جستجوی مقالات مرتبط با کلیدواژه "روش های تجربی" در نشریات گروه "آب و خاک"

One of the first steps for optimal management of water consumption in the agricultural sector is to estimate water needs by determining evapotranspiration. There are several direct and indirect methods for estimating evapotranspiration; each one has advantages and disadvantages. Due to the importance of measuring evapotranspiration in most hydrological studies and estimating the water requirement of plants and due to the limitation of the possibility of direct measurement, there is a serious need for experimental methods to estimate evapotranspiration. In the present study, reference evapotranspiration was initially estimated at selected stations in the east of Lake Urmia. Then, experimental methods of calculating the pan coefficient were used to calculate the reference evapotranspiration using evaporation pan data considering the FAO standard method.

The aim of this study was to evaluate the accuracy of pan coefficient estimation methods to calculate daily evapotranspiration in the east of Lake Urmia basin. There are several direct and indirect methods for estimating evapotranspiration; each one has advantages and disadvantages. The evaporation pan method has been used to estimate evapotranspiration values. For this purpose, data from Tabriz, Sarab, Maragheh, Bostanabad and Herris synoptic stations located in the east of Urmia Lake basin were used. The meteorological data utilized in the current study are minimum, average and maximum temperature, sunny hours, minimum, average and maximum relative humidity, wind speed, and evaporation from the pan. It is worth mentioning that due to the limitation of recording evaporation pan data, the present study was carried out using data for 6 months of the year (May to October) in which continuous data are available. The values of the pan coefficient were estimated using six experimental methods including Konica, Allen and Parvit, Snyder, modified Snyder, Orang and Mohammad et al. To determine the best method for estimating the pan coefficient, the evapotranspiration values obtained from the application of each method were compared with the evapotranspiration values obtained from the standard FAO-Penman-Monteith method. Furthermore, statistical meters of R, RMSE, MAE and box and violin plot diagrams were used to evaluate the obtained results.

In this study, six experimental models were used to estimate the pan coefficient. Based on the obtained results, the highest range of average monthly changes of the pan coefficient is related to the Orang method. Also, considering the average monthly values obtained for the pan coefficient, the Orang method estimates the reference evapotranspiration to a considerable amount. The results showed that in Bostanabad and Harris modified Snyder method, in Sarab and Maragheh method of Mohammad et al. and in Tabriz Allen and Parvit method are the best methods for estimating pan coefficient. Also, in general, in all stations, the Orang method has the highest error in estimating pan coefficient. In order to use experimental models for estimating the pan coefficient to calculate evapotranspiration, it is necessary to determine the appropriate model for each region based on the climatic conditions.

Due to the importance of estimating reference evapotranspiration in most hydrological studies as well as estimating the water requirement of plants, several direct and indirect methods have been developed. In the present study, six models of estimating the pan coefficient were evaluated in order to calculate the daily reference evapotranspiration using evaporation pan data. The obtained results showed that in general, the models for estimating the coefficient of the pan with acceptable accuracy can be used to calculate evapotranspiration. Meanwhile, due to the effect of climatic factors in these models, it is necessary to evaluate the efficiency of each model in different climatic conditions and determine the appropriate model for each region. For example, the results of the present study showed that the Orang method for the study area (east of Lake Urmia) does not provide suitable results and if this model is used for the east of Lake Urmia, it is necessary to calibrate the model. Also, based on the obtained results, the accuracy of other methods is close to each other. In Bostanabad and Herris, the modified Snyder method, in Sarab and Maragheh, the method of Mohammad et al., and in Tabriz, the method of Allen and Parvit, are the best methods in estimating daily reference evapotranspiration.

Evaporation is one of the most important factors in the hydrological cycle and is one of the determinants of energy equations at the ground level and water balance, which is estimated in various fields such as meteorology, hydrology, agriculture, and water resources management. Evaporation is also one of the main causes of water loss and stress on water resources. Therefore, knowing its amount as one of the hydrological variables is very important in agricultural research and soil and water conservation and modeling. Evaporation is a physical process that has a direct and close relation with atmospheric factors, the most important of which are temperature, wind speed, relative humidity and solar radiation. Researchers have been able to analyze evaporation using mathematical and empirical methods and their combination, as well as using intelligent neural methods. Due to the importance of evaporation in the water cycle and its effect on the quantity and quality of surface water resources, the study and accurate knowledge of this phenomenon is one of the important issues in the study of water resources. Using pan evaporation is one of the most common methods of estimating evaporation. But in most areas, the number of evaporating stations is not enough and they do not have suitable spatial distribution. Therefore, indirect methods such as hybrid relations, intelligent neural systems, data mining methods and remote sensing techniques have been considered by researchers.

In the present study, the evaporation of free water zones in the Urmia Lake basin has been estimated. For this purpose, the efficiency of combined empirical methods including deBruin, Tichomirov, Penman and Meyer as well as intelligent neural methods including artificial neural networks (ANN), random forests (RF) and gradient boosted trees (GBT) were compared and evaluated using statistical indices of R, NRMSE, MAPE and also Taylor diagram. Moreover, in order to increase the accuracy and efficiency of the combined methods, these relations were calibrated for the Urmia Lake basin. In order to evaluate the different combinations of meteorological variables to estimate the evaporation of free water zones in intelligent neural systems, 14 scenarios were considered with the aim of increasing the accuracy of evaporation estimation. In these scenarios, various combinations of meteorological parameters were defined that were used as variables of the combined empirical relations to estimate evaporation of free water zones. Also, pan evaporation data were used to estimate the rate of evaporation of free water zones by applying the pan coefficient and the obtained results were used as a basis for evaluating combined methods and intelligent neural systems.

The results showed that among the studied combined methods at six considered stations, the deBruin method is more accurate than other methods. Only in Tekab station, the Meyer method with NMRSE value of 30.00% and MAPE of 19.99% had higher accuracy. After calibrating the relations, the deBruin method also had the highest accuracy in all stations compared to other relations. Among the intelligent neural methods in 4 of 7 studied stations, the ANN method was introduced as the best and most accurate intelligent method for estimating evaporation of free water levels. In Maragheh, Mahabad and Sarab stations, RF method had the highest accuracy, while in all of the stations, the GBT method had the weakest performance.

Despite the overall improvement in the results of the evaporation estimation and the reduction of the error values of the calibrated empirical combined relations, the NMRSE values indicated different efficiencies of the combined relations in estimating the evaporation of free water zones. So, the calibrated combined relations were not accurate at any of the stations. Moreover, evaluating the results of intelligent neural methods indicated the high accuracy of them compared to combined relations in estimating evaporation of free zones of water. Also, the obtained results showed that the temperature and radiation parameters in the model obtained from the best scenario of intelligent methods have been used in all stations, which indicated the importance of these two parameters in evaporation modeling. Also, the results showed that although the calibration of the relations generally improved the accuracy of the combined relations; however, according to the statistical analysis, the combined relations did not have the suitable accuracy in estimating evaporation. Therefore, the use of intelligent neural systems in estimating evaporation of free zones of water was recommended. Among all of the studied methods, the ANN method had the highest accuracy in estimating the pan evaporation. Thus, this method was introduced as an accurate model in 4 stations with NRMSE values less than 10%.

The gradual increase in the world’s population requires continues increase in agricultural production. Climate change is one of the challenges of our society and frequent droughts affect large areas of the world, which requires more accurate management of water resources, both globally and in local catchments. Accurate estimation of components of the hydrological cycle is essential for proper irrigation scheduling. Most of the precipitation received by the earth is returned to the earth’s atmosphere by the process of evapotranspiration. On the other hand, because every process that takes place in the plant is dependent on water and one of the most common uses of water in the plant is evapotranspiration, so reducing amount of the water will have adverse effects on photosynthesis, crop production, product quality, etc. The complex and nonlinear relationship between the factors affecting the process of evapotranspiration, has caused researchers today to use new methods to accurately identify and predict this parameter. Reference evapotranspiration is a concept that uses the crop coefficient to obtain the actual water requirement. According to the FAO proposal, the FAO- Penman- Monteith equation was introduced as a benchmark method for calculating reference evapotranspiration values when measurements of this parameter are not available and there is no access to lysimetric data. One of the major advantages of this model is its physical basis and global validity, but this equation needs a large number of meteorological parameters that are often not available, instead empirical equations with low meteorological variables or modern methods such as artificial intelligence and machine learning methods can be used.

In this study, meteorological data related to two stations of Astara located in the humid region and Sirjan located in the arid region of Iran in the period of 2000-2020 were studied to predict the crop evapotranspiration values. As mentioned, the FAO- Penman- Monteith method has used as a standard method for calibration and evaluation of the other functional equations and machine learning methods. In this study, four types of empirical equations including Hargreaves –Samani, Makkink, Turk and Dalton were evaluated against the FAO- Penman- Monteith model. Also, modelling was performed using Support Vector Regression, Random forest and M5P Tree model. In this study, 70% of data were considered for training and 30% for testing. Finally, statistical parameters including root mean squared error (RMSE), correlation coefficient (R), scatter index (SI), Nash-Sutcliffe coefficient (NS) and Wilmot index (WI) were used to determine the performance of each mentioned methods in estimating reference evapotranspiration values.

Using different meteorological parameters in accurate prediction of evapotranspiration using 4 combined scenarios, calibration calculations were performed on 70% of data and validation calculations were performed on 30% of testing data implementing Weka software. The obtained results showed that the SVR3 and M5P3 models in Astara station with all meteorological parameters and having R= 0.993, RMSE= 0.201 and also, the SVR3 model in Sirjan station with R= 0.982, RMSE= 0.410 compared to the studied empirical methods provided better results in estimating the reference evapotranspiration and scenario 3 with all meteorological parameters was introduced as the top scenario. Among the empirical methods, Hargreaves- Samani was superior to some models only in Astara station. At Sirjan station, none of the empirical models performed better than the machine methods.

Accurate estimation of reference evapotranspiration in water resource management is essential. In this study, meteorological data from Astara and Sirjan stations were used to evaluate the ability of machine learning methods including SVR, RF and M5P to estimate the values of reference evapotranspiration and compared the results with empirical methods. The results showed that the high accuracy of the SVR3 model in both stations and in the next position M5P3 model for humid area. Empirical methods except Hargreaves- Samani had poor performance compared to data- driven models. Finally, the use of SVR and M5P methods in irrigation scheduling is recommended.

  Evapotranspiration is a main component of the water cycle and is important in agricultural water management. There are a whole host of factors that affect evapotranspiration, which makes it difficult to estimate accurately. Accordingly, several methods have been proposed for its estimation. Measurement of evapotranspiration using weighing lysimeter is the most accurate method. But due to expensive build of Weighing lysimeter, simplified empirical methods for calculating potential evapotranspiration are widely used and its results were compared to reference evapotranspiration (FAO-Penman-Monteith) method. In this study a cross comparison of FAO-Penman-Monteith method and 20 Empirical Equations for Calculating Potential Evapotranspiration in a period of 10 years from 2007 to 2016, in Rasht, Sari and Gorgan synoptic stations in coastal zone of Caspian Sea, was made. These stations were selected for climatic variation from humid climates to Mediterranean climates. The Root mean squared error (RMSE), Mean Absolute Error (MAE) and Percent error (PE) were used to measure accuracy of these methods. The results showed that daily evapotranspiration values obtained from Ravazzani method were very closer to FAO-Penman-Monteith method than those of the other models. So that results in terms of the measured errors involve RMSE, MAE and PE, which obtained 0.77, 0.54 (mm/day) and 21.6% for Rasht, 0.8, -0.39. (mm/day) and 9.4% for Sari and 0.99, -0.42 (mm/day) and 9.88% for Gorgan. Also, the results showed that the accuracy of Romanenko model was lower than other models. Overall, in all of the studied climates, the Ravazzani et al, Berti et al, Hargreaves- Sammani, Irmak et al and Valiantzas models had better performance than other models in estimating the reference evapotranspiration The results of this study showed decrease in the accuracy of ET models compared to reference model of FAO-Penman-Monteith for moving from humid to Mediterranean climate. The mass transfer based models like Mahringer and Tabari showed relative undesirable efficiency in coastal zone. Therefore, it can be noticed that empirical equation can lead to variable efficiency in different climate. The results of this study showed decrease in the accuracy of ET models compared to reference model of FAO-Penman-Monteith for moving from humid to Mediterranean climate. The mass transfer based models like Mahringer and Tabari showed relative undesirable efficiency in coastal zone. Therefore, it can be noticed that empirical equation can lead to variable efficiency in different climate.
Keywords: Coastal Regions, Empirical methods, FAO-Penman-Monteith, Reference Evapotranspiration

The water resources are severely affected by hydrological cycle.Estimation of evapotranspiration which is the main component of the hydrological cycle plays an important role in water resources management. This phenomenon is non-linear and many factors affect on that and its estimation is very difficult. Various methods have been employed to estimate evapotranspiration although they have some limitations or problems. Some of these methods are costly and time-consuming such as lysimeters, and other empirical methods have local authority. Accordingly, applying a method that can be able to model the evapotranspiration regard to the nature of the gathered data and usage of minimum climate parameters is necessary. Nowadays, Artificial Neural Network (ANN) as a novel intelligent method are used in various sciences. In this study, the daily data of two climatological stations, namely Farokhshahr and Shahrekord airport in the interval of 2004-2013 including minimum temperature, maximum temperature, average relative humidity, sunshine, and wind speed at the height of two meters under different scenarios were utilized. Initially, empirical methods of reference evapotranspiration were approximated.The used empirical methods in this research have been Hargreaves, Blany Criddle, Priestley Taylor, and Jensen Hayes. The ANN model has been designed based on different scenarios of input data through MATLAB (R2012 b) Software. In this step, different ANN architectures were evaluated based on sensitivity and accuracy So, threshold functions such as tangent sigmoid and log sigmoid in hidden layers, linear function output layer were tested in topology where as Levenberg Marquardt employed as learning function.To evaluate the models, Penman Monteis FAO 56 model was employed.The statistical indexes, namely RMSE, MAE and R were calculated. Ten scenarios have been examined, and the results demonstrated that Scenario one with five parameters had the lowest error in comparision to FAO 56 technique. Furthermore, the perposed model show superior performance than empirical methods. However, between the empirical methods, Priestley Taylor and Hargreaves had better performance. On the other hand, the sensitivity analysis illustrated that the maximum temperature and wind speed had the greatest influence on reference evapotranspiration in these regions.

The Part of precipitation that directly meets the water requirements of a plant, is termed the effective rainfall. Effective rainfall estimation methods consist of: direct measurement, empirical methods, and soil-water balance modeling. Due to the high costs of measurements and storing devices such as lysimeter, empirical and balance methods are most commonly used to estimate the effective rainfall. In this paper, as well as introducing the main methods of effective rainfall estimation, the benefits, drawbacks, and applications of each method are discussed. Based on the reported literature, the balance method, in spite of its need for exact data regarding the physical elements, after the direct measurement methods, has the most accuracy. The potential evapotranspiration ratio method is the most effective approach for basic projects. The Renfrew equation because it is very empirical, is therefore not a suitable method. The USDA method is also not appropriate for areas where the quantity of rainfall is more than the potential evapotranspiration rate. The highest effective rainfall amount is given by the USDA method and the lowest level is given by the Reliable method. In addition, the results of the empirical approaches, as well as the US Soil Conservation Service, US Department of Agriculture and Percentage method give similar results. In arid and semi-arid areas, the potential evapotranspiration to precipitation method and the soil conservation department of the United States provide the best results. In wet areas, the Reliable method is chosen as the most appropriate approach to planning because it provides more realistic values.

Sediment deposition is the principal problem affecting the useful life of reservoirs. Here we quantify the physical properties of sediments deposited in Dez dam reservoir by field investigations and empirical methods. Upon field investigation performed in the reservoir, 79 undisturbed sediment samples were taken from 12 cross sections of a 25 km of the reservoir upstream dam body where the sediments deposited have always been submerged. Results showed that grain size and bulk density of sediment deposited in the studied reach (up to 25 km upstream Dez dam body) are almost uniform and empirical methods for calculating bulk density in comparing of field data has averagely an error of 30%. This shows that the empirical methods arent capable accurately to predict bulk density of fine sediment in Dez reservoir. Analysis of the results showed that the error mostly refer bulk density calculation of the clay sediments.

There is a need for proper water demand management since water scarcity is a fact in Iran due to the arid nature of the region. Seepage losses constitute the large amount of losses in agriculture. In some cases, the rising of underground water levels in adjacent lands to irrigation canal cause drainage and salinity problems for surface layers of soil.Therefore, it is important to find ways to limit losses through seepage. In Iran, empirical equations are mostly used to determine seepage in unlined channels in the most of the irrigation networks. In this study, the seepage losses in three earthen canals in Gazvin plain were determined by Ponding Test method and also the empirical equations were used to estimate the rate of seepage losses in these canals. Empirical equations such as Ingham, Davis and Wilson, Moritz, Molesworth – Yennidumia and Misra were evaluated with Ponding Test. The seepage rates obtained by the ponding test on canals No. 1 to 3 were determined to be 163.6, 567.7 and 379.5, respectively. Among empirical equations, Misra and Moritz methods found to have acceptable precision for channel seepage determination at the study area. Hence, these two formulas are recommended to be used.