مدل‌سازی و مدیریت آب و خاک

Introduction The agriculture sector, as the biggest consumer of water to produce more food, has faced the challenge of water shortage. One of the problems ahead in the agricultural industry is the sustainable use of available resources such as land, water, and labor to increase agricultural production and development, which requires proper planning and management policies. Plant models can be used to investigate the long-term effects of quantitative and qualitative changes in irrigation water on crops, soil salinity, evaporation and transpiration, deep infiltration, and surface runoff. One of the widely used plant models is the AquaCrop model, which was presented and developed by the World Food and Agriculture Organization. The Aquacrop model is one of the crop yield estimation models that can be used for a wide range of crops including fodder crops, vegetables, grains, fruits, oil crops, and tubers. In this model, the state of various stresses including water and soil salinity, simulation of lack of irrigation, and crop yield are considered. Various studies have been conducted regarding the calibration and validation of crop forecasting models in our country, and much research has been conducted on wheat at the global level. In this research, the AquaCrop model was used to predict the biomass and grain yield of wheat in Qazvin. This model can be a good substitute for field measurements and can be used in areas where there is a lack of ground information. Materials and Methods In the present research, the data of water wheat cultivation in a lysimeter in Ismailabad, Qazvin were used. The input information of the AquaCrop model includes information on climate, soil, management, and plant characteristics. To calibrate and verify the model, some farm information was needed to be compared with the output of the AquaCrop model. The biomass of the wheat plant was determined by taking random samples of the 0.5×0.5 m2 with two replications per sampling hectare. To measure grain yield in the fields, four samples were taken at the end of the growing season at the end stage. The validated AquaCrop model was used to estimate the effect of three planting dates and three low irrigation conditions on wheat grain yield. In this step, the average regional information around the farms was used so that the implementation of the model is not unique to the conditions of a particular farm. Results and Discussion In terms of the investigated meteorological factors, the model has moderate sensitivity to maximum and minimum temperature and low sensitivity to rainfall. The change in the maximum temperature in this region increases the error of the simulation on average. Regarding the soil parameters, the sensitivity of the model to the crop capacity moisture, wilting point, saturated moisture, and saturated hydraulic conductivity, especially in saturated conditions, is low to medium. The most sensitive of the AquaCrop model was the change in the reference harvest index. The model simulated biomass values with higher accuracy than yield. In the calibration stage, the values CRM, NRMSE, and d for biomass were -0.15, 0.17, and 92% respectively. These values were obtained in the validation stage for biomass -0.1, 0.24, and 92 % respectively, and for yield -0.03, 0.06, and 80 % respectively. By running the model in different climatic scenarios, it was determined that the maximum delay in the planting date is on November 15. A 25% reduction in irrigation water reduced grain yield in wet, normal, and dry years by 15%, 20%, and 28 %, respectively, and a 50 % reduction in irrigation water reduced its amount by 20, 25, and 45 %, respectively. Conclusion Evaluation of the AquaCrop model for common plants in a region plays an important role in comparing crop performance in different conditions. In this research, the ability of AquaCrop 6 model to estimate the yield and biomass of wheat in Ismailabad Qazvin was investigated. The results showed that the model is capable of simulating these factors with high accuracy. The accuracy of the model in biomass simulation was higher than the grain yield. By implementing the calibrated model in different climatic scenarios, planting dates, and irrigation deficits in two regions, it was determined that to achieve optimal performance, the wheat planting date should not exceed 15 November. It was the use of calibration coefficients by spending a long time in the AquaCrop model so that a calibrated model can be used in many areas with proper accuracy. More accuracy in the simulated results can be achieved by using more calibration factors, but it is clear that the use of more calibration factors requires spending more time and money. Finding a general recalibrated model that can be used in large areas is a good solution in crop management at the farm-to-regional scale. Comparing the statistical parameters obtained in this study with previous studies on wheat yield modeling by the AquaCrop model shows that the results of this study are within an acceptable range.

Introduction Irrigation decision support systems (IDSS) are among the approaches considered a tool in complex decision-making for water resource managers due to the enormous development of computer systems. Modernizing at different levels of water consumption can significantly increase water productivity indicators. Performing these conditions requires technological changes. The primary pillar of any IDSS system is its ability to adapt to environmental changes. This process allows the prediction model to compare predicted values with actual results and adjust automatically. IDSS systems for designing cropping patterns and optimal irrigation programs have the critical capabilities to control and manage optimal irrigation on large levels and water rights. These systems suggest the optimal cultivation pattern and dynamically provide the water consumption optimization schedule. According to previous studies, the most critical challenge of irrigation management is the limited amount of available water, which leads to the complexity of the optimal use of agricultural water in real conditions. One of the most important strategies to save Lake Urmia is to take necessary measures to reduce water consumption in the agricultural sector. One of the primary solutions to reduce water consumption in the agriculture sector is to decrease the loss of valuable and non-useful uses of agricultural water through the improvement of irrigation management. For this purpose, in the present study, the details of the adaptability of a developed IDSS system to improve irrigation management with the conditions of irrigation and drainage network, water and soil resources, climate, and vegetation in Mahabad Plain have been discussed. Materials and Methods To evaluate the IDSS, the downstream farms of the Mahabad irrigation and drainage network located southeast of Lake Urmia were selected. The Mahabad irrigation and drainage network consists of a diversion dam, two main canals, 11 2nd-grade canals, 69 main drains, and 10 water pumping stations. Four sites from the Mahabad irrigation and drainage network were chosen as selected sites. In each site, 20 farms were considered for monitoring the IDSS. The rest of the farms were under the control of the farmer, and only optimal irrigation programs were provided to the farmer by the IDSS. The general framework of the IDSS has been developed to achieve the goal of optimal management of water consumption in agriculture, taking into account the time and amount of water availability using international methods. The IDSS provides the optimal irrigation schedule for the cropping pattern in the farm by using online information on agricultural meteorology, water access conditions of the farm, soil, and crop characteristics, and the type of irrigation system used in the farm. The IDSS can suggest the optimal cropping pattern for farm conditions. During the crop growth period, the farmer can introduce farm events as feedback to the system. In this situation, IDSS simulates new scenarios according to the existing situation in the farm and represents the new optimal irrigation schedule for the next few days. Results and Discussion To adapt the IDSS for irrigation planning the physicochemical characteristics of soil and water, water right, soil texture, crop characteristics, and etc. were considered. It is possible to update soil and water resource details during the growing season in the system. The information on irrigation systems can be loaded separately in the IDSS. According to the uploaded details, the optimal irrigation schedule was designed. IDSS takes advantage of seven-day agricultural meteorological forecasts, which leads to the maximum use of rainfall in the region and a proper matching between the provided irrigation schedule and the forecast of meteorological information in the coming days. To adapt the IDSS for irrigation planning the physicochemical characteristics of soil and water, water right, soil texture, etc. were considered. The virtual agricultural meteorological station launched by IDSS estimated the minimum temperature, maximum temperature, and sunshine hours with a good degree and relative humidity with a very good degree compared to the regional synoptic station data. Based on statistical indicators, the performance of IDSS for simulating volumetric soil moisture is evaluated as good to very good. Also, IDSS is adapted to the social conditions, the agricultural structure of the study area, and the knowledge level of farmers.Conclusion IDSS has up-to-date simulations and is suitable for providing an optimal irrigation schedule within the study area. Determining the effectiveness of IDSS in water consumption showed that the irrigation schedule provided by IDSS reduced the water consumption in the area. In farms that are under the basin irrigation system, the use of irrigation planning provided by IDSS has resulted in an average increase of 13.5% in water consumption and 8.6% in crop yield. The reason for the increase in water requirements in the basin irrigation systems is the high advance time in the farms under monitoring, and to meet the water requirments at the end of the irrigation farms, IDSS has inevitably increased the water consumption. The use of irrigation planning provided by IDSS has been able to reduce water consumption by 41% and 14% and increase the crop yield by 10.3% and 8.6% respectively in farms under drip and sprinkler irrigation systems. Therefore, the potential application of IDSS as an irrigation consultation and the degree to which this system has improved the irrigation management of agricultural farms can be used in most areas of the Lake Urmia basin. Next, it is suggested that IDSS be evaluated for other areas and crop yields, emphasizing the application of IDSS in humid and semi-humid climates.

IntroductionAgriculture is the largest consumer of fresh water in the whole world. Currently, almost 11% of the Earth’s total land surface is arable, which is expected to reach 13% by 2050. Roughly 17% of these lands are subject to modern form of irrigation management, which constitutes about 30-40% of the gross agricultural output. Reducing water consumption and increase water productivity in agriculture, requires a correct understanding of the biological response of crop to water. The ever-increasing growth of the population and the limitation of fresh water resources have led irrigation experts to new and efficient approaches in making decision to increase water productivity. The lack of scheduling in irrigation or their incompatibility with weather conditions, soil, irrigation system, agricultural restrictions and different phenological stages of crop, has caused severe losses in irrigated fields. Crop modeling and field data measurement can help irrigation experts improve irrigation scheduling in field and reduce water losses. Coupling in-situ measurement and crop modeling during the growing season is one of the useful solutions to improve irrigation scheduling in different farm conditions. In this paper, AquaCrop was calibrated for maize (Zea mays L.) in research farm of Ferdowsi University of Mashhad with comprehensive dataset. Variations of soil water content at different depths and also different growth indices was monitored during one growing season. The main novelty of this research is the targeted use of AquaCrop software to reduce water consumption by knowing the phenologically sensitive stages. Materials and MethodsIn-situ and in-lab measurements along with plant modeling, have been the two main parts of this research. First, the input files of the AquaCrop software were prepared and calibrated for maize during one growing season. The outputs of software including variations of moisture, biomass produced during the growing season and final yield were compared with the values measured in the field. After ensuring the accuracy of calibrated software, an improved irrigation scheduling was investigated. Subsequently, by means of crop modeling, the sensitive intervals of the crop to soil water stress as well as the thresholds of yield reduction in different stages of growing season were determined with the aim of improving irrigation scheduling in maize field. Biomass reduction, dry yield production, and the changes in water use efficiency during the growing season were investigated according to different amounts of moisture reduction in root zone, and irrigation scheduling was fulfilled by applying stress to less sensitive stages. The software was first run in Net Irrigation Requirement mode. Then, different amounts of drought stress were applied to each of the growth stages of the crop, and the other stages were kept constant in non-stressed condition. Dry yield and biomass reduction as well as water productivity changes were obtained for each stage. According to the threshold values and the amount of yield reduction at each stage, it is possible to fine-tune the time and amount of applying stress to crop. Furthermore, according to the moisture profile of the root zone, the drained water was significantly reduced in field. In this research, in order to increase accuracy in moisture measurement and prevent errors, an equation was developed to convert the mV output of sensor to volumetric moisture. At each moisture measurement, the PR2 sensor reports a number in mV for each depth, which is converted to volumetric moisture using device's conversion equation. This equation can vary according to field and soil condition. To develop the conversion equation, six same pots with a height of 40 cm and diameter of 30 cm were filled with the desired field soil, and the access tubes were placed horizontally inside them. The soil inside these pots was completely saturated and exposed to air for drying. During the drying period of the pots, the soil moisture was measured regularly using PR2 sensor and at the same time by weight method. After these measurements, calibration curves for PR2 sensor were obtained using the alpha mixing method for different depths.Results and DiscussionThe Pearson correlation coefficient for measured and simulated moisture by software, was 0.84 and the root mean square error was 12 mm. Also, the value of Pearson correlation coefficient for measured and simulated values of biomass was equal to 0.99 and mean square root of error was 1.3 ton/ha. The results showed that the vegetative stage of crop was sensitive to drought stress and caused a significant yield reduction at the end of the growing season. The stage of germination and flowering were less sensitive, such that the decrease of moisture up to 12.3% compared to the Net Irrigation Requirement mode would not change the final yield of the crop. The improvement made in the field resulted in no change in the amount of biomass and dry yield (0.38% increase in biomass and 0.52% increase in dry yield), at the same time 26.6% decrease in depth of irrigation water, 85.6% decrease In drainage and increasing the efficiency of evapotranspirated water, it has increased from 4.66% to 4.67% kg/m3 compared to condition before modify. Before modify, the farm was managed in traditional way.ConclusionMore research in the field of irrigation scheduling and providing comprehensive instructions to farmers in different weather conditions and different irrigation systems, taking into account different management approaches in allocating water to different parts of the farm along with investigating the effects of saline water irrigation, can be topics for the next research should be for experts in this field.

Introduction The Fe iron significantly affects the quantity and quality of agricultural production. Factors affecting the absorption of this element increase its efficiency. Meanwhile, the pH of the nutrient solution plays an important role in iron absorption. Iron is one of the essential elements for plant growth and plays an essential role in chloroplasts. Due to iron deficiency, the activity of several enzymes such as catalase, cytochrome oxidase, and ferroxin is significantly reduced. The amount of iron in the soil is high, but plants only absorb two-capacity of soluble soil, which is negligible compared to the total iron. Soil environmental conditions affect the amount of iron by the plant, so it is difficult to control the uptake of iron by the plant. It has the highest ability to absorb iron and manganese at a pH less than six. For this reason, acetic acid was used to stabilize the pH of the solution. Acetic acid has all the properties of an acid. Acetic acid is a polar solvent and an organic compound. The use of inexpensive organic acid and citric acid in agriculture, despite its positive effects on calcareous soils and their reasonable price, is still not common in Iran. Acetic acid has a carboxylic group and therefore has all the properties of an acid. Materials and Methods The pH solution plays a role in absorbing iron elements. This experiment was carried out in the form of a split-split plot in a randomized complete block design on a strawberry plant of diamant cultivar in the research station of the University of Mohaghegh Ardabili during the years 2015-2017. Ardabil City in northwest Iran is located between Sablan and Baghrou mountain ranges. Due to its high altitude and mountainous nature, this city is colder than other cities in Iran and is considered one of the semi-arid cold regions. The average rainfall of this city is reported to be 400 mm. Factors included acetic acid (zero, one, two, and three percent), iron in two levels (sequestration 25 gr and nano one gr), and two levels of agricultural soap agents (zero and 7.5 %) as foliar spraying. Foliar application of pH nutrient solution from the three-leaf stage of the plant (mid-April) to the end of May, a total of five times 10 days apart in both years, was done. Two weeks after the last foliar application (June of the second year) plant growth indices included leaf fresh and dry weight, root fresh and dry weight. Root length, flower-to-fruit ratio, chlorophyll greenery a, b, and total, leaf iron content and fruit yield per plant were measured. Results and Discussion The results of the analysis of variance showed that the three-way effect of treatments in all studied traits except root length and yield at one percent probability level on leaf fresh weight, leaf dry weight, and chlorophyll a, b and total at five percent probability level on fresh weight and root dryness, flower to fruit ratio and leaf iron content were statistically significant. The three-way interaction of acetic acid, iron, and agricultural soap data showed that the highest leaf fresh weight, leaf dry weight, root fresh weight, root dry weight, ratio of flowers to fruits, chlorophyll b, total chlorophyll and the amount of leaf iron related to the treatment acetic acid two percent of sequestration iron in combination with agricultural soap (7.5 %) and in chlorophyll a, it was related to the treatment of acetic acid one percent of sequestrin iron in combination with agricultural soap. The lowest amount of fresh and dry weight of leaves, fresh root weight and dry root, flower-to-fruit ratio, and leaf iron content were related to the treatment of acetic acid zero with Nano iron with agricultural soap (control). The lowest Chlorophyll b and total chlorophyll were related to the treatment of acetic Acid three percent with Nano iron with agricultural soap (control). In addition, the interaction between acetic acid and iron at the level of five percent probability on root length and yield was significant. Conclusion It can be concluded that acetic acid two percent on the absorption of iron fertilizer, along with agricultural soap (7.5 %) application, has better results than other treatments and qualitative traits of strawberry fruit. In general, it can be concluded that the pH of the nutrient iron solution can improve plant growth and crop yield. The iron level of sequestration iron with two percent acetic acid with agricultural soap 7.5 % due to increasing the bicarbonate of the soil in the Ardabil region has caused the absorption of nutrient elements, especially iron by strawberries. The shelf life of the solution on the leaf surface had a more favorable effect on most growth indices in the strawberry Diamant cultivar in Ardabil soil conditions.

Introduction The capacity of groundwater systems to offer various services depends on their geographically varying properties and is dynamically influenced by ongoing natural and human processes. Because groundwater is often perceived as a private resource (closely connected to land ownership, and in some jurisdictions treated as privately owned), regulation and top–down governance and management are difficult. Governments need to fully assume their role as resource custodians given the common-good aspects of groundwater. The participatory modeling approach included several stakeholder groups from groundwater users, policymakers, environmental groups, and other organizations involved in groundwater management. The scarcity of water resources and ever-increasing demand for these vital resources require identification, quantification, and management of groundwater in a way that prevents overexploitation and consequent economic and environmental damage while satisfying the demand for water from competing sectors. Participatory groundwater management is envisaged to take a significant step in groundwater management at the grassroots level to enable the community and stakeholders to monitor and manage the groundwater as a common pool resource themselves. Materials and Methods Marand Plain located in East Azarbaijan province, Iran. There are 502 agricultural wells in the Marand Plain, which are used by 16,000 farmers for agriculture. The total volume of consumed water is 226.82 mm3 and 191.66 mm3 of it is supplied from underground water sources. The participatory system dynamics modeling process includes five main steps: 1- Identifying the problem through participatory workshops: it aims at collectively defining the problem to be solved and the objectives of the model. 2- Formulating a dynamic hypothesis to explain the causes of the problem, leads to the development of a conceptual model or causal loop diagram. 3- Formulation of a system dynamics quantitative simulation model. This step includes the development of decision rules, the quantification of variables, building the stock and flow diagram, and model calibration using parameters to define initial conditions. 4- Ensuring the model is appropriate for the task through model validation. 5- Formulation of potential strategies and the evaluation of the simulated results. It requires the identification of scenarios, i.e., alternative strategies, and the analysis and discussion of the simulated results generated by the model for each scenario over time. Results and Discussion The problem statement was created in a qualitative way using the focus group discussion (FGD) method. The key variables of the system (61 variables), the system boundary (including the internal and external variables of closed loops), and the modeling period (20 years) were defined. For formulating the dynamic hypothesis, 9 CLDs were obtained. In the third step, developing the simulation model, all mathematical relationships, behavioral functions, and differential equations were formulated in the form of a stock and flow diagram (SFD). In the fourth step, model reliability tests were conducted in three categories: structural test (including structure verification test and limit condition test), behavioral test (including reference behavior reconstruction test), and sensitivity analysis test. Also, the stakeholders’ feedback test was conducted in a mixed method (quantitative-qualitative). In the policy design and analysis stage, the five general actions in the participatory management of groundwater resources in the Marand Plain, including modifying the cultivation pattern, increasing the irrigation efficiency by implementing modern irrigation networks, decentralizing the water governance and management, creating public institutions for water management (rural production cooperatives), water supply from outside the basin (transfer from the Aras River), were obtained. These actions have the greatest effect on the Marand Plain aquifer reservoir deficit. Conclusion The results of our participatory system dynamics simulation show that the balance and restoration of the aquifer are mostly affected by the "reform of the decision-making structure in the governance of groundwater" in the form of institutionalization (empowered farmers association) and decentralization of water management. Other adopted policies will be effective only if these components are present. To get out of the vicious cycle of short-term and unstable policies and decisions in the field of groundwater, fundamental reforms should be made in the structure of governance and groundwater management, and fully equal opportunities should be provided for local communities to participate effectively in the decision-making process. Also, if the process of implementation of laws and decisions is done through the channel of associations of the local community, they will have more executive support. Improving the water use behavior of farmers is the consequence and output of changing the structure and processes of groundwater management and governance.

Introduction Urban flooding is caused by the lack of capacity of the harvesting channel network and occurs when the amount of precipitation exceeds the network's capacity. One of the two main factors contributing to the aggravation of damage caused by urban floods is population growth and the expansion of urbanization, and the second factor is heavy rainfall caused by climate change, which plays an essential role in intensifying and accelerating the hydrological cycle and may change the amount and frequency of precipitation. This factor affects the probability of flooding, runoff volume, and peak flow. It is more visible in arid and semi-arid areas where rainfall usually occurs briefly but with high intensity. Urban flooding is a growing threat to urban infrastructure and public health, posing significant challenges to urban resilience and sustainability. One of the urban infrastructures that is of great importance is the runoff collection network. The increase of impervious surfaces wear and tear on the network. changes in the rainfall pattern due to climate change have increased the occurrence of urban floods and raised the importance of network redesign to minimize the system vulnerability. Materials and Methods In this research, the runoff harvesting network of ten districts of Tehran Municipality was redesigned and optimized. This area, with a population of 327,000 people, is located in the relatively dense fabric of the Tehran metropolis, and its area is 807 ha. Simulating the runoff collection network and checking the performance of the network by two indicators of vulnerability and reliability requires an accurate model with great details. For this purpose, in this research, SWMM version 5.1 software was used to simulate the runoff collection network. The study area was divided into 285 sub-basins to simulate the sub-channels in better detail. Then, information such as slope, area, and percentage of impervious space was introduced through ArcMap software version 10.3.1 as information under the watersheds. The sub-watershed width parameter was calculated by dividing the sub-watershed area by its most significant length using Q-GIS software and applied to the sub-basins. The LARS-WG model has also been used for the exponential micro-scale output of climate models. To simulate the network in the current or present situation, the historical precipitation information of the Mehrabad synoptic station was used, and to affect the network in future conditions, the precipitation information of the climate models of the sixth climate change report was used. Among the predictions of climate models, the most incremental changes in threshold precipitation were selected as a pessimistic scenario, and a system redesign was done to reduce vulnerability under this scenario. Results and Discussion This study was conducted to assess the performance of Tehran municipality's runoff collection network under current and future conditions. The SWMM hydraulic model was employed to simulate the network under various rainfall scenarios. Current conditions revealed a total runoff volume of 45.9, 51.14, and 59.7 thousand m3 for return periods of 2, 5, and 10 years, respectively. This runoff volume resulted in a vulnerability increase from 10.4 to 12.2% and a reliability reduction from 97.5 to 95.8%. To evaluate the network's performance under future climate change scenarios, the SWMM model was used with data from the IPCC sixth assessment report. Among the top five climate models, the one with the highest precipitation was selected as the pessimistic scenario. Simulation results under future conditions indicated a significant runoff volume increase, reaching 64.04 and 72.18 thousand m3 in 5- and 10-year return periods, respectively. This increase corresponded to vulnerability indices of 12.7 and 13.9% and reliability indices of 95.3 and 94.3% for the same return periods. To enhance the network's resilience, a genetic algorithm-based optimization approach was employed. Cost, reliability, and vulnerability index were considered optimization objectives with specific weighting functions. The algorithm converged to an optimal design solution in the 168th iteration, resulting in a 7.6% vulnerability reduction and a 98.1% reliability enhancement. Conclusion The vulnerability index in the return periods of 5 and 10 years is equal to 12.7 % and 13.9 %, respectively, and the reliability index is equal to 95.3 % and 94.3 %. After checking the network in its current state and future conditions, an optimal network redesign was done to improve system performance in both current and future conditions. For this purpose, the genetic algorithm was used for optimization, and the objective function consisting of cost, vulnerability index, and reliability index and the importance weights of each, were defined. Then, MATLAB software did the optimal network redesign by connecting the simulator and optimizer model. The results showed that in the 168th iteration, the algorithm reached its final answer of 0.3, which remained constant until the 300th iteration. Also, the optimal redesign has reduced network vulnerability by 7.6% and increased reliability by 98.1%. This research showed that with an optimal redesign and solving the current network problems, the system's ability to face future climate change threats could also be increased. Of course, spending the least money to achieve the best result was one of the main goals of this research. In future studies, it is recommended to use low-impact development tools along with optimal redesign to fix defects and improve the performance of the runoff collection network.

IntroductionThe lack of proper water productivity and the increase of water per capita over time have led to the drying up of rivers and lakes as well as the drop in the level of underground water. Currently, many countries are facing a great challenge in producing food from limited water resources. Lack of water along with improper exploitation of resources does not meet the growing demand of the current population for food. The lack of water resources for the production of various agricultural products, including wheat, is one of Iran’s main challenges and concerns and the primary solution is to improve and promote water productivity. In this regard, consuming less water should produce the most production. One of the biggest problems for wheat production is the lack of water resources and the fragmentation of different water management methods, fields, and irrigation systems. On the other hand, for water and farm management to have the greatest effect in increasing the yield, efficiency, and productivity of water consumption, it must have various inputs such as hybrid and modified seeds, chemical fertilizers and pesticides, the use of advanced tools and machinery, and appropriate planting operations, and harvesting should be done optimally, correctly, and at the right time-place. Materials and MethodsThe current study has targeted the main areas of wheat production in Iran. Due to its high nutritional value and strategic importance, wheat is cultivated in almost all areas of the country. According to the Pareto principle, the areas where 80% of wheat cultivation and production (irrigated and rainfed) are carried out are the main wheat production areas. Since in Iran, the statistics of the agricultural situation are mainly presented based on political divisions, therefore, the major regions will be the set of provinces in which the above hypothesis is true. Accordingly, 13 provinces of Golestan, Kermanshah, West Azarbaijan, East Azarbaijan, Hamedan, Ardabil, Lorestan, Central, Khuzestan, Fars, Razavi Khorasan, Kurdistan, and Zanjan have been identified as the main wheat production areas in Iran and in this study also, the main focus was on these areas. Since the vast country of Iran has a diverse climate, to ensure that all the country's climate groups are represented in the selected regions, an adaptation of these regions to the country's climate groups was also done. Results and DiscussionThe results showed that with the data of the 2020-2021 crop year, the reduction of agricultural water consumption by developing the method of irrigation the Raised Bed Planting Systems (RBPS) in the main areas of wheat production will lead to a reduction of 154.2 million m3 of water. The implementation cost of this solution based on the average implementation of each hectare is 50 million Rials in 2021, equal to 5059 billion Rials. Among the limitations in the stages of planting, planting, and harvesting wheat with the irrigation method of the raised bed planting systems, we can point out the need for cultivators equipped with furrows, but in the operation of planting, this method is facilitated. Regardless of the results of the research, limiting the movement of the wheels of agricultural machines to the furrows (traffic control), facilitating the operation, and the smoother movement of water in the furrows are some of the strengths of the raised bed planting systems. The advantages of this method can be mentioned as follows: increasing the yield of wheat per hectare, Improvement in soil ventilation and more growth of roots, Increasing the germination percentage of seeds and reducing seed consumption, reducing seed suffocation, reducing soil erosion, Reducing the amount of herbicides used, increasing the efficiency of using poisons and fertilizers, Reducing the risk of flooding and fungal diseases, and planting wheat on raised beds due to the elimination of irrigation borders as a combine-friendly method. ConclusionAs a result, using the new method of cultivation the raised bed planting systems can lead to the improvement of water productivity of wheat in the country as a management tool. The raised bed planting system is the most modern method implemented in developed countries. In this method, the seed is planted on long and wide beds, which increases the economic efficiency of the raised bed planting systems. It is expected that the yield of wheat fields will increase by 15% compared to the traditional method. In the cultivation system on raised beds, both the raised bed and the irrigation furrows are prepared at the same time. Also, fertilizer and seeds are distributed on the beds at the same time.

Introduction In recent years, one of the most significant environmental crises has been the phenomenon of wind erosion and dust emission. Wind erosion is considered one of the effective factors in desertification and land degradation in dry regions. The process of wind erosion, due to the transport of soil nutrients along with fine particles, is recognized as one of the limiting factors for soil fertility in many parts of the world. Evaluating this type of erosion and estimating the soil loss requires the installation of various measuring stations. Establishing and equipping these stations and providing the necessary equipment require large costs and a long time. In addition to direct measurement methods in wind erosion, the use of modeling results, especially in combination with remote sensing techniques, to study and predict environmental effects, trends, and risk assessment has greatly contributed to research in the last two decades. Therefore, this study aims to monitor and investigate the warning zones of wind erosion in northwest Golestan Province using the wind erosion hazard index (WEHI) model. Materials and Methods To assess wind erosion in the western part of Golestan Province, the WEHI model was implemented. This model predicts the sensitivity of the landscape to wind erosion by considering a set of surface and climatic thresholds and using a geographic information system (GIS). In this model, wind erosion severity is determined in three classes: low, moderate, and severe, by multiplying wind speed by the percentage of bare soil and dividing it by the percentage of soil moisture percentage. To monitor wind erosion, the normalized difference moisture index (NDMI) was used to evaluate soil moisture, and the modified bare soil index (MBI) was used to assess bare soil. For validation, these indices were compared to field data and plots in each working unit. Additionally, three statistical parameters, Pearson correlation coefficient, coefficient of determination (R2), and root mean square error (RMSE), were employed to calculate the correlation between these indices and ground data. Furthermore, a Markov chain model was used to examine changes in wind erosion classes. Finally, after monitoring wind erosion and considering a threshold value for this model, the area of warning zones during the statistical period was investigated. Results and Discussion The results obtained from the WEHI model indicate severe wind erosion intensity in the working units of Atark floodplain deposits, saline lands, longitudinal dunes, Barchan dunes, bare lands, and margins area of wetlands. According to the WEHI model, the region was divided into three classes: low (54% frequency), moderate (21% frequency), and severe (25% frequency). The model classified the northern regions into severe and moderate classes, while the southern areas of the region fell into the low wind erosion class. High correlation coefficients between the WEHI model indices based on remote sensing and field data demonstrate the model's ability to monitor wind erosion over time and at different scales. Wind erosion monitoring results showed that the high wind erosion class increased from 59,940.88 ha to 71,698.3 ha, indicating an increase of 11,757.43 ha. Spatial analysis of wind erosion classes indicated that most changes occurred in central areas, with most areas around the Sangartappeh playa and central regions changed to severe wind erosion class, while western, eastern, and central areas changed to the low wind erosion class. Finally, monitoring the warning zones revealed an increase of 41,000 hectares in the areas under warning, in the western, northwestern, and central regions. Conclusion In this study, the performance of the WEHI model in assessing wind erosion risk in the western part of Golestan Province was confirmed. Although factors such as roughness, soil structure, and organic matter content are not directly considered in the model, they are indirectly incorporated in scoring the percentage of bare soil. To control wind erosion in these areas, planting salt-tolerant species and implementing soil fertility enhancement strategies, such as soil mulching in heavy-textured areas, are recommended. Finally, mechanical operations and the establishment of windbreak networks are suggested for controlling wind erosion in abandoned land units. This research can serve as a useful approach for planning and managing vulnerable areas to wind erosion in northwest Golestan Province.

Introduction Climate change is one of the greatest challenges of our time to achieve sustainable development. The dangers of global warming and climate change can be recognised and planned for, so efforts should be made to identify the consequences and methods of adaptation to reduce their impact, as trees and plants are sensitive to climate change, heat and moisture stress, and forest ecosystems are affected by these changes. Climate change is one of the greatest challenges of our time to achieve sustainable development. The threats of global warming and climate change can be identified and planned for, so efforts should be made to identify the consequences and methods of adaptation and reduce their impacts because the sensitivity of trees and plants to climate change, heat and moisture stress, forest ecosystems are affected by these changes. Forest degradation due to climate change is a global phenomenon affecting many tree species. One of the major challenges to Iran's natural resources is the degradation of oak forests. The increase in temperature in recent years has lengthened the growing season of Iranian oak, and due to the lack of rainfall, the intensity of water stress on this forest species has increased. Understanding the extent of climate change in the Zagros ecosystem based on standard indicators that show climate change can help managers and planners take action to adapt to conservation and restoration conditions.Materials and Methods In this study, based on the standard indicators established and updated by the World Meteorological Organization, the occurrence of climate change in six sites monitoring the decline of oak trees in Ilam Province, including Sarabeleh, Mala Siah and Dalab Strait, was detected in both north and south directions. The trend slope line, trend slope error and trend significance of the indices in the statistical range P value=0.05 were calculated using ClimPACT software in R program environment. The characteristics of heat, cold, combined, and precipitation indices such as day, number of occurrences, and continuity of the index in the nearest synoptic station to each site were identified, and daily data of minimum temperature, maximum temperature, and precipitation parameters were used between 1987 and 2019. The variations of each index were plotted and analyzed as graphs in the R software environment. Accordingly, the trend slope and significant indices affecting heat stress (hot temperature indices and cold-temperature indices) and water stress were calculated with the software ClimPACT in the R programme environment.Results and Discussion The results of the trend and significance of thermal indicators for climate change detection showed that of the 16 heat indices studied, in Ivan station, 11 indicators showed a significant trend of about 70% and in Ilam 9 indicators showed a significant trend of about 56%.The heat-based temperature indices, especially those based on maximum temperatures, showed a significant change towards warming, while those based on minimum temperatures showed no significant trend. The study of heat waves showed that the northern and northwestern regions of the province were subjected to several periods of heat waves, which occurred with greater frequency and continuity in the northwestern part of the province (Malah Siah and Dolab Strait sites) than in the northern part (Sarab sites) of the province, confirming the emergence of high mountain regions as a result of climate change. Cold-based temperature indices of 8 studied indices in Ivan-e-Sorchrove station, 7 indices were above 87% and Ilam 3 was about 38% the trend was significantThe cold-based temperature indices in Ivan West station show a significant change in the direction of a decrease of frost and cold and their persistence and warming, and in Ilam station the decrease of cold and warming of air after 2008 indicates a decrease of cold and warming. The results of the trend and significance of the combined indices for climate change detection in Ivan West station were 100% and in Ilam station 50% showed significant trend. The combined indices confirm climate change in terms of increase in air temperature due to increase in growing season and increase in trend of change in maximum temperature. The study of the trend and significance of ten precipitation indices related to the frequency and intensity of heavy rainfall and the length of dry and wet periods showed only one index in Ivan-e-West station, which was about 10%. The station Ilam did not show significant changes in the precipitation index.Conclusion It can be concluded that zagros vegetative region in Ilam province has experienced severe thermal changes during 1987-2019 and climate change in the north of Zagros vegetative region is a type of temperature increase that has been extended to the northwestern regions of this region since 2008. The results show that in deterioration sites, in addition to increasing temperature, some precipitation has increased. Therefore, it can be said that the occurrence of climate change phenomenon causes increasing temperature, occurrence of floods, emergence of droughts and creating dust phenomenon, especially from neighboring countries and intensifying it in the country, all of these factors are the starting factors of the decline of Oak Trees in Ilam province. What seems to be important is to take necessary measures to prevent and mitigate the effects of climate change. These two measures can be effective and useful in reducing the effects of flood severity and risk. Therefore, managers and planners of forest ecosystems should have adaptive strategies to climate change in order to maintain and revive their executive plans so that Zagros can survive as a forest ecosystem. Although the occurrence of climate change phenomenon is one of the most important factors of drought and deterioration of Zagros forests, conservation and restoration of oak forest ecosystems can help reduce greenhouse gas emissions and climate change.

Introduction Rainfall-runoff modeling is one of the most important components of hydrological processes in water resources management and accurate estimation of runoff and river flow in the short and long term can be of great help to various sectors of water engineering. In several types of rainfall-runoff models, the unit hydrograph methods are still a useful tool for flood estimation in many, except non-gauged, basins. The unit hydrograph is the same as the unit pulse response function of a linear hydrological system. The tank model is one of the hydrological models for analyzing river flow characteristics. In hydrological analysis, simulation models are often used to describe and predict basin response to rainfall events based on mathematical and physical knowledge. In this article, using the concept of linear system theory, the pulse response functions of the runoff components (surface runoff and base flow) using the reservoir model for several flood events related to the two Navrood basin in Gillan province and Liqvan basin in the East Azerbaijan province has been extracted. Since Simulink can schematically show the dynamic relationship between hydrological components such as rainfall, runoff, storage, evapotranspiration, and runoff, it can be useful for rainfall-runoff modeling. Therefore, Modeling is done in the Simulink MATLAB environment. The modular design and block library can help users focus on hydrological analysis, including modeling strategy development, parameter estimation, and model application. Materials and Methods In the present study, to evaluate the capability of the tank model in different climates, the model has been implemented for two regions with dry and wet climates to evaluate the effectiveness of the tank model by comparing the results. Therefore, the Navrood representative basin in Gilan province was selected for a wet climate, and the Liqvan representative basin in East Azerbaijan province for a dry and semi-arid climate. The response of the entire basin to the rainfall that fell on its surface has been determined using the conceptual model of the tank. In order to extract the unit pulse response function for the runoff caused by precipitation, the model of three tanks in series with holes on the side and bottom has been used to show the types of currents prevailing in the process of forming runoff. The internal dependence of reservoirs is described using exponential functions of model parameters. Estimating the model parameters was performed using the cluttered evolution optimization method or SCE-UA for short, a conceptual optimization method. In addition, to extract unit pulse response functions and evaluate the model's efficiency in predicting flood events, it was tried to select events that have rained in the entire basin and correspond to flood events in terms of occurrence time. Results and Discussion The results obtained in this research showed that the tank model provides good results in estimating the peak discharges and the time to reach the peak discharges in the two representative basins of Navrood and Liqvan with two different climates. The parameters of the model, which actually reflect the geomorphological characteristics of the basin, are almost constant, and only the changes in soil moisture storage are variable in the runoff calculations. Response functions as exponential functions of model parameters have simulated the different roles of flow components (quick surface, quick subsurface, delayed subsurface, and underground flow) in relation to the precipitation process. As can be deduced from the results obtained for the Navrood basin, the slow runoff accounts for a major part of the total runoff in the falling limb of the hydrograph. The relations extracted for the response functions of the unit pulse for ru as a unit input that happened in the duration of ∆t have been obtained parametrically, therefore, for different combinations of ru and ∆t, several unit pulse response functions can be extracted. Examining the parameters of the model obtained from the SCE-UA algorithm shows that due to the large value of the b2 parameter compared to other parameters, a major part of the volley losses is penetration losses. In fact, the high permeability of the surface layers of the soil justifies the high value of the b1 parameter compared to other parameters. Conclusion The performance results of the tank model showed that the model has a relatively good capability in predicting the runoff affected by the rainstorm. The comparison of the computational and observed hydrographs shows that the mentioned values have a good correlation. According to the results, it can be seen that the fast runoff, which usually appears in the form of surface flow and waterways in the basin, occupies a significant part of the entire flood hydrograph during the flooding process, and in terms of durability has a shorter duration than slow runoff. Slow runoff hydrograph is slowly affected by precipitation during the flooding process, but in terms of durability, it has a longer duration.

Introduction Landfill leachate, a liquid resulting from waste decomposition, contains nutrients like ammoniacal-N, Na, K, and organic matter. Biological treatments effectively remove degradable organics from young landfill leachate, but aged leachate with recalcitrant organics requires combined physical-chemical and biological methods or advanced technologies, leading to higher treatment costs. Even after treatment, leachate may not meet environmental standards for release. In arid and semi-arid regions with water scarcity and low soil organic matter, leachate application to soil presents a potential solution. Soil’s properties enable it to retain and degrade pollutants while utilizing leachate’s nutrients to enhance fertility and crop growth. However, leachate composition and application rates are critical factors due to potential negative impacts from total nitrogen, salinity, and heavy metals. Alkaline pH in aged leachate reduces heavy metal contamination risk. Detailed leachate characterization before soil application is crucial to prevent environmental and functional problems. This review examines existing research on leachate irrigation’s effects on soil properties and plant nutrition, contributing to sustainable leachate management and agricultural practices in water-limited regions. Additionally, the review explores potential risks associated with leachate irrigation, including soil salinization, heavy metal accumulation, and groundwater contamination. By understanding both the benefits and drawbacks, informed decisions can be made regarding the suitability and implementation of leachate irrigation in specific contexts. Materials and Methods To carry out this study, keywords such as "Landfill leachate", "Composition of landfill leachate" and "Landfill leachate irrigation" were searched in the Web of Science, Google Scholar, ScienceDirect, and SID databases. For these keywords, 205 articles were found from 1989 to 2023. After the screening, quality review, and removal of repetitive and unrelated articles, 110 relevant articles were used. The main criterion for selecting articles was the effects of landfill leachate irrigation on the various properties of soil, and the nutrition of different plant species. The quality of the articles was evaluated through the Scimago Journal Rank (SJR) index, the citation, the Impact Factor, and the source normalized impact per paper (SNIP) index. Results and Discussion Landfill leachate presents a complex environmental challenge due to its potential for both soil contamination and enrichment. Leachate's xenobiotic and heavy metal components can induce soil contamination, altering the natural environment. Studies have documented reduced hydraulic conductivity, increased gas production, and altered microbial communities, ultimately impacting soil productivity. Leachate percolation can also modify physicochemical characteristics, including reduced microbial biomass, phosphorus-fixing capacity, and pH shifts, depending on waste composition. Conversely, research highlights the potential benefits of leachate application in arid and semi-arid regions facing water scarcity and low soil organic matter. Leachate can contribute to the increased organic content, improved soil structure, and regulated pH, enhancing soil fertility and crop productivity. The presence of macro and micro-nutrients such as Fe, Mn, N, P, and Zn further supports leachate's potential as a fertilizer. However, concerns remain regarding inhibitory chemicals in leachate and their potential detrimental effects on plant growth and yield. Studies report instances of leaf injury, reduced yield, and poor survival rates in certain plant species. In contrast, research demonstrates the positive effects of diluted or low-strength leachate application, stimulating plant growth and enhancing yield, particularly for Brassica species and tree species like Acacia confusa, Leucaena leptocephali, and Eucalyptus tortellini. These contradictory findings underscore the intricate interplay of factors influencing leachate irrigation outcomes. Soil characteristics, plant species, leachate source and composition, application methods, and their interactions all play significant roles in determining the success or failure of leachate irrigation. Conclusion Landfill leachate, characterized by its elevated nitrogen and nutrient levels, presents a potential alternative water and fertilizer source for agricultural practices, particularly in arid and semi-arid regions facing water scarcity. However, responsible leachate utilization necessitates a comprehensive approach that balances maximizing benefits with minimizing environmental risks. Prior to agricultural application, detailed leachate characterization is crucial to determine its precise composition and suitability for irrigation. This includes quantifying heavy metal concentrations, salinity levels, and the presence of potentially toxic organic compounds. Concurrent plant selection is equally important, prioritizing species with demonstrated tolerance to leachate constituents. Given the potential for salinity and heavy metal accumulation, continuous application of raw leachate, especially for sensitive crops, should be avoided. Implementing alternating irrigation regimes with conventional water sources can mitigate these risks while providing essential nutrients for plant growth. Monitoring soil health indicators, including pH, organic matter content, and microbial activity, is vital to assess long-term impacts and implement necessary soil amendments. Determining optimal leachate application rates requires a multifaceted approach that considers plant-specific nitrogen requirements, leachate toxicity levels, and soil infiltration capacity. This ensures adequate nutrient supply without exceeding the assimilative capacity of plants and soil, preventing environmental contamination. Further research is needed to investigate the long-term impacts of leachate irrigation on soil health, crop quality, and potential groundwater contamination. Developing standardized guidelines for leachate treatment and application, tailored to specific regional contexts and crop types, is crucial for promoting sustainable and responsible leachate utilization in agriculture.

Introduction The soil water curve is one of the most critical soil hydraulic characteristics. This characteristic is used to determine soil water in the field capacity point and the permanent wilting point (PWP) beside it has a vital role in the application of soil water models in the study of soil-plant-water relationships. This curve is known as the quality soil index which has an effective role in the explanation of agricultural, ecological, and environmental problems. Impressive and efficient management of soil and water resources, water flow and solute transport survey, soil pollution, and contaminant leakage into water sources are dependent upon the accurate estimation of soil water curve parameters. Moreover, this index has a functional role in applying numerical and hydrological models. On the other hand, to better identify and understand its role, different models were provided to describe this curve mathematically. The efficiency of these models depended on the accuracy of estimated parameters in the model structure that was defined. Soil water curve is known as a non-linear relationship that is used to describe the relation between soil and water content or degree of soil saturation. The soil water curve provides essential information for using irrigation methods and about soil resistance and soil mechanical properties. In this research, the performance trend of two meta-heuristic algorithms, including the differential evolution (DE) and particle swarm optimization (PSO), was studied to estimate hydraulic parameters of soil water curves based on the van Genuchten and the Brooks and Cory models in four soil texture classes; loam, silt loam, sandy loam, and sandy clay loam. Besides, this study evaluated the performance of the meta-heuristic algorithm to RETC software. This software has a non-linear square local algorithm. This study can evaluate the ability of the meta-heuristic algorithms to estimate parameters for exponential relationships and nonlinear models. Materials and Methods At the agricultural farm of the University of Birjand, a study was conducted to analyze soil water content in different texture classes. The research involved the random selection of four soil texture classes and the random sampling of 20 points from each class. The soil water content was measured using a sandbox and pressure plate device, covering a broad suction range of 0-15000 cm. In the first phase, soil water curve parameters were estimated for each soil texture using the van Genuchten model and the Brooks and Cory model in the RETC software. Subsequently, the Matlab desktop environment was utilized to apply meta-heuristic algorithms (DE and PSO) to estimate the soil water curve parameters based on the two models. An objective function was defined to minimize the Root Mean Square Error (RMSE) of the meta-heuristic algorithms' performance. Finally, the study compared the performance of the meta-heuristic algorithms (DE and PSO) with the RETC software in estimating soil water curve parameters based on the van Genuchten and Brooks and Cory models, using statistical indices such as RMSE and R2. The soil texture classes play a crucial role in influencing soil water content and nutrient retention, making them an essential factor in agricultural management and crop suitability. The study's findings can contribute to a better understanding of soil water dynamics and the development of improved agricultural practices. Results and Discussion The obtained results of the statistical indices (RMSE and R2) showed that the least value of RMSE was acquired by the differential evolution algorithm (DE) performance. The values of RMSE during the application of the DE algorithm as an estimated method based on the van Genuchten model were 0.0008, 0.0005,0.0004, and 0.0006 also based on the Brooks and Cory were 0.006, 0.006, 0.005, and 0.0005 in sandy clay loam, sandy loam, loam, and silt loam respectively. Also, the highest value of the R2 index was obtained equal to 0.995, 0.996, 0.994, and 0.994 by the utilization of the DE algorithm based on the van Genuchten model in the sandy clay loam, sandy loam, loam, and silt loam respectively. The values of RMSE by the utilization of the PSO algorithm based on the van Genuchten model were 0.0021, 0.006, 0.0057, and 0.006 in the sandy clay loam, sandy loam, loam, and silt loam classes respectively. The highest and lowest values of the RMSE and R2 indices by the application of RETC software were obtained equal to 0.017 and 0.912 (sandy clay loam), 0.01and 0.963 (sandy loam), 0.085 and 0.972 (loam), and 0.01 and 0.924 (silt loam) based on the van Genuchten model. Conclusion It could be concluded that RETC software has poor performance in the estimation of soil water curve parameters in all soil texture classes studied based on the van Genuchten and Brooks and Cory models. This trend represents the weakness of the local algorithms to solve multivariable problems where an exponential relationship exists between the variables and they are influenced by each other. On the other hand, the results show the meta-heuristic algorithms have sufficient ability to estimate parameters in multivariable problems. It could be concluded that the meta-heuristic algorithms have better performance in estimating the parameters of soil hydraulic models. The DE algorithm is the best method to estimate soil hydraulic parameters. The PSO algorithm has the nearest performance to the DE algorithm but the best performance to RETC. Finally, meta-heuristic algorithms are suitable options for estimating soil water curve parameters based on various hydraulic models.

Introduction Irrigation water salinity is a very serious problem in different parts of the world, especially in arid and semi-arid regions. Increasing fresh-water demand due to population growth causes the pressure on water resources to increase in the future causing the water supply through saline and unconventional water to become a serious issue, especially in areas facing water scarcity. On the other hand, agriculture is the world's greatest water consumer where saline water reduces the products, destroys the soil structure, and damages the environment. Wastewater desalination and water reuse is a relatively new approach in the water industry that solves saline-water problems through various methods. But it is uneconomical due to high equipment costs and energy consumption, especially in agriculture where water consumption is much higher. To remove pollutants, various studies have used different adsorbents such as biochar, activated carbon, zeolite, and resin among which biochar can effectively remove pollutants from aquatic environments because it is an effective, inexpensive, polar, high-porosity adsorbent. Ion exchange, complex formation, surface adsorption, electron sharing, and biochar (carboxylic and pHenolic) - functional group interaction are among various mechanisms where the presence of negative charge on the biochar surface and positive charge on metal ions improve the adsorption process. As the activated carbon is made from cheap materials as wood, coal, oil, coke, sawdust, and plant waste, it is quite economical and highly capable of removing a wide range of organic and inorganic pollutants from aquatic and gaseous environments. Materials and Methods To prepare biochar, this research used the sugarcane bagasse as primary biomass by 1) washing it several times with ordinary and distilled water and drying it in the open air to remove its remaining salts, 2) crushing it with an industrial mill and placing it in an oven at 60 °C for 24 hrs to remove its excess moisture, 3) grinding the crushed bagasse with a small mill for further milling, 4) passing it through 60 and 100 mesh sieves in two stages for more uniformity and 5) placing it in closed containers. Biomass was converted to biochar (BC) using a heat-programmable electric furnace where the temperature rise was set at five °C/min for a uniform heat distribution. Bagasse was placed inside a steel reactor into which nitrogen gas was injected at a fixed flow rate and prevented oxidation. Biomass was kept at 600 °C for two hrs thereafter the furnace was turned off, while nitrogen gas was injected, and the temperature was slowly lowered to that of the lab. Considering the sizes of the furnace and reactor, each time 20 g biomass was placed in the reactor and about five g biochar was produced after the carbonization process; the biochar production efficiency under these conditions was about 25%. Nano biochar (N-BC) was made by a planetary ball mill with ceramic cups and bullets where the bullet-to-biochar weight ratio was 15-to-one and the rotation speed was 300 rpm. The good mill-activity time was two, four, and six hrs. It worked for three min and rested for one minute to prevent the temperature from rising and cohesive masses from forming in the samples; as size and uniformity of particles were important, use was made of a gradation device. Results and Discussion In all treatments, by increasing the initial Chlorine concentration, the Chlorine removal had an increasing trend. on average, this was, using activated nano biochar 74.4% more than activated non-nano biochar. Magnetizing nano-absorbents reduced the Chlorine removal by 18.8%, on average. The highest and lowest Chlorine removal reductions due to the adsorbent magnetization were 31.6 and 10.9%, respectively. The highest Chlorine removal in all three activated non-nano, activated nano, and magnetically activated nano adsorbents (200 and 400 W treatments) was measured for an activator-to-biochar ratio of three. According to the results, Chlorine adsorption by magnetically activated nano absorbent reached equilibrium after 480 min in the treatment with 200 and 700 W microwave power and after 540 min in treatment with 400 W microwave power. Increasing the initial Chlorine concentration from three to 25 g l-1, increased the Chlorine removal by the magnetically activated nano absorbent by three, 3.5, and 2.6 times in 200, 400, and 700 W microwave power treatments, respectively. Conclusion The pseudo-first-order kinetic model had a good correlation with the data and the pseudo-second-order kinetic model did not correlate well with the data in times less than 60 min; hence, the dominating adsorption mechanism was not chemical in this interval. Intraparticle diffusion was an effective Chlorine-adsorption factor from the beginning of the adsorption process. Considering the correlation coefficient and sum of squared errors, the pseudo-first-order kinetic model and the intraparticle diffusion model had the highest correlation with the measured data. The average correlation coefficient for Langmuir and Freundlich models was found to be 0.9938 and 0.886, respectively. Therefore, the Langmuir isothermal model conformed better to the measured data than the Freundlich model.

Introduction Runoff and the rainfall-runoff relationship are one of the most fundamental research topics in hydrology. Due to the increasing trend of flood occurrence and the resulting damages, it is necessary to determine the flood-producing priority areas and prioritize the sub-watersheds in terms of flood control projects and integrated management of watersheds. The primary contributing areas in runoff generation and the affecting factors should be identified in a flood management project. Understanding the flood occurrence potential of watersheds can be useful in formulating different flood management plans, allocating necessary funds, water resources management, watershed management, and erosion control programs. Watershed Modeling System (WMS) as an integrated flood modeling software can simulate flood hydrographs considering the required parameters. Among the common runoff estimation methods, the SCS curve number method is the most common in estimating flood volume and flood runoff height. Land use changes as an important factor in the alteration of watershed hydrologic response can accelerate soil erosion and biodiversity loss. Land use change affects the curve number and consequently, discharge and flood hydrographs, as assessed in the current study. Materials and Methods The Khiavchai Watershed, with an area of about 134 km2 has been chosen as the study area. The annual rainfall of the study area is 343.8 mm. Toward the hydrologic modeling in the study area, the slope map of the watershed has been derived from the DEM of the study area using the ArcMap software. The maximum daily rainfall of 11 rating gauge stations has been obtained and the raw data has been processed using a common statistical test to check the data quality and homogeneity of SPSS software. The maximum 24-h rainfall data were analyzed using Easy Fit software to select the best statistical distribution. The 3-parameter Pearson distribution (as the best probability distribution function), has been used to calculate the rainfall values in 2 to 100-year return periods. The maximum daily rainfall values were entered into the ArcGIS software and the spatial distribution mapping was done using the IDW method. The average maximum daily precipitation with different return periods was converted to 6-h precipitation. The SCS and WMO rainfall patterns were compared in the study area, and the WMO rainfall patterns were selected as the appropriate input for the model. The maximum annual instantaneous discharge values have been used in estimating the flood discharge in 2, 5, 10, 25, 50, and 100-year return periods using EasyFit software. The input model parameters (slope map, slope direction, curve number, and soil hydrological group) were prepared in ArcGIS and Arc-Hydro software. Results and Discussion The results showed that the maximum flood discharge increases intensely with an increase in the return period. The average CN value of the basin was obtained at 76, the initial loss coefficient was obtained at 0.202, and the STRTL value was obtained at 16.203. According to the increasing curve number values, the infiltration time and the time to reach the peak are reduced. Therefore, by increasing the curve number by 5, 15, and 25%, respectively, the time to reach the peak is 240, 180, and 135 min, and the base time of the hydrograph has decreased by 1035, 885, and 750 min. Meanwhile, the peak value of the simulated flood hydrograph has increased from 1.74 to 6.466, 27.491, and 109.694 m3 s-1. With an increase of 25% curve number, the peak discharge in the return period of 10, 25, 50, and 100 years has increased to 13, 9, 7.5, and 6.3, respectively. The results show that the effect of changes in curve number values on the flood discharge, in the low return period is much more than the high return period. So in the return period of 100 years with 25% changes in curve number, the peak flood discharge value is 6.38 times in the 2-year return period. The results indicate that the least change in the type of land use (in order to reduce permeability) causes a considerable increase in flood discharge in the region. Conclusion The effects of changes in CN values have been assessed in the current research in a modeling framework to estimate the flood hydrograph components. Comparison of the estimated flood hydrograph components against observation values has been evaluated using relative error and root mean square error. The values of these statistical indices were obtained with the least error related to the 3-parameter lognormal distribution of about 10.32% and 8.68 m3 s-1. By comparing the results of the maximum flood analysis and the WMS model, it can be concluded that the simulated data are consistent with the observed flood records in the Pole-Solatani river gauge station located at the outlet of the Khiavchai Watershed.

IntroductionA landslide is one of the mass movements on the top surface of the earth. Landslides have resulted in notable injury and damage to human life and destroyed infrastructure and property. Landslides represented approximately Nine percent of the natural disasters worldwide during the 1990s. According to studies, this trend is expected to continue due to increased human development. Many studies have been done to determine the factors affecting mass movement. In large part of Iran including the mountain areas, tectonic activity and seismic high with diverse geological and weather conditions led to many countries prone to landslide. Landslides cause wide damage to natural resources, human settlements, infrastructure, mud floods, and filling reservoirs. Landslides cause extensive property damage and occasionally result in loss of life. Besides, should not be ignored the social and environmental impacts resulting from the occurrence of this phenomenon, such as immigration and unemployment. One of the strategies for reducing losses due to a range of movements is the identification and management of unstable slope areas. To identify unstable regions pay to landslide hazard mapping. The main purpose of this research is to assess the effective parameter on landslide occurrence and to compare different machine learning models including SVM, GP regression, and RF for landslide susceptibility zoning. Materials and Methods The study area is a part of the Haraz Watershed, Mazandaran Province, Iran, occurrence many landslides are damaged after each heavy rain. So, it was selected as a suitable Watershed to evaluate the landslide susceptibility mapping (LSM). The vegetation covers and land mainly consists of rangeland. The geology of the study area consists mainly of Quaternary and Shemshak formations. The first step for the assessment of landslide susceptibility is gathering the necessary data and preparing information. These data were determined based on several factors. Considering the literature review, the local conditions, and previous studies. In this study, nine parameters such as slope angle, slope aspect, elevation, geology, land use, the distance of fault, the distance of the road, the distance of the river, and precipitation were identified as key factors for the prediction of landslide susceptibility. To assess the effectiveness of GP-PUK, GP-RBF, SVM-PUK, SVP-RBF, AND RF to estimate the landslide susceptibility map (LSM), data used in the present study were taken from field data. In this study, the dataset contains 148 observations of landslide occurrence and landslide non-occurrence points. The landslide data have been randomly separated into training (70% of landslides; 103) and testing (30% of the landslides; 45). To judge the performance of the soft computing techniques, statistical evaluation parameters were used. In this research, three statistical evaluation parameters were used. These parameters are the correlation coefficient (C.C.), root mean square error (RMSE), and Nash–Sutcliffe model efficiency (NSE). Results and DiscussionAccording to the results of the comparison of methods, RF was the best model and the accuracy of the RF model was more suitable for the estimation of the landslide occurrence. So, in this study, RF was used for the landslide susceptibility map. Single-factor ANOVA test suggests that there is an insignificant difference between observed and predicted values of landslide occurrence and landslide non-occurrence using GP_PUK, GP_RBF, SVM_PUK, SVM_RBF and Random Forest approaches. According to the results of the comparison of methods, RF was the best model and the accuracy of the RF model was more suitable for the estimation of the landslide occurrence. The map of landslide susceptibility map was divided into five classes from none susceptible to very high susceptibility. According to the final Landslide susceptibility map, the area belonging to the “non-susceptible” class covers 35.86 km2, “low susceptibility” class 36.19 km2, “moderate susceptibility” class 15.06 km2, “high susceptibility” class 10.95 km2 and “very high susceptibility” class 14.46 km2 of Haraz Watershed. Sensitivity analysis was performed to find the most significant input parameter in the prediction of landslide occurrence and landslide non-occurrence. The result shows that aspect has a major role in predicting landslide occurrence and landslide non-occurrence in comparison to other input parameters, respectively. Conclusion Due to all results, some zones are potentially dangerous for any future habitation and development. Thus, there is an immediate need to implement mitigation measures in the very high-hazard and high-hazard zones, or such zones need to be avoided for habitation or any future developmental activities. The results of this research can be used by the local authority to manage properly, and systematically and plan development within their areas.

AbstractIntroduction Machine learning is a new artificial intelligence method that seeks to write a program with the best performance by using learning experience. Machine learning models with different algorithms can be predictive or descriptive or have both properties and be used in different fields. On the other hand, for better management of flood risk reduction, it is necessary to know the effective factors in each region and flood sensitivity analysis. Since so far, few researchers have analyzed the threshold of influence of variables affecting the occurrence of floods using machine learning methods, the current research is new in this respect. Based on this, the current research has been conducted to identify the threshold of variables affecting the zoning of flooded areas using machine learning and remote sensing data in the Karun Basin area. The results can be put on the agenda of the relevant managers in identifying the influence limits of different variables on the occurrence of floods and the management of flood-sensitive areas by relying on the effective limits of the variables in the study area. Materials and Methods Landsat OLI 8 images on April 8, 2019 were used to identify flooded areas. In this regard, to identify groundwater, the corresponding image of the previous year of the region was used to separate and identify groundwater zones. Then, the remaining pixels of the study area as whole samples and flooded areas were entered into the modelling process as target samples. Therefore, flooded areas with a code of one and other areas with a code of zero entered the modelling process as dependent variables. Also, the variables that were entered as independent variables in the machine learning process include actual evaporation and transpiration, land use, soil density mass, soil clay percentage, soil water deficit, DEM, NDVI, land cover index, Palmer drought severity index, potential evaporation and transpiration, precipitation. cumulative, soil sand percentage, soil texture, soil moisture, minimum and maximum temperature. Next, by entering these variables and performing the machine learning process, the models were evaluated and TreeNet was selected as the best model. Then the threshold of each of the studied components on flood zones was obtained from machine learning. Also, in the present study, learning and test data were used in a ratio of 70% to 30% and completely randomly. It is worth noting that the number of 200 trees with at least six nodes was set for modelling. Results and Discussion Different components have certain thresholds at the beginning of land flooding so regarding vegetation as the most important effective factor in flood zoning, it shows that the lack of vegetation causes flooding, and the higher the level of vegetation, the more it prevents flooding. Also, the cumulative precipitation threshold for flooding the studied area was 15 mm of rainfall, and less than that, the incoming rainfall did not pose a risk of flooding the studied lands. The amount of 15.5 mm of rainfall was the turning point and the threshold of the beginning of the flooding in the study area. Regarding the soil moisture deficiency index, it shows that the threshold of flooding based on this index was 144, in other words when the soil moisture profile is more than the mentioned value, the incoming precipitation must compensate for the soil moisture deficiency, and as a result, floods will be prevented. On the other hand, most flooding conditions have existed at a height of 16 m, and as the height increases, the risk of flooding the studied area decreases, so that there is a failure at a height of 19 m, and when the height reaches 22.5 m, the risk Flooding disappears, and at a height higher than 26 m, flooding is restrained and will reach a steady state. The reason for this can be the plainness of the studied area and the widening of the flood zone in the plain. ConclusionThe results showed that the components of the vegetation cover index, cumulative precipitation, soil water deficit, Palmer drought index, height, and surface soil moisture respectively had the greatest effect on the flooding of the studied area. Also, in the studied area, the effect of soil sand percentage, soil clay percentage, soil density, potential evaporation and transpiration, slope direction, maximum daily temperature, and soil texture on flood zoning was insignificant. The evaluation of the efficiency of the model with the indicators of ROC, specificity, sensitivity, and overall accuracy is 0.95, 91.2, 90.43, and 91.12, respectively, which indicates accuracy. The results of flood zoning with the ground reality indicated R2 and MAE equal to 72.8% and 0.27%, which confirms the accuracy of the zoning results with the ground reality relatively well. The analysis of the results shows that there will be an increased risk of flooding in the wetland and swamp areas due to the high humidity and water level. The results of the present research can be used by planners and managers of natural hazards to reduce floods.

Introduction Rangeland ecosystems cover more than half of the earth's land surface, these ecosystems have high carbon sequestration and cause the formation of about 10% of the total biomass carbon reserves and 30% of soil carbon. Carbon sequestration potential differs according to plant species, habitats, and management methods. Each ecosystem has a certain potential which is determined by utilizing of natural vegetation, climatic conditions, and physical and chemical properties of the soil. Moreover, the amount of carbon in the soil and plants depends on the characteristics of topography, and any change in the elevation and slope of the habitat will affect the amount of organic carbon in the soil and plant cover and carbon sequestration. Therefore, it is important to determine the effects of elevation and slope characteristics, type of vegetation and canopy cover, and soil characteristics on the amount and changes of carbon sequestration. In this study, the carbon sequestration capacity and its relationship with some physical and chemical characteristics of soil, topography, and rangeland habitat have been investigated. Materials and Methods Sampling was conducted in six sites in elevation gradient (from 1400 to 3600 m above sea level; rainfall from 350 to 700 mm; the mean temperature varies from -0.24 to 10.1 °C) in the north of Sablan Mountain, Meshginshahr County, Iran, including three sites with the dominant physiognomy of grass-shrublands and three sites with grassland in three elevation levels of less than 2000, 2000-2500 and more than 2500 m above sea level. In each site, soil sampling was conducted at two depths of 0-15 and 15-30 cm. The random-systematic method was used to study vegetation variables. In this way, 10 plots of one m2 (based on the distribution pattern of plants) were established in each of the three transects with a length of 100 m along each transect. After determining the normality of the data and the homogeneity of the variance of the data, two-way analysis of variance general linear model (GLM) was used for the overall comparison. Results The results showed that with the increase in elevation in the two investigated habitats (grassland and grass-shrubland), the carbon sequestration in the soil increased. Moreover, the results indicate that the depth of the soil has a significant effect on the carbon sequestration capacity. Thus the highest amount of soil carbon deposition is in the soil depth of 0-15 cm in the grassland habitat with an elevation of more than 2500 m (70.53 gr cm-2), then the depth of 0-15 cm in the grass-shrub habitat. At an elevation of more than 2500 m (63.98) and a soil depth of 30-15 cm, the grassland habitat is at an elevation of 2000-2500 m (62.73), and its lowest amount belongs to a soil depth of 30-15 cm. The grass-shrub habitat is at an elevation of less than 2000 m. The results of the correlation analysis of carbon sequestration in habitats and different elevation classes indicate a negative relationship with the percentage of sand, acidity, and electrical conductivity of the soil, and a positive correlation with the percentage of clay and silt, the percentage of organic carbon and organic matter, the percentage of carbon, particulate organic matter, soil nitrogen, and total vegetation cover has shown. Discussion and ConclusionThe distribution of carbon stocks between biomass and soil varies among ecosystems and is influenced by elevation, so that the carbon sequestration potential is more significant in high elevation classes than in lower classes. Based on the estimated carbon values in the studied habitats, it can be concluded that the higher percentage of canopy cover and density preserves more moisture and prevents evaporation, which in turn can affect the amount of cover and density. Therefore, the effect of vegetation in different elevation classes on the amount of changes in soil carbon deposition is confirmed based on the results obtained in this research. Overall, the carbon sequestration potential is different according to habitat and elevation classes; therefore, by better understanding these factors and investigating the management factors that affect the sequestration process, we can take steps towards strengthening carbon sequestration and sustainable management of rangelands.

Introduction Effective approaches and policies including identifying priorities and optimal water allocation techniques, especially in basins with different users are considered essential for sustainable development in each region. With 1100 m3 of renewable water per person per year, Iran is considered to be the most critical region in the world in terms of water resources. Unfortunately, most plans in the water sector of such countries are based on local economic growth, and no attention is paid to the amount of available water resources. Considering the issue of a water crisis and the droughts of the last few years, the issue of water resources management has gained high importance. To overcome the mentioned problems, it is inevitably essential to use newly developed water management techniques based on advanced approaches. Although optimization techniques are well-known tools in these issues, the simulation method is utilized as a helpful approach. To simulate water management in the basin, there are various available models. RIBASIM, MIKE BASIN, WEAP, and MODSIM models are famous and user-friendly ones in this collection. WEAP software is a comprehensive and advanced water resource system simulation tool widely used in watershed management and can consider physical and hydrological processes. The scenarios that can be investigated with this software include population growth, economic development, changing the policy of operating reservoirs, extracting more from underground water resources, saving water, allocating ecosystem needs, integrated use of surface and underground water, reuse of water, etc. Materials and Methods This study was conducted in the Nahand catchment area which is located in East Azerbaijan province. Nahand river is the main draining course of this catchment, on which a dam has been built to supply a part of Tabriz's drinking water. To control the performance indicators of the reservoir, several management and exploitation scenarios were developed and evaluated in the WEAP model. The WEAP model was presented in 1990 by the Stockholm Environment Institute (SEI). It is a comprehensive and advanced model for simulating water resource systems, which is extensively used in the management of water resources in watersheds. This model has provided a practical tool for water resource planning and policy analysis to put all the issues related to water resources and uses in a single environment. The WEAP model is capable of simulating issues related to consumption such as water consumption patterns, water reuse strategies, costs, and water allocation patterns, as well as issues related to resources such as river flow, groundwater resources, reservoirs, and water transmission lines. The inputs of the WEAP model include data on the population of Tabriz City, per capita consumption of drinking water per person, the amount of water wastage in the distribution network, the inlet discharge of the Nahand reservoir, the information of the Nahand dam, the amount of cultivated area, etc., and to evaluate the model R^2, RMSE, and MAE statistical indicators were used in two periods of calibration and validation. Then, various operating conditions were investigated by compiling the Reference (continuation of the status quo), SC1 (increase of input flow by 10%), and SC2 (decrease of input flow by 10%) scenarios. Besides, Reservoir performance indicators are used to measure its performance under different operating circumstances. Results and Discussion The simulation results of the studied area indicated that the WEAP model with evaluation criteria including R2, RMSE, and MAE in the calibration stage was 0.89, 1.16, and 1.01 MCM, respectively, and in the validation stage were 0.88, 6.22, and 6.01 MCM, respectively. The results also showed that the amount of water demand for the near future period (2021-2040) will increase due to the increase in population, and therefore, the resources in the basin will not be able to meet all assumed needs. The findings showed that the studied system for the near future period (2021-2040) under the reference (continuation of the status quo), SC1 (increase in flow by 10 %) and SC2 (decrease in flow by 10 %) scenarios from the drinking water supply point of view, will result in a shortage of 28.1, 7.3 and 44.3%, respectively, and from the supply of agricultural needs point of view will result in 31.4, 18.3 and 44.4%, respectively. Also, by evaluating the reservoir's performance indicators, it was found that under all assumed scenarios, the system will fail under the condition of supplying 100% and 80% of the needs, whereas the reservoir will be more sustainable by applying the SC1 scenario in comparison with the other two scenarios. Conclusion To choose the best management and exploitation scenarios, due to existing circumstances and limitations such as time limitation, cost, possible risks to the environment, etc., it is not possible to apply all scenarios in the basins and, thence, it is logical to choose the most suitable scenario. Therefore, software tools can help experts to make decisions by considering all limitations. By examining the results of the reservoir performance indicators, it can be seen that the reservoir will encounter failure in supplying 100 and 80% of the needs in the future period under all scenarios and the sustainability index of the reservoir (remedial stability) in supplying 100%. The needs under the Reference, SC1, and SC2 scenarios will reach 31, 49, and 22%, respectively, and in meeting 80% of the needs, the sustainability index will be slightly higher.

Introduction Recently, the presence of pathogenic bacteria in the municipal water network has been observed and proven. Applying animal manure in agricultural lands with improper drainage is the main cause of this pollution. Identifying and investigating the movement of intestinal bacteria, especially E. Coli, which is the source of their distribution in most waters, agricultural activities, and urban sewage, is considered one of the appropriate and necessary ways to preserve drinking water resources Some of the soil characteristics that affect the movement of bacteria are: particle size distribution, structure, porosity and apparent density of the soil, in addition, plant roots and pores and cracks are caused by root activity. Plants and animals in the soil create fast water passages to facilitate the transport of pollutants. These routes are called preferential pathways and the flow is named preferential flow. Therefore, considering the environmental importance of the movement of E. Coli bacteria as a pathogen in the soil, so far, most of the studies on the transfer of bacteria without the presence of plants and its effect on the release of bacteria have been investigated. Therefore, this research aims to investigate bacteria transport from cow manure in four granulation levels in the presence of grass plants. Materials and Methods This study was conducted in the greenhouse of Shahrekord University to investigate the transport of E. Coli bacteria caused by the addition of cow manure in four levels of granular size in the soil profile with/without grass cultivation. Some physical and chemical characteristics of the soil were measured by usual methods. In this research, cow manure with a scale of 36 tons per hectare with four granulation levels of 0.25, 0.5, one, and two mm was used as a source of bacteria. The grass was prepared at a height of five cm and was placed on the surface of the soil columns for 14 days to stabilize the roots. The used columns were 24, made of polyethylene and in the form of a cylinder with an external diameter of 160 and a height of 350 mm. First, the soil was passed through a two mm sieve and then the columns were filled with soil up to a height of 300 mm. The treatments included grass cultivation in two levels (without cultivation and with grass cultivation) and the size of manure particles in four levels (0.25, 0.5, one, and two mm). The columns were irrigated with the usual irrigation schedule (once every two days) with the same volume and flow in the surface method until the field capacity was reached. After seven irrigations, the transfer test was performed. The transfer test with municipal water in the columns continued up to seven pore volumes (PV) and sampling was carried out in pore water volumes of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8,0.9, 1, 1.2, 1.4, 1.6, 1.8, 2, 2.5 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5 and 7 were done for each treatment. After the end of the transfer test, to measure the population of bacteria in the soil profile, samples were taken from every five cm of soil depth. In this study, the live count method was used to measure the bacterial population. Results and Discussion There is no significant difference in the relative concentration curve of bacteria in the state of cultivation and without the cultivation of grass. It can be said that the effect of the cultivation of grass in the transfer of bacteria was not observed for 2 mm fertilizer particles, but the shape of the curves has changed in diameters less than 2 mm. It can be stated that in all treatments, the larger the amount of fertilizer, the higher the relative concentration of bacteria in low PVs. In other words, by washing the bacteria from the surface of the fertilizer particles, they are freed and enter the soil, and by continuing the washing, the maximum relative concentration of bacteria in the treatments without grass cultivation and in the diameters of 2.0, 1.0, 0.5 and 0.25, respectively, is 0.6. 0.7, 0.6, and 0.9 times the pore volume occurred. These values were equal to 0.7, 1.0, 0.9, and 1.0 times the pore volume in the treatments with grass cultivation, respectively. After this period, the concentration of released bacteria decreased sharply. The results showed that the presence of grass in the soil for all diameters of fertilizer, except the diameter of 0.25 mm, caused the peak of the breakthrough curve to be delayed. In addition, it is observed that the relative concentration of bacteria in the treatments with grass cultivation has decreased with a gentler slope compared to the treatments without grass cultivation. The amount of zero torque in the treatments with grass cultivation in all fertilizer sizes was more than the same treatment as compared to the conditions without cultivation, and this indicates that the presence of grass caused more bacteria to escape from the drainage of the columns. For fertilizers with particle sizes of 0.25, 0.5, and 2.0 mm in the condition of no cultivation, there is not much difference in the delay factor with the similar treatment in the condition of grass cultivation, but in the treatment with the particle size of one mm in the condition of grass cultivation, the rate of fertilization is delayed. has had a significant increase. Conclusions The results showed that for two mm fertilizer particles, the amount of bacteria transfer increased in the case of no grass cultivation compared to the one, 0.5, and 0.25 mm treatments. The maximum relative concentration of bacteria in the breakthrough curve for 0.25 mm fertilizer particles was lower between one and two mm compared to larger fertilizer particles and was observed with a delay compared to coarser fertilizer particles. In the treatment without grass cultivation, the maximum concentration per fertilizer with the particle size of 0.25 mm was observed at PV 0.9, while in the treatment with the particle size of two, one, and 0.5 mm, the maximum relative concentration of bacteria was 6.6, respectively. About 0.0, 0.7, and 0.6 times the pore volume occurred. In the presence of grass in the soil, the bacteria reached the bottom of the soil column at a faster rate. One of the causes of this phenomenon is the role of plant roots in accelerating the transfer of bacteria in the soil in such a way that the preferential flow paths created by grassroots have moved the bacteria down.

Introduction Water quality assessment is paramount for various sectors, including environmental planning, public health, and industrial operations. With the increasing importance of ensuring safe water sources, especially for drinking and irrigation purposes, modern methodologies like data mining offer valuable tools for predictive analysis and classification of water quality. Knowledge of water quality is considered one of the most important needs in planning, developing, and protecting water resources. Determining the quality of water for different uses, including irrigation and drinking in different areas of life. The use of modern data mining methods can be beneficial for predicting and classifying the quality of provider water. In the current study, the water quality of the Qizil-Uzen River was evaluated at Qara Gunei stations. In this regard, the drinking water quality index (WQI) using the chemical compounds of glass hardness, alkalinity (PH), electrical conductivity, total dissolved substances, calcium, sodium, magnesium, potassium, chlorine, carbonate, bicarbonate and sulfate in the statistical period of 21 years (2000-2020) was estimated. Water quality assessment is paramount for various sectors, including environmental planning, public health, and industrial operations. With the increasing importance of ensuring safe water sources, especially for drinking and irrigation purposes, modern methodologies like data mining offer valuable tools for predictive analysis and classification of water quality. Materials and Methods Due to the relatively large number of variables, principal component analysis and independent component analysis methods were used to reduce dimensions, and then different machine learning algorithms including decision tree, logistic regression, and multi-layer perceptron artificial neural network were used to model the water quality index. By using these methods, the number of parameters needed to calculate the quality index was reduced from 12 to 2. Reducing the dimensions of the data saves the time of sampling, monitoring the samples, and determining the quality of the water and reduces the costs required for modeling to a significant amount. The results showed that among the dimensionality reduction methods, the principal component analysis method can perform better than the independent component analysis method. In the current research, the WQI index was modeled using machine learning algorithms including decision tree, logistic regression, and artificial neural network method. The quality of water in the Qizil-Uzen Qara Gunei river station has been evaluated. Then, to estimate the numerical values of the WQI index, TH, pH, EC, TDS, Ca, Na, Mg, K, Cl, CO3, HCO3, and SO4 parameters of the mentioned station in the statistical period of 21 years (1378-1398) were used. PCA and ICA methods have been used to select different input parameters. Modeling has been done in a Python programming environment. Among the available samples, 75% are considered for training and 25% for testing. Results and Discussion In the present research, to model the water quality index in the first stage, different dimensionality reduction methods such as PCA and ICA were used to reduce the time and cost of implementation. In the second stage, machine learning methods such as decision tree, linear regression, and multilayer perceptron were used. In the method used by Tripathi and his colleagues, by using the principal component analysis method, they reduced the number of parameters needed to calculate the quality index from 28 to 9 and calculated the water quality index with the number of 9 parameters. Examining the two methods of PCA and ICA has reduced the dimensions of the problem from 12 dimensions to 2 dimensions. The results show that the PCA method can help us improve performance with little cost and high accuracy. Because of the PCA dimensions. The comparison of the results of the models was done using different numerical and graphical evaluation criteria, including R2, RMSE, and modified Wilmot coefficient as numerical criteria and Taylor diagram as graphical criteria. Because the PCA algorithm can help reduce noise in data, feature selection, and generate independent and unrelated features from data. The results show that multi-layer perceptron, decision tree, and logistic regression methods accurately perform the water quality index. In this research, for the first time, using the ICA dimension reduction algorithm, while reducing the dimensions of the problem, the water quality index is predicted with an accuracy of over 90%. Conclusion Water quality index modeling holds significant relevance in agricultural practices, where access to clean water is crucial for irrigation and crop growth. Surprisingly, only a limited number of studies have explored variable reduction methods in water quality index modeling, with none incorporating the relatively novel Independent Component Analysis (ICA) method for dimensionality reduction. Thus, the current research fills this gap by employing PCA and ICA techniques to reduce the dimensionality of large datasets in water quality index modeling. By utilizing these advanced methods, the study aims to enhance efficiency and accuracy in assessing water quality, thereby offering valuable insights for agricultural water management. Following dimensionality reduction, the dataset is then subjected to modeling using various machine learning algorithms. This approach not only optimizes computational resources but also facilitates a deeper understanding of the complex interrelationships among water quality parameters. Through this pioneering research endeavor, the efficacy of ICA alongside PCA in addressing water quality index modeling challenges is evaluated. By integrating these techniques with machine learning methodologies, the study endeavors to provide actionable intelligence for agricultural stakeholders, aiding in informed decision-making and resource allocation. Moreover, by venturing into unexplored territory with the inclusion of ICA, the research contributes to expanding the methodological toolkit available for water quality assessment. As agriculture faces increasing pressure from climate change and resource scarcity, such innovative approaches hold promise in ensuring sustainable water management practices.