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In Iran, pressurized irrigation systems cover a large area of agricultural land, but water use efficiency remains low because farmers tend to do deficit (over) - irrigation due to their lack of knowledge of crop water requirements. To address this issue, irrigation systems can be automated, and it is important to estimate crop water requirements accurately. This can be done based on soil moisture deficit or meteorological data. The water required can then be applied using a volume meter or by determining irrigation time based on the sprinkler flow rate. The study aimed to compare crop water requirement estimates based on soil moisture deficit and meteorological data, as well as the amount of water applied using volume and time-based methods.The study was conducted in the research farm of Razi University, Kermanshah, Iran, on a sprinkler irrigation system equipped with pressure and flow measuring devices, pressure switches, and electrical valves. The field was under corn cultivation, and four types of irrigation management were evaluated, which included a combination of two methods of determining crop water requirement (soil moisture deficiency and meteorological data) and two methods of irrigation application (time or volume). The four treatments were soil moisture - time (MT), soil moisture - volume (MV), weather - time (WT), and weather - volume (WV). The crop water requirement was calculated using the Penman-Monteith formula based on daily weather data. Soil moisture was measured at different depths one day before irrigation, and the soil moisture deficit was calculated to determine the crop water requirement based on soil moisture. The irrigation volume for each sprinkler in the irrigation cycle was calculated using equations that written in the paper.In the volumetric-based method (treatments WV and MV), the volume of water applied was measured using a water meter with a precision of 0.1 liters, and irrigation was stopped after passing the required volume of water. In the time-based method (treatments WT and MT), the irrigation time was calculated by dividing the irrigation volume by the average flow rate of the sprinklers (3 liters per second), and irrigation was stopped after the calculated duration. The actual sprinkler flow rate was calculated based on the volume of applied water and irrigation time in each treatment and irrigation round. Crop yield was measured at the time of harvest in the studied treatments and a control treatment managed by the Faculty of Agriculture. The irrigation treatments were not applied in the first month of the growth period due to field limitations.The results show that the crop water requirement calculated based on meteorological data at the beginning and end of the growing period was more than the method based on soil moisture. In total, the amount of crop water requirement calculated based on soil moisture was 8% more than the meteorological-based method. The volume of applied water in treatments of MT and WT was 14 and 8% more than in MV and WV treatments, respectively.The actual flow rate of sprinklers was different from the design flow rate due to irrigation situations in other parts of the farm. The average discharge of sprinklers (12 irrigation events) in WT, MT, WV, and MV treatments was 2.79, 3.03, 3.27, and 3.12 l/s, respectively. The irrigation time in volume and time-based methods also showed a significant difference. The irrigation time in MT and MV treatments was 10 and 18% longer than in WT and WV treatments, respectively. The study found that due to the non-uniformity of sprinkler discharge, applying irrigation by volume method is better than the time-based method. The results suggest that the MV treatment, which determined the amount of irrigation based on soil moisture deficit and applied it using a volumetric method, is a suitable option for automating sprinkler irrigation systems in the studied region.

In areas with dry and semi-arid climates, underground water is one of the main sources of water supply for agriculture, industry and drinking. The aim of this research is to investigate hydrodynamic coefficients, plain balance and aquifer thickness in the study area of Kermanshah through aquifer simulation using MODFLOW model. In this method, first the conceptual model and then the numerical model were built in two steady and unsteady states, then it was calibrated and validated. The results show that the maximum thickness of the aquifer in the north and northeast of the plain is about 160.96 meters, and the lowest thickness is 118.14 and 120.69 in the southwest and west of the plain, respectively. Also, the coefficient of hydraulic conductivity varies between 0.09 and 40 m/day and the coefficient of specific yield varies between 1 and 35%. Balance calculations showed the negative water balance in the western and southwestern areas of the plain and the positive balance in the northern, northeastern, and southeastern areas.

Maximum sprinkler flow rate, maximum irrigation interval, and leaching requirement are the primary factors in designing sprinkler irrigation systems, which are determined based on the physical and chemical properties of water and soil. This study aimed to prepare the spatial maps of the mentioned parameters in the plains of Islamabad-Gharb district, Kermanshah province. In previous studies, kriging and IDW methods have been found to be suitable for zoning the physical and chemical properties of groundwater and soil. Asadzadeh et al. (2019) investigated the spatial changes of groundwater quality indicators in Ardabil plain by kriging method to investigate the water situation in this area for irrigation systems. Yonesi et al. (2020) also zoned some critical water quality parameters (such as nitrate) of the Najafabad aquifer using the kriging method. Kumar and Sangeetha (2020) used the IDW method to classify groundwater quality in the study area and classify it according to drinking and agricultural uses. Karami and Basirat (2015) evaluated the spatial variations of some surface soil characteristics of Arsanjan plain by kriging and IDW methods. on the other hand, a specific method can not be considered the best method for intermediation and zoning of these parameters. In this study, both methods were used for mediation.

Evapotranspiration (ET) is one of the most important parameters in agricultural water management which has spatial and temporal variations. Remote sensing-based algorithms of ET estimation have been applied in several studies. Using MODIS satellite images ET can be estimated on a daily basis at the pixel size of 1000 meters while using Landsat 8 images can be estimated every 16 days in the pixel size of 100 meters. Various methods are available for combining satellite images with low (such as MODIS) and medium (such as Landsat 8) spatial resolution to estimate evapotranspiration on a daily basis in the lower pixel size. Downscaling is one of these methods which was evaluated in this study is one of the plains in Kermanshah province. For this purpose, the actual ET over the study area was calculated during the summer of 2014 using the SEBAL algorithm based on six satellite images of Landsat 8 and MODIS. Although there are several methods for downscaling the estimated evapotranspiration based on MODIS images, in this study two simple downscaling methods (ratio and subtraction). were used to convert the Landsat 8 image scale. Results of downscaled images of MODIS were compared with the results of Landsat 8 images graphically and statistically. Given the fact of small size and mixed land use types in the study area results of downscaling methods were not acceptable.

Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region. Area of study: Kermanshah, Iran. Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data. Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield. Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.

The crop evapotranspiration (ET) has spatial and temporal variations within the region due to changes in meteorological parameters, phenological stage, and farm management conditions. The main objective of the present study was to compare the trend of temporal and spatial variations of grain maize evapotranspiration in Mahidasht using satellite images with available resources (due to lack of Lysimeteric data). In this study, the daily evapotranspiration for the four maize farms in the different parts of the plain was calculated using Landsat 8 satellite images and the SEBAL algorithm on six dates during the growing period of maize in the year 2014. Since there was no field measurement of ET to verify the results of estimated ET based on the SEBAL algorithm, the final results (evapotranspiration) and intermediate results (Land surface temperature, Albedo, and NDVI) calculated by the SEBAL algorithm were compared with acceptable values in the literature. Results showed a similar trend of estimated evapotranspiration by the SEBAL algorithm and AGWAT software. The trend of increasing and decreasing land surface temperature was rational. The average of NDVI during the growth period in the studied farms was between 0.47 and 0.53 which was acceptable. Albedo with an average of 0.17-0.18 had slight variations between the selected farms. Results showed rising in land surface temperature, evapotranspiration, and NDVI to the middle of the growth period and then reducing of these parameters. The average estimated maize evapotranspiration based on the SEBAL algorithm was between 3.35 to 8.53 mm/day for the six imagery dates.

This study was carried out in a randomized complete block design with four treatments (120, 100, 80 and 60% of field capacity) in order to investigate the effects of deficit irrigation on growth, qualitative, qualitative and water use efficiency of grain maize in Kermanshah. Three replications were designed and conducted in the form of furrow irrigation. The results showed that deficit irrigation effects on grain yield, biomass, leaf area index, harvest index, crop canopy, grain fat productivity, weight of stems, weight of leaves, weight of ears, number of kernels per ear, length of kernels, water use efficiency in grain, water use efficiency in biomass and water use efficiency in fat were significant. The impacts of water stress on the characteristics of maize different in different stages of growth and were higher at the end of the growing season (grain filling stage) so that the application of 40 percent water stress in grain yield, biomass yield, 1000-seed weight and number of grain per ear to the values 3631(Kg), 12143(Kg), 211.66(g) and 288, respectively, reduced 69, 49, 11 and 21 percent compared to the control treatment. The maximum water use efficiency in grain yield ,biomass and fat in the treatment of excess Irrigation was obtained at 1.15, 2.74 and 0.0548 Kg / ha respectively. Therefore, deficit irrigation of maize (KSC 706) is not recommended in Kermanshah.

Abstract Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

Accurate estimation of dam lake evaporation is of great importance in reservoir water management. This factor has led to the installation of meteorological stations in most dams. At the meteorological station, the rate of evaporation from the lake surface is measured using an evaporation pan. Remote sensing methods are cost effective and can be generalized to the whole area. In this study, the daily evaporation rate from the surface of Suleiman Shah Lake was calculated using a combination of SEBAL algorithm and meteorological data and using five Landsat 8 satellite images. The obtained results were compared with the evaporation rate obtained from different methods. The results showed that among the combined methods, Penman Kimberly 1996 method, among other methods, Blanche-Kridel method had the highest agreement and Hafner method had the least agreement with the measured data indicated with R2, RMSE and MAE equal to 0.96, 2.78 and 5.66, respectively. The results of this study can be used to estimate evaporation in other regions with similar climates.

Introduction: Evapotranspiration (ET) is one of the key parameters in water resource planning and design of irrigation systems. ET could have spatial variations in a plain due to the diversity of plant species and spatial variability of meteorological parameters. Common methods of ET measurement are mostly point based and generalization of their results to the regional level are costly, time consuming and difficult. During the last three decades, several algorithms have been developed to estimate regional ET based on remote sensing techniques. Verstraeten et al. (2008) classified remote sensing-based methods for ET estimation into four categories i) methods based on the surface energy balance, ii) Penman-Monteith equation, iii) water balance and iv) the relationship between surface temperature and vegetation indices. SEBS (Surface Energy Balance System), SEBAL, METRIC and SEBI are examples of the algorithms which is developed based on the surface energy balance approach. SEBS is developed by Su (2002) and has been evaluated by several researchers. However this algorithm has been examined in the several studies in the world,it has been used rarely in Iran. The aim of the current study was to assess the results of SEBS algorithm in Mahidasht, Kermanshah, Iran. The study area is located at the latitude of 34º 5' – 34º 32' N and longitude of 46º 31' - 47º 06' E. Materials and Methods: A brief description of the SEBS algorithm (in Persian) as well as its procedure to calculate ET based on Landsat images were presented in this paper. All equations of the algorithm were coded in the ERDAS Imagine package software using model maker tools. Actual ET over the study area was estimated using SEBS algorithm during the growth period of grain maize in the year 2010. For this purpose, available LANDSAT TM satellite images during the growing season of maize in 2010 (25 June, 11 July, 27 July and 12 August) were downloaded free of charge from the http://glovis.usgs.gov website (last visited: 26 November 2015). A Lysimetric study was carried out to obtain reliable amounts of ET to assess the accuracy of calculating actual ET by SEBS algorithm. Because of the absence of the weighing Lysimeters in the study area, Drainable Lysimeter was used. Since the maize was the major crop in the study area, 10 ha maize was planted on 15 May 2010 at the research farm of the Mahidasht agricultural research station. At the same time, maize was cultivated in the Drainable Lysimeter (1m*1.5m*1.5m) which was located almost in the middle of the research farm. Actual ET of maize was calculated with the Lysimeter for each irrigation interval (10 days) based on water balance equation. The Results of the SEBS algorithm were evaluated on two levels (farm and regional). At the farm level, average of calculating ET at the pixels of research farm was compared with the average of measured ET at the Lysimeter. The absolute and relative differences between the calculated and measured values of ET was used to describe the accuracy of the algorithm. Due to the absence of regional ET measurement, maximum ET estimated by the SEBS algorithm in the plain was compared with the calculated potential crop reference evapotranspiration (ETO). ETO was calculated using the Penman - Monteith formula based on daily weather data obtained from Mahidasht weather station. Results and Discussion: Results indicated that an average of ET in the study area increased from June to August which coincides with increasing air temperature and vegetation density in the irrigated fields of the study area. The highest and lowest values of actual ET over the study area were determined in the irrigated farms and mountainous area, respectively. The results of Lysimetric study indicated that daily actual ET of maize on 25 June, 11 July, 27 July and 12 August was 4.13, 7.74, 7.45 and 8.05 mm.day-1, respectively. The value of ET estimated by SEBS algorithm was less than actual measured ET by Lysimeter for the all mentioned dates. The maximum absolute difference between estimated ET by SEBS and measured ET with the Lysimeter was occurred on 27 July with the amount 0.34 mm.day-1. Considering the maximum relative difference of 4.56 % between calculated and measured ET, it could be concluded that estimated ET by SEBS algorithm can be acceptable. Due to the absence of ground-based measurements of evapotranspiration at the regional level, the maximum amount of ET estimated by SEBS algorithm was compared with ETO. The highest and lowest ratio of maximum ET over ETO were calculated as 1.02 and 1.22 which are acceptable values for the crop coefficient (Kc) in the studied period. The maximum difference between estimated ET by SEBS algorithm with ETO was 1.53 mm.day-1 which is equal to 21.86% of ETO in the same date (12 August). Conclusions: The results of the current study showed that the SEBS algorithm can estimate the actual ET of maize with the acceptable accuracy in the Mahidasht. In the absence of measured ET data at the regional level, it was difficult to have a reasonable judgment on the accuracy of the estimated values of ET by SEBS algorithm at this scale. It is recommended to do the same study on other remote sensing-based approaches of ET estimation.