aliheidar nasrolahi

This study aimed to assess the virtual water of sugarcane under two irrigation scenarios in the Khuzestan province of Iran. The first scenario (S1) reflected current water consumption conditions in sugarcane fields, while the second scenario (S2) involved a 30% reduction in water usage on these farms. In S1, the average virtual water content was 0.42 m3.kg-1, which decreased to 0.32 m3.kg-1 in S2. The virtual water variation among sugarcane units in S1 was 0.62 m3.kg-1, while in S2, it was 0.53 m3.kg-1. In S1, the virtual water contribution of all sugarcane units to the water stress index of Khuzestan's agricultural sector was approximately 20.1%. This indicated that sugarcane production accounted for one-fifth of the water resources in the province's agricultural sector, primarily for export. This contribution decreased by 14.1% in S2. The changes in virtual water were comparable among different sugarcane units in both scenarios. The water stress index, based on the virtual water per total allocated water resources in the agricultural sector in Khuzestan (VKA), revealed high values (2%<VKA) in the Mirza Kochuk Khan, Debal-Khazai, Farabi, Karoon, and Dehkhoda units, moderate values (1% <VKA< 2%) in the Imam Khomeini and Salman Farsi units, and low values (VKA<1%) in the Haft Tappeh and Mianab units. Overall, the virtual water value and cultivated area significantly influenced the share of each sugarcane unit in the total virtual water amount. Notably, the Haft Tappeh and Mianab units had the smallest virtual water share, attributed to their smaller cultivated areas.

This research investigates the utilization of copula functions in the water resources field, encompassing meteorological and hydrological aspects. A review of the Web of Sciences archive revealed 15143 studies featuring copula keywords. Notably, 40% of these studies pertain to copula-based simulation within this field. Groundwater studies were the least conducted, accounting for only 3% of all studies in the field. Regarding copula functions, studies were generally divided into two parts: frequency analysis and simulation, encompassing all dimensions of copula functions. Researchers confirmed the performance of copula functions in both parts. Studies in copula functions have revealed a new approach in joint frequency analysis and conditional probability estimation, based on the marginal distribution of data and their conditional density. The results indicate that in more than 2 dimensions, the tree sequence of vine copula has reduced computational complications and allows for the determination of different structures based on independent and dependent variables. Various studies have shown that the use of copula functions has been successful due to its lack of assumptions and restrictions and has good performance. For this reason, this approach is considered. The approach has been increasingly utilized in 2-dimensions and multi-variables, and continues to progress and develop.

Climate change is a phenomenon that affects many natural processes, including the hydrological cycle. Evapotranspiration is also an important part of the hydrological cycle, which is crucial in water resource management and agricultural planning. Since the estimation of evapotranspiration is always associated with uncertainties, this study examines the effects of climate change on the evapotranspiration process at the Crumbed station in Preston province. The study uses the SAP1-2.6, SAP2-4.5, SAP3-7.0, and SAP5-8.5 scenarios according to the Sixth Assessment Report (AR6) in three future time periods: near future (2023-2048), mid future (2049-2074), and far future (2075-2100). The reference evapotranspiration for the base period and future periods is calculated using the Hargreaves method. The results show that the maximum temperature at the Crumbed station will increase by an average of 0.26 to 6.3 degrees Celsius by the year 2100, compared to the base period (1988-2014). The minimum temperature will also increase by an average of 0.32 to 4.9 degrees Celsius during the same period. Additionally, the average evaporation-transpiration in all periods will increase compared to the base period. The average evaporation-transpiration in the near future will range from 4.69 to 4.82, in the mid-term future from 4.7 to 4.94, and in the far future, from 4.72 to 5.04

This research was conducted in order to investigate the interaction of different levels of irrigation and nitrogen fertilizer on the yield of Forage Corn in Khorramabad. The experiment was conducted as factorial in a randomized complete block design with three replications. The first factor consisted of four irrigation levels including I125 (125 Percent of water requirement), I100 (100 Percent of water requirement), I75 (75 Percent of water requirement), and I50 (50 Percent of water requirement) and the second factor consisted of three levels of nitrogen fertilizer including N400 (400 Kg/ha), N250 (250 Kg/ha), and N100 (100 Kg/ha). Based on the evaporation pan method, the total irrigation requirement for treatments I125, I100, I75 and I50 during the period were 761, 642, 524 and 406 mm, respectively, which were more than the amount of water requirement reported in the national water requirement document of plants.The result showed that the maximum amount of wet and dry matters, plant height, leaf area index which were 80 and 21.2 tons per hectare, 9595 mm2 and 220 cm, respectively, were obtained in I125N400 treatment, which had a significant increase (5 Percent) compared to the control treatment (I100N250). The minimum amount of these traits which were 30 and 10.64 tons per hectare, 7096.133 mm2 and 100 cm, respectively, were obtained in I50N400 treatment, which had a significant decrease (5 Percent) compared to the control treatment (I100N250). It was found from the results of this research that at the levels of I125 and I100, the values of wet matter and leaf area index were increased as the amount of nitrogen fertilizer increased. At irrigation levels of 75 Percent and 50 Percent of water requirement, increasing the amount of fertilizer did not cause significant changes in the measured traits. In this research, the maximum value of water productivity for wet and dry matters was obtained in I100N400 treatment with values of 10.9 and 2.91 kg/m3, respectively.

Evaporation and wind loss causes a decrease in the amount of water entering the soil and a change in the spraying pattern, which changes parameters such as uniformity coefficient (CU), water distribution uniformity (DU), potential efficiency of application in the lower quartile of land (PELQ), actual efficiency in Lower quartile (AELQ) and total efficiency (ET) are irrigated. The current research was conducted in a factorial way in the agricultural year of 2020-2021 in the agricultural lands of Chaghahroshi plain in Khoram Abad city. The studied factors include land type (F1: sugar beet field and F2: onion field), sprinkler height (L1: 75 cm and L2: 100 cm) and irrigation time (T1: morning, T2: evening and T3: night) with three repetitions. Evaporation and wind loss in the sugar beet field was about 0.95% more than the onion field, which caused the decrease of other parameters including uniformity coefficient (5.06%) and total efficiency (5%) in the sugar beet field compared to It became an onion farm. With the increase of sprinkler height, evaporation losses and wind blowing increased by 1.56%. The reduction of evaporation and wind loss in the sugar beet field at night compared to morning and evening was 2.1% and 5.6%, respectively. The increase of uniformity coefficient and uniformity of distribution in the sugar beet field at night was 5.8 and 6.5%, respectively, more than irrigation in the morning. The increase in the values of these parameters compared to evening time was 11.3 and 14.9 percent, respectively. Changing the irrigation time in the onion field had a greater effect on improving the irrigation efficiency indicators than the sugar beet field. The reason for that was the increase in losses in the morning (4%) and evening (6.5%) compared to night irrigation in the onion field.

The remote sensing technique is a suitable method for estimating actual evapotranspiration (ETa) at the large-scale due to spatial and temporal resolution. The present study aims to assess the ETa using the SEBAL and different algorithms to survey the effect of the LST and their impact assessment on the ETa fluctuation. Field measurement, including canopy temperature and the volume of inflow and outflow of water consumption was done based on lysimeters during 2018-2019. After the necessary pre-processing on the satellite images, the Land Surface Temperature (LST) was estimated using Planck's and split window algorithms. The result showed that the performance of Split window was better than to the Planck algorithm. Also, ETa was estimated by the SEBAL algorithm based on two temperature scenarios including the Planck and split window. The results showed, the cold pixel of SEBAL algorithm had compliance with the Lysimetric measurement. Moreover, the cold pixel of the split window algorithm with RMSE=0.56, NRMSE=0.084 and NS=0.992 (mm/day) had the highest consistency with the lysimeter data. Also, the rMBE index of the split window algorithm was associated with underestimation in the range of -4.07 to -3.22%, while the Planck function algorithm fluctuated with overestimation in the range of 4.76 to 12.65%. This research has been verified to the cold pixel of satellite for crop with no stress conditions and for better investigation at crop stress condition, precise instruments are needed.

In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of bat support vector regression, bat support vector regression, gray wolf support vector regression for four piezometric wells of Sarab Pardah, Naservand, Sally and Baba Hossein Bridge, which have homogeneous statistics. and it was done without missing data. For modeling, rainfall (P), temperature (T) and underground water level (H) and withdrawal from water resources (q) have been used in the monthly report of the models during the period of 1380-1400. It should be noted that for modeling, 80% of the data is chosen for training and the remaining 20% ​​for testing, randomly, which covers a wide range of data types. Correlation coefficient (R), root mean square error (RMSE), mean absolute value of error (MAE) and Sutcliffe coefficient of vitality (NS) were used to evaluate and compare the performance of the models. The results showed that the combined structure provides better performance than other structures in all the investigated models. Also, the results showed that the wavelet support vector regression model based on the evaluation indicators, in the piezometric well of Sarab Pardah has R=0.978, RMSE=0.221 m, MAE=0.011 m, NS=0.985 and also in the piezometric well of Naservand has R=0.981 . 010 m, NS=0.986 and finally piezometric well, Baba Hossein Bridge has 5 R=0.98, RMSE=0.101 m, MAE=0.007 m, NS=0.995, compared to other models, it can be used to create a favorable result.

The phenomenon of the exploitation of groundwater recently has caused a sharp decline in groundwater levels, resulting in both subsidence and desertification caused by the groundwater withdrawal. Thus, reliable prediction of groundwater level has been an important component in sustainable water resources management. In this study, a data-driven prediction wavelet neural network model (WNN) was proposed for groundwater level in Azna-Aligodarz, Dourod-Brojerd, Delfan, Selseleh plain forecasting, and the results were compared with the artificial neural network. Parameters including precipitation, temperature, flow rate and water level balance during the period of the previous month were used as input of the model and level of the water table in each period as the output of the model through monthly scale (2002-2019) were selected. The criterion of the correlation coefficient, Root-Mean-Square-Error and average absolute error and coefficient of Nash Sutcliff for evaluating and the comparison of performance models were used.  The results of the hydrograph analysis indicated that the increase of rainfall has an effect on groundwater resources, and also the findings evaluation of criteria showed that WNN has better performance and less error than the artificial neural network.

Irrigation has greatly contributed to crop production in arid and semi-arid regions. In addition, agricultural production in irrigated lands is more sustainable than in rain feed lands, which has helped significantly stabilize food prices in the world (Hanjra et al., 2009; Rosegrant and Cline, 2003). Considering the role of irrigation and scarce water resources, the United Nations Development Program has predicted that the future of food supply for the world's growing population will be affected by the water crisis, not the crisis of arable land (Du Toit, 2011). Therefore water is a major player in increasing agricultural production (Hanjra and Qureshi, 2010). At the same time, competition for water between the agricultural, industrial and urban sectors is also increasing (Dubois, 2011). Under such circumstances, any method or technology that can increase the productivity of water consumption (product per unit volume of water) will contribute to the development of food security. Naturally, cheap and simple methods and technologies, especially among low-income farmers in developing countries, will become more common; deficit irrigation is one of them.Beans are an important source of protein, fiber, and nutrients and have for centuries played an important role in the human diet (Yonts et al., 2018). The bean is a water-consuming plant, its net water requirement in the Khorramabad Plain (Project Area) is about 5500 m3/ha, which is 2.5 times more than wheat and 2 times more than rapeseed (Zabihiafrous et al., 2018). Deficit irrigation of beans can be an effective solution to reduce the irrigation water consumption of this crop and increase its water productivity.

Estimation of evapotranspiration of the reference plant is one of the important components in the design of irrigation systems and water management for irrigation of plants. In this study, four coefficients of evaporation pan Schneider (1992), Aurang (1998), Allen and Perot (1991) and Konica (1989) to estimate the reference evapotranspiration compared to the FAO-Penman-Monteith model using indices RMSE, MAE, AARE, MR and Spearman coefficient were evaluated. Information required for different coefficients for estimation (ETo) was collected from Khorramabad synoptic station. The results of statistical evaluation showed that in the daily period, Orang (1998) and Schneider (1992) pan coefficients with RMSE equal to 1.004 and 1.13 (mm.day-1), respectively, as the most appropriate and Allen and Prot coefficients ( 1991) and Konica (1989) were selected as the most unsuitable coefficients with RMSE of 1.83 and 1.47 (mm.day-1), respectively. On the other hand, the results of statistical evaluation for the 10-day period of Konica (1989) and Schneider (1992) pan coefficients with RMSE of 6.01 and 8.25 (mm.day-1), respectively, as the most appropriate coefficients and coefficients of Allen and Perot (1991) and Aurang (1998) were selected as the most unsuitable coefficients with RMSE of 13.90 and 9.43 (mm.day-1). Spearman correlation test also showed that the results of all coefficients have an acceptable correlation with the reference method.

Accurate estimation of reference evapotranspiration (ETo) in each region is necessary due to its significant contribution to water resources management and proper irrigation planning.  Since direct (ETo) measurement methods are time consuming and costly, therefore, in many cases, experimental and mathematical models are used for this purpose. These models, however, can’t be used unless they are validated and verified using lysimetric data. A various methods can be used to estimate evapotranspiration; but the performance of these equations are varied from one climatic condition to another. Therefore, in the present study, lysimetric data have been used to evaluate 15 ETo estimation models in Aleshtar region. The data required in different (ETo) estimation models were collected from Aleshtar synoptic station. According to the statistical evaluation results, the Jensen- Haise (RMSE = 1.14) and Turk (RMSE = 1.42) models from the radiation group and the Hargreaves–Samani (HS) model (RMSE = 1.72) from the temperature group were found to be the most accurate ETo estimation models in the study area. On the other hand, Meyer (RMSE = 4.78) and WMO (RMSE = 4.73) models from mass transfer group were found to be the least accurate ETo estimation models, respectively. Finally, taking into account the significance of the FAO Penman-Monteith model in evapotranspiration calculations, comparing lysimetric data with the values estimated ​​by this model showed that the FAO-Penman-Monteith model ​​(RMSE = 5.83 and R2= 0.10) has the least accurate to estimate ETo in the region.

Due to the importance of irrigation management and optimum use of fertilizer in each region, a factorial experiment based on randomized complete block design was conducted to investigate the effect of different irrigation and fertilizer levels on yield and water productivity of autumn wheat (Chamran-2) in Khorramabad. Totally 27 plots including three irrigation levels (100%, 75% and 50% of water requirement), three fertilizer levels (100, 150 and 200 kg ha-1) and three replications were used in the research farm of the Faculty of Agriculture and Natural Resources of Lorestan University. In this study, grain yield, biological yield, thousand seed weight, pod length, plant height, irrigation water productivity (grain yield and biological yield) and rain water productivity (grain yield and biological yield) were investigated. The results showed that the highest amount of traits were obtained in I100N200 treatment, so that the grain yield and biological yield were measured to be 3407/41 and 11097/51 kg ha-1, respectively. Also, the lowest amounts of grain yield and biological yield were observed in I50N100 treatment with 45.65 and 32.60% reduction in yield, respectively, as compared to full irrigation treatment. Interaction of water stress and nitrogen in all irrigation treatments showed by increasing nitrogen fertilizer up to 200 kg ha-1, the grain yield and biological yield increased. The results also showed that the interaction of irrigation and nitrogen fertilizer treatments on irrigation water productivity (grain yield) and rain water productivity (grain and biological yield) is significant at 1% level. The I50N200 treatment with 3.24 kg m-3 (grain yield) and 10.78 kg m-3 (biological yield) had the highest irrigation water productivity.

Around the world, the Penman-Monteithe-FAO model is used as a reference method to estimate reference ‎evapotranspiration. This method requires a lot of input data, which in many cases are difficult to access, so ‎it is necessary to replace simpler models with low inputs and good accuracy. Therefore, the purpose of this ‎study was to evaluate the accuracy and capability of Bayesian Network and Support Vector Machine ‎models in estimating reference evapotranspiration and comparing it with the Penman-Monteithe-FAO ‎model. For input data, monthly data of Khoramabad synoptic station including: maximum and minimum ‎temperature, maximum and minimum relative humidity, solar radiation and wind speed in period 1990-‎‎2016 (420 months) were used. Based on the effect of input parameters on output, six input patterns were ‎determined for modeling. 70% of data were used for training and 30% for model validation. The results ‎showed that pattern number 5 includes: maximum Temperature, wind speed, solar radiation, minimum ‎temperature and minimum relative humidity in has the best accuracy all models. This model in test phase, ‎has R2 = 0.97 and RMSE = 0.93 in the Bayesian network and 8.9 R2 = 0 and RMSE = 0.41 in support ‎Vector Machine with radial basis functions kernel. Comparison of the performance of the models showed ‎the superiority of the vector machine model over the other models with AARE of 0.0525 and MR of 0.005.‎

This research was carried out to investigate changes in bean water stress index and its relationship with soil moisture under different regimes of drip irrigation at Lorestan University College of Agriculture.For this purpose, three varieties of Pinto bean Contains sadri (S), COS16 (C) and Kusha (K) in main plots and irrigation regimes in four levels including T120 (120% crop water requirement), T100 (100% crop water requirement) T80 (80% crop water requirement) and T60 (60% crop water requirement) were subplots.The results showed that in all three cultivars of the beans, the highest values of CWSI during the season were related to severe tension (T60) which was equal to 0.3 and in contrast to (T120), the lowest values of water stress index and Equal to 0.14. The correlation coefficient between of soil moisture content and CWSI was found to be 0.91 which was significant at 1% probability level, and Therefore a relation was used to estimate the soil moisture content using the crop water stress index. Finally, using the proposed relationship and the allowable drainage coefficient, the threshold value of CWSI was 0.21.

In order to the investigation of  Zn nano-chelate foliar application effects on photosynthetic parameters, glutamine synthetase, protein, grain yield and yield components of soybean under water deficit, a split-plot experiment was conducted as a randomized complete block design with three replications at Research Farm, Faculty of Agriculture, University of Lorestan, in 2016. The treatments consisted of irrigation regime (100, 80, 60 and 40% of plant water requirement) in the main plots and zinc nano-chelate foliar application (Control or distilled water and zinc as zinc nano-chelate containing 20% zinc absorbable ion) in the subplots. The results showed that by decreasing of water requirement some traits such as photosynthesis, transpiration, stomatal conductance, glutamine synthetase, the number of pods per plant, the number of grain per pod, 100-grain weight, grain yield, protein percentage of grain and protein yield were reduced. In contrast, internal CO2 concentration (Ci) and SPAD value increased in stress condition. In normal condition, zinc nano-chelate treatment was not any effect on SPAD value, the number of seed per pod, 100-seed weight, seed and protein yield. In water deficit conditions, zinc nano-chelate foliar application increased the mentioned traits by 4.2, 13.8, 17.3, 45.7 and 39.3 percent, respectively. In addition, in water deficient, zinc nano-chelate decreased Ci and stomatal conductance of leaves. In general, zinc nano-chelate application mitigated effects of water deficit and it was positive for improving soybean yield. Therefore, its application may improve tolerance of soybean.

Lack of available water in arid and semi-arid regions has a negative effect on the production of soybean (Glycine max L.). Nano zinc chelate (ZnO) has the potential to alleviate this issue through overcoming zinc deficiency and thereby improve plant function. Therefore, to evaluate the effectiveness of ZnO application on soybean in drought stress conditions, a split-plot experiment based on randomized complete block design was conducted. Irrigation treatments (100%, 80%, 60% and 40% water requirement) was the main plot and foliar application of nano-zinc (2ml.L-1 foliar treatment vs. no-application control) was the subplot. The results showed that drought stress had negative effects on plant yield and productivity. Under drought, grain yield, seed oil percentage, oil yield, palmitic acid, stearic acid, cis-oleic acid and linoleic acid were decreased. ZnO application significantly increased proline content, catalase and peroxidase activities. However, the percentage of palmitic acid, stearic acid, linoleic acid and α-linoleic acid decreased with the ZnO foliar spray. In general, results showed that, ZnO treatment, with enhancing antioxidant enzymes activity and changing physiological parameters decreased adverse effects of drought stress on soybean plants.

The expansion of urbanization along with industrial and agricultural activities on the banks of the Alshar River and subsequent discharge of wastewater into water sources have caused water contamination by various pollutants. In this study, seasonal and annual changes in the long-term water quality changes in the Alshar River were evaluated. For this purpose, water quality data used were recorded at the Sarab Sayyed Ali hydrometric station located at the outlet of the Alshar River from 1970 to 2018. For analysis, 11 water quality variables were selected. Man-Kendall test was used to examine the trend of changes. The results showed that the patterns were similar for changes in parameters at both annual and seasonal scales, with decreasing concentrations of HCO3, Na, pH and SAR and increasing concentrations of Mg, Ca, TH, TDS, EC, Cl, and SO4. In the annual period, the trend of change of all qualitative parameters except Na was statistically significant at 99% level. During the summer seasons, there were more intense upward and slower downward trends, with the strongest increase in concentration during this season. However, in the winter, the results were in contrary with the summer. In most of the qualitative parameters, the decreasing and increasing trends were higher in spring than in autumn. The reason for this could be attributed to the fact that winter snow melting from the altitudes occurs in the spring, causing increased surface currents and a relative decrease in concentrations.

Irrigation scheduling is very important under water stress condition. To investigate the crop water stress phenomena, several indices have been presented, of which one is CWSI. In order to investigate the ability of this index in irrigation scheduling of Maize (SC704), a research in a randomized blokes design with four experimental treatments was conducted in Khoram Abad region in 2011. Experimental treatments including IR1: 100% water requirement, IR2: 80% water requirement (by Regulated deficit irrigation), IR3: 60% water requirement (by Regulated deficit irrigation) and IR4: 50% water requirement (by Partial root zone drying management).  The results showed that the amount of CWSI was significantly affected by water stress.  The CWSI index for IR1, IR2, IR3, and IR4 treatments were 0.17, 0.21, 0.39, 0.29 and the amount of yield were 21560, 19500, 12560 and 14860 Kg/ha, respectively. In this study, the best treatment was IR1 (with the minimum CWSI and the maximum Yield) and the worst treatment was IR3 (with the maximum CWSI and the Minimum Yield).  Based on CWSI index, the value of water stress in IR4 treatment was less than the water stress value of IR3 treatment (The CWSI index dropped by 11.4 percent and the amount of yield increased by 18.3 percent). In this research, the relationship between the corn forage yield and CWSI index was obtained with high correlation. Regarding the variation of CWSI under different treatments, it can be stated that the CWSI index is able to evaluate the water stress and estimate the corn forage yield.

This study was carried out using AquaCrop plant growth model to simulate potato crop yield in irrigation levels of 100, 80 and 65 percent of plant water requirement under climate change conditions in Shahrekord region. The data of two years of 2013 and 2014 were used to calibrate and validate the AquaCrop plant growth model. In order to investigate the effect of climate change on product yield, the output data of the HadCM3 general circulation model under two scenarios A2 and B1 for the periods 2011- 2030 and 2046-2065 are downscaled using LARS-WG model and used as inputs of the growth model. Based on the results, the simulation of potato yields with the AquaCrop plant growth model was carried out with high precision so that the average difference between observed and simulated values in calibration and validation stage was 0.56 and 0.68 ton/ha, respectively. Also, the mean absolute error (MAE) for simulating minimum temperature, maximum temperature, sunshine hours and precipitation with LARS-WG model was 0.05, 0.03, 0.01 and 0.16, respectively which reflects the good performance of the model in producing climate data for the future periods. Simulation results showed that the potato yield for the periods 2011-2030 and 2046-2065 under the A2 scenarios was 15.8 and 24.5 percent, respectively, and under B1 scenario, 11.8 and 22.4 percent increase compared to the base period. Also, for I80 and I65 treatments, the yield value decreases in both the future periods and the two scenarios campared to the base period.