Irrigation
Sh. Nourinezhad; M.M. Rajabi; T. Fathi
Abstract
Introduction Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is ...
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Introduction Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is the uncertainty analysis of water quality predictions, especially about the vital phosphorus parameter. Phosphorus is one of the important quality variables in water, and its increase in water resources can cause eutrophication phenomena in streams and reservoirs of dams. Due to the importance of the phosphorus parameter and the fact that water quality modeling has not been employed in the Karaj catchment area so far, in this research, total phosphorus has been modeled as a water quality parameter along with the flow and sediment discharge. This study aims to identify the most sensitive parameters of the model to flow, sediment, and total phosphorus discharge and calibrate, validate and analyze the parametric uncertainty of the SWAT model in predicting these three variables in a mountainous catchment. The case study was the catchment area of the Karaj River upstream of Bileqan pond, which is one of the mountainous watersheds in Iran. There are two critical water structures along the Karaj River, namely Amirkabir dam and Bilqan pond. Amirkabir dam (Karaj) is a multi-purpose project that is constructed to supply drinking water to Tehran and regulate water for irrigation and agriculture in the suburbs of Karaj. The Bileqan pond is also the essential point of supply and transfer of drinking water in Tehran. Given the importance of this region in supplying water for different uses, providing a calibrated model for quantitative and qualitative variables of water can be the basis for decisions to apply future management scenarios in this basin.Materials and Methods The case study was the Karaj River catchment area upstream of Bilqan Basin, which with an average height of 2880 meters, is one of the mountainous areas located in the Alborz Mountains. This basin with an area of 1076 square kilometers in the north, includes parts of Mazandaran province. In the east and south of the catchment area includes parts of Tehran province and most of it is located in Alborz province. The average annual temperature and rainfall in this basin are 12.1 °C and 480 mm, respectively, and the average of 117 glacial days during the year is observed in this area. The long-term daily data of synoptic stations adjacent to the study area from the beginning of 1998 to the end of 2018 (21 years in total) was introduced to the model. Also, daily data of relative humidity, rainfall, minimum and maximum temperature, solar radiation hours, and wind speed as meteorological parameters measured at stations in the study area were introduced to the model. It should be noted that there was a lot of missing data in meteorological information, especially for daily temperature data. In addition to the above information, daily flow data discharged from Amirkabir dam and technical specifications of the dam were introduced to the model. In addition, orchard management information, including irrigation periods and information related to phosphate fertilizers used in regional orchards, were presented to the model. The global sensitivity analysis method was used to determine the sensitive parameters of the model. Furthermore, the SUFI2 algorithm was used in SWAT_CUP software to calibrate and analyze the parametric uncertainty of the SWAT model. This algorithm quantifies the output uncertainty by 95% prediction uncertainty boundaries.Results and Discussion According to the results of sensitivity analysis, the parameters Baseflow alpha-factor (ALPHA_BF), Manning’s “n” value for overland flow (OV_N), and Precipitation Laps rate (PLAPS) were the most sensitive parameters to flow, sediment, and total phosphorus, respectively. The best Nash-Sutcliffe (NS) coefficients for runoff, sediment, and total phosphorus simulation obtained in all stations and after full calibration and validation periods were equal to 0.76, 0.56, and 0.92, respectively. Simulation of the peak points of the diagram of all three quantities was also associated with increased uncertainty and decreased model prediction accuracy, but due to the placement of more than 70% of the measured runoff and sediment values and nearly 60% of the measured total phosphorus values in the prediction uncertainty boundaries generated by SUFI2 algorithm the final value of the parameters used in the calibration process can be appropriate for simulating future scenarios in similar mountain catchments. The main weakness of the model is simulating the peak points of flow and sediment discharge. In the case of flow and sediment discharge, the liability of modeling can be generalized due to the lack of accurate prediction of the snowmelt inflow to the river in spring, which begins to increase in February and reaches the peak point in May. A considerable number of missing data in meteorological stations can effectively reflect the lack of accurate model prediction at the peak points. In this region, missing daily temperature data compared to other meteorological parameters has been significant. The dependency of the SWAT model on many experimental and quasi-experimental models such as SCS-CN and MUSLE can be another factor affecting the weakness in predicting the peak points of the sediment discharge, as well.Conclusion According to the uncertainty analysis results, most of observed flow, sediment and total phosphorus discharge values were within the uncertainty prediction boundaries generated by the SUFI2 algorithm. The NS coefficient for all three variables has met the satisfactory modeling threshold. Therefore, it seems that the sensitive parameters identified and used in the calibration process in this study and their final values can be appropriate for modeling future scenarios for this study area and similar mountain catchments. One of the limitations of the present study was a large number of missing data in meteorological stations, especially for the temperature variable. Thus, providing required measured meteorological data to the model may emhance the simulation, especially at peak points.
E. Mehrabi Gohari; H.R. Matinfar; Ruhollah Taghizadeh-Mehrjardi; A. Jafari
Abstract
Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and ...
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Introduction: Soil texture is the most important environmental variable because it plays a very important role in reducing the quality of land and water transfer processes, soil quality control and fertility. On the one hand, soil texture components are the basis of environmental predictive models and digital mapping of soil and on the other hand, soils are temporally and spatially variable, thus distinguish zoning and their monitoring with traditional sampling methods and laboratory analysis is very costly and time consuming. As a result, the development of methods for analyzing the soil and for required information has become very important. Visible and near infrared spectroscopy (VIS-NIR) is widely used to estimate soil physical properties and estimate soil texture. The present study aims to predict soil texture using spectral measurements and artificial neural network models and partial least squares regression.
Materials and Methods: The study area in southeastern Iran is approximately 70 km from Kerman. In the study area, based on the hypercube technique, 115 profiles were identified and then horizons were sampled. In this way, for each point of study, the necessary information, including the location of the profile on the ground, the type of geomorphic unit and the type of materiel, were recorded and taken from the horizons of each profile. In all soil samples, after drying and passing through 2 mm soil, the soil texture was measured by hypercube. Spectral radiometer was used to measure the spectral reflection of soil samples. The soil samples were air dried and sieved and then placed in a petri dish with an approximate diameter of 10 cm and transferred to the dark room for spectral analysis. Each specimen was tested four times (for each 90 degree sequential rotation) to remove the effects of a change in the radiation geometry. Soil samples were scanned, and absolute reflections at a spectral range of 2500-350 nm yielded 2150 spectral data points (SDPs) per soil sample with a spectral resolution of one nanometer. Finally, to construct a suitable model for forecasting the percentage of clay, sand, and silt, the least squares model was used with the number of factors 1 to 10 by Artificial Neural Network (ANN) modeling using JMP software Work.
Results and Discussion: The reflectance spectrum of the visible range - near infrared - was measured for specimens. Since preprocessing of spectral data has an effective role in improving the calibration, in order to perform spectral preprocessing, two first nodes of the first and the end of the spectra were first removed in the range of 350-400 and 2450-2500 nm. In addition, the interruption due to the change in the detector in the range of 900 to 1000 nm was also eliminated. Types of preprocessing methods were performed on spectral data. Then, using partial least squares regression analysis, the best model was produced when the first derivative was fitted to reflection values. The explanation coefficients for this low and unacceptable model were obtained. Therefore, using partial least squares regression analysis, the best wavelengths were selected to predict the percentage of clay, sand, soil, and extracted from the model. Then it was used as input in the neural network model. To determine the best combination, root error index and error coefficient were used. The results of artificial neural network showed that the number of neurons 9.8 and 10 had the best composition for predicting clay, sand and soil silt. The root-squared error results for clay, sand, and soil silt were 3.42, 6.94, and 4.383 respectively. Also, the results of the explanatory factor were 0.84, 0.83 and 0.81, respectively. After obtaining the optimal structure in the artificial neural network training phase described above, the trained network has been tested on the test data to determine the accuracy of this model to predict clay, sand and silt of surface soil. The root-squared error results for clay, sand and silt components were obtained at 5.54.9.14 and 7.01. Also, the results of the explanatory factor were 0.76.0.70 and 0.73 respectively. The best result of the prediction for partial least squares regression was obtained for the sand sample. The results indicate that the neural network performance is better than partial least squares regression, which is consistent with Mouazenet. al (2010) and also ViscarraRossel R. et. al (2009). Acceptable performance of the artificial-neural network can be attributed to the ability of this model for non-linear behavior of soil texture in visible spectroscopy. In this study, specific wavelengths, which Ben Finder et al. (2003) obtained in the study on the soils of Israel, were used. This conclusion confirms that various types of soil can be modeled using specific wavelengths. The advantage of this study is that, when using the artificial neural network, no pre-processing of reflection data is required before applying the model. Since the relationship between the percentage of soil particles (clay and gravel) and the reflection of the soil is not linear, the neural network method is very useful for analyzing the relationship between soils. Finally, the map of clay, sand and silt and map of soil texture was prepared by artificial neural network method in GIS environment.
Conclusion: The results of this study showed that the neural-dynamic network has a better performance than partial least squares regression. Calibration models designed and used in this study can be transported for use with other soils. When the partial least squares regression model was implemented, it had a very low accuracy (R2 ~ 0.1-0.3); on the contrary, the neural network-based method had high accuracy and less error. Note that although neural-dynamic modeling estimates higher precision results from soil texture, both approaches depend on wavelength selections, and so wavelengths should be selected before using any of the two models. To be finally, a meaningful relationship between the selected wavelengths and the percentage of clay, sand and silt in the present study indicates that soil texture is not only possible but also reliable by reflection spectroscopy.
Saeed Farzin; Reza Hajiabadi; Mohammad Hossein Ahmadi
Abstract
Introduction: Dynamic nature of hydrological phenomena and the limited availability of appropriate mathematical tools caused the most previous studies in this field led to the random and the probabilistic approach. So selection the best model for evaluation of these phenomena is essential and complex. ...
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Introduction: Dynamic nature of hydrological phenomena and the limited availability of appropriate mathematical tools caused the most previous studies in this field led to the random and the probabilistic approach. So selection the best model for evaluation of these phenomena is essential and complex. Nowadays different models are used for evaluation and prediction of hydrological phenomena. Damle and Yalcin (2007) estimated river runoff by chaos theory. khatibi et al (2012) used artificial neural network and gene expression programming to predict relative humidity. Zounemat and Kisi (2015) evaluated chaotic behavior of marine wind-wave system of Caspian sea. One of the important hydrological phenomena is evaporation, especially in lakes. The investigation of deterministic and stochastic behavior of water evaporation values in the lakes in order to select the best simulation approach and capable of prediction is an important and controversial issue that has been studied in this research.
Materials and Methods: In the present paper, monthly values of evaporation are evaluated by two different models. Chaos theory and artificial neural network are used for the analysis of stochastic behavior and capability of prediction of water evaporation values in the Urmia Lake in northwestern of Iran. In recent years, Urmia Lake has unpleasant changes and drop in water level due to inappropriate management and climate change. One of the important factors related to climate change, is evaporation. Urmia Lake is a salt lake, and because of existence valuable ecology, environmental issues and maintenance of ecosystems of this lake are very important. So evaporation can have an essential role in the salinity, environmental and the hydrological cycle of the lake.
In this regard, according to the ability of chaos theory and artificial neural network to analysis nonlinear dynamic systems; monthly values of evaporation, during a 40-year period, are investigated and then predicted. So that, 10 years of data are applied to model validation and a four-year time horizon is predicted by each model. In the present paper, a multi-layer perceptron network with a hidden layer are used. Number of neurons in the hidden layer is determined by try and error. Also different input combinations are used to find out the best artificial neural network model. Prediction accuracy of models is evaluated by three indexes. These three indexes are mean absolute error (MAE), root mean squared error (RMSE) and determination coefficient (R2).
Results and Discussion: Results of chaotic parameters such as a positive lyapunov exponent and the correlation dimension non-integer slope indicate that evaporation values in the Urmia Lake have chaotic behavior. So these values have not stochastic behavior and can be predicted by suitable models. Chaos theory and artificial neural network are used for prediction in this paper. Values of MAE, RMSE and R2 for validation data are 10.96, 14.67 and 0.97 for artificial neural network and 13.47, 16.92 and 0.97 for chaos theory, respectively. The determination coefficient is the same in the two models while the values of MAE and RMSE is lower in the artificial neural network. So error indexes indicate that the artificial neural network is slightly better than the chaos theory. In order to prediction by artificial neural network, The best input combination includes four time delays that they are values of a month ago, two months ago, eleven and twelve months ago. Because in the chaos theory only the evaporation time series is applied, in order to better comparison of artificial neural network and chaos theory, in the artificial neural network model only the evaporation time series is used. Results of the four-year time horizon indicate somewhat similar behavior of two models especially in the minimum and maximum values of time series. In the maximum and minimum value chaos theory and artificial neural network predict similar values while in the other values there are some difference and the artificial neural network model predicted values less than chaos theory.
Conclusions: The results obtained from the chaotic nature determination parameters of the evaporation data, positive lyapunov exponent and the correlation dimension non-integer slope; indicate the chaotic behavior of study time series. Therefore, the system has a hidden pattern (i.e., the system isn’t Stochastic). The verification results indicate the high accuracy of chaos theory and neural network models - a little more accurate - and it was found that both models have similar accuracy in prediction of the future evaporation values or data that haven't been recorded in the past.
N. Khalili Samani; A. Azizian
Abstract
Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables ...
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Interduction: Spatial and temporal improper distribution of precipitation is one of the major problems in the water district. Increasing population and reduction per capita fresh water has made freshwater resources as a renewable to a semi-renewable source (1).
Rainfall is one of the climatic variables that influence the ground water resources. The existence of models for predicting the annual precipitation and subsequent management of water resources in arid, semi-arid and also humid regions is useful . In this study, the simple regression models that relate the annual precipitation to the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) and mean annual precipitation (Pm), in Khuzestan (2), Kerman (3) and southern and western provinces of Iran (4) were evaluated using long-term daily precipitation data of Shahrekord and Yazd Weather stations and, if necessary, modified equations.
Materials and methods: In this study, long-term daily precipitation data of Shahrekord and Yazd Weather stations (1360-1392) from Meteorological Administration of Chaharmahal and Bakhtiari and Yazd were prepared, completed and used for analysis. At each station the duration of 42.5 and 47.5 mm of precipitation from the beginning of autumn (t42.5 and t47.5, respectively) for each year, annual precipitation and mean annual precipitation for subsequent calculations were extracted. Then, the homogeneity and adequacy of data were checked using RUN Test. Equations of 1 to 8 were used for predicting the annual precipitation using 70% of the data. The relationship between observed and predicted annual precipitation were evaluated. Then the coefficients of equations were corrected by 70% of the data set using SPSS Software in Shahrekord and Yazd Weather Stations. The remaining 30% of data were used to validate the modified models. Index of agreement (d) and normalized root mean square error (NRMSE), were used to evaluate the models. The NRMSE values close to zero and d values close to 1 indicate proper operation of the model.
Results and Discussion: Results showed that the models with straight and reverse relationships between t42.5 or t47.5 and Pm were not suitable to estimate the annual precipitation in Shahrekord. However, these models were relatively acceptable for Yazd. While the simple regression model using t42.5, t47.5 and the long-term Pm as independent inputs could be able to predict the annual precipitation of Shahrekord and Yazd stations with acceptable accuracy.
Conclusion : Using the relationship between t42.5, t47.5 and Pa (equations of 1, 3, 4 and 7) for estimating the annual precipitation in Shahrekord and Yazd stations, NRMSE values obtained greater than 0.3 and d index less than 0.7 (Fig. 3 and 4). Furthermore , the models included t42.5, t47.5 and Pm versus Pa (equations of 2, 5, 6 and 8), had not acceptable results (Fig. 5 and 6). By modifying the above mentioned equations (models of 10 to 14 for Shahrekord and 15 to 19 for Yazd) and comparison of measured and predicted annual precipitation by the modified models, the results showed that the linear and inverse relationship between t42.5, t47.5 and annual precipitation could not be an appropriate model for Shahrekord Station (Fig. 7-A and 7-B and 7-C) and results of the evaluation of these relationships for estimating of the average annual precipitation of Yazd were relatively acceptable (Fig. 8-A and 8-B and 8-C results in Yazd station). While the simple linear model including the relationship between those time periods (t42.5, t47.5 ) and the long-term average annual precipitation with corrected coefficients could accurately estimate the annual rainfall in the Shahrekord and Yazd stations (Fig. 7-d and 7-H for Shahrekord and 8-D, 8-H for Yazd station). In order to validate the above results, the models were evaluated with the remaining 30% of the data . Results showed in Figs. 9 and 10. The NRMSE values in Figs. 10-A, 10-B and 10-C, confirm the validity of the relationship between t42.5, t47.5 and annual precipitation.
M. Ghamghami; J. Bazrafshan
Abstract
Today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. Nevertheless, the accuracy of such models or predictions should be improved in short timescale. In the present paper, it is assumed ...
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Today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. Nevertheless, the accuracy of such models or predictions should be improved in short timescale. In the present paper, it is assumed that the rainfall occurrence/non-occurrence sequences follow a two-layer Hidden Markov Model (HMM) consist of a hidden layer (discrete time series of rainfall occurrence and non-occurrence) and an observable layer (weather variables), which is considered as a case study in Khoramabad station during the period of 1961-2005. The decoding algorithm of Viterbi has been used for simulation of wet/dry sequences. Performance of five weather variables, as the observable variables, including air pressure, vapor pressure, diurnal air temperature, relative humidity and dew point temperature for choosing the best observed variables were evaluated using some measures oferror evaluation. Results showed that the variable of diurnal air temperatureis the best observable variable for decoding process of wet/dry sequences, which detects the strong physical relationship between those variables. Also the Viterbi output was compared with ClimGen and LARS-WG weather generators, in terms of two accuracy measures including similarity of climatic statistics and forecasting skills. Finally, it is concluded that HMM has more skills rather than the other two weather generators in simulation of wet and dry spells. Therefore, we recommend the use of HMM instead of two other approaches for generation of wet and dry sequences.
B. Shabani; M. Mousavi Baygi; Mehdi Jabbari Nooghabi; B. Ghareman
Abstract
Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and ...
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Nowadays, modeling and prediction of climatic parameters due to climate change, global warming and the recent droughts is inevitable. Maximum and minimum temperatures are including climatic parameters that are important in water resources management and agriculture. In order to model the maximum and minimum monthly temperatures of Mashhad plain, the long- term data of Mashhad and Golmakan were used for the joint period from 1987 to 2008. The SARIMA(0,0,0)(0,1,1)12 model for maximum monthly temperature and the SARIMA(0,0,0)(2,1,1)12 model for minimum monthly temperature were determined as the final models using time series. High correlation coefficientsindicate acceptable adaptation of modeling and actual values in the calibration and validation of models. Finlay, predictions were performed based on models fitted for the next 10 years (2009-2018). Comparison of results for future period (2009-2018) and the base period (1987-2008) represents maximum temperature mean 1 °c increase and minimum temperature mean 1.4 °cincrease.
A.A. Keikha; M. Mosannan Mozafari; M. Sabouhi; Gh. Soltani
Abstract
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 ...
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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. Therefore it is proposed that Copula is used for Helmand river flow modeling. Also this method use for simulating other river flows and also using other Copulas for river flow modeling could have the subject of future researches.
A. Hezarjaribi; F. Nosrati Karizak; K. Abdollahnezhad
Abstract
Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose ...
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Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose of this research was to investigate CEC estimating using easily accessible parameters with Artificial Neural Network. In this study, the easily accessible parameters were sand, silt and clay contents, bulk density, particle density, organic matter (%OM), calcium carbonate equivalent (%CCE), pH, geometric mean diameter (dg) and geometric standard deviation of particle size (σg) in 69 points from a 1×2 km sampling grid. The results showed that Artificial Neural Network is a precise method to predict CEC that it can predict 82% of CEC variation. The most important influential factor on CEC was soil texture. The sensitivity analysis of the model developed by using of Artificial Neural Network represented that clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size, OM% and total porosity were the most sensitive parameters, respectively. The model with clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size as inputs data was selected as the base model to predict CEC at studied area.
