B. Sarcheshmeh; J. Behmanesh; vahid Rezaverdinejad
Abstract
Introduction: Drying Urmia Lake, located in northwest of Iran, is mainly related to the reduction in rivers flowing into the lake and hydrological parameters changes. Considering the importance and critical ecological conditions of Urmia Lake, the purpose of this research is to accommodate the environmental ...
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Introduction: Drying Urmia Lake, located in northwest of Iran, is mainly related to the reduction in rivers flowing into the lake and hydrological parameters changes. Considering the importance and critical ecological conditions of Urmia Lake, the purpose of this research is to accommodate the environmental water requirement in managing rivers leading to the lake, including Zarrinehrood as the largest river to the lake. Moreover, water scarcity was assessed by QQE approach in this basin.
Materials and Methods: Tennant method is easy, rapid, inexpensive, and is based on empirical relationships between the recommended percent of the MAF. The ecological conditions of the river have been determined for use in this method. In this study, different levels of EFR were calculated to protect the relevant levels of habitat quality defined in the Tennant method. Also the fraction of Blue Water Resources (BWR) required to protect a “good” level of habitat quality was considered as the suitable EFR. If it is less than the lower limit, the habitat quality will be in degraded status.
,
SQQE is a complete index to demonstrate water scarcity by considering water quantity and quality and EFR indicator.
, ,
The Smakhtin method provided an indicator for assessing the water scarcity.
WSI =
Where WSI is the index of water scarcity, MAR is the mean annual flow and EWR is the environmental water requirement of river. If the water scarcity index is more than one, the river would suffer from water shortage and not be able to meet the environmental water requirement. When the water scarcity index is between 0.6 and 1, the river would be under stress, and if it is between 0.3 and 0.6 Harvesting conditions from the river is moderate, and if it is less than 0.3 the river is ecologically safe and has no shortage.
Results and Discussion: According to the Smakhtin method, can be noticed that the calculations of this method are the same quantitative index of the other method used in this research. Only the quantitative conditions are evaluated in the Smakhtin method. However, in addition to the quantity (blue water footprint), environmental requirement and water quality are also included in the other method used in this research. Figure 1 shows the mean annual flow (MAF) and environmental flow requirement (EFR). As shown in figure 1, the majority river flow has been conducted from January to June and the rest from July to December. The annual BWR in the Nezamabad station was equal to 1208 × 106 (m3/year). To protect the habitat health of Zarrinehrood river at a good level, 400×106 (m3) of water must be left in the river per year. Therefore EFR was equivalent to 33.11% of the annual BWR. It is about one-third of total BWR.
In this station, EFR ranged from 60×106 (m3/year) as severely degraded to 2400×106 (m3/year) as maximum habitat health situation by using the Tennant table (Fig 2).
Figure 1- Environmental flow requirement (EFR) and mean annual flow (MAF) for the (Nezamabad station) Zarrinehrood river basin
Figure 2- Different levels of total environmental flow requirement (EFR) in the (Nezamabad station) Zarrinehrood river. Habitat quality levels with the flows shown in table 3 (Tennant) have be matched
The BWF and the BWA for the studied station were calculated 830×106 and 808×106 (m3/year), respectively. The BWF is 1.02 times the BWA. Therefore, the WSI Smakhtin and S Quantity will be 1.02.
The total GWF in this station was 1.08 times the BWR. Thus, the S Quality will be 1.08.
P is a demonstrator that shows the percentage of EFR in total BWR. It is related with the EFR to protect the habitat quality in a “good” level.
As you know, the number in the bracket shows that 33.11% of the total BWR of the basin is required as EFR, for maintaining the ecological habitat condition at the ‘good’ level. Other percentages of EFR are used to represent other ecological levels of habitat condition.
The S Quantity and S Quality for the Nezamabad station in Zarrinehrood river basin were obtained 1.02 and 1.08, respectively. Both indices are above the threshold (1.0), and the basin suffer from both qualitative and quantitative deficiencies. Thus, the final water scarcity indicator, SQQE, is 1.02 (33.11%) |1.08.
Conclusion: The EFR for protecting the good ecological level is not enough in some months during a year. Water scarcity was evaluated by simultaneously considering water quantity, water quality and EFR in the Zarrinehrood river basin in Iran. Compared with the Smakhtin method as another method of water scarcity assessment, the Smakhtin Index is only quantitatively, but the SQQE Index provides a comprehensive assessment of the water scarcity. The results imply that the studied region is suffering from both water quantity, water quality problems. The water pollution has a big role in causing the water scarcity in the river basin. This shows that only aiming on reducing water consumption cannot help impressive reduce the water scarcity. It is necessary to pay attention to reduce water pollution and water conservation. Even in the areas that the hydrological and ecological data are rare, the QQE approach as a holistic method could be used.
vahid Rezaverdinejad; M. Shabanialasl; S. Besharat
Abstract
Introduction: Greenhouse cultivation is a steadily developing agricultural sector throughout the world. In addition, it is known that water is a major issue almost all part of the world especially for countries which have insufficient water source. With this great expansion of greenhouse cultivation, ...
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Introduction: Greenhouse cultivation is a steadily developing agricultural sector throughout the world. In addition, it is known that water is a major issue almost all part of the world especially for countries which have insufficient water source. With this great expansion of greenhouse cultivation, the need of appropriate irrigation management has a great importance. Accurate determination of irrigation scheduling (irrigation timing and frequency) is one of the main factors in achieving high yields and avoiding loss of quality in greenhouse tomato and cucumber. To do this, it is fundamental to know the crop water requirements or real evapotranspiration. Accurate estimation on crop water requirement is needed to avoid the excess or deficit water application, with consequent impacts on nutrient availability for plants. This can be done by using appropriate method to determine the crop evapotranspiration (ETc). In greenhouse cultivation, crop transpiration is the most important energy dissipation mechanisms that influence ETc rate. There are a large number of literatures on methods to estimate ETc in greenhouses. ETc can be measured or estimated by direct or indirect methods. The most common direct method estimates ETc from measurements with weighing lysimeters. Thisalsoincludes the evaporation measuring equipment, class A pan, Piche atmometer and modified atmometer. Indirect method includes the measurement of net radiation, temperature, relative humidity, and air vapour pressure deficit. A large number of models have been developed from these measurements to estimate ETc. Due to the fast development of under greenhouse cultivation all around the world, the needs of information on how it affects ETc in greenhouses has to be known and summarized. The existing models for ETc calculation have to be studied to know whether it is reliable for greenhouse climate (hereafter, microclimate) or not. Regression and artificial neural network models are two important models to estimate ETc in greenhouse. The inputs of these models are net radiation, temperature, day after planting and air vapour pressure deficit (or relative humidity).
Materials and Methods: In this study, daily ETc of reference crop, greenhouse tomato and cucumber crops were measured using lysimeter method in Urmia region. Several linear, nonlinear regressions and artificial neural networks were considered for ETc modelling in greenhouse. For this purpose, the effective meteorological parameters on ETc process includes: air temperature (T), air humidity (RH), air pressure (P), air vapour pressure deficit (VPD), day after planting (N) and greenhouse net radiation (SR) were considered and measured. According to the goodness of fit, different models of artificial neural networks and regression were compared and evaluated. Furthermore, based on partial derivatives of regression models, sensitivity analysis was conducted. The accuracy and performance of the employed models was judged by ten statistical indices namely root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R2).
Results and Discussion: Based on the results, the most accurate regression model to reference ETc prediction was obtained three variables exponential function of VPD, RH and SR with RMSE=0.378 mm day-1. The RMSE of optimal artificial neural network to reference ET prediction for train and test data sets were obtained 0.089 and 0.365 mm day-1, respectively. The performance of logarithmic and exponential functions to prediction of cucumber ETc were proper, with high dependent variables especially, and the most accurate regression model to cucumber ET prediction was obtained for exponential function of five variables: VPD, N, T, RH and SR with RMSE=0.353 mm day-1. In addition, for tomato ET prediction, the most accurate regression model was obtained for exponential function of four variables: VPD, N, RH and SR with RMSE= 0.329 mm day-1. The best performance of artificial neural network for ET prediction of cucumber and tomato were obtained with five inputs include: VPD, N, T, RH and SR. The RMSE values of test data sets for cucumber and tomato ET were obtained 0.24 and 0.26 mm day-1. Moreover, the sensitivity analysis results showed that VPD is the most sensitive parameter on ETc.
Conclusion: The greenhouse industry has expanded across many parts of the word and the need of information on a reliable ETc method especially by indirect method is crucial. In this research, the artificial neural network models indicated good performance compared with linear and nonlinear regressions. The evaluated method could be used for scheduling irrigation of greenhouse tomato and cucumber.
vahid Rezaverdinejad; M. Hemmati; H. Ahmadi; A. Shahidi; B. Ababaei
Abstract
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: ...
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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.
vahid Rezaverdinejad
Abstract
In order to investigate impactes of furrow firming on furrow irrigation performance, a field experiment was conducted during Sugarbeet growing season in Nagadeh. Four furrow irrigation treatment of furrow firming includes B1: furrow firming with once roller, B2: furrow firming with twice roller, B3: ...
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In order to investigate impactes of furrow firming on furrow irrigation performance, a field experiment was conducted during Sugarbeet growing season in Nagadeh. Four furrow irrigation treatment of furrow firming includes B1: furrow firming with once roller, B2: furrow firming with twice roller, B3: furrow firming with thrice roller and B0: without furrow firming were considered to collect field data for 1388 period and all evaluation parameters were collected. The surface irrigation model: WinSRFR, was calibrated and evaluated by using field measurements data. Furrow Infiltration and Roughness parameters, was calibrated by multilevel optimization method.The maximum relative error for estimation of advance and recession times and runoff were obtained 2.1, 4.7 and 4.5%, respectively. For 13 irrigation events assessment, application efficiency of B0, B1, B2 and B3 were obtained 50.03, 55.77, 60.22 and 62.31%, respectively. So as to increase irrigation performance, optimal combinations of cutoff time and inflow rate were extracted for all irrigation events and treatments. Under B3 furrow firming, the mean water productivity increased about 17.8% compared with without furrow firming. Beside with assumption of optimal cutoff time and inflow rate, water productivity is increasable about 27%.
Z. Taghizadeh; V.R. Verdinejad; H. Ebrahimian; N. Khanmohammadi
Abstract
The low irrigation application efficiency is the major problem of surface irrigation systems due to weak management and poor design. In this research, in order to analyze the performance of furrow irrigation system, a field experiment was conducted during maize growing season. Three furrow irrigation ...
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The low irrigation application efficiency is the major problem of surface irrigation systems due to weak management and poor design. In this research, in order to analyze the performance of furrow irrigation system, a field experiment was conducted during maize growing season. Three furrow irrigation methods; conventional furrow irrigation, fixed alternate furrow irrigation and variable alternate furrow irrigation were considered to collect field data and, then, to evaluate the performance of WinSRFR (surface irrigation model). This model was calibrated and evaluated based on the experimental data with Zero-Inertia (ZI) and Kinematic Wave (KW) solutions. The sensitivity analysis of WinSRFR showed that the most sensitive parameters were inflow rate, cutoff time and parameters of the infiltration equation, respectively. There was a small difference between ZI and KW to estimate advance time, runoff and infiltration due to high field slope. The minimum absolute error for estimation of advance times was obtained about 1.5% (0.8 minute). The minimum absolute error in estimating runoff and infiltration were 5.7 and 5.0%, respectively. Using operations analysis of WinSRFR, the iso-performance contour plots of furrow irrigation system was obtained to optimize cutoff time and inflow rate under maximizing of application efficiency and distribution uniformity and minimizing of runoff and deep percolation. Application efficiency iso-performance contour plot of fixed alternate furrow irrigation, indicated by managing of cutoff time and inflow rate, application efficiency could be increasing from 54.5% in current evaluation to 74%, provided water supply of Dreq. Also based on this contour plot, increasing of application efficiency more than 74% was impossible provided water supply of Dreq, under current furrow geometry parameters and it was possible with changing furrow geometry parameters.
V. R. Verdinejad; H. Ebrahimiam; H. Ahmadi
Abstract
A transient drainage simulation model, SWAP, was used to evaluate the performance of subsurface drainage system. SWAP model was calibrated by measured daily data including water table depth, drain discharge rate and soil and water drain salinity collected from Behshahr Ran drainage system for 120 days ...
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A transient drainage simulation model, SWAP, was used to evaluate the performance of subsurface drainage system. SWAP model was calibrated by measured daily data including water table depth, drain discharge rate and soil and water drain salinity collected from Behshahr Ran drainage system for 120 days during 1385. Calibration of SWAP model was done by inverse modeling via linking with WinPEST model. In order to calibrate drainage quantity parameters, two objective functions were defined to minimize difference between measured and simulated values of the water table depth and drain discharge rate, simultaneously. To calibrate drainage quality parameters, another objective function was also defined to minimize difference between measured and simulated values of soil salinity. There were good agreements between measured and simulated values of drain discharge rate and water table depth. The absolute error of estimation was 7 and 4 % for water table depth and drain discharge rate, respectively. Measured cumulative drainage was 7.5 % (5.3 mm) greater than its simulated value. The SWAP model could also simulate soil and drainage water salinity with a reasonable accuracy. The results of this study indicated that the performance of the SWAP model could be considerably improved using inverse modeling.
V.R. Verdinejad; S. Besharat; H. Abghari; H. Ahmadi
Abstract
Abstract
To optimal use of available water, irrigation scheduling is important to over scarcity of water resources in arid and semi-arid area. In this research to estimate of maximum allowable deficit (or: management allowed depletion) and irrigation scheduling of Fodder Mays based on canopy-air temperature ...
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Abstract
To optimal use of available water, irrigation scheduling is important to over scarcity of water resources in arid and semi-arid area. In this research to estimate of maximum allowable deficit (or: management allowed depletion) and irrigation scheduling of Fodder Mays based on canopy-air temperature difference, a field study was conducted in agricultural faculty of Karaj. The lower limit baseline (potential transpiration) and upper limit baseline (zero transpiration) were estimated by a wet treatment: (keeping soil water content at Field Capacity) and a dry treatment: (complete depletion of available water), respectively. To estimate the maximum allowable deficit, four soil moisture depletion to permanent wilting point treatments were applied in four different growth stages including settlement, vegetating, flowering and ripening of Fodder Mays with three replications. The measured data were wet and dry air temperature, canopy temperature, air relative humidity, root depth, soil water content in root depth and air vapor pressure and based the measured data, equations were extracted for lower and upper limit baselines of Fodder Mays. By comparison of canopy-air temperature difference of four treatments of soil moisture depletion with wet treatment, the maximum allowable deficit for four growth stages were estimated 42.8, 59.2, 58.9 and 67.5 percentages, respectively. The location of upper limit baseline (zero transpiration) was obtained +3.2 °C based on dry treatment. To irrigation scheduling in different growth stages by canopy-air temperature difference, crop water stress index was used and irrigation time was determined by direct method of canopy temperature.
Keywords: Canopy temperature, Evapotranspiration, Fodder Mays, Irrigation scheduling, Karaj