WATER AND SOIL MANAGEMENT AND MODELING
Year:2024 | Volume:4 | Issue:3|Papers Count:20

IntroductionDue to the limited water resources in Iran, optimal utilization of water resources is necessary to achieve food security and sustainable development of the agricultural sector. One of the suitable solutions for the optimal use of water resources is to estimate the virtual water content and the physical and economic water productivity of agricultural products. Virtual water is the amount of water that an agricultural product consumes during the production process to reach the stage of evolution, and its amount is equivalent to the total amount of water consumed in the different stages of the production chain from the beginning to the end. The amount of virtual water required to produce a product differs according to climatic, cultural, management, and planning conditions in each country and even region. This issue makes it necessary to study the estimation of virtual water content in each region. In recent years, many types of research have been conducted to estimate the virtual water content and the physical and economic productivity of water in crops. Dehloran County is the main hub of agricultural production in Ilam Province. So far, no study has been conducted in the field of estimating the virtual water content of crops in this county. This study was therefore conducted to estimate virtual water content and physical and economic water productivity of crops (i.e., wheat, barley, maize, rapeseed, tomato, cucumber, carrot, melon, watermelon, and sesame) of Dehloran County. Materials and MethodsThe Dehloran County, located in the south of Ilam Province, with 6229 km2 constitutes about one-third of the total area of Ilam Province. This county has 103000 ha of suitable agricultural land, of which 67000 ha are irrigated and 36000 ha are rainfed, and it is considered the agricultural hub of Ilam Province. Most of the irrigated area in the study area is devoted to cultivating crops such as wheat, barley, maize, rapeseed, tomato, cucumber, watermelon, melon, sesame, and carrot. This study investigated the virtual water content and the physical and economic water productivity of these crops in the agricultural year 2021-2022. The virtual water content of crops was estimated from the sum of green, blue, gray, and white virtual water. This study used the FAO Penman-Monteith and USDA methods in CROPWAT 8.0 software to estimate reference crop evapotranspiration and effective rainfall. Crop yields, nitrogen fertilizer consumption rates, production costs, and sales prices of crops were obtained from the Agricultural Jihad of Dehloran County. To check the validity of the mentioned data, an interview was conducted with the farmers of Dehloran County. The results of the interview confirmed the validity of the data. According to the data obtained from the water resources affairs of Dehloran County, the irrigation efficiency is equal to 85%, which was used to estimate the gross irrigation requirement. Crop per drop (CPD), benefit per drop (BPD), net benefit per drop (NBPD), and unit virtual water value (UWV) indicators were used to estimate the physical and economic water productivity of crops. Results and DiscussionThe results showed that the virtual water content of wheat, barley, rapeseed, maize, watermelon, melon, sesame, tomato, cucumber, and carrot crops is equal to 1.82, 1.64, 3.90, 1.49, 0.31, 0.29, 6.99, 0.49, 0.30, and 0.35 m3 kg-1, respectively. The amount of physical water productivity (CPD index) of wheat, barley, rapeseed, maize, watermelon, melon, sesame, tomato, cucumber, and carrot products is equal to 1.53, 2.04, 0.63, 0.81, 4.17, 5.45, 0.22, 2.59, 5.45, and 3.94 kg m-3, respectively. The amount of BPD index of wheat, barley, rapeseed, maize, watermelon, melon, sesame, tomato, cucumber, and carrot crops is equal to 0.17, 0.19, 0.14, 0.09, 0.13, 0.16, 0.10, 0.09, 0.19, and 0.12 million rials m-3, respectively. The amount of NBPD index of wheat, barley, rapeseed, maize, watermelon, melon, sesame, tomato, cucumber, and carrot crops is equal to 0.06, 0.09, 0.04, 0.06, 0.07, 0.05, 0.10, 0.04, 0.11, and 0.07 million rials m-3, respectively. The amount of UWV index of wheat, barley, rapeseed, maize, watermelon, melon, sesame, tomato, cucumber, and carrot crops is equal to 0.06, 0.06, 0.06, 0.08, 0.10, 0.10, 0.06, 0.07, 0.12, and 0.09 rials m-3, respectively. According to the CPD index, cucumber, melon, watermelon, carrot, and tomato crops are respectively placed in the first to fourth priorities for cultivation. According to the BPD index, cucumber, barley, wheat, melon, and rapeseed crops are placed in the first to fourth priorities for cultivation. According to the NBPD index, cucumber, sesame, barley, watermelon, and carrot crops are placed in the first to fourth priorities for cultivation. According to the UWV index, cucumber, melon, watermelon, carrot, and maize crops are placed in the first to fourth priorities for cultivation. ConclusionExamining the amount of CPD, BPD, NBPD, and UWV indicators of the studied crops shows that cucumber has the highest physical and economic water productivity compared to other studied crops and its cultivation in Dehloran County reduces water consumption and implies high economic benefits for farmers. Melon has the lowest virtual water content and the highest physical water productivity compared to other studied crops. However, the high cost of melon production has caused this crop to not have high water economic productivity. Therefore, it is necessary to adopt supportive policies from the government to reduce production costs and increase the relative advantage of this crop. Sesame has the lowest yield and the highest virtual water content compared to other studied crops. Sesame is placed in the last priority of cultivation based on the CPD index. However, the low cost of production and high income of sesame has caused this crop to be the second priority for cultivation based on the NBPD index. The high NBPD index of sesame necessitates the need to focus on indicators that are effective in increasing the yield of sesame. Rapeseed has the second rank in terms of low yield and high virtual water content among the studied crops. Rapeseed has been placed in the eighth priority for cultivation based on the CPD index. In addition, according to the NBPD index, rapeseed has been placed in the last priority of cultivation compared to other studied crops. Therefore, it is recommended to remove this crop from the cultivation pattern of Dehloran County and replace it by planting crops with high economic productivity and low water requirements.

IntroductionBiochar is a potential soil amendment produced by pyrolyzing waste organic materials. Biochar with improving soil quality indicators could increase soil sustainability. Amending soil with biochar enhances soil quality and stimulates plant growth. So far, most studies have investigated the potential impacts of biochar on soil fertility, soil biota, soil chemical properties, soil greenhouse gas emissions, and remediation of contaminated soils. Comparatively, a minimal number of research has been carried out on the implications of biochar application on soil's physical and mechanical properties on the field scale and in the presence of plants. This study aimed to investigate the effect of Conocarpus erectus biochar as a modifier on some mechanical properties of soil (shear strength (SS), coefficient of linear extensibility (COLE), liquid limit (LL), plastic limit (PL) moistures, and plasticity index (PI)) as well as some physical properties includes soil porosity, soil moisture retention (field capacity (FC), permanent wilting point (PWP), and plant water available content (AWC)), soil air capacity (SAC), and bulk density (BD). Materials and MethodsThe research experiment was conducted in a completely randomized block design with three replications. The treatments including biochar at three levels (zero, three, and six ton ha-1) were added into a calcareous soil. The biochar was produced from Conocarpus erectus wood through the slow pyrolysis process at 550 °C. Before being applied to soil plots, the biochar was crushed to pieces smaller than 0.5 cm. The biochar was mixed to around 20 cm soil depth and soil moisture was kept at 70% of field capacity for three months. The corn plant was then planted and harvested after three months. Then soil samples were collected and used for physical and mechanical experiments. Some physical and mechanical properties of soil include SS, COLE, LL), PL moistures, PI, BD, porosity, SAC, FC, and PWP moisture were measured. The surface functional groups analysis of the biochars was detected using Fourier transform infrared spectroscopy (FTIR). Furthermore, the surface morphology of bulk biochar was portrayed by a scanning electron microscope (SEM). Results and DiscussionThe results of the analysis of variance (ANOVA) indicated that the addition of Conocarpus erectus biochar had a significant effect on the soil's physical properties (P< 0.01). The results revealed that the biochar significantly enhanced soil porosity, air capacity, and moisture content at FC and PWP, while diminished soil bulk density (P <0.01). The amount of soil porosity, air capacity, FC, PWP moisture, and AWC in the treatments of three and six ton ha-1 of biochar had a significant difference with the control treatment (P< 0.01). The increase of FC, PWP, and plant available water by raising the amount of biochar was attributed to the porosity of the biochar particles. The results of SEM images revealed that synthesized biochar is a porous material that significantly can enhance the total soil porosity and water retention capacity. Furthermore, the FTIR spectra of the synthesized biochar functional groups such as carboxylic acid, phenolic, ketone, ester, and, amine were detected. The findings of the ANOVA also show a significant effect (P< 0.01) of Conocarpus erectus biochar on the soil mechanical properties (SS, COLE, LL, PL moistures and PI). Moreover, the results of the mean comparison test revealed that three and six ton ha-1 of biochar treatments had a significant difference with the control treatment (P< 0.01); The difference between three and six ton ha-1 of biochar was also significant (P< 0.01). Application of the biochar increased LL, PL, and PI; while diminished shear strength and COLE index. In the treatments of three and six ton ha-1 of the biochar, the amount of LL increased by 40.32 and 77.74%, respectively, and PL increased by 40.8 and 70%, respectively, compared to the control treatment. Furthermore, the value of PI was enhanced by 38.33 and 71.66% in the biochar treatments of three and six ton ha-1 compared to the control treatment. While, the amount of shear resistance in the treatments of three and six ton ha-1 of the biochar decreased by 23.94 and 34.75%, respectively, compared to the control. The amount of decrease in COLE index at the three and six ton ha-1 of the biochar compared to the control was 20.28 and 36.95%, respectively. The results also revealed that the application level of six ton ha-1 biochar treatment increased the amount of porosity, SAC, FC, PWP, and AWC by 70, 13.7, 6.2, 5, and 8 %, compared to the control treatment. The application of biochar reduced the COLE index significantly; therefore, biochar has the potential to improve the mechanical characteristics of expandable soils. ConclusionThis study showed that the biochar of Conocarpus as a suitable modifier improves the quality of the physical and mechanical properties of calcareous soils. According to the findings, it can be concluded that biochar by reducing soil bulk density, and shear strength, increasing porosity, water retention, plant available water, and air capacity, and improving soil consistency (Atterberg Limits) can provide suitable conditions for plant growth. The application of biochar not only has positive effects on the transport of nutrient elements, gases, heat, and water movement in soil but also by increasing soil porosity and water retention capacity provides beneficial conditions for plant growth. Therefore, in arid and semi-arid regions such as Khuzestan Province, which is facing the problem of lack of water resources and organic matter, biochar can be a valuable soil amendment. Overall, the use of biochar of Conocarpus could improve soil physical and mechanical properties at the field scale but long-term studies in different soils under plant cultivation are needed for a better understanding of its performance as a soil amendment.

IntroductionWater scarcity is an increasingly critical global issue, causing a rise in arid lands and highlighting the need to address wasteful water usage in agriculture. Population growth, climate change, industrialization, and human conflicts have exacerbated water shortages, particularly in arid and semi-arid regions. According to the Falcon Mark index and the United Nations, Iran is experiencing water stress and a severe water crisis, threatening food security, economic development, public health, and national security. With over 92% of water consumption attributed to the agricultural sector, efficient water usage and reducing irrigation system losses are paramount. This study focuses on improving furrow irrigation efficiency by investigating surface irrigation efficiency and providing appropriate solutions. Materials and Methods The SIRMOD model, capable of simulating hydraulic surface irrigation, was employed to obtain the cut-off to Advance Time Ratio (CR) indicator. Diagrams based on soil texture, inflow rate, farm length, and the CR indicator were generated to enhance the design and efficiency of furrow irrigation systems. To ensure the accuracy of the simulation and results, the SIRMOD model was validated, and optimal CR indicators were determined for a furrow irrigation system with four different lengths and sandy-loam textured soils. A suitable field under the row irrigation system was selected, and the soil texture was determined using double-cylinder tests. Field operations included establishing forward and backward stations and smoothing the water path to prevent water from exiting the furrow. A pipe at the end of the furrow measured the output runoff using the volumetric method, and standard siphons were chosen to maintain the input flow rate. After turning on the siphons, advance and retreat times were recorded, and instantaneous runoff was estimated during irrigation. This process was repeated for three inflow rates: 0.5, 0.8, and 1.15 l s-1, with three repetitions for each rate l s-1. Results and DiscussionThe advance times in the first, second, and third furrows were 44.22, 45, and 42.88 min, respectively, while the water recession times were 293.1, 290.73, and 292.7 min, respectively. The relatively high water speed in light-textured soils and the short water regression times indicated the light texture of the farm soil and validated the test results. For an inflow rate of 0.5 l s-1, measurements revealed that half of the water volume entered the furrow along its length, while the other half exited as runoff. Simulation results for an inflow rate of 0.5 l s-1 yielded CR values of 8.37 for a 100 m length, 6.99 for a 120 m length, 5.41 for a 150 m length, and 3.31 for a 200 m length. For an inflow rate of 0.8 l s-1, optimal CR indicators were 8.0, 7.25, 6.19, and 4.63 for lengths of 100, 120, 150, and 200 m, respectively. At an inflow rate of 1.15 l s-1, the optimal CR indicators for lengths of 100, 120, 150, and 200 m were estimated to be 7.42, 6.53, 6.11, and 5.06, respectively.  ConclusionThe study's findings highlight a significant breakthrough in optimizing water usage in agriculture, a sector heavily reliant on water resources. By meticulously experimenting with different inflow rates and furrow lengths, the highest water application efficiency was attained with a specific set of parameters. An inflow rate of 0.5 l s-1, coupled with a furrow length of 200 m, resulted in an impressive water application efficiency of 83%. This efficiency correlates with a cut-off to advance time ratio (CR) value of 3.31, indicating a well-balanced water distribution system. The implications of this discovery are far-reaching, especially in regions facing water scarcity and agricultural challenges. By implementing these optimized settings, farmers can maximize their water usage while minimizing waste. This not only ensures the efficient utilization of a precious resource but also contributes to sustainable agricultural practices. Furthermore, the consistency in results, achieved through the SIRMOD model's validation, underscores the reliability of these findings, providing a solid foundation for future irrigation system designs and improvements. The expanded text emphasizes the significance of the study's findings, highlighting the efficient water usage and its potential impact on sustainable agriculture, especially in water-scarce regions. It also underscores the reliability of the results through the model's validation, providing confidence in the optimized settings for furrow irrigation systems. The best water application efficiency in the farm, 83%, was associated with the inflow rate of 0.5 l s-1 and the length of 200 m, and the CR equaled 3.31.

Introduction Heavy rains are one of the climate phenomena that leads to human and financial losses. Since Iran is located in a dry and low rainfall region, this type of rain leads to flooding in a short period and causes huge damages that cannot be compensated in the short term. Due to its mountainous nature, the western region of Iran is suitable for the intensification and expansion of rainfall and flooding. Because mountains play an important role in increasing atmospheric precipitation by trapping air humidity. Today, the general and expanding climate change phenomenon has become one of the most important aspects of meteorology. Global warming is the driver of climate change and simply means an increase in the average temperature of the atmosphere. It has started continuously, creeping, and increasing since 1950 due to the accumulation of greenhouse gases in the atmosphere. One of the most important aspects of this large-scale general event (climate change) is the increase in entropy or anomaly of the earth's atmospheric system. Following the increase in atmospheric anomalies, some extreme events such as severe droughts, torrential rains, floods, extremely hot temperatures (heat waves) in summer, and late frosts in spring have increased significantly across the country. The behavior of the natural climate of the region tends towards disorder and abnormality in a significant way every year. Accordingly, the current research was done to reveal the dynamic patterns of heavy rains causing floods in Lorestan Province based on synoptic systems.   Materials and Methods In this research, three types of data were used. The first category is the daily rainfall data of the synoptic stations from 2000 to 2020, which was obtained from the meteorological organization of Lorestan Province. The second set of data related to the factors of the middle and lower levels of the atmosphere, including geopotential height, sea level pressure, Omega, moisture, wind, an orbital and meridional component of wind level 300 hectopascal for 5 March and 1 April 2019, 15 February 2006, 29 October 2015 from NCEP/NCAR. The third group of data is related to the data of the upper atmospheric station of Kermanshah which were obtained from the University of Wyoming database for selected days. using the 95th percentile method, the heavy rainfalls of the studied stations were determined. In the following, the statistical features of rainfall of four selected heavy rainfall were analyzed. The synoptic conditions that produced these rains were investigated using the data of the middle and upper levels of the atmosphere of the ECMWF database version ERA-Intrim. To examine the patterns of the middle and upper levels of the atmosphere, the maps of the daily synoptic factors of these two precipitations are analyzed. Finally, using thermodynamic indices, the thermodynamic status of the upper atmosphere of the region is investigated.   Results and Discussion At the level of 500 millibars, which is the main level of observing the systems that produce the main atmospheric phenomena, in two rain waves on 25 March and 12 April 2018, a very strong low-altitude core with a central height of 5550 geopotential meters was closed over the eastern Mediterranean Sea and the western part of Iran is located in the front part of a very deep trough, which indicates the dominance of a very strong circulation system in the west of Iran. Low pressure was visible on the surface of the earth in the west of Iran. The moisture map of level 850 for 15 February 2014 showed that the moisture cores are completely located in Lorestan Province. One of the moisture cores from the Persian Gulf has strengthened with a curve of 5 to 9 gr and covers the south to the northeast and has provided the conditions for the creation and fall of heavy rains in the province. The moisture sources of the system are provided by the Black Sea and the Red Sea, as well as the Persian Gulf. The trough of the Red Sea is associated with the establishment in the middle level of the atmosphere, and it has brought heavy rains and floods in the west. In the humidity map, it can be seen that a circulation center has formed over the Oman Sea and the Persian Gulf, which directs moisture from the Oman and Arabian Seas to the south and southwest of Iran. In addition, another circulation center has formed over the Mediterranean Sea, which it sends the Mediterranean and Black Seas to the west and southwest of Iran. This means that these two centers concentrate the moisture of the South, West, and North-West seas of the country on Lorestan and Kermanshah provinces, and the atmospheric systems are fully nourished in terms of moisture.   Conclusion The analysis of the co-occurrence patterns of extreme rainfall events showed that a similar co-occurrence pattern was the generator of heavy rainfall waves in the region. In the studied days, the presence of a deep trough on the eastern Mediterranean Sea and the western part of Iran is located in the front part of a very deep trough, which has provided the conditions for the ascent and entry of low-pressure systems for the west of the country. The instability indices of the upper atmosphere, which were analyzed with emphasis on the upper atmospheric stations of Kermanshah, did not confirm the existence of extreme instability in the region. The Skew-T diagram indicated that a global synoptic system involved the entire region and the local convection factor did not play a critical role.

Introduction In recent years, advancements in computer technologies, remote sensing systems, software, and various models have enabled the prediction of ecological niches for diverse plant and animal species. Over the past decades, alterations in human lifestyles, industrialization, and production processes have resulted in increased atmospheric pollutants, leading to severe climate change. Global climate change has induced shifts in plant growth ranges, with an expansion of warm-weather-adapted plants and a decline in cold-weather-adapted ones. These changes consequently modify the structure and ecosystems of the entire planet, directly and indirectly impacting ecosystem services crucial for human well-being and economic prosperity. Consequently, predicting the effects of climate change on plant distribution has emerged as a pivotal research area to inform conservation strategies and programs. Species distribution models primarily predict the impact of climate change on plant growth ranges. Accurate predictions of species distribution are essential for effective conservation planning and sustaining forest ecosystem services in the face of climate change. Given the significance of this issue, this research aimed to identify the most critical climatic and environmental factors influencing the distribution of Rhume ribes L. species and ascertain its current geographical range within Razavi Khorasan Province, located in northeastern Iran. Materials and Methods For this purpose, 68 bioclimatic variables including soil characteristics (45 cases), topographical factors (four cases), and climatic factors (19 cases) were first subjected to correlation analysis as predictive variables and variables with high correlation (above 80%) were removed. Due to the large size of the studied area, sampling of presence points was done with field visits during the period of 1400-1401 of the introduced areas, and a total of 232 presence points from eight regions were registered as presence points using the global positioning system (GPS). Then all the environmental data and presence points in R software using Biomed 2 package models which include GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt models in determining the relationship between vegetation and environmental factors in pastures Razavi Khorasan Province was predicted in the present time. The accuracy of the models was evaluated using the values of KAPPA, TSS, and ROC indices, which are prominent and widely used indices for determining and identifying the potential areas. Results and Discussion The results of this research showed that according to the accuracy evaluation index, the best modeling for the current time is the random forest model with an accuracy of 95.5%, which indicates the accuracy of the modeling at an excellent level. Also, the relative importance of the selected models and the variables that have had the greatest impact at present include digital elevation model (DEM), Average monthly (BIO2), This is the sum of all total monthly precipitation values (BIO12), The average temperatures experienced during the wettest quarter (BIO 8) and the amount of sand at a depth of 15-30 cm from the soil surface (Sand 15-30), which indicates the great influence of climatic factors on the distribution of this species, and in the next stage, the height above sea level and finally the soil factors have the greatest influence. The most distribution of Rhume ribes L. species at present is in the east of Razavi Khorasan Province including the cities of Bakharz, Torbat Jam, Taibad, Zaveh, Khaf, and Rashtkhwar in the form of a strip on their border and in the west of the Province on the border of Koh Sorkh and Neishabur cities and the north of the Province on the border Binaloud, Zabarkhan and Mashhad cities and the south of the Province in Gonabad city has spread in a strip and limited way. Conclusion The results of this research can be used to improve, protect, and economically exploit and expand the habitat of the Rhume ribes L. species. Destructive human activities, such as livestock grazing and the corrupt exploitation of rhubarb, combined with climate change, have endangered the current habitats of this species in Razavi Khorasan Province. These unprincipled exploitations, disregarding environmental capacities in natural resource management, are a significant problem in Razavi Khorasan Province and the country, gradually leading to water, soil, and plant loss in the region. While this study sufficiently examined current climatic and soil factors to identify areas suitable for rhubarb species, a deeper understanding is required to effectively restore damaged areas, preserve those at risk, and enhance the predictive capabilities of ecological models. In addition to climatic and soil factors, the potential habitats of plant species are influenced by various factors, including human activities, exploitation methods, livestock grazing, wildlife, economic and social conditions, and other direct and indirect impacts on distribution. Numerous studies have been conducted on different plant species. This research evaluated various machine learning-based species distribution models, selecting random forests as the most suitable. Species distribution models are valuable, cost-effective tools for natural resource managers, increasing their awareness and decision-making abilities regarding the effects of climate change on species.

IntroductionThe average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran. Materials and MethodsTo investigate the effects of climate change on the climatic classification of Iran, the De Martonne aridity index has been used. To show the effects of climate change in the past and the future on Iran's climate, data from 120 meteorological stations of Iran, which are distributed in different locations with different climates, were collected and analyzed in the statistical period of 1933-2022. The climatic condition of Iran in the base period was determined according to the De Martonne aridity index. In addition, to investigate the effects of climate change in the coming periods on the climatic classification of Iran, the data related to the output of the CanESM2 model, which is one of the CMIP5 models that is hybridized by the Canadian Center for Climate Modeling and Analysis (CCCMA) by combining CanCM4 and CTEM models, were used. To examine the changes in climatic classes of Iran under different scenarios and conditions, the output of two release scenarios, RCP2.6 and RCP8.5, were utilized. Due to the large-scale output of General Circulation Models (GCM), the output of this model was downscaled using the LARS-WG model. The LARS-WG model, which is considered one of the most famous and widely used models for downscaling weather data, was used to generate precipitation values, minimum and maximum temperatures, as well as daily radiation, under base and future climate conditions. Results and DiscussionAccording to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran. ConclusionIn this research, by using the latest available data, Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics

Introduction Life on Earth is influenced by precipitation. Precipitation is one of the most significant factors that affect the hydrological cycle. Considering that precipitation is non-linear, complex, and can be changed according to spatial and temporal, estimating the amount of this important atmospheric factor in each month or year for each region and watershed is particularly important in managing and optimizing water resources. Various optimization models and algorithms have been proposed for modeling hydrological systems in recent decades. These algorithms have significantly reduced errors and increased accuracy. Still, since hydrological systems rely on random events, none of the methods can be completely and accurately selected as a superior model for modeling and estimating. The honey badger algorithm is an innovative algorithm that requires a few iterations to achieve an optimal solution, and this increases the speed of reaching the desired results. In current study investigates the performance of three models, including multiple linear regression (MLR), artificial neural network (ANN), and hybrid artificial neural network with honey badger optimization algorithm (HBA-ANN) for modeling the temporal and spatial precipitation in East Azarbaijan province. The best-developed model was selected by evaluation criteria such as R, RMSE, NRMSE, MBE, and NSE, the best model is selected. Materials and Methods The MLR model is one of the methods to analyze and investigate several variables. In this method, the model has one dependent variable and several independent variables, so that a linear equation is generated between the independent variables called X1, X2, ..., Xn and the dependent variable Y. ANN is a black box model of neural networks in the human brain. One of the most used methods is the BP method, which includes two stages. In the first stage, which is entitled feed-forward, the error value is calculated, after comparing output and objective values. In the second stage, which is labeled the back-propagation, the error value calculated in the previous step is corrected. The mentioned two stages continue until the output of the model approaches the desired output. The HBA is a new algorithm that simulates the honey-seeking behavior of a creature called the honey badger. The HBA includes two stages. In the first phase, the locations of this creature are calculated, and in the second phase, the exact distance between the HBA and the prey (dj) is calculated based on the honey intensity (S) and the honey smell intensity (Ij), as well as its new and optimal location for the prey Xnew. In the HBA-ANN model, the HBA algorithm is used to determine the most optimal output value in the ANN and increase performance in modeling. Therefore, the developed hybrid model can have the characteristics of both ANN and HBA methods. Results and Discussion In this study, in the first stage, the temporal modeling, and in the second stage, the spatial modeling of the monthly precipitation of 18 stations in East Azarbaijan province during the period of 1996-2022 using MLP, ANN, and HBA-ANN models has been paid. For temporal modeling of precipitation, one and two-month precipitation delay steps of the stations were used as input parameters. The first 70% of the dataset was selected for the training phase and the last 30% of the dataset was selected for the testing phase. Based on the results obtained from evaluation criteria and graphic diagrams, it can be concluded that the HBA-ANN model indicated significant accuracy compared to other models in the temporal modeling of precipitation. Also, by comparing the results of the stations in the HBA-ANN model, the Heris station with R =0.94, RMSE=2.25, NSE=0.79, NRMSE=0.04, and MBE=1.06 in the testing stage performed better compared with other stations. For spatial modeling of precipitation, the geographic coordinates of the stations, which include longitude, latitude, and altitude, are used as input parameters, and average monthly precipitation is used as the output parameter. From eighteen stations, 70% of the stations were selected for the training phase and 30% of the stations were selected for the testing phase. Based on the results obtained from R=0.95, RMSE=1.03, NSE =0.92, NRMSE = 0.03, and MBE = -0.81 and graphical diagrams, it can be concluded that the HBA-ANN model revealed significant accuracy compared to other models in spatial modeling of precipitation. Conclusion Precipitation is one of the most important factors that significantly change the hydrological cycle. Therefore, modeling and estimating this parameter is vital. In this study, the performance of multiple linear regression (MLR), artificial neural network (ANN), and hybrid ANN using honey badger algorithm (HBA-ANN) models were used for the spatial and temporal modeling of precipitation in East Azarbaijan province. For spatial modeling, the time delay steps of one and two months of station precipitation were selected as input parameters. Also, for temporal modeling, the longitude, latitude, and altitude parameters were used. The mentioned models were evaluated by R, RMSE, NSE, NRMSE, and MBE assessment criteria. According to the results of temporal modeling, the HBA-ANN model for all stations, especially Heris station with R equal to 0.94, RMSE equal to 2.25, NSE equal to 0.79, NRMSE equal to 0.04, and MBEequal to 1.06 is selected as the superior model. Also, based on the results obtained from spatial modeling, the HBA-ANN model with R equal to 0.95, RMSE equal to 1.03, NSE equal to 0.92, NRMSE equal to 0.03, and MBEequal to -0.81 was selected as the best model. The MLR and ANN models, respectively, presented a relatively poor performance compared to the developed hybrid model.

Introduction The cow manure application is a common way to improve soil properties, however, the uses may cause bacteria contamination in the soil profile as well as the surface water and groundwater. Thus it is crucial to monitor the amount of bacterial transport and retention on the soil surface and deeper under cultivation conditions. The literature review shows that the structure of manure, soil structure, cultivation, soil moisture, soil salinity, and irrigation methods are important in the transport and retention of bacteria in the porous media. The plant’s roots cause cracks, creating preferential flow in the soil profile. Preferential flow accelerates pollutant transportation in the soil. Therefore It is essential to survey the transport of bacteria in the presence of plants. In this research, it has been investigated the effect of grass cultivation conditions and the size of the particles on the retention of Escherichia coli bacteria in the soil profile. Materials and Methods This research was conducted in the greenhouse and column experiments condition with grass cultivation. Cow manure was dried for 72 hours and passed through 0.25, 0.5, 1.0, and 2.0 mm sieves. The grass was prepared and placed uniformly on the surface of the soil columns. Then 14 days were given for the stabilization and settlement of the grass roots into the soil columns. The columns were irrigated once every two days by custom surface method until the soil moisture to the field capacity. The column’s height was divided into six equal layers, in which each layer was sampled to measure the remaining E. coli bacteria. The live counting method was used to determine the bacteria accumulation on the soil profile. For the measurement, a nine cubic centimeter of distilled water was added to one gr of each soil sample and they were put and kept in a shaker for 30 minutes. After preparing the soil samples’ dilutions, 100 µl of each dilution was cultured on the Eosin Methylene Blue (EMB) culture medium. The plates were retained at 37 °C, the temperature suitable for E. coli growth. After 24 hours, the bacteria colonies were counted and reported per ml. Due to the difference in the initial concentration of bacteria in the cow manure samples, the relative concentration index of bacteria was used to compare the concentration of bacteria in the effluent and the soil profile. This is the bacteria concentration ratio to the concentration of input bacteria, at each depth of the soil with the S/C0 parameter, it was shown that C0 and S are the bacteria concentration in the manure columns and the bacterial growth rate in the soil depth respectively. Results and Discussion In this research, the distribution of the soil bacteria is studied at the end of the test period. The results show that in treatments without grass cultivation, it can be seen that the maximum S/C0 for all cow’s manure sizes in the surface layer was 10 cm. With increasing depth, the relative concentration of the bacteria in all treatments has decreased. In the conditions of grass cultivation on the soil surface, the relative concentration of bacteria for the manure particle size of 0.25 mm at lower depths was more than the treatment with a larger particle size. The smaller size of the manure particles of 0.25 mm due to their transfer to the lower layers has led to the observation of a higher amount of bacteria compared to other sizes at lower depths. In other words, due to their small size, cow manure particles with a diameter of 0.25 were more easily transported to the lower layers of the soil column by flowing through the pores created by the plant roots. The reason for the decrease of the bacteria absorption in the soil surface layer compared to the treatments without cultivation can be attributed to the soil structure. The root growth causes the creation of macropores and large pores on the soil surface and increases the transfer of bacteria to the lower layers.Conclusion The effect of grass cultivation and different sizes of cow manure particle size (2.0, 1.0, 0.5, and 0.25 mm) were evaluated on the distribution of Escherichia coli bacteria in the soil profile. It shows that the bacteria retention rate decreases with increasing depth in the soil profile. Manure particles with a size of 0.25 mm cause more contaminant transport to the lower depth of the soil profile. Preferential flow in the conditions of grass cultivation caused the transport of bacteria to the lower depths. Grass cultivation causes bacteria retention at lower depths compared to the conditions without cultivation. The average bacterial retention index was 68% for a manure size of 2.0 mm at a depth of 10 cm and 48% in the treatment with a manure size of 0.25 mm. It concluded that the possibility of deep soil contamination with the application of manure with finer particles and grass cultivation is higher than with coarser particles and without grass cultivation.

Introduction Although a flood is an extreme and exceptional flow, every exceptional flow will not turn into a destructive flood, different factors must be changed in nature to cause destruction, damage, and casualties. In general, floods can be divided into four groups flash floods, river floods, urban floods, and coastal floods. Urban floods usually cause fewer casualties and mainly create damage caused by flooding, disruption of traffic, interruption in socio-economic activities, and problems of this kind. The damage caused by non-urban floods is often heavy and sometimes accompanied by high and catastrophic casualties. According to the Mediterranean climate, Iran is the seventh country in the world in terms of flooding. The flood-prone areas of the country are estimated to be around 91 MHA. In other words, 55% of the country's surface has contributed to the production of surface runoff, of which about 42 MHA have moderate to very high flood intensity. The review of sources shows the development of knowledge-based methods, statistical methods, and artificial intelligence algorithms in predicting flood-prone areas in urban and non-urban watersheds in different regions worldwide. However, a hybrid method of the Fuzzy, Delphi, and Analytic Hierarchy Processes (FDAHP) in urban flood susceptibility has not been used. Regarding the questions, what are the most important factors in urban flood occurrence? Is it possible to determine flood-prone areas in urban areas using the FDAHP hybrid model?, this study aims to identify the factors influencing the occurrence of floods and predict flood-prone areas in Sanandaj City. Materials and Methods  In this study, which has a descriptive-analytical-comparative approach, to predict floods in Sanandaj City, the FDAHP was used. First, each of the conditioning factors (14 factors) was scored by flood experts and completed using the scores obtained from other stages of the FDAHP model. After collecting the opinions, the relative weights of the indicators were determined using the hybrid model, and finally, the flood susceptibility map of Sanandaj City was prepared using ArcGIS 10.5 software. The different stages of modeling with the FDAHP method are as follows: 1) experts' opinions, First, with the help of experts (technical and executive experts of the Kurdistan Province and Sanandaj Municipality's Natural Resources and Watershed Administration) the decision-making parameters according to their importance qualitatively or, if possible, quantitatively they rate. (Opinion scales are: very important with a nine score, importance with a seven score, average importance with a five score, low importance with a three score, and no importance with a one score). 2) Calculation of fuzzy numbers. After the preliminary stage, which includes a survey of experts in the form of a qualitative or quantitative questionnaire, fuzzy numbers were calculated based on the results of this survey. 3) Forming the matrix of fuzzy pairwise comparisons. 4) Calculating the fuzzy weight of the parameters. 5) De-fuzzification of model parameters. 6) Evaluation of the accuracy of the output of the spatial flood prediction model. Results and Discussion The findings showed that slopes of less than 10% (flat), areas with an elevation of fewer than 1400 m above sea level, slope aspect and curvature (flat), urban land use with high building density, rainfall of more than 369 mm and rock type Qt2 (Quaternary alluvium) have the highest susceptibility to floods compared to other types in Sanandaj City. Also, the results show that, on the one hand, increasing the distance from the residential areas, the distance from the roads, and the distance from the waterways decreases the susceptibility to urban flooding. On the other hand, with the increase in building density, the density of roads and the density of waterways in Sanandaj increases the susceptibility to flooding. These results are consistent with the other studies that concluded the residential areas with the largest area have the highest risk of vulnerability. Our findings indicated that the density of the waterway, the slope, and the distance from the waterway have the most influence on the occurrence of floods in Sanandaj City. Rainfall, road density, building density, distance from residential areas, distance from roads, flow accumulation, elevation, land use, lithology, and slope curvature are the next priorities in terms of importance in the occurrence of floods in Sanandaj City. Conclusion The flood prediction map showed that a large part of Sanandaj City including the City's northern, western, southern, and center, which is crowded, has more potential for urban flooding. For example, the old and dilapidated buildings of the City center are highly susceptible to floods, but the border areas around the City have less exposure to this phenomenon. Therefore, as the distance from the City center and the residential regions increases, the potential for flooding decreases. Based on the value of 80.56% of the area under the curve, the validation results indicated that 80.56% of the areas where urban flooding is visible have been correctly predicted. The FDAHP hybrid model had a high ability to estimate the areas prone to urban flooding and, therefore it can be tested and evaluated as a management tool to identify urban floods in other similar areas. Since our aim concerning the occurrence of floods is more on flood mitigation, according to the mentioned theoretical bases and the existing views in the field of flood prediction maps, it can be stated that the ruined canals and the network of waterways, unauthorized constructions, high density, and elevation of buildings, old and dilapidated buildings and the irregularity of the canals have aggravated the urban flooding. Overall, it can be said that obtaining an accurate and reasonable urban flood prediction map can help City managers and planners identify flood-prone areas to manage the urban flood crisis.

IntroductionActual evapotranspiration, one of the most important components of the hydrological cycle, causes 60% of precipitation to return to the atmosphere. This value increases up to 90% in dry and semi-arid areas. In recent years, with population growth, increasing water demand, and climate change, the importance of this phenomenon has doubled. Despite its importance, actual evapotranspiration remains largely unknown and its estimation by direct measurement methods is costly and time-consuming. In this regard, the Penman-Monteith method, the most accepted method for estimating reference evaporation and transpiration, also requires a lot of meteorological data. Despite the weakness of the conceptual model, using experimental methods to estimate evaporation and transpiration is still common due to its simplicity. In addition, examining the conceptual model and statistical methods without prejudice makes it possible to identify the influential variables and create experimental relationships compatible with the conceptual model. Materials and Methods The case study, with an area of 13 ha, is located in the southwest of Sicily (Italy), about five km from Castelvetrano. The landscape is flat and the soil type is relatively homogeneous. The main crops are olives (70% coverage), vineyards (24%), fruit trees (2.6%), and other garden products (3.4%). The plants are about 3.5 m tall and are arranged in a regular grid of five m by eight m (density of 250 plants per ha). The climate of the region is Mediterranean and the soil texture class, according to the USDA classification, is silty clay loam. In this research, one-hour meteorological data from 23 meteorological variables from the Sicily meteorological-agricultural station and actual evapotranspiration data extracted from the Eddy covariance method for the statistical period of 2009-2016 were used. Linear regression methods were used to investigate the relationships between the 24 variables and actual evapotranspiration. Integrated hierarchical clustering was used also to classify the 24 variables. Results and Discussion Evaluating the relationships between the 24 variables (23 meteorological variables and actual evapotranspiration) using the linear regression method led to the extraction of relationships between parameters in the form of a 24x24 matrix. In other words, to predict each variable (as a dependent variable), 23 relationships with other parameters (as independent parameters) were extracted. Then, the priority of the independent variables to predict each dependent variable was determined based on the correlation coefficient (R). The average of the 23 numerical ranks of an independent variable to predict other dependent variables indicates the degree of competence of that variable to predict other variables. The result of integrated hierarchical clustering with a 70% correlation is seven clusters. The members of cluster number one are different temperature variables (instantaneous, minimum, maximum, and average), cluster number two, rainfall variable (the only single member cluster), cluster number three, humidity variables (instantaneous, minimum, maximum, and average), cluster number four, pressure variables (station pressure and sea level pressure), cluster number five, variables of total solar radiation and evaporation and transpiration (the only cluster with non-identical members), cluster number six, different wind speed variables (instantaneous, minimum, maximum and average at the height of two and 10 m) and cluster number seven showed the wind direction variables. The summary of the classification results generally shows that the meteorological variables are independent except for the variables of the same name (such as temperature variables) all Variables with the same name were placed in a cluster, and the only variables with different names that were located in a cluster were total solar radiation and evaporation and transpiration. The representative of each cluster is the best predictor (based on rank) among the members of that cluster to predict other variables. Based on this, average temperature variables, rainfall, average relative humidity, sea level pressure, total solar radiation, maximum wind speed at a height of two m, and wind direction at a height of 10 m were determined as representatives of seven clusters. Also, the best predictor of these representatives was determined from inside and outside the cluster members. Based on the regression analysis, the best predictor of actual evapotranspiration with a correlation coefficient of more than 70% in total solar radiation. Instantaneous and minimum relative humidity variables with a correlation coefficient of about 50% (inverse relationship) took second and third place respectively to estimate actual evapotranspiration. The fourth and fifth ranks also belong to the average and maximum humidity with a correlation coefficient of about 49%. The independent variable of the duration of wetness of the leaves with a correlation coefficient of 40% has taken the sixth place. The characteristics of wind speed, temperature, wind direction, total annual rainfall, and pressure have the next ranks to estimate the actual evapotranspiration, respectively, with correlation coefficients of less than 30%. Conclusion In general, the high correlation between total solar radiation and actual evapotranspiration in a cluster indicates the key role of this meteorological variable in estimating evaporation and transpiration and is a justification for using methods based on energy balance to estimate this parameter. The high correlation between the estimation of actual evapotranspiration with the total solar radiation, considering the dependence of this variable on other climatic and hydrological variables, can be a useful point for use in watersheds lacking data and information. Also, the state of relative humidity ranks first and second respectively to predict other variables and actual evapotranspiration, indicating the key role of this variable in the case study. Contrary to some research about the key role of precipitation in the estimation of evaporation and transpiration in the Mediterranean climate, in this research no acceptable correlation was observed between the independent variable of precipitation and the dependent variable of actual evapotranspiration, although this issue may be related to the form of the equation. In this regard, instantaneous, minimum, average, and maximum relative humidity were ranked after total solar radiation. On the other hand, the total solar radiation in the estimation of the actual evapotranspiration with a correlation coefficient (71%) compared to the independent variable of relative humidity is in the first rank of the predictors, although it is ranked after the average and minimum relative humidity in the estimation of 23 meteorological variables. However, total solar radiation and relative humidity (average and minimum) were identified as two independent variables that are effective in estimating meteorological variables, especially actual evapotranspiration, and it is suggested that more research be done in watersheds with different climatic variations to discover the internal relationships of actual evapotranspiration and other climatic variables. 

IntroductionDue to climate change in recent years, the water crisis has become much more serious than before. Therefore, water shortage is one of the most important problems in the world in the last century. Evaporation from the soil surface can be considered the most important component in the water balance. Using soil amendments is a strategy to reduce the adverse effects of drought stress on plants. Water-absorbent polymers can be used as a modifier to improve soil health, which improves water holding capacity. Natural or artificial absorbents superused in agriculture are hydrophilic compounds that increase the water-holding capacity in the soil, reduce the leaching of soil nutrients, reduce the amount of evaporation from the soil surface, and increase the soil ventilation, causing better growth and development of plants and increased yield in drought stress conditions. These polymers can absorb water several times their weight in their building and gradually return water to the soil by reducing the water in the surrounding soil, so the soil is moist for a longer period without re-irrigation. Biochar and bentonite are among the important natural superabsorbents, whose effect on increasing the water-holding capacity in the soil has been proven. Despite many studies in this field, the impact of combining these two absorbents super has not been studied, so in the current study, the simultaneous effect of these two absorbents super on evaporation from the soil and the amount of residual moisture in the soil was investigated. Materials and MethodsThis research was conducted at Shahrekord University. To prepare biochar, thin and dry walnut tree branches were first collected, and after chopping, the wood was placed in cylindrical metal containers with lids and low oxygen conditions at two temperatures of 400 and 600 ̊C in an oven for two hours. The bentonite was also purchased from the market and its purity was checked in the laboratory. This research was a factorial experiment using a completely randomized design and three replications. The treatments include the combination of bentonite (Be) at three levels of two, five, and 10 % by weight with biochar (B) prepared at two temperatures of 400 and 600 ̊C at two levels of two and three percent by weight and a control treatment (a ClayLoam soil). In this research, the water balance method was used to obtain the amount of evaporation. In this method, the amount of water entering and leaving the lysimeters (pots) is measured using a graduated container, and since no plants are grown in the pots, the amount obtained from the balance equation is the amount of evaporation from the soil surface. The humidity of the treatments was also measured by the SM150 hygrometer at the surface and 15 cm depth of the pots. The analysis of the data obtained from this experiment, including analysis of variance and means comparison, was done in Statisca software and drawing figures in Excel software. Results and DiscussionThe results showed that the changes in the amount of evaporation from the soil surface in all the investigated months as well as the total evaporation of the entire period are significant at a significant level of 0.01. The comparison of the means for the total amount of evaporation in different treatments showed that the amount of evaporation in all the investigated treatments was lower than in the control treatment. The lowest rate of evaporation is related to the soil mixed with two percent bentonite and biochar produced at 600 ̊C. The highest evaporation from the soil surface after the control treatment (432 mm and equivalent to 3.1 mm per day) was observed in the soil treatment mixed with 10% bentonite and two percent biochar produced at a temperature of 400 ̊C. In total, the amount of evaporation from the soil in different treatments has decreased between seven and 14 % compared to the control treatment. The amount of soil moisture in all the investigated treatments has increased significantly compared to the control treatment. In other words, the combination of biochar and bentonite has helped to maintain moisture in the soil. The highest amount of soil moisture was obtained in the treatment of three percent biochar produced at 600 ̊C and 10 % bentonite and it was 25.4 %, which is 18 % more than the moisture content of the control treatment. Increasing the amount of bentonite added to the soil has also increased soil moisture. ConclusionThe results showed that the soil evaporation in the simultaneous use of three percent biochar with different levels of bentonite is higher than the use of two percent biochar. Therefore, it can reject the use of a three percent biochar composition to reduce evaporation from the soil. Based on this, the best treatment should be sought among the two percent biochar treatments. Among the treatments of two percent biochar, the greatest effect on reducing evaporation occurred in the two treatments of two percent biochar produced at 600 ̊C and two percent bentonite and the treatment of two percent biochar produced at 400 ̊C and five percent bentonite. Choosing one of these two treatments for use in large areas requires an economic study of the prevailing conditions in the region and the availability of bentonite minerals and raw materials for biochar production. In one treatment, the temperature of biochar production is higher (B2-Be2-T600), and in the other, the amount of bentonite used is higher (B2-Be5-T400). Optimum use of water in the field and scientific management of water, require appropriate tools that, in addition to being available, do not impose large costs on farmers. In addition to all the things mentioned above, not causing pollution to the soil and plants is also a very important parameter in this field. Therefore, according to the results of this research, it is recommended to use a combination of two or five bentonites with two percent biochar for use in fields.

Due to the large number of parameters affecting surface irrigation and their temporal and spatial changes, surface irrigation management is very complicated in obtaining high efficiency and uniformity. Therefore, improving the irrigation performance based on variables with minimum implementation cost, ease of use, and the possibility of implementation in the field, is essential, and due to the high losses in surface irrigation, it is necessary to use tools that can maximize water efficiency with proper planning. Achievement is inevitable. Considering the current conditions of Lake Urmia due to its environmental crisis, the lack of water, and the importance of saving the lake from the current situation, one of the basic ways is to increase the efficiency of surface irrigation in fields. Therefore, the objectives of this research are a detailed examination of actual efficiency, calibration, and verification of WinSRFR software for the region, evaluation of the performance of the software in estimating the advance time parameters, and Evaluation of the software in estimating the efficiency of water use. In other words, the main goal is to evaluate the irrigation performance and achieve the appropriate performance through proper management so that we can move towards this goal with proper design. To achieve the aforementioned goals, field evaluation was done during the wheat growing season. Materials and MethodsTo collect field data, experiments were carried out in selected fields of Urmia Plain in 2015. These tests were carried out in five fall wheat fields, which were all irrigated by the closed-end strip method under the management of the farmers. In this way, one strip was selected as a test strip in each farm and the evaluation parameters were done for the desired strip. In this research, the hydrograph information of the incoming flow, the time of flow interruption, the geometric parameters of the strip, the amount of soil moisture discharge in the root zone, the progress stages, and other characteristics were measured and evaluated. The inflow hydrograph was measured by installing WSC flumes at the beginning of the strips. To measure the advance speed, the strip under test was nailed at 10 m intervals and the advance times were recorded with the movement of the water flow. The cutoff time was applied and recorded according to the opinion of the farmer. The geometric parameters of the strip, including the length, width, and slope of the strip, were obtained by mapping the tested strip. The net depth of irrigation was calculated based on the amount of soil moisture discharge in the root zone by sampling the soil just before irrigation and the gross depth of irrigation was calculated based on the amount of water given. Results and Discussion The model overestimated in some irrigations and underestimated in others. The maximum efficiency of irrigation application in the second farm (f2) and the third irrigation round (Irrg.3) with a value of 65.51% due to the application of irrigation management to reduce the flow and the minimum efficiency in the fourth farm (f4) and the first irrigation round (Irrg.1) with a value of zero percent due to the heavy rain a few days before irrigation (the soil was completely wet and its net water requirement was zero) and the farmer's re-irrigation, zero efficiency was achieved. Also, the WinSRFR software could not simulate the cut-back method well. The average efficiency in all tested farms was estimated at 23.58%. The reason for this is irrigation with low frequency and high depth of irrigation. Also, the non-uniform leveling and inappropriate slope of the strips can be considered as another factor in reducing the efficiency, which causes the slow movement of water along the strip and the increase of deep penetration losses at the beginning of the strip. The model has been able to correctly predict the progress of water flow in all irrigations. In the second irrigation of the third field, the advance time of the model has more accurately simulated the advance time of the flow compared to other irrigations. ConclusionAccording to the results, the simulation of the progress of the water flow and the prediction of the application efficiency by the zero inertia model by WinSRFR software were close to the measured and calculated results; So, it can be said that WinSRFR software can model with ideal accuracy (NRMSE<10%) the closed strip irrigation field under farmer management. The failure to accurately estimate the parameters of the Kostiakov-Lewis infiltration equation and the Manning roughness coefficient, which is different in each irrigation cycle, is the cause of the error between the measured and predicted values. The results of this research showed that in the closed field fields under the management of farmers, excessive watering of the plants by the farmers caused deep infiltration losses from the root zone and the main factor of reducing efficiency in the mentioned fields. Also, the uneven and low slope of the fields, which the farmer is unwilling to level due to the high cost of leveling the land and causing the water to not move well in the strip, can be another reason for the decrease in efficiency in the fields. For accurate evaluation, it is suggested to use volumetric meters to measure the volume of water delivered to farms. Because the fluctuations resulting from the pump during irrigation cause errors in evaluation and modeling. On the other hand, due to the low willingness of farmers to implement irrigation systems under pressure, to reduce water losses, the efficiency of surface irrigation should be increased.

Introduction Soil contamination due to heavy metals is a global environmental issue. One vital aspect for understanding the impact of a contaminant in porous media is to describe their transport behavior using appropriate models. The governing equations for solute transport in soil consist of the convection–dispersion equation (CDE) and the mobile–immobile model (MIM). Mathematical models are usually used to evaluate solute transport in porous media. The first model used to express the transport of solutes and pollutants in porous media is CDE it provides acceptable and satisfactory results in homogeneous soils in laboratory tests. Hydrus-1D is a modeling environment for simulating water, heat, and solute movement in one-dimensional variably saturated media. Sensitivity analyses and model identification are standard approaches in modeling applications to investigate the relative importance of model components that control the system’s behavior. The sensitivity analysis is applied to identify the parameters that influence the model performance most. The sensitivity analysis is defined as the rate of variation in the model outputs due to changes in the input parameters. This study is a fundamental practice for analyzing the behavior of a model under different conditions of an application. The sensitivity analysis could be a practical and powerful tool for investigating the role and importance of model components, such as parameters and forcing data on the model responses. Materials and Methods The loamy soil samples were collected in both disturbed and undisturbed forms from a farm in the Qaramalek area with appropriate humidity located in western Tabriz, Iran, at 38º 5' 59.89'' north and 45º 12' 38.57'' east. To determine and present breakthrough curves, concentration values are required throughout the laboratory columns at different times. To simulate the CDE model, Hydrus software was used. Solute transport parameters such as diffusion coefficient (D), distribution coefficient (Kd), and dispersion coefficient (β) were estimated using soil hydraulic parameters and data related to the metal concentration of cadmium, nickel, and zinc by an inverse modeling method. A sensitivity analysis was carried out for the identification of the most influential factors on the model output. This method examines the impact of input data on a given model and its actual conditions. In line with this purpose, in each run, one input data was changed to a value equal to Positive and negative five to 15%, and the other input data was kept constant. To identify the effect of the input parameters of a given model on its output, the sensitivity analysis for the Hydrus model was utilized. The parameters of hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were changed between five to 15 %. Sensitivity analysis was carried out on cadmium, nickel, and zinc metals with densities equal to 50, 100, and 150 mg.l-1 in two disturbed and undisturbed soils. Results and Discussion Examining the breakthrough curves of cadmium in disturbed and undisturbed soils shows that the fitted curves using the Hydrus model and the measured curve almost coincide with each other, which is more obvious in disturbed soils. It should be noted that the model fits better in the disturbed soil than in the undisturbed soil. This may be due to the disruption of the structure the increase in the contact surface of the particles in the disturbed soil and the presence of heterogeneity in the undisturbed soil column. The simulation results show the transport of heavy metals (Zn, Ni, Cd) and Hydrus output have the highest and the lowest sensitivity to dispersion coefficient β and diffusion coefficient (D), respectively. In general, the impact of input parameters can be reported as follows: spreading parameter (β) > distribution coefficient (Kd) > dispersion coefficient (D). Therefore, it can be observed that D has a negligible effect on the model results; and consequently, measurement errors can be ignored. Conclusion Sensitivity analysis is used to analyze model behavior under different conditions. This analysis is used to investigate the relative importance of model components that control the system’s behavior. In this research, the transfer of hydraulic parameters of heavy metals Cd, Ni, and Zn in disturbed and undisturbed loam soil columns with initial concentrations of 50, 100, and 150 mg.l-1 was performed under the simulation of the Hydrus-1D model. The comparison of the simulated BTCs of the Hydrus-1D model and the measured data indicates a high agreement between the simulation curves and the measured data. Solute transport parameters such as hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were estimated using soil hydraulic parameters and data related to Cd, Ni, and Zn metal concentration by inverse modeling method. Based on the results of sensitivity analysis, the spreading parameter (β) and hydrodynamic dispersion coefficient (D) had the highest and lowest sensitivity, respectively. In other words, due to the significant effect of β changes on the output values of the model, this parameter should be measured more accurately and on the other hand, the measurement errors of parameter D can be ignored. The degree of sensitivity of the parameters was independent of the initial concentration of the elements.

Introduction Due to water resources limitations in the country, future decisions will be based on future status. In the present study, to investigate the effect of climate change on hydrological components (minimum temperature, maximum temperature, precipitation, and sunshine hours), the information of Kamalvand water gauging station from the headwaters of Kashkan River located in Khorramabad City and Khorramabad meteorological stations were used. For this purpose, taking into account the climate information by LARS-WG software, their values were predicted in the future under three scenarios including A1B, A2, and B1 in three time periods of 34 years from 2011 to 2113. Considering the limited water resources, making management decisions will require knowing the future state of water resources. This phenomenon can cause considerable damage in vulnerable areas. Therefore, as water and its related issues are among the main concerns of mankind in the coming periods, it is necessary to evaluate the occurrence of climate change and the extent of its impact on water resources. According to the importance of knowing the amount of river flow in hydrological studies and water resources management, and the lack of information about the changes in the amount of river flow in the coming years, this study was designed and implemented to predict the daily flow of the Kashkan River in Khorramabad City in the coming years. To achieve this purpose, with the application of the atmospheric general circulation model and various intelligent neural models, the prediction of river flow with high accuracy under different climate change scenarios was examined. Material and Methods This watershed forms an important part of the water-rich tributaries of the Karkhe River and covers about one-third of the land of Lorestan Province. In this study, the data of the Kamalvand River gauge station from the headwaters of the Keshkan River located in Khorram Abad have been used. In this study, relying on the ability of the artificial neural network, the application of this method was evaluated along with two hybrid models including neuro-fuzzy (CANFIS) and neuro-genetics (ANN-GA). The water crisis can be considered one of the challenges facing different regions of the country in the coming years. Managing water resources and dealing with the water shortage crisis requires knowing the state of hydrological components in the coming years. For this purpose, in this study, the status of meteorological parameters and the amount of river flow in the coming years were investigated. To achieve this goal, the capability and application of the LARS-WG model in forecasting meteorological parameters and intelligent neural models were used in river flow forecasting. Results and Discussion Based on the obtained results, the trend of increasing minimum and maximum temperature and evaporation and transpiration was predicted until 2113. Regarding the parameters of precipitation and solar radiation, a decrease in precipitation and an increase in radiation were predicted from 2080 to 2113. Comparing the performance of intelligent neural models in predicting river flow showed the superiority of the neural-fuzzy model over artificial neural and neuro-genetic models. river flow prediction with the neuro-fuzzy model until 2113 under scenarios A1B, A2, and B1 indicated that the lowest amount of river flow will be observed in scenario A1B and the highest amount will be observed in scenario B1. The temporal changes of the river flow during different seasons showed that the river flow will increase in spring, autumn, and winter. In general, according to the changes in meteorological parameters and the observed values ​​of the river flow, the description of the changes in the river flow in scenario A1B was closer to reality. This makes it necessary to properly manage the river flow, especially in the summer season of 2080-2113. Results The results indicated that minimum and maximum temperatures and evapotranspiration during the next years will increase. In scenario A2, the precipitation changes trend was decreasing and solar radiation was increasing, however in other scenarios trend of increasing and decreasing. Then the discharge amount under different scenarios was calculated. The forecasting discharge values by intelligent models showed that the CANFIS model had more accuracy than the ANN and ANN-GA. The results of the optimized structure of CANFIS illustrated that the minimum discharge value in the future will occur in scenario A1B and the maximum discharge amount will be recorded in scenario B1. The evaluation of the seasonal trend showed that the flow rate increased in spring, autumn, and winter compared to the base period by 20.60, 17.31, and 9.27%, respectively. The lowest river flow in summer will occur under the A1B scenario during 2080-2113. rivers are one of the most important effective factors in the geomorphological processes of the earth and the hydrological cycle. Effective factors in very diverse hydrological processes and their applications in designed models are very difficult and the existence of high uncertainties and strong nonlinearity of the data complicates the issue. Long-term records of hydrological data show the temporal changes in discharge caused by climate change and vegetation changes.

IntroductionThe amount of sediment production, the manner and time of sedimentation, the size and composition of sediment grains, and transport among the waterways network are important features of the sedimentation regime of Watersheds; Because changes in each of these factors cause changes in Watershed performance. Therefore, sediment production is a reflection of the importance and amount of erosion processes and sediment sources in the upstream parts of the Watershed and how sediment is transported and deposited from the moment of movement of erosion materials from the point of separation to the exit of it. Sediment connectivity indicators indicate the spatial changes of connectivity patterns in different parts of the Watershed and provide a suitable estimate of the contribution of sediment sources and sediment transport routes. For this purpose, investigating the spatial pattern of sediment flux at the Watershed scale is of particular importance in developing comprehensive management plans and measures to control erosion and sedimentation. Although various indicators and models have been developed in this field, their performance has not been evaluated based on observational data and statistical methods. This research aims to analyze the sediment flux pattern of the Neyriz Watershed located in the east of Fars Province, based on sediment connectivity and sediment transfer capacity indicators, and compare their performance based on field sediment evidence. Materials and Methods At first, a digital model of the ground elevation of the Neyriz Watershed with a spatial resolution of 12.5 m was prepared and its drainage network was extracted. Then, the sediment connectivity index was calculated by considering the upstream and downstream features of the Watershed and by considering the roughness factor as the sediment movement resistance factor, and the sediment connectivity map of the Watershed was made. Then, the sediment transport capacity index map was also prepared based on the concept of topography and using the digital model layer of land height. For this purpose, the sediment transport capacity index was calculated for each pixel of Neyriz Watershed in SAGA-GIS software, and a sediment transport map was prepared. Based on field visits to different parts of the Watershed, 30 positions that had evidence of sediment transfer were recorded as observation points of the sediment transfer event. Also, 30 other positions that did not have signs and evidence of sediment transfer were added to the validation database as observation points of no sediment transfer event. The corresponding values of each index were also extracted in the geographic information system and based on the available information, using evaluation methods based on the error matrix including true skill statistic (TSS), efficiency (E), and F score (F-score), the validation of the mentioned indicators was done quantitatively. Results and Discussion The value of the sediment connectivity index of the Neyriz Watershed in Fars Province varied from -7.24 to 2.43 and its median value was -4.36. The spatial pattern of the sediment connectivity index in this Watershed is such that the middle and western parts have a low amount and the northern, southern, and eastern parts have more amounts. In this research, the drainage network of the Watershed was introduced as the target of receiving the sediment; Parts of the slopes of the Watershed level, which had the conditions of sediment production and transfer were connected to the drainage network in terms of the transfer path, have shown a higher value of connectivity index. This index provides valuable information for land management by considering the upstream characteristics of each point as well as the characteristics of the transfer path to the sediment-receiving target. The value of the sediment transfer capacity index varied from 7.2 to 23.16 and the average value was 10.19. The value of this index is high in the marginal parts of the Watershed where there are sloping lands, and the middle parts of the Watershed have a small value. Based on the findings, the index of sediment connectivity with the true skill statistic (TSS) of 0.833, the efficiency value (E) of 0.916, and the F score of 0.915 is a better performance than the sediment transport capacity index (TSS= 0.633, E=0.816, F-score=0.825). In addition, based on the values of the false positive component in the error matrix, the sediment transport capacity index predicts high sediment flux potential in many situations; While in the field observations, it was not true. Conclusion Based on statistical evaluation criteria, the sediment connectivity index has been able to better describe the state of sediment flux and is more consistent with field realities. So that the sediment connectivity index, in addition to considering the characteristics of the upstream area of each point of the Watershed, is possible to consider the path of the sediment particle to the target location of the receiver (such as the nearest branch of the network drainage) has made it possible. Based on the findings obtained in this research, although the sediment transfer capacity index in some parts of the Neyriz Watershed of Fars Province was in line with the sediment connectivity index; However, due to the energy-based nature of the flow, this index only considers the local conditions of the points and ignores the features of the upstream area as well as the process of transporting sediment particles to the downstream. Therefore, it is suggested to use the sediment connectivity index in the erosion and sedimentation studies of Watersheds. Because when information about the amount of sediment production is not available; the Sediment connectivity index can provide useful information about the sediment transfer process in the Watershed.

IntroductionWater is one of the most important factors for the production of agricultural products and as one of the vital things for life, it is very important. However, in many regions of the world, water resources are limited and due to population growth and climate change, the use of water resources has become a big challenge. Therefore, choosing the best irrigation method for the production of agricultural products is very important and necessary. Today, there are different irrigation methods for the production of agricultural products, each of which has its characteristics and advantages. For example, irrigation methods such as drip irrigation, subsurface irrigation, sprinkle irrigation, surface irrigation, and irrigation using smart systems are very popular and widely used today. To choose the best irrigation method, one should pay attention to various factors such as soil type, crop, weather conditions, and the amount of water needed for the crops. In addition, methods that are economical and less harmful to the environment should be used. The proposed irrigation method in the improvement plan, on the one hand, should have sufficient capabilities to supply water requirements and reduce operating and maintenance costs, and on the other hand, it should be designed and selected according to the technical, economic, and social conditions of the region. The factors that are effective in choosing irrigation methods include soil conditions, topography, slope, water quality, climatic conditions, irrigation efficiency, area of agricultural units and land use system, groundwater level, plant water requirement, investment, maintenance and operation costs, existing experiences, and socio-economic considerations. At the same time, irrigation methods can include the improvement of existing irrigation methods to completely advanced systems. Materials and MethodsThe area studied in this research was Tabriz Grand Park in the northwest of Tabriz city. This surface area includes 864 hectares, of which 328 ha are located in area one and 536 ha in area two. The only source of water in the area is a well, and surface water sources are not used in the agricultural lands of Tabriz Park. On the other hand, the source of water supply for the agricultural lands of Hokmabad is groundwater sources (agricultural wells) and the common irrigation method in the agricultural lands of Tabriz Grand Park is the gravity irrigation method of the basin type. In this research, Charles Burt's scoring method was used to select the best irrigation method in the study area. By using this method, it is possible to easily identify the weak and strong points of irrigation systems and improve their efficiency. The criteria and features that were considered for scoring included the following items: Crop (corn, cotton in a wet place, cotton in a dry place, alfalfa, small grains, etc.), Water source (groundwater well, flexible surface water distribution, inflexible surface water distribution, etc.), Climate (temperature conditions, wind, rain, frost, etc.), Land (irregularly shaped land, obstacles in the land, steep slopes, steep rocky lands, etc.), and socio-structural conditions (access to parts, type of management, labor, user skill, etc.). Each irrigation system has a specific efficiency for each criterion, which is indicated by positive, zero, and negative signs. A positive sign means that this irrigation system is a suitable system according to this criterion and feature. The zero sign indicates that the criterion does not play a role in choosing the system, and the negative sign indicates that the irrigation system is not suitable for this specific feature. Results and DiscussionWhat is important in choosing an irrigation method is to choose methods that can be implemented. The purpose of this research is to select the most suitable irrigation method for the agricultural lands of Tabriz Park based on effective physical factors such as crop, water source, land, climate, and socio-structural conditions and using Charles Burt's scoring method. To choose the appropriate irrigation system for the study area, the physical factors effective in choosing the irrigation system in six areas of crop, water source, land, climate, and social-structural conditions have been examined and the results are presented below. According to Charles Burt's scoring method, in sprinkle irrigation, the classical fixed system received a score of one negative, which indicates that the system is not suitable for the study area due to these characteristics. Also, in surface irrigation and drip irrigation, the highest scores of three positive and two positives were observed, respectively, and this means that these irrigation systems will be a suitable system for the studied area according to these criteria and characteristics. The results showed that basin irrigation is the most appropriate in the agricultural lands of Hokmabad, Tabriz city. ConclusionIn general, based on the scoring method of Charles Burt, among the options of sprinkle irrigation, the classic fixed system, the drip methods, the tape system, and the surface methods, the basin system was suitable for the agricultural lands of Hokmabad, Tabriz city. The results showed that the scoring method used, despite its simple instructions, is a practical method for choosing the most suitable irrigation system. The results showed that the basin system is suitable for the study area in terms of crop, climate, water source, and socio-structural conditions. In rain irrigation, the classic fixed system in terms of land, crop type, and socio-structural structure received a score of zero, which indicated that there is no relationship between the irrigation system and these factors in the region. Among the drip methods, the tape system received a score of one positive in terms of crop type, land, and water source, which showed that the system is suitable for the region due to its characteristics. However, due to the need for high management and skill and the possibility of vandalism, it has problems from a socio-structural point of view, but from the point of view of the crop, land, and water source, it is considered among the best options. Also, among the surface irrigation methods, the basin system is a suitable system from the point of view of the type of crop and land.

Introduction Floods cause financial losses and countless lives in the world every year. Identifying flood-prone areas is one of the basic steps in flood management. In the past, careful observation and note-taking of the mechanism of occurrence and the natural course of the cause and effect of the effective factors that led to the final occurrence of the phenomenon in a chain manner helped to understand the pattern and process of the occurrence to some extent. Because the processes of creating floods are numerous and affected by various factors and the flood phenomenon is multidimensional and dynamic, all-natural, human, and organizational-management factors affect the occurrence, intensity, extent, and continuity of floods. So far, many efforts have been made to use data-mining models and artificial intelligence in the spatial prediction of floods. The models try to better and more accurately estimate the distribution of the flood phenomenon by examining the relationships between each flood event (dependent factor) and the set of underlying and stimulating factors (independent factors) and fitting them to educational evidence. Since the applicability of the flexible discriminant analysis model has not been fully investigated in the field of flood susceptibility prediction, this research quantitatively evaluated its performance using real-world flood data. Materials and MethodsBased on the availability of periodic history of flood events, the Zarrineh-Rood Watershed of Kurdistan Province, Iran, was chosen as the study area. It was tried to select the factors based on different criteria such as familiarity with the process of flood inundation, ease of data preparation, having the most spatial variability at the regional level (not uniform), and containing the most information for the model should be selected to separate areas with different levels of flood susceptibility. Thirteen diverse geo-environmental factors including elevation, aspect, slope percent, land use, drainage density, lithology, plan curvature, profile curvature, mean annual precipitation, soil texture, stream power index, distance from the stream, and topographic wetness index were used as independent variables. The maps of elevation, aspect, slope percent, plan and profile curvatures, stream power index, and topographic wetness index were produced using a digital elevation model. Hydrological layers including distance from the stream and drainage density were produced using the stream network layer. The location of the flooding events was also collected as the dependent variable. The spatial data of flooding were randomly divided into two groups of training and validation with a ratio of 70:30. After running the model (i.e., Flexible Discriminant Analysis) based on the training group, the flood susceptibility map was produced. The validation of the model results was conducted using the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS) metrics. Results and DiscussionThe results indicated that the FDA model with the value of AUROC= 0.96 and TSS= 0.86 efficiently and accurately produced the flood susceptibility map. The flexible nature of the model in the selection of regression equations, as well as the possibility of weighting, and determining the priority of the evidence of presence over the evidence of absence, are among the special capabilities of the FDA model, which many machine learning models lack. Using probability distribution estimation algorithms in the model is very important and can not only extract the hidden spatial pattern of occurrence from a set of data but also help to predict flood-prone areas in data-scarce Watersheds. Based on the results, about 14% (62 thousand ha) of the study area was categorized in the high and very high flood susceptibility zones, which include the northern, northwestern, and southeastern areas. Spatial analysis of the flood susceptibility map showed that in total 25897 ha (18.12%) of agricultural lands, 343 ha (50.91%) of garden lands, and 2126 ha (39.93%) of residential areas located in high and very high susceptible zones. Considering the successfulness of the FDA model in goodness-of-fit and validation phases, the flood susceptibility map can be used as a basis for planning flood control and management measures. ConclusionThe findings of this study proved that the flexible discriminant analysis model provides the possibility of processing diverse and big geo-environmental data to predict the flood susceptibility of Watersheds and it had a high efficiency in this context. There is a lot of spatial correspondence between the vegetation status map and the flood susceptibility map; in such a way that the places that had a high flood susceptibility degree, their upstream areas were generally destroyed in terms of vegetation. The results of this research showed that a significant area of the Zarineh-Rood Watershed had a high and very high flood potential, which was characterized by the interaction of low slope and flat areas, formations and soils with low penetration and dense drainage network, and more importantly, flood-prone areas located in the northern, northwestern and southeastern parts of the Watershed. The situation of the flood probability of the Zarineh-Rood Watershed has been determined and managers and decision-makers must put the critical areas in the priority of flood management programs. More flood-driver factors are suggested to be used as predictor variables in flood susceptibility modeling in future studies. On the other hand, it is very important to determine the role of predictor variables in the flood susceptibility degree at the Watershed scale, which can be investigated in future research.

 Introduction The increased fresh-water demand due to the population growth will cause the pressure on water resources to increase in the future causing the water supply through saline and unconventional water to become a serious issue, especially in areas facing water scarcity. As reuse of the agricultural wastewater reduces the pressure on water resources and improves environmental conditions, and some field wastewater is rich in sodium, this research has studied the sodium removability by sugarcane bagasse sorbents. agriculture is the greatest water consumer in the world where saline water not only reduces the products but also destroys the soil structure and damages the environment. Wastewater desalination and its reuse is a relatively new approach in the water industry that solves saline-water problems through various methods, but it is uneconomical due to high equipment costs and energy consumption, especially in agriculture where water consumption and costs are much higher. Therefore, inexpensive primary saline-water modification methods can reduce desalination costs. To remove pollutants, various studies have used different adsorbents such as biochar, activated carbon, zeolite, and resin among which biochar can effectively remove pollutants from aquatic environments because it is an effective, inexpensive, polar, high-porosity adsorbent. Materials and Methods To produce biochar, this study utilized sugarcane bagasse as the primary biomass. The process involved several steps: 1. Washing and drying: the bagasse was washed multiple times with both ordinary and distilled water, then air-dried to eliminate residual salts. 2. Crushing and drying: it was crushed using an industrial mill and then placed in an oven at 60°C for 24 h to remove excess moisture. 3. Grinding: the dried bagasse was further ground using a small mill. 4. Sieving: the ground bagasse was passed through 60 and 100 mesh sieves in two stages to ensure uniformity. 5. Storage: the processed bagasse was stored in closed containers. The biomass was then converted to biochar using a heat-programmable electric furnace, with the temperature increased at a rate of 5°C.min-1 for even heat distribution. The bagasse was placed in a steel reactor, and nitrogen gas was injected at a constant flow rate to prevent oxidation. The biomass was maintained at 600°C for 2 h, after which the furnace was turned off, and the temperature was gradually reduced to room temperature while continuing the nitrogen gas flow. Each batch consisted of 20 g of biomass, yielding approximately five grams of biochar, resulting in a production efficiency of about 25%. Nano biochar (N-BC) was produced using a planetary ball mill with ceramic cups and bullets, maintaining a bullet-to-biochar weight ratio of 15:1. Results and Discussion In all treatments, increasing the initial sodium concentration enhances removability, with activated nano biochar showing higher removability under similar conditions compared to non-nano adsorbent. The greatest difference between the two is 179.5% in the treatment with 200 W microwave power for an initial sodium concentration of two g/l. Magnetic-activated nano biochar's removability is 18.8% less than that of activated biochar. The highest reductions are 40.3% and 68% for initial concentrations of four g/l and two g/l in activated non-nano adsorbent, while the lowest are 24.9% and 46.9% for similar concentrations in activated nano adsorbent. This indicates that sodium removability by activated nano adsorbent is less affected by reductions in initial sodium concentration, performing better at low sodium concentrations than the other two adsorbents. The highest reductions are 25.5% and 15.5% at 200W and 700 W powers for activated non-nano adsorbent, and the lowest are 12.9% and 5.8% at similar powers for activated nano adsorbent. This shows that activated nano adsorbent is less affected by non-optimal microwave power. The highest cavities that removed 99.9% of methylene blue were at 900W and 20 minutes. Average correlation coefficients are 0.994 and 0.886 for Langmuir and Freundlich models, respectively, with the former being more consistent with the measured data. The nf parameter is greater than one for all three adsorbents, indicating that the sodium adsorption process is mostly physical. According to the results, Langmuir and Freundlich linear models better match the real values, with the Langmuir model providing more accurate estimates than the Freundlich model. Conclusion Wastewater desalination and its reuse is a relatively new approach in the water industry that solves saline-water problems through various methods, but it is uneconomical due to high equipment costs and energy consumption, especially in agriculture where water consumption and costs are much higher. Irrigation water salinity is a very serious problem in different parts of the world, especially in arid and semi-arid regions. The present research showed that increasing the initial sodium concentration enhanced sodium removal, with activated nano biochar. In addition, magnetizing nano-adsorbents reduced sodium removal. The highest sodium removal for all three adsorbents (activated non-nano, activated nano, and magnetically activated nano) in the 200 and 400 W treatments was observed at an activator-to-biochar ratio of three. The average correlation coefficients for the Langmuir and Freundlich models were 0.994 and 0.886, respectively, indicating that the Langmuir isothermal model better matched the measured data than the Freundlich model.

Introduction Water resources are the common aspect of the goals and challenges of sustainable development, the lack of which is one of the big multidimensional problems of the current century and is one of the main reasons for positive and negative developments in the world. Therefore, the water poverty index (WPI) is one of the indices defined for this purpose. This index shows the effect of the combination of effective factors on the scarcity and stress of water resources. It provides the conditions for prioritizing and developing management versions in different regions. To determine water scarcity and poverty in each region, attention should be paid to the conditions of water resources in the studied region, the ability to calculate the index and the existence of information and data in the studied region, as well as the selection of selected criteria and components in that region. In this study, the water poverty index is used to investigate the shortage and tension of water resources and for its influence on drought, its relationship with univariate drought indices based on precipitation including standardized precipitation index (SPI) and Z score index (ZSI), and variable indices based on precipitation and evapotranspiration including standardized precipitation evapotranspiration index (SPEI) and reconnaissance drought index (RDI) were searched. Materials and Methods The study area in this research is the Hashem-Abad meteorological station in Gorgan Township, and the statistical period for calculating the water poverty index based on the data available in the study area was considered to be 13 years (2003-2015). The water poverty index in this research is calculated based on five main components, which include the resource (groundwater loss), meteorological (temperature and precipitation), consumption (water need), capacity (river discharge), and environmental (salinity). Each of the components must be weighed after calculating to calculate the water poverty index. For this purpose, the AHP hierarchical technique was used. First, a questionnaire was prepared and the components were scored based on the opinion of regional water experts and university professors, then, using Expert Choice software, the weight of the main components of the water poverty index was determined, and finally, the WPI for the study area in this research was also estimated. Then, in the next step, drought indices SPI, SPEI, RDI, and ZSI were calculated in 6-month and 12-month time windows. To calculate the drought indices, the precipitation and temperature data at the Hashem-Abad meteorological station for a period of 30 years (1990-2019) were considered, which were sorted monthly and the coding necessary to calculate the SPI and SPEI indices in time windows 6 and 12 months was done by R programming and statistical software. Also, two indicators, RDI and ZSI, were calculated in the Excel software. Finally, the relationship between drought indices and the water poverty index was searched based on simple one-to-multivariate correlations. Results and Discussion The results of the water poverty index’s components showed that the resources and environment component had the highest value in 2009 and 2010 and the lowest value in 2010 and 2016, respectively. About meteorological, capacity, and consumption components, the highest values were in the years 2010, 2004, and 2009, respectively, and the lowest values occurred in the years 2010, 2016, and 2016, respectively. Questionnaire analysis of WPI components with AHP showed that resources and environment components had the highest and lowest weights with values of 0.354 and 0.041, respectively. However, by multiplying these weights by their related components, it was found that the components of consumption, environment, resources, meteorology, and capacity had the greatest effect in calculating the water poverty index. The range of WPI changes during the years (2004-2016) varies from 26 to 82, so 2014, which is one of the driest years, the region was in the poorest state of water resources and the year 2008 had the best conditions. Considering the average WPI of about 55, out of the 13 years studied, the WPI was lower than the average in 8 years. In the next step, due to the lack of data, there was no possibility of non-linear modeling, therefore, simple one-to-multivariate correlations were used. The results of these correlations showed that the use of the multivariate linear regression method by considering the drought index in a 12-month time window along with two six-month time windows related to the first and second half of the year increases their correlation with the water poverty index. Examining the effect of the time window considered for the drought index on the water poverty index shows that the 12-month time window has a higher correlation than the six-month time window. Also, among the six-month time windows, in the SPEI index, the first six months of the year, which includes the spring and summer seasons, had a higher correlation with the water poverty index. Correlation results between drought indices and WPI showed that the annual time interval is more suitable than the 6-month time one. And among the 4 indices studied, the SPEI index with R2=0.90 had the highest correlation while the ZSI index with R2=0.81 had the lowest correlation with WPI. Conclusion Based on the results of the components of the water poverty index in this research, it was observed that the consumption component in the Gorgan region had the biggest role in the WPI estimation, so water conservation can have a great contribution to solving water poverty. Due to the high volume of water consumption in the agricultural sector, some measures should be taken to manage water consumption and choose the appropriate cultivation patterns. The high correlation of WPI with drought indices, especially the SPEI variable index, makes the importance of creating a drought monitoring and forecasting system more tangible, and due to global warming and climate change in the future, which this region is not exempt from, it can make the problems of water poverty and lack of water more severe in this region.

Introduction The lack of scale-dependent information related to soil properties has largely led to limitations in agriculture, hydrology, climate, ecology, and environmental studies. In this regard, the study aims to analyze the scale-dependent changes in estimating the spatial distribution of soil pH and salinity in Tabriz. The proposed method applies the decomposition approach to increase the accuracy of soil properties spatial distribution estimation which includes creating a relationship between soil salinity and pH with the sub-series of input variables from the decomposition method, construction of a relationship between soil salinity and pH sub-series with sub-series of input variables and finally, using a data reduction approach for the effective sub-series determination from decomposition. Materials and Methods The sampling points of the study are related to Tabriz City in East Azerbaijan province. 60 and 38 samples were collected from zero to 20 cm depth for soil pH and salinity monitoring, respectively. Changes in soil properties are evident from one region to another. The cyclical behavior of soil properties is called periodicity. Previous studies in the spatial changes description have mainly focused on the spatial similarities obtained between properties in a region, in which the change of spatial arrangement-spatial dependence or periodicity of soil properties has not been regarded. Spectral analysis can measure the periodicity in spatial changes by approximating a series of spatial data with the sum of sine and cosine functions. The purpose of discrete wavelet transformation is to decompose the signal into the sub-series to obtain a comprehensive input signal analysis. Discrete wavelet transform is used to calculate approximation coefficients in a signal. Maximum overlap discrete wavelet transform (MODWT) is similar to discrete wavelet transform in which low and high pass filters are applied to the input signal at each level. However, the elimination of coefficients is not done by MODWT. Principle component analysis (PCA) was used as the reduction method to find the effective sub-series of decomposition. Results and Discussion In this study, 48 and 12 points were used in soil pH modeling, and 30 and eight points were used in soil salinity modeling in the calibration and validation periods, respectively. Longitude, latitude, height above sea level, slope, and slope aspect were considered as the spatial variables, and the longitude, latitude, and height of sampling points were recorded by GPS, but the slope and slope aspect of sampling points were taken from the production maps of slope and direction. They were extracted from the digital elevation model (DEM) map. The changes in accuracy measurement indices show the variation in the estimated soil salinity and pH against different inputs of the support vector regression (SVR) model. In the case of salinity, analyzing only the aspect of slope has been able to increase the accuracy of the measurement indices, the reduction rate of RMSE, RRMSE, and Var from the previous optimal state without decomposition to decomposition state is three, four, and 20%, respectively, and the rate of the residual predictive deviation (RPD) increase is equal to four percent. Based on the values of the accuracy measurement indices, PCA can increase the accuracy of the estimated values. The box plot of data related to the use of principal component analysis has become closer to the box plot of the observation data compared to the case where only the decomposition method was used. This problem shows the increase in accuracy with a combination of the MODWT spectral approach and the PCA data reduction method. ConclusionIn recent years, there has been an increasing demand for soil spatial distribution information in environmental decision-making and land use management. One of the issues that can affect the accuracy of spatial modeling of soil properties is the spatial information increase of input variables to the model. Modeling sub-series of input variables with sub-series of soil properties and finally the sum of estimated sub-series of soil properties could not increase the accuracy of estimated values. Series decomposition could increase the accuracy of estimates. The factors that can affect the accuracy of the proposed method for determining the spatial changes of soil properties include the type of input variables, the type of used model in the modeling process, the use of the appropriate method in spectral analysis, and determining the effective factors in the decomposition method. Therefore, the combination of spectral analysis and artificial intelligence as an effective option can increase the accuracy of the spatial distribution of soil properties.