Irrigation
M. Arjmand Sharif; H. Jafari
Abstract
Introduction: In hydrological studies, time series are observed as continuous or discrete. Groundwater level and rainfall can be considered as discrete time series. The most common way to measure the dependence between two variables in a discrete time series is to calculate the Pearson correlation coefficient ...
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Introduction: In hydrological studies, time series are observed as continuous or discrete. Groundwater level and rainfall can be considered as discrete time series. The most common way to measure the dependence between two variables in a discrete time series is to calculate the Pearson correlation coefficient (r). Pearson correlation test is a parametric test that quantitatively measures the linear relationship between variables. This coefficient is essentially a dimensionless index that describes the relationship between two variables numerically. The groundwater level is more or less influenced by rainfall, and this influence may be delayed for a variety of reasons. The process of comparing two time series in different time steps is called cross-correlation. In the cross-correlation analysis, the time-dependent relationship between the dependent and the independent variables is analyzed by computing the coefficients of cross-correlation for various time lags. Results are plotted on a graph called a cross-correlogram.Mashhad-Chenaran aquifer with an area of about 2527 km2 is the most important aquifer in Khorasan Razavi province. Unfortunately, so far in the Mashhad-Chenaran aquifer, the recharge lag time has not been calculated due to the very complex geological and hydrogeological conditions of the aquifer. In this study, an attempt has been made to calculate the groundwater recharge lag time.Materials and Methods: In this study, 15 years (Sep. 2001 to Sep. 2016) data of monthly depth to water-table and rainfall have been used . There is 74 active observation well in Mashhad-Chenaran aquifer. Out of 74 wells, 31 well were selected based on geological and hydrogeological conditions. To calculate the rainfall at the observation wells, the daily rainfall data from rain gauge and evaporation stations (25 rain gauge stations and 9 evaporator stations) have been used. First, the cumulative daily rainfall at each station for one month (from 15 months to 15 months later) was calculated. Then, a monthly rainfall raster was prepared using ArcGIS.Finally, the rainfall at the observation well was extracted from the raster file.Results and Discussion: The correlation coefficient between the groundwater level and rainfall was calculated for the 31 wells at two confidence levels (α = 0.05 and α = 0.1). The lag time was calculated based on the highest correlation coefficient for the two confidence levels. Results showed that the cross-correlation coefficient varied from at least 0.129 in the Tanglshour-Morgh Pardak observation well (very weak) to 0.495 in the Kalateh Sheikhha observation well (moderate). The coefficients of cross-correlation for various time lags were plotted on the cross-correlogram. In cross-correlogram, the month zero was equivalent to October and the month 11 was equivalent to September of the next year. It was observed that the trend of correlation coefficient followed the two specific patterns. In the first group, the water table usually reacts to rainfall after the second month. Then, the correlation coefficient gradually increased. The correlation coefficient reached its maximum in the fourth and fifth months and then decreased with a gentle slope. From the seventh month to the eleventh month the correlation coefficient has become negative. Although there was a significant relationship during these months, there was no cause-and-effect relationship between changes in the water table and rainfall. In the second group, the relationship between the groundwater level and rainfall was not significant at the 95% confidence level. This group includes Doghai observation wells, Qarachah, Shurcheh, Mochenan, Yekehlengeh, Chamgard, Ghahghahe, Tangleshour - Morgh Pardak, and Shorcheh. Changes in the correlation coefficient of these wells were very irregular and the relationship between rainfall and water table changes was probably influenced by other factors. The map of lag time showed that the spatial variations of the lag time completely followed the pattern of the Iso-depth map. In general, the lag time was a function of the depth to the water-table in the Mashhad-Chenaran aquifer. With increasing water depth, the lag time also increased. A closer look at the map showed that in the northern and southern margins of the first hydrogeological unit, the lag time was more than its center. In the northern and southern hydrogeological units, the lag time showed the greatest compliance with the groundwater depth. The amount of lag time from the northern margin of the aquifer to the south gradually increased and finally reached its maximum value in the Akhlamad, Torqabeh-Shandiz.Conclusion: As discussed previously, the groundwater level was influenced by rainfall, and this influence may be delayed for a variety of reasons. In this study, the groundwater response to rainfall has been estimated from 31 observation wells by cross-correlation method in a period of 15 years (Sep. 2001 to Sep. 2016). The correlation test results showed that after about 2 to 3 months, the effect of rainfall was gradually observed on the groundwater level and the correlation coefficient at the confidence level α = 0.05 and α = 0.1 for 77 % and 97% of wells became meaningful, respectively. The minimum lag time was 2 months and the maximum was 7 months. In general, the estimated lag time was well matched to the groundwater depth and fully followed the Iso-depth map pattern. The amount of groundwater recharge throughout the Mashhad-Chenaran aquifer was mainly controlled by the unsaturated area properties such as thickness, material, etc. Changes in groundwater depth were the major factor affecting the lag time. It seems that with the start of rainfall in late October, groundwater recharge in most wells begin in mid-autumn and continues until late spring. Most of the groundwater recharge takes place in late winter. In summer, rainfall has a very small role in groundwater recharge. In this period, the uncontrolled extraction of water from the aquifer and consequently a sharp and continuous drop in groundwater level plays a major role in water table fluctuations.
B. Kamali; A. Mahdavi; A. Sotoodehnia
Abstract
Introduction: Over application of phosphorous-containing fertilizers is common among the farmers. Excess amounts of phosphorus can potentially cause more phosphorous losses through water flow on the soil surface or leaching into the soil profile. The ability of highly phosphorus-fertilized soils to maintain ...
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Introduction: Over application of phosphorous-containing fertilizers is common among the farmers. Excess amounts of phosphorus can potentially cause more phosphorous losses through water flow on the soil surface or leaching into the soil profile. The ability of highly phosphorus-fertilized soils to maintain excessive amounts of phosphorus and prevent losses largely depends on the phosphorus adsorption capacity of the soil. The purpose of this study was to investigate and compare phosphorous adsorption isotherms in soil samples of four agricultural areas located in Qazvin plain and determine the most appropriate equation to describe the equilibrium adsorption in the studied samples. Identification of the most accurate model of adsorption kinetics using the investigated kinetics equations in one of the soil samples was another objective of this study. The linear regression analysis and correlation between physical and chemical properties of different soils with adsorption coefficients of Langmuir equation was also investigated. Based on mentioned points, the results of this study can help to increase the availability of applied phosphorous for plants, reduce phosphorous losses and proper management of phosphate fertilizers consumption in the study areas.
Materials and Methods: In order to study the soil properties and phosphorous adsorption, soil samples of four villages included Zaaferan (A), Koochar (B), Mehdi Abad (C) and Kamal Abad (D) were taken from 0 to 30 cm depth and stored in plastic bags after air drying. Batch experiments using a standard method recommended by the SERA-IEG17 group were used to determine the amount of phosphorous adsorbed to soil particles. The steps to perform the equilibrium were as follows:
1- Dry soil samples were crushed and passed through a 2 mm sieve.
2- One gram of the soil sample was placed in a 60 ml container.
3- 0.01 M CaCl2 solution was prepared and different concentrations of phosphorous including 0, 5, 10, 15, 20, 30 and 80 mg/l were created by adding certain amounts of KH2PO4 to this solution. 25 ml of these solutions were added to each soli sample to give a 1:25 soil to solution ratio and three drops of chloroform were added to each container to prevent microbial activity.
4- The suspension samples were placed in a shaker machine (250 rpm) at 25°C for 24 hours.
5- Then, the samples were removed from the shaker and allowed to settle for one hour and then passes through a fine filter (Mesh 42).
6- Phosphorous concentration was measured by the molybdate-vanadate method followed by spectrophotometric determination at 470 nm.
7- The amount of phosphorous adsorbed in each soil sample was calculated from the difference of the initial and secondary concentration values.
The adsorption kinetics experiment was similarly performed, with the exception that one soil sample with average adsorption value (sample C) was selected and the phosphorous solution at a concentration of 20 mg/l added to the soil samples. Phosphorous contact times with soil were considered as 0.17, 0.5, 1, 2, 4, 8, 16, 24, 48 and 72 hours. In this study, using CurveExpert 1.4 software and by matching Pseudo-first-order, Pseudo-second-order, Intra-particle diffusion, Kuo and Lotse (1974), Barrow and Shaw (1975) and Panda et al. (1978), equations on the data obtained from kinetics adsorption experiments, and the coefficients were estimated in these equations (adsorption parameters). Furthermore, by calculating the coefficient of determination (R2) of these equations and the standard error of the estimate (s), the most appropriate and accurate model of phosphorous adsorption kinetics for the soil sample was determined. Similarly, from Langmuir, Freundlich, Linear and Van Huay equations, the most appropriate isotherm was determined for estimating phosphorous equilibrium adsorption in the studied areas. Also, correlation and linear regression analysis were performed to determine the relationship between the physical and chemical parameters of the soils and the coefficients of Langmuir isotherm using Minitab software.
Results and Discussion: According to the results, the highest coefficient of determination (R2) and the lowest standard error of the estimate (s) for all four samples were related to Langmuir, Freundlich, Van Huay and Linear equations, respectively. Therefore, in this study, Langmuir isotherm was the most accurate model for estimating equilibrium adsorption of the phosphorus to the soils of the study areas. However, the Freundlich and Van Huay equations also showed a good correlation with the laboratory data. Comparison of the results of various studies in these fields showed that the type of isotherm corresponds to phosphorous adsorption data in each experiment is related to the physical and chemical properties of soil and adsorption sites. The amounts of maximum phosphorous adsorption capacity (qm coefficient in Langmuir equation) for the soil samples A, B, C and D were 0.49, 0.31, 0.42 and 0.4 mg/g, respectively. In kinetic study, Although, Kuo and Lotse, Barrow and Shaw and Panda et al. equations had a coefficient of determination (R2) above 0.95 ; the highest accuracy was related to the Kuo and Lotse equation with R2 of 0.974. The coefficients of this model included k (reaction rate) and m (constant coefficient) were 0.007 l/gr.min and 13.2, respectively. Based on the results of this study and other adsorption studies, soil physical and chemical properties including EC, PH, soil calcium content, clay content and porosity were among the parameters affecting adsorption rate and the type of the most accurate equation of adsorption estimation. Considering the soil properties that were most correlated with adsorption coefficients, it can be concluded that the high percentage of clay and low levels of organic matter and soluble calcium are the main causes of the high phosphorous adsorption in soil. The correlation coefficients (r) of these three soil parameters with the maximum adsorption capacity (qm) were 0.61, -0.97 and -0.92, respectively.
Conclusion: According to the results of this study, Langmuir was the most accurate isotherm model and the soil sample of Zaaferan area has the most adsorption capacity with qm of 0.49 mg/g, which is related to low levels of soil organic matter. Therefore adding organic matter to the soils can be used as a solution to increase cultivated plants access to applied phosphorous and reduce phosphorous adsorption into the soil and thus reduce losses and leaching of excess phosphorous in the profile or soil surface.
T. Rajaee; R. Rahimi Benmaran
Abstract
IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses ...
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IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN), Wavelet Neural Network combination (WANN) and multi linear regression (MLR) models, to predict next month the Nitrate (NO3) and Chloride (CL) ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated.
Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL) has been put related to the past amounts of the same time series (NO3 / CL) and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data) as training set, next 10% of data (12 months) and 10% of the end of time series (terminal 12 months) were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q) and mentioned qualityparameters(NO3 / CL) were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E) and the Root Mean Square Error (RMSE).An efficiency of one corresponds to an accurate match of forecasted data to the observed data. RMSE indicates the discrepancy between the observed and predicted values
Results Discussion The results indicates that the accuracy and the ability of hybrid model of wavelet neural network had been better than the other two modes; so that hybrid model of Wavelet artificial neural network was able the improve the rate of RMSE for Nitrate ions in comparison with ANN and MLR models respectively, amounting to 30.13% and 71.89%, for chloride ion as much as 31.3% and 57.1%. In the WANN model increasing the decomposition level, in level 1 to Level 3, increases the model’s performance, but increasing the decomposition level, in levels over Level 3, decreases the model’s efficiency, because high decomposition levels lead to a large number of parameters with complex nonlinear relationships in the ANN technique.The WANN model needed 1 to 7 neurons in the hidden layer for the best performance result. In prediction of high NO3 values the amount RMSE for ANN, MLR and WANN models are 1.487, 2.645 and 0.834 ppm, respectively. Also, for CL values the mentioned statistical parameter is 0.990, 3.003 and 0.188 ppm, respectively for models.The results exhibits that the combined model of WANN the forecast was better than the other two models.
Conclusion Wavelet transforms provide useful decompositions of original time series, so that wavelet-transformed data improve the ability of a predicting model by capturing useful information on various resolution levels. The main advantage of this study is that only from the Q and slightly quality of parameter time series are used until the same quality of parameter in one month ahead is predicted. The purpose of entering Q time series with quality of parameter as inputs of models is analysis the efficacy of Q in the accuracy of prediction. owing of the high capability wavelet neural network in the prediction of quality parameters of river's water, this model can be convenient and fast way to be proposed for management of water quality resources and assurance from water quality monitoring results and reduction its costs.
Ahmad Gholamalizadeh Ahangar; F. Sarani; M. Hashemi; A. Shabani
Abstract
Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting ...
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Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting organic carbon content in 192 samples of surface soil (0 to 30 cm) of Sistan plain (Miankangi region). In this study, Only 25% of organic carbon variations were explained by variables used in linear regression model in the study area (R2= 0.25). Moreover, simple co-kriging (with clay as co-variable) which was the best geostatistical method in the current study, predicted organic carbon content weakly (R2= 0.23 and RMSE= 0.127). However, using latitude and longitude parameters, ANN performed much better than linear regression and geostatistical methods for predicting organic carbon content (R2= 0.79 and RMSE= 0.044).
H. Kashi; H. Ghorbani; S. Emamgholizadeh; S.A.A. Hashemi
Abstract
With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed ...
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With respect to the problem of direct measurement of soil parameters in recent year using indirect method such as artificial neural networks has been considered. In the present study, 200 soil samples were collected from Ghoshe location in Semnan province. Half of samples were collected from disturbed agricultural lands and the other half were collected from undisturbed nearby lands. Some soil chemical as well as physical properties such as electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ration (SAR) and bulk density were considered as easy and fast obtainable features and soil cation exchange capacity as difficult and time consuming feature. The collected data randomly divided in two categories of training (70%) and testing (30%) and they used for train and test of two artificial neural networks, multi-layer perception using back-propagation algorithm (MLP/BP) and Radial basis functions (RBF) and nonlinear regression model. Results of this research show high efficiency of artificial neural network compared with nonlinear regression and also MLP network was better than RBF network. Sensitivity analysis was also performed for all parameters to find out the relationship between soil mentioned parameters and soil cation exchange capacity for both disturbed and undisturbed soils. At last, the correlation between soil parameters and soil cation exchange capacity was determined and most important parameters which could influence the soil cation exchange capacity were described.
