مجله آبیاری و زهکشی ایران
سال هفدهم شماره 3 (امرداد و شهریور 1402)

Salinity and drought stresses is one of the serious problems in producing of agricultural products in many parts of the world, especially in arid and semi-arid regions. Based on this, a pot research was carried out in order to investigate the combined effects of salinity and drought stresses on yield and yield components of safflower in winter 2021 and spring 2022. A factorial experiment based on completely randomized design with three replications including; two factors of the amount of irrigation water and salinity was carried out. Three levels of irrigation water amount including full irrigation W0 =100% , W1 =75% and W2 = 50% water requirement and four levels of salinity including S0 = 0.7 (drinking water), S1 = 4, S2 = 8 and S3 =12 dS/m were applied in a sandy loam soil. The variance analysis results showed that salinity and drought stress on seed weight per plant, capitula per plant, seed per capitula, 1000 seed weight, water productivity were significant at one percent level. The highest simultaneous level of drought and salinity stresses reduced seed weight per plant and water productivity in compared to control treatment by 64.17% and 43.29% respectively. Increasing in salinity and drought stresses simultaneously, seed weight per plant decreased in compared to control treatment 45.19 and 61.1% , respectively. The results also indicated that the salinity threshold of irrigation water for reducing of safflower grain yield was obtained about 4 dS/m under deficit irrigation. In highest level of salinity, changing in irrigation level from W1 to W2, reduced water productivity by 27.63 percent. Changing in irrigation level from W0 to W1 and W1 to W2 was not reduced water productivity significantly under irrigation with non-saline water. Considering of findings in this research, it can be said that W1S0 is recommended for the study region.

Accurate monitoring of groundwater levels and estimating their fluctuations in the future is of importance, especially in arid and semi-arid areas. regarding the high capabilities of AI-based models in the modeling of hydrologic phenomena, this research used the MP5 decision tree, in combination with the wavelet transform, to predict groundwater level fluctuations of the Kerman-Bagheyn plane. To develop the wavelet-decision tree (W-M5p) hybrid model, the wavelet transformation outputs were exported to the MP5 as inputs. Several statistical criteria, including coefficient of correlation (R), agreement index (Ia), and scattering index (SI), were used to evaluate the performance of the hybrid model compared to the single model. The results indicated that, even when the inputs of the hybrid model includes only the meteorological data from a synoptic station (the water level of previous periods were not used in the analysis), that the performance of the WM5P was superior to the single model in the prediction of groundwater fluctuations. The WM5P model with three months of forecast horizon with the Coif4 wavelet and decomposition level of 6 reduced the SI value from 0.6394 to 0.0181 and, at the same time, increased the Ia from 0.6898 to 0.9998. Consequently, the Coiflet4 with decomposition levels of 5 and 6 was the most efficient wavelet in the hybrid model for reliable estimation of the Kerman-Bagheyn plane groundwater level.

In spite of the drought conditions and lack of water in most parts of Iran, fodder corn is one of the main producers of silage fodder in the country. For the purpose of investigating the effects of subsurface drip irrigation systems on fodder corn cultivation and determining the efficiency of water consumption, a split plot design was conducted at Jopar Kerman Agricultural Research Station for a period of two crop years using randomized complete block designs with three replications. In the research, three levels of irrigation (I1=100, I2=75, and I3=50 percent of the water requirement) were assessed as the main factor, and two depths of subsurface pipes with drippers (inline) were assessed as the submain factor. Among the treatments of different irrigation levels, the treatment of 100% water requirement obtained the highest yield by producing 77250kg/ha of wet fodder and 15447kg/ha of dry fodder. A 20% and 52% yield difference was observed between the 100% water demand treatment and the 75% and 50% water demand treatments. In terms of WP, there was a significant difference between the two treatments of 30 cm soil depth and 40 cm soil depth; the highest WP was observed in the 30 cm soil depth treatment. In both years of the study, the irrigation treatment of 50% of the water requirement had the lowest WP (average 1.2 kg/m3). Additionally, the average amount of water consumed by the treatments of 50, 75, and 100% of fodder corn water requirements was 3200, 4100, and 5200 m3/ha, respectively. As a result of the results of this research, it has been recommended that for the production of fodder corn in the Jopar Kerman region, a planting depth of 30 cm with a 100% water requirement under subsurface drip irrigation with a water consumption of 5200 m3/ha will be used

Evaporation is one of the most effective and massive loss factors in water resources, and with its accurate calculation, effective solutions can be implemented to reduce the effects of drought and to protect and efficiently manage water resources. In this research, the common experimental relationships in estimating the daily and monthly evaporation rate for four stations of Suleiman Tange, Mohavvateh Edareh Sari, Farim Sahra and Telmadarreh were investigated and evaluated based on the observed data of the evaporation pan, and the best experimental relationships for estimating the daily and monthly evaporation values was introduced. For this purpose, the 10-year statistics of the measured values of evaporation from the pan with the values calculated from the experimental methods of Meyer, Marciano, Shahtin, Hefner, Ivanov, and the American civil engineering office, with two-time scales, daily and monthly, with the aim of estimating losses due to evaporation it was compared from the lake level of Shahid Rajaei Sari Dam. Then, the performance of empirical relationships was evaluated based on MSE, R2 and EF statistics. The results of the evaluation of the experimental relationships of evaporation calculation showed that Meyer's method is the best relationship for estimating daily evaporation in the studied stations. After Meyer's empirical relation, Ivanov's relation was introduced as the best relation. The values of MSE, R2 and EF statistics in the estimation of monthly evaporation using Meyer's relation at Suleiman Tangeh station, which is the closest station to Shahid Rajaei dam, were obtained as 436.9, 0.97, and 0.3, respectively. Also, among the experimental methods used in this study, the American Civil Engineering Bureau method was determined as the weakest equation for estimating monthly evaporation at Suleiman Tangeh station.

In order to study the effect of deficit irrigation on antioxidant enzymes content, soluble carbohydrates, chlorophyll concentration and yield of two sweet corn cultivars a field experiment based on randomized complete block design (RCBD) arranged in factorial with three replications was conducted at the Education-Research Farm (With geographical position: 37°33' north, 59°11' east), Mashhad Branch, Islamic Azad University, Mashhad, Iran, during in cropping season 2018-2019. The experimental factors were included at three levels (0 (Full irrigation), 20 and 40% less than the irrigation requirement) and two sweet corn cultivars (Amylapop and Merit). The results showed that with increasing of deficit irrigation (increasing stress) content of catalase, peroxidase, superoxide dismutase enzymes and soluble carbohydrates increased. However, concentration of chlorophyll a, b and total, husked cob yield, dehusked cob yield and conservable grain yield decreased. The content of catalase, peroxidase, superoxide dismutase, husked cob yield, dehusked cob yield and conservable grain yield were higher in Amylapop cultivar than Merit. In general, the results of this research showed that the tested cultivars do not have the necessary stability under conditions. Therefore, applying the method of deficit irrigation in the studied cultivars it will be justified only if a 20 and 40 percent reduction in the amount of water used for this product is more beneficial than the reduction in yield.

Wheat, as one of the most important crops in Iran and all around the world, has taken a significant share of the area under cultivation and production. The lack of water resources in the dry climate of Iran makes it necessary to use the available water resources to the optimal level. The analysis of optimal water consumption in agricultural production is done by using the water efficiency index. In this study, rainwater productivity in rainfed wheat cultivation in Kurdistan province was estimated using the AquaCrop-GIS model. Performance information was collected from the Agricultural Products Insurance Fund in the six-year period from 2014-2015 to 2019-2020. Also, information from synoptic meteorological stations and the global soil data bank were used for the required basic information. The model results show a 90% agreement index in the simulation process. Also, according to the average water productivity index calculated by the model, a zoning map of the areas prone to rainfed wheat cultivation was presented. Kamyaran district, having an average water productivity index of 0.68 kg/m3, was identified as the most suitable part of the province for dry wheat cultivation. According to the special conditions of Kurdistan province, it is suggested that the development of the cultivated area should be done according to the priority of more susceptible areas and based on the rainwater productivity index in the province.

Floodplain vegetation can alter the flow characteristics of a river through the application of redundant drag forces. In this study, turbulence characteristics and flow structure were investigated under the influence of partially double-layered vegetation in a compound channel. To investigate the phenomenon, a three-dimensional numerical model was used to solve the Navier-Stokes equations and track the evolution of the free surface. To ensure the performance of the model, the numerical results were validated using data from previous experimental studies. The validation results showed that this model captured the flow dynamics with high accuracy. In the next step, the model was used to predict the free surface fluctuations and velocity field of the steady flow in the layered vegetated floodplains. The modeling results showed that the formation of a velocity gradient at the interface between the main channel and the floodplain can lead to the development of secondary flows and the mass and momentum exchange at this interface. In addition, turbulent kinetic energy and turbulent dissipation of the flow through vegetation in floodplains was observed in the numerical results. It was concluded that the layered vegetation can increase the flow turbulence and the dissipation rate of the flow energy. The maximum values of turbulence kinetic energy and turbulence intensity were observed at the interface between the floodplain and the main channel. Therefore, the flow disturbance at the interface between the floodplain and the main channel may increase the mass and momentum exchange in this region.

Stage -discharge rating curve is mainly developed from measured data in any hydrometry station. Measured data usually obtain in low to medium flow discharge, because in most cases it is very difficult to measure the flow discharge during flood. Therefore, the stage-discharge is extrapolated beyond the measured data to compute the flood which may estimate low or higher value. This is because during the high flow, the bed form is developed which causes the flow resistance to change. In order to establish a better stage - discharge rating curve, it is important to apply methods which consider the bed form resistance. In this research, Einstein-Barbarossa, Shen, White, Engelund and Brownlie methods were used to complete the final part of the Stage- discharge rating curve obtained from the measurement at the Ablo hydrometry station located on the nekarud river or to construct the Stage – discharge rating curve at the mentioned station. After collecting the required data including river cross section, discharge and related stage and bed material gradation in this station and calculating the corresponding Stage – discharge using the mentioned methods in the water year 2021-2022, various statistical comparisons were made between calculated and measured discharges. The results showed that the Einstein-Barbarossa method has the lowest absolute mean error of 6.9 and minimum root mean square error of 2.7 compared to other methods.

Hydrological response is one of the most important issues in watershed studies. Various researches have been performed in order to determine the hydrological response, including the kinematic wave model, and the results indicate the advantages and disadvantages of the models. In this paper, the numerical and analytical methods of solving the kinematic wave equation were investigated and the results were evaluated in a laboratory watershed. So, the finite difference method was used in the framework of HEC-HMS software, and to evaluate the analytical method, by the kinematic wave equation and using GIS software, the flow routing in the laboratory watershed were investigated. The results of the two mentioned methods were placed next to the results of the time-area method in the HEC-1 model, and finally the results of these three methods were compared with the observational results of the rainfall-runoff events in the laboratory watershed. The error of the kinematic wave analytical model, the kinematic wave numerical model and the HEC-1 model in determining the maximum flow rate, according to the relative error index, was about one percent. Also, according to Nash-Sutcliffe index, the kinematic wave analytical model had the best performance with a value of 0.926 and the numerical solution of the kinematic wave with a value of 0.838 was weak compared to the other two models in derivation of the hydrograph. However, the regression index of the HEC-1 model was 0.944 and analytical method with a value of 0.932 ranked second, and the numerical model of the kinematic wave with a value of 0.899 was ranked third.

Local energy loss is one of the basic design parameters of underground aqueducts or culverts. So far, numerous studies have been performed in this regard, and several formulas have been developed to predict the local energy loss coefficient. Previous studies have shown that the energy loss coefficient is a parameter dependent on the hydraulic variables of the flow and culvert geometry. However, due to the uncertainty in the phenomenon, the existing relationships have not led to comprehensive and accurate results. In this study, several models were developed by considering different input parameters by using experimental data to predict the local energy loss coefficient in slope-tapered culverts and the efficiency of the intelligent method of Gaussian Process Regression (GPR) as a kernel-based approach was evaluated. The results were compared with Gene Expression Programming (GEP) method. Also, for determining the effect of input variables, sensitivity analysis was performed. The results proved the high efficiency of the method used in the research in estimating the energy loss coefficient. The results of GPR modeling showed that the model with input parameters of Froude number (Fr), ratio of water depth to culvert diameter (Hw/D), and reducer length (Lr) is the superior model. In this case, the evaluation criteria values for the test data series were obtained as CC=0.85, DC=0.799 and RMSE=0.2. Also, by performing sensitivity analysis, it was observed that Froude number has the most impact on the local loss coefficient and could cause a significant increment in model efficiency. The use of the Froude number increased the accuracy of modeling by almost 40%.

In order to plan and make correct policies in agricultural sector in terms of sustainable application of water resources and ensuring food security and self-sufficiency in the production of strategic products such as wheat, it is necessary to make an accurate prediction of dryland wheat yield. Recently, use of artificial intelligence methods for this purpose has increasingly attracted researchers' attention. In this study, the performance of three ANN algorithms, BR, GD, and LM were evaluated and compared to estimate dryland wheat yield. Meteorological data (2004-2018) from 10 meteorological stations, located in Kermanshah province, were used as input data in the proposed models. After determining the correlations between meteorological parameters and dryland wheat yield, relative humidity (RH) and precipitation (P) variables under three different input data combinations were used in the proposed models. Comparison of the predicted and observed data of dryland wheat yield showed acceptable performance of all three models. The R2 values of training step for the best combination of input data for the ANN algorithms (BR, GD and LM) were 0.85, 0.64 and 0.74, respectively, and the RMSE values were 0.09, 0.16 and 0.18 tons per hectare, respectively. Comparison of the results of different input data combinations showed that the P parameter has the most importance in predicting the yield of dryland Wheat, however, the use of P and RH data simultaneously as the third scenario leads to the highest accuracy. Finally, the BR algorithm by combining the inputs of P and RH with R2 and RMSE values for the test data equal to 0.85 and 0.09 ton/ha respectively, as the optimal model in estimating the drtyland Wheat Yield compared to other algorithms and input combinations were known.

In this study, the energy footprints of the surface, drip, and sprinkler irrigation systems were investigated. In this regard, the water requirements of 15 different crops were considered in Alborz province. The data of the wells of Alborz province, the data of the ground water level of the observation wells located in the Alborz plain in the statistical period of 2019 were collected from the regional water authority of Alborz province. The water table and dynamic level of 11 wells were checked and calculated.. The required electric power to pump the water on the ground was then calculated. Taking both static and dynamic water levels, the electric energy required for different irrigation systems was calculated according to the net water requirement and consumed energy of the water losses. Also, taking three scenarios for the return flow to the aquifer and irrigation non-useful losses, the return flow to the aquifer and the associated energy footprint were calculated for both static and dynamic groundwater levels. Results indicated that the highest energy loss is related to surface irrigation and the lowest amount is related to drip irrigation.the irrigation useful losses calculated using the dynamic water level were always higher than that calculated by the static water level. For example, the amount of useful irrigation losses of the dynamic and static water levels of alfalfa crops (40% of the useful losses of drip irrigation) is about 15 and 11 (m3/ha), respectively. Also, the highest and the least irrigation useful losses were obtained for horticultural and summer crops respectively. The highest amount of losses in horticultural crops, agricultural crops and vegetable crops is related to walnut, alfalfa and onion, respectively, and the lowest losses in horticultural crops, agricultural crops and vegetable crops is related to grape, barley and spring cucumber, respectively.

In addition to the over extraction of groundwater resources, one of the problems with sustainable use of groundwater, especially in the arid areas, is the occurrence of successive droughts. Therefore, an important challenge in managing groundwater resources, is to consider the effects of drought in the hydrological system. In this study, due to the necessity of separating human activities from natural droughts, a new groundwater drought index was developed and its capability evaluated in the Najaf Abad plain. In the new index, first, the effects of human activities, such as groundwater extraction or land use changes, were separated by HARTT model, and then the natural drought values are determined, by fitting the best probabilistic distribution function and standardizing that. The results of the evaluation of NGDI index in the Najafabad plain showed that, natural drought conditions prevailed over the groundwater resources in the plain between 1992 and 2000. According to the obtained results, the NGDI index provides the ability to determine the natural drought in the groundwater and distinguish the human effects from it and predict drought from several months or even more than a year before it occurs. This index can be used as an appropriate tool for managers to better control and manage groundwater resources. Therefore, the use of this index is recommended in management strategies for prediction and drought warning systems.

The purpose of this research was to evaluate the SWAP model in simulating the yield and water productivity of corn under different conditions of irrigation water and nitrogen fertilizer management. For this purpose, the data from two separate research projects were used. In the first research project, fertilizer in four levels (N1: 100, N2: 80, N3: 60 percent of fertilizer requirement and N4: control) and fertilizer splitting (T1: three and T2: four equal splitting) and in the second project, the factors of irrigation water amount in four levels (W1: 120, W2: 100, WI3: 80 and W4: 60% of water requirement) and nitrogen fertilizer in four amounts (N1: 100, N2: 80, N3: 60 and N4: zero percent of fertilizer requirement) were considered. The results showed that the SWAP model for simulating yield and water productivity in the first project provided low (0.55 ton.ha-1) and acceptable (0.25 kg.m-3) errors, respectively, and in the second project has low (0.51 ton.ha-1) and high (0.47 kg.m-3) error, respectively. The accuracy of this crop model for simulating the yield and water productivity in the first project was in the excellent (NRMSE≤0.1) and good (0.1<NRMSE≤0.2) categories, respectively, and in the second plan, in the excellent (NRMSE≤0.1) and poor (NRMSE<0.3) categories, respectively. Splitting of nitrogen fertilizer from T1 to T2 caused a decrease in yield simulation error from 6.6 to 4.4% and water productivity from 6.6 to 0.4%. The yield simulation error in treatments N1 to N4 were 0.11, 0.12, 0.14 and 0.17 percent, respectively and in treatments W1 to W4 were 0.12, 0.18, 0.12 and 0.12 percent, respectively. There was no proper trend between the changes of water productivity error in different treatments. Based on the results, application of the SWAP model is suggested for the presence of nitrogen fertilizer.

The limitations of satellite sensors make it impossible to access thermal bands with high spatial and temporal resolution simultaneously. Therefore, downscaling methods are essential because they provide simultaneous access to thermal data with high spatio-temporal resolution. LST parameter is critical in agriculture, it is one of the most important in estimating the amount of evapotranspiration and significantly impacts crop growth. LST images of the MODIS with spatial resolution of 1000 meters are available daily. Still, the low spatial resolution in these images is a limitation that makes it impossible to use for agricultural management. On the other hand, high humidity changes in irrigated regions cause errors in LST downscaling process. this research was carried out with the aim of downscaling the LST of the MODIS sensor from 1000 meters to OLI resolution from Landsat 8 satellite (30 meters) in irrigated regions. In the first stage, the DisTRAD model was implemented for the downscaling of the LST of MODIS in Amirkabir and Mirzakoochak Khan Farms. The results show the poor performance of the DisTRAD model in the downscaling of LST from 1000 meters to 30 meters. Next, to check the results of LST downscaling of the MODIS by TOTRAM and OPTRAM soil moisture estimation models, the root mean square error (RMSE) statistic was used. The results indicate that the average value of RMSE in the downscaled images by the OPTRAM-TOTRAM model shows a decrease of about 2.53°C compared to the DisTRAD model. Also, the average value of RMSE in the first six months of the year when irrigation has been done, compared to the DisTRAD model, shows a decrease of about 4.11 °C. As a result, the use of the OPTRAM-TOTRAM model offers much better performance than the DisTRAD model in downscaling the LST of the MODIS in irrigated regions.