نشریه مدل سازی و مدیریت آب و خاک
سال چهارم شماره 1 (بهار 1403)

Today, fresh water sources are very important in human life. During the past years, population growth, development of industries, and agriculture have increased the need for water and subsequently increased the withdrawal from groundwater sources. This increase in withdrawal has occurred without considering the capacity of the aquifers. While logically, the exploitation of an aquifer should be limited to its dynamic storage, in most regions, the static reserves of aquifers have been used to a large extent, and it seems impossible to return these aquifers to their original state. The dynamic storage of the aquifer is actually a part of the storage volume of the aquifer, which has fluctuations, and by examining and determining these fluctuations, it is possible to estimate the permissible amount of exploitation of the aquifer. But the static storage of the aquifer, which is very important, is among the old and stable storage that has been stored for many years. The use of static aquifer storage causes events such as subsidence. The drying up and lack of water in many aqueducts, springs, and extraction wells across the country is one of the consequences of the overexploitation of groundwater resources. In the arid and semi-arid climates of Iran, factors such as climate change and water scarcity, along with overexploitation of water resources, especially groundwater resources, have caused the groundwater levels to decrease. In some areas, we are witnessing irreparable subsidence. As a result, it is necessary to manage and control the extraction of groundwater resources before the crisis occurs and reaches an irreversible level. In this regard, monitoring and controlling the condition of the aquifer using computer models is essential.

In the present study, the modeling and quantitative investigation of the groundwater condition of the South Mehyar-Dasht Asman aquifer in the Gavkhoni catchment area has been done by the MODFLOW 2005 model and groundwater modeling system )GMS 10.4.5( software. GMS software is one of the few with good performance and has been used in groundwater studies in many countries worldwide. This software simulate groundwater both quantitatively and qualitatively. The MODFLOW (Modular Ground Water Flow) model is used to simulates groundwater flow in aquifers with specific boundary conditions, assuming the necessary values for hydraulic conductivity and other aquifer parameters. The program allows the user to select only the modules needed to study the desired system for specific hydrogeological processes and activate or deactivate a particular part. These features have made the MODFLOW model the most efficient and accessible groundwater model today. For this purpose, the statistics and information from 15 years ending in 2013 of the Iran Water Resources Management Company and topographical information extracted from satellite data have been used to model the groundwater of the South Mehyar-Dasht Asman aquifer.

After running the model, the parameters of hydraulic conductivity, anisotropy, or HANI, RCH, SY, and parallel lines of the groundwater level were obtained. In the next stage, or the calibration stage, the model was calibrated by the parameters mentioned to minimize the difference between the observed water level and the calculated water level. In the sensitivity analysis stage, the calibrated value of the parameters is systematically changed to determine the model's sensitivity to the parameters. In this stage, it was found that the model received the most influence from the parameters of anisotropy and hydraulic conductivity. To validate the model and measure its accuracy, validation was done. The RMS error was 2.21, which is a good value, but for more certainty, the RMSE was also calculated, and its value was 23 %, which is very appropriate considering the long-term simulation period. Finally, the numbers extracted from the FlowBudget engine based on the calibrated MODFLOW model revealed that during the 12-month simulation period, a particular share of the aquifer's fixed storage was reduced on a daily average basis, resulting in an average drop of 7.5 m in the aquifer's groundwater level during this period. We see that the most significant drop is related to the eastern part of the aquifer.

In this research, the South Mehyar-Dasht Asman aquifer was simulated and modeled by the MODFLOW model and GMS software, and the groundwater level was investigated during the simulation period. The model was calibrated to reduce the difference between the observed and the calculated water level. The sensitivity analysis section also examined the influence of the model parameters. Validation was also done to increase the accuracy of the model's performance. The coefficient of determination or R-squared correlation was 0.9971, which seems to be a good number compared to other studies. The results showed that the water withdrawal is greater than the aquifer's recharge, and a specific volume of the aquifer storage is always reduced during the simulation period, which caused a drop of 7.5 m in the groundwater level. The groundwater level of the South Mehyar-Dasht Asman aquifer continuously decreases from the western part to the eastern region, and the lowest groundwater level is related to the east part of this aquifer. Declining groundwater levels, water shortage conditions, and overexploitation, increase the risk of subsidence, which is an irreversible event. To prevent this from happening, the amount of water taken from the aquifer should be proportional to its capacity, and control and management measures should be carried out on a large scale. Computer models and up-to-date methods help to maintain and manage these water resources as well as possible. Factors such as the dryness of the Zayandehroud River, excessive harvesting, and lack of water have led to the aggravation of the adverse environmental effects of the Gavkhoni International Wetland, and this has increased the importance of simulating the existing aquifers in this basin. Due to the fact that no study has been done on the simulation of the groundwater of South Mehyar aquifer-Dasht Asman and also the duration of the simulation period is 15 years, this research can be a basis for future studies and provide the possibility of comparison with other studies.

The increase in water demand, especially in the production of agricultural products, has led to increased competition for fresh water. Therefore, improving agricultural water productivity and reducing water stress caused by agricultural production is an important measure to improve the sustainable use of water resources. One of the most important indicators proposed for water management is the concept of water footprint, which can be used as a useful tool to measure and predict the amount of water consumed in the agricultural sector and the required demand. In addition, water stress and water poverty indicators are among the other widely used indicators to evaluate water scarcity. Iran is an arid and semi-arid country that has faced severe water shortage and this has had adverse effects on the economy, ecosystem functions, and the welfare of the country's people. The agricultural sector is one of the most important and largest consuming sectors of water resources in Iran, so more than 92% of freshwater resources are consumed in this sector, so the knowledge of water resources allocated for the production of agricultural products is important for managers. And the country's policymakers are very important. So, one of the main solutions to reduce water shortage is to reduce water consumption in the agricultural sector. Among agricultural products, rice is one of the most important food products, which feeds more than half of the world's population. Therefore, the purpose of this study is to use the indicators of water footprint, water stress, and water poverty to evaluate the water scarcity of water in rice production in Iran.

Among the agricultural products, rice is a valuable food and the most important and widely consumed grain. The study area is the rice-producing provinces. which feeds more than half of the world's population. It accounts for about 19% of the world's dietary energy. After wheat, this product is known as one of the most important food items. The average area under rice cultivation (ha), production (ton), yield (tons/ha) as well as the necessary data and information were collected concerning the water resources available for rice production from the Ministry of Jihad Agriculture and the Water Resources Management Company of Iran. The evaluation of water footprint components, including blue, green, and gray water footprints, is based on the method provided by Hoekstra et al. (2011). The water stress index of rice is calculated as a ratio of the total water footprint in rice production to the total water resources available in the region. The amount of water poverty caused by rice production is defined by the product of the total water footprint in rice production and the value of the rice water stress index. Finally, the amount of export and import of virtual water due to rice production in Iran has been estimated.

On average, the total footprint of rice is 3037 m-3 t and the total volume resulting from its production is 4313 MCM, with the share of blue, green, and gray water footprints being 91.68, 6.93, and 1.39 %, respectively. The available water resources (AWR) for rice production in the producing provinces are 21,992 MCM, of which 6,872 and 15,210 MCM are related to blue water and green water, respectively. The results of the investigation of the water stress index (RWSI) caused by rice cultivation in Iran, which is the result of dividing the total water footprint in rice production by the available water resources, is on average equal to 0.5 (out of 1.9), which shows Iran is in moderate water stress of rice production. Changes in water stress in rice production on a provincial scale showed that the provinces of Qazvin, Zanjan, Isfahan, North Khorasan, Razavi Khorasan, and Sistan and Baluchistan have water stress with a value between 0.6 and 1.2 are high and very high water stress in rice production, while the provinces of Mazandaran, Guilan, Golestan, Fars, and Khuzestan are in the range of less than 0.3 (low water stress) Also, the water poverty caused by rice production is equal to 1073 MCM in Iran, which is the highest and lowest amount of water poverty in Guilan and Khuzestan provinces (290 and 11 MCM).

Water sustainability in rice production in Iran has been investigated using water stress, water poverty, and water footprint, as well as the amount of exports and imports. Among the components of the water footprint, the highest value is related to the blue water footprint, and the lowest is related to the green water footprint. The high blue water footprint shows that most of the surface water and groundwater is used for rice production, and the low green water footprint shows that the amount of rainfall is not enough for rice cultivation in Iran. According to the results, the amount of gray water footprint in rice production is more than the green water footprint, this issue shows that the low yield of rice in the studied provinces as well as the high consumption of fertilizers and chemical pesticides cause the increase of gray water footprint in rice has been produced. Assessing water scarcity using the water footprint approach can be useful for identifying the risks of high rice production due to the potential of water scarcity. The dependence and high consumption of blue water compared to green water have increased the water shortage related to the production of rice. Therefore, it is necessary to change the pattern of water resource allocation based on the status of water resources and water scarcity indicators.

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.

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.

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.

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.

The main activity of people in the Sistan region is agriculture and animal husbandry, which due to the drought and the unavailability of surface water and the intermittent flow of water in the Sistan River, the digging of irrigation wells has expanded in this area. The Sistan River continues of Afghanistan's Helmand River, which passes through agricultural and urban areas. Therefore, it is possible that some kinds of pollutants, especially heavy metals, can come from different sources. On the other hand, after entering the Sistan Plain, the river affects the water level of wells along the river. Therefore, in case of contamination, it may increase the concentration of heavy metals in wells. However, until now, the concentration of heavy metals in the water wells has not been investigated and only the salinity factor has been considered from the point of view of water quality. The quality of drinking water is associated with the concentration of physicochemical compounds such as nitrate, phosphate, various anions and cations, and organic and inorganic pollutants such as heavy metals. Drinking water contaminated with metals is turning into a primary health concern for human health care. Therefore, surveying the quality of water can be helpful in management. The aim of this study is to investigate the concentration of heavy metals in the wells along the Sistan River, in order to ensure the quality of water for agriculture, husbandry, and human consumption.

In this research, in order to ensure the appropriate quality of water in terms of the concentration of heavy metals and also the possible effect of EC and pH factors on their changes, the concentration of some elements and factors were mentioned in the water samples of 26 active wells. The samples were collected along the Sistan River (with a maximum distance of 1000 meters buffer zone) from the border point to the entrance to Hamoun Hirmand (Afzal-Abad branch, Lorg Bagh and Khwaje mountain) from January to March 2022. According to the investigation of the Afzal-Abad branch and the absence of wells, no samples were obtained from this section. At the same time as the sampling, the characteristics of the well, such as the year of establishment and the type of water usage were recorded. Sampling was done in three repetitions and during sampling pH, EC, TDS, and salinity variables were measured by a portable calibrated device and recorded three times. The collected samples were transferred to the laboratory in order to measure heavy metals in sterilized frosted glass containers. The standard method of the American Public Health Association (APHA) was used to measure each of the considered factors. After the preparation of samples, the concentration of heavy metals was measured with ICP. The obtained results were zoned using the normal kriging and co-kriging methods based on the selected model resulting from the prediction standard error. Geostatistical kriging methods (such as simple kriging, normal kriging, and co-kriging) were used for interpolation of heavy metals distribution. In the semi-variable analysis, the variability of the factors with respect to the spatial distance was organized by different linear, spherical, etc. functions using ArcGIS software.

The average amount of pH was measured at 8.31. The concentrations of salinity and TDS were 3.74 and 4.62 g l-1, respectively. EC value was also measured as 6322 µS/cm. The average concentration of Cr, Fe, Ni, Cu, Zn, and Pb was also obtained at 2.64, 124, 10, 5.9, 33, 17, 1.43, and 2.79 µg l-1 respectively. The trend of elements obtained Fe>Zn>Ni>Cu>Pb>Cr>Cd. The results indicated a low concentration of Cd, Ni, Cr, Cu, Fe, and Zn While Pb concentration was higher than the standard. The low concentration of the mentioned elements is due to the alkaline pH of water, which acts as a buffer and causes the elements to become insoluble and precipitate. Some dangerous elements such as mercury could not be measured due to their low concentration. The amount of salinity and EC factors also showed that the well water is not suitable for agriculture and livestock. The result of element zoning also showed that the concentration of metals increases from the border towards the lake. According to the age of the wells, it can be said that the reason for the decrease in the concentration of metals is due to the longer life of the wells and the chance of water mixing during the Sistan River water harvesting.

The result showed that the concentration of metals, except lead, is lower than the standard value. The low concentration of elements may be due to the alkaline condition of water, which acts as a buffer and causes the insoluble and finally precipitate. Or it can be due to the lower concentration in the bedrock, which can be obtained by surveying the geology of the bed of the wells. The variation in metal concentration among the sampling sites may be due to the age of the well. So up to the Sistan dam, where the wells are older, the concentration of elements is lower. It may be due to river flow, which can cause dilution of metals in wells during high water season. According to the amount of salinity and EC, irrigation with water can cause a quality decrease of soil that leads to loss of cultivated area. Moreover, the fact that water is not suitable for livestock and alternative sources should be introduced.

Nitrate pollution in groundwater and drinking water reservoirs has increased alarmingly in different parts of the world. The high concentration of nitrate in surface and groundwaters is due to the excessive use of chemical fertilizers and improper disposal of wastes caused by human activities and animal manure. Due to its high mobility, nitrate anion is easily washed from the soil and enters the surface and groundwaters. If the concentration of nitrate exceeds the limit (50 mg l-1), it causes the disease of children with methemoglobinemia, as well as the formation of carcinogenic nitrosamines. Various methods have been proposed to remove nitrate. These methods besides having side effects on water, are not economically viable. In recent years, the development of effective technologies for keeping nitrates in the soil has received much attention. Adding biochar to the soil is one of the effective ways to reduce nitrate leaching. Biochar is a carbon-rich and porous substance, that is produced by heating biomass such as organic waste, animal manure, plant residues, sewage sludge, wood, etc. in limited or oxygen-free conditions. Due to its high specific surface area, high porosity, and diverse functional groups, biochar increases the water retention capacity, cation exchange capacity, and surface absorption capacity after adding it to the soil. Therefore, this research aims to investigate the effect of biochar and biochar coated with trivalent iron on the amount of nitrate absorption from aqueous solution.

Biochar can be produced from materials with low economic value and is a suitable and inexpensive adsorbent for nitrate removal from water sources. According to the studies conducted for biochar production, the temperature and duration of storage in the furnace are the most important factors controlling the quality and strength of biochar in nitrate removal. In this research, four types of rice straw, rice husk, sugarcane bagasse, and dicer wood chips were used to produce biochars. First, the samples were passed through a 2 mm sieve and dried in an oven at 70°C for 24 h. Then they were converted to biochar for 3 h at 300 and 600°C in an electric furnace under oxygen-free conditions. To determine the best adsorbent with maximum nitrate absorption, 0.5 gr of each adsorbent was weighed and poured into a 50 ml centrifuge tube. Then, it was contacted at a constant time (60 min) at an initial concentration of 50 mg l-1 of nitrate solution. After determining the best adsorbent, kinetic experiments were done to determine the equilibrium time, optimum pH, and adsorbent dosage. The adsorption isotherms were conducted for soil, rice husk 300˚C (RSB 300), and Fe-coated RSB 300.

The results showed that among the eight types of biochar produced at two temperature conditions of 300 and 600 ˚C, RSB 300, with the initial concentration of nitrate solution of 50 mg l-1 and contact time of 60 min, had the most amount of nitrate absorption. The kinetic experiments were continued on this type of biochar. The kinetic experiment results showed adsorption nitrate with an initial concentration of 50 mg l-1 an equilibrium time of 90 min, pH 7, and an adsorbent dosage of 1.25 g l-1 was 23580 mg kg-1. The result of the adsorption isotherms study showed that the adsorption of nitrate on RSB and Fe-coated RSB were fitted to the Langmuir isotherm model. This result indicates the uniform or single-layer distribution of active sites on the absorbent surface. The maximum adsorption capacity of nitrate by RSB and Fe-coated RSB were 38.16 and 43.66 mg g-1, respectively.

The use of cheap absorbents can be a suitable solution for removing environmental pollution. In general, biochar can absorb pollutants and nutrients by its potential physicochemical properties, including high specific surface area, high porosity, high cation and anion exchange capacity, high surface charge density, low volume mass, and the presence of functional groups. The results showed that among the eight types of biochar tested, RSB with the initial concentration of nitrate solution of 50 mg l-1 and contact time of 60 min, had the highest absorption rate. The optimal conditions for nitrate absorption are estimated at 90 min of contact time, pH 7, and adsorbent dosage of 1.25 g l-1. The results showed that wastewater treatment by surface absorption process using biochar produced from vegetable waste is a very useful and effective method. Besides, the results of isotherm adsorption on the nitrate adsorption test data by biochar produced from RSB and Biochar Fe-coated RSB showed that nitrate adsorption on these adsorbents according to its correlation coefficient (R2=0.994) is consistent with the Langmuir isotherm model. The maximum absorption capacity RSB is 38.16 mg l-1, which is more absorbable than other studies. Now, when the above biochar was coated with Fe, the maximum nitrate absorption capacity increased by 43.66 mg g-1, which is a very high absorption. It can be concluded that RSB, especially when it has a Fe-coating, is a suitable adsorbent for removing nitrate from water. Therefore, it is suggested to investigate the effect of biochar covered with different cations on the mobility of other pollutants that are in anionic form.

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.

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.

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.

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.

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.

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.

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.

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.

In most regions of Iran, the requisite water for population and industry is supplied by groundwater resources. Drought is an important factor, which affects groundwater quality. Groundwater is a renewable, limited, and vital resource for human life, social, and economic development. It is a valuable part of the ecosystem and is vulnerable to natural and human effects. Recognizing the quality of underground water, as one of the most important and vulnerable sources of water supply, has been a matter of course in recent decades. The effects of corrosiveness and sedimentation in water supply systems, water transmission, and distribution can increase operating costs and also create negative effects on human health. Therefore, the science of water quality will remain an important issue for engineers and scientists for years to come. Many methods and techniques have been invented and developed to investigate the chemical quality of water. Excessive harvests and recent meteorological droughts have changed the quality of underground water, but so far no study has been conducted in the entire Sefid-Rud Basin area to investigate the quality and the process of its changes. Therefore, the purpose of this study is to investigate the chemical quality of Sefid-Rud water which is used for the industry.

In this research, the chemical quality of underground water sources in the Sefid-Rud Basin has been studied and analyzed using the results of the qualitative analysis of water samples in deep wells, semi-deep wells, springs, and aqueducts, separately for three periods and according to the common statistical period of 18 years (2001-2018). The analyzed statistics and information include the results of a complete chemical analysis of water and parameters such as electrical conductivity (EC) values, total dissolved substances (TDS), pH, cations (Ca, Mg, Na, and K), anions (Cl, SO4, HCO3, and CO3), Na, and sodium absorption ratio (SAR) have been investigated. Water used by industries depending on the type of consumption in different sectors should also have certain qualities and characteristics. In this research, the Langiler index, which is an index for Corrosion and Scaling, was used to classify water quality for industrial purposes. To this end, the chemical quality of water in terms of chemical balance and occurrence of corrosion and scaling phenomena of underground water was classified into three categories: sedimentation, balanced, and corrosive. The Langelier index was used to measure water quality for the industry.

In the statistical analysis of groundwater in the Sefid-Rud Basin, while determining the maximum, minimum, and average values of qualitative parameters at the level of the study areas, the trend of qualitative changes at the level of their aquifers according to the EC map of the groundwater, has been analyzed. In the whole period of 18-year statistics (2001-2018) in the Goltapeh-Zarinabad and Tarom-Khalkhal areas, the average values of EC and TDS are higher than in other areas. there has been an increasing trend in the Goltapeh-Zarinabad area during the three study periods. Among the cations, the calcium ion had the highest amount (except for Goltapeh-Zarinabad and Tarom-Khalkhal areas), followed by the sodium ion. The highest amount of Na ion was observed in the area of Tarom-Khalkhal, the highest amount of Ca ion was observed in the Divandareh-Bijar, and the highest amount of Mg ion was observed in the Taleghan-Alamot. HCO3 and then SO4 ions were the main anions in the entire statistical period and all the study areas. It followed a relatively constant trend in all the study periods. According to the qualitative classification of industrial water, about 67% of the samples have corrosive properties and about 32% are depositors.

The values of EC and TDS in the three statistical periods in the area of Goltapeh-Zarinabad had an increasing trend. The sodium absorption ratio (SAR) in the entire statistical period was 2.9% with a maximum in the Tarom-Khalkhal range and a minimum of 0.8% in the Manjil and Sojas ranges. It shows the maximum values of cations for Ca ions with 3.6 in the Divandareh-Bijar range, Na with a value of 5.8 in the Tarom-Khalkhal range, and Mg with a value of 3.8 in the Goltapeh-Zarinabad range. Besides, the maximum amounts of anions including HCO3, SO4, and Cl were in Astane-Kuchesfehan, Divandareh-Bijar, and Tarom-Khalkhal areas, respectively. Qualitative assessment of groundwater resources in the Sefid-Rud Basin area for industrial use based on the Langiller index shows that the changing trend of this index has been relatively stable in the three periods, and out of 360 sources in the basin, 241 are corrosive sources, 115 are sedimentary sources and four sources have had a balanced situation. The analysis of qualitative zoning maps for industrial purposes showed that the Astana-Kuchsefahan and Divandre-Bijar aquifers, the central part of the Zanjan aquifer, and the western areas of the Goltape-Zarinanabad aquifer have sedimentation characteristics and other aquifers have corrosive characteristics.

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.

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.

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.

The 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.

The cost of building dams is very high and their failure can be hazardous. On the other hand, they are vital for every country as freshwater storage. Deterministic and traditional algorithms can not answer the multidimensional and complex problems of dam construction, and it is necessary to use hybrid methods based on probabilities. The problems of fluid movement in their nature have a complexity that modeling and finding requires using an advanced algorithm that can finally interpret its non-deterministic nature. Earth dams have a porous, multiphase, and complex medium, and the hydraulic and mechanical variables in different parts are associated with uncertainty. For this reason, in recent years, the regulations for the design of dams have been reforming in the direction of applying non-deterministic and probabilistic variables in the calculations. A probabilistic engineering view leads to a more realistic understanding of design than deterministic approaches. In the research, artificial intelligence (AI) methods have been used to analyze the data, which provides a predictive model of behavior for seepage discharge flow through the earth dam. In general, the present research has two purposes a) to estimate the effect of uncertainty of the hydraulic conductivity dam on seepage discharge and b) to provide a model to estimate seepage discharge in a dimensionless way with the gene expression programming (GEP) and support vector machine (SVM) methods.

Monte Carlo simulation (MCS) with 2000 iterations was executed for stochastic analysis. The first step of the Monte Carlo simulation is the choice of the deterministic performance function. In the second step, the input variables were defined to the performance function and the probability distribution for variable/variables. By repeating the process n times, n random answers were extracted for the resulting problem, and finally, probability density function (PDF) and cumulative density function (CDF) graphs were drawn for the results. In the Fortran code of this research and to check the convergence, the hydraulic heads were compared to achieve the difference obtained in iteration n with the obtained value in iteration n-1, and if the difference is less than the tolerance error, then the program stops. In the next section of the algorithm, the obtained data (from the repeated execution of the MCS) are converted into a model for a description relationship between the effective seepage discharge (ESD) and the input variables by using the metaheuristic methods that include; gene expression programming (GEP) and support vector machine (SVM). After GEP and support vector regression (SVR) modeling the predicted and observed results were compared by statistical indexes such as MSE, RMSE, MAE, and Correlation coefficients.

The different models of earth dams were implemented in the Fortran program, and the average and standard deviation of the seepage discharge flow in the uncertainty state were obtained. To determine the relationship between the ESD value, indicators had been defined that these parameters used for the Gene Expression Programming model include; Kx/Ky, W/B, Bd/B, Bu/B, Hdam/B, Hu/Hdam, and Hd/Hu. These were the factors influencing the seepage discharge of the earth dam, and the discharge component is also defined as the effective seepage discharge (ESD) in a dimensionless manner. Kx and Ky are soil permeability in the direction of the horizontal and vertical axes respectively (m/s), W is the width of the crest, B is the width of the base of the earth dam, Bd is the horizontal distance of the dam tip from the downstream side from the crest, Bu is the horizontal distance of the dam tip from the upstream side crest, Hdam height of the dam, Hu height of the reservoir level, Hd water height downstream of the dam, all the variables are in meters. By increasing the Kx/Ky ratio of horizontal to vertical hydraulic conductivity by 49%, the Effective Seepage Discharge increases by 14%. If the horizontal variable of permeability is increased by 25%, the ESD rate increases by 4.56%, similarly, if the vertical variable is increased by 25%, the ESD decreases by 4.72%.

After finite element analysis, and modeling with two methods of gene expression programming (GEP) and support vector regression (SVR), the statistical analysis of the methods showed that the two calculation models had a good prediction of the ESD with a correlation coefficient above 0.9. Vertical hydraulic conductivity (Ky) has a greater effect on the ESD rate than horizontal hydraulic conductivity (Kx). The results of the geometric investigation of the dam also show that the increase in the ratio Hdam/B has a direct impact on the ESD and also the lower the slope downstream of the dam leads to the lower the ESD. The statistical analysis was used to compare the results of the data obtained from Fortran output for SVR and GEP models. In general, the SVR model is closer to the model resulting from the Fortran code rather than the GEP model, and it has a low root mean square error (RMSE) and a high correlation coefficient.

Irrigation efficiency is one of the most important key indicators in the macro-planning of water supply, allocation, and basic water consumption in various sectors such as industry, drinking, environment, and other biological resources, especially agriculture. In many countries such as Iran, irrigated agriculture is the main factor in food production and irrigation has played a key role in increasing the production of agricultural products in the last 50 years. One of the biggest problems for wheat production is the need for more water resources. Therefore, the necessity of optimal use of available and extractable water resources and increasing water consumption efficiency is inevitable. Experiments and experiences of the last half century in the science and technology of irrigation have shown that the effect of various inputs such as modified seeds, chemical fertilizers, appropriate planting operations, and harvesting has a positive and appropriate effect on plant growth. Irrigation management, which includes a set of actions that provide water in a certain amount and at the required time to the plant, must be done in a good way.

The 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.

The comprehensive analysis indicated that the area proposed for implementing the drip irrigation system (tape) in 2020-2021 was equal to 75,000 ha, of which 53,523 ha (71.4%) belong to the 13 main producing provinces or the main wheat production areas. Also, the results showed that the reduction of agricultural water consumption with the development of a drip irrigation system in the main wheat production areas leads to a reduction of 134.1 million m3 of agricultural water. The implementation cost of this solution based on the average implementation of each ha of drip irrigation system is 400 million Rials in the year 2021, equal to 21409 billion Rials. Among the limitations of wheat irrigation with the tape method, we can point out the spreading and distribution of tapes in the field, which is time-consuming, but with the introduction of sowing and distribution devices, this problem will be solved. At the beginning stage, care should be taken not to tear the tapes during the spraying and fertilizing stage. In the harvesting phase, before harvesting the crop, tapes should be collected from the field. In the whole system, strip drip irrigation (type) can be used for dense and row crops such as all kinds of vegetables, summer crops, corn, wheat, fodder, watermelon, melon, tomato, onion, potato, strawberry, sunflower, sugar beet, and cotton.

One of the pressurized irrigation methods that have been developed for wheat cultivation in recent years is the drip (tape) irrigation system, most of the research and field investigations in Iran have confirmed the high efficiency of this method in increasing wheat water productivity. As a result, using the modern irrigation system as a management tool can lead to the improvement of wheat water productivity in Iran. The main problem in this system is the high cost and low knowledge of the farmer. We can also mention the weak points of increasing the cost of constructing the irrigation system, the problems of collecting strips for the next crop, and the accumulation of salt in areas with salty water. Among the strengths, we can mention easier irrigation management, less labor, increased yield, reduced water consumption, increased productivity, and most importantly, increased cultivated area with the same amount of water. Another advantage is the use of the water-fertilizer system, which will be very precise, the operation of fertilizing and irrigation will be done for cultivated crops.

The 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.

The 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.

The 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.

As 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.

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.

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.

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.

The 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.

The limitation of water resources, increase in water demand for food supply, land use changes, climate change, and reduction of soil fertility are the most important challenges facing the world's food security. Various approaches have been used to reduce water demand and increase production per unit area of ​​agricultural products, including modifying cropping patterns and planting methods, using scheduled deficit irrigation, and implementing new policies and laws in water resources management. Among these approaches, some researchers suggest that intercropping is the most sustainable method to improve the use of production resources. The intercropping system is the simultaneous growth of two or more crops in one part of the land. Intercropping has potential benefits such as higher crop yields, more efficient use of resources (sunlight, water, and fertilizers), reduced disease and pests, and improved environmental conditions. The intercropping farming can be an efficient solution to improve the productivity of water resources, especially in areas with limited water resources such as Iran. So far, no study has been conducted on the efficiency of water and land use in the maize-mung bean cropping system. So, the purpose of this study was to investigate the effect of maize-mung bean intercropping in different cropping patterns and different water stress conditions on water productivity.

The field experiment was a split plot experiment based on a randomized complete block design with three replications in the research farm, Faculty of Agriculture, Ilam University during the 2020-2021 growing season. Treatments included four levels of irrigation including 40, 60, 80, and 100 % of crop water requirement as main plots, and four planting pattern levels including additive intercropping series (100 % corn + 50 % mung bean), replacement intercropping series (50 % corn + 50 % mung bean) and monocultures of mung bean and corn, as subplots. The application of drought stress started after the establishment stage of the plant and continued until the harvest. The irrigation requirement was determined based on the TDR method and was applied using the drip irrigation method. At the end of the growth period, some parameters of both plants were measured, including plant height, thousand seed weight, grain yield, biological yield, and harvest index. Moreover, water use efficiency (WUE), land equality ratio (LER), and water equality ratio (WER) indicators were used to check the effectiveness of the intercropping system. Finally, analysis of variance (ANOVA) was performed using Minitab 17 software, and the means were compared by Duncan's test at a 5 % probability level (p≤0.05).

The results showed that the water use efficiency of mung bean was significantly lower than that of mung bean monoculture in two patterns of mixed and incremental cultivation. The highest mung bean water use efficiency occurred at the irrigation level of 80% of the water requirement and single crop (0.51 kg m-3). Maize water productivity in all cropping patterns decreases with increasing irrigation water consumption. There was no significant difference between the water productivity of the additive intercropping and the monoculture of maize, but the water productivity of the replacement intercropping had a significant difference with the other two cropping patterns. The highest value of LER was obtained in the additive intercropping as 1.56 and 1.52 in 80 and 100% of water requirement, respectively, which was equivalent to a 56 and 52% increase in profitability compared to a monoculture of the two species. WER was greater than 1 in all water levels and two intercropping patterns, which indicated the superiority of intercropping with less water than monoculture. Also, the results showed that the highest WER value was 1.46 in the additive intercropping treatment with an 80% water consumption level. Additionally, the comparison of LER and WER indices showed that intercropping makes more efficient use of water and land resources. Also, the results indicated that the highest LER and WER occurred in the additive intercropping and the irrigation level was 80%. Therefore, additive intercropping and the use of an 80% irrigation level make the most efficient use of water and land resources.

The higher WER and LER indexes in this study suggest that an intercropping system may save 45%–56% of water and farmland, achieving similar yields in comparison to sole maize and sole mung bean. Thus, the intercropping system would be an advantageous cropping system for sustaining crop productivity and improving water and land use efficiency. However, additional research is required to comprehend the resource capture mechanism of intercrop species in intercropping systems under the changing climate. Especially, a series of comprehensive studies to develop the best management approaches regarding fertilization, irrigation, and pest control would support the sustainability of intercropping systems and farmer adoption. The successful adoption of the intercropping system will play an important role in meeting the food requirements of the increasing population, especially in arid and semiarid countries such as Iran which are facing challenges in resource limitation. The results of this study showed that by using intercropping system and optimal management of water consumption, it is possible to achieve performance similar to single crop cultivation with 45% less water and 56% less land use. However, more research is needed to understand the mechanism of how resources are absorbed by mixed species in intercropping systems, especially under different climatic conditions.

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.

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.

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.

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.

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.

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.

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.

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.

Irrigation planning is one of the management strategies, which is based on determining the exact water requirement. The lysimetric method is the most accurate method of determining the water requirement of the plant, but due to the high cost and the need for high technical knowledge, it cannot be used anywhere. The basis for determining the water requirement in the Ardabil Plain is the use of NETWAT software output information, which is known as the National Water Document of Iran. This software calculates the water requirement using the Penman-Monteith FAO model. The output of this software due to the need to update climate information, not introducing the exact range of plains, ignoring sub-climates in some plains and catchments (including Ardabil Plain), and not considering some important crops in the plain (lack of potato water requirement in Ardabil Plain) and the creation of new databases in recent years, should be reconsidered. 

This study in Ardabil Plain and water requirement of the dominant crop pattern including wheat, barley, potato, alfalfa, and bean crops was calculated by the Penman-Monteith method and CROPWAT software. To calculate the net irrigation requirement, first, the evapotranspiration potential of the plain was obtained using climatic information from three stations Ardabil, Abi Biglou, and Namin. Then, the effective rainfall of the plain was extracted by the information from Ardabil, Abybeigloo, Namin, Koozeh Topraghi, Gilande, and Samian rain gauge stations. Required information on plain soil was prepared using 22 points in the plain. In the last step of the information preparation phase, the characteristics of the cropping pattern plants were defined using field measurements, local experiments, and FAO publication No. 56. The cropping pattern (91.4% of the cultivated area of Ardabil Plain) included wheat, barley, potatoes, alfalfa, and beans, which according to the five-year statistics ending in 2021, the cultivated area of these crops was 18,300 (32.6%), 10300 (19.4%), 15700 (28%), 5200 (9.2%) and 1200 (2.2%) hectares. After preparing the case information, the water requirement was calculated for each of the wheat, barley, potato, alfalfa and bean products in each of the soil sampling points in 10-day periods during the growing season. Based on the point information obtained, a zoning map of net irrigation needs in the Ardabil Plain was prepared.

Based on the obtained point information, a zoning map of net irrigation needs in the Ardabil Plain was prepared. The results showed that the zoning of the net need for irrigation divides the Ardabil Plain into three separate parts in this regard. The northern part and the southern part are divided into high consumption, the eastern and southeastern parts are low consumption, and the western part and parts of the center are divided into medium consumption. In addition, according to the zoning results, the average, minimum, and maximum net irrigation needs of the crops were calculated. For the wheat crop, the average, minimum, and maximum net irrigation requirements were 164, 314, and 259 mm, respectively. For the barley crop, the average, minimum, and maximum net irrigation requirements were 110, 255, and 205 mm, respectively. For the potato crop, the average, minimum, and maximum net irrigation requirements were calculated as 325, 613, and 484 mm, respectively. In addition, for alfalfa and bean crops, the mean, minimum, and maximum net irrigation requirements were estimated at 425, 872, and 670 mm and 337, 637, and 497 mm, respectively.

The results showed that if the average of the whole plain is used for wheat, barley, potato, alfalfa, and bean crops, instead of point or regional information, about 18, 20, 21, 23, and 22% deficit irrigation, respectively, and in Low consumption sector accounts for about 58, 86, 49, 58, and 48% of excess irrigation. Also, the results showed that using the output numbers of NETWAT software will cause wrong water management in Ardabil Plain. Therefore, using the results of the National Water Document (NETWAT) will lead to incorrect water management due to the problems mentioned. That is if the results of the national document are used as the basis for determining the water requirement in the Ardabil Plain, compared to the minimum and maximum numbers obtained from this research, about 32 and 38 MCM will occur in the exceeding irrigated and deficit irrigated plains, respectively (without considering the impact potato crop due to not calculating its water requirement in the national water document for Ardabil Plain). Considering climate change and also the development of different databases, it is suggested to use up-to-date information for water requirement calculations. Also, considering that the Ardabil Plain is divided into three separate parts in terms of the net need for irrigation, therefore it is recommended that instead of using one number as the consumption in the whole plain, from point or regional information obtained from this research for wheat, barley, Use potatoes, alfalfa and beans.

Agriculture 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.

In-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.

The 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.

More 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.

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.

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.

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.

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.

Preservation and proper management of water resources are one of the essential fields of study in the world. In arid and semi-arid regions like Iran, quantitative and qualitative management of underground water resources is particularly important. In most hydrological issues and groundwater resources studies, groundwater statistics and information availability are critical. To collect information without side effects, comprehensive and sufficient data collection with the help of a groundwater monitoring network is very important. In line with the sustainable management of renewable water resources, the need for a network of underground water observation (monitoring) wells to accurately measure the water level is necessary and necessary. Considering the complexities of the underground water environment and the high costs of conventional monitoring methods, inventing new technologies and using advanced methods in this matter will significantly help improve the underground water systems. One of the parameters of particular importance in monitoring groundwater quantity is the groundwater level. Therefore, this parameter should be measured or estimated as accurately as possible. In recent decades, the use of computer and calculation models to monitor the level of underground water has developed significantly. Considering the importance of underground water resources and network monitoring, to save time and money, in this research, principal component analysis and Shannon's entropy theory were used to monitor the underground water network of the Dezful-Andimeshk Plain.

This research used monthly groundwater level information from 77 observation wells in the Dezful-Andimeshk Plain during 2018-2019. Groundwater level information is collected twice a month. Principal component analysis and Shannon entropy methods were used for monitoring. In the current research, the number of statistical periods for each well is 24, less than the total number of observation wells. Twenty-four observation wells around it were used to monitor each well. In groundwater level monitoring, the relative importance of each well is defined by the ratio of the number of times that well is recognized as a compelling well to the number of times that well is included in the analysis of the main components. This ratio shows the importance of each well compared to other wells. Therefore, to save time and costs, less important wells can be removed in the monitoring of the underground water level. In 1948, Shannon showed that events with a high probability of occurrence show less information, and on the contrary, the lower the probability of an event, the more information it provides.  In this method, the weight of each well was obtained using Shannon's entropy theory. Any well that has a higher Shannon entropy weight contains more important and unpredictable information and should be preserved. On the contrary, a well that has a lower Shannon entropy weight can be removed from the network. Principal component analysis and Shannon's entropy method in the current research were done with the help of coding in Matlab software due to the high volume of calculations.

To rank the wells, the threshold limits are equal to zero, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and one considered. At threshold one, only wells that have a rank of one remain (wells that are recognized as effective wells in all analyses) and threshold zero includes all wells (effective and ineffective). According to the obtained results, increasing the error in the threshold zero to 0.7 is gradual, but in the thresholds 0.8, 0.9, and one, the error value increases with a high slope. So, the amount of error in the thresholds of 0.7, 0.8, 0.9, and 1 has been calculated as 12.2, 17.7, 25.3 and 34.2 respectively. Therefore, the threshold limit in the current research is considered to be 0.7. However, the number of wells effective in monitoring the underground water level is reduced from 77 to 32. Shannon's entropy weight values were also calculated for all wells. 11 wells have the highest value of Shannon's entropy weight, which shows that they contain the most information.

The general comparison of the results of the two methods showed that all 11 wells with the highest weight in the Shannon entropy method were also observed as effective wells in the principal component analysis method. By knowing the effective wells in the region, firstly, in the face of lack of time and money, it is possible to use known effective wells for monitoring secondly, by removing ineffective wells, there will be little change in the average level of underground water. It is not possible, or in other words, the tracking error does not increase significantly. Comparing the results of the two methods showed that the remaining wells in Shannon's entropy theory are among the wells identified in the principal component analysis method. Also, considering that the wells in the region were built by the Khuzestan Water and Electricity Organization considering the types of uses, removing the ineffective wells will not affect the process of using the information of the wells. It is recommended to use principal component analysis and Shannon entropy for groundwater quality monitoring in the study area. Additionally, it is suggested to monitor the quality of the underground water network in the study area using the methods used in future research.