JWSS - Journal of Water and Soil Science

Phytoremediation models are important to understand the processes governing phytoremediation and the management of contaminated soils. Little effort has been made for evaluating the potential of the phytoremediation of metals based on the mathematical models. Therefore, the purpose of this study was modeling the phytoremediation of the nickel-contaminated soils. For this purpose, a model was recommended for estimating the rate of the phytoremediation of nickel from the soil by means of relative transpiration reduction and concentration of nickel in the plant functions. To evaluate the model, soil was contaminated with different levels of nickel by nickel nitrate. Then, the pots were filled with contaminated soil and Basil (ocimum tenuiflirum L.) seeds were planted. To avoid the dry tension, the pots were weighed and irrigated to the point of field capacity (FC) at short time intervals (48 hours). The plants were harvested in four times. At each harvesting stage, the relative transpiration values and nickel concentration in the soil and plant samples were measured. The performance of the model was evaluated by statistical methods such as Maximum Error, Root Mean Square Error, Coefficient of Determination, Efficiency of Model and Coefficient of Residual Mass. Results demonstrated that in the case of nickel contamination in soil, changes in the relative transpiration of Basil can be measured by the two proposed models and the linear model (R2=0.94) has a better performance compared to the nonlinear one (R2=0.84). Also the model obtained from the combination of linear function and nickel's concentration in soil has a relatively good (R=0.7) fit with the measured values of the remediation rate of nickel in soil.

Erosion and sedimentation as a river natural behavior lead to the loss of soil and cause irreparable damages to water development projects. Since the phenomenon of erosion and deposition is one of the most complex natural processes, the complete understanding of the effective factors involved in this phenomenon is really a big problem. This study was done to optimize the parameters affecting sediment yield and determine their sensitivity in the Doiraj river basin in the West of Iran, using SWAT, a semi-distributed model, and SUFI2 algorithm, and the monthly sediment yield from 1994 to 2004. In the first run of the model, coefficients R2, NS, and Br2 were respectively obtained as 0.43, 0.39 and 0.28. The results showed that performance of the model with the default data was not satisfactory and we needed to determine the used optimal values. Then, the optimal values of parameters were determined using SUFI2 algorithm and reverse modeling and the model was run with the new values. Based on the new results, coefficients R2, NS, and Br2 were respectively obtained as 0.75, 0.73 and 0.65 and performance of the model improved and its accuracy increased acceptably. In the next step, from among the studied 30 parameters, (CH_N2), (USLE_K), (USLE_P) and (OV_N) were identified as the important parameters effective in the output of the sediment yield from basin. Also, USLE_K as was diagnosed as the most sensitive parameter. Results of this survey can be used in the management of the effective parameters in sediment load. Determining exact values of the effective parameters in other studies can be helpful to improve the simulation results in similar basins, too.

Nowadays, due to the high potential of advanced simulation models for groundwater, these models are comprehensively applied in the management and exploitation of groundwater resources. The aim of this study was to investigate and simulate the groundwater resources in Kouhpayeh-Segzi watershed and in particular estimate the hydrodynamic coefficients of unconfined aquifer. After preparation of input layers, efficient parameters in modeling, boundary conditions and aquifer gridding were determined. Then, based on the available data, the model was run and calibrated in a steady state for the water year 2002 and in a transient state for water years between 2002 and 2004. The simulation outputs were confidently verified for the water year 2005. The results indicated that the hydraulic conductivities and storage coefficients were ranged on sub-basin from 15.26 to 19.87 m/day and 0.0107 to 0.0186, respectively. From aquifer's hydrograph for a period from 1995 to 2012, water level dropped about 25 cm. This may be due to two irrigation networks (green area). This leads to rising water level. By ignoring these recharge areas, water level declined up to 80 cm per year. With sensitivity analysis in transient state to evaluate the efficacy of each parameter, the accuracy of the results of calibration model was confirmed. In addition, the hydraulic head values computed by MODFLOW were in good agreement with those that were collected from all piezometers.

Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.

Simulation of water and solute transport in soil is very useful for optimum management of water and fertilizer use. In this study, the HYDRUS-1D model was used to simulate water and nitrate transport in furrow irrigation of sugarcane. For this putpose, a large-scale experiment was performed as a split plot design based on the randomized complete blocks with 3 replications in a 25-hectare piece of land in the Dehkhoda Sugarcane Agro-Industry Company from March 2012 to October 2013. The main factor was split application of fertilizer at three levels: two, three and four splits. The sub-main factor was fertilizer amount, applied at three levels (i.e. 350, 280 and 210 kg urea corresponding to 100%, 80% and 60% fertilizer requirements, respectively). Soil hydraulic parameters were estimated through inverse modeling using moisture data collected during more than 4 months of the sugarcane growing season. Solute transport parameters were then estimated using the hydraulic parameters and nitrate concentration data. In this study, statistical criteria including R², RMSE, ME and SSQ were used to compare the observed and simulated values of moisture content and nitrate concentration. The results indicated that R² for simulated moisture content and nitrate concentration in four splits and 60% fertilizer requirement treatment (i.e. calibrated treatment) were 62.7 and 91.2 percent, respectively. Cumulative infiltration depths were about 46 and 58 mm for calibration and validation treatments, respectively. For these treatments, the cumulative evapotranspiration rates were 50 and 60 mm, respectively. Soil moisture content in the surface layer varied from 21 to 45 and 21 to 42 percent, for calibration and validation treatments, respectively while the changes in the deep layer moisture content were 33 to 38 percent, for both treatments.

Splash erosion is one of the most important water erosion types, causing initiation of other types of water erosion. The objective of this study is to model the splash erosion using fuzzy logic approach in part of northern Karoon basin. The major land usage in the area are irrigated farming, dry land farming, pasture and degraded pasture. For the purposes of this study, soil properties including organic matter; CaCO₃; surface shear strength (SSS); particle size distribution; mean weight diameter (MWD) and soil splash erosion were measured under four different slope conditions (S:%) and rainfall intensity (RI:mm.h-1): 5-50, 5-80, 15-50, 15-80, respectively, using multiple splash sets (MSS) at 80 different locations. Splash erosion was modeled based on combinational rule of inference under five conditions for selection of different operators. The efficiency of the models was evaluated using mean square error (MSE) between observed and estimated values. Results revealed that all models are capable of predicting splash erosion. Also slope, rainfall intensity, MWD, SSS, fine sand and coarse silt attributes were found to be appropriately and precisely using splash erosion.

In this research, a comprehensive simulation model for water cycle and the nitrogen dynamics modeling including all the important processes involved in nitrogen transformations such as fertilizer dissolution, nitrification, denitrification, ammonium volatilization, mineralization, immobilization as well as all the important nitrogen transportation processes including nitrogen uptake by the plant, soil particles adsorption, upward flux, surface runoff losses and drain losses, was used for fertilizer management modeling in a sugarcane farmland in Imam Khomeini Agro-Industrial Company using a system dynamics approach. For evaluating the model the data collected from Imam Agro-Industrial Company equipped with a tile drainage system with shallow ground water and located in Khuzestan province, Iran, were used. The statistical analysis of the observed and simulated data showed that the RMSE for determining the accuracy of simulation of the nitrate and ammonium concentration in drainage water is 1.73 mg/L and 0.48 mg/L, respectively. The results indicated that there is good agreement between the observed and the simulated data. Nine scenarios of fertilization at different levels of urea fertilizer were modeled including one scenario of 400 kg/ha, two spilit scenarios of 350 kg/ha, two spilit scenarios of 325 kg/ha, two spilit scenarios of 300 kg/ha, one scenario of 280 kg/ha and one scenario of 210 kg/ha. Results of the modeling showed that the scenario of 210 kg/ha has the highest nitrogen use efficiency (52.3%) and the lowest nitrogen losses consisted of denitrification, ammonium volatilization and drainage losses (17.82, 7.16 and 92.59 kg/ha, respectively). The results revealed that increasing the consumption of urea fertilizer greater than 210 kg/ha increased the overall nitrogen losses and reduced the nitrogen use efficiency. Meanwhile, this model can be used for managing the fertilizer and controlling the nitrate and ammonium concentrations in the drainage water to prevent the environmental pollution. Also, the system dynamics approach was found as an effective technique for simulating the complex water-soil-plant-drainage system.

Soil erosion is undoubtedly one of the most important problems in natural areas of Iran and has destructive effects on different ecosystems. Considering that calculation of the sediment rate in sediment stations and direct measurements of erosion process is costly and difficult, it is critical to find ways to accurately estimate the amount of sediment yield in catchments especially in arid and hyper arid areas because of their high ecological sensitivity. One of the most commonly used methods in these areas is the sediment rating regression method. Therefore, in this study sediment observed data for 48 events (the corresponding discharge and sediment) in a 23-year period from Fkhrabad basin (Mehriz) were compared to the estimated data obtained from Multi-line rating method, extent middle class, middle class rating curve with correction factor QMLE, SMEARING correction coefficient FAO and Artificial Neural networks (ANNs). Finally, the accuracy of these methods were assessed using different evaluation criteria such as Root Mean Square Error (RMSE), coefficient of determination (R2) and the standard Nash (ME). Results showed that ANN outperformed the other methods with the RMSE, R2 and ME of 203.3, 0.86 and 0.66, respectively. The results suggest that these methods should be used cautiously in estimating the suspended sediment load in arid and hyper arid regions due to the nature of the observed data and temporal and seasonal flow systems in these regions. It was also indicated that the artificial neural network models have higher flexibility than other methods which makes them to be useful tools for modeling in poor data conditions.
 

Appropriate criteria and methods are required to assess desertification potential in various ecosystems. This paper aimed to assess desertification levels in Segzi plain located in east part of Isfahan, with a focus on soil quality criteria used in MEDALUS model. Bayesian Belief Networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Soil samples were collected from 17 soil profiles in all land units and some of their characteristics such as texture, soluble sodium and chlorine, organic material, Sodium Absorption Ratio (SAR), Electrical Conductivity (EC) and CaSo4 of all soil samples were determined in soil laboratory. The effects of measured soil quality indicators on desertification intensity levels were assessed using sensitivity and scenario analysis in BBNs. Results showed that the used integrated method can appropriately accommodate uncertainty in the desertification assessments approaches created as a result of the influence of different soil characteristics on desertification. According to the results of MEDALUS model, 28.28 % and 71.72 % of the study area were classified as poor and moderate areas in terms of soil quality respectively. Sensitivity analysis by both models showed that soil organic matter, SAR and EC were identified as the most important edaphic variables responsible for desertification in the study area. Evaluating the effects of various management practices on these variables can assist managers to achieve sound management strategies for controlling desertification.
 

Effective soil conservation requires a framework modelling that can evaluate erosion for different land-use scenarios. The USLE model was used to predict the reaction of appropriate land-cover/land-use scenarios in reducing sediment yield at the upland watershed of Yamchi Dam (474 km²), West Ardabil Province, Iran. Beside existing scenario, seven other land-use management scenarios were determined considering pattern of land-use through study area within a GIS-framework. Then, data inputs were prepared using terrain data, land-use map and direct observations. According to the model results, the generated erosion amount was 3.92 t/ha/yr for the current land-use (baseline scenario). For this purpose, conservation practices in dry farming slopes and implementing the scenario 5 (contour farming and remaining crop residuals) can reduce the sediment to 2.02 t/ha/yr. The lowest and highest decreases in sediment yield are projected to be through implementation of scenario 6 (irrigated farming protection with plant residuals) and 7 (biological soil conservation in dry and irrigated farming). The results indicated that, implementing scenario frameworks and evaluating appropriate land-use management scenarios can lead to the reduction of sediment entering the reservoir, and prioritizing soil conservations in the studied area.