JWSS - Journal of Water and Soil Science

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Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.

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Sediment sources fingerprinting is needed as an autonomous tool for erosion prediction, validation of soil erosion models, monitoring of sediment budget and consequently for selecting soil conservation practices and sediment control methods at the catchment scale. Apportioning of eroded-soil into multiple sources using natural tracers is an integrated approach in soil erosion and sediment studies. The objectives of this study, as a first work, are to assess spatial variations of biochemical tracers and theirs validation in discriminating sediment sources under different land uses and water erosions at catchment scale and to apply them as fingerprints to determine relative contributions of sediment sources in Zidasht catchment, Iran. In view of this, 4 enzyme activities as biochemical tracers were measured in 42 different sampling sites from four sediment sources and 14 sediment samples. The results of discriminant function analysis (DFA) provided an optimum composite of two tracers, i.e. urease and dehydrogenase that afforded more than 92% correct assignations in discriminating between the sediment sources in the study area. Sediment source fingerprinting model was used based on optimum composite of two tracers resulting from DFA to explore the contributions of sediment from the four sources. The results showed that the relative contributions from rangeland/surface erosion, crop field/surface erosion, stream bank and dry-land farming/surface erosion sources were 11.3±5.3, 8.1±3.8, 75±8.5 and 3.6±2.5, respectively. Therefore, we can conclude that fingerprinting using biochemical tracers may help develop sediment fingerprinting models and as a first step facilitate a more complete tool for fingerprinting approach in the future.

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Rivers as a main sources of supplying water for urban areas, agriculture and industry, are very important. This point reveals the necessity of the control, improvement and solving the problems of rivers, especially all problems relating to water quality. In this study, transport of the suspended sediment is numerically modeled. The Saint-Venant hydrodynamic equations and also advection-dispersion equation (ADE) are applied for modelling flow and suspended sediment transport. It is necessary to choose appropriate empirical and/or semi-empirical equation to accurately estimate the equilibrium suspended sediment discharge, as well as the appropriate equation describing longitudinal dispersion coefficient. In this research, 5 and 6 equations were applied in the ADE for estimating equilibrium suspended sediment discharge and longitudinal dispersion coefficient, respectively. 30 combinations of these equations were made and the final model was run for each of them separately. Comparison of the predicted suspended sediment concentrations and the corresponding measured values at the survey site, Abdelkhan Station, for the calibration and verification periods showed that the combination of the Van Rijn's equilibrium suspended sediment equation and the Fischer's longitudinal dispersion equation performed very well. The maximum percentages of errors in estimation of suspended sediment concentrations were 19.56% and 26.3% for the calibration and verification periods, respectively.

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Discrete Models of Spatial Variability (DMSV) have limitations for soil identification in traditional soil maps. New approaches, generally called digital soil mapping (DSM), using continuous methods (CMSV), try to predict soil classes or soil properties based on easily-available environmental variables. The objective of this study was to map the soil classes of the Borujen area, Chaharmahal-va-Bakhtiari province, using digital elevation model (DEM) and its attributes and Soil-Land Inference Model (SoLIM). To do this, eighteen terrain attributes were derived from the DEM of the area. The primary analysis showed seven attributes are the most important derivatives. These derivatives as well as three dominant soil subgroups and seven soil families of the region (41 profiles from 125 profiles) were used to construct the input data matrix of the model. Then, output fuzzy soil maps of SoLIM were converted to polygonal soil map, using ArcGIS. Results showed that different combinations of DEM attributes have different accuracy rates for soil prediction. The accuracy of the interpolation was twice that of the extrapolation. Although SoLIM had an acceptable accuracy for soil nomination, and identification of soil map units’ types, it did not have enough accuracy for the location of soil classes. It seems that using other data like parent material and geomorphic surface maps will increase the accuracy of the model prediction.

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Hardsetting phenomenon is an indicator of poor soil physical quality. Hardsetting soils are soils with high rate of mechanical strength increase upon drying and are hardened and/or compacted when dry out. It is difficult to till such soils. Hardsetting soils have additional limitations such as poor aeration at wet conditions, low infiltrability and high runoff and erosion. Most of Iran soils have low organic matter content and it is expected that hardsetting phenomenon occurs in some of these soils. This study was conducted to investigate the hardsetting phenomenon on 9 soil series collected from Hamadan province. Three types of mechanical strength consisting tensile strength (ITS), unconfined compressive strength (UCS), and penetration resistance (PR) were measured on the repacked soil samples prepared in the lab. The ITS, UCS and PR tests were done on the soil cores which had been prepared at bulk density (BD) equal to 90% of critical BD for root growth (0.9BDcritical). The effects of intrinsic properties on the hardsetting phenomenon were studied, too. Based on the suggested definition in “International Symposium on Sealing, Crusting and Hardsetting Soils” to International :::union::: of Soil Science, in which a hardsetting soil has air-dry tensile strength ≥ 90 kPa, one soil (medium-textured) out of the studied soils showed the hardsetting phenomenon at 0.9BDcritical. It might be concluded that medium-textured soils are more susceptible to hardsetting. For all of the studied soils, the ITS increased with the increase in clay content. The increasing impacts of clay and carbonate contents were also observed for the UCS and PR, respectively. Calcium carbonate could act as a cementing agent in between the soil particles and brings about the soil susceptibility to hardsetting. Moreover, the decreasing trend of all soil mechanical strengths was observed with water content increase. Slope (b) of the exponential model (fitted to the soil mechanical strength characteristic curve), as an index of hardsetting, had positive correlation with the sand content and negative correlation with the silt content. Overall, texture and calcium carbonate content are major and effective properties in terms of hardsetting phenomenon in Hamadan soils.

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The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff from rainfall events. The ratio of initial abstraction (λ=Ia/S) to maximum potential retention (S) was assumed in its original development to be equal to 0.2 (λ=Ia/S=0.2) in SCS-CN method. Application of the initial abstraction ratio equal to 0.2 out of the area where it has been developed may lead to a non logical estimation of runoff. Thus, the study was conducted to determine the initial abstraction ratio (λ=Ia/S) by analyzing measured rainfall-runoff events. The dataset consisted of 58 rainfall-runoff events during 15 years (1987-2001) of rainfall and runoff measurements from Taham-Chay watershed, northwest of Zanjan, Iran. Based on the results, the estimated runoff value on the basis of Ia= 0.2S was 26.7 times higher than the measured value, on average. There was a very low relationship between the measured and estimated runoff values (R2=0.09) and mean model error was 0.13. The Ia/S values varied from 0.004 to 0.008 with an average of 0.006. When Ia/S value was modified to 0.08, ratio of the measured to estimate runoff value was 1.4 and the determination coefficient (R2) of the relationship between the two was 0.41. When seven rainfall events that had the low rainfall intensity values (lower than 0.14 mm/h) and two events that had the high rainfall depth (bigger than 10.47 mm) during the past five days were removed from the data analysis process, ratio of the measured to estimated runoff value decreased to 1.3 and the determination coefficient (R2) of the relationship between the two enhanced to 0.90. The mean model error for the modified Ia/S value also decreased to 0.007. It also improved model efficiency coefficient (EF) to -0.089 compared with 0.91 for traditional Ia/S value (0.2).

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The task of modern agriculture is to safeguard the production of high quality food, in a sustainable natural environment under the precondition of pollution not exceeding accepted norms. The sustainability of current land use in agro-ecosystems can be assessed with respect to heavy metal accumulation in soils by balancing the input/ output fluxes. The objectives of this study were to model accumulation rate and the associated uncertainty of Zn in the agro-ecosystems of 3 arid and semi-arid provinces (Fars, Isfahan and Qom). Zinc accumulation rates in the agro-ecosystems were computed using a stochastic mass flux assessment (MFA) model with using Latin Hypercube sampling in combination with Monte-Carlo simulation procedures. Agricultural information including crop types, crop area and yield, kind and number of livestock, application rates of mineral fertilizers, compost and sewage sludge and also metal concentration in plants and soil amendments were used to quantify Zn fluxes and Zn accumulation rates. The results indicated that Zn accumulates considerably in agricultural lands of the studied townships especially in Najafabad (3009 g ha-1yr-1). The major Zn input routes to the agricultural soils (and due to agricultural activities) were manure and mineral fertilizers and the major part of the uncertainty in the Zn accumulation rate resulted from manure source.

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Models are helpful tools to predict runoff, sediment and soil erosion in watershed conservation practices. The objectives of this research were to investigate sensitivity analysis, calibration and validation of EUROSEM model in estimation of runoff in Tangh-e-Ravagh sub-basin of Karoun watershed. The model was tested in a one hectare experimental test site. The area was divided into nine elements according to EUROSEM user's manual. A triangular weir was installed at the outlet of the area to collect runoff in specified time periods for six rainfall events. Sensitivity analysis of the model was performed by a ±10% change in the dynamic parameters of the model and examining the outputs for a rainstorm. Sensitivity analysis showed that total runoff was sensitive to saturated hydraulic conductivity and insensitive to soil cohesion. Sensitivity analysis indicated that the model sensitivity depends on evaluation conditions and it is site-specific in nature. Calibration and validation of the model was performed on input parameters. Calibration of hydrographs was performed by decreasing saturated hydraulic conductivity and capillary drive and increasing initial soil moisture. Validation results showed that EUROSEM model simulated well the total runoff and peak of runoff discharge, but it could not simulate well the time of runoff, time to peak discharge

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The present paper tries to describe the advantage and improvement of a numerical model when predicting government processes on Flow Rivers. With regard to the important effect of the flow velocity and shear stress forces on river bank erosion, we apply a Two-Dimensional numerical model, named CCHE2D, to simulate river flow pattern at a meandering river Khoshk-e-Rud River of Farsan, 30 Km west of Shahr-e- Kord. Various algorithms and parameters were implemented in a computational fluid dynamic model (CFD) for simulation of two-dimensional (2D) water flow to gain an insight into the capabilities of the numerical model. At this surveying, at first, we applied the topographic maps of the studied location and then, made the model geometry and calculation mesh with diverse dimensions. Finally, using the measured properties of the river flow and the Depth-Average, Two-Dimensional Hydrodynamic Model was run. Then, we obtained the results of model, such as depth and flow velocity at the river meander. Within the scope of the test cases, the model simulated water flow pattern processes at an intake, as well as a steady flow regime in a sine-shaped meandering channel by a 90_channel bend, which is the free-forming meander evolution of an initially straight channel. Because of high accuracy of this numerical model and multiple content of its internal parameters, the evaluation result of model, confirmed the measurement results. Therefore, the parameters gained from the model showed good conformity with measurement parameters at field cross-section. All results matched well with the measurements. The results also showed that using computational fluid dynamics for modeling water flow is one step closer to having a universal predictor for processes in Meandering Rivers

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Reference evapotranspiration (ET0) is a necessary parameter for calculating crop water requirements and irrigation scheduling. In this study, a method was presented as ET0 is estimated with NOAA satellite imagery in the irrigation network. In this method, a pixel from a set of pixels within the irrigation network was chosen with the highest vegetation index, and its surface temperature (Ts) with extraterrestrial radiation parameter (Ra) was used as inputs of the model. The M5 model tree for converting Ta and Ra to ET0 was used as input variables. In this research, Gazvin irrigated area was selected as a case study. A total of 231 images of NOAA satellite related to irrigation season of the study area were used. The results obtained by the M5 model were compared with the Penman–Monteith results, and error values were found within acceptable limits. The coefficient of determination (R2), percentage root mean square error (PRMSE) and the percentage mean bias error (PMBE) were found to be 0.81, 8.5% and 2.5%, respectively, for the testing data set.

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The objective of this study was to investigate the effect of potassium zeolite on ammonium ion sorption and retention in a saturated sandy loam soil in laboratory conditions with four treatments of 0, 2, 4 and 8 g zeolite per kg soil. The study was conducted as a completely randomized block design. Simulation of ammonium ion leaching was performed using Hydrus-1D model in the soil columns. Ammonium nitrate fertilizer with a concentration of 10g per liter was added to soil columns and then leaching was performed. Results of the study showed that adding potassium zeolite to soil causes reduction in the mobility of ammonium ion and increase in the retention of ammonium in soil. Also, the results of the Convection- Dispersion (CDE) and Mobile- Immobile (MIM) models investigation indicated that the ammonium ion sorption by soil followed the Freundlich isotherm model. Absorption isotherms and diffusion and dispersion coefficients were determined using the inverse modeling technique. Based on the results obtained, optimized values of Freundlich isotherm of model were much less than the observed amounts. This shows that the Hydrus-1D model is not able to predict the ammonium ion mobility in soil macropores, and as a result, reduces greatly the amount of absorption parameters. Because the soil was disturbed, CDE model estimation was closer to the observed values in all four treatments

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Excess pore water pressure in clay core dams during construction and primary filling reservoir (first impounding) causes initiation and progression of hydraulic fracture. In this research, the instrumentation data during construction and first filling reservoir (first impounding) was analyzed. It measured internal deformations, pore water pressures and total vertical stresses and compared with the analysis results in Masjed-e-Soleiman dam. To do this analysis, GEOSTUDIO 2004 V. 6.02 software was used. The staged construction of the dam was the model in the form of 2D coupled consolidation. The Non-linear elastic model for the core material and Linear Elastic model for other zones were incorporated into the models. For exact assessment and to obtain correct parameters of the constitutive model, the triaxial tests were performed on the core material of Masjed-e-Soleiman Dam and acceptable results were obtained.

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Nitrate leaching from agricultural lands can pollute groundwater, and the degree of pollution caused significantly depends on agricultural practices implemented on farms. Field studies required to evaluate the effects of various agricultural management strategies on nitrate leaching are expensive and time consuming. As a result, it is suggested to use crop models to simulate the effects of management practices on nitrate leaching. Plant growth models such as DSSAT software package can simulate daily plant growth and development, and also are capable of simulating daily nitrate leaching and nitrogen uptake by plants. However, it is required to evaluate the performance of any model before using it for any specific region. In this study, the performance of nitrogen balance model of DSSAT software package was evaluated to simulate nitrate leaching from the root zone of silage maize at different levels of applied water and nitrogen fertilizer. The experiment consisted of three levels of nitrogen fertilizers, including zero, 150 and 200 kg N ha-1 and four levels of applied water 0.7SMD (soil moisture depletion), 0.85SMD, 1.0SMD and 1.13SMD. Nitrate-nitrogen leaching from 36 plots at the 60 cm depth during the growing period was measured by soil moisture suction equipment (ceramic suction cups, CSC). After calibrating the model by using field data, its performance was evaluated to simulate nitrate leaching. Maximum amount of N leaching 8.4 kg N ha-1 was obtained from over irrigation treatment with the application of 150 kg nitrogen per hectare. The model simulated nitrate leaching for this treatment as 7.8 kg N ha-1. The model consistently underestimated the nitrate leaching however, it followed the behavior of nitrate leaching during the growing season. In deficit irrigation treatments, the nitrate leaching was very low and close to zero and the model simulated the same result accordingly. The results showed that the model, in addition to phenological stages and performance indicators, can simulate nitrate leaching from the root zone and could be used to evaluate the effects of various irrigation and fertilizer management strategies on nitrate leaching.

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The real estimation of the volume of sediments carried by rivers in water projects is very important. In fact, achieving the most important ways to calculate sediment discharge has been considered as the objective of the most research projects. Among these methods, the machine learning methods such as decision trees model (that are based on the principles of learning) can be presented. Decision tree method is a hierarchical multi step method which is a recursive data collection technique to binary and smaller sub-divisions until the final analysis cannot be divided. Decision trees consider a priori known set of data and derive a decision tree from it. Then, tree can be used as the set of laws to predict unknown features. In this research, the efficiency of this technique for predicting the suspended sediments in Ilam dam basin has been investigated. To evaluate the accuracy of the methods (written by MATLAB software), statistical criteria such as R, BIAS, RMSE, r2 and MAE were computed. The results showed that based on all the statistical criteria, decision tree in comparison with the sediment rating curve had most consistency with the observed data. Meanwhile, the most important factors for creating tree in the model (that had high correlation with sediment data) are the corresponding discharge and daily rainfall.

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Physicochemical characteristics of soil, land cover/use and human activities have effects on heavy metals distribution. In this study, we applied Classification and Regression Tree model (CART) to predict the spatial distribution of zinc in surface soil of Hamadan province under Geographic Information System environment. Two approaches were used to build the model. In the first approach, 10% of total data were randomly selected as test data and residual data were used for building model. In the second approach, all data were used to build and evaluate the CART model. Determination coefficient (R2) and Mean Square Error (MSE) were applied to estimate the accuracy of model. Final model included 51 nodes and 26 terminal nodes (leaf). Calcium carbonate, slope, sand, silt and land use/cover were determined by the CART model to predict spatial distribution of Zn as the most important independent variables. The regions of western Hamadan province had the highest concentration of Zn whereas the lowest concentration of Zn occurred in the regions of northern Hamadan province. The results indicate good accuracy of CART model using R2 and MSE indices.

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Zinc (Zn) is an essential trace element for plants as well as for animals and humans. There is a significant relationship between soils, plants and humans Zn status in a certain agro-ecosystem. The objectives of this study were to assess Zn status of soils in 3 arid and semiarid provinces of Iran and to model the relationship between wheat grain Zn and agro-ecosystem parameters. About 137 soil and wheat samples were collected randomly from the agricultural soils of Fars, Isfahan and Qom and were analysed in laboratory. Modeling the relationship between wheat grain Zn and agro-ecosystem parameters was done using least square based and robust methods. The results indicated that total Zn concentration of soils (range, 21-149 mg kg-1 mean, 75.2 mg kg-1) was in normal ranges. The DTPA-extractable Zn concentrations were below the critical level (0.8 mg kg-1) in 16% of the surveyed fields. The Zn concentration in 80% of wheat grains was sufficient (more than 24 mg kg-1) with respect to plant nutrition (range, 11.7-64 mg kg-1 mean, 31.6 mg kg-1). However, Zn bioavailability for consumers was generally low in more than 75% of the samples. This is because of high phytic acid to Zn molar ratio (more than 15). Soil DTPA-extractable Zn and available P were entered in to most of regression models significantly. Regression analysis showed that most of models fitted to wheat grain Zn concentration and soil Zn and influenced by agro-ecosystem parameters had a weak prediction power, despite their high determination coefficient. This means that factors other than those considered here have a strong influence on the uptake of Zn by wheat in these soils.

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The modeling of yield response to water is expected to play an increasingly important role in the optimization of crop water productivity (WP) in agriculture. In this study, the CropSyst model was used to simulate two local rice varieties yield response under five irrigation treatments consisting of continuous flooding irrigation and irrigation at 0, 3, 6 and 9 days after the disappearance of water from the soil surface. The experiment was conducted at Rasht region during two growing seasons of 2003 and 2004. The model was calibrated using the first year data and validation of that was done using the second year data set. The result of F test shows that there was not a significant difference between the measured and simulated yield at confidence level of 99%. The relative errors of yield estimation were obtained between -0.81 to 12.58% and -2.4 to 19.42% for Binam and Hasani cultivars in 2003, respectively. These values were 0.83 to 16.4% and -2.82 to 21.27% in 2004, respectively. The results showed that due to the CropSyst model ability in simulating yield of rice under different irrigation regimes, this model can be used to explore management optimum options to improve rice water productivity

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Soil erosion is one of the most important factors affecting soil quantity and quality and is environmental problems in developing countries like Iran. It can have deteriorating effects on ecosystems. This research was carried out in farm lands of the Chehel-Chai watershed, Golestan province to investigate the effect of soil properties on runoff and soil loss. Runoff and soil loss were measured in a completely randomized design in 36 plots with 10×10 m sizes in farm lands. For this reason, this study was conducted using rainfall simulator with 2 mm/min intensity and 15 min duration in 4 replicates. Soil samples were also taken in each plot. Sampling was conducted in October 2009. Results of the Pearson correlation showed that among soil properties, the contents of the lime, silt and fine sand had positive correlations with runoff at 1% confidence level. Also, soil surface resistance at 1% confidence level, the contents of the organic matter and nitrogen at 5% confidence level had negative correlations with soil loss. Finally, the results of multiple linear models showed that the content of lime is effective in estimating runoff and soil surface resistance, and organic matter is effective in estimating soil loss.

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Different root water uptake models have recently been used. In this article, we use evapotranspiration data and soil water content data obtained from lysimeter measurements and root distribution in soil data obtained from olive tree to evaluate the accuracy of root water uptake models in predicting the soil water content profiles. Depth of lysimeter was 120 cm which was filled with clay-loam. Lysimeter recorded values of input and output of water and accurate value of evapotranspiration was also calculated. Soil water content distribution was measured using a TDR probe in lysimeter during the experiment. Feddes model with the root length density was used to account for the role of root distribution in soil. The flow equations were solved numerically with the measured evapotranspiration data as input, and the predicted soil water content profiles were compared with the measured profiles to evaluate the validity of the root water uptake models. The comparison showed that the average of relative error index for Feddes model was 10 %. Based on the results, about 90% of root uptake in olive tree happened at the depth of 40 centimeter

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Shahrekord aquifer is depleted by almost 800 deep and semi-deep wells, the majority of which are agricultural wells and some have urban usage. In southern parts of the plain, the water table has fallen strongly because of immoderate discharge and decreased the quality of water by urban wastewater. The main objective of this study is investigation of subsurface dam construction and its effects on water table in consumption locations, reduction of deliveries costs and interception of contaminant transport. Therefore, the Shahrekord aquifer model was simulated with hydrodynamic coefficients calibration by PMWIN5.3 Software. The southern outlet of plain (near Bahram-Abad village) was selected to study subsurface dam construction, then a horizontal-flow barrier in this place was set with mean hydraulic conductivity equal to 0.5 m/day. Water table situation and nitrate concentration were analyzed using ArcGIS9.2 software before and after dam construction. The results showed that the subsurface dam rises groundwater level in 4 kilometers distance of upstream areas. Also, the available volume of water increased about 1.5 Mm3. Nitrate concentration didn't show to be considerably different from the initial state. But, it is likely that contamination in the storage resource will rise because it is located near Shahrekord water treatment plant and also due to the discharge of wastewater wells.