Journal of Water and Soil
Year:2014 | Volume:28 | Issue:3|Papers Count:22

Pore pressure, seepage and drainage discharge through in earthen dams are the most important characteristics that have to be defined in order to analysis an earth dam. These parameters have been measured since beginning of building dam. In this investigation, a physical model of homogenous earth dam created in the flume with three different angles of toe drain, then pore pressure of earth dam was measured by piezometers that put on the wall of the flume. Discharge of drainage was measured too. The seepage analysis was conducted by PLAXIS software then Result of the PLAXIS compare with physical model by SAS (Statistic software) and finally the best angle and without dimension index, p/h, propose for toe drain according to results & observations.

Interception is one of the important and effective parameters on ET and hydrological relation, which is ignored in many situations. In order to investigate the effectiveness of LAI and extinction coefficient on amount of interception, in this study wheat and soybean were cultivated in thelysimeters of agricultural school of Fredowsi Uni. of Mashhad, in Spring and Summer 2012 in the same treatments. The results showed that there is relationship between interception and LAI and extinction coefficient. By increasing LAI, interception increased significantly (slope 0.15). The maximum amount of interception was 1.19 cm in soybean by 6.19 LAI and in wheat cultivars was 1.1cm in 4.58LAI. Also by decreasing the extinction coefficient, interception increased by the rate of 1.023. Results showed that in the same LAI (3.2), wheat interception was more than soybean, 0.74 and 0.5 respectively. While in the same extinction coefficient interceptions was the same in two crops.Standardization the amount of interception by LAI, showed that the effect of the crop on interception is still remained, while by standardize the interception by extinction coefficient, the influence of crop on standard interception removed. The obtained result showed that the type of crop has a significant effect on interception, which can be shown by extinction coefficient.

Due to inadequate flood data it is not always possible to fit a frequency analysis to at-site stations. Reliable results are not always guaranteed by a single clustering algorithm, so a combination of methods may be used. In this research, we considered three clustering algorithms: single linkge, complete linkage and Ward (as hierarchial clustering methods), and K-mean (as partitional clustering analysis). Hybrid cluster analysis was tested for up-to-dated of floods data in 68 hydrometric stations in East and NE of Iran. Four cluster validity indices were used to find the optimum number of clusters. Based on the Cophenetic coefficient and average Silhouette width, single linkge, and complete linkage methods were performed well, yet they produced nonconsistent clusters (one large and numerous small clusters) which are not amenable for flood frequency analysis.It was shown that hybridization was efficient to form homogeneous regions, however, the usefulness was dependent to the number of classes. Heterogeneity measure of Hosking was negative, due to inter-correlation of floods in the clusters. The hybrid of Ward and K-mean was shown to be the best combination for the region under study. Four homogeneous regions were delineated.

In this study, the FAO agro-hydrological model was investigated and evaluated to predict of yield production, soil water and solute balance by winter wheat field data under water and salt stresses. For this purpose, a field experimental was conducted with three salinity levels of irrigation water include: S1, S2 and S3 corresponding to 1.4, 4.5 and 9.6 dS/m, respectively, and four irrigation depth levels include: I1, I2, I3 and I4 corresponding to 50, 75, 100 and 125% of crop water requirement, respectively, for two varieties of winter wheat: Roshan and Ghods, with three replications in an experimental farm of Birjand University for 1384-85 period. Based on results, the mean relative error of the model in yield prediction for Roshan and Ghods were obtained 9.2 and 26.1%, respectively. The maximum error of yield prediction in both of the Roshan and Ghods varieties, were obtained for S1I1, S2I1 and S3I1 treatments. The relative error of Roshan yield prediction for S1I1, S2I1 and S3I1 were calculated 20.0, 28.1 and 26.6%, respectively and for Ghods variety were calculated 61, 94.5 and 99.9%, respectively, that indicated a significant over estimate error under higher water stress. The mean relative error of model for all treatments, in prediction of soil water depletion and electrical conductivity of soil saturation extract, were calculated 7.1 and 5.8%, respectively, that indicated proper accuracy of model in prediction of soil water content and soil salinity.

Considering the necessity of desirable operation of limited water resources and assuming the significant role of dams in controlling and consuming the surface waters, highlights the advantageous of suitable operation rules for optimal and sustainable operation of dams. This study investigates the hydroelectric supply of a one-reservoir system of Karoon3 using nonlinear programming (NLP), genetic algorithm (GA), genetic programming (GP) and fixed length gen GP (FLGGP) in real-time operation of dam considering two approaches of static and dynamic operation rules. In static operation rule, only one rule curve is extracted for all months in a year whereas in dynamic operation rule, monthly rule curves (12 rules) are extracted for each month of a year. In addition, nonlinear decision rule (NLDR) curves are considered, and the total deficiency function as the target (objective) function have been used for evaluating the performance of each method and approach. Results show appropriate efficiency of GP and FLGGP methods in extracting operation rules in both approaches. Superiority of these methods to operation methods yielded by GA and NLP is 5%. Moreover, according to the results, it can be remarked that, FLGGP method is an alternative for GP method, whereas the GP method cannot be used due to its limitations. Comparison of two approaches of static and dynamic operation rules demonstrated the superiority of dynamic operation rule to static operation rule (about 10%) and therefore this method has more capabilities in real-time operation of the reservoirs systems.

Bridge failures due to scour at bridge abutments clarify importance of scour prediction and scour countermeasures. Most of bridge abutments are located in floodplains. One of common cases in floodplains is the existence of vegetation cover. Scope of this study is investigation on effect of vegetated floodplain on vertical wall abutment scour in compound channels. Results show, for constant abutment length and flow depth, with increasing the vegetation concentration, scour depth decreased with an increasing rate. For constant vegetation concentration and abutment length, with increasing the flow depth, effect of vegetation in reduction of scour depth decreased. In abutment with the lengh of 26 cm and concentration with s=8, with increasing the flow depth from 4.8 to 11.8 cm, percentage reduction in scour depth decreased from 40 to 22 percent. Also, vegetation reduces scour hole dimensions and has effect on downstream topography.

Evaluation procedures have been developed for effective design and management of irrigation systems.Furrow irrigation is one of the main types of applying water in field. In the present study, a new procedure for field evaluation of water distribution in furrow irrigation is proposed. For this purpose, application efficiency (Ea), water requirement efficiency (Er), deep percolation (DP) and tail water ratio (TWR) have been introduced based on the average of the non-dimensional infiltrated depth values. The relationships between coefficient of uniformity (CU) and distribution uniformity (DU) have been described by opportunity time values of each quarter and using the linear equation instead of infiltration function. The proposed method was compared to other procedures by 9 scenarios of field data with different discharges and lengths. The results show that the evaluation indicators of furrow irrigation is calculated with non-dimensional parameters depend on opportunity time and new coefficients of linear infiltration equation. Also, there was no observed any significant difference on the suggested indices values and classical performance indicators.

River flow modeling has special importance in water resources management. Since the actual river flow data are often low and they correlate and depend yearly and monthly, making the data similar to historical data is so difficult and complex. In this study, 50 year data and Seasonal Auto Regressive Moving Average (SARMA) and Clayton and Frank Copulas which are the prediction and simulation methods of the river flow molding, were used to generate random flow data of Helmand River. Results show, SARMA model forecasts minimum river flow data very good, but the generated data hasn’t correlation of historical data and usually the maximum river flow is greater than real data. Otherwise, Copula preserved concordance of real data and make the data that are similar to real river flow. Also Root Mean Square Error of Copula method was 0.3 that is was less than SARMA method (0.4). Therefore Copulas are good methods for Helmand river flow modeling.

Forecasting of river discharge is a key aspect of efficient water resources planning and management. In this study, two models based on Wavelet Analysis and Artificial Neural networks (ANNs) were developed for forecasting discharge of Behesht-Abad River. For this purpose, mean daily discharge data of mentioned river as well as precipitation data of 17 meteorological stations were used in the period 1999-2008. In the first method, called Cross Wavelet (CW), complex Morlet wavelet was used as analyzer function. Wavelet analyzing was performed for every daily rainfall and average discharge time series, separately. The model equation derived for 1, 2, 3 and 7 days ahead forecasting horizon. In the second method, called Wavelet Neural Networks conjunction (WNN), a preprocessing was done on the initial input matrix using Meyer wavelet. Then the elements of the initial input matrix were normalized and the second input matrix was created. A three layer Feed Forward Back Propagation (FFBP) was formed based on the second input matrix and target matrix. After training the model using Levenberg-Marquardt (LM) algorithm, the river discharges were predicted for short term time horizons.The results showed that the WNN method had higher accuracy in short-term forecasting of river discharge in comparison with CW and ANN methods. In testing stage, in CW model when forecasting horizon reduced from 7-days to 1day, the R2 value increased from 0.5113 to 0.9388, and RMSE decreased from 17.9171 to 8.3226 m3/s. In ANN model when forecasting horizon reduced from 7-days to 1day, the R2 value increased from 0.6705 to 0.9166, and RMSE decreased from 5.9828 to 2.5600 m3/s. Whereas, in WNN model, when forecasting horizon reduced from 7-days to 1day, the R2 value increased from 0.8424 to 0.9927, and RMSE decreased from 3.4678 to 0.8145 m3/s.

To drainage design and management it is necessary water flow toward drain, water table variation between drains and drainage discharge have been simulated. With recent development in numerical method, it is possible the none-linear differential equation governing saturated-unsaturated flow in soil is numerically solved. In this study a computer model has been developed in which two dimensional equation of saturated-unsaturated flow in soil is solved using finite volume method and Crank-Nicolson scheme. The soil hydrodynamic properties function and soil moisture characteristic curve proposed by Van Genuchten were employed. The result of model calibration revealed that the recent finite volume method endorse the result of finite element models. After model calibration and evaluation, water table variation between two drains with 20 m distance and installation depth of 1.2 m was simulated. The result showed during discharge phase water table falls very fast at the first and then falling speed reduces until reach a constant value. During recharge phase water table raises very low at the first and then rising speed increase. Drainage discharge has similar behavior same as water table. Drainage discharge has a lag time related to time that recharge begins. In this study the lag time was 3.125 day.

With due attention to the low availability of iron in calcareous soils and different ability of plant species in iron acquisition, the study and identification of iron-efficient plants is necessary to reduce the use of chemical fertilizers. A greenhouse experiment was conducted to study iron uptake efficiency in different wheat varieties, as a randomized complete blocks design with factorial arrangement. Treatments consisted of three levels of Fe (0, 2 and 6 mg kg-1) and three varieties of wheat (Falat, Parsi, Pishtaz) with three replications. The results showed that iron application had no effect on shoot dry weight and iron uptake in Parsi and Pishtaz varieties, but increased those in the Falat variety. The study of influx and root-shoot ratio, which are two important factors in the iron uptake by plant, showed that the Parsi and Pishtaz varieties had higher influx than the Falat variety. In the Parsi variety as well as influx, the root length had an important role in iron uptake efficiency. In the Falat variety with increasing amounts of iron uptake, relative shoot dry weight, influx and root-shoot ratio were increased. Therefore, the Falat variety in comparison to two other varieties was not an iron efficient plant. No response to Fe fertilizer was observed in the Pishtaz and Parsi varieties which indicated some other specific uptake mechanisms were involved at low Fe levels in soil. Finally, the results showed that the Pishtaz and Parsi varieties were more efficient in iron uptake than Falat variety.

Soil quality is important for evaluating the soil fertility and physical condition. Soil physical, fertility and chemical indicators should be regarded for determining the soil quality. This research was conducted to study the effect of organic matter on quantitative value of soil quality. Three levels (15, 30, and 60 ton/ha) of different organic compounds including municipal waste compost, sewage sludge, cattle manure, and wheat straw together with control treatment in three replications were applied to a loamy soil. The treated soils were kept for 6 months at 70% of field capacity moisture in greenhouse conditions. Then, soil quality was determined based on non-linear score function using 14 physical and chemical indicators. The results showed that the score of control treatment was 52.7 belonging to class 4, i.e. low quality. Addition of different organic matter improved the soil quality score and soil quality class increased one to 2 degrees. Among the studied treatments, the highest score of soil quality was obtained in 60 ton/ha sewage sludge and 30 and 60 ton/ha compost. Also, the addition of other organic treatments increased the quantitative soil quality score in relation to control, and soil quality class increased one degree. Among the studied indicators, iron (Fe), manganese (Mn), zinc (Zn) micro-nutrients, aeration porosity (AC), and mean weight diameter of aggregates (MWD) were the important limiting indictors in degrading the soil quality in control treatment, and applying the compost and sewage sludge increased their amounts and scores.

One of the most important soil contaminants are heavy metals. Chemical analysis of the samples can be used to evaluate the contamination but these methods are expensive and time consuming. Thus, for rapid evaluation, other techniques such as magnetic susceptibility are considered. The aim of this study was to compare the spatial distribution of magnetic susceptibilityand cadmium, lead, nickel and copper in soil series of Isfahan, Khomeinishahr and Zayanderood in Lenjan at region, Isfahan province. Estimation of heavy metals via pedotransfer functions using magnetic susceptibility was also investigated. Total concentration of Cd, Pb, Ni and Cu in soil samples was determines and the magnetic susceptibility of the samples was also measured. Results showed magnetic susceptibility does not have high accuracy for estimation of heavy metals contents in the soils of this region. On the other hand, similar trends of continuous maps for heavy metals and magnetic susceptibility suggest that magnetic susceptibility can be a good indicator for trend of soil contamination in this area.

Wastewater is one of the resources which is using, recently, for irrigation due to the scarcity of water resources. In this regard, using adsorbents such as zeolites is recommended to improve the indexes of the wastewater. The results show that although natural zeolite decreases the amount of pollutions, it decreases the permeability of the soil, which could adversely affect the soil structure. This study was done in 2012 at Shahrekord University in which 21 PVC columns were used to study the effect of modified zeolite particles on permeability and quality of sewage. The experiment consisted of two factors: the type of the microzeolite (natural zeolite, modified zeolite) and application method of the micro zeolite (mixed, layered) with three replications in 7 treatments. Injection of sewage into the soil was through flood irrigation and repeated fifteen times with a weekly frequency. Volume of sewage used in each injection was equal to pour valume. In frequency injections of 1, 3, 5, 7, 11, 15, infiltration was measured by using Falling Heads. The results showed that treatment of modified zeolite (included mixed, middle layer and layer on the surface) had the highest infiltration rate respectively and treatment with natural zeolite (included mixed, middle layer, layer on the surface) had lowest infiltration rate. Further, modified treatments decreased Na effluent rate 111 percent with respect to natural Zeolite and therefore caused modified treatments to decrease SAR amount to 45%, comparing to control treatments and 132% to natural zeolite. Ultimately the modified zeolite increased the final infiltration rate of the soil and improved effluent quality.

The right selection of an appropriate cultivar, which can be adapted with a particular pollutant and environmental conditions, is a crucial factor for a successful phytoremediation technology. Sunflower might be a suitable plant to remove the toxic metals from soil of polluted sites due to its rapid growth and high biomass production. In order to evaluate the efficiency of six sunflower cultivars in lead (Pb) phytoextraction from a contaminated soil, an experiment was carried out using a completely randomized design with three replications in Research Station of Shahrekord University. Sunflower cultivars used in this experiment were Alestar, Serena, Sanburu, Hysun 33, Record and Euroflor. Results showed that there was a significant difference in shoot lead concentration, translocation factor and total lead harvested by shoot among sunflower cultivars at 1% probability level. Generally, due to translocation factor of all cultivars was less than 1, this can be stated that none of cultivars had the proper efficiency for extraction of lead from contaminated soil. However, high root lead concentration and low translocation factor of these cultivars show that efficiency of them for use in phytostabilization technique is more than phytoextraction technique.

To determine the origin of heavy metals, the effects of parent materials, soil genesis, and human activities on the content and distribution of selected metals in soils near Mofateh Martyr powerhouse, Hamedan, were assessed. Six types of parent materials including shale, schist, limestone, alluvial plain, alluvial terraces and fan deposits were identified and soil genesis were studied. Total content of Cd, Cu, Mn, Ni, Zn, Pb, Fe were determined in soil horizons and parent materials. Concentration of the metals in four different chemical phases, including acetic acid extractable, reducible, oxidizable and residual fractions, was determined with four-step sequential extraction procedure. Soil development is limited in the studied region so that the discrepancy between solum and parent material in terms of heavy metal content is not great in general. Calcareous soils and limestone have the lowest amount of copper, manganese, nickel, zinc, lead and iron. Independent of soil types and parent materials, most of the heavy metals, except Mn, were present in the residual fraction. The concentration of Mn in all profiles is highest in reducible fraction. Low degree of soil development and the prevalent presence of metals in residual fraction show the influential role of parent materials in controlling metal concentration and distribution; pedogenic processes have minor effects. The role of human activities is limited for most of the selected metals; however, the tangible presence of Pb and, in some cases, Cd in acetic acid extractable fraction, reflects the impact of human activities on the concentrations of these two metals.

Estimation the amount of radiation reaching the Earth's surface (Rs) is an important factor in the energy balance models simulation of plant growth and evapotranspiration estimation. Most Estimation models to radiation reaching the Earth's surface use satellite data and they are based on land surface temperatures. In this study, the Accuracy of solar radiation estimation is investigated Using four different models of neural networks (with the names of ANN1, ANN2, ANN3, ANN4) with the inputs Including products land surface temperature MODIS sensor (models 1 and 2, and models 3 and 4 are based on MOD11A1 MYD11A1 products, respectively), extraterrestrial radiation (Ra) and relative sunshine (n/N). The results show that four neural network models are able to estimate the amount of radiation reaching the Earth's surface with good correlation (R2>.85). However, models based on MOD11A1 products have a higher accuracy than models based on MYD11A1 products. Neural network model of ANN1 (based on MOD11A1 products, relative sunshine and extraterrestrial radiation (Ra)) with the coefficient of determination (R2) equal to.9332 and the root mean square error (RMSE) equal to 1.4448 MJ per square meter per day is more accurate on the estimation of solar radiation than other models. The results also showed that the Neural network model ANN2, comparing with Hargreaves and Samani models based on air temperature and extraterrestrial radiation, is More accurate in estimating of solar radiation.

Accurate and reliable precipitation data is very important for hydrological studies, water resources management and planning. In this study, to obtain the most appropriate method for estimating regional precipitation in Khuzestan province, the estimated daily precipitation data from the optimal interpolation method and APHRODIT daily grid precipitation data were compared with measured data from 44 meteorological stations during the 2000-2007 years. Four interpolation methods i.e. Inverse Distance Weighted, OrdinaryKriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation. For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian were fitted on the precipitation data. Near neighbor method was used to compare APHRODIT daily precipitation data with measured data in stations. Cross validation was using to evaluate different methods and the most appropriate method was determined based on Root Mean Square Error, Mean Bias Error, Mean Absolute Error indices and regression analysis.The results from evaluation of errors in interpolation methods showed that the regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. The Comparison of error indices and regression analysis of regression Kriging method and APHRODITE grid indicated that Estimation accuracy of regression Kriging is higher than the APHRODITE in the most days. Therefore to obtain the spatial distribution and estimate of regional daily precipitationin Khuzestan Province, Regression Kriging interpolation method is more accurate than estimate data of APHRODITE.

Most of drought evaluation systems are based on precipitation data. However the quality of drought spatial variability is strongly influenced by short period of the measured data, inappropriate distribution of the weather stations and undesirable quality of the measurement networks. Therefore, it is necessary to recognize others reliable climatic data resources. Then to overpass the difficulty, after verification, the data is used to complete or substitute the existing data. Accordingly, in this research to monitor drought in Khorasan Razavi province using data from 10 synoptic stations and 107 rain gauges around the province, the monthly data of TRMM satellite was validated. To do this, standardized precipitation index (SPI) of 1, 3, 6 and 12 months are calculated for a 13 years period (1998-2010) and compared with those of satellite data for the same period. The evaluation was measured by using CSI (%) (Critical Success Index) and R2 (Coefficient of Determination). The results showed that there was a very good consistency between earth borne and satellite data borne SPIs for all time scales except for 1 month time scale. Consistency value for all time scales over most regions of the province is more than 50%. Based on the results, for achieving the accuracy more than 60%, time scales of 1, 3, 6 and 12 months should be used as following: 1 months only for the northern regions, 3 months for all regions except the eastern part, 6 months for all regions except the northern part and 12 months for all regions except the northern region and central part of the province.