JWSS - Journal of Water and Soil Science

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Soil erosion is an important economical, social and environmental problem requiring intensive watershed management for its control. In recent years, modeling has become a useful approach for assessing the impact of various erosion-reduction approaches. ِDue to limited hydrologic data in mountainous watersheds, watershed modeling is, however, subject to large uncertainties. In this study, SWAT2000 was applied to simulate runoff and sediment discharge in Beheshtabad watershed, a sub-basin of Northern Karun catchment in central Iran, with an area of 3860 km2. Model calibration and uncertainty analysis were performed with SUFI-2. Four indices were used to assess the goodness of calibration, viz., P-factor, d-factor, R2 and Nash-Sutcliffe (NS). Runoff data (1996-2004) of six hydrometery stations were used for calibration and validation of this watershed. The results of monthly calibration p-factor, d-factor, R2 and NS values for runoff at the watershed outlet were 0.61, 0.48, 0.85 and 0.75, respectively, and for the validation, these statistics were 0.53, 0.38, 0.85 and 0.57, respectively. The values for calibration of sediment concentration at the watershed outlet were 0.55, 0.41, 0.55 and 0.52, respectively, and for the validation, these statistics were 0.69, 0.29, 0.60 and 0.27, respectively. In general, SWAT simulated runoff much better than sediment. Weak simulation of runoff at some months of the year might be due to under-prediction of snowmelt in this mountainous watershed, model’s assumptions in frozen and saturated soil layers, and lack of sufficient data. Improper simulation of sediment load could be attributed to weak simulation of runoff, insufficient data and periodicity of sediment data.

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Water and fertilizer applications management should be improved due to scarce resources and environmental protection aspects. An analysis of crop yield production and profit maximization was conducted to determine the optimal water and nitrogen allocation. In this study, maize grain yields were predicted for 25 different amounts of irrigation water (350-1700 mm) and 46 different rates of nitrogen application (0-450 kg N/ha) were predicted using MSM (Maize Simulation Model) model. Irrigation water was distributed in growth period based on maize evapotranspiration. 30% and 70% nitrogen fertilization was used 19 and 50 days after planting date, respectively. Based on field operational costs and present market value in Fars province, optimal amounts of applied water and nitrogen were determined in different conditions of maximum yield (Wm and Nm, respectively), maximum profit under limited land (WL and NL, respectively) and maximum profit under limited water (Ww and Nw, respectively). At present market value ( 88 Rls m-3 for water, 1946 Rls kg-1 for nitrogen and 1570 Rls kg-1 for maize grain), the amounts of Wm, WL and Ww were 1336, 1008, 844 mm, respectively, and the amounts of Nm, NL and Nw were 450 kg N ha-1. Because of the low price of nitrogen, the optimum amounts of nitrogen in the analyzed conditions were similar. If the price of nitrogen and water are increased (i.e. 30000 Rls kg-1 N and 1000 Rls m-3 water), the optimum amounts of applied nitrogen and water in the analyzed conditions are changed to 450, 120 and 210 kg N ha-1, and 1336, 899 and 874 mm, respectively.

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Under different climatic conditions of Iran, the evaluation of evapotranspiration (ETo) models sensitivity to meteorological parameters, prior to introducing the superior performance model, seems quite necessary. Using a 35-year (1971-2005) climatological observations in Hamedan, this study compares the sensitivity of different commonly used evapotranspiration models to different meteorological parameters within the IPCC recommended variability range of 10 to 20% during the growing season (April-October). The radiation and temperature-based ETo models include: Penman-Monteith -FAO56 [PMF56], Jensen-Haise [JH1,2], Humid Turc [TH], Arid (semi) arid Turc [TA], Makkink [MK], Hansen [HN], and Hargreaves-Samani [HS]. Results indicate that all the above-mentioned ETo models show the highest sensitivity to radiation and temperature parameters. This implies that special care is required when we apply model-generated radiation and albedo parameters in such ETo models. It is predicted that by 2050, as a result of global warming, the cold semi-arid climates of Iran will cause an average evapotranspiration rise of about 8.5% in crop reference during the growing season.

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At present, soil surface characteristics (SSC) are recognised as key parameters controlling infiltration rates, runoff generation and erosion. Microtopography of surface among SSC is the main one. The work presented in this paper is based on a set of digital elevation models (DEMs) supplied by two different methods: Laser roughness-meter and photogrammetry method. We used two maquettes. The used maquettes correspond to varying roughness (rough and soft roughness). These methods were compared using different statistical parameters of SSC such as heights and slopes histograms. In addition, we studied estimation of Random Roughness (RR) coefficient and Maximum Depression Storage (MDS). RR is considered as an indicator of microtopography and it is one of the main parameters influencing erosion and runoff-infiltration processes. The obtained RR by photogrammetry method showed, on average, 10 percent difference from laser method for soft maquette and 5 percent for the rough maquette. The range of this difference for the MDS varies from 2 to 34 percent, i.e., maximum 0.17 millimetres. In this study, photogrammetric method gives the DEMs with a lower slope for the rough maquette (on average 40.5 versus 46 for the laser method) and higher slope for the soft maquette (about 23.5 versus 20.7 for the laser method). The results showed the DEMs provided by photogrammetric method is able to perform accurate estimation for RR and provides good estimation for the MDS. Therefore, it can be useful in erosion and hydraulic studies.

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The use of suitable empirical models for estimation of soil erosion and sediment yield is essential because of nonexistence or shortage of associated data in many watersheds. In the present study, the applicability of the USLE and its different versions Viz. MUSLE-S, AOF, MUSLT, MUSLE-E, USLE-M and AUSLE in estimation of storm-wise sediment yield from standard plots installed in dry farming, ploughed and rangeland treatments was evaluated. To conduct the study, the entire input data were collected from plots installed in three replicates in each treatment in Khosbijan Natural Resources Research Station in Arak Township. The models’ estimates were then compared with the observed sediment data for 12 storm events. Contrary to high correlation among different models’ estimates, the models used in estimation of measured sediment data were found inapplicable. However, significant relationship (r=94.4%) and non-significant relationship with correlation coefficients less than 50% were found between MUSLE-E, and MUSLE-S and MUSLE-E estimates and measured data in rangeland, dry farming and ploughed treatments, respectively.

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Low flow estimation and its characteristics play an important role in hydrologic studies. However, some low flow events are ignored compared with the lowest annual low flow that may have high risk. These events are taken into consideration by the use of partial duration or peak over threshold models. In this study, a 7-day low flow was applied for frequency distribution and threshold, and the lower events were considered as the number of low flow event ( ) to study seasonal variation of low flows together with two graphical methods. The results showed two major low flow seasons, and for other times of the year, the low flow events are negligible. At last, the region was divided into homogeneous groups based on seasonal variation of low flows.

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Variable rate technology-spraying technologies are new aspect of sustainable agriculture. In these technologies, the chemical is applied in the needed level of farm where there is a high intensity of pests and weeds. The purpose of this paper was to study the agricultural specialists' attitude toward and intention to use variable rate technology-spraying technologies in Jihad-e-Keshavarzi organization of Fars and Khuzestan provinces. A survey was conducted using a stratified random sampling to collect data from 249 agricultural specialists. The results showed that the specialists of Fars and Khuzestan provinces have intentions to use the variable rate technology-spraying technologies. The variables including attitude toward application, trialing, perceived usefulness, compatibility, and attitude of confidence all influence the intentions to use the variable rate technology-spraying technologies. Based on high positive intention of agricultural specialists, we recommend the use of these technologies in agricultural practices of the two provinces.

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An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.

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Phytoremediation is a new, in-situ and emerging remediation technology for contaminated soils. This technology, compared to other methods, is a sustainable, natural, relatively cheap and applicable to large scale area. Modeling phytoremediation provides quantitative insight for the governing process as well as for managers to assess the remediated sites. The objective of this study was to introduce a macroscopic phytoremediation model for Ni and Cd- polluted soils. The proposed model assumes that relative transpiration reduction function can resemble total soilNi and Cd concentrations. Combining the related functions of soil and plant responses to soil Ni and Cd concentrations, the phytoremediation rate of Ni and Cd was predicted. In order to test the proposed model, large quantities of soil were thoroughly polluted with Ni and Cd. Upland Cress (Lepidum sativum) and Ornamental Kale (Brassica olerace var. Viridis) seeds were then germinated in the contaminated soils. The experimental pots were irrigated with fresh water to reach field capacity. Upland Cress and Ornamental Kale were harvested three and four times, respectively. At each harvest, relative transpiration, Ni and Cd contents of soil samples and plants were measured. Comparison of the maximum error, root mean square error, coefficient of determination, modeling efficiency and coefficient of residual mass indicated that the non-threshold non-linear model provide high efficiency to predict relative transpiration for Upland Cress and Ornamental Kale, respectively. The results also indicated that the proposed macroscopic model can well predict the phytoemediation rate of the Ni and Cd by Upland Cress (R2>0.83) and Ni by Ornamental Kale (R2=0.78).

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Deficit irrigation is one of the strategies used to obtain products with maximum profits in recent years. In this context, research on determining appropriate levels of deficit irrigation is essential. Since determining the different levels of performance through field experiments is difficult, the use of simulation models is a strategy through which we can examine the water balance data, simulate the growth process, and to study different managerial scenarios. The purpose of this study was validation and evaluation of CropSyst, a plant growth model, to determine suitable cropping patterns in deficit irrigation conditions. Applying three deficit irrigation scenarios in model, with values of 10%, 20% and 30% on six crops, fava bean, bean, wheat, potato, sunflower and rice, we concluded that the applied deficit irrigation of 10% to bean, potato and beans, 20% to sunflower and 30% to wheat had been suitable, and it is better not to apply deficit irrigation in rice. Also, since in final selection, the rate of water productivity is one of the basic criteria in each crop mentioned above, determining net benefit based on drop index (NBPD) per cubic meter showed that the most NBPD is related to bean with 6853 Rials per cubic meters and the lowest amount is related to sunflower with a value equal to 2809 Rials per cubic meters.

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Soil bulk density measurements are often required as an input parameter for models that predict soil processes. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm was introduced and tested to estimate soil bulk density from other soil properties, including soil textural fractions, EC, pH, SP, OC and TNV. As many as eight nearest neighbors, based on cross validation technique were selected to perform bulk density prediction from the attributes of 136 soil samples. The nonparametric k-NN technique mostly performed equally well using Pearson correlation coefficient (r=0.86), root-mean-squared errors (RMSE=2.5) maximum error (ME=0.15), coefficient of determination (CD=1.3), modeling efficiency (EF=0.75) and coefficient of residual mass (CRM=0.001) statistics. It can be concluded that the k-NN technique is an alternative to other techniques such as pedotransfer functions (PTFs).

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In diversion flows, a portion of stream flow which enters the intake is diverted from upstream of the intake denoted by a surface and is called dividing stream surface (DSS). The amount of flow and sediment discharge entering the intake as well as design of submerged vanes to control sediment depends on determination of dividing stream width. In this study, the experimental tests were carried out at a 30 degree water intake from a trapezoidal section. Three components of velocity data were obtained for different flow conditions. Then numerical SSIIM2 model was calibrated and verified using tests data. More flow conditions such as the main channel with rectangular section were run using SSIIM2 model to get enough hydraulic data. From analysis of these datas it was found that the dividing stream width in different distances from the bed depends directly upon the diversion flow ratio. It was found that in comparison to the rectangular section, in trapezoidal cross section, the DSS dimensions are modified in such a way that its width is increases at the surface and reduced at the bed for the same flow conditions. Relations for predicting the dividing stream width and diversion flow ratio have been presented in this paper for intake from both rectangular and trapezoidal cross sections.

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Selecting an appropriate particle size distribution (PSD) model for a particular soil may be important for a precise estimation of soil hydraulic properties. Various models have been proposed for describing soil PSDs. The objective of this study was to compare the quality of fitting of eight PSD models (Fredlund, Gompertz, van Genuchten, Jaki, Logarithmic, Exponential, Logarithmic-Exponential and Fractal) in 71 soil samples collected from Lordegan and Saman in Charmahal-va-Bakhtiari province, Iran. Coefficient of determination ( ) and Akaike’s information criterion ( ) were used to compare the goodness-of-fit of the models to the experimental data. Results showed that Fredlund model is best for describing PSD of silt loam, silty clay loam, silty clay and sandy loam soil textures. While Fractal, Exponential and Logarithmic-Exponential models produced the poorest-fit in silt loam, silty clay loam and silty clay, they had the best performance in sandy loam texture. The performance of Fredlund and Gompertz models improved with an increase in clay and silt content from 25 and 40 percentage, respectively. The performance of Fractal, Exponential and Logarithmic-Exponential models improved by increasing the sand content. Reverse correlation was observed between silt content and the performance of the Fractal model.

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Soil water characteristic curve shows the relationship between soil water content and matric suction, which has an important role in water movement in the soil. The measurement of this curve is expensive and time-consuming in laboratory therefore, many methods have been proposed for its estimation including pedotransfer functions. By using the pedotransfer functions, soil water characteristic curve can be estimated based on other easily measured soil physicochemical properties. Parametric pedotransfer functions have been offered for parameters of the existing soil water characteristic curve models. In this study, 12 internal and external parametric pedotransfer functions of Brooks and Corey, Campbell and van Genuchten models were used and evaluated for 30 top soil samples in Fars province. To this end, the soil water characteristic curve and other necessary soil properties were measured, and then all soils according to the texture were divided into three groups of fine, medium and course textures. The results showed that the parametric pedotransfer functions of van Genuchten model were better than the other models, beacause of the better fit of this model to the measured data. Also, the results demonstrated that the parametric pedotransfer functions of Wosten et al. were the most appropriate method for estimating the soil water characteristic curve for the selected soils in Fars province, and that internal pedotransfer functions were not appropriate

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Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient, , return period, , at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.

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In recent years, simulatiom modelling of yield has been the focus of attention for many researchers. Because, while reducing adminestrative costs, it can easily provide simulation models of different situations. In this study, while a subroutine on simulation of canola was added to CRPSM model, effect of different water treatments on canola was also investigated. In this research, canola (Talaye) under 5 irrigation treatments (full irrigation treatment during the growing period, water stress treatment at the spring re-growth stage, the flowering stage and pod formation, the grain formation stage and dry land treatment) was sown in complete randomized block designs at the college of Agriculture, Shiraz University during 2007-2008, and then the model was calibrated based on available information (soil-location -plant-water). Review of statistical indicators between simulated and measured yield show high accuracy in the estimation of crop yield (R2=0.98) and soil water content. The result of model validation with independent data series also showed that the result of soil water content is desirable except in dry treatment, and the corrolation coeficient between simulated and measured crop yield (R2=0.98) was acceptable.

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Due to the important role of climatic parameters such as radiation, temperature, precipitation and evaporation rate in water resources management, this study employed time series modeling to forecast climatic parameters. After normality test of the parameters, nonparametric Mann-Kendall test was used in order to do trend analysis of data at P-value<0.05. Relative humidity and evaporation (with significant trend, -0.348 and -0.42 cm, respectively), as well as air temperature, wind speed, and sunshine were selected for time series modeling. Considering the Autocorrelation function (ACF) and Partial Autocorrelation function (PACF) and trend of data, appropriate models were fitted. The significance of the parameters of the selected models was examined by SE and t statistics, and both stationarity and invertibility conditions of Autoregressive (AR) and Moving average (MA) were also tested. Then, model calibration was carried out using Kolmogorov-Smirnov, Anderson- Darling and Rayan-Joiner. The selected ARIMA models are ARIMA(0,0,11)*(0,0,1), ARIMA(2,0,4)*(1,1,0), ARIMA(4,0,0)*(0,1,1), ARIMA (1,0,1)*(0,1,1), ARIMA (1,0,0)*(0,1,1) for relative humidity, evaporation, air temperature, wind speed and sunshine, respectively. The fitted models were then used to forecast the parameters. Finally, trend analysis of forecasted data was done in order to investigate the climate change. This study emphasizes efficiency of time series modeling in water resources studies in order to forecast climatic parameters.

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Investigation in to the sediment delivery of watersheds and its variation is an important element of ecosystem management. Since sediment load depends on runoff quantity, and runoff is considered as a unique indicator of sediment load, in this research the two modified versions of the MUSLE model were evaluated for 9 torrential events in two subwatersheds of the Kamal Saleh watershed in the Markazi Province of Iran. To this end, first all factors of the model including runoff, erodibility, topographic, cover management, and support practice were estimated using routine equations of the model. Then, the power coefficient in the runoff factor was corrected, applying two methods: “m correction coefficient” and “average correction coefficient. The evaluation criteria showed that the “m correction coefficient method” (compared to the “average correction coefficient method”) reduces the difference of the observed and estimated sediment load of small and large torrential events remarkably. In fact, the application of this modified method increased the accuracy of the MUSLE by decreasing the standard deviation of prediction. Also, the validation analysis of the modified method showed that the coefficient of efficiency indexes for the Hasan-Abad station (Rudkhane Bozorg subwatershed) and Ghal'e-No station (Ashur-abad subwateshed) were 0.997 and 0.811, respectively. This result confirms the efficiency of application of “m correction coefficient method”. However, it is suggested that the performance of this method be evaluated using a sufficient number of individual hydrographs and their sedimentgraphs in other watersheds.

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Soil saturated hydraulic conductivity (k) and effective porosity (f) are the most important parameters to simulate the processes associated with irrigation, drainage, hydrology, leaching and other agricultural and hydrological processes. Present methods to measure these parameters are often difficult, time consuming and costly. Therefore, a method which provides more accurate estimates of these parameters is essential and is considered inevitable. The purpose of this study was simultaneous estimation of k and f using approach inverse problem. In this study, analytical drainage model of Glover-Dam was used to simulate the inverse problem method. Also, genetic algorithm was used as an optimization technique for determination of optimal values of k and f. In order to measure the data required for calibration and evaluation of the proposed inverse problem model, a physical model was designed and constructed in the laboratory. The results showed that the proposed method is good for simultaneosly estimating simultaneous soil k and f. Also with variable f assumption, the prediction error of water table around the drainage was reduced significantly.

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In Guilan province, Sefidrud River, as the main source of irrigating rice in Guilan province, has been subjected to increasing salinity and a decreasing discharge because of decreasing in the volume of sefidrud dam, diverting water upstream and entering different sewages into the river. This research tries to determine optimum irrigation depth and intermittent periods in proportion to salinity resistance at different growth stages using optimization- simulation model. After calibration, Agro-hydrological SWAP model was used to simulate different growth stages of rice. Optimization results were obtained for managing fresh and saline intermittent water, 8-day intermittent period, for salinity of 0.747 dS/m in sensitive maturity stage and salinity of 3.36 dS/m in resistant vegetative, tiller and harvest growth stages. It is suggested that the depth of irrigation water be 1, 3, 3 and 5 cm for vegetative, tiller, maturity and harvest stages, respectively. Comparing managements of irrigation and saline based on the resistance of different growth stages to salinity and exploitation of irrigating water with a constant salinity during growth periods of the plant showed that irrigation management based on resistance of different growth periods of the plant to salinity causes rice yield to be improved by 23percent.