مجله تحقیقات آب و خاک ایران
سال پنجاه و ششم شماره 1 (پیاپی 109، فروردین 1404)

Erosion caused by water and sediment transport in watersheds leads to on-site and off-site effects that can cause significant damage to lands and infrastructure. Understanding the source of sediment yield in river systems is essential for effective watershed management. A key challenge in sediment source fingerprinting is the use of appropriate tracers. Therefore, the purpose of this study was to investigate the effectiveness of weathering indicators and geochemical elements in detrmining the contribution of sub-basin sediment sources in Alvand watershed by using a Bayesian sediment fingerprinting model. First, 27 samples were taken from the outlest of three sub-basins as sediment sources and 9 samples were taken from the outlet of the main basin as sediment target. 9 geochemical elements (Al-Ca-Fe-K-Na-Mg-Si-Ti-P) were measured for the predominant particle size farction (<125 µ) and 42 weathering indices were calculate based on the geochemical elements in sediment source samples and target sediment samples. Using Kruskal-Wallis test and discriminant function analysis three geochemical elements (Na, Mg, Si) and three weathering indices (CPA, ALK, R) were selected as compsite fingerprints. The sediment source apportionment technique was prepared based on the Bayesian model, and the percentage contribution of each sediment source was determined. For the three sediment sources in the Alvand watershed, namely Basin 1 (Ghaleh Shahin), Basin 2 (Patagh), and Basin 3 (Rijab), the estimated percentages were 97.7%, 0.8%, and 1.1%, respectively. The Ghaleh Shahin sub-basin was identified as the dominant sediment source. The results showed that a combination of weathering indicators and geochemical elements can create an appropriate combination of tracers to be used in sediment provenance.

The present study aimed to investigate the effects of rice straw, sewage sludge, and their hydrochars on some agronomic traits and chlorophyll index of rice plants in a sandy loam soil contaminated with lead (Pb) under greenhouse conditions. The research was conducted as a factorial experiment in a completely randomized design with three replications and two factors of organic matter at 11 levels (control, rice straw, rice straw hydrochar, sewage sludge, sewage sludge hydrochar, rice straw + rice straw hydrochar, sewage sludge + sewage sludge hydrochar, rice straw + sewage sludge, rice straw hydrochar + sewage sludge hydrochar, rice straw hydrochar + sewage sludge, sewage sludge hydrochar + rice straw), and Pb at two levels of 0 and 500 mg/kg as lead nitrate. The results showed that soil contamination with Pb significantly reduced leaf chlorophyll index, plant height, panicle dry matter, panicle length, leaf number, leaf width, and stem diameter. By application of sewage sludge and its hydrochar, agronomic traits of rice were significantly increased, and this increase was even higher in the presence of sewage sludge than its hydrochar. The application of rice straw decreased panicle dry matter and leaf number and increased the ratio of shoot dry matter to that of root and had no significant effect on other traits. The application of rice straw hydrochar reduced shoot and root dry matter, leaf number, and leaf length. The results showed that integration of sewage sludge and its hydrochar with rice straw and its hydrochar can reduce the negative effects of rice straw and its hydrochar and Pb toxicity.

The adsorption and desorption processes play a crucial role in the solubility of boron (B) in soil solutions.  To investigate the effect of leonardite on the adsorption and desorption processes of B, an experiment was conducted on four calcareous soil samples at three weight levels of 0%, 2%, and 4%, along with nine B concentration series (0-120 mg/L) using a 0.03 M sodium chloride background solution. A 0.03 M sodium chloride solution was utilized for the desorption of boron. The B adsorption data were fitted to Langmuir (R² = 0.85-0.96), Freundlich (R² = 0.62-0.88), Temkin (R² = 0.77-0.98), and Dubinin-Radushkevich (R² = 0.94-0.99) models. The results indicated that the addition of leonardite reduced the adsorption parameters of B, including the maximum single-layer adsorption of Langmuir (qmax) and the Langmuir adsorption energy (KL), as well as the maximum adsorption capacity of Dubinin-Radushkevich (qs). Applying 4% of leonardite resulted in a 32% reduction in qmax in soil 1. The average adsorption energy (E) calculated using the Dubinin-Radushkevich equation indicates a physical adsorption of B in soils, as E is less than 8 kJ/mol. Furthermore, application of leonardite inceased desorption of B in soils, leonardite's effect was 4% more than 2%. Therefore, the application of leonardite can increase the boron toxicity levels in soils, especially in calcareous soils.

The aim of this study is to combine organic, inorganic materials and selected microorganisms to modify the physical, chemical and biological characteristics of a non-saline sandy Entisol (0-20 cm), from NajmAbad, Karaj, aeolian sediments. This research was conducted in the frame of factorial Completely Randomized Design (CRD) with three levels of bentonite 0, 20 and 40 Mgha-1, carboxymethyl cellulose 0, 3 and 5 gkg-1 and a mixture of two isolates of endophytic bacteria (Pseudomonas sp) and rhizosheath (Pantoea agglomerans) at three levels 0, 20, and 40 mLkg-1, in a total of 27 treatments with 3 replicates (81 samples). Multivariate statistical methods, including Principal Component Analysis (PCA), hierarchical clustering, and two-step clustering were utilized for the analysis. Based on the results of the two-step clustering, the following variables were identified as the most significant variables: available Fe (RI=1), mean weighted diameter of peds (RI=0.99), field capacity (RI=0.97), total N (RI=0.87), available K (RI=0.83), and dehydrogenase activity (RI=0.8). Variables including Fe available with a range of 1.75 to 7.08 mg kg-1, mean weight diameter with a range of 0.31 to 1.15 mm, total nitrogen with a range of 0.014 to 0.06%, field capacity with a range of 8.03 to 12.82%, available K with a range of 280.8 to 416.66 mg kg-1 and dehydrogenase enzyme activity with a range of 0.07 to 0.148 μgTPF (g soil 24 h)-1, had the highest effect in the clustering treatments. The results of this study showed that the treatments B20C5M20, B40C5M10, B40C5M20, B40C3M20 and B40C3M10 have better performance to modify certain soil properties compared to other treatments.

Irrigation performance evaluation is needed for hydrological planning and as the first step to improve water management, one of the performance evaluation criteria is the relative annual irrigation supply index (ARIS), which is used in agriculture and water resources management to evaluate the adequacy of water supply for irrigation in the length of a cropping period is used. Therefore, the purpose of this research is to investigate the ARIS index and water productivity indices for different plants (wheat, seed corn, corn, maize, sugar beet, cotton, alfalfa and soybean) in two surface and center pivot irrigation systems in It was Moghan Plain where three indices 〖WP〗_T ،〖WP〗_eg and 〖WP〗_en were used to check water efficiency. This research was conducted in 13 combinations of irrigation system - crop in the area of 647 hectares. The results showed that the ARIS index for the studied area varied from 0.73 to 2.21, so that the overall average was 1.27 with an overall standard deviation of 0.15, which indicated the application of heavy irrigation in the fields. Regarding the irrigation systems, the average ARIS for surface and center pivot irrigation was 1.60 and 0.89, respectively. Also, in terms of plant type, soybean and corn had the lowest and highest ARIS values with 0.73 and 1.73, respectively. On the other hand, the investigation of technical and economic efficiency of water for main crops and irrigation systems showed that maize and seed corn and cotton in the surface irrigation system had the lowest value, while sugar beet and corn under center pivot irrigation system had the highest value of water economic efficiency index. Considering the volume of water consumption, water productivity and gross productivity, the best option for cultivation in the wheat crop area was recommended. Also, in terms of water consumption and productivity, according to the type of irrigation system, center pivot system is more efficient than surface system, and the development of this system is recommended.

One of the most important problems in the soil erosion research is the dependency of measurements and estimates on the scale. Recently, studies in the field of soil erosion scaling have resulted in development of some different models. This research aimed to investigate the scaling of rill erosion in field conditions. Artifitial rills with length of 1 to 8 meters and width of 5 cm were created in a plot with a slope of 5% in an agricultural soil with sandy loam texture and weak aggregate stability. The experiments were caried out under two target flow rates of 0.11 and 0.24 liters per second. Sediment concentration was measured during the event for 30 minutes, and its time changes were investigated along different rills. To scale rill erosion, the changes with rill length of the mean sediment concentration and particle detachment rate, both at three initial unsteady, final steady and total event conditions, were evaluated and modeled. The temporal changes of sediment concentration exhipited an exponentially decreasing trends almost in all cases. The average sediment concentration increased linearly with rill length (spatial changes) at lower flow rates, and exponentially (reaching a certain limit) under the higher flow rates in all three conditions. Also, the particle detachment rate decreased exponentially with rill length under both flow rates in all three cases of unsteady, steady and total event averages. The results generally confirmed the models and theories of the exponential decrease in the particle detachment rate with increasing rill length.

Urban wetlands are rapidly deteriorating due to urbanization and wastewater disposal, posing a threat to water quality and human livelihoods. Assessing the water resources carrying capacity (WRCC) of these wetlands is crucial to facilitate sustainable development and synergy between economic growth and water resource conservation. This study evaluated the WRCC of Anzali Wetland, located in the Fumanat region of Gilan Province, over a 10-year period (2011-2021). eight evaluation indicators were defined based on available data and information, considering three subsystems: water resources, economy, and environment. Each indicator was classified into four levels: I (loadable), II (weak), III (critical), and IV(Overload). The weight of each indicator was calculated using three methods AHP, Entropy, and CRITIC. The weights obtained from the three methods were combined using the geometric mean. Subsequently, the annual values of each indicator, along with their corresponding weights and four evaluation levels, were applied to a model combining Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, based on the annual WRCC results, the obstacle factors were identified. The WRCC assessment indicated that the wetland has been in a overloading condition (IV) since 2013. The water resources subsystem is in a critical or overloading condition, reducing the overall carrying capacity, while the economic subsystem contributes to this capacity. According to the obstacle degree model results, the four main factors affecting the WRCC include the quality of incoming water, the percentage of water supply required, the percentage of water supply for upstream areas, and the ratio of total available water to the population.

Monitoring and controlling the level and sources of dust are crucial in the face of climate change and the development of suitable predictive approaches that directly impact the environment and human health. This study aims to estimate the concentration of PM10 in the city of Ahvaz using various machine learning models. Climate variables and the Aerosol Optical Depth (AOD) index, derived from the MODIS sensor at a wavelength of 476 nanometers, were used as influential variables in estimating PM10 concentration in three scenarios: combining AOD with PM10 (scenario 1), combining climate variables with PM10 (scenario 2), and combining climate variables and AOD with PM10 (scenario 3) .Using six machine learning algorithms, namely Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Artificial Neural Networks (ANN), AdaBoostR with DTR, Support Vector Regression (SVR), and Decision Tree Regression (DTR), the PM10 concentration was estimated in different scenarios, considering accuracy and precision coefficients. The most influential variables in estimating PM10 concentration were determined to be sunshine hours, minimum visibility, maximum wind speed, and the AOD index. The GBR linear regression model, with R2, MAE, RMSE, and IOA coefficients of 0.76, 0.31, 0.49 and 0.93 respectively, was found to be the most suitable model for estimating PM10 concentration in scenario 3. the results showed that incorporating the AOD index alongside climate variables improved the model's performance in estimating PM10 concentration. The proposed final model can be used for daily estimation of PM10 particles.

Measuring the flow of rivers is one of the most important issues in river management, that's why it is always tried to use accurate methods for its measurement. The aim of this study is to enhance the performance of Support Vector Regression (SVR) model using the Gray Wolf Optimization (GWO) algorithm for monthly river flow modeling. For this purpose, the monthly data of river flow, precipitation and temperature during 15 years (from 1400 to 2015) are used. The trial and error procedure is used to select the best input variables to the SVR and GWO-SVR models. Based on the results of this method, Q(t-1), R(t-1), T(t-1) are the best independent variables for simulating the variable Q_t. 80% of all data are used for training and 20% for validating the SVR and GWO-SVR models. Also, R^2, RMS and NSE indices are utilized to evaluate the efficiency of the models, linear (LKF), polynomial (PKF), radial basis function (RBF), and sigmoid (SKF) activation functions are used to develop the models. First, the trial and error procedure is used to determine the parameters of the activation functions. Based on the results of this study, the SVR model with the polynomial activation function has the best performance in the training and validation stage, and the worst performance with the linear activation function in the training and verification stages. Then, the GWO algorithm is used to determine the parameters of the activation functions. Based on the results, the SVR model performs better with the GWO algorithm. Therefore, to simulate the monthly flow of river using this model, it is better to use the GWO algorithm instead of the trial and error procedure.

Drought and desertification are significant environmental and agricultural challenges in Iran and worldwide, which exacerbated by inefficient water consumption patterns. Given the considerable extent (43 million hectares) of low-yield desert lands in Iran, which generally have light-textured soils, it is essential to rehabilitate these lands and manage their water resources to enhance soil productivity. Therefore, the primary objective of this study is to determine the optimal amounts of bentonite and nano-bentonite as natural soil amendments and to evaluate their effectiveness in improving the physical and chemical properties of sandy soil. In this study, each level of bentonite, including rates of 10, 20, and 30 ton ha-1, was applied in combination with nano-bentonite levels of 0.25, 0.75, and 2.5 ton ha-1. These experimental treatments were mixed with the soil and incubated for three months at a temperature of 25°C and 80% field capacity. After the incubation period, soil properties, including saturation percentage (SP), bulk density (BD), texture, electrical conductivity of the saturated paste extract (ECe), Ph and cation exchange capacity (CEC), were measured. The experiment was conducted in a completely randomized design with 10 treatments in three replications for bentonite and nano-bentonite, and the experimental data were analyzed using Minitab 16 software. Results showed that the treatment with bentonite at 30 ton ha-1 and nano-bentonite at 2.5 ton ha-1 had significant positive effects on the measured physicochemical properties of the soil. Analysis of variance revealed that bentonite and nano-bentonite treatments had significant effects on soil CEC, ECe, BD, and SP. Clustering of the results further indicated that the treatments of 30 ton ha-1 of bentonite plus 0.75 ton ha-1 of nano-bentonite and 30 ton ha-1 of bentonite plus 2.5 ton ha-1 of nano-bentonite had the greatest impact on improving the physical and chemical properties studied. Considering the experimental data, statistical results, and clustering of the treatments, the application of 30 ton ha-1 of bentonite plus 2.5 ton ha-1 of nano-bentonite was selected as the most suitable treatment for amending sandy soils in arid and semi-arid regions. This treatment effectively contributed to improving the physical and chemical properties of sandy soil, including reducing BD, increasing SP, enhancing CEC and OC content, and lowering soil ECe.

Determining and predicting runoff is a crucial step in the proper use of water resources. This research aims to calibrate and validate the HEC-HMS model and simulate rainfall-runoff for continuous data series in the Tajan basin based on climate change Scenarios. In this research, the data recorded during the years 2002-2009 were used for the calibration stage and the data recorded during the years 2010-2016 were used for the verification stage. The results of model calibration showed that the correlation coefficient between observed and simulated discharge values was equal to 0.81 and the Nash-Sutcliffe coefficient was equal to 0.68, which indicates a good match between the mentioned data. Also, in the verification stage, the correlation index and Nash-Sutcliffe coefficient were equal to 0.80 and 0.63, respectively, confirming that the model shows a good estimate for the observations. In the next step, data values in the daily time scale for the period of 2020-2049 were loaded in the model and basin runoff was simulated for this period. The results showed that the amount of discharge in the future period compared to the base period would decrease strongly under both RCP 8.5 and RCP 2.6 climate change scenarios, and only in the RCP 8.5 scenario, the discharge will increase a little compared to the base period in August, September, October and November. The simulation results showed that the amount of discharge in summer and autumn under the RCP 8.5 scenario will increase more than in the RCP 2.6 scenario.

Nowadays, the successful countries in agricultural water management have attempted to address the challenges in this sector by “reducing their being in charge” and "transferring the management of agricultural water to stakeholders". In Iran, despite valuable experiences in efficient traditional water management and water users’ associations, significant achievements have not been gained. Therefore, the aim of this paper is to accurately identify the factors influencing the participatory management, its contexts, motivations, impediments, and ultimately to propose some mechanisms for implementing participatory management in agricultural water management. A content analysis method was employed, utilizing library resources (A total of 78 cases from 1971-2024). The results of this research indicate that the main reasons for the lack of successful achievements in participatory water management are mainly embedded in the absence of belief and conviction at the national level and the lack of necessary will among decision-making institutions to institute for participatory water management; the dominance of a technocratic approach in the water sector; the unclear legal status of water user associations and their weakness in technical, financial, economic, legal, and cultural aspects for accountability; the instability of supportive policies in establishing and organizing these associations. To institutionalize participatory irrigation management, it is essential to establish and formalize water user associations, delegate responsibilities and authorities in accordance with the country's seventh development plan, clearly define and provide water services, ensure mutual participation and accountability between the government and stakeholders, and ultimately establish a demand-driven support system based on cost-sharing.

The side weir is a widely used structure for the diversion of flow in channels and waterways. In this study, a new type of side weir, referred to as the pivot side weir (a type of inclined sharp-crested weir) with a sill, was introduced to investigate its discharge coefficient and efficiency. The experiments were conducted in the hydraulic laboratory of the Faculty of Water Engineering at Shahid Chamran University of Ahvaz. The performance of the models PSW (pivot side weir with a sill and open overflow sides), PSW-C (pivot side weir with closed overflow sides), and PSW-HC (pivot side weir with an open upstream side and closed downstream end) was evaluated against the control model R (rectangular sharp-crested side weir) in relation to the upstream Froude number ranging from 0.3 to 0.54, under a constant ratio of upstream depth to weir height and at opening angles of 30, 45, and 60 degrees towards the main channel. The results indicated that the models introduced in this study were more efficient than the control model, exhibiting higher discharge coefficients and efficiencies. Calculations showed that in the primary models, the discharge coefficient increased by up to 80% and efficiency by up to 70% compared to the control model. Furthermore, when comparing the primary models with each other, it was observed that the average discharge coefficient and efficiency of the PSW-HC model increased by approximately 7% and 5%, respectively, compared to the PSW model, and by 22% and 8% compared to the PSW-C model.

This research aims to determine and validate the relationship between the electrical conductivity (EC_e), cations and anions concentration in the extract of saturated soil paste and the relevant parameters in the extract of soil-to-water ratios namely 1:1, 1:2.5, and 1:5 in non‐gypsiferous soils with different textures. Accordingly, 64 soil samples from different regions of Iran were selected and EC, and cations and anions concentration in both saturated past extract and soil-to-water ratios extract were determined. Moreover, the soils were categorized into three texture classes: fine-, medium-, and coarse-textured. To develop and validate the overall relationships between EC_e and EC of different ratios, 43 soils were used for relationship development, and 21 soils were used for relationship validation. The results demonstrated that in the fine-textured soils, 〖 EC〗_e=2×EC_ (1:1) =4×EC_ (1:2.5) =8×EC_ (1:5); in the medium-textured soils, 〖 EC〗_e=2×EC_ (1:1) =5.5×EC_(1:2.5)=10×EC_(1:5); and in the coarse-textured soils, 〖 EC〗_e=2×EC_(1:1)=3×EC_(1:2.5)=6~8×EC_(1:5). Therefore, despite the limited number of soils, it can be recommended to use EC_ (1:1) for estimating 〖 EC〗_e since the regression relationship between these two methods remains constant and equal to two times of 〖 EC〗_e across three texture classes. Regardless of the soil texture, the following relationship was obtained for all studied soils; 〖 EC〗_e=2×EC_ (1:1) =5×EC_ (1:2.5) =10×EC_ (1:5). Also, the results of the validation of different relationships showed that the soil-to-water ratio of 1:1 due to lower errors, closer equation slope to 1:1 line and higher determination coefficient (R^2=0.99) gives a more accurate 〖 EC〗_e estimation compare to the other two ratios. Regarding the concentration of cations and anions, it can be recommended to use the 1:1 extract instead of saturated past extract, especially for non-saline soils, and provides a larger volume of extract.

Human pathogens such as Salmonella enter agricultural soils from various sources. Salmonella is one of the most common food-borne microorganisms and is a causative agent of infections shared between humans and animals. In the current study, some plant growth-promoting bacteria were used to control the pathogen Salmonella typhimurium. The results showed that, in order, 1) Bacillus rugosus CS5, 2) Bacillus vallismortis AS4, 3) Priestia aryabhattai CL1 and 4) Bacillus sp. SS4 had the highest inhibitory effect on the grrowth of Salmonella in liquid medium. However, in solid medium, the bacteria 1) B. vallismortis AS4, 2) B. rugosus CS5, 3) P. aryabhattai CL1 and 4) Bacillus sp. SS4 exhibited the greatest inhibition of this pathogen's growth. The ability to produce siderophores, proteases, lipases, and hydrogen cyanide was investigated in these four bacteria. All four bacteria were capable of producing siderophore. The highest protease production index, with values of 2.5 and 2.9, belonged to P. aryabhattai CL1 and B. rugosus CS5, respectively. B. vallismortis AS4 was capable of producing lipase. Both P. aryabhattai strain CL1 and B. rugosus strain CS5 had the ability to produce HCN, with values of 0.48 mg/mL and 0.2 mg/mL, respectively. The simple impact of four separate bacteria and a microbial consortium on the Salmonella population in soil was investigated. The results showed that the Salmonella population decreased 15 days after inoculation due to the treatments. The greatest reduction in the the population of this pathogen was related to the microbial consortium and the Bacillus sp. strain SS4 treatment, with a 92 % and 98 % reduction in population compared to the control, respectively. The findings indicate a positive effect of plant growth-promoting bacteria in soil for controlling S. typhimurium, which could reduce the negative impacts of the pathogen's presence in soil, thereby enhancing human health and food security.