Journal of Water and Soil Conservation
Year:2013 | Volume:20 | Issue:5|Papers Count:15

Water management and fertilizer nitrogen in agricultural products should be improved due to lack of resources, environmental problems and health. Modeling in recent years has had an important role in improving irrigation, fertilizer operations in new irrigation systems. The aim of this study was to evaluate the capability of Hydrus-1D model to simulate the effect of different combinations of water and nitrogen regimes in a drip irrigation system for the dung.This study was done in a drip irrigation fertilizer system and under field conditions for onion cultivation. Moisture levels and nitrogen concentration were obtained in field with 48 hr irrigation and fertilizer nitrogen injection with the concentration of 0.116 hr. Applying boundary conditions and initial in Hydrus-1D model, moisture and nitrogen were estimated at four depths 0-15, 15-30, 30-45 and 45-60 cm. In this study, changes of moisture content and nitrogen concentration during different growth months by Hydrus-1D model were evaluated.Results indicate higher moisture content of the three at 30, 60 and 90 days after transplanting bulbs in the ground layer 15-0 cm and 10 cm at the location of emitters. The humidity was higher in the first layer of soil at 10 cm eye drops due to the settlement of the soil.Measurement and modeling of nitrogen showed increasing nitrogen content at 30 and 90 days after transplanting in 15-0 cm layer of soil at the site of emitters than two months after transplanting. Reducing nitrogen at 10 cm droppers and 60 days after transplanting could be due to plant bulbs at 10 cm from droppers, increasing vegetation cover and food for the plant.Compared to moisture simulations at depth of 60 cm and 10 cm at place droppers showed that root mean square error were 0.0067 and 0.0062, mean absolute error were 0.0044 and 0.0047 and the coefficient of determination were 0.9 and 0.99, respectively. Similarly, compared to nitrogen simulated with observed in place dropper and 10 cm showed the root mean square error were 0.015 and 0.011, the mean absolute error were 0.014 and 0.009 and the coefficient of determination were 0.92 and 0.99, respectively. Calculating error statistics indicate low error and high coefficient of determination in application of Hydrus-1D model and accuracy of this model in the simulation of moisture and nitrogen distributed in the root zone.

The rainfall erosivity factor is one of the important parameters for the correct prediction of soil loss amount by water. However, the used rainfall erosivity indices in any region must be accurate. The aim of this research was studying the regression relations between rainfall erosivity indices and related soil losses from experimental plots after individual rainfall event in order to investigate the appropriate rainfall erosivity index in Semnan province. For this reason, rainfall events were recorded from a recording raingauge at the Jashloobar Researches Station in Semnan province, from 2008 to 2010. Also, soil loss amounts after any rainfall events were collected and determined from 6 plots with standard dimensions in two slopes, 3 and 15 percentage and 3 replications. Then, daily rainfall amounts, maximum intensities in different base times, total kinetic energy and other rainfall parameters for calculation of different indices were calculated and relations between these indices and related sediment amounts were studied.With statistical analysis of mentioned relations, ÖP. (I60) 2 index, showed strongest correlation, r=0.651 and significant at 0.001 level. Also, long-term average of selected index and seven rainfall amount-based indices at 10 stations with raingauge in Semnan province for periods of 1 to 25 years were calculated. The regression relationships between ÖP. (I60) 2 and readily available indices were investigated. Mean annual rainfall, standard deviation of annual rainfall and maximum monthly rainfall, showed good correlations (r=0.83, 0.82 and 0.81, respectively) at 0.001 level.

One of the ways to improve characteristics of hydraulic jump is using corrugated beds in location that hydraulic jump is occurred as the corrugated beds by causing strong turbulence in flow, increased Reynolds stress and reduced velocity and second depth of jump. In this study, by applying VOF method and turbulent models of k-e and RNG k-e, a series of hydraulic jumps on corrugated bed by 13 mm height and 68 mm wave length in a range of Froud number from 3.5 to 8.5 were performed and results were compared by some experimental data.Results of this research showed that the proposed model by applying RNG k-ε turbulent model that has fine ability to modeling flow with high shear stress, estimated second depth, length of jump and velocity distribution of hydraulic jump on corrugated beds, very well. In addition, the results indicated that the numerical model could estimate shear stress coefficient of jump on corrugated bed close to the experimental results. The estimated value of shear stress coefficient on corrugated bed by numerical model was 10 times of this value in classical hydraulic jump.

Alluvial rivers generally have wide widths, and hence, the lateral velocity profile in this condition is very variable. Calculation of this profile is necessary in many procedures such as determination of flow discharges in main channels and floodplains, sediment transport in rivers, pollutant transport, shear stress prediction and identification of reaches with erosion or deposition across the river. Currently, the field measurements of point velocities across the river using the current meter or quasi two dimensional mathematical models are being used for lateral velocity profile prediction. Field measurement of velocity takes much cost and time and is dangerous during flood. Also, mathematical models theory is rather complex and may be make some limitations for hydraulic engineers. In this study by using genetic algorithm, a simple and dimensionless equation has been derived for velocity profile prediction. Dimensionless variables of relative depth and coherence parameter have been used as input parameters. For this study, experimental and field data of velocity distributions have been categorized to two groups of calibration and validation. With statistical comparison of results, the determination coefficient and root mean squared error of calibration data were obtained as 0.93 and 0.063 and for validation data as 0.91 and 0.127, respectively.

Studying and quantifying the uncertainty of the parameters of rainfall-runoff models have been raised mainly in the last decade and now is considered as one of the major challenges in international level. Despite the easy use of most conceptual rainfall-runoff models, due to the large number of parameters and lack of physical understanding of these models, use of them will be difficult in the calibration phase. In this study, uncertainty of the parameters of WetSpa model was investigated in Gorganrood catchment, Golestan province by Mont carlo method.WetSpa is a GIS-based distributed hydrological model that operates on catchment scale and has developed for flood prediction and watershed management. The model is physically based and simulates hydrological processes continuously both in time and space, for which the water and energy balance are maintained on each raster cell. Most of Uncertainties in model simulations results from the model structure and because some of the parameters have not properly defined. So for the correct prediction of runoff, uncertainty of the parameters and also forecast uncertainty should be taken into consideration. An appropriate parameter range was obtained by application of the Mont carlo method. The results show that the correction factor for measured evaporation data Ke has the highest relative sensitivity, because the margin of uncertainty of this parameter is the smallest, while parameters of g0 and Krain have the highest uncertainty.

Effluent generally includes nutrient elements like nitrogen and phosphorus. Its discharge to water resources leads to an increase in the growth of different types of algae and at last eutrification. Hence it is necessary to decrease the amount of phosphorus and nitrogen as much as possible before discharging of effluent to water resources. The use of aguatic plants is one of the effective methods for refining nitrogen and phosphorus in polluted water. For this propose, an experiment was conducted on thypha latifolia with 4 treatments and three replicates in a completely randomized design with factorial arrangement in experimental reactors with the batch. Treatments included effluent along with the plant, effluent diluted (dilution grade 1/2) with the plant and control (no plant) in two cases (initial and diluted effluents). Parameters of nitrate, ammonium, organic nitrogen, total nitrogen, dissolved phosphorus, BOD and COD in time of 8, 16 and 24 days in effluent samples and total nitrogen of plant tissue at the beginning and end of experimental period were measured. The results showed that the concentration of nitrate, ammonium, organic nitrogen, total nitrogen and dissolved phosphorus in plant treatment was significantly lower than treatment without the plant and there was non significant difference in pH values of the treatments. Typha latifolia aquatic plant was efficient in the reduction of BOD of initial effluent only in second time. It is concluded that, influence of plant system are more significantly efficient than without plant system in the decrease of concentration of nitrogen and phosphorus nutrient elements.

Soil erosion as a human-induced phenomenon has bad side effects. Therefore measuring soil erosion using 137Cs and preparing maps are important. In this study which has been performed in paired-sub-catchments of Golestan Province, spectrometry of 60 sites in every sub-catchment was performed using portable High Pure Germanium Detector and then conversion model of Mass Balance II was used to obtain soil redistribution rate. Average of soil erosion in all 60 sites in sample and testifier sub-catchments was 10.93 and 10.12 Mg ha-1yr-1, respectively.Geostatistical simulation and prediction (Kriging) of soil redistribution rate was performed using some packages with R software. Gaussian semivariogram model with effective range of 380 m was determined for both sub-catchments. Average soil erosion by geostatistical simulation are (10.3 and 18.08 Mg ha-1yr-1) and for prediction (9.93 and 11.22 Mg ha-1yr-1) for testifier and sample sub-catchments, respectively. The highest and the lowest global uncertainty in sample sub-catchment were 10.70 and 11.45 Mg ha-1yr-1. The highest and the lowest local uncertainty in Testifier sub-catchment was 10.02 and 11.55 Mg ha-1yr-1 that these areas are 720 m far from each other.

The target of this article is hydrochemical of assessment Aghili plain ground water resources in the eastern Khuzestan province. In order to do this several parameters such as TDS and cation and anion that exists in the water were used.AHP method as one of the most widespread method using decision-making tools was applied. In order to do comparison, a coefficient of danger for each parameter was calculated. Then based on this coefficient, the value of each parameter and specified weight coefficient was determined. For analyzing and assessment of several criterion, parameters should be accommodated for measurement's scale. For this purpose, the logic fuzzy method using specific function, devoting the most value (number one) to the maximum of membership and the lowest value (number zero) to least membership was applied. In order to use the linear function the Schoeller classification was used to define maximum and minimum quantities of each parameter. The results indicate that ground water in most area of the Aghili Plain for drinking is in good level and just small parts indicate unsuitable quality in the south-west plain.

Knowledge of the soil saturated hydraulic conductivity (Ks) is essential for irrigation management purposes and hydrological modeling, but it cannot often be measured because of practical and/or cost-related reasons. In this research, common geostatistical approaches with one type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm, was compared and tested to estimate saturated hydraulic conductivity (Ks) from other easily available soil properties. In this research 151 soil samples were collected from arable land around Bojnourd and saturated hydraulic conductivity (Ks) was estimated from other soil properties including soil textural fractions, EC, pH, SP, OC, TNV, ρs and ρb. The nonparametric k-NN technique performed mostly equally well, in terms of Pearson correlation coefficient (r), modeling efficiency (EF), root-mean-squared errors (RMSE), maximum error (ME) and coefficient of residual mass (CRM) statistics (r=0.76, EF=0.655, RMSE=42.87, ME=26.89 and CRM=-0.11) and after that, Co-Kriging and simple kriging methods, performed better than others. It can be concluded that the k-NN technique is a competitive alternative to other techniques such as pedotransfer functions (PTFs) to estimate saturated hydraulic conductivity especially when for new data set redriving these functions is essential.

Potential Evapotranspiration (ETo) determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Many variables needed in estimating evapotranspiration and lack of access to some of the required data has made problems in accurate estimation of ETo in these areas. This paper examines the ability of artificial neural networks (ANNs) in estimating ETo from minimum climatic variables. In this study, Multi Layer Percepteron (MLP) Neural Network used with Back-Propagation learning rule (BP) and the weather parameter data used as input were the mean of monthly maximum temperature, minimum temperature, mean relative humidity, wind speed and actual sunshine duration that were available at Mashhad synoptic weather station, from 1951 to 2005. ETo was estimated by Penman-Monteith and used as output. For evaluation of Neural Network utility under situations of absence of some parameters, a comparison was made between the estimations provided by the Neural Networks models and the following empirical modeles: Hargreaves- Samani, Blaney-Criddle, Makink. Neural Networks have obtained better results than the empirical methods for modeling potential evapotranspiration. Based on these results, under situations of limited climatic parametrs, the Neural Networks was able to estimate ETo properly even with the minimum temperature, maximum temperature and extraterrestrial radiation in the study of area (RMSE=0.286).

Explore for optimized water use of limited water resources is a serious challenge in the agricultural sector. To achieve this strategy, it is necessary to assess the plant response to water for obtaining the highest water use efficiency. A field experiment was conducted using line-source sprinkler irrigation to evaluate effects of different water amounts on yield and water use efficiency in three soybean cultivars (Sahar, G3 and DPX) for two years (2005, 2006). Four irrigation treatments (W1, W2, W3 and W4) based on distance from the pipeline (W1 nearest and W4 farthest) as main fixed and soybean cultivars that were randomly distributed within each irrigation treatment were examined under a strip plot design with four replicates. Although, results have shown that there were similar trend in soybean yield response to water but, they had different slopes. Generally, reducing the amount of irrigation water has increased the WUE and maximum water use efficiency was obtained by DPX cultivar. Overall mean indicated that WUE in SAHAR, G3 and DPX were 5.1, 6.2 and 7 Kg.ha-1.mm-1, respectively. These results imply that the highest water use efficiency does not conform to the highest yield. In other words, by increasing the amount of water from the point of maximum water use efficiency may lead to increase soybean yield, so that the amount of water use efficiency reduced, but this strategy is not suitable for water shortage areas.

Chehelchai watershed in Golestan Province, northeast of Iran, covers steep slopes in a mountainous region and has been facing forest cover change to dry farming leading to accelerated soil erosion. This necessitates a holistic management approach to the watershed and to this end, we need good estimation of soil erosion and sedimentation. The aim of this study is application of SWAT which has performed well in similar studies- for modeling erosion distribution over the entire watershed. Our results indicated rainfed agriculture comprising 14.64% of the watershed and located on slopes 8-50% steep is producing the highest average weighted amount of erosion and sediments (25 tons per hectares). Hence, the rainfed agriculture is considered as the highest priority for management practices. The average weighted erosion and sediment yield were estimated at 9.26 t ha-1y-1 using the SWAT model. The sediment leaving the watershed was estimated at 7.71 t ha-1y-1 using the model. Based on the reasonable results we obtained from the application of the model concerning location and mount of erosion, we recommend its application to manage Chehelchai Watershed for erosion and sedimentation control.

Soil compaction which results from soil agricultural machinery traffic, changes hydraulic and pores of soil properties, thus influences root growth, and water and solute transport through the soil. Therefore knowledge of the properties of active soil water conductive pores is important. The objective of this study was to evaluate the effects of compaction on soil hydraulic properties, included porosity, number of meso and macropores and also contribution of pores to the water flux in soil. For this purpose, the soil hydraulic parameters were measured using a disk infiltrometer at 14, 4, 1 and 0 cm water pressure suction at a silty loam soil in a field experiment. The treatments included control (no tractor passes), 2 and 4 tractor passes. The results showed that in control treatement, meso and macropores in the upper layer were present, while in the other two treatments, because of compaction caused by passing tractor, macro and mesopores were both destroyed and decreased. In spite of the very low macroposity volume, in all three treatments, macropores were the main effective pores for water flow.

One of the major issues making difficult to model soil water and crop relationship is difficulty of determining soil hydraulic characteristics as soil moisture and unsaturated hydraulic conductivity curves. Because these characteristics are affected by chemical and physical agents. In this study, some readily available soil parameters such as sand, clay and silt percentage, moisture versus matric suction, bulk density, organic matter, saturated electrical conductivity (EC) and sodium adsorption ratio (SAR) were measured. Also soil hydraulic parameters are determined by optimization method. Finally, soil unsaturated moisture parameter is formulated by M5 decision tree method as functions of matric suction, sand, clay and silt percentage, bulk density, organic material, sodium adsorption ratio and saturated electrical conductivity. Also computed moisture data is compared with measured soil moisture curve data. Next, the R2 Determination coefficient, root mean square error (RMSE) and mean of bios error (MBE) is determined between measured and decision tree model (M5) fitted moisture data for all soil textures and salinities. Result showed that besides matric potential data. The sand, silt and clay data have also special effect on precision of moisture determining with M5. But matric potential data has greater effect on valid moisture prediction comparing to observed data. Generally, results of modeling with decision tree model (M5) showed that although some physical and chemical soil data could be available and measurable but matric potential data is required as key input parameter for accuratly simulation moisture retention curve.

Application of optimizer algorithms, in cross section optimization of gravity dams and finding optimal geometric dimensions of structure using this method is one of the problems which have been considered by designers. Genetic Algorithm (GA) is one of the intelligent algorithms that apply Fitness, Selection, Mutation and Crossover, as operators to solve problems. In this research, the sensitivity of the prepared simulator-optimizer model was investigated in relation to variation of the replication rate, population size, mutation rate and the selection rate. In optimization of gravity dams cross section by Genetic algorithm, at the best state, replication rate, the initial population, the mutation rate and the selection rate were obtained 30, 50, 0.3 and 0.5, respectively. This data can be used as input parameters in optimization model to study about other dams.