Showing 220 results for Model
A. H. Boali, H. Bashari, R. Jafari, M. Soleimani,
Volume 21, Issue 2 (8-2017)
Abstract
Appropriate criteria and methods are required to assess desertification potential in various ecosystems. This paper aimed to assess desertification levels in Segzi plain located in east part of Isfahan, with a focus on soil quality criteria used in MEDALUS model. Bayesian Belief Networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Soil samples were collected from 17 soil profiles in all land units and some of their characteristics such as texture, soluble sodium and chlorine, organic material, Sodium Absorption Ratio (SAR), Electrical Conductivity (EC) and CaSo4 of all soil samples were determined in soil laboratory. The effects of measured soil quality indicators on desertification intensity levels were assessed using sensitivity and scenario analysis in BBNs. Results showed that the used integrated method can appropriately accommodate uncertainty in the desertification assessments approaches created as a result of the influence of different soil characteristics on desertification. According to the results of MEDALUS model, 28.28 % and 71.72 % of the study area were classified as poor and moderate areas in terms of soil quality respectively. Sensitivity analysis by both models showed that soil organic matter, SAR and EC were identified as the most important edaphic variables responsible for desertification in the study area. Evaluating the effects of various management practices on these variables can assist managers to achieve sound management strategies for controlling desertification.
Engineer H. Talebikhiavi, Engineer M. Zabihi, Dr. R. Mostafazadeh,
Volume 21, Issue 2 (8-2017)
Abstract
Effective soil conservation requires a framework modelling that can evaluate erosion for different land-use scenarios. The USLE model was used to predict the reaction of appropriate land-cover/land-use scenarios in reducing sediment yield at the upland watershed of Yamchi Dam (474 km2), West Ardabil Province, Iran. Beside existing scenario, seven other land-use management scenarios were determined considering pattern of land-use through study area within a GIS-framework. Then, data inputs were prepared using terrain data, land-use map and direct observations. According to the model results, the generated erosion amount was 3.92 t/ha/yr for the current land-use (baseline scenario). For this purpose, conservation practices in dry farming slopes and implementing the scenario 5 (contour farming and remaining crop residuals) can reduce the sediment to 2.02 t/ha/yr. The lowest and highest decreases in sediment yield are projected to be through implementation of scenario 6 (irrigated farming protection with plant residuals) and 7 (biological soil conservation in dry and irrigated farming). The results indicated that, implementing scenario frameworks and evaluating appropriate land-use management scenarios can lead to the reduction of sediment entering the reservoir, and prioritizing soil conservations in the studied area.
E. Mehrabi Gohari, H. R. Matinfar, R. Taghizadeh,
Volume 21, Issue 3 (11-2017)
Abstract
Typical routine surveys of soils are relatively expensive in terms of time and cost and due to the fact that maps have been traditionally developed and considering their dependence on experts' opinions, updating maps is time consuming and sometimes not economical as well. While soil digital mapping, using soil various models - the Landscape, leads to simplification of the complexity found in natural soil systems and provides users with quick and inexpensive updates. In fact, the model represents a simplified form of the complex relationships between the soil and the land. This study aims to consider inferential model Soil-Land (SOLIM) in mapping and estimating soil classes in Aran area, Isfahan province. For this purpose, the SOLIM model inputs are digital geological and environmental layers of digital elevation model (DEM) including elevation, slope in percent, slop direction, curvature of the earth's surface, wetness indicator, flow direction, flow accumulation, and satellite images of Landsat 8. The seven subcategory of soil in the study area are input data of SOLIM model. Then fuzzy maps were prepared for seven types of soil and final maps of soil prediction were created by non-fuzzy action. Results showed that the SOLIM using environment variables has very high ability to separate soil types in greater detail and soils with different parent materials, geology, climate and vegetation can be separated from each other by this model with a high degree of accuracy. Comparing error matrix shows that the overall accuracy of the map derived from the model SOLIM is 92.36%.
S.a. Mohseni Movahed, M. Koochakzadeh, P. Salehimoghadam,
Volume 21, Issue 3 (11-2017)
Abstract
Basin irrigation is one of the conventional surface irrigation methods used to irrigate many crops all over the world. EDOBASIN, a new mathematical model for evaluation, design and optimal operation of basin irrigation has been introduced in this paper. In this model the linear combination of desired efficiencies are considered in an objective function, and based on decision variables that include discharge, length and width of basin and also restrictions of parameters, the optimal design is performed. This model is a simulation-optimization model in which the volume balance equations are used for hydrodynamic simulation and SA method are used for optimization steps. Optimization capabilities with various decision options and allowing weights to the indicators are good characteristics of this model. Comparison of advance time in this model with the well-known model SIRMOD for a real condition showed a good accuracy in the evaluation phase. In addition, the significant improvement of efficiencies after optimizing them simultaneously indicate that the model is an efficient tool for optimal design and use of this model for a data plot of an experimental field could enhance the efficiency of deep percolation ratio and application efficiency to 20% and keep constant the level of 100% for the efficiencies of water requirement and distribution uniformity.
M. Golabi, M. Albaji, A. Naseri,
Volume 21, Issue 3 (11-2017)
Abstract
In the present study Hydrus-1D software was used to simulate electrical conductivity, pH and sodium, potassium, calcium, magnesium, chloride and sulfate ions. Field experiments were performed at the Sugarcane Research Center located in south of Ahvaz on sugarcane varieties CP48-103 with four water treatments (one treatment was Karun river water and three treatments were diluted drainage water) and three replications. The samples were collected from 0-30, 30-60 and 60-90 cm soil depth before irrigation and electrical conductivity and anions and cations of soil were measured in the laboratory. Sensitivity analysis and calibration were first performed with the aim of verifying the Hydrus-1D software. The sensitivity analysis indicated that the software had maximum sensitivity to the saturated volumetric water content. Minimum sensitivity was for the inverse of the air-entry suction, tortuosity parameter, residual volumetric water contents and moderate sensitivity was for hydraulic conductivity at natural saturation. Also, the software did not show any sensitivity to empirical parameter related to the pore size distribution that is reflected in the slope of water retention curve. In calibration stage the amount of hydraulic conductivity at natural saturation, residual volumetric water contents, saturation volumetric water contents, the inverse of the air-entry suction, empirical parameter related to the pore size distribution and tortuosity were obtained as 18 (cm/day), 0.04 (cm3/cm3), 0.63(cm3/cm3), 0.012 (cm-1), 1.2 and 0.6 respectively. The results showed that the coefficient of determination of all parameters was more than 0.85 which confirms the appropriate capabilities of the model in simulation of electrical conductivity, pH, anions and cations. In the modeling carried out the amount of NRMSE was between 11 and 18 percent which indicates good performance of the model. The Nash-Sutcliffe efficiency criterion was obtained 0.72 to 0.8 that indicates a good match of the model with reality. The coefficient of residual mass in this paper was positive for electrical conductivity, pH and sodium, potassium, calcium, magnesium and negative for chloride and sulfate. The positive and negative coefficient of the residual mass shows less and over estimation of the model.
A. R. Vaezi, M. Ahmadi,
Volume 21, Issue 3 (11-2017)
Abstract
Modified Universal Soil Loss Equation (MUSLE) is one of soil loss estimation models which has been developed based on the runoff characteristics in the event scale. However, it needs to be evaluated in the plot scale for the semi-arid rainfall events. With this aim, a field study was designed using twenty one plots. Runoff and soil loss were measured using 5-min samples under seven rainfall intensities consisted of 10, 20, 30, 40, 50 60, and 70 mm h-1 for 60 min. Soil loss was estimated using the MUSLE based on the runoff volume (Q) and runoff peak discharge (qp) and the values were compared with the observed values. The estimated soil loss was about 3.89 times bigger than the observed value on average. In order to improve model estimations, the power of rainfall erosivity index was modified from 0.56 to 0.62, (Q qp)0.62. The modification of the MUSLE model improved model efficiency (ME) from -5.5 to 0.47 and decreased the root mean square error from 0.000137 to 0.000031. This study revealed that the MUSLE overestimates soil loss from the small plots in the semi -arid regions. Therefore it is essential to calibrate runoff erosivity index using the data observed in the area. The modified MUSLE can be reliably used to predict soil loss in the small plot scale in semi-arid regions.
A. Khalili Naft Chali, A. Shahidi, A. Khashei Siuki,
Volume 21, Issue 3 (11-2017)
Abstract
In recent years and in many countries, overusing groundwater resources had been higher than their annual feeding amount. This issue caused drop in the groundwater levels, followed by drying wells, qanats and springs. In this study, given the importance of Neyshabur plain in supplying agricultural, industrial and drinkable water of the area, lazy algorithms of KNN, KSTAR and LWL and M5 tree model have been utilized under seven different scenarios in order to estimate groundwater level of this aquifer. To compare the results, the Statistical parameters of root mean square error, correlation coefficient and the average absolute error were analyzed. The results showed that the ‘f’ scenario which contains the volume of water discharged and total precipitation parameters is less efficient because the ground surface level parameter was not taken into account. In ‘a’, ‘b’ and ‘g’ scenarios, an optimum estimation has been maintained for the groundwater level by considering the parameters of total rainfall in the previous month, total rainfall in the last two months and the ground surface level. Among the models of lazy algorithms and M5 decision tree model, the ability of KNN model under ‘a’ scenario was more than other models in December (Azar) by the statistical parameters RZ=0/96 , RMSE= 6.56 and MAE= 3.53. Also, study of evaluation criteria showed that the LWL is not an appropriate model to predict the level of the water table.
R. Mostafazadeh, Sh. Mirzaei, P. Nadiri,
Volume 21, Issue 4 (2-2018)
Abstract
The SCS-CN developed by the USDA Soil Conservation Service is a widely used technique for estimation of direct runoff from rainfall events. The watershed CN represents the hydrological response of watershed as an indicator of watershed potential runoff generation. The aim of this research is determining the CN from recorded rainfall-runoff events in different seasons and analyzing its relationship with rainfall components in the Jafarabad Watershed, Golestan Province. The CN values of 43 simultaneous storm events were determined using SCS-CN model and the available storm events of each season have been separated and the significant differences of CN values were analyzed using ANOVA method. The Triple Diagram Models provided by Surfer software were used to analyze the relationships of CNs and rainfall components. Results showed that the mean values of CN were 60 for summer and winter seasons and the CN values in the spring and autumn seasons were 50 and 65, respectively. The inter-relationships of CN amounts and rainfall characteristic showed that the high values of CNs were related to high rainfall intensities (>10 mm/hr) and rain-storms with total rainfall more than 40 mm. Also the CN values were about >70 for the storm events with 40-80% runoff coefficient values.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (2-2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
K. Qaderi, R. Jafarinia, B. Bakhtiari, Z. Afzali Goruh,
Volume 22, Issue 1 (6-2018)
Abstract
The investigation of local scour below hydraulic structures is so complex that makes it difficult to establish a general model to provide an accurate estimation for the local scour dimension. During the last decades, Data Driven Methods (DDM) have been used extensively in the modeling and prediction of unknown or complex behaviors of systems One of these methods is Group Method of Data Handling (GMDH), that is a self-organization approach and increasingly produces a complex model during the performance evaluation of the input and output data sets. So, the objective of this study was to investigate the potential of the GMDH method in the accurate estimation of local scouring geometry (maximum scour depth, the distance of maximum local scour depth till Ski-jump bucket and length of local scour) below the Siphon spillway with Ski-jump bucket energy dissipaters for a set of experimental data. 80% of data set was used for the training period and the remaining data set was used for the test period. The average values of MSRE, MPRE, CE and RB for the nonlinear second order transfer function (FUNC1) were calculated to be 0.92, 0.02, 8.74, -0.01; also, for the nonlinear first order transfer function (FUNC2), they were 0.85, 0.02, 10.43 and -0.02, respectively. The results indicated that the performance of FUNC1 was better than FUNC2. Also, the value of the coefficient of determination (R2) for the estimation of local scour dimension using different methods such as s linear regression, nonlinear regression and ANN indicated the high performance of the developed model of GMDH in the accurate estimation for local scour dimensions.
H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (6-2018)
Abstract
Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The percentages of the model in estimating the region's annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.
Sh. Zand-Parsa, S. Parvizi, A. R. Sepaskhah, A. A. Kamgar Haghighi,
Volume 22, Issue 1 (6-2018)
Abstract
In this study, the values of moisture and soil temperature were estimated at different depths and times under unsteady conditions by solving the Richards’ equation in an explicit finite difference method provided in Visual Studio C#. For the estimation of soil hydraulic parameters, including av and nv (coefficients of van Genuchten’s equation) and Ks (saturated hydraulic conductivity), soil moisture and temperature at different depths were measured by TDR probes and the stability apparatus, respectively. The objective function [equal to Root Mean Square Error (RMSE)] was minimized by the optimization of a parameter separately, using the Newton-Raphson method, while, the other parameters were considered as the constant values. Then, by replacing the optimized value of this parameter, the same was done for other parameters. The procedure of optimization was iterated until reaching minor changes to the objective function. The results showed that soil hydraulic parameters (coefficients of van Genuchten’s equation) could be optimized by using the SWCT (Soil Water Content and Temperature) model with measuring the soil water content at different depths and meteorological parameters including the minimum and maximum temperature,, air vapor pressure, rainfall and solar radiation. Finally, the measured values of soil moisture and temperature were compared to the depth of 70cm in spring, summer, and autumn of 2015. The values of the normalized RMSE of soil water content were 0.090, 0.096 and 0.056 at the soil depths of 5, 35 and 70 cm, respectively, while the values of the normalized RSME of soil temperatures were 2.000, 1.175 and 1.5 oC at these depths, respectively. In this research, the values of soil hydraulic parameters were compared with other previous models in a wider range of soil moisture varying from saturation to air dry condition, as more preferred in soil researches.
Z. Nazari, N. Khorasani, S. Feiznia, M. Karami,
Volume 22, Issue 1 (6-2018)
Abstract
The purpose of this research was source identification of aerosols in atmosphere using geochemical properties in the city of Kermanshah. The concentrations of twenty elements consisting of K, Na, Ca, P, Cu, Ni, Pb, Cd, Se, Zn, Fe, Mg, B, Cr, Co, As, Mo, V were analyzed by ICP for 55 soil samples (in the height range of 600-1600m) and 41 aerosols samples. Source identification of aerosels using geochemical tracers was performed in two steps. In the first step, appropriate combination of tracer elements with high ability in the resolation of aerosol sources was chosen using the means comparison test and discriminate analysis. In the second step, the multivariate mixing model was used to determine the contribution of aerosol sources (geological and geomorphology types) to the production of aerosols in the study area. The results obtained from determination of the contributions of sources of aerosols (geological and geomorphological types) showed the UF formation (consisting of red marl and sandstone), with the height of 0-1400 mand the slope of 0-5%, could be regarded as the main contributor to the production of aerosols located in the city of Qasreshirin.
H. Sarmadi, E. Salehi, L. Zebardast, M. Aghababaei,
Volume 22, Issue 2 (9-2018)
Abstract
Since the introduction of cities and urbanization, healthy water supplement and urban wastewater treatment have been considered as an important factor to evaluate progress in the urban areas. Tehran as a megacity is facing the lack of water. Tehran water supplement is far from its area; therefore, Tehran-Karaj plain has been considered in this study. So, Tehran water quantity index using the DPSIR model (Driving force, Pressure, Status, Impact and Response) in a period of 3 years (2008-2010) was considered in this paper. Driving forces included population, urbanization, green spaces, and industries. Pressures on urban water included water consumption, water losses, rainfall and evaporation. Then, Tehran water quantity status was investigated based on the existing water in dam reservoirs and groundwater resources. Their impacts on urban area were evaluated and finally, appropriate responses were presented. Some of the presented approaches included industries transmission, improvement of water consumption pattern, improvement of the water treatment plants status and unconventional water resources reuse, identification and rearrangement of subterranean, improvement of irrigation systems operation, and reduction of Tehran urban population.
A. Uossefgomrokchi, A. Parvaresh Rizi,
Volume 22, Issue 2 (9-2018)
Abstract
In the recent decades, due to the development of the pressurized irrigation systems, the relationship between the water and energy has been extended more than ever. So, according to problems due to the water shortage, energy saving is considered as one of the most important challenges in the agriculture section. In this study, by considering the capabilities of the pumping systems, variable speed pumps have been examined in an agro-industrial region of Ashrafiyeh (Qazvin province, Iran) with an area of 85 ha. The energy consumption during the ten-year operation was analyzed in the five operation scenarios by the MATLAB/SIMULINK software. The results showed that the consumed electrical energy by using variable speed pumps was approximately decreased up to about 18 percent, as compared to the fixed speed pumps. The results of the evaluation of the consumed energy showed that the current operation circumstance increased energy losses up to about 60 percent, as compared to the other operation methods. The results also revealed that the overall energy efficiency for the current operation circumstance was 52 percent (78 percent of Nebraska Performance Criteria).
S. Parvini, Z. Jafarian, A. Kavian,
Volume 22, Issue 2 (9-2018)
Abstract
Due to the lack of necessary equipment for measuring and recording changes in watershed runoff and flood situation after the implementation of corrective actions, using hydrologic models is considered as an efficient tool to assess the undertaken actions and simulate the behavior of the watershed before and after the implementation of these measures. The present study aimed to simulate the effects of corrective actions on runoff components using HEC- HMS hydrological models in the form of a rangeland and watershed plan in 2006 and the predicting plan of applicable operations in a region in the Meikhoran watershed, Kermanshah. For this purpose, three scenarios including the conditions before running the rangeland and watershed plan, the conditions after running the project and requirements and enforcement actions resulting from the proposed location map were considered in the spring of 2006. First, a map of the curve number (CN) changes was prepared under all three scenarios caused by the vegetation changes and by implementing HEC-HMS model, the curve number criteria, the peak discharge and flood volume were determined to assess the changes in hydrological basins and their values for all three scenarios were calculated and compared. The results showed that the HEC- HMS model for the base period (first scenario) with Nash-Sutcliffe coefficient 0/551 and the coefficient of determination 0/63 had an acceptable accuracy in predicting runoff. Nash-Sutcliffe coefficient for the second and third scenarios was 766/0 and 0/777, respectively. Also, the results showed that in the second scenario, there was an 8/85 and 7/74% decrease in the peak flows and runoff volumes, respectively, and these values for the proposed operation were estimated to be 12.84% and 6.33%, respectively. Overall, the results indicated the considerable impact of rangelands and watershed management (third scenario) on the reduction of effective runoff components, particularly flood peak, on the basis of the location model.
A. Mansouri, B. Aminnejad, H. Ahmadi,
Volume 22, Issue 2 (9-2018)
Abstract
In the present paper, fluctuations of inflow into the Karun-4 Dam under different scenarios of the climate change for the future period of 2021-2050 were investigated. For this purpose, the outputs of the HadCM3 model under the scenarios of B1 (optimistic) and A2 (pessimistic) were utilized for the fourth report; additionally, the outputs of the ensemble model under RCP 2.6 (optimistic) and RCP 8.5 (pessimistic) scenarios were used for the fifth report. Moreover, in order to estimate runoff in the future period, the artificial neural network was considered as a rainfall-runoff model. The results indicated that the average annual precipitation in the five study stations under B1 and RCP 2.6 scenarios was increased by 15 and 5%, respectively, while it showed a decrease equal to 8 and 6%, respectively under the scenarios A2 and RCP 8.5. Furthermore, the average annual temperature in all scenarios showed increase, which was at least 1.06 ⁰C under the scenario B1 and 1.89 ⁰C under scenario RCP 8.5. Examining the input inflow into the Karun-4 dam showed that under both B1 and RCP 2.6 scenarios, the annual inflow will be increased by 1.8 and 1.5%, respectively; under the two scenarios A2 and RCP of 8.5, the annual inflow will be decreased to 10.4 and 9.8%, respectively.
A. Fariabi, H. Matinfar,
Volume 22, Issue 3 (11-2018)
Abstract
One of the problems with the traditional mapping of soils is the expert’s opinion, it time-consuming and timely preparation, and the updating of the maps. While digital soil mapping, using different soil-earth models leads to the simplification of the complexity of the soil system. The purpose of this study was to investigate Soil-Environment Inference (SIE) in soil mapping with an emphasis on using the expert knowledge and fuzzy logic. For this purpose, the digital layer of geology and peripheral layers were derived from a digital elevation model including elevation, slope, and curvature of the ground surface, and auxiliary index, which comprised the input data of the SIE model. Then, the fuzzy maps prepared for the five soil types and the final map of soil prediction were created by hardening. The results showed that the SIE model, which used environmental variables, had a high ability to isolate soil types with more detailed compositions of soils with different maternal materials. The comparison of the error matrix showed that the overall accuracy of the derived map of the SIE model was equal to 75%, and the matching of the digital mapping results with conventional mapping accounted for 74.71% of the results. The difference in the compliance rate could be attributed to the difference in the nature of the two methods.
E. Gravandi, A. Kamanbeadst, A. R. Masjedi, M. Heidarnejad, A. Bordbar,
Volume 22, Issue 3 (11-2018)
Abstract
Rivers has long been regarded as one of the most basic human water supplies. If the topography, a morphology, water requirements conditions, etc. allow water to be transferred to gravity, the use of the dike can have a significant impact on the flow rate and the sediment input to Intake. Dike design needs to consider several parameters such as position, length, type, etc. Using a good design can increase the input flow rate and reduce the sediment entering it. In this study, to evaluate the dike impact on flow hydraulic conditions in the Intake with different situations, 30, 45, 60 and 90 degrees two simple L-shaped dikes in the upstream and downstream Intake and for five inlet flows (0.7, 1.12, 2.84, 5.04 and 6.23 Lit/s) were considered in the laboratory flume made by the author as a physical model to simulate the flow of the basin; then different effects of the dike on the hydraulic flow were studied. The results of the tests showed that the L-shaped dike in the upstream and downstream Intake in the internal arc flume increased the inflow flow rate into the Intake. Also, the best angle of deviation for the maximum flow entered the Intake angle of 60 degrees.
M. Yazdekhasti, M. Shayannejad, H. Eshghizadeh, M. Feizi,
Volume 22, Issue 3 (11-2018)
Abstract
Due to the dry climate and limitation of fresh water resources, using fresh and salt water is a solution for crop production under salinity conditions. This study was conducted at Isfahan University of Technology as a randomized complete block design with three replications and five irrigation management treatments in 2014. The treatments included irrigation with saline water (with the salinity of 5 dS/m, based on the relative yield of 75%), irrigation with fresh water (municipal water), alternate irrigation (irrigation with saline water and the next irrigation with fresh water), conjunctive irrigation (half of irrigation with saline water and the other one with fresh water) and irrigation with fresh water to reach the raceme stage, and irrigation with saline water. The maximum wet yield, dry yield and grain yield were related to the fresh water treatment with 4.14, 2.45 and 0.588 kg/m2 and the minimum values were obtained for water their water treated with 1.34, 0.765 and 0.0957 kg/m2 respectively. The conjunctive treatment had the highest yield after fresh water treatment. The various statistical indices showed that this model could be used for sorghum in Isfahan. The determination coefficient for yield was 0.65.The priority of model for yield simulation was salt water at the last stage, alternate irrigation, saline water, conjunctive irrigation and fresh water treatments, respectively.
