نشریه فناوری های پیشرفته در بهره وری آب
سال چهارم شماره 1 (بهار 1403)

The reduction in renewable water resources in arid and semi-arid regions of the world, and the increase in the demand for high-quality water, on the other hand, have led water resources with higher quality to be allocated for urban and drinking purposes and lower-quality water resources (such as treated sewage) to be used for purposes such as agriculture, industry and green space. However, the accumulation of heavy metals in different parts of crops irrigated with wastewater is still unclear. Therefore, due to the risk of high concentration of heavy metals in the human food cycle, it should be monitored regularly.

In this study, the effects of irrigation with treated municipal wastewater on the accumulation of heavy metals in the roots, aerial parts and seeds of the faba bean plant (Vicia faba L.) were investigated and compared with concentration of heavy metals in the faba bean plant irrigated with well water (as a control treatment).

The results showed that the concentration of all the heavy metals in different parts of the faba bean plant under the wastewater treatment was significantly higher than the well water treatments, so that the concentration of iron and cadmium in the root (non-edible part) of the faba bean plant was higher than the standards’ limits. The concentration of copper and manganese in different parts of faba beans was lower than the permissible limits. The concentration of cadmium and zinc in the grain is higher than the standards’ limits, so measures should be taken to reduce the concentrations of those metals in the effluent.

Understanding the accumulation of heavy metals in plant tissues is critical to address environmental and food safety concerns. By comparing the concentration of heavy metals with existing standards, potential risks can be identified and strategies can be developed to minimize the impact of heavy metal pollution. This research is very important and necessary to maintain the health of the ecosystem and human well-being in the face of increasing demand for the use of non-conventional water in arid and semi-arid regions due to successive droughts and the crisis of water resources. Efforts to reduce the pollution of heavy metals cadmium and zinc in the effluent of treated wastewater, which in this research were found to be hazardous to human health, include specific and additional treatment of these two metals from the effluent, implementation of appropriate waste management practices, improvement of industrial processes, Regulating the use of specific products and using effective technologies for wastewater treatment. Supervisory and management measures are also necessary to control the release of heavy metals in water resources.

Gilan-e Gharb Reservoir Dam in Gilan-e Gharb County in the southwest of Kermanshah Province is one of the dams designed to provide agricultural water to downstream lands in a hot and dry region. This dam is one of the structures where the calculation errors in the estimation of the water entering the reservoir have caused the failure of the planned goals in such a way that to compensate for this calculation error and to supply agricultural water to the Gilan-e Gharb dyke area in front of the Gilan-e Gharb spring flow, it was designed and was executed After 25 years of dewatering this dam, the volume of the reservoir has not yet reached the estimated value. The studying consulting engineers company mentioned the reason for this failure due to the quality of the reservoir dam inlet station, the quality of statistics and information, the occurrence of drought, and finally the effects of watershed operations in the catchment area of this dam. The main purpose of this research is to evaluate the methods of estimating the inflow to the Gilan-e Gharb dam reservoir and to investigate the factors affecting the calculation errors by using HEC-HMS software to analyze the runoff rainfall model of the Gilan-e Gharb dam reservoir and to investigate the effect of water management operations on the productive runoff and peak flood discharge.

In this study, maps of land use, geology, vegetation and soil hydrological groups were prepared along with field assessments, and then the CN map of the basin was prepared from the integration of the said maps using GIS software. According to the historical record of the statistics and information of the inlet station of the Gilan-e Gharb Reservoir Dam in recent periods and even before the construction of the Reservoir Dam, the volume of the reservoir due to several reasons such as the low amount of incoming runoff, the existence of karst formations that collect most of the runoff, the volume of runoff with the amount 17 million cubic meters does not match.

The results of this research showed that the estimation of the dam inlet was accompanied by gross errors due to the lack of knowledge of the hydrological conditions of the reservoir dam area. Taking into consideration that the watershed operations in the carried-out periods have also had an impact on the water intake of the mentioned reservoir dam, the watershed cannot be the main obstacle for not reaching the predicted watershed.

The results of this study showed that the methods of estimating the water input to the Gilangreb dam reservoir due to the use of statistical relationships and its extension could not be in accordance with the basin realities and the construction of this dam with high costs actually did not reach the pre-designed goals.

Climate change has been a crucial environmental issue in recent years, with global warming, water crisis, and ecosystem changes drawing significant attention. Scientists have been developing various scenarios of greenhouse gas emissions to model future climate changes.Prior research has focused on forecasting rainfall using the fifth climate change report, with little attention to the future socioeconomic impacts. This study addresses monthly rainfall prediction for the Kermanshah synoptic station using data from the sixth climate change report, considering various social and economic scenarios.

The Kermanshah province, located in the western part of the country, spans 24.64 square kilometers, in Iran. It stretches between the latitudes of 33° 36' to 35° 15' north and the longitudes of 45° 24' to 48° 30' east. The Kermanshah synoptic station is situated at the coordinates of 34° 21 longitude and 47° 10 latitude.One challenge with using AOGCM model outputs in regional climate change studies is the mismatch in spatial scale between the model's computational cells and the study area. At the same time, regional studies need data at a resolution of 50 kilometers or less to assess the effects of climate change. The current study utilized the Delta Change Factor (DCF) method to reduce the scale of GCM model data. In CMIP6, models typically feature enhanced resolution and improved dynamic processes. The simulation of future climate changes has utilized the Shared Socioeconomic Pathways (SSP) scenarios. These scenarios, employed in CMIP6, forecast global socioeconomic changes until 2100. The study utilizes the SSP5_8.5, SSP2_4.5, and SSP1_2.6 scenarios.To assess model accuracy, RMSE (Root Mean Square Error) and NSH (Nash-Sutcliffe Efficiency)are used as indicators.

In this study, three climate models - CanESM5, MRI-ESM2-0, and MIROC6 - were utilized to forecast monthly rainfall variability in Kermanshah synoptic station for the sixth climate change report across three future periods: 2026-2050, 2051-2075, and 2076-2100 under SSP126, SSP245, and SSP585 scenarios. The comparison of the trend of monthly rainfall changes in the corrected data indicates that the MIROC6 model predicts the median for the Kermanshah region with the least error compared to the other two models.In all scenarios, there is a decreasing trend in monthly precipitation. The lowest amount of precipitation is related to the months (JUNE-JULY-AUGUST-SEPTEMBER), while the highest amount of precipitation occurs in the months (APRIL-MARCH-NOVEMBER) across the near future, mid-term, and far future. The average rainfall in the SSP126 scenario is predicted to be higher for the MIROC6 model than for the other two models shortly (2026-2050). In the mid-term future (2051-2075), the MIROC6 model predicts less rainfall than the other two models, and in the far future (2076-2100), the CanESM5 model predicts the highest average rainfall for April. In the SSP245 scenario, shortly (2026-2050), the MRI-ESM2-0 model predicts the highest amount of rainfall for November, and the rest of the values are similar. In the mid-term future (2051-2075), the predictions of the models are almost similar, and in the far future (2076-2100), the MIROC6 model predicts the highest average rainfall for March, the CanESM5 model predicts the highest average rainfall for April, and the MRI-ESM2-0 model predicts the highest average rainfall for November. In the SSP585 scenario, shortly (2026-2050), the MRI-ESM2-0 model predicts more rainfall than the other two models. In the mid-term future (2051-2075), the CanESM5 model predicts the highest amount of rainfall for November, and in the distant future (2076-2100), the MRI-ESM2-0 model predicts the highest amount of rainfall for November, while the CanESM5 model predicts approximately the highest average predicted rainfall.

The most significant changes in monthly rainfall are expected to occur after bias correction in the first period (2026-2050) in the MRI-ESM2-0 model scenarios. The MIROC6 and CanESM5 models show similar predicted rainfall changes. In the second period (2051-2075), the trend of monthly rainfall changes for all three scenarios is more similar. However, the MIROC6 and CanESM5 models under the SSP126 scenario have predicted greater increases in rainfall compared to the SSP245 and SSP585 scenarios of the MRI-ESM2-0 model during this historical period. The MRI-ESM2-0, MIROC6, and CanESM5 models are suitable for use in the study area after bias correction, based on the validation index values for all three scenarios and models. Nonetheless, there are differences in the accuracy of the models for examining various climate change parameters and different climate regions. For instance, the MIROC6 model exhibits the highest accuracy for predicting monthly rainfall, while the MRI-ESM2-0 model has the lowest accuracy. The accuracy of the MRI-ESM2-0 model for predicting monthly rainfall in this study area is lower, but its accuracy for other climate change parameters and different regions may yield different results. The comparison of observational data and historical scenario model data in the study area for predicting future monthly rainfall shows that the best models for the study area are MIROC6 and CanESM5.

In order to reduce the amount of evaporation from water reservoirs, various methods have been proposed, which can be divided into two physical and chemical categories. In the physical methods, by using physical coverings, such as floating balls, metal and polymer plates, or tree leaves, and by covering the water surface, evaporation waste is greatly reduced. These covers reduce a large amount of solar energy reaching the water surface and reduce vapor transmission by slowing the air flow.

The present study was conducted with the aim of investigating the effect of the coverage and shape of polypropylene sheets on reducing the evaporation rate and the effect of meteorological variables on the efficiency of these sheets efficiency. This research was carried out in two parts, in the first part, the efficiency of square and triangular polypropylene plates was investigated in comparison with floating balls, and in the second part, the efficiency of 100, 70, 50, and 30% coverage of polypropylene plates was evaluated.

The results obtained from the tests showed that in the conditions of using floating balls, the reduction of evaporation was more intense and this coating was able to reduce evaporation to a greater extent compared to square and triangular floating plates. Also, using the square and triangular polypropylene plates and floating balls reduced evaporation by 30.71%, 14.86% and 48.7%, respectively, compared to the control pan.

The results of two-way ANOVA on different meteorological variables showed that the percentage of relative humidity, which was the most important factor in evaporation from the pan, lost its significance due to the presence of floating balls and polypropylene sheets on the water surface. The comparison of rectangular covers with different densities showed that by increasing the density from 30, 50 and 70% to 100% respectively, evaporation decreases by 2.3, 1.5 and 0.8 times.

The weather has an ongoing impact on human living and working environments. Drought is a natural disaster that ranks first in frequency of occurrence, financial losses, and even human casualties among natural disasters that endanger humans and their environment. Due to its complexity and imperceptibility, this phenomenon—one of the primary and recurring features of various climates—has significantly impacted the human environment more than any other hazard. Its effects can also accumulate gradually over time and last for years afterward.Drought cannot be avoided, but if its nature and characteristics are researched and understood, we may be able to forecast when it will occur, lessen its adverse effects through planning and preparation, and perhaps even control it.

The political region of Iran is the subject of this study (Figure 1). Soil moisture and monthly precipitation are among the data used. The necessary data, which included 516 precipitation files and 516 soil moisture files with a spatial resolution of 0.5*0.66 in NC format, as well as drought and its characteristics calculated for all points, were acquired from the MERRA website for 43 years (1980–2022) to conduct this research. Iran's droughts were estimated over 3, 6, 9, 12, and 24 months using the MSDI. The characteristics of droughts, such as frequency, duration, severity, and magnitude, were computed, analyzed, and presented as a map in addition to examining the actual droughts.

Analyzing the drought characteristics for 1546 points across Iran revealed that the frequency and number of drought periods decrease as the time scale increases. In contrast, the values of other traits—such as continuity, intensity, and magnitude—also rise as the time scale increases. The increase in drought characteristics with increasing time scale, in terms of magnitude, duration, and severity, has been highlighted in studies by Nouri and Homai (2020) and Torabinejad et al. (2023). Geographically, the southeast and eastern parts of Iran have seen the highest frequency of droughts throughout the 43 years; in the southeastern part of the country, which is centered on the provinces of Sistan and Baluchestan, Kerman, and Hormozgan, there have been 28 to 34 periods of drought in 3 months. Conversely, the least amount of drought occurred in the northern coasts during the short-term periods of three and six months, with six to twelve periods, and in the coasts of the north and the southwestern region of the country during the long-term periods of twelve and twenty-four months, with two to six periods. The provinces of Sistan Baluchistan, Kerman, and Hormozgan in the southeast, as well as the far east of Khorasan Razavi and South provinces in the east of the country, experienced the worst droughts in terms of severity. Severe droughts lasting 12 or 24 months are concentrated in the southwest, in Bakhtiari and Chaharmahal, Kohgiluyeh and Boyer Ahmad, and part of Khuzestan.The findings from the computation and analysis of the duration and magnitude of droughts in Iran over the period under discussion indicated that, for two features, the country's northern regions, centered on the north coasts, mainly Mazandaran province, had more prolonged and severe droughts than other regions within the time scales of three and six months. By looking at the country's southwest from the perspective of location over periods of 12 and 24 months, the provinces of Fars, Bushehr, south of Kohgiluyeh and Boyer Ahmad, and Khuzestan province make up the central core of the most prolonged and most extensive droughts. 

Based on soil moisture and precipitation data, the results of a drought calculation in Iran demonstrated that droughts occur throughout the country with varying degrees of severity and weakness. Their occurrence has become a permanent feature of the country's climate, particularly in recent decades. The analysis of Iran's drought chronology revealed that, according to the MSDI index's range, most of the country's droughts are weak and moderate. It was also discovered that as time scales increase, the frequency of droughts decreases while the continuity, magnitude, and severity increase. Geographically, the country's southeast and east saw the highest frequency of droughts, while its northern coast—mainly the province of Mazandaran—and southwest—where the provinces of Chaharmahal & Bakhtiari and Kohgiluyeh & Boyer Ahmad—saw the lowest frequency. The maximum severity of drought in the country is -1.93 on the 3-month scale in the southeast and -2.2 on the 24-month scale in the southwest. Two of the study's notable findings are the continuity and high magnitude of the droughts that struck the country's north and southwest. Thus, the most extensive droughts, with values between -16 and -31 on short-term scales and between -35 and -68 on 12- and 24-month scales, have happened in the country's north and southwest, respectively. These results suggest an overall decrease in soil moisture and precipitation in these regions.

The fluctuation of underground water level is one of the important criteria required for decision-making in many water resources exploitation models. The lack of reliable and complete data is one of the most important challenges in analyzing the decline and predictions of the underground water level in water management. In recent years, the use of different numerical models has been noticed as a reliable solution. These models are able to estimate based on extensive statistics and information and based on various land maps and measurements such as pumping tests, geophysics, soil and land use maps, topography and slope data, different boundary conditions and using complex equations. The level of underground water in any region.The studied area is Sanghar plain in the west of Iran, located at a distance of 100 km northwest of Kermanshah city (Figure (1)). Sanghar plain is one of the fertile plains in Kermanshah province, whose needs are provided by two systems of surface water and underground water. Part of the water needed in the plain is provided by Suleimanshah Dam (Shahda) and the rest is provided by 278 deep wells dug in the south and west of the plain. 

In the present research, first, by using available statistics and information and maps, the fluctuations of the underground water level of Sanghar Plain were simulated by the GMS model, and the accuracy of the model was evaluated in two stages of calibration and validation. Then, due to the need for much less data volume in machine learning methods, GWO-ANN and PSO-ANN hybrid methods and LSTM and SAELM models were used. Based on the general direction of the underground water flow in the entire Sanghar plain, the grid direction was considered to be 250x250 meters in the north direction. Therefore, the model network was built with 2596 cells (44 rows and 59 columns) with 250 meters intervals, which included 908 active cells and 1688 inactive cells. In this study, the general load boundary package was used to simulate the entry and exit borders of Sangar plain. In this package, the inlet or outlet flow is affected by the hydraulic gradient at the boundary and the conductance of the boundary cell. Using the prepared geophysical sections and the data log of the wells, a rock map of the plain was prepared. Also, the DEM map of the plain was used to determine the upper limits of the layer in the groundwater model. In the GMS model, the WELL package was used to simulate exploitation wells in Dasht Sangar (278 wells) and well cells were identified. The feeding of the plain is one of the important parameters in the groundwater model. Usually, due to the different characteristics of soil, geology, vegetation, rainfall intensity and the slope of the land, the amount of groundwater recharge is different in different places. In the GMS model, the RCH package is used to consider the feeding. The zoning method was used to estimate the hydrodynamic parameters of the aquifer. The zoning of the area for hydraulic guidance and special drainage was done based on the drilling logs of observational, exploratory and piezometric wells, as well as geophysical sections prepared from the area. According to the type of soil and sediments of each zone, the initial values of hydraulic conductivity and specific drainage were estimated. Finally, after performing the calibration process, for each zone, the optimized value of hydraulic conductivity and specific drainage was taken into account. In the underground water simulation section, after the calibration and validation tests of the model in two permanent and non-permanent modes and ensuring its accuracy, the final zoning of the main parameters of the model, i.e. hydraulic conductivity and specific drainage, was prepared so that the model can predict the changes in the underground water level for 6 years. Simulate consecutively. Because all the required information was available for 6 years (October 2019 to September 2015). 

The results showed that the output of the SAELM model had the best fit with the observational data with a correlation coefficient equal to 0.97, and it also had the best and closest distribution of points around the 45 degree line, and in this sense, it is considered the most accurate model. Therefore, to predict the level of underground water in the whole plain, instead of using the complex GMS model with a very large volume of data and also a very time-consuming calibration and validation process, SAELM model can be used with confidence.  

This approach greatly helps the researchers of the underground water sector to predict the changes of the underground water level in dry and wet years without using numerical models with a complex and time-consuming structure using artificial intelligence with high accuracy