مجله تحقیقات آب و خاک ایران
سال پنجاه و سوم شماره 11 (پیاپی 83، بهمن 1401)

Selenium is one of the essential micronutrients for human health. Since the amount of selenium in the plant is related to its amount in the soil and also the physical and chemical properties of the soil affect the amount of selenium uptake by the plant, therefore it is important to study the relationship between selenium uptake by the plant and physical and chemical properties of soil. In this study, first, different methods of selenium extraction were investigated to determine selenium concentration in the soil and rice grain in Silakhor plain of Lorestan province in 2021. Then, the correlation between soil properties and soil selenium content was investigated. The results showed that the extractable selenium with phosphate had a significant correlation with selenium uptake by plant (available selenium) and 0.1 M monopotassium phosphate solution (KH2PO4) could be used as the best extractor of selenium in the plant. There was also a significant correlation between soil selenium and some soil properties such as organic carbon content (-0.547*), calcium carbonate content (0.648**), clay percentage (-0.519*) and sulfate concentration (-0.275. The regression coefficient between soil and selenium properties of soil and plant was 0.72 and 0.68, respectively. Selenium concentration of rice grain in the region was between 1.016 to 1.985 mg / kg. Also, the concentration of selenium in the soils was between 0.17 to 0.52 mg / kg, which indicates that there is no deficiency of selenium in rice -cultivated soils in the Silakhor plain of Lorestan province.

Soil class maps contain useful information that helps stakeholders to understand soil behavior in response to different management programs. As well as, their numerical prediction is dependent on the appropriate scale of environmental variables. Therefore, the current research intends to use the deep learning approach (CNN) and the spatial information of geomorphometric attributes and the sentinel 1/2 satellite images along with band ratios to predict the soil subgroup classes with its uncertainty map. Also, comparing the results of CNN and the random forest (RF) model in prediction of soil classes and different environmental variables was not well documented.

CNN model was runed in the Google Collaboratory online environment and the RF model was performed by the "rf" function in the "caret" package in the RStudio environment. The models were calibrated with 80% of the data set along with six different window sizes and validated according to 20% of rest data based on two indices of overall accuracy (OA) and F1-Score.

Six covariates i.e., DEM, SWI, WE, SH, MRVBF, DIFF were selected as the most effective variables among 33 geomorphometric attributs, with 12 individual bands and the indices of sentinel 1/2. Totally, 13 soil subgroups including nine from Aridisols, three Inceptisols subgroups and, one Entisols subgroup are recognized in the study area. The overall accuracy for two models with a slightly difference of 7% in the window size (15*15) was observed with 43% and 50% for CNN and RF models, respectively. The CNN model has three patterns (increasing-decreasing), small and large optimal window size, and the same pattern observed in the scaled RF model, too. The OA was zero in all window sizes for the Sodic Xeric Calcigypsids subgroup in the CNN model and the Xeric Calcigypsids, and Typic Xerorthents subgroups in the RF model. In addition, the Xeric Haplocalcids and Xeric Haplogypsids only predicted by the RF model in 3*3 and 5*5 window size, respectively. By increasing the window size from three to nine, and 15, the Typic Calcixerepts shows a mild increasing trend in the F1-Score and also a mild decreasing trend after reaching the peak. The amount of F1-score for Typic Calcixerepts in CNN and RF models was 69% and 77%, respectively. The F1-Score values of Gypsic Aquisalids and Xeric Haplogypsids increase by 30% and 17%, by increasing the window size from three to five, and immediately a sharp downward trend, which indicates the appropriateness of the small window size in order to predict.

     In general, despite the limited number of observation profiles (n=278), the CNN model provides an acceptable prediction in mapping the soil subgroup classes, and although a slight difference in the overall accuracy with the RF model, while, the CNN presents a lower uncertainty map in comparison to RF. In future studies, this model and its procedure can be used to predict soil class maps in other arid and semi-arid regions.

Lead is one of the most abundant and toxic heavy metals which can have lethal effects on plants. The Pb contamination is produced by mining, industrial, activities and fossil fuel burning. Some metallophytes plants belong to Brassicaceae family are able to grow in soils contaminated with heavy metals. The purpose of this study is to evaluate the resistance and lead accumulation of the metallicolous species Matthiola flavida collected from the vicinity of the Irankouh Pb/Zn mine in Isfahan, Iran, which is compared with its non- metallicolous species Matthiola incana to used Pb phytoremediation. Non-metallicolous (Matthiola incana) and metallicolous plants (Matthiola flavida) were transferred to hydroponic mediums and after proper vegetative growth, they were exposed to 0, 10, 50, 100, 150 mg/L treatments of lead for 14 days. The results showed by increasing lead concentration, the growth of both species significantly decreased, but this reduction in growth was always greater in the non-metallicolous species, so that at the highest stress level, the dry weight of shoots and roots decreased in the metallicolous to %7.1 and % 28.8, but in the non-metallicolous to %69.9 and %60.8 in comparison with their control, respectively. With increasing the concentration of Pb in the medium, the accumulation of lead in the roots of both species are enhanced, but Pb concentration in the roots and translocation factor of the metallicolous species compared to the non-metallicolous species was more than 4-folds at the lowest stress level, which decreased with increasing lead concentration. Then, the compatible mechanisms of the metallicolous species have the ability to control the transfer of lead to the shoot in different concentrations, which makes it suitable for growing in lead-contaminated areas.

Vegetation plays an essential role in modifying the flow characteristics of natural channels, such as rivers. Efficient hydraulic modeling of flow in compound channels with vegetated floodplains is necessary to understand and identify natural flow processes. In both natural and artificial channels, there are various types of vegetation with differing densities and heights. The main goal of this study is to investigate the effect of double-layered vegetation on flow characteristics in compound channels using the FLOW-3D model. The results of the numerical model have been calibrated and validated with the results of the physical model of the research of Takuya et al., 2014. Experiments were conducted in 2014 in the hydraulic laboratory of the Akashi National University of Technology in Japan, and in a straight trapezoidal channel with a length and width of 4.8 and 0.8 m respectively. During calibration, the model's estimation error for the depth-averaged velocity was within 4 to 6%, which was reduced to approximately 1.5% during validation. The average error in water depth estimation was around 3%. The numerical and physical models showed good agreement in simulating the flow pattern. The numerical model showed that, for larger floods when vegetation is submerged, the vertical profile of velocity in the floodplain is S-shaped. However, during smaller floods or when short and tall vegetation is emergent, the vertical velocity profile is relatively uniform or logarithmic. The resistance caused by the presence of vegetation in the floodplains leads to a decrease in the flow velocity in the floodplain area of the river and an increase in the capacity of flow transfer in the main channel.

Climate change is one of the critical environmental issues that has significantly influenced the water resources availability in recent years. The purpose of this research is to investigate the climate change adaptation strategies in Maroon Basin, Iran, to balance the pre-defined water demands and the future supplies by changing the area and pattern of cultivation in the basin. To investigate the impacts of climate change on precipitation, temperature, and inflow to Maroon Dam, simulations of five General Circulation Models (GCMs) namely MIROC-ESM, MPI -ESM-MR, HadGEM2-ES, EC-EARTH, and GFDL-ESM2M were studied at three future periods of 2021-2040, 2041-2060, and 2061-2080. First, Maroon Dam inflow was simulated using the SWAT hydrological model. Then the model was calibrated and validated against monthly flow at the Idanak and Tang-e-Takab hydro-stations. The Nash-Sutcliffe efficiency (NSE) index at Idanak and Tang-e-Takab stations were estimated 0.69 and 0.67 for the calibration and 0.65 and 0.59 for the validation step. The downscaling of precipitation and temperature data under the four RCP scenarios was done using the LARS-WG model. The calibrated SWAT model was forced using the downscaled data to simulate the inflow to the dam during the three future periods. The results showed that the temperature increase projected in the catchment would significantly reduce the runoff formation in the catchment, despite the projected increase in the precipitation. The present and future Maroon Dam inflow were used as essential inputs to the MODSIM model. Water allocation to each requirement in all the periods was done by collecting other necessary information and calculating the reliability performance index. Compared with the current status, on average, allocation to the defined demands will reduce by 2 to 6.25 percent. In this research, an attempt has been made to maintain the cultivated area and reliability index in the current status by altering the cultivation pattern. Therefore, 9% decrease in alfalfa plantation area, 15% decrease in autumn grain maize, 17% decrease in wheat, 5% decrease in potato and 9% increase in barley and sesame, 15% increase in autumn silage maize, 17% increase in tomato, and 5% increase in bean in class B3 (as the most effective climate change conditions), would fulfill the premise of the study to maintain the overall cultivation area and reliability index under the climate change impacts.

The FAO Penman-Monteith method as a standard method requires a lot of meteorological data. The accurate preparation of these data is not possible in all regions; as a result, alternative methods that require less data are investigated. Prestley-Taylor method require a few meteorological data and its application can be useful in areas where meteorological data is not available. The Sistan region in the southeast of Iran is one of the regions that is unique in Iran due to the 120-day winds and high day-night temperature changes. The purpose of this research is to evaluate Prestley-Taylor method compared to the PMF-56 method and to modify this equation according to the wind conditions for the region of Sistan. For this purpose, 30 years of meteorological data in Sistan region were used. The coefficient of Priestley-Taylor equation (α_PT) is one of the most important parameters which is used for evaluation of the equation. The results showed that the value of the evaporation coefficient in the main equation (1.26) for the Sistan region is too low and should be corrected. Its correction value varied between 1.02 and 6.11. The average value of α_PT was equal to 2.16, which is 71% different from the default value (1.26). Also, a regression relationship between wind speed and α_PT was presented. The results show that the amount of evapotranspiration obtained using the correction factor based on the wind speed (α_(PT-U2)) has the best results.

Taleghan Dam is the main suplier of water required by the agricultural sector of Qazvin plain. Reducing the amount of water allocated to the Qazvin plain irrigation network and uncertainty about the welfare provided by farmers due to the interruption of the time between the production of the crop and its supply to the market, have caused farmers to seek welfare by reducing fallow and draining groundwater resources. In this study, with the aim of preserving groundwater resources and providing livelihood to farmers, the groundwater level in the irrigation network of Qazvin plain is simulated under six scenarios: 1) Continuation of current harvest without increasing cultivated area 2) Increasing cultivated area by reducing fallow 3) Simultaneous optimization of Cultivation pattern and distribution water with price forecasting 4) Simultaneous optimization of water Cultivation pattern and distribution water with price forecasting and reduction of functional vacuum 5) Simultaneous optimization of Cultivation pattern and distribution water with price forecasting and increasing efficiency Irrigation 6) Simultaneous optimization of Cultivation pattern and distribution water with price forecasting, increasing irrigation efficiency and reducing the yield gap using the system dynamics during the years 2002-2037 in Qazvin plain irrigation network. The results showed that the drop in groundwater level under scenario one and two will be on average 1.2 and 2.4 meters per year. Simultaneous optimization of the cultivation pattern and water distribution with price forecasting in scenario three, four and five droped groundwater level to 1.6, 1.4, 1.3 and 1.2 meters annually, while the welfare of farmers is compensated, too.

Recent researches have proven the effect of salinity of water and soil resources on the bioavailability of cadmium.In this study, the effect of different sources of salinity including sodium chloride, sodium sulfate and calcium chloride on cadmium transport in two different soils was investigated.Soil columns with a height and inner diameter of 50 and 10 cm, respectively, were subjected to leaching of cadmium solution at a concentration of 100 mg/L along with various salts of sodium chloride, sodium sulfate and calcium chloride at a concentration of 100 me/L for 10 days. The drained solutions were analyzed at three depths of 3, 7 and 50 cm. The simulation of cadmium chemical species in the soil solution was performed using Visual MINTEQ 3.The presence of salinity along with cadmium solution caused more transport and drain of cadmium than the control soil. The highest cadmium concentration drained from the columns was belonged to the soils which received calcium chloride with the mean of 19.6 mg/L whereas the lowest was determined in soil received sodium sulfate with the mean of 6.1 mg/L. Due to the formation of chloro-cadmium complexes, treatments containing chloride had a more obvious effect on the transport of cadmium in the soil columns. The simulation of cadmium speciation in solution containing chloride showed that CdCl+ and CdCl2(aq) account for more than 80% of the cadmium species, while in those containing sodium sulfate, this dominant mainly included two species of Cd(SO4)2- 2 and Cd(SO4)(aq). Generally, salinity conditions affected by chloride ions in cadmium polluted lands can increase the potential of this toxic metal entering water sources and food chain.

Water stress is one of the dominant stresses limiting crop growth and yield, particularly in arid and semi-arid regions. The best strategy to deal with water stress is choosing a water-tolerant plant based on reliable screening methods such as polyethylene glycol (PEG). To evaluate different predictive models and quantitatively investigate camelina response to drought stress during the germination stage, an experiment was conducted in a completely randomized design with three replications. Water stress treatments included six matric potentials of PEG6000: 0 (control, without stress), 3000, 6000, 9000, 12000, 15000, 18000 cm. Germination indicators were calculated using the SeedCalc package in R software and MATLAB was used for programming and models fitting. Water stress models including Feddes et al. (F), van Genuchten (VG), Dirksen and Augustijn (DA) and Homaee (H) were assessed and compared afterwards. Data analysis was performed using SAS software (V. 9.4). The threshold values under germination rate (GSI), mean seedling length (M-SL), mean root-to-stem ratio (Razao) and seed vigor (SV-S)) were then obtained for the control, 6000, 3000 and 3000 cm, respectively. According to these four germination indices, the camelina is very tolerant to water stress. In all four indicators of GSI, M-SL, SV-S and Razao, the H model presented the best performance. The highest values of GSI, Razao, M-SL, stem and root length were obtained at matric potentials of control, 3000, 6000, 3000 and 6000 cm, respectively, indicating the suitability of camelina cultivation in areas under water shortage.

Soil is the base of plant growth and has a significant effect on agricultural production. On the other hand, this section of the ecosystem is strongly affected by climate factors. The aim of this study was to investigate the relationship between meteorological variables with soil temperature at different depthes and to use the most effective factor for estimation of it  using the regression method without the need for more complex models in stations of Guilan province. Therefore, the relationship between meteorological data including air temperature at 2m-elevation, cloudiness, sunshine hours, rainfall, relative humidity, evaporation, and wind speed with soil temperature at depths of 5, 10, 20, 30, 50, and 100 cm at stations of Guilan province in a 10-year period from 2009 to 2018 was studied by correlation analysis. Finally, a regression equation was developed based on 70 percent of the data and it was validated by another 30 percent of the data to estimate soil temperature at different depths. The results illustrated that among the various independent variables, the average daily temperature at 2m-elevation had the highest correlation with the soil temperature at different depths. The correlation coefficient for different station was 0.70 - 0.97. Finally, it can be concluded that the regression method is an acceptable method for estimation of soil temperature at different depths, especially at shallower depths. So that the RMSE values range from 1.7 to 4.9 ° C and the determination coefficient values range from 0.62 to 0.96.

Phosphorus (P) is often lacking in calcareous soils. To investigate the effect of organic matter and elemental sulfur (S) on P availability and fractions in two calcareous soil with different textures (Clay ‌loam and Loamy sand) and total iron and aluminum concentrations, incubation studies were carried out for 12 weeks. This study was conducted in the soil science laboratory of Malayer University in 2019. The treatments include the simple treatment of sheep manure (organic matter=OM) (0, and 2 %), S (0, 0.25, and 0.5 %), and integrated treatment of OM and S in the presence of Thiobacillus bacteria. Every week, P concentration in control and treated soils was measured. At the end of incubation period, EC, pH, ECC percentage, gypsum percentage, and inorganic P fractions in control and treated soils were measured, too. At the end of the incubation period, EC and gypsum percentage increased in both treated soils. The presence of OM decreased the gypsum in soils and increased P concentration in the soils. In Clay loam soil treated with 0.25, and 0.5 percent of S, adding OM decreases the rate of P transformation, from 0.226 to 0.153 and from 0.168 to 0.154 mg kg-1 week-1, respectively and in Loamy sand soil caused an increase from 0.129 to 0.161 and from 0.125 to 0.184 mg kg-1 week-1, respectively. In both soils, simple and integrated treatments increased the dicalcium phosphate fraction and decreased the octacalcium phosphate fraction, and the apatite fraction decreased in integrated treatments. In Loamy ‌sand soil, simple and integrated treatments increased the aluminum phosphate fraction. The results showed that integrated treatment of OM with 0.25% of S in both soils increased the P availability, and despite the fixation of P in Loamy ‌sand soil, due to high total iron and aluminum content, OM increased the P availability in Loamy ‌sand soil.

It is necessary to use mathematical models to determine the river's response to pollutants. These models are able to simulate the effect of existing and future pollutants and facilitate related decisions. In this study, the water quality of Dez River and its main feeding drains were extracted and analyzed through sampling and laboratory analysis from Dez diversion dam downstream of Loreh drainage during seven months from March to September 2018. The results of the measured samples were compared with those of the simulated values through the HEC-RAS model. The river path was investigated in the study period and seven sampling stations were determined along the river and six stations at the point of drainage into the river to determine the sampling stations, as well as the sources of pollution of Dez River. The results indicated the lack of self-purification of DO, BOD, NO3, NO2 parameters in Dez River during seven months of 2018 due to the inflow of drains and lower river discharge in this period. Also, the base on NSFWQI index, in drought seasons the first half of the river quality index is less than 68, that indicates average quality, and in the second half of the river, the quality index is less than 50 (poor quality). This decreasing trend of NSFWQI quality index is consistent with the trend of qualitative variables of HEC-RAS simulation and indicates the lack of relative self-purification of the river during the study period.

Watershed sediment yield estimation is an important process in watershed management. Because of limited information on sediment yield in small watersheds, empirical methods are developed in different watersheds. Sediment connectivity is an important characteristic of watershed sediment yield and is compared for better estimation of sediment delivery ratio (SDR). This research aims to compare and evaluate the spatial changes of SDR values and connectivity index on a small watershed scale. This study was conducted in the Marcheshme watershed of Semnan province with an area of 2418 ha. At the outlet of this watershed, an earthen dam was built by the natural resources and watershed management organization of the province (1995). No conservation and management measures were carried out in the watershed. In 2013, the amount of sediment trapped behind the dam was measured by the research center of Semnan province. It should be noted that there isn’t any overflow of the earth dam between 1995 and 2013. The WaTEM/SEDEM model was calibrated to calculate the SDR using the measured amount of sediment, and the SDR values for the entire watershed were distributed. A distribution map of the sediment connectivity index was prepared. The highest value of the SDR map was close to 0.5, the lowest value was close to zero, and the sediment connectivity index varied from -6.71 to 3.04. SDR and IC indices show that the amount of sediment transport and delivery near the outlet is higher than the average value in the watershed area. In the upper part of the watershed, erosion is more than the one in other parts, although SDR and the possibility of sediment connection are less than the ones in other parts. These two indicators and distribution of them can be important for prioritizing areas in terms of soil erosion and sediment management. This study shows the up-to-date application of available data and develops their potential for further analysis.

Investigation of soil organic carbon stock (SOCS) in agricultural lands and the role of factors affecting its variability and digital modeling are important for predicting possible scenarios of future carbon stock. The purpose of this study was to investigate the spatial variability and to estimate SOCS at 0 to 100 cm depth based on two generation of machine learning approaches in a part of Qazvin plain. SOCS of about 211 legacy soil data were prepared. The environmental variables including 11 geomorphometric variables and 25 spectral indices with 10-meter spatial resolution were used. Further, the dataset was divided into two parts: 70% of data were chosen as training and 30% of data for model validation. Two algorithm were used for SOCS modeling in the study area. Validation results indicated that the QRF had a higher coefficient of determination than the RF. According to the results of the relative importance of environmental variables, DEM and Valley depth parameters are more important in the spatial modeling of SOCS than other variables. Generally, it is suggested to investigate hybrid models in the process of modeling secondary soil characteristics.

Climate change is an undeniable phenomenon, which affects virtually every aspect of life on Earth. The agricultural sector is heavily connected to the environment and thus, gets affect by climate change the most. Climate change affects agriculture by affecting temperature and rainfall. The elevated air temperature increases the potential evapotranspiration. Variation in precipitation is generally unfavorable. Increase in precipitation causes waterlogging and subsequently erosion in soil, while decreased precipitation causes water stress in crops. It is necessary to assess the climate change impact on agriculture in order to change policies accordingly. This paper sought to assess the impact of climate change on sugar beet net irrigation water requirement and potential yield. In this research, the climatic information of the meteorological station located in Mohammadshahr, Karaj for the period 1970-2014 and the crop parameter of the sugar beet research farm located in Mehrshahr, Karaj were used. Future climatic data from six global climate models, namely ACCESS-ESM1-5, CanESM5, EC-EARTH3, IPSL-CM6A-LR, MRI-ESM2-0 and NorESM2-LM, under three optimistic, intermediate and pessimistic scenarios (SSP126, SSP245 and SSP585) for the period 2015-2100 were downscaled for Karaj using empirical quantile mapping method. Taylor diagram was used to evaluate the downscaling results. Temperature and precipitation data as well as reference evapotranspiration, which was calculated by Hargreaves-Samani method, were given to AquaCrop model. The effective rainfall calculation approach was set to USDA-SCS method in AquaCrop model. The trend and fluctuations of the climatic and crop variables were examined using the Mann-Kendall and Pettitt tests, respectively.The results indicate that the sugar beet sowing and harvest dates advance by 39 and 71 days, respectively, which means a decrease of 29 days in the growing season length (from 171 to 142 days). The reference evapotranspiration under SSP585 will increase by 14.8% (6.5% under SSP126) at the end of the 21st century in Karaj. The water requirement can increase up to 7.8% under SSP585 (3.7% under SSP126). The amount of irrigation water requirement in the future period will increase up to 10.5% under SSP585 (5.8% under SSP126). The biomass and yield variations at the 21st century ending period will be 11.8% and 19.2% increase under SSP585 (4.2% and 5.9% increase under SSP126), respectively. All the climatic and crop variables showed strongly significant trend in the study period (1995-2100) under the pessimistic scenario, while the fluctuations of the variables were not as significant as their corresponding trends under the same scenario.According to the results of this paper, the increasing temperature caused by the elevating atmospheric CO2 concentration, under the pessimistic scenario, increases the sugar beet net irrigation water requirement by increasing the crop evapotranspiration. The decreasing precipitation also contributes to the obtained result. The potential yield showed a contradicting result, i.e., the elevated CO2 under the pessimistic scenario favors the yield production due to the fact that more CO2 contributes to more efficient photosynthesis. The results can be used in climate change adaptation policies regarding water allocation and optimal planting date determination for sugar beet cultivation in Karaj.