Irrigation
H. Ramezani Etedali; F. Safari
Abstract
IntroductionEvaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors ...
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IntroductionEvaluation of plant models in agriculture has been done by many researchers. The purpose of this work is to determine the appropriate plant model for planning and predicting the response of crops in different regions. This action is made it possible to study the effect of various factors on the performance and efficiency of plant water consumption by spending less time and money. Since the most important agricultural product in Iran is wheat, so proper management of wheat fields has an important role in food security and sustainable agriculture in the country. The main source of food for the people in Iran is wheat and its products, and any action to increase the yield of wheat is necessary due to limited water and soil resources. Evapotranspiration is a complex and non-linear process and depends on various climatic factors such as temperature, humidity, wind speed, radiation, type and stage of plant growth. Therefore, in the present study, by using daily meteorological data of Urmia, Rasht, Qazvin, Mashhad and Yazd stations, the average daily evapotranspiration values based on the results of the FAO-Penman-Monteith method are modeled and the accuracy of the two methods temperature method (Hargreaves-Samani and Blaney-Criddle) and three radiation methods (Priestley-Taylor, Turc and Makkink) were compared with FAO-56 for wheat.Materials and MethodsThe present study was conducted to evaluate the accuracy and efficiency of the AquaCrop model in simulation of evapotranspiration and biomass, using different methods for estimation reference evapotranspiration in five stations (Urmia, Qazvin, Rasht, Yazd and Mashhad). Four different climates (arid, semi-arid, humid and semi-humid) were considered in Iran for wheat production. The equations used to estimate the reference evapotranspiration in this study are: Hargreaves-Samani (H.S), Blaney-Criddle (B.C), Priestley-Taylor (P.T), Turc (T) and Makkink (Mak). Then, the results were compared with the data of the mentioned stations for wheat by error statistical criteria including: explanation coefficient (R2), normal root mean square error (NRMSE) and Nash-Sutcliffe index (N.S).Results and DiscussionThe value of the explanation coefficient (R2) of simulation ET and biomass in the Blaney-Criddle method is close to one, which shows a good correlation between the data. The NRMSE and Nash-Sutcliffe values for both parameters and the five stations are in the range of 0-20 and close to one, respectively, which indicates the AquaCrop model's ability to simulate ET and biomass. On the other hand, the value of R2 in the Hargreaves-Samani method for biomass close to one, NRMSE in the range of 0-10 and Nash-Sutcliffe index is more than 0.5, which indicates a good simulation. The NRMSE index in the evaluation of ET and biomass wheat is excellent for the Blaney-Criddle method and about Hargreaves-Samani for ET is poor and for the biomass is excellent.The Turc method with NRMSE in the range of 0-30, explanation coefficient close to or equal to one and a Nash-Sutcliffe index of one or close to one can be used to simulate ET and biomass at all five stations. Also, for biomass simulation, Priestley-Taylor and Makkink methods have acceptable statistical values in all five stations.Based on the value of explanation coefficient (R2) of estimation ET and biomass wheat for radiation methods, the correlation between the data in all three radiation methods is high. Percentage of NRMSE index of Makkink method for wheat in ET evaluation in Qazvin station is poor category and in Urmia and Rasht is good and in Mashhad and Yazd is moderate and about biomass in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd) is excellent category, the error percentage of Priestley-Taylor method for wheat in ET evaluation in Yazd station is good and the rest of the stations is poor, about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd). The error rate of Turc method for wheat in ET evaluation in Urmia, Rasht and Mashhad stations is good and in Qazvin and Yazd is poor and about biomass is excellent in all five stations (Qazvin, Rasht, Mashhad, Urmia and Yazd).ConclusionAccording to the results obtained using Blaney-Criddle method with R2 value close to one, NRMSE in the range of 0-20% (excellent to good) and Nash-Sutcliffe index close to one and Turc method with R2 value close to one, NRMSE in the range of 0-10% (excellent) and Nash-Sutcliffe index close to one was showed a good accuracy of AquaCrop model in simulation of evapotranspiration and biomass with these methods of estimation of evapotranspiration compared to other methods.
Irrigation
A. Sedaghat; N.A. Ebrahimipak; A. Tafteh; S.N. Hosseini
Abstract
IntroductionThe accuracy of determining reference evapotranspiration (ET0) is an important factor in estimating agricultural and garden water requirements. The complexity of the evapotranspiration process and its dependence on meteorological data have made it difficult to accurately estimate this variable. ...
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IntroductionThe accuracy of determining reference evapotranspiration (ET0) is an important factor in estimating agricultural and garden water requirements. The complexity of the evapotranspiration process and its dependence on meteorological data have made it difficult to accurately estimate this variable. Non-linearity, inherent uncertainty and the need for diverse climatic information in ET0 estimation have been the reasons that have made researchers interested in data mining methods such as artificial neural network (ANNs), random forest (RF) and support vector machine (SVM). Dos et al. (2020) evaluated the performance of machine learning methods to estimate daily ET0 with limited meteorological data. Their results showed that machine learning methods estimate ET0 with high accuracy, even in the absence of some variables. The use of artificial intelligence models in estimating ET0 with high accuracy has become popular in recent years, but the complexity of these models makes it difficult to apply them to regions with different climatic conditions) Feng and Tian, 2021.( Therefore, the aim of this study is to show that different data mining methods are suitable for daily ET0 estimation, which can reach a comprehensive and simple model with high accuracy by using minimal weather data.Materials and MethodsIn this research, the accuracy of data mining methods in estimating ET0 was evaluated in comparison with the plant water requirement system (FAO-Penman-Monteith standard method). For this purpose, data related to meteorological parameters such as sunshine hour, air temperature, wind speed, and relative humidity air were collected from ten synoptic stations and five climatology stations of Qazvin province in a period of 10 years (1389-1399). The ET0 extracted from the plant water requirement system was calculated based on the Penman-Moanteith method of FAO 56 and on a daily time scale, which is the actual value (measured) with the estimated values obtained by data mining methods (ANNs, RF and SVM) were evaluated. In order to validate the obtained results, the data of each station was divided into two sets of training (two-thirds of data) and testing (one-third of data). Finally, the generalizability of the mentioned methods in estimating ET0 was investigated based on NRMSE, R2, RMSE, MBE, EF and d Criteria.Results and DiscussionThe results showed that the ET0 values of the plant water requirement system have a good correlation with the estimated ET0 values of ANNs, RF, and SVM methods. In this research, the accuracy of the results of ANNs method was relatively higher than the other two methods. The results of statistical investigations and diagrams showed that ANNs, RF and SVM methods, considering all meteorological parameters (mean air temperature, average relative humidity, sunshine hours and wind speed) as input to the model, in Qazvin synoptic station with altitude 1279 meters and the climatology station of Rajaei power plant with a height of 1318 meters, estimated ET0 with higher accuracy in both training and testing steps.In the ANNs method, the values of NRMSE and R2 at Qazvin synoptic station in both training and testing steps are equal to 0.11 and 0.97, respectively, and at Rajaei Power Plant climatology station in both training and testing steps are equal to 0.10 and 0.97, respectively. In this research, the accuracy of estimating the value of ET0 in two ANNs and RF methods is close to each other and higher than the SVM method. On the other hand, the fitting speed of the ANNs method is very long compared to the RF method, and considering all aspects, it can be said that the RF method has a more suitable approach for estimating the ET0 value. The results of this research showed that the value of ET0 is not only based on air temperature, but may change under the influence of other factors such as air pollution, and is also strongly influenced by regional conditions such as topography and altitude.ConclusionThe results of this research, in addition to better investigation of ET0, help to know more influential factors in each region and can be used in regions with similar climatic conditions. For example, in the current study area, it was found that the role of average air temperature is greater than other climatic parameters and has a greater impact on ET0. Therefore, it can be said that increasing the average daily air temperature will increase ET0 and subsequently increase the water requirement of plants. As a result, by using these methods and paying attention to these points, it is possible to avoid water stress and possible reduction of the production.
F. Khadempour; B. Bakhtiari; S. Golestani
Abstract
Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to ...
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Introduction: In drainage and irrigation network capacity design and determination, reference evapotranspiration (ETo) plays significant role. Methods applied for estimated reference evapotranspiration classified in two direct and computational methods. Amongst computational methods it might point to Penman-Monteith method. This method requires radiation, temperature, humidity and wind speed data with high reliability rate in vast ranges of climates and areas represent precise outcome from reference plant Evapotranspiration.
Materials and Methods: Study stations in De Martonne classification system are divided into 6 climates such as Hyper-arid, Arid, Semi-arid, Mediterranean, Humid and Very humid (a) climates. Study stations statistical span during 19 years (1996-2015) were selected and temperature, relative humidity, sunshine hours, and wind speed in 2 meter height daily data were used. Figure 1 showed studied stations position all over the country. In this study, in order to obtain daily ETo, Penman-Monteith standard method represented by FAO-56 was used. In local sensitivity analysis, factors local influences on model output were shown. Such an analysis usually carried out through output functions minor deviants computation due to input variables. In this analysis, usually it was used one-factor- at-a- time method (OAT), so that, one variable factor and other input factors kept constant.
Figure 1. The geographical location of weather stations
The FAO-56 PM model for estimating ETo is as follows (3).
(1)
where ETo is reference crop evapotranspiration (mm day−1), Δ is the slope of vapor pressure versus temperature curve at temperature Tmean (kPa°C−1), γ is the psychometric constant (kPa °C−1), u2 is the wind speed at a 2 m height (m s−1), Rn is the net radiation at crop surface (MJ m−2 d−1), G is the soil heat flux density (MJ m−2 d−1), T is the mean daily air temperature at 2 m height (°C), and (es-ea) is the saturation vapor pressure deficit (kPa).
Results and Discussion: Weather parameters in stations showed that mean temperature sensitivity coefficient ( ) in all study stations varied between 0.21 to 0.78 so that the maximum temperature sensitivity coefficient related to Bushehr station in arid climate (in April, May, June, July, October and November) and minimum temperature sensitivity coefficient related to Shahrekordstation in semi-arid climate (in January, March, April and November). Maximum and minimum net radiation sensitivity coefficient value ( ) related to Rasht and Zahedanstations respectively. Also, maximum and minimum wind speed sensitivity coefficient value ( ) related to Zahedan and Ardebilstations are 0.54 and 0.07 respectively. Yazd station in Hyper-arid climate showed minimum relative humidity sensitivity coefficient value ( ) about 0.20 and Rasht station in very-humid (a) showed the maximum values 0.45. So the northern coastal areas are more sensitive to and SRH. The highest value is in northern coastal areas and lowest in southern coastal and southwest areas of the country. Some other studies showed that in many climates evapotranspiration was more sensitive to Rn (6, 14 and 17).In current study, also, showed the highest sensitivity in Very-humid climate (a) includes Rasht station in February, March, April, October and November. For example, = 0.82 means that 100% increase in Rn parameter result in 82% increase in ETo.
Conclusion: Sensitivity analysis experiment on FAO Penman-Monteith standard method is one of the most efficient methods to understand various climate parameters influence on reference evapotranspiration (ETo). In this study, results showed that computed ETo in all climates showed highest sensitivity to Rn and temperature respectively. Temperature sensitivity coefficient showed the highest value at April. May, June, July, October and November and Rn showed its highest value at March, April, October and November. While, minimum in all of months but May and July and maximum value showed in January, July, August and September by 0.07 and 0.54 respectively. So, in most months of the spring and the fall was larger and smaller during the winter months. Sensitivity coefficient related to mean temperature is higher during summer season and lower during winter season. Results of this study may be useful for assessing the response of the standardized FAO Penman-Monteith model in different climatic conditions. The results can also be used to predict changes in ETo values with respect to climatic variable changes obtained from climate change models.
F. Ahmadi; S. Ayashm; K. Khalili; J. Behmanesh
Abstract
Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good ...
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Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19).
Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering.
A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1) and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9).
SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15) based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12).
The SVM is originally a two-class Classifier that separates the classes by a linear boundary. In this method, the nearest samples to the decision boundary called support vectors. These vectors define the equation of the decision boundary. The classic intelligent simulation algorithms such as artificial neural network usually minimize the absolute error or sum of square errors of the training data, but the SVM models, used the structural error minimization principle (5).
Results Discussion Based on the results of performance evaluations, and RMSE and R criteria, both of the SVM and ANFIS models had a high accuracy in predicting the reference evapotranspiration of North West of Iran. From the results of Tables 6 and 8, it can be concluded that both of the models had similar performance and they can present high accuracy in modeling with different inputs. As the ANFIS model for achieving the maximum accuracy used the maximum, minimum and average temperature, sunshine (M8) and wind speed. But the SVM model in Urmia and Sanandaj stations with M8 pattern and in other stations with M9 pattern achieves the maximum performance. In all of the stations (apart from Sanandaj station) the SVM model had a high accuracy and less error than the ANFIS model but, this difference is not remarkable and the SVM model used more input parameters (than the ANFIS model) for predicting the evapotranspiration.
Conclusion In this research, in order to predict monthly reference evapotranspiration two ANFIS and SVM models employed using collected data at the six synoptic stations in the period of 38 years (1973-2010) located in the north-west of Iran. At first monthly evapotranspiration of a reference crop estimated by FAO-Penman- Monteith method for selected stations as the output of SVM and ANFIS models. Then a regression equation between effective meteorological parameters on evapotranspiration fitted and different input patterns for model determined. Results showed Relative humidity as the less effective parameter deleted from an input of the model. Also in this paper to investigate the effect of memory on predict of evapotranspiration, one, two, three and four months lag used as the input of model. Results showed both models estimated monthly evapotranspiration with the high accuracy but SVM model was better than ANFIS model. Also using the memory of evapotranspiration time series as the input of model instead of meteorological parameters showed less accuracy.
M. Makari; B. Ghahraman; S.H. Sanaeinejad
Abstract
The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration ...
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The objective of this study is to analyze the sensitivity of ETo for five models including FAO-Penman-Monteith, modified Blaney-Criddle, Hargreaves, Hargreaves-Samani and Priestley –Taylor. Daily meteorological data of Bojnourd synoptic station including air temperature, relative humidity, actual duration sunshine and wind velocity were used for sensitivity analysis of five models. In order to produce random data at a specific range, Monte-Carlo simulation was performed. Annual and seasonal were calculated to indicate the sensitivity of ETo in simultaneous variations of meteorological variables in each model.The results obtained in this study showed that the sensitivity of in simultaneous variations of meteorological variables is higher in summer. In all models, the most sensitivity was seen in summer and spring and the least sensitivity was occurred in autumn and winter. Among the studied models, FAO-PM and BC models had the most annual sensitivity and PT model had the least annual sensitivity. All of the models had fairly high correlation coefficient with FAO-PM model but the quantity of and was different in each model. BC model had the most and the least and was seen in and PT. According to the findings in this study, it can be concluded that SH model is fairly suitable for estimation of in synoptic station.
A.A. Sabziparvar; S. Tanian
Abstract
The main aim of this research is investigating the effect of ENSO phenomenon on reference evapotranspiration (ET0) on monthly, seasonal and annual time scales, using Southern Oscillation Index (SOI). For this purpose, 13 sites located in cold climate regions with 50 years (1957-2006) meteorological data ...
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The main aim of this research is investigating the effect of ENSO phenomenon on reference evapotranspiration (ET0) on monthly, seasonal and annual time scales, using Southern Oscillation Index (SOI). For this purpose, 13 sites located in cold climate regions with 50 years (1957-2006) meteorological data were selected. In the first step, the reference evapotranspiration rates were determined for the selected sites by using FAO recommended approach. In the second step, different phases (El Nino, La Nina and normal) were separated in terms of SOI and the mean deviation of ET0 values at each phase were compared by Mann-Whitney test. At statistical significant levels (p< 0.1), good correlation were found between the ET0 values and SOI. About 72% of correlations were positive and the rest (28%) were negative. In positive SOI-ET0 correlations, the monthly averages of ET0 values during El Nino phases were 14.8% and 10.8% lower than ET0 of La Nina and Normal phases, respectively. On the contrary, the average ET0 rates in La Nina phases were 13.1% higher than the corresponding values of normal pahses. The mean time lag to observe the highest impact of ENSO on ET0 was 3.2 months. The highest effective months in the study sites was found to be November, October and December, respectively. In seasonal time scale, 68% of the statistical significant affecting cases were occurred in autumn. It was found that the cold climates were more sensitive to the ENSO signals than warm climates. The results can be useful for policy makers in water resources management and agricultural sectors.
