نشریه دانش آب و خاک
سال سی و چهارم شماره 3 (پاییز 1403)

Due to the location of Iran in an arid and semi-arid climate, the optimal use of water resources systems and better management in water shortage conditions are necessary. One of the appropriate tools in the field of water resources management is the use of simulation, optimization techniques and the combination of simulation and optimization methods. The main goal of this research is to provide a solution in which, according to the capacity of the reservoir, in addition to achieving the acceptable reliability of supplying demands in the whole period, the percentage of meeting the needs in dry months also increases.

To achieve the optimal operation of the water resources in the Marun and Jareh dams located in the south of Iran, the multi-objective particle swarm optimization (MOPSO) algorithm is linked to the WEAP model to provide a new structure for the water resources management, especially in periods of low water by obtaining the optimal values of water release from the reservoir.In this structure, the WEAP simulator is called directly in the MATLAB environment and executed by the optimization algorithm. In this research, for optimal operation of the system, the amount of water released from the reservoir every month is considered as a decision variable. However, due to the large number of available variables and the high volume of calculations, in the body of the MOPSO optimization model, the flow release coefficients that are considered on a monthly basis are used. Based on these coefficients, every month, a percentage of the water volume in the Marun and Jareeh reservoirs is released to supply the needs of the downstream uses, and the rest is stored for the better management of the reservoir, especially in water shortage conditions. Therefore, according to the application of these coefficients on a monthly basis, 12 variables are considered for each dam and 24 decision variables in the whole multi-dam system in the entire next 30-year operation period.

The optimization process is carried out using the MOPSO multi-objective algorithm. The number of iterations of the algorithm to reach convergence is considered to be around 1000. Finally, after the optimization, according to the population size of 48 and the implementation of the MOPSO model for 1000 iterations, the solutions close to to optimal are obtained, and the optimal exchange curve (Pareto-optimal front) between the optimization objectives (the function of maximizing the supplying percentage and the function of minimizing the violation of the minimum operation level) is achieved (Figure (1)). In Pareto curve the solution with the least amount of penalty due to the violation of the reservoir operation capacity and the highest supplying percentage is chosen as the best answer. Then, these optimal variables are entered in the WEAP surface water model.The average percentage of supplying the needs and the level of reliability of meeting the needs according to Table (1) for different uses in optimization scenario. According to this table, in this scenario, the drinking and industrial demands are fully provided in all months. Also, the average percentage of meeting the agricultural demands of the Marun and Jarrahi basins for August, September, October and November was improved by about 15 to 16%, which is significant and shows a decrease in the severity of failure in these low water months. The supplying percentage in July also increases by about 7%.

The results obtained from the implementation of the optimizer model showed that the percentage of demand supply in the months that was 0% in the reference scenario reached 30-60% and in most of the dry months, it was calculated around 45%. This showed that the optimizer model was able to reduce the failure severity in the worst case and in years with three to eight consecutive dry months. The results showed that according to the application of hedging in the model, some of the need is stored in the high water months to be consumed in the low water months. This research showed that planning water resources and allocating them to existing uses only by relying on maximizing the reliability of supplying needs in the entire period, especially in areas with dry climates where we inevitably face severe water shortages in several months of the year, is not a suitable solution and leads to irreparable financial losses and social consequences. Instead, using the solution of this research will lead to better management of the reservoir and reduce the severity of the failure to supply the needs in the dry months of low water.

Previous researches have often used the Firefly Algorithm (FA) as a single-purpose operation of the reservoir. For example, Garousi-Nejad and Bozorg-haddad (2014) used FA for the first time in water science and reservoir operation, using ten-year statistical data of the Aidoghmoush reservoir located in East Azerbaijan province of Iran. They used it to meet the needs of downstream irrigation. In order to show the efficiency of FA, the results of this algorithm were compared with the global optimum results obtained from the nonlinear programming (NLP) method. The results showed that FA responses differed by less than one percent from NLP. Garousi-Nejad et al. (2016) used the FA to operate the reservoir and used the superiority of this algorithm over GA using five mathematical tests, the problem of reservoir operation for agricultural water supply, and the problem of reservoir operation to generate electricity. The results showed the superiority of FA performance in terms of global optimum convergence rate compared to GA results. Therefore, further investigating the capability and application of the multi-objective of this algorithm in the multi-purpose reservoir operation is necessary.In this study, first, the performance of the MOFA algorithm is compared with the well-known and common NSGA-II algorithm from three perspectives of execution time, generation distance, and distance metric in two mathematical test functions. After ensuring they perform correctly in the test functions, an evaluation is performed for a reservoir operation problem. Then, three reservoir operation policies are selected from the policy set and reviewed.

Aidoghmoush dam is one of the dams built in East Azerbaijan Province, located at 19 km from Mianeh city. Aydoghmoush dam is located in the Sefidrood catchment area and Aidoghmoush sub-basin on the Aidoghmoush river. The structure of the dam is a clay-core rockfill. In this study, the Aidoghmoush reservoir's operation is examined from 1990 to 1996. The FA algorithm was developed by Yang (2008). This algorithm is based on the idealization of the behavior and flashing patterns of fireflies.The test functions used in the present study are (1) Schaffer test function (SCH) and (2) Fonesca test function (FON) (Coello et al. 2007). The MOFA algorithm was used to solve two multi-objective test functions, and its performance was compared with NSGA-II. The Generational Distance (GD) metric, introduced by Van Veldhuizen and Lamont (1998), was used to measure the proximity of the non-dominated solutions obtained to the true Pareto front (PFtrue) (Coello et al. 2007). The spacing (SP) metric introduced by Schott (1995) was also used to measure the distribution of the obtained non-dominated solutions.

In both test functions, the MOFA with good solution quality has a significant time difference in execution time compared to the NSGA-II algorithm; the MOFA is 30% faster in the SCH test function and 15% more quickly in the FON test function than the NSGA-II. Therefore, the MOFA can be a good algorithm for optimizing complex and time-consuming problems such as reservoir operation. The MOFA algorithm in two groups of parameters compared with the NSGA-II algorithm was evaluated in the problem of reservoir operation to maximize reservoir storage and minimize deficit. Extracted solutions from the MOFA algorithm improved by 11% compared to the NSGA-II algorithm by considering better parameter values in terms of proximity to the PFtrue. Also, the execution time of the MOFA algorithm was improved by 13% compared to the NSGA-II algorithm's execution time.

Nature-inspired meta-heuristic algorithms have been considered in recent decades due to their suitable ability to derive optimal reservoir operation policies compared to classical methods. In this research, the Multi-Objective Firefly Algorithm (MOFA) is compared with the widely-used Non-Dominated Genetic Algorithm (NSGA-II) in two test functions [Schaffer (SCH) and Fonseca (FON)]. These algorithms are then used to solve the optimizing the reservoir operation problem. Aidoghmoush reservoir, located in East Azerbaijan province, is used as a case study. Two conflicting objective functions are considered, including the first objective function of maximizing reservoir storage and the second objective function of minimizing deficiency. The algorithm runtime, generation distance, and Spacing matric are used to evaluate and compare the studied algorithms. The results showed that the execution time of the MOFA algorithm in the two test problems examined was, on average, 22% less than the execution time of the NSGA-II algorithm, and the quality of the answers could be better or worse than NSGA-II. The results obtained in the multi-objective operation of the reservoir showed that the set of solutions obtained from the MOFA algorithm became closer to the PFtrue averagely of 13% by considering the parameters α, β, and γ equal to 10, 1, and 0.1, instead of considering the parameters α, β, and γ equivalent to 0.25, 1 and 1, and the average execution time of the algorithm was improved by 3%. This improvement of the solution's quality and runtime was 13 and 11, respectively, compared to the NSGA-II algorithm.Since the MOFA algorithm is fast, the quality of the resulting set of answers can be improved by adjusting parameters, modifying, or integrating with other methods, such as different optimization algorithms or machine learning algorithms, and using it to Optimize real-time problems that are important in terms of computational time and burden. It is further suggested that the MOFA algorithm be developed for more complex models such as multi-purpose reservoir operation with more than two objectives of multi-reservoir systems, and its performance in reservoir operation management problems be compared with other meta-heuristic algorithms.

Climate change is one of the fundamental challenges of mankind in the last century and the coming years, which affects the status of water resources at the global level, especially at the scale of the watershed. In addition, climate change affects the hydrological cycle at the basin level and thus the management of water supply and demand. The Intergovernmental Panel on Climate Change (IPCC) emphasized that climate change is an inevitable phenomenon. Therefore, it is necessary to estimate water demand sites under the influence of climate change and evaluate different climate scenarios. Several software and models have been developed to estimate the effects of climate change and integrated management of water resources at the watershed level, among which we can mention the Water Resources Planning and Management System (WEAP).By using the WEAP model, it is possible to investigate, manage, and allocate different policies for the optimal use of water resources under the influence of different management scenarios, including investigating the impact of climate change on the amount of water allocated to different supply and demand sites in Therefore, according to the importance of the topic in this research, the evaluation of different climatic scenarios of the Mahabad watershed was done.

In this research, IPCC data were extracted under two scenarios RCP 4.5 and RCP 8.5, and then in order to use the data extracted in the study area, these data were used using the LARS_WG model with a smaller scale. In the following, the IHACRES model has been used to simulate rainfall-runoff in the future period in the study area. The outputs of the IHACRES model were introduced as the input of the Weap model, which is a comprehensive and integrated model in water resources. Then, the impact of climate change on supply and demand sites under different climate scenarios was simulated using the Weap model.

In this study focused on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCPs) scenarios (RCP4.5 and RCP8.5) for 2020–2039 were used. The results obtained in the mentioned scenarios for the statistical period of 2020-2039 show a decrease in precipitation and an increase in temperature. In order to estimate the productive runoff of the basin under the influence of climate change, the IHACRES rainfall-runoff model was used and the performance of the model in the study area was evaluated with correlation coefficient (R) and bias error. It was found that in the calibration and validation period, the correlation coefficient and bias error were 72%, 0.08, 69%, and 0.59, respectively, which shows the acceptable performance of the model in the region. In the following, the outputs of the IHACRES model were introduced as input to the WEAP model and it was determined that with the priority of allocating one for the drinking site and two for the agricultural site of the MIROC 8.5 scenario, the highest amount of unmet demand with the amount of 121.4 million cubic meters, especially for the year 2039 will have. On the other hand, the GFDL 4.5 scenario will have the highest reliability with 72% volume reliability and 42% time reliability.

By examining the results, it can be concluded that according to the MIROC 8.5 scenario, the amount of rainfall will decrease by 4.8% compared to the observation period. This reduction trend was also observed in three other scenarios. Then, using the IHACRES model, the monthly average amount of flow for the periods of 2020 to 2039 was produced and it was observed that the amount of runoff produced by the model will decrease from 2036 onwards. In the following, the runoff produced by the IHACRES model as the input of the simulator-optimizer modelWEAP entered. Then, the information related to climate change was checked under four scenarios and it was found that the highest unsupplied water needs for drinking sites considering allocation priority one and agriculture with priority allocation two are related to the MIROC 8.5 scenario with the amount of unsupplied demand of 121.4 million. Is cubic meters. Of course, it is worth mentioning that in this study, underground water supply and water allocation to the industrial sector have not been modeled.Keywords: Integrated water resources management, Climate Change, IHACRES, WEAP, Mahabad river basin.

Salinity is one of the most important environmental stresses after water stress in Iran, which has affected about 50% of agricultural lands with different intensities. The growth and development of crop plants in these areas are constantly exposed to salinity and other stresses at the same time, which causes morphological, physiological, and biochemical responses. Alleviating the negative effects of salinity in the crops is mainly done by agronomic and breeding methods. The use of appropriate microorganisms is one of the agronomic methods that can directly and/or indirectly reduce some limitations of salinity stress. In recent years, the necessity of biological study in rhizosphere microorganisms to improve nutrition and plant growth and, to control stress factors has been considered. Some species of Trichoderma fungi are known as plant growth stimulants in normal and stressful conditions. Since the bio-treatment of Trichoderma fungus for alleviation of salt stress in the Pinto bean has not been evaluated, so, the inoculation effects of different species of this fungus were studied on the plant growth and development under salinity conditions.

A field split-plot experiment based on a randomized complete block design with three replications were carried out at the Nuclear Agriculture Research Institute in Karaj on Pinto bean plants of Saleh 2. The main factor (salinity) was in two levels of non-saline (ECe=1.1 dS m-1) soil and saline (ECe=6 dS m-1), soil and the secondary factor with 11 levels, including; T. harzianum (MW718882), T. lixii (MW719563), T. ghanens (MW719590), T. virens (MW719876) and (MW719255) and their mutant isolates, namely NAS108 M1, NAS114-M17, ON545796, NAS115 M17, and NAS112M2, respectively, and the control treatment was without inoculation.

The results showed that the effect of salinity and the biopriming on the characteristics of emergence percentage, plant height, harvest index, number of pods, number of seeds per pod, seed weight, seed yield, biological yield, branching, seed protein and leaf area was significant (p≤0.01). Also, the results showed that the interaction effect of salinity and biopriming on emergence percentage, plant height, seed yield, biological yield, seed protein and leaf area was significant at p≤0.01, however, harvest index, the number of pods per plant, The number of seeds per pod and the seed weight were not influenced by treatment interactions. All studied characteristics were significantly reduced under salt stress compared to the control. The decrease in yield component traits was more than vegetative traits. The biopriming of Trichoderma species except T. lixii improved all traits of growth, development and seed protein of the plant in saline and non-saline conditions. T. harzianum, T. ghanens and T. atroviride along with their mutants produced the highest salinity resistance induction. Among them, the effect of T. atroviride was more obvious on saline condition while its mutant was superior in non-saline condition. The symbiosis efficiency of Trichoderma with this plant shows that under non-saline condition the efficiency varies between 16 to 70%, and the lowest is in T. lixii m and the highest in T. atroviride m. Also, except in T. atroviride treatment, the index was declined by salinity to the range of 3 to 58% for the rest of the fungi .

In general, the results of the experiment showed that the growth and development of pinto beans are affected by salinity, inoculation by the Trichoderma species and their interactions. As a result, the effects of different Trichoderma species are not the same under saline and non-saline conditions. Although under salinity treatment, the potential of the fungi in supporting of the vegetative traits, yield and yield component decreased, however, the values of all the traits in saline conditions were higher than the control without inoculation. This article shows that choosing the right and sustainable fungal treatment can partially alleviate the effects of the moderate salinity in the pinto bean plants. Therefore, it is possible to optimize bean production by inducing salinity resistance by selecting suitable Trichoderma. Overall and according to the results, seed biopriming treatment with Trichoderma species improves the vegetative and reproductive traits of pinto bean in saline and non-saline conditions, and the use of T. harzianum, T. ghanens and T. atroviride along with their mutants is recommended to maximize of the plant yield in the field conditions .

Evapotranspiration is considered as the water requirement for plants. Therefore, its measurement is necessary for all agricultural and irrigation projects. Evapotranspiration is one of the main components of the hydrological cycle associated with agricultural systems. Usually, evapotranspiration can be obtained using reference evapotranspiration (ET0). Accurate estimation and prediction of ET0 is essential in managing water resources, planning irrigation, and determining the water requirement of plants. Prediction of the ET0 by providing information about the future state in different time scales can help to make appropriate decisions, plan, and apply water resources management methods. Also, assessing agricultural drought conditions by well-known indices such as the Standardized Precipitation-Evaporation Index (SPEI) and Palmer Drought Severity Index (PDSI) directly requires ET0 of the region. The sharp decrease in the level of Lake Urmia and the threat to the region's ecosystem have also made the need for accurate calculation of ET0 more significant than in the past. One of the solutions to calculate ET0 is to use the FAO-56 Penman-Mantis equation (FAO-56 PM), an acceptable alternative for the scarce lysimeter data. However, the Penman-Mantis equation is highly dependent on the wind speed parameter, so a small error in the wind speed measurement causes a significant error. Therefore, this study aims to provide an innovative and reliable model for estimating ET0 without the need for wind speed parameters in Tabriz and Urmia stations.

In this study, to predict daily ET0 different intelligent models including multi-layer perceptron neural network (ANN-MLP), support vector regression (SVR), and support vector regression combined with firefly algorithm (SVR-FFA) were used in Urmia and Tabriz stations during 2002-2022 period. The input parameters of the models included minimum relative humidity (RHmin), maximum relative humidity (RHmax), average relative humidity (RHavg), sunshine hours (SSH), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg), and average soil temperature (Tsoil) which were obtained from Iran Meteorological Organization (IRIMO). Also, four different scenarios were used to run the models. The selection of different input combinations was based on the correlation coefficient, so the first combination had the lowest correlation and the last combination had the highest correlation concerning ET0. Also, data from 2002-2015 for 14 years were considered for model training and from 2016-2022 for 6 years for model testing. Correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and normalized root mean square error (NRMSE) indices were used to evaluate the used models.

The comparison and evaluation of the models used in Tabriz station showed that the SVR-FFA-4 model was chosen as the best model in this station with the root mean square error of 1.23 mm day-1. Among the SVR models, the SVR-4 model showed a good performance with the root mean square error of 1.95 mm day-1 after the combined model. Finally, the ANN-4 model also obtained an acceptable accuracy compared to other ANN combinations by having the root mean square error of 1.99 mm day-1. Finally, the evaluation of the results used for the Urmia station shows that the SVR-FFA-3 model has made the best predictions compared to other models with a root mean square error of 1.16mm day-1. The SVR-3 and SVR-4 models had a higher accuracy than other SVR combinations with a root mean square error of 1.78 mm day-1, but the third scenario was chosen as the appropriate model in the SVR model due to having less input. Among the ANN combinations, the ANN-3 model has a good performance compared to the other combinations of this model with the root mean square error of 1.81 mm day-1.

The results of this study showed that in both studied stations, the hybrid model showed higher accuracy than the individual models. So, in Tabriz station, the SVR-FFA-4 model had the best performance with an error rate of 1.23 mm day-1. In the Urmia station, the SVR-FFA-3 model showed good accuracy with an error rate of 1.16 mm day-1. Finally, it is suggested to use the hybrid model to predict the daily reference evapotranspiration in the northwest of the country. One of the limitations of this research is the lack of access to the parameters of dew point temperature and solar radiation. Therefore, it is suggested to use these parameters in the subsequent studies.

Increasing effluents production and discharging them as untreated into aqueous and terrestrial ecosystems, leads to contamination and imbalance of the ecosystems. Contaminants may include heavy metals or even nutritional elements. Small amounts of heavy metals such as iron (Fe) around living things are needed for their life, but high amounts of these metals are toxic and the accumulation of these metals in habitats is fatal for them. Bioremediation has been suggested as a clean technology, in which microorganisms are used in order to remove contaminants. Bacteria are efficient structures for metal ion detoxification due to their abundance, small size and large specific surface area. Also, they have high stability to survive in polluted habitats. In this research, the ability of two species of bacteria, Bacillus subtilis and Pseudomonas putida, which had a high capacity for the accumulation of metal ions, were compared in absorbing iron from aqueous solution in two living and non-living states, keeping the absorption efficiency characteristics constant. It is obvious that both alive or dead masses of these bacteria, which have a high function in absorbing and storing iron, can be used to make bio-purifiers or bio-fertilizers.

In this study, the effects of two bacterial species as alive or dead biomass were investigated on the biosorption of iron. Biomasses were prepared and purified from two species of bacteria, Pseudomonas putida and Bacillus subtilis. Their dead mass was made by sterilization. Moreover, a constant volume of 1.5 ml of the biomass of bacteria grown in broth media was isolated in the bottom of 2 ml vials. Then sorption isotherm experiments of Fe from iron solutions with different concentrations (0, 5, 10, 20, 50, 75, 100, 120, 150 and 200) mgL-1 in 0.01 M sodium nitrate solution and fixed pH values of 2.5 were investigated. In this research, by keeping constant the parameters affecting absorption such as initial pH and fixed volume of solution, amount of biomass, equilibrium time and temperature, the ability and speed of absorption by bacteria was tested only by increasing the concentration of Fe. Then, by measuring the difference in the amount of ions remaining in the solution from the initial amount used, the amount of absorbed ions was obtained. Linear, Langmuir, Freundlich and Temkin isotherm models were used to analyze the adsorption data. This was a factorial experimental research with a completely randomized basic design that was done in three replications. The average absorption of Fe was tested by Duncan's method and SAS 9.4 software.

The isotherm of iron absorption in bacteria showed that the maximum amounts of iron absorption in Bacillus subtilis in live and dead forms were 41.9 and 49.2 mg/g and in live and dead Pseudomonas putida were 24.6 and 25.1 mg/g, respectively. This shows the ability of Bacillus subtilis for absorption of iron from aqueous solution was higher than Pseudomonas putida by increasing Fe concentrations in constant pH, biomass quantity and temperature. On the other hand, the absorption of iron in the dead mass of bacteria compared to their live mass was higher. The isothermal investigations in elimination of iron metal from aqueous solution showed that among the tested models, the Linear, Freundlich and Temkin models were suitable. This study was done in 3 replicates, so the effect of experimental factors (bacteria species and viability) on the coefficients of the Linear, Freundlich and Temkin equations were also statistically tested. Among the experimental factors, only the bacterial species had significant effects on a and b coefficients of the Linear model and the Bt coefficient of the Temkin model, so the mean tests were only done for these parameters.

Examining absorption diagrams and also testing coefficients a and b of Linear equations and Bt of Temkin showed that Bacillus subtilis has more affinity and better retention capacity to absorb iron from aqueous solutions. Examining the Temkin model coefficient showed that the heat released after the absorption process in this bacterium is less than Pseudomonas putida. However, Bacillus subtilis had higher capacity and affinity for absorption of iron than Pseudomonas putida, so it was a more suitable adsorbent in all Fe concentrations. The ability of the living and non-living masses of Bacillus subtilis to absorb iron makes it a good candidate for research in the fields of biomining of heavy metals, bioremediation of contaminated waters and/or making biofertilizers.

Considering the lack of renewable water resources in the world and the high share of the agricultural sector in water consumption, makes increasing water productivity very important. Corn is an important type of fodder that is harvested for consumption as silage of the whole plant (Coors et al., 1997). Despite having one harvest stage, this plant has a high dry matter yield. Its silage is easily prepared and it is a tasty fodder with stable quality for livestock and has higher energy than other fodder (Curran and Posch, 2000). Research results in Hamedan province in two traditional and modern irrigation systems showed that the average physical water productivity for fodder corn in traditional and modern irrigation systems was 5.11 and 6.67 kg per cubic meter of water, respectively. The average economic water productivity for fodder corn in traditional and modern irrigation system was 7678 and 10068 rials per cubic meter of water, respectively (Seyedan and Mottaghi, 2019). The main purpose of this article was to measure the water received in the field of fodder corn fields in Behbahan city and to investigate their physical and economic water productivity. To do this, the applied irrigation water, physical and economic water productivity under different agricultural management and in irrigation networks and systems can be compared and evaluated.

This research was carried out in the field and in order to investigate the effects of independent variables on applied irrigation water, physical and economic water productivity in the cultivation of fodder corn in irrigation methods and networks under the management of farmers in the cropping season (2016-2017). It was implemented in Behbahan city. Thus, in 12 farms of the farmers, the applied fodder corn irrigation water was measured (without interfering in their irrigation program). The effective rainfall of the 2016-2017 crop year in Behbahan city was calculated using the United States Department of Agriculture (USDA) method. To determine the applied irrigation water, first, the inflow rate from the selected water source was measured with a suitable device (WSC flume, water meter and ultrasonic flow meter). The SPSS16 software was used for step-by-step linear multivariate regression analysis. Linear multivariate regression analysis was used to investigate the effects of independent variables on dependent parameters. One-way analysis of variance was performed with SPSS16 software. The purpose of using one-way analysis of variance was to statistically show a significant difference between the average of two or more "independent" groups. In order to compare the indicators of physical and economic water productivity , there was a need to resolve the difference. Therefore, to solve the scale difference, the common method of Z-Score standardization was used.

The results of the analysis of variance in the regression model showed that the growth period of the plant had the most significant positive effect on the amount of irrigation water with the t-statistic of 5.288. Performance with a t-statistic of 5.919 had the most significant positive effect on economic water productivity. Among the independent variables, yield and applied irrigation water had the most significant positive and negative effect on the physical productivity of water with the values of 6.419 and -6.381, respectively. The applied irrigation water varied from 4826 to 14733 cubic meters per hectare. The results of the analysis of variance and the descriptive results of the mean of the traits showed that the highest value of physical water productivity was equal to 10.128 kg/m3 in rain irrigation and the lowest value was 4.818 kg/m3 in surface irrigation in the traditional network. They had a significant difference. The calculated Z-Score values showed that 50 and 33% of the sprinkler and surface irrigation farms (modern network) had acceptable economic productivity, but 100% of the surface irrigation farms in the traditional network did not provide acceptable economic productivity.

The amount of irrigation water used in the fields with sprinkler irrigation method was 46 and 33% less than traditional and modern network fields that were irrigated by surface irrigation method. Also, the consumption of irrigation water in the surface irrigation method in the traditional network was 20% more than the surface irrigation in the modern irrigation network. This increase in water consumption was effective in reducing the irrigation efficiency of traditional networks compared to modern networks that irrigated with surface irrigation. The reason for the higher consumption of irrigation water in the surface irrigation method in the modern network compared to sprinkler irrigation can be attributed to the following factors: 1- The lack of irrigation management during the change of the irrigation shift of the fields, which sometimes happens due to the absence of the irrigation worker during the shift change. 2- lack of control of the incoming water of the fields due to the lack of meters or devices to measure the incoming flow. 3- Improper leveling of lands under the modern irrigation network, which increases the irrigation time of these fields. By covering the traditional irrigation canals with concrete or other coverings suitable for the climate of the region, water losses can be reduced. Also, the necessary training for the correct use and management of irrigation water according to the different stages of fodder corn growth will be effective in reducing water consumption. Water according to the stages of growth and development of the plant prevented the occurrence of under-irrigation stress in the stages of flowering and seed filling, which is the most important stage sensitive to water stress.

Water shortage and the occurrence of drought is a major challenge in agriculture. By 2050, Iran will experience a 2.6 0C increase in temperature and a 35% decrease in rainfal. Plant growth promoting bacteria could alleviate the water stress effect on crops. Auxin and ACC-deaminase production are best known for their mechanisms of action, but recently extracellular polysachrid (EPS) production was considered. EPS could support bacterial life by absorbing water and keeping the hydrative environment around bacteria. In such situations, plants are indirectly affected by increasing available water as well as getting enough time to genetic responses to water deficit. Bacteria of the genus Bacillus are considered as one of the most important microbes in the rhizosphere of plants. Their ability to form dormant spores resistant to heat and dehydration makes them a desirable option in biofertilizers production. Moreover, using extracellular polymer producing strains for plants inoculation maybe more successful compared to other bacteria. So far, no study has been reported on the effect of EPS producing Bacillus simplex inoculation on plants growth. Therefore, this research study was carried out to investigate the effect of this bacterium on pepper seedling under different watering conditions.

Bacillus simplex selected as a plant growth promoting bacterium (PGPB) due to the ability of auxin and ACC-deaminase production. This bacterium was obtained from the soil biology lab collection at the University of Maragheh. Its tolerance to water shortage and EPS production was evaluated by culturing it on nutrient agar medium treated with polyethylene glycol (PEG) to simulate water stress conditions. To assess the effects of EPS-producing B. simplex on pepper growth, a greenhouse experiment was conducted. The experiment was conducted as factorial in a completly randomised design with three replications. The experimental treatments consisted of B. simplex inoculation and three different soil water states, namely 80%, 60%, and 40% of the soil's field capacity water content.The plants were harvested after 50 days of planting date and various parameters were recorded. These included total dry weight, leaf surface area, leaf dry weight, root dry weight, root fresh weight, root volume, specific leaf area (SLA), specific leaf weight (SLW), leaf weight ratio (LWR), root length, root surface area, root diameter, and root surface area density.

Based on visual evaluations, it was observed that the bacterial colonies of B. simplex exhibited significant changes under water stress conditions compared to non-stressed conditions. The colony color changed from milky to semi-transparent, and its fluidity and stickiness increased in PEG nutrient agar containing PRG compared to nutrient agar medium.Its inoculation could significantly increase the total dry weight of aerial organs by 23%, 27% and 46% and the dry weight of the leaves by 15%, 19% and 37%, respectively, under normal watering, moderate and severe water stress conditions. The leaf areaincreased respectively by 12% and 29% just in the conditions of moderate and severe water stress. Bacterial inoculation in normal watering and medium water stress conditions increased the root dry weight by 37% and 42%, respectively, but the wet weight of root in severe water stress decreased by 28%. The inoculation of studied bacterium caused to increase root volume by 27%, 31% and, 51% and root surface by 16%, 20% and 38% respectively, under normal watering and modarate and severe water stress conditions. Root diameter, root tissue density and root surface density increased, by 38%, 29% and 17%, respectively, at severe water stress conditions by bacterial inoculation. The regression relationship between root traits and the total dry weight of the aerial part of pepper showed that there is a direct and significant correlation between them. Root dry weight, root fresh weight, root volume, explanation factor, root length, root surface density, root tissue density, and root surface area showed positive correlations with the total dry weight of the pepper seedlings. The coefficients of determination (R2) for these relationships were 0.86, 0.86, 0.86, 0.90, 0.91, 0.91, 0.60, and 0.91, respectively. However, there was no correlation between the total dry weight and leaf indicators.

Based on the correlation coefficients between the total dry weight of pepper seedlings and their root and leaf traits, it appears that the increase in yield by B. simplex is associated with improvement in the characteristics of both leaves and roots, particularly under severe water stress conditions. Bacterial metabolites, such as auxin, are known to stimulate root growth, root size, weight, and branching, which ultimately leads to a larger soil volume accessible to the plant and improved nutrient availability, thus enhancing overall plant yield. Therefore, based on the results the use of B. simplex might improve the establishment of pepper plants in all soil water conditions, particularly under water deficit conditions.

Soil temperature is one of the important factors in agriculture and hydrology, and its accurate measurement is very important to ensure the optimal growth and development of the plants. Soil temperature is a factor that affects many processes such as seed germination, soil moisture level, aeration, nitrification and availability of plant nutrients. Because the soil temperature data is measured in some synoptic stations, most of the data have limitations or are incomplete. However, choosing the best method to estimate soil temperature with other available meteorological data is an efficient approach in many fields. Soil temperature depends on several factors including color, slope, vegetation, density, humidity and amount of sunlight. Currently, some physical models are available that are intrinsically related to the state of soil heat flow and energy balance in underlying soils to estimate soil temperature. The importance of soil temperature in agricultural sciences and hydrology, on the one hand, and the existence of many difficulties in recording this vital parameter, have led researchers to seek a relationship between soil temperature and other parameters in order to be able to estimate soil temperature with optimal accuracy.

In this research, daily soil temperature values were collected during the time period of 1990-2022 in Ramsar, Arak and Shiraz stations. On the other hand, the parameters of minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tm), maximum relative humidity (Umax), minimum relative humidity (Umin), average relative humidity (Um), average wind speed (FFM) and Sunshine hours (SSHN) was considered as the input parameters and soil temperature (T-soil) as the target parameter. It is worth mentioning that the way of choosing different input compounds to estimate the value of soil temperature in the studied models is based on having a higher correlation with soil temperature based on the thermal map. Moreover, the ability of data-driven models of Gaussian process regression (GPR), support vector regression (SVR), M5P algorithm, linear regression (LR), and multilayer perceptron (MLP) neural network in estimating soil temperature was evaluated using different statistical parameters of correlation coefficient (R), root mean square error (RMSE), Nash Sutcliffe coefficient (NS), average absolute value of percentage error (MAPE) and Wilmot agreement index (WI).

The evaluation of five GPR, SVR, M5P, LR and MLP models for three stations of Arak, Ramsar and Shiraz shows that the 8th M5P scenario and the 8th LR scenario with lower root mean square error respectively (0.899 and 0.889) for Ramsar station, (0.958 and 0.949) for Arak station and (0.966 and 0.953) for Shiraz station have better performance than other studied models. Also, the evaluation of the impact of the input parameters in creating the scenario for the models shows that the parameters of relative humidity and air temperature had more important role than other input parameters. So that by adding parameters of relative humidity and air temperature, the accuracy of the model has increased. Therefore, these parameters are among the most key and important parameters of soil temperature.

The analysis and evaluation of soil characteristics has an important impact in the fields of hydrology, agriculture and climate. On the other hand, soil temperature has a direct relationship with the amount of moisture available to the plant, so that an increase in soil temperature can increase the transpiration rate of plants, and as a result, soil moisture decreases. Soil temperature is also an essential factor in agriculture because it determines whether plants can grow, and controls soil chemistry and biology and atmosphere-land gas exchange. Therefore, predicting soil temperature is very important for successful crop management and yield optimization. So, In this research, five data-driven methods of GPR, SVR, M5P, LR and MLP were used to predict soil temperature in Arak, Ramsar and Shiraz stations during the time period of 1990-2022. The obtained results were compared using statistical parameters and it was concluded that the 8th M5P scenario and the 8th LR scenario have shown the best performance in three stations with the lowest error compared to all scenarios. Therefore, the application of the mentioned models to predict the soil temperature has proper accuracy and is recommended for management and evaluation in terms of environmental and civil aspects.

Composting is an effective approach to organic waste management, but it also has disadvantages such as greenhouse gases emission (CH4, CO2, and N2O), bad smells, nitrogen loss, and contamination of soil and water resources. Therefore, it is crucial to develop a method to reduce gas emissions and improve the quality of compost at the same time. To overcome these disadvantages, some additives are usually used. The use of additives at the beginning of the composting process to produce valuable compost is known as "co-composting". Various materials can be incorporated into waste during composting. These materials fall into three categories: organic, mineral, biological, or a combination of these. Some of these added materials serve as bulking agents, primarily affecting the physical structure of the compost (such as aeration). However, most of the time, these substrates also have direct or indirect impacts on other composting factors and can be considered as additives. Additives play a role in enhancing the composting process by reducing leaching and gas emissions, improving aeration, accelerating organic matter breakdown, and enhancing nutrient content and availability in the final product. The research published so far showed that the effect of wood biochar along with leonardite and coal on the quality of compost obtained from animal manure and forest organic matter had not been studied in Iran, and considering the importance of the feasibility of improving the quality of compost, this research was conducted.

In this research, the effect of wood biochar, leonardite and coal on some characteristics and quality indices of the co-compost of animal manure and forest organic matter was studied. The biochar was produced at 400° C from the pyrolysis of mixed pruning plum and pomegranate brunches. Leonardite and coal were also prepared from companies active in this field. The experiment was conducted as a factorial in a completely random basic design with three replications. The first factor of treatment included wood biochar, leonardite and coal at two levels of 2 and 4% by dry weight and the second factor of time included 12 weeks. During the composting process, temperature, pH, EC, total nitrogen concentration, organic carbon content and some quality indices of composting such as humification index (HR), humification ratio (HI), degree of polymerization (DP) and total materials Humic (HS) were measured.

According to the results, the treatments were significantly different from the control regarding the temperature of the compost pile, and the coal treatment showed the highest temperature at the level of 2%, and the duration of the thermophilic phase in the leonardite treatment was 2% longer than the other treatments. Among the additives, 2% and 4% leonardite treatments created the highest total nitrogen content in the compost. The additives used in this research did not make a significant difference in compost pH, but coal at the level of 4% caused a significant increase in electrical conductivity (EC). Biochar and coal increased organic carbon concentration in the compost pile. Leonardite treatments of 2% and 4% produced the highest values of humification indices (HS, DP, HI, HR), but they were not significantly different from each other. The biochar used in this research increased the C/N ratio of co-compost.

In general, leonardite treatments were found to be useful in terms of the indicators of final compost and biochar and coal treatments to accelerate composting in the early stages. But the results of this research showed that it can be used at the end of composting processes in order to enrich and improve the quality of the produced compost. The results of this research showed that considering the costs of raw materials, coal is a suitable treatment to accelerate the production and improve the quality of compost. The results obtained about the effect of biochar obviously cannot be generalized to all biochars and different results may be obtained depending on the type of biomass and pyrolysis conditions. Therefore, it is recommended to use the combination of organic and biological or organic and inorganic additives in future research. Other traits such as indices of microbial contamination and abundance of weed seeds should also be measured and the effect of treatments should be studied. Finally, it is recommended to study the effect of final compost on the characteristics of calcareous and acidic soils.

Water shortage and the need for its optimal use in arid and semi-arid regions, including Iran, has led water officials and farmers to use modern irrigation systems, such as drip irrigation with the aim of making optimal use of water resources. Drip irrigation has been welcomed in most parts of the world due to its high efficiency and the possibility of irrigation in different environmental conditions. The most important reason for the superiority of drip irrigation over other irrigation methods is the controllable amount of water for each plant. Drip irrigation is a method in which water is poured out of the net at low pressure through an orifice or device called an emitter and dripped into the bottom of the plant. This irrigation system, like other methods, requires accurate knowledge of the parameters affecting it to achieve the desired efficiency. One of the most important parameters for the irrigation system is the distribution of moisture in the soil and in fact the shape of the moist bulb. Therefore, knowledge of how to distribute water in the soil is essential for the proper design and management of subsurface drip irrigation systems. Since testing is very difficult and time consuming to detect the shape of moisture distribution in the soil, the use of numerical and analytical simulation can be an effective and efficient way to design these systems.

In order to determine the progress of the moisture front in drip irrigation, first the soil texture type and physical properties of the soil were determined. It should be noted that the emitter flow rate was measured and adjusted in volume at the beginning of the test to minimize the difference between the emitter flows along the three side tubes. Evaluation experiments were performed with three outflows of 2, 4 and 6 liters per hour. With the start of the system, the progress of the moisture front at different times was measured by digging a trench using a scale. Numerical simulation of moisture front progress was performed using HYDRUS model based on Richard equation and analytical simulation was performed using Moment Analysis. HYDRUS software was used to numerically simulate the progress of the moisture front. The simulation range was considered to be 100 cm by 100 cm on the two-dimensional plane. In these simulations, 3956 nodes are used to represent the entire simulation range and also, relevant equations were used to calculate the two-dimensional spatial Moment of the wetting pattern.

The simulations show that the initial volumetric moisture content is 0.11 and the saturation volumetric moisture content is 0.380 and the water dispersion rate increases over time on the x and z axes. With increasing flow, the maximum dispersion is in the x-axis, which occurs in flow of 6 liters per hour. The result for flow of 6 liters per hour based on the data used is slightly higher than the desired value. The reason why the value of M00 in the flow rate of 6 liters per hour is higher than expected, is that in the simulation flow rate of 6 liters per hour change in the size of the inlet diameter and the amount of flux changes the amount of water entering the soil and moistens a large volume of soil. Due to the different amount of moisture applied to the area at different times, the value of z_c,σ_x^2,σ_z^2 is different and has caused a change in the size of the oval in different flows. The increase in the size of the ovals indicates the high dispersion of water in that area. The results showed that the Moment analysis was able to express the position of the center of mass of water distributed in the soil with correlation coefficient of 0.986 in linear mode and 0.982 in logarithmic mode. By comparing the values of diameter and depth obtained from the HYDRUS and the drawn ovals, it can be concluded that both methods provide close results. The accuracy of the Moment analysis method in simulating different types of moisture patterns resulting from drip irrigation under different flows with the use of different volumes of water is similar to the HYDRUS model and therefore it is possible to use this feature to predict the pattern of moisture from a certain flow using a specific volume of water.

In this study, the accuracy of Moment analysis in simulating various moisture patterns resulting from drip irrigation under different flows with the use of different volumes of water was investigated and the possibility of using this feature to predict the pattern of moisture from a given flow using a specific volume of water checked. In order to investigate the Moment of the amount of water distributed in the soil by subsurface drip irrigation, simulation was performed by two-dimensional HYDRUS software for three discharges of 2, 4 and 6 liters per hour with an inlet water volume of 12 liters. Then, using the results of simulation of moisture distribution range by a programming language including MATLAB software, and by calculating the Moments, it was determined that the Moments are able to express the position of the center of mass of water distributed in the soil and how it is distributed relative to x and z axes. The increase in the size of the ovals indicates that more water is distributed in that area. Comparing the diameters and depths of the moisture front between the simulated HYDRUS model and the Moment analysis model, it was found that the Moment analysis is an efficient way to study the distribution of water moisture by drip irrigation and this method can be used as an alternative input to estimate parameters.

Microorganisms make changes to their lives, when they are exposed to contaminants; these changes include physiological and genetical changes. Antibiotics are used in the livestock industry as well as in medicine. Among several methods for assessing the risks of contamination of soil ecosystems; one of which is the pollution induced community tolerance. This study, the effects of different levels of OTC on microbial activity were investigated in a silty loam soil. Different levels of OTC (10, 20, 30, 40 and 50 mg.kg-1) were applied to the pots containing 2 kg of soil with three replications and kept at room temperature for 120 days. The soil moisture level was adjusted to 50-70% of field capacity. Soil Dehydrogenase activity as an indicator of soil microbial activity was measured at 3, 7, 15, 30, 60, 90 and 120 days of incubation. The ΔIC50 diagram was plotted against different soil OTC concentrations for each time point and the trend of microbial tolerance changes was evaluated. The results showed with increasing incubation time and concentration of OTC, induction of tolerance in the microbial population was gradually enhanced. Overall the increase of tolerance in microbial population started on day 15 and reached the highest values on days 30 and 60 at OTC concentration of 50 mg.kg-1 OTC. A marked change in the slope of ΔIC50 was occurred on day 30 at 30 mg.kg-1 OTC which can be regarded as critical level of OTC in soil at this time point which led to occurrence of microbial tolerance.

Replacing the pressurized irrigation system with surface irrigation is one of the proposed solutions to increase irrigation efficiency. The low-pressure network consists of pipes that receives water from main and/or secondary canals or pipes and usually delivers it to farm units or hydro-flume pipes for surface irrigation with a pressure of lower than 10m of water. Various parameters affect the feasibility and design of low-pressure irrigation, which determine the economic justification of the plan, energy consumption, and its social desirability. In recent years, there has been more interest in implementing low-pressure irrigation instead of open tertiary canals. The reason for this is the reduction of water losses in the distribution and delivery process, the possibility of better management of water distribution, the possibility of volume delivery of water to agricultural plots, and the possibility of irrigating more lands. But there are still ambiguities in its economic and technical issues, and no significant research has been done on its design issues and parameters affecting its economic aspect. In this research, the design of a low-pressure distribution system instead of the open canals and the simultaneous consideration of social and executive issues in an irrigation area are considered. In addition, network design based on different design options (pressure-energy consumption), evaluation and comparison of two types of sub-networks from an economic and operational view have been proposed as research objectives. Focusing on the correct selection of the pump (with a lower energy consumption approach) and estimating for the economic comparison of the two methods of irrigation have been done.

The land of Keyzarin village (with an area of 144 hectares) is located on the right bank of the Tangab river in Fars province. The lands of the right bank use the springs of the region, but the low efficiency of water distribution in the traditional canals has caused a significant reduction in the delivered water to the farms. The characteristics of the irrigation network in the current situation are as follows: a canal with a length of 610meters and a design flow of 360 lit/s, leads a part of the water of the "Qomp Atashkade" spring to the dividing point and divides it equally into two canals. In order to design low-pressure instead of the open canal, the layout of water conveyance and distribution lines is determined. The initial pipes’ diameter is determined according to the optimal speed, and then, the appropriate pump is selected based on the design flow rate and the topography of the area. In this research, the pressure control system is selected according to the topography of the region and the slope of the land, the type of closed system.Socially, the available option for the layout of pipes is to follow the arrangement of the traditional canals. From a technical point of view, there are several options that, the main difference between them is in the hydraulic design and the type of pump used; So that, accordingly, the number of pumps, the commercial diameter of the pipes and the amount of energy consumed in each option are determined. Therefore, these options need to be compared technically and economically. In this research, 14 scenarios are defined based on 7 types of pump designs and two output pressures. The energy consumption of each scenario has been calculated according to the flow rate and cropping pattern in each irrigation season. Then, the comparison of costs in low-pressure and open canals network is calculated and compared.

The cost of constructing an open tertiary network per hectare of land is equal to 990 million-Rials. The costs of earthworks with machines, which are related to the volume of cut and fill, and then the cost of in-situ concrete, constitute the largest percentage of the total costs of the canal networks. Based on the results of the quantity surveying and estimating of the low-pressure sub-network, the cost of constructing for each hectare is equal to 1654 million-Rials. However, if the operation and maintenance costs are not met and if the maintenance and operation of the low-pressure pipes is neglected, the resulting problems will disable the network completely. The construction of the low-pressure network in the studied area can make 3.6 hectares of land available to farmers due to the less space occupied by this network and also increase the cultivated area by 21.6hectares (A total of 17.5% is added to the cultivated land in the irrigated area). Considering the quality of land in the region, the price of each hectare of irrigated land and rainfed land in the region is estimated to be 1 billion-Rials and 300 million-Rials respectively. Therefore, as a result of the implementation of the low-pressure network, the profit from the increase in the price of agricultural land (conversion of rainfed to irrigated) will be equal to 18 billion-Rials, which is equal to 83% of the total cost of the construction of the low-pressure network. Proper layout of lands in low pressure irrigation can reduce the costs of construction of the network. Also, the amount of more land that can be cultivated after the implementation of low pressure irrigation and the amount of reduction of land waste compared to open canals can be effective in the economic justification of low pressure irrigation. In low-pressure networks, repairs and maintenance, unlike open canals, are somehow mandatory.

At the end of this plan, low-pressure networks were chosen as the superior option over the tertiary canals due to various technical advantages and also to compensate for the initial implementation costs by increasing the water transmission efficiency and increasing the cultivated area. Based on the results obtained, it can be said that despite the acceptability of low-pressure irrigation, comprehensive studies should be done in each region for its implementation, and the specific economic-social conditions in a region may not be consistent with its implementation. Also, special attention should be paid to the final output pressure, of the pumps based on different options and the type of low-pressure irrigation network.

Flood is a natural disaster that causes a lot of damage in different parts of the world every year, because it has caused a lot of financial and human losses in many countries. One of the main problems of floods is soil erosion and the production of sediments that are transported along the river during the flood and destroy the buildings on the banks of the river, reduce the capacity of canals and reservoirs of dams, and damage agricultural lands. Since the amount of sediment in a river is a random variable that is a function of several correlated random characteristics such as flow rate, suspended load and bed. Therefore, estimation of sediment load based on univariate probabilistic analysis is not a reliable criterion. For this reason, multivariate analyzes are of special importance. The bivariate behavior of the flood discharge and the resulting sediment load depends on their joint cumulative distribution function, which can be implemented with the help of copula functions. Since hydrological variables are multidimensional and copula functions allow multidimensional analysis of variables and give us more information about hydrological processes. Therefore, the use of copula functions can be an important step in promoting hydrological research. Based on this, the aim of the current research is to investigate the two-variable behavior of flood discharge and sediment load using copula functions and to analyze the frequency of both variables in the Minab watershed.

In this study, Archimedean and elliptical copula functions were used as a tool for bivariate analysis of flood (Qw) and sediment discharge (Qs). First, marginal distribution functions were fitted on flood (Qw) and sediment discharge (Qs) variables. Then, using conventional correlation methods such as spearman, Pearson and Kendal tau, the correlation between the variables was checked. In the next step, elliptic copula (T-Student and Gaussian) and Archimedean (Frank, Joy, Clayton, Gamble) functions were fitted to the variables based on the maximum likelihood method, and the best fitted function was the two variables of flood discharge and sediment. The copula was normal. After the coupling of the variables, the single and double return period of 2 to 1000 years in "AND" and "OR" mode and in conditional modes with scenarios T(Qf/Qs<=qs) and T(Qf/Qs>=qs) It was calculated and finally two-variable return period analysis was done based on return period tables and graphs.

The results of the correlation test showed that there is a positive and significant relationship between the investigated variables in the study area. Based on the results of Chi-plot and Kendall-plot, there is an acceptable correlation between the two investigated variables. The results of the copula fit showed that the normal copula has an acceptable performance on the investigated variables. Examining the return period of flood discharge and sediment discharge showed that the changes in sediment discharge are far greater than the changes in flood discharge in higher return periods, so that with the increase in the return period, the amount of flood discharge increases exponentially. The AND scenario gives much higher values than the return period of OR and is a suitable tool for risk analysis of hydrological events. Also, among the conditional scenarios, the scenario T(Qf/Qs>=qs) showed greater values of the return period than T(Qf/Qs<=qs). Finally, the risk analysis in the studied area showed that the risk of flooding and sedimentation in the two-year return period with the AND and OR scenarios is without risk, but for the two conditional cases, it has a low risk. Or in the 20-year return period, the risk of the 50-year project for all scenarios except the OR scenario and the rest of the scenarios have predicted critical conditions.

The results of the return period analysis showed that in the case of "AND" return period, the return period values are much larger than the "OR" values. In the same way, it has a higher risk. Also, considering the conditional scenarios were also significantly different. Therefore, ignoring the correlation between flood discharge and sediment may significantly overestimate or underestimate the actual amount of sediment, as a result of which the probability of the corresponding occurrence increases or decreases. Therefore, depending on the purpose and needs in the region, the following scenarios can be used.

In many fields of water sciences, the moisture movement in the soil profile is very important. Due to the non-linearity of the governing equation in moisture movement in the soil profile, the comprehensive analytical solution of this equation is impossible, and there is a need to solve it numerically. The abundance of data and simulation steps reduces the flexibility in using existing software to solve the Richards equation and using this data as initial conditions. Therefore, having a numerical solution plan for the Richards equation will be very useful. Among the existing methods, the fully implicit finite difference method can continue the simulation unceasingly due to its unconditional stability in providing a non-iteration solution. However, using the finite difference method to solve the Richards equation leads to significant mass balance error. Some usual methods to reduce the mass balance error could not be applied to the non-reverse solution. By creating the Richards equation numerical solution in MATLAB software, this research introduces a method to reduce mass balance error, which is a simple method to apply to all kinds of numerical solutions.

In this research, a Non-Iteration implicit solution for the Richards equation was developed. After creating the Richards equation numerical solution, the difference between the entering flux to the soil column and the added water to the same soil column, is added to each node to reduce the mass balance error. This amount is added to the nodes that their matrix pressure head difference (PD) compared to the initial state, is more than a threshold value. This amount applies as a coefficient proportional to the humidity of each node. To investigate the effect of various factors on the performance of the created numerical solution, a series of 11 experiments were designed with different characteristics in terms of infiltration rate, infiltration duration, duration of moisture redistribution in the soil profile, and initial volumetric water content. The optimal value for PD in each experiment was determined in such a way as to minimize the mass balance error. For the integrity of the results, the average PD values equal to 10 cm were used to simulate all cases. These series of experiments were simulated by two-dimensional HYDRUS software, and the results were used to verification of the created numerical solution.

The results showed that the most mass balance error is forced in the infiltration phase. Using the proposed method to preserve the mass balance caused a significant reduction in the mass balance error. The absolute value of the mass balance error varied in a narrow range and was always less than 3.5 percent. It is expected that time and space steps have a considerable effect on the mass balance error. The absolute mass balance error decreased linearly from 3.36 to 3.33, with the infiltration duration increasing from 10 to 30 minutes at a constant infiltration rate of 1 mm 〖min〗^(-1). The absolute mass balance error increased linearly from 3.36 to 3.37% by the initial volumetric water content increasing from 13 to 17% and decreased from 3.40 to 3.35 percent by the increase in the duration of the moisture redistribution in the soil profile from 300 to 900 minutes. Also, the Mean Absolute Relative error (MAE) increased from 1.17 to 9.53% by the infiltration time increasing from 10 to 30 minutes; the MAE increased from 1.17 to 8.89% by the infiltration rate increasing from 1 to 3 mm 〖min〗^(-1); and the MAE increased from 1.17 to 6.89% by the volumetric water content increasing from 13 to 17%.

The results showed that this method leads off the simulation to a significant reduction in the mass balance error; so that, in all experiment cases, the mass balance error ever fluctuated in a small range, less than 3.5%. The results comparison of the created simulation and the HYDRUS software showed that this method provided acceptable results, in cases such as summer rains where the infiltration duration is much less than the water redistribution time in the soil profile. Also, the highest value of the MAE in comparing the created simulations with the obtained results from the HYDRUS software is less than 10%. It proves a good agreement between the created simulation and the HYDRUS software.