مجله تحقیقات آب و خاک ایران
سال پنجاه و پنجم شماره 2 (پیاپی 98، اردیبهشت 1403)

The Water Productivity (WP) of the Darbasti Irrigation System (DIS) was evaluated in Alfalfa cultivation in the Markazi Province. The evaluation included three patterns of sprinkler placement: 8x8 meter (SS-88), 8x10 meter (SS-810), and 8x12 meter (SS-812). The Distribution Uniformity (DU), Coefficient of Uniformity (CU), Application Efficiency of Low Quarter (AELQ), and WP were determined in the years 2018 and 2019, after the third (HA-3), fourth (HA-4), and fifth (HA-5) yield harvests. The results showed that the average CU in the SS-88 pattern was 84.76% in the first year and 86.34% in the second year. This pattern had the highest CU compared to the other two patterns. The DU in SS-88, SS-810, and SS-812 patterns in the first and second year was 77.3%, 79.31%, and 75.38%, 77.66%, and 70.11%, 70.88%, respectively. These results indicate very good, good, and relatively good performance in each of the patterns, respectively. Moreover, there was a significant difference at the one percent level in the AELQ between the first and second year for all three patterns. The results showed a significant difference at the level of one percent between the SS-812 model and the other two models. The WP of alfalfa was 1.84 kg/m3, 1.87 kg/m3, and 2.28 kg/m3 for SS-88, SS-810, and SS-812 patterns, respectively, in the first year. In the second year, this index was 2.43 kg/m3, 2.3 kg/m3, and 2.4 kg/m3 for the same patterns, respectively. Generally, the SS-810 pattern of sprinkler placement is a suitable choice based on performance indicators.

Arranged delivery is recommended for higher irrigation management flexibility and water productivity in irrigation networks. For arranged delivery, the spatial and temporal variations of numerous requests increase the complexity of the operation and the probability of operational errors. The objective of this research is to consider the operational errors and analyze their impact on output uncertainty. To this end, in a canal, two options of increase and decrease in demand, and four scenarios of structural operational errors are studied. The structural operational error scenarios include one check structure, one check and one intake, two check structures, two checks, and one intake. In each scenario, random numbers for structural adjustment were generated using the Monte Carlo simulation method. The canal system, operational scenarios, and delivery options were simulated using the Irrigation Conveyance System Simulation (ICSS) model. The analyzed outputs are delivery discharge, adequacy, efficiency, stability, and depth control error. The uncertainty of outputs is calculated for the operational error range of 38 to 95 %. The uncertainty band of the flow delivery for the increasing scenario was between 38 to 95%, and 33 to 85% for the decreasing scenario. Therefore, increasing scenarios produce higher uncertainty, and require a more accurate operation. By increasing the number of structures that encounter operational error, the uncertainty of almost all outputs has increased. The highest increment of 12% was seen in the stability index for the increasing scenario and an 8% uncertainty band increase in the depth index for the decreasing scenario. For increasing scenarios, delivery discharge with an uncertainty band of 38 to 95% is the most sensitive parameter. For decreasing scenarios, the depth control parameter with an uncertainty band of 36 to 82% is more sensitive compared to delivery flow. Therefore, for increasing scenarios the delivery discharge, and for decreasing scenarios, the water depth are more important parameters to be controlled.

The Caspian Sea as the largest lake in the world is very important from the different aspects of economic, social, tourist, and environmental. In this regard, monitoring the water quality of the southern shores of the Caspian Sea has a special importance in order to study it according to national and international standards and control the possible pollution of the coastal strip of Iran. Therefore, the National Research Center of Caspian Sea was analyzed and checked the quality of water in terms of amount heavy metal by establishing 14 stations in the coastal strip from Miankala to Astara. In this study, the concentration of heavy and dangerous metallic and non-metallic elements in depth 1 and 7 meters from Farah Abad station to Lisar and at two depths of 1 and 4 meters in Miankala and Astara stations were measured by ICP-MS method and heavy metal evaluation index, pollution degree index, heavy metal pollution index and Spearman's correlation coefficient were calculated according to the concentration data. The obtained results show that the average concentration of heavy and dangerous metals B(2.37ppm), Ba(24.9ppb) and Zn(18.3ppb) at a depth of one meter and B(2.44ppm), Zn(26.5ppb) and As(17.5ppb) at a depth of seven meters are the highest concentrations recorded in the monitoring stations. According to the data, the highest value of the HEI(1.58), the Cd(-5.96) and  the HPI(68.49) of heavy metals were obtained at depth of seven meters in Farah-Abad station and one meter Faridoonkanar and Miankale station, respectively.

Preparation and proper implementation of water scarcity adaptation programs at the provincial level requires diverse, reliable and integrated information related to the province's water resources. To obtain this information in an integrated and dynamic manner, a system called ‘System for regional Agricultural Water balance and water Accounting’ (SAWA) was prepared for Fars province. First, the irrigated agricultural lands of the province were divided into 17 homogenous Agro-Ecological-Zones (AEZ). Then, a crop model (SSM-iCrop2) was calibrated and set up to simulate the growth, yield and field water balance of 35 agricultural plants under potential and farmers’ conditions in the 17 AEZ. The simulations were done using weather data of 2011-2021. The outputs of the system are produced on a daily basis and for the end of the growing season. The system is also able to produce monthly outputs of water balance that are essential information for water scarcity adaptation programs such as cropping pattern. Some of the outputs of the system are crop planting date and the date of bud burst in trees, the time of occurrence of important phenological stages, total biomass, leaf area index and field water balance components such as runoff, evaporation, transpiration, deep drainage, weeds’ transpiration and applied irrigation water. The outputs of this system are available for each plant or all plants in each of the zones, counties and the whole province. The testing of the system showed that the simulated yields and applied irrigation water are in satisfactory agreement with the measured ones.

Determining the future climate situation by using climate models seems necessary to consider in the field of adaptation or reducing the adverse effects of climate change. In this research, the temporal trend of rainfall, minimum and maximum temperature in the four stations in the Qarasu basin and in addition to, investigated using Thiessen's interpolation method. Among the five models of the CMIP6, three models were selected as the best models and used for MME. Biass Correction was done with CMHyd software for scenarios SSP2-4.5 and SSP5-8.5 in periods 2026-2050, 2051-2075 and 2076-2100. The trend of variables in the base period (1990-2014) and future were investigated with Mann-Kendall test and sens slope. The results of analysis significant trends annual average maximum and minimum temperature of all stations and in catchment area according to SSP2.4-5 scenario in two near and middle future periods and for SSP5-8.5 scenario in all three future periods have a significant trend at the 99% level. In analysis significant trend seasonal rainfall according to SSP2.4-5 scenario in the summer season distant future all stations and in near future of the station area of Gorgan regional water company at the 95% level and for the SSP5-8.5 scenario only in the winter season in the distant future Ghafarhaji station has a significant trend at the 99% level. The future monthly rainfall in the catchment area according to scenario of SSP2.4-5 in August at the 99% probability level and SSP5-8.5 in March at the 95% probability level have a significant trend.

Land suitability is a crucial factor in land use planning and sustainable agricultural production. Evaluating land suitability helps optimize land use, promote sustainable land use, protect the environment, and ensure optimal use of natural resources. This study was conducted in the Abiek region of Qazvin province in northwest Iran, covering an area of 60,000 hectares. After collecting data from 300 soil profiles and determining land suitability classes for wheat cultivation with surface irrigation using the FAO classification system, digital elevation models, Landsat-8 and Sentinel-2 satellite images, and environmental variables extracted from the digital elevation model were used to create digital maps using both traditional and machine learning methods. The results showed that the machine learning method had a higher accuracy rate of 74% and a Kappa index of 68 compared to the traditional method with an accuracy rate of 62% and a Kappa index of 53. The most important environmental variables used in the machine learning model were those extracted from the digital elevation model and Landsat-8 satellite images. The largest area for wheat cultivation with surface irrigation was found in the relatively suitable class (S2), with 30,753 hectares in the random forest method and 21,028 hectares in the traditional method. In contrast, the smallest area belongs to the unsuitable class (N), with 3,052 hectares in the forest method. Additionally, random fields and 7185 hectares were identified in the traditional method. Also, 15,000 hectares of the study area are suitable for wheat cultivation without restrictions.

This study was conducted to evaluate the relationship between leaf iron and some easily-available soil properties in citrus orchards in the southern region of Kerman province by artificial neural network modeling and stepwise regression. For this purpose, 40 orchards were selected from the study area and the physical and chemical properties of soil and iron in the plant leaves were measured. Using artificial neural network in different models with different data from soil properties as input and leaf iron as output, the ability of these models to predict leaf iron concentration was evaluated. The results showed artificial neural network with variables of organic carbon, pH, clay, phosphorus, TNV and electrical conductivity with explanation coefficient of 0.86 and 0.81 and root mean square error (RMSE) of 14.60 and 20.13 mg.kg-1 for data Training and testing were the best models in estimating leaf iron. Comparison of regression and neural network models in the test data showed that the neural network had a higher accuracy with an explanation coefficient of 0.81 than stepwise regression with an explanation coefficient of 0.2. The amount of RMSE in the neural network also improved and increased from 27.72 mg.kg-1 in the stepwise regression model to 20.13 mg.kg-1 in the neural network. Artificial neural networks have been able to predict the iron in plant leaves based on the easily-available properties of the soil, so that by choosing organic carbon as the input of the first model to the best model by selecting organic carbon, pH, clay, phosphorus, TNV and electrical conductivity, model accuracy increased.

Due to participation in the process of eutrophication, nitrate has caused a lot of damage to the environment. In this research, Clinoptilolite nanozeolite granules modified by hexadecyltrimethylammonium bromide surfactant named HD-Clinoptilolite were synthesized. The clinoptilolite zeolite of Sabzevar region was converted into nanozeolite by ball mill and then its granules were prepared. In this study, a continuous reactor with a fixed bed equipped with a peristaltic pump has been used to provide the necessary flow to remove nitrate from polluted water. For adsorption process, a continuous flow reactor with a diameter of 3 cm and a height of 54 cm, for investigation of pH parameters, flow intensity, initial concentration, and column height have been fabricated. A Uv-vis Array spectrophotometer was used to measure nitrate. Also, Thomas, Bohart-Adams, and Yoon-Nelson models have been used to predict column behavior. According to the results, with increasing nitrate concentration, the adsorption capacity increased from 3.16 to 95.21 due to the increased presence of nitrate ions. Also, with increasing pH and column height, the adsorption capacity increased, while with increasing flow intensity, the adsorption capacity decreased due to the reduction of contact time. The highest adsorption capacity occurred at a concentration of 200, pH equal to 8 and a column height of 54 cm. At a column height of 54 cm, the adsorption capacity is equal to 91.26 mg/g. The results indicate that the clinoptilolite nanozeolite granules modified with ammonium bromide ligand has the ability to remove nitrate from drinking water to a high extent.

Rivers play a crucial role as a primary water source for societies, emphasizing the need for continuous monitoring of their quality. In this article, we focus on the assessment of water quality in Dinachal River, located in Gilan province. The evaluation was conducted through field studies, sampling, and laboratory methods. Specifically, we measured and analyzed five hydraulic parameters and thirteen water quality parameters, including nitrate, phosphate, temperature, and acidity. The study encompassed seven selected sections along a 25 km stretch of Dinachal River in September 2021. To analyze the data, we employed various methods such as Schoeller, Piper, and Wilcox, along with the FAO and WQI indices. Spatially comparing the river water quality revealed a consistent increase in most parameters from the upstream section, with a steeper rise observed from the station near SafarMahaleh village (section4) towards the river's end. Notably, three parameters electrical conductivity, nitrate, and total dissolved solids experienced significant increases. Electrical conductivity rose from 315.67 µS/cm to 712 µS/cm, total dissolved solids increased from 202.03 to 455.68 mg/l, and nitrate levels elevated from 11.27 mg/l to 69.47 mg/l along the river. Conversely, nitrate levels rose from 19.4 mg/l to 21.4 mg/l, and electrical conductivity increased from 328 µS/cm to 416µS/cm. When comparing data between 1996and2016, specifically for the months of July and June, we noted that agricultural drains had caused nitrates to exceed the permissible limit. The findings indicate an overall deterioration in certain parameters, particularly in relation to electrical conductivity, nitrate levels, and total dissolved solids. These results emphasize the need for effective measures to mitigate pollution sources and preserve the river's water quality for the well-being of the surrounding communities.

The constraints of water resources and the imperative to enhance water efficiency in the production of vegetables and summer crops, on one hand, along with the economic importance of cucumber production in the country, on the other hand, reveal the necessity of investigating management indicators in cucumber production. The present study was conducted on a national scale with the objective of directly determining the applied water and water productivity indices in cucumber production fields across the country during a single crop year (1399-1400) in more than 180 selected farms, including about 70% of the cultivated area of cucumbers in the country. The results of the study revealed a highly significant disparity in various parameters among the selected provinces, including the volume of irrigation water, applied water (the sum of irrigation and effective precipitation), yield, and water productivity indices. The volume of applied water for cucumber cultivation exhibited notable variation, ranging from 4158 m3/ha in Hormozgan province to 8898 m3/ha in Razavi Khorasan province. The weighted average of applied water volume was calculated to be 7043 m3/ha. Similarly, the average yield of cucumbers displayed considerable diversity, ranging from 12750 Kg/ha in Zanjan province to 32956 Kg/ha in Razavi Khorasan province. The weighted average yield stood at 25219 Kg/ha. The calculated water productivity indices for both irrigation water and applied water were 4.27 Kg/m3 and 4.20 Kg/m3, respectively. Notably, the province with the lowest applied water productivity was Zanjan (2.21 Kg/m3), while the highest was observed in Fars province (6.59 Kg/m3). Based on the results, the total water requirement for cultivating cucumbers across an area of 55000 hectares in the country was estimated to be 330 MCM.