability errors. Hence, it is important to assess the degree of uncertainty involved in agro-hydrologic modeling. The generalized likelihood uncertainty estimation (GLUE) method has been widely used for uncertainty analysis in hydrologic modeling because of its simplicity, ease of implementation, and less strict statistical assumptions about model errors. In GLUE, parameter uncertainty accounts for all sources of the model uncertainty. The drawback of the GLUE is its prohibitive computational burden imposed by its random sampling strategy, which hinders the efficient application of the method. In this study, a hybrid high-dimensional uncertainty analysis method was developed, combining GLUE with an evolutionary optimization algorithm, Unified Particle Swarm Optimization (UPSO), to improve the computational efficiency of the GLUE framework. UPSO is a modification of Particle Swarm Optimization (PSO) that aggregates its local and global variants, combining their exploration and exploitation capabilities without additional objective function evaluations. The hybrid GLUE-UPSO framework was used for uncertainty analysis of SWAP distributed sub-daily agro-hydrological modeling for a sugarcane farming system with combinational free/controlled subsurface drainage management.

The source code of the SWAP model was modified and extended to consider the duration of the irrigation events, simulation of sub-daily reference evapotranspiration, sub-daily precipitation interception, ratooning, and implementation of subsurface controlled drainage during the simulation period. The GLUE-UPSO framework was coded in FORTRAN and C++ and integrated into SWAP source code. The developed framework was applied to a dataset collected from a field with a combinational free/controlled (70-cm depth) subsurface drainage management located at Shoaybiyeh Sugarcane Agro-industrial company farms, Khuzestan province, Iran. The simulation was performed from 2010-07-19 to 2011-12-11 (481 days) for planted sugarcane (CP48-103 cultivar). A soil profile of 550 cm depth (depth of impermeable layer) was specified during simulations. The soil profile was divided into two layers. To consider the heterogeneity of irrigation scheduling at different parts of the studied field, the field area ( 21 ha) was divided into ten homogeneous simulation units, termed as hydrotopes. Hydrotopes have similar agro-hydrological properties except for irrigation scheduling. The model was calibrated, using the measured soil moisture profile, soil solute concentration profile, groundwater level, subsurface drainage outflow, drainage outflow salinity, Leaf Area Index (LAI), cane yield, and sucrose yield in a parallel manner. The weighted average of simulated values derived for each hydrotopes was compared with the corresponding measured data. Totally, 45 parameters were estimated through the GLUE-UPSO framework. The accuracy of the model in calibration and validation stages was evaluated, normalized root mean square error NRMSE and Nash-Sutcliffe model efficiency coefficient EF. The behavioral parameters were identified, using NRMSE > 0.2 for solute transport (soil water solute concentration and drainage outflow salinity) and EF > 0.7 for hydrological (soil water content, water table level, and drainage outflow) and biophysical (cane yield, sucrose yield, and LAI) simulations. For each parameter set, the objective function values were used as the likelihood measure to calculate the corresponding likelihood weights. The 95% prediction uncertainty (95PPU) bands were calculated at the 2.5% and 97.5% levels of the cumulative posterior distribution (realized from the weighted behavioral parameter sets) of the simulated state/flux variables.

The results revealed a significant nonuniqueness of the calibrated parameters and the necessity of an uncertainty assessment for the SWAP simulations. Strong parameter correlations highlighted the need for calibration of the model parameters against diverse calibration data in a simultaneous manner. The 95% prediction uncertainty bands obtained for the model's hydrology (soil water content, water table level, sub-surface drainage outflow), solute transport (soil water solute concentration and sub-surface drainage outflow salinity), and biophysical (leaf area index, cane, and sucrose dry yield) components enveloped 41-87%, 18-67%, and 75-100% of the corresponding total observed data (including both calibration and validation datasets), respectively, with a r-factor (the ratio of the average thickness of the 95PPU band to the standard deviation of the corresponding measured variable) of 0.71-1.14, 0.33-1.14, and 0.84-0.98. The results indicated that the hybrid GLUE-UPSO framework offers an efficient alternative to provide traditional calibrated parameters as well as uncertainty analysis of computationally expensive hydrologic models.

In order to evaluate the SWAP model a study was conducted in 2017 in the farm of the Department of Irrigation and Reclamation Engineering, University of Tehran, located in Karaj as a factorial experiment in a randomized complete block design. The treatments consisted of three maize hybrids SC-704, SC-400 and SC-260 (V1, V2 and V3, respectively) and three levels of irrigation water salinity 0.7, 3 and 5 dS m-1 (S1, S2 and S3, respectively). In each maize cultivar, for the model calibration, the crop yield data measured in the field from the salinity level of irrigation water 3 dS m-1 and for its validation, the salinity levels of 0.7 and 5 dS m-1 were used. Also, to evaluate the model in estimating soil moisture and salinity, field data of V1S2 (calibration) and V1S3 (validation) were used. Based on the obtained results, SWAP model has a good performance in estimating soil moisture so that in the validation stage in three soil layers (0-20, 20-40 and 40-60 cm) with RMSE (Root Mean Square Error) of 0.03, 0.03 and 0.04 cm cm-3 respectively and has a good accuracy in predicting soil salinity of 0-20 cm surface layer (RMSE = 0.67 mg cm-3) but with increasing soil depth, the accuracy of the model decreases so that RMSE of 1.16 and 1.19 mg cm-3 were obtained in two layers of 20-40 and 40-60 cm, respectively. The SWAP model detected the inherent differences between different cultivars of maize and the best simulation results were obtained for SC 704.

The agro-hydrologic simulations are subject to varying degrees of uncertainty. In this regard, uncertainty analysis can provide useful insights into the level of robustness of the model outputs. In this study, a hybrid multi-objective uncertainty analysis scheme, GLUE-UPSO, based on strained Monte-Carlo simulations was developed, combining Generalized Likelihood Uncertainty Estimation (GLUE) and Unified Particle Swarm Optimization (UPSO). The developed scheme was used for uncertainty analysis of distributed agro-hydrological modeling with SWAP for a sugarcane farming system with subsurface drainage located at Shoaybiyeh Sugarcane Agro-industrial Company farms, Khuzestan, Iran. The results highlighted the strong nonuniqueness of most of the calibrated parameters and the importance of uncertainty analysis of the SWAP simulations. Strong parameter correlations revealed the need for the simultaneous calibration of the model parameters against diverse calibration data. The 95% prediction uncertainty bands obtained for the model's hydrology (soil water content, water table level, sub-surface drainage outflow), solute transport (soil water solute concentration and sub-surface drainage outflow salinity), and biophysical (leaf area index, cane, and sucrose dry yield) components enveloped 67%-90%, 23%-71%, and 75%-100% of the corresponding observed data (including both calibration and validation datasets), respectively, with an r-factor of 0.58-1.34, 0.43-1.07, and 0.70-0.98. The results of the study indicated the acceptable level of model uncertainty and the capability of the developed framework for simultaneous calibration and uncertainty quantification of model components.

Soil moisture is a determinative parameter in many of the complex environmental processes and plays a decisive role in the occurrence of agricultural drought. So, in this study, based on estimated soil moisture data by SWAP model and Fifth Report Data of Climate Change, agricultural drought was determined by Soil Moisture Deficit Index for the upcoming period (2020-2039) for the wheat field of Faroub in Neyshabour. The climatic data were estimated using six models of GCM and two emission scenarios of 4.5 and 8.5 and were downscaled by LARS-WG model. Then the downscaled climatic data along with field, irrigation and soil data were entered into the SWAP model. Finally, using soil moisture data of 0-30 cm depth, agricultural drought was evaluated using SMDI index. The results showed that the minimum and maximum temperatures and precipitation in the upcoming period have increased compared to the base period and 8.5 scenario have estimated a higher temperature and lower rainfall than the 4.5 scenario. Also, the average SMDI in the upcoming period has increased relative to the base period for both scenarios. The certainty results of GCM models for estimation of SMDI index also showed that under the 4.5 scenario, the IPSL and MIROC models have the highest certainty and the Canesm2 model has the lowest certainty. Under the 8.5 scenario, MIROC model has the highest certainty and Ciromk-3.6 and GFDL models have the lowest certainty.

The soil moisture estimation is essential for optimal water and soil resources management. Surface moisture is an important variable in nature's water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Therefore, in this study, soil moisture simulation in the upcoming period (2020-2039) was compared to the base period (1992-1992) from the Faroub maize farm located in Neishabour plain. The climate data was estimated using six GCM models and two RCP4.5 and RCP8.5 release scenarios. The LARS-WG model was quantified using SWAP model. The results of the change in climate parameters showed that the minimum and maximum temperatures for AOGCM models in the upcoming period will increase compared to the base period, and in some models the future rainfall will decrease compared to the base period and the RCP8.5 scenario shows a higher increase compared to RCP4.5. Changes in soil moisture at a depth of 30 cm showed that the moisture content in the soil in the upcoming periods of 2020-2039 compared with the base period for each two-year period is negligible however, the RCP8.5 scenario shows lower moisture content than RCP4.5 scenario for the six AOGCM models. Estimated annual moisture values showed that in RCP4.5 scenarios at 30 cm depth of the IPSL model, the lowest moisture content and GISS-ES-R and GFDL models had the highest annual moisture content and for RCP8.5 at depths of 30 Centimeters GFDL Model and GISS-ES-R model have the lowest moisture content and the highest annual moisture content during the weeks after plant growth.

Reduction of water resources in arid and semi-arid areas requires the application of management methods to achieve optimal performance. With the logical application of saline water as a source of irrigation water, we can supply a part of the crop water requirement (Hamdy, A., Abdel-Dayem, S. and Abu-Zeid, M., 1993), using various applicable management techniques. The optimal management is, in turn, considered as the use of conjunctive irrigation. Two commonly used solutions include mixing salty and fresh water to obtain water with the optimal salinity; and also the periodic application of fresh and salty water (Amer, 2010; Aslam, & Prathapar, 2006). In effect, salt mainly enters the surface layers of the soil through irrigation and the solute moves vertically from the unsaturated to the saturated zone and towards the groundwater. In turn, the SWAP model is often used to simulate the solute transfer in soil. However, field measurement of the solute concentration changes is very difficult in soil profiles. A simulation model can, thus, be used to estimate the accumulation of solutes in the soil profiles. (Van Dam, Huygen, & Wesseling, 1997)

Moisture content of surface soil is an important variable in nature's water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological, and meteorological processes. Soil moisture is a determining factor in many complex environmental processes and plays a determinative role in the occurrence of agricultural drought. In this study, based on estimated soil moisture data by SWAP model and data of the IPCC Fifth Assessment Report, agricultural drought was determined by the use of soil moisture deficit index for the future period. The climatic data was estimated using six GCM models and two RCP4.5 and RCP8.5 emissions scenarios, and downscaled by LARS-WG model, and was entered into the SWAP model. Finally, by using soil moisture data of 30 cm depth, agricultural drought was evaluated using SMDI index. The results of climate parameter changes showed that the minimum and maximum temperatures and rainfall in the future period would increase compared to the base period and RCP8.5 scenario estimated higher temperatures and less rainfall than RCP4.5 scenario. Results of estimated SMDI values for the future period showed that RCP4.5 scenario has a higher average of SMDI amount than RCP8.5 scenario. Also, both scenarios show the normal moisture amount for future period and the predicted SMDI amount for the future period is higher than the base period.

Since agricultural sector is the largest consumer of water, it is crucial to introduce effective policies to manage water resources in this sector. In the present study, the economic analysis of allocation and pricing of irrigation water on cropping pattern and water demand management in Sistan plain was investigated by use of a positive mathematical programming model (PMP). In this regard, an economic modeling system including a state wide agricultural production model (SWAP) was used. The GAMS software version 24.1 was used to solve the proposed modeling system. The results showed that the use of pricing and allocation policies for irrigation water led to a decrease in the total area of designated cropping products and a reduction in the farm gross margins in Sistan plain. However, the aforementioned policies resulted in savings of 4.594 to 46.256 and 7.123 to 29.484 million cubic meters of water consumed in the region as a result of applying allocation and pricing, respectively. Decrease in total area of cultivation of selected crops under different scenarios of irrigation water quotas policy, especially higher economical crops such as watermelon, melon, and onion. Total gross yield of Sistani farmers would be reduced from 1425694 to 1292677 million Rials, reducing the gross profit by 2.17% to 9.33% in the region's agricultural pattern. Thus, applying the policy of water allocation could save about 30 million cubic meters of water available to farmers. Compared to pricing policy, the use of water allocation policy is recommended due to its superior effect on preserving water resources in the Sistan plain.

Due to the dry climate and limitation of fresh water resources, using fresh and salt water is a solution for crop production under salinity conditions. This study was conducted at Isfahan University of Technology as a randomized complete block design with three replications and five irrigation management treatments in 2014. The treatments included irrigation with saline water (with the salinity of 5 dS/m, based on the relative yield of 75%), irrigation with fresh water (municipal water), alternate irrigation (irrigation with saline water and the next irrigation with fresh water), conjunctive irrigation (half of irrigation with saline water and the other one with fresh water) and irrigation with fresh water to reach the raceme stage, and irrigation with saline water. The maximum wet yield, dry yield and grain yield were related to the fresh water treatment with 4.14, 2.45 and 0.588 kg/m2 and the minimum values were obtained for water their water treated with 1.34, 0.765 and 0.0957 kg/m2 respectively. The conjunctive treatment had the highest yield after fresh water treatment. The various statistical indices showed that this model could be used for sorghum in Isfahan. The determination coefficient for yield was 0.65.The priority of model for yield simulation was salt water at the last stage, alternate irrigation, saline water, conjunctive irrigation and fresh water treatments, respectively.

Due to absolute dependence of agriculture on water, determine of drought condition in each region is very useful in planning the food sourcing. Unfortunately, there is no same definition about the “drought condition”, so there are some indexes to determine it. Standardized Precipitation Index (SPI) is one the meteorological indexes, which widely used to determine agricultural drought conditions. Evapotranspiration Deficit Index (ETDI) was also designed for this purpose. This index is used to determine agricultural drought conditions in arid and semi-arid region. Although there were most research about other drought indexes such as SPI, but there is few studies in oversea countries about use of ETDI index. Thus in this study tried to determine drought by ETDI and SPI indexes in Neyshabur plain by used of climate change models
This research was conducted to determine drought condition in Neyshabur plain located at longitude between 58˚ 13’-59˚ 30’ N and latitude between 35˚ 40’-36˚ 39’ E, Iran. Evapotranspiration Deficit Index (ETDI) was developed based on weekly evapotranspiration deficit to determine drought condition in this region. In order to comparison of the ETDI results to other drought indices, we used Standardized Precipitation Index (SPI) as one the most common drought index. The data were collected from Neyshabur meteorological station for irrigated farms (wheat in Soleymani and Faroub farms, barley and corn) and rain-fed farms (rain-fed wheat) during 1992-2011. In order to estimate weather data for each index in the irrigated farms during two future periods (2020-2039 and 2080-2099), HADCM3, ECHOAM and CGCM3 T47 models were used based on A2, B1 and A1B scenarios and the climate model that has been used in rain-fed farm is the HADCM3 based on A2 and B1 scenarios. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used to comparison of the ETDI and SPI results.
Results showed that average ETDI were in initial wet condition for Faroub farm during base period (1992-2011) while it will be in drought condition during future periods (2020-2039 and 2080-2099). ETDI index was in normal condition for Soleymani farm during base period. Average ETDI indexes for these farms were in normal and initial dry condition during 2020-2039 and 2080-2099 periods, respectively. For barley and corn, ETDI indexes were in normal and initial dry condition during base period, respectively. This index was in normal statute for both of them during future periods. The ETDI value for rain-fed wheat was less compared to irrigated wheat during base period, although, this index will be increased during future periods. In most of scenarios, ETDI indexes showed negative values. It means that high drought condition will be happened during future periods due to deficit evapotranspiration. Results according to SPI index revealed that this region was in moderately drought condition and this situation will not change.
High differences were obtained between ETDI and SPI results. Since agricultural drought depends on evapotranspiration deficits, ETDI is better index compared to SPI. The value of RMSE revealed poor adaptation between two indexes during future periods. In addition, ETDI were not correlated with SPI for all the scenarios in all scenarios. These differences are reasonable because SPI index only uses precipitation data and ETDI uses evapotranspiration. According to the results, it seems that SPI cannot be suggested as a good index in agricultural studies.

Water resources management and planning globally have become a challenging task due to climate change uncertainties. So in the research, the uncertainty of emissions scenarios were investigated in soil moisture estimation during the growing season for wheat in Faroub field of Neyshabour by SWAP model. The Climate data were estimated by ten of AOGCM model and two of emissions scenarios A2 and B for period of future. The Temperature and precipitation data were downscaled by LARS_WG model and was used in SWAP model. The data of soil moisture in depths of 30cm and 60 cm during the growing season of wheat were calculated and compared for periods of baseline and future. The result showed that in the last weeks of growth wheat of Faroub field, the soil moisture for future period was lower than baseline and plant will encounter with the most aridity tension. Also, result of uncertainty bands of emissions scenarios showed that scenario of 2 than B1 have less certainty in estimation of soil moisture for future period during growth weeks.

In recent years, in order to solve problems were presented the way tasks in the agricultural sector. Among these, growth simulation models have been provided for water management, which it possible to reduce the difficulties in agricultural water. The main advantage of these models is that, they can optimize irrigation schedule economically. SWAP model is of the most widely used models in the world which has shown good results for variety of crops. So that, in this practice the performance of this model for simulating the growth of Canola was evaluated according to the data relating to two cultivation periods in Badjgah. The studied parameters consist of soil water content, evapotranspiration, dry matter, and seed yield. The results indicated that, in the calibration and evaluation phases for seed yield respectively, mean normalized error was 8.0 and 14.2. What is more, mean normalized error for simulation of dry matter was 3.9 and 10.8 in calibration and evaluation periods, respectively.

The estimation of soil moisture plays an essential role in the hydrologic modeling, drought prediction and management, climate change analysis, and support of agricultural decision making. Therefore, by use of meteorological parameters variations in the future period, effect of climate change on soil moisture status were investigated by use of three models of AOGCM and emission scenarios namely B1, A2 and A1B. So, firstly, the values of temperature, rainfall, relative humidity, wind speed and radiation were determined by these three scenarios and AOGCM models and secondly downscaled by the LARS-WG model and change factor method. Results of the present study showed that the values of temperature, relative humidity, wind speed and rainfall would increase in average by mean of 8%, 7%, 1% and 5% respectively, in the future period (2020-2039) in comparison with the baseline (1992-2011) and also temperature and relative humidity would increase in average by 30% and 19% in the future period (2080-2099) as compared with the baseline. Also, soil moisture at the depths of 30 and 60 cm were determined by use of meteorological parameters, soil and water data in SWAP model for future and base line periods. The results showed that the soil moisture in the future period of 2020 would increase as compared with the baseline, but in the future period of 2090 would decrease. Furthermore, the 5th and 21th weeks of the wheat growing season were determined as drought stress warning weeks for the Faroub and Soleimani farms, respectively.

Climate change has important impacts on most of the natural processes, including hydrological cycle. Evapotranspiration, as a part of hydrological cycle, will also undergo these changes. Due to the importance of evapotranspiration in water resources and agricultural management, this research was conducted to study climate change effect on evapotranspiration in Neyshabour plain. Evapotranspiration was calculated for five farms in Neyshabour plain using SWAP software and meteorological and agronomic data. In irrigated farms, the HADCM3, ECHAM5OM and CGCM3T47models were used to calculate crop actual evapotranspiration for 2020-2039 and 2080-2099 periods based on A2, B1 and A1B scenarios and the climate model used in rainfed farms was the HADCM3 based on A2 and B1 scenarios. The greatest calculated difference in evapotranspiration was found between the period 2080-2099 and base period (1992-2011) in the A2 scenario. Also, evapotranspiration values for the period 2080-2099 will increase compared to the period 2020-2039 in all three scenarios. Among the crops of investigate, wheat will have the greatest changes (12%) in evapotranspiration in the future periods compared to the base period, while changes of maize will be only 3%. However, the average daily evapotranspiration of maize during the growing season (about 12 mm/day) will be more than the other crops.

Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing land use on water yields. Different methods are available for ET estimation including Bowen ratio energy balance systems, eddy correlation systems, weighing lysimeters.Water balance techniques offer powerful alternatives for measuring ET and other surface energy fluxes. In spite of the elegance, high accuracy and theoretical attractions of these techniques for measuring ET, their practical use over large areas might be limited. They can be very expensive for practical applications at regional scales under heterogeneous terrains composed of different agro-ecosystems. To overcome aforementioned limitations by use of satellite measurements are appropriate approach. The feasibility of using remotely sensed crop parameters in combination of agro-hydrological models has been investigated in recent studies. The aim of the present study was to determine evapotranspiration by two methods, remote sensing and soil, water, atmosphere, and plant (SWAP) model for wheat fields located in Neishabour plain. The output of SWAP has been validated by means of soil water content measurements. Furthermore, the actual evapotranspiration estimated by SWAP has been considered as the “reference” in the comparison between SEBAL energy balance models.
Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate actual ET fluxes from Modis satellite images. SEBAL is a one-layer energy balance model that estimates latent heat flux and other energy balance components without information on soil, crop, and management practices. The near surface energy balance equation can be approximated as: Rn = G H λET
Where Rn: net radiation (Wm2); G: soil heat flux (Wm2); H: sensible heat flux (Wm2); and λET: latent heat flux (Wm2). Simulations were carried out by SWAP model for two different sites in Faroub and Soleimani fields. The SWAP is a physically based one-dimensional model which simulates vertical transport of water flow, solute transport, heat flow and crop growth at the field scale level. The period of simulation covered the whole wheat growing season (from 1st of December2008 to 30th of July2009. 16 MODIS images was used to determine evapotranspiration during wheat growing season. Inverse modeling of evapotranspiration (ET) fluxes was followed to calibrate the soil hydraulic. While SWAP model has the advantage of producing the right amount of irrigation and evapotranspiration at high temporal resolution, SEBAL can estimate crop variables like leaf area index, NDVI index, net radiation, Soil heat flux, Sensible heat flux and evapotranspiration athigh spatial resolution.
Actual and potential evapotranspiration were estimated for SWAP Model during the whole wheat growing season around669.5 and 1259.6 mm for Farub field and 583.7 and 1331.2 mm for Soleimani field, respectively. In contrast with NDVI and net radiation,spatial distribution of SEBAL parameters indicated that soil heat flux, sensible heat flux, and surface temperature of land have the same behavior. At the planting date, evapotranspiration was low and about 1 mm/day, but at the peak of plant growth, it was about 9 mm/day. Moreover, evapotranspiration declined at late growing season to about 3 mm/ day. SWAP model has been calibrated and validated with meteorological data and the data of field measurements of soil moisture. The amount of RMSE of 0.635 and 0.674 (mm/day) and MAE of 0.15 and 0.53 (mm/day) and also coefficient of determination (R2) of 0.915 and 0.964 obtained from comparison of SEBAL algorithm with SWAP model for Farub and Soleimani fields showed that no significant differences was seen between results of two models.
The present study supports the use of SEBAL as the most promising algorithm that requires minimum input data of ground based variables. Results of comparison of SEBAL and SWAP model showed that SEBAL can be a viable tool for generating evapotranspiration maps to assess and quantify spatiotemporal distribution of ET at large scales. Also, it feels that SEBAL and SWAP models can be applied in a wide variety of irrigation conditions without the need for extensive field surveys. This helps significantly in identifying performance indicators and water accounting procedures in irrigated agriculture, and to obtain their likely ranges.

Accurate quantification of ET in irrigated agricultural lands is crucial for planning for water allocation, optimizing crop production irrigation management, evaluating the effects of changing land use on water yields. For this purpose in the research, evapotranspiration calculated by three kind of different methods remote sensing, agro-hydrological model and computational method for maize field located in Neyshabour plain. Methods of evapotranspiration determination consist of Surface Energy Balance Algorithm for Land (SEBAL) algorithm and Modis product satellite in the long of during growth, SWAP agro-hydrological and computational methods of FAO Penman-Monteith and Hargreaves-Samani and FAO Blany Criddle. Correlation coefficient 0.67 to 0.91 between SEBAL algorithm with SWAP model and computational method showed high potential for SEBAL algorithms in the evapotranspiration estimation. In between methods for potential evapotranspiration determination, FAO Blany Criddle method has better result in comparison with Penman-Monteith and Hargreaves-Samani methods. Also, this study found that the SEBAL algorithm could be used to determine evapotranspiration in areas with shortages of data and for evaluation of computational methods and hydrological models.

Nowadays, it’s very usual to use these models in almost all sciences. use of these models has provided quick, appropriate and economic answers to many questions. Modeling is developing In different fields of agriculture, including irrigation and drainage. Field tests are useful to determine and analyze the different irrigation management but there are significant limitations. The most important limitation is the validity of the experiment that is limited by the physical conditions of the area where the experiment performes there. One of the models that is used in agricultural science is the SWAP model that is a model for simulating of soil, water, atmosphere and plant. This model is able to analyze interactions between water movement, growth of plant and transfer of solutions, prediction of yield under different regimes of water and salt, long-term simulation and irrigation scheduling. Although SWAP model is able to simulate relations between soil, water, atmosphere and plant under different management field conditions completely but it has limitations too. One of these limitations is many numbers of input data. So far many researches have been done using this model but by searching on the scientific articles, there is no complete source that offers the basic equations used in the model and also the practical guide is not available. In this article, according to the model guide, the equations that used for model calculations, has been described. Also a useful and practical guide for users who want to use this model has been presented.