نشریه تحقیقات کاربردی خاک
سال دهم شماره 1 (بهار 1401)

It is essential to use of an inexpensive and eco-friendly method to reduce the environmental hazards of heavy metals. In this study, the effect of humic acid (HA) and fulvic acids (FA) on the availability of copper and cadmium and their uptake by ornamental castor oil was investigated in the greenhouse of Shahid Bahonar University of Kerman. Treatments including two levels of HA and FA (0.5 and 1 %) were added to a Cu-Cd polluted soil with five replications. A treatment received no organic acid as a control. After 90 days, some plant growth characteristics, available Cu and Cd and shoot, and root Cu, Cd concentration were measured. The results showed that application of 0.5% of humic acid to soil increased the growth characteristics of ornamental castor. In contrast, the addition of humic acid at the rate of 1% and fulvic acid at the rate of 0.5 and 1% significantly reduced plant growth characteristics. Soil application of 0.5 and 1% of humic acid increased available Cu by 19 and 37% and available Cd by 30 and 44%, respectively. Available Cu and Cd in soils treated with 0.5 and 1% of fulvic acid were 35 and 39%, and 42 and 54% higher than the control. Shoot and root Cu and Cd concentration were significantly increased in plants treated with 0.5 and 1% of humic and fulvic acids. Bioaccumulation and translocation factors were less than 1 in all treatments and increased in soil treated with 1 % of HA and FA. Based on these results, ornamental castor been could not be considered as a hyperaccumulator plants. However, humic and fulvic acids can increase the phytoremediation ability of ornamental castor for Cu and Cd removal.

The process of soil particles splash by raindrops and their transport by splash or surface shallow flow is considered as interrill erosion, which can reduce soil fertility and cause surface water pollution. Slope gradient and soil type can be the main factors affecting interrill erosion processes in semi-arid regions; therefore, it is important to determine which soil textures in such slopes are more sensitive to interrill erosion processes. This research was conducted to investigate the effect of surface slope on interrill erosion in different soils. For this purpose, laboratory experiments were carried out on eight soils with different texture (silty clay, clay, sandy loam, sand, loam, sandy clay loam, clay loam, sandy loam) on tree slopes (10, 20 and 30%) under simulated rainfall (a constant intensity of 30 mm.h-1) with three replications (72 test units). The interrill erosion was measured at intervals of one minute from beginning of runoff in each experimental unit. The results showed that interrill erosion in all soils is affected by soil texture (P<0.0001), surface slope (P<0.0001) and their interaction (P<0.0001). Loam soil was the most susceptible to interrill erosion (10.76 g.m-2.s-1) and sandy soil was the most resistant soil (0.75 g.m-2.s-1). Surface slope had a significant effect on the amount of interrill erosion in all soils (P <0.000). Also, with increasing the slope percentage, the amount of interrill erosion in all textures increased significantly. This shows that it is necessary to prevent surface erosion in fine textured soils, especially on slopes in Zanjan region.

Pollution and accumulation of heavy metals in the soil are the most important environmental challenges that threaten the lives of plants, animals and humans. One of the effective methods to reduce the harmful effects of heavy metals in contaminated soils is the application of biochar as a soil amendment. In this study, the efficiency of almond soft husk and rice husk biochars (temperature of 500 °C) as well as their modified biochars with NaOH and HNO3 were investigated to reduce soil contamination by copper. The experiment was performed in a factorial experiment in a completely randomized design with three replications. For this purpose, two biochar treatments of almond soft husk and rice husk (unmodified and modified with NaOH and HNO3) were used in two levels (without biochar as a control and 4% by weight). Copper was added in the soil of each pots in three levels (0, 50 and 200 mg kg-1) from the copper sulfate salt source (CuSO4.5H2O). In this regard, different kinetics desorption models of copper were evaluated. Copper concentrations in different time periods (from 30 minutes to 48 hours) were determined using EDTA method in the samples. The results showed that the application of modified biochars at different times caused a significant reduction in copper desorption compared to the control treatment. There was a statistically significant difference between almond soft husk and rice husk and also between different Modified biochar at 5% level. Modified rice husk is more effective than almond soft husk. Sodium hydroxide-modified rice husk biochar reduced copper by 21% and 37%, respectively, compared to almond soft husk modified biochar at 50 and 200 mg kg-1 in 480 minutes, respectively. The power function was introduced as the best Copper desorption kinetics equations due to its high values coefficient of determination (R2) and low values standard error of estimation (SE).

It is important to detect the range of soil moisture at which plant growth is associated with the water potential, soil resistance and least limiting water range (LLWR). The present study was conducted to investigate the effect of sunflower mulch at different levels on the subsurface resistance (Q) and on the LLWR in a completely randomized factorial design. Experiments were performed to improve the condition of soil moisture range at three levels of sunflower mulch and five levels of moisture with three replications. Q was measured by a laboratory automatic microprocessor by adjusting the depth and velocity of the device cone in the soil. The moisture content in Q critical for control treatment (without mulch) and treatment with sunflower mulch (10 and 20 tons per hectare) occurred in 28.4%, 22% and 19.1% by volume, respectively. The critical moisture content (Q) of 10 and 20 tons of mulch per hectare treatment decreased by 6.4 and 9.3%, respectively, compared to the control treatment. This decreasing trend means an improvement in the moisture content at critical Q (2 MPa). The moisture content in the Q critical of the 20-tons mulch per ha treatment has decreased by 2.9% compared to the 10-ton mulch treatment per hectare. This means that better results are obtained by increasing the amount of mulch per hectare. The LLWR for control treatment and 10 and 20 tons mulch per hectare was calculated to be 5.11, 9.09 and 9.17% by volume, respectively. This increasing of moisture content in LLWR indicates the positive performance of sunflower mulch.

Accurate estimation of temperature at various soil depths is crucial for land-atmosphere interactions. In this study, the application of six different machine learning models including artificial neural network (ANN), decision tree (DT), cubist (CB), random forest (RF), support vector machine (SVM) and linear regression (LR) for modeling of daily soil temperature was studied at six different depths of 5, 10, 20, 30, 50 and 100 cm in Kerman. A set of accessible meteorological data including maximum and minimum temperatures, relative humidity, dew point, evapotranspiration and atmospheric pressure were used as input to the models. The degree of importance and correlational analysis was performed for the input variables based on the data of the 18-year statistical period. According to the results, the performance of all six models based on evaluation criteria (R2 >0.86, RMSE <2.8 ◦c and Bias <0.14 ◦c) was acceptable at all depths. However, RF, ANN and SVM showed very high efficiency in estimating soil temperature (R2 <0.97). Also, the DT model and then the LR model performed lower than the others. Examination of the importance of variables showed that among the input parameters, maximum and minimum temperature had the greatest effect on predicting soil temperature in all models. Finally, it can be safely acknowledged that machine learning models such as random forest, artificial neural network and support vector machine have the ability to estimate surface and depth soil temperatures in arid climates in the absence of measuring equipment. A set of meteorological data including maximum and minimum temperature, relative humidity, dew point, evapotranspiration and atmospheric pressure were used as input to the models.

Dust phenomenon is one of the destructive and common phenomena in arid and desert areas of Iran and the world that has harmful effects on human life and the environment. The purpose of this study is to statistically investigate the number of dust occurrences for the period 1990 to 2016 for the whole of Iran. In this study, the number changes of dust occurrences in months of the studied years (1990-2016) were analyzed at 30 stations in the country, and the correlation between stations at four regions and the whole country was investigated. The results of the occurrence number of dust in different seasons in the country indicated that the highest and lowest occurrence of dust in the country were in summer and winter, respectively. The highest and lowest dust levels were on July and December, respectively, in most stations in the country compared to other months. Also, the results showed that Zabol and Zanjan had the highest and lowest dust levels with 3666 and 130 events during the year, respectively. Most of the stations in the country had a significant correlation in terms of dust occurrence at the level of 95 and 99%, which indicates that almost most of the country have similar conditions in terms of dust storm. The results also showed that the stations that were geographically close to each other had a higher correlation coefficient.

The study of spatial distribution of soil properties for optimal soil management and proper utilization of non-renewable soil resources is of particular importance. The copula function is one of the new interpolation techniques that are widely used in various sciences such as hydrology. Thus, the aim of this study was to evaluate the spatial variation of some soil properties using the copula function and to compare with geostatistics techniques. Sampling by regular networking was done in a 484 ha area in the west of Baft city, Kerman province, and 121 surface soil samples were collected. After air drying and passing through a 2 mm sieve, the percentage of organic matter and clay were determined in soil samples. To interpolate, four functions of the Archimedean copula including the Clayton, Frank, Gumbel and Joe functions, and geostatistics techniques including simple kriging, ordinary kriging, universal kriging and disjunctive Kriging and the Inverse Distance Weighting (IDW) method were used. The results were analyzed using Root Mean Square Error (RMSE), determination coefficient (R2), mean absolute error (MAE), and Mean Bias error (MBE). In order to fit the copula function on the data, the distribution function of the studied variables was determined. The results showed that the distribution of each of the studied variables is different and is explained by different distribution functions. Also, with increasing distance, the value of correlation for all studied variables decreased so that after a distance of 2000 meters, they do not show any spatial correlation. Comparison of the Copula function and geostatistical techniques based on evaluation criteria showed that the Copula function had a better performance in estimating the studied variables and the estimation error for the Copula function were calculated less. In general, the results of this study showed that due to the skewed nature of soil data, Copula function have the ability to fully express the probabilistic dependence and can be considered in spatial studies.

In recent years, inverse numerical solution methods have been considered by many researchers to address the problems of water movement in soil. In this study, HYDRUS-2D/3D software was used to simulate water infiltration through double-rings infiltrometers in soils with different textures using the inverse solution approach. For this purpose, the infiltration data obtained by double-rings method from 63 points of different regions in Isfahan were used as model input. The studied soils were classified into seven textural classes including Sandy Loam (SL), Sandy Clay Loam (SCL), Loam (L), Silty Loam (SiL), Clay Loam (CL), Silty Clay Loam (SiCL) and SiC (Silty Clay). Good agreement was observed between the measured and simulated cumulative infiltration data, in all soil textures. Coefficients of determination (R2) were 0.998, 0.999, 0.992, 0.996, 0.983, 0.976 and 995 for SL, SCL, L, SiL, CL, SiCL and SiC textures, respectively. Increasing the percentage of clay in the soil textures increased the simulation error. The highest simulation error was observed in SiC (NRMSE = 0.045) and the lowest simulation error was observed in SL (NRMSE = 0.015). In general, the simulated double-rings infiltration data using HYDRUS -2D / 3D software and the inverse numerical solution approach had acceptable accuracy and high reliability in all studied textures.

Over the last decades, soil spectral data as a rapid, low-cost, and non-destructive method has been widely applied to estimate basic soil properties. In this study, the feasibility of using spectrotransfer functions (STFs) and pedotransfer functions (PTFs) was explored to estimate the parameters of fractal and experimental models of Soil Water Retention Curve (SWRC). For this purpose, a number of 100 soil samples were collected and their spectral reflectance over 350-2500 nm region were measured using a handheld spectroradiometer apparatus. Some soil physical properties and parameters obtained from fitting fractal and experimental models of SWRC to the measured data were determined. After spectral preprocessing, stepwise multiple linear regression was applied to derive PTFs and STFs using basic soil properties and soil spectral reflectance as input, respectively. According to the results, the parametric PTFs had high accuracy in estimating the fractal dimension of the soil particle size distribution (Dpsd) (R2 = 0.96), while the derived parametric functions had moderate predictive accuracy in estimating other studied fractal and hydraulic parameters including DSWRC-TW, DSWRC-B, λBC, nvG and bC (R2 = 0.40 – 0.59). The results also showed that the proposed spectral transfer functions (STFs) had moderately accuracy in estimating Dpsd (RPD = 1.40) and had poor accuracy in estimating DSWRC-TW, DSWRC-B, λBC, nvG and bC (RPD = 1.13 – 1.37). Overall, the results of this study showed that despite of the relatively lower accuracy of spectral parametric functions compared to pedotransfer functions, the use of soil spectral data due to simultaneous estimation of several parameters, lower cost, less time and field data (especially with development of soil information databases and spectral libraries), can be used as an indirect, rapid and novel method in estimating parameters of fractal and experimental models of SWRC.

In semi-arid area the dangerous floods are usually generation by runoff. Therefore, spatial analysis of runoff is necessary for crisis management. Hydrological models are useful tools for simulation of water cycle-related processes and predict future events. Among the hydrological models, SWAT is one of the most popular of these models which the soil map and information are one the most important input data in SWAT model. On the other hand, the qualitative and quantitative soil maps are not available in most of the watershed in Iran. The main purpose of this study was to determine the spatial analysis of runoff generation using global soil data and using SWAT model in Damghanrood watershed in Semnan province. For this purpose, climatic data from 2008 to 2018 were used to simulate runoff. The data from 2010 to 2014 was used for model calibration and data from 2015 to 2018 was used for model validation. Sensitivity analysis, calibration, validation and model uncertainty were performed in SWAT-CUP software using SUFI-2 algorithm. The model was evaluated with coefficients of determination (R2) and Nash Sutcliffe (NS) statistics. The parameters of curve number, mean slope length, manning channel roughness coefficient and saturated soil hydraulic conductivity were determined as more sensitive parameters. The values of the R2 and NS statistics were 0.48, 0.47 for the model calibration stage and 0.46 and 0.45 for the validation stage, respectively. The results showed that the performance of the model in runoff simulation with global soil data was acceptable. Spatial analysis of runoff was performed using the average weight of runoff per unit area. The results of spatial analysis of runoff showed that the most important sub-basins in runoff generation was sub-basin number 5 and the least important sub-basins were number 1 and 11. Results indicated that sub-basin location and land use were most effective variables in runoff prioritization. SWAT model divide the watershed into the homogeneous units, therefore, it is possible to determine the critical areas of runoff generation.