M.T. Pozan; M.M. Chari; P. Afrasiab
Abstract
Introduction: Infiltration is found to be the most important process that influences uniformity and efficiency of surface irrigation. Prediction of infiltration rate is a prerequisite for estimating the amount of water entering into the soil and its distribution. Since the infiltration properties are ...
Read More
Introduction: Infiltration is found to be the most important process that influences uniformity and efficiency of surface irrigation. Prediction of infiltration rate is a prerequisite for estimating the amount of water entering into the soil and its distribution. Since the infiltration properties are a function of time and space, a relatively large number of field measurements is needed to represent an average of farm conditions (Bautista and Wallender, 1985). In recent years, researchers have proposed methods to reduce the requirement of the regional and field data in order to describe water dynamic in the soil. One of these methods is scaling which at the first was presented by Miller and Miller (1956) and developed on the similar media theory in the soil and water sciences (Miller and Miller, 1956; Sadeghi et al., 2016). According to similar media theory, soils can be similar, provided that different soils can be placed on a reference curve with ratios of a physical characteristic length, called "scaling factor". The objective of the present study was scaling the Philip infiltration equation and analyzing the spatial variability of infiltration characteristics by using minimum field measurements. In this research, a new method was presented for scaling infiltration equation and compared with previous methods scaling including: based on sorptivity (), transmissivity (), the optimum scaling factors () arithmetic, geometric and harmonic.
Materials and Methods: The basic assumption of scaling through this method was “the shape of the infiltration characteristics curve is almost constant despite the variations in the rate and depth of infiltration”. The data required for infiltration scaling were a reference infiltration curve (whose parameters are known) and the depth of water infiltrated within a specified time period in other infiltration curves. In this method, first, equation infiltration parameters are specified for one infiltration curve, called the reference infiltration curve (). If, for other infiltration equations, the depth of water infiltrated is obtained after the specified time(ts) (for example, depth of infiltration water after 4 hours), the scale factor (Fs, dimensionless) is equal to the depth of water infiltrated after ts in the reference infiltration equation to depth of infiltrated water after ts even infiltration equation is as follows:
(1)
where Ii (i=1,2, …,n) is depth of infiltrated water after a given time (ts) for each infiltration families and is depth of infiltrated water after a given time in reference, and and are parameters of reference curve.In order to assess the proposed scaling method, root mean square error (RMSE), mean bias error (MBE) and coefficient of determination (R2) were used for a totally 24 infiltration tests.
Results and Discussion: The parameters of this model (i.e. sorptivity S and transmissivity factor A) showed a wide variation among the study sites. The variation of these parameters showed no significant difference between sorptivity and transmissivity factors. In addition, Talsama et al. (1969) illustrated that there is a weak relationship between sorptivity and saturated hydraulic conductivity. Results showed that scaling achieved using αA was better than that obtained using αS. Mean curve was chosen as reference curve and scale curve was obtained by different methods. The results of statistical analysis showed that the proposed method had the best performance (RMSE=0.006, MBE=0.0019 and R2=0.9996). In order to evaluate the effect of the reference curve selection on the results, the scaled cumulative infiltration curve based on different reference curves (different infiltration equation) was evaluated. The results showed that the selection of the reference infiltration curve is optional and each cumulative infiltration families can be selected as the reference curve. For defining the relationship between and , , αS، αA ، ، ، data, a statistical analysis was performed. According to our results, had the highest correlation with .
Conclusion: In this study, a new method for penetration scaling was presented. In this method, the infiltration curve can be obtained using the minimum information including a reference curve and the depth of infiltrated water after a given time. The selection of the reference infiltration curve is optional and each cumulative infiltration equation can be selected as the reference curve. In the light of the results of this research, it can be concluded that the proposed method in this study is promising to be used for surface irrigation management.
Esmaeel Dodangeh; .Kaka Shahedi; karim solymani
Abstract
Introduction: The proper management of water resources in a watershed requires precise understanding and modelling of the hydrological processes. HSPF model uses an infiltration excess mechanism to simulate streamflow and requires the hourly precipitation data as input. Despite the high accuracy of the ...
Read More
Introduction: The proper management of water resources in a watershed requires precise understanding and modelling of the hydrological processes. HSPF model uses an infiltration excess mechanism to simulate streamflow and requires the hourly precipitation data as input. Despite the high accuracy of the HSPF model, the lack of rainfall data at short time scales (hour and less than hour) restricts implementation of the model especially for long time simulations. Some studies have applied simple division for daily rainfall disaggregation into the hourly values to provide data required by the HSPF model. In simple division, each rainfall event is divided into 24 pulse stochastically and the peak flows may not be simulated correctly due to the lower rainfall intensities.
Materials and Methods: In this study, Random Parameter Bartlet-Lewis Rectangular Pluse (BLRPM) model was used for daily rainfall disaggregation into the hourly values to provide data needed by the HSPF model. The model parameters were calibrated using the 1, 24 and 48 hour rainfall data time series of the rain gauge stations inside (Jovestan and Zidasht) and outside (Kalk Chal) the watershed for the period of 2006-2009. To cluster the wet days, the BLRPM model was run several times and a generated sequence which had the best match with the original one in terms of daily totals was selected. Then, the synthetic sequence of hourly rainfall depths was modified based on a proportional adjusting procedure to add up exactly to the given daily depths. The calibrated model was then implemented to disaggregate the daily rainfall data of the watershed for the period of 1995-2005. The resultant hourly rainfall data were then fed into the HSPF hydrologic model to simulate the daily runoff. Parameterization of the BLRPM and HSPF models was also done while keeping the Kalk Chal station out of the calibration.
Results and Discussion: Sum of weighted squared error was calculated to be 1.03 when the data recorded in Kalk Chal station was also considered for parameter estimation of the BLRPM model. Maximum weighted square error was equal to 0.7 for lag-1 auto covariance of daily rainfall data. Keeping the Kalk Chal station out of the BLRPM model parameterization resulted in improved performance of the model. Sum of the weighted error decreased to 0.36 by removing the Kalk Chal station data. The results indicated that the weighted square error values decreased for all of the BLRPM model parameters when Kalk Chal station was not considered for calibration. The lag-1 auto covariance of daily rainfall data had the greatest reduction in weighted square error from 0.7 to 0.07 with and without including the Kalk Chal data set, respectively. The BLRPM model parameters also varied when data of the Kalk Chal station were removed from the calibration process. The k parameter value increased and the values of λ, and v decreased due to removal of the Kalk chal station data. The highest variation was observed for v decreased from 2.74 to 0.33 by removing the Kalk Chal station. The calibrated BLRPM model, with and without taking into account the Kalk Chal station data set, was employed to disaggregate daily rainfall data into the hourly values. The HSPF model was calibrated using the daily observed streamflow data recorded in Galinak station to simulate daily streamflow in reach 27. The daily streamflow simulations in reach 27 were conducted by implementing the hourly generated rainfall data sets. The results showed that inclusion of the hourly rainfall data recorded in Kalk chal station for parameterization of the BLRPM model caused the reproduction of high-intensity rainfall data in disaggregation process and consequently led to the overestimation of peak flows by HSPF model. Exclusion of the Kalk Chal station for BLRPM model parameterization improved the daily streamflow simulation with Nash-Sutcliff efficiency = 0.76, coefficient of determination = 0.79 and RMSE = 7.11 m3.s-1. These results demonstrated the sensitivity of HSPF model to the weather station selection and rainfall intensities.
Conclusions: The Kalk Chal station located outside of the studied region, with high intensity-short duration rainfall pattern caused heterogeneity of the input hourly rainfall data for parameter estimation of BLRPM model. Parameter estimation of the BLRPM model with inclusion of the hourly rainfall data of Kalk Chal station resulted in generation of greater intensities in disaggregation process. Despite the same values of daily rainfall data in streamflow simulations, the high rainfall intensities caused by the data set of Kalk Chal station led to the overestimation of peak flows. The results indicated the high sensitivity of HSPF model to the rainfall intensities.
M.R. Khaledian; S.A. Moussavi; H. Asadi; M. Norouzi; M. Aligoli
Abstract
Introduction: With increasing awareness of human beings towards the environment, researchers pay more attention to process and redistribution of water flow and solute transport in the soil and groundwater. Moreover, determination of soil hydraulic conductivity is necessary to determine the runoff from ...
Read More
Introduction: With increasing awareness of human beings towards the environment, researchers pay more attention to process and redistribution of water flow and solute transport in the soil and groundwater. Moreover, determination of soil hydraulic conductivity is necessary to determine the runoff from basins. Water movement within the unsaturated zone is often described by the formulae proposed by Richards. To solve this equation, initial and boundary conditions of the hydraulic conductivity and the soil water pressure should be determined as functions of soil water content. Beerkan method was developed to identify retention and hydraulic conductivity curves. In this method, van Gunechten with Burdine condition and Brooks and Corey equations were used to describe water retention and hydraulic conductivity curves. Recognition of the spatial pattern of studied parameter using semivariogram and then preparing zoning map with interpolation methods such as IDW and kriging can help us in relevant watershed management. The aim of this study was to spatial analyze of saturated hydraulic conductivity from 50 infiltration tests at watershed scale using Beerkan method and then preparing zoning map for the Navroud watershed.
Materials and Methods: Navroud-Assalem watershed with an area of about 307 km2 is located in the west part of Guilan province, within the city of Talesh. Of the total watershed area of Navroud, about 41 km2 is plains and the rest of it is about 266 km2, corresponding to the mountainous area. The study area includes an area with a height above 130 m. In order to complete the database of the studied watershed the present study was designed to assess soil saturated hydraulic conductivity. In this study, a 2×2 km network was designed in Navroud watershed with a surface area of 307 km2, and then infiltration tests were carried out in each node using single ring of Beerkan. Beerkan method derives shape parameters from particle-size distribution and normalization parameters from infiltration test with a near zero pressure head. Evaluation of spatial variation was done using GS+ and zoning map was prepared with ArcGIS software. Statistical evaluation of recorded data was done using SPSS software package.
Results and Discussion: Results showed that the soil bulk density was of 1.07 gr cm-3 in average. Furthermore, the results showed that the average of saturated hydraulic conductivity (Ks) in the watershed was of 3.96 cm hr-1 with a coefficient of variation of 151%. The watershed Ks is classified in the moderate class. Regarding the high value of Ks variation coefficient, using geostatistics is necessary to analyze Ks spatial variation. The results indicate the absence of the anisotropy. Using GS+ software, exponential model was fit on the empirical variation (r2=0.953 and RSS=0.0057 cm hr-1). The effective range was of 2280 m. The difference between the amounts reported by other studies and this study was because of the effect of the difference in the study area (307 km, in this study), scale (the field or watershed) and the distance between measured points. Two usual methods of interpolation including inverse distance weighting and ordinal kriging were verified. The results showed that ordinal kriging performed better than inverse distance weighting method (RMSEs for ordinal kriging and inverse distance weighting were 8.97 and 9.75, respectively). Zoning map of Ks was prepared according to the results of GS+ software using ArcGIS software. The correlation coefficients between Ks and sand, silt and clay percents were -0.04, 0.01 and 0.07, which demonstrate a weak effect of soil texture on the Ks as compared with soil structure. The correlation coefficient of the soil bulk density with Ks was of -0.45 which demonstrate a stronger effect as compared with the soil texture.
Conclusions: The results of this study can be used to proper management of watersheds. One of the main information needed to manage a watershed is Ks. Determining the spatial variability of soil saturated hydraulic conductivity at watershed scale in spite of its difficulty is one of the main prerequisite parameters to provide detailed maps of a watershed. The aim of this study was to analyze the spatial variability of Ks in Navroud-Assalem watershed, Guilan province. After analyzing spatial data, using ordinary kriging interpolation method, zoning map of Ks was prepared. This map can be used to find the optimal management of watershed, such as determining the amount of basin runoff and groundwater recharge.
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: ...
Read More
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%.
