جستجوی مقالات مرتبط با کلیدواژه « تعرق مرجع » در نشریات گروه « آب و خاک »

Evapotranspiration has a main role in water budget assessment and management. In plant water requirements and evapotranspiration volume calculation, firstly reference evapotranspiration (ET0) have to be computed and then plant water requirements can be estimated using different methods. In this research, firstly the reference evapotranspiration factor was calculated by standard FAO-Penman-Monteith formula via climatic data of Tabriz station, East Azerbaijan province. Climatic parameters include mean, minimum and maximum of air temperature also mean, minimum and maximum of relative humidity, rainfall, wind speed and sunshine hours were considered as an input of genetic programming and M5 tree models to estimation of monthly reference evapotranspiration as an output. Results showed that both of two approaches present exact results (determination coefficient for M5 tree model equal to 0.99 and for GP equal to 0.96) in estimating monthly reference evapotranspiration in Tabriz region, but M5 model tree, provides understandable, applicable and simple linear relations to estimate reference evapotranspiration.

Reference evapotranspiration is one of the most effective components of agricultural water use and management of water resources. Determination of the water requirements of various plants during the growing season is necessary in order to prevent water waste and proper planning (7). In recent years the use of artificial intelligence technics and hybrid model in forecasting of hydrological parameters has become very popular (12). The objective of this study is to evaluate GMDH neural network and wavelet-GMDH hybrid models in forecasting of daily reference evapotranspiration at Ahvaz synoptic station.
For this purpose, 10-year period (2000 to 2009), 7 years (2000-2006) for training and 3 years (2007-2009) to test different models were considered. Reference crop evapotranspiration time series generated using standard penman-monteith equation. Different combinations of inputs (different delays) and various mother wavelets were examined. To test different models were considered different combinations of inputs (9 different delays); and different mother wavelets (13 mother wavelets). A total of 126 models 117 of them related to hybrid models wavelet- GMDH and 9 for GMDH neural network were carried out. To choose the best model, statistical criteria such as coefficient of determination (R2), root mean square error (RMSE) and the mean percentage error (MARE) was used.
The results showed that wavelet-GMDH hybrid model (RMSE = 0.31 mm / day) compared with GMDH neural network (RMSE = 1.22 mm / day) has higher accuracy in forecasting daily reference evapotranspiration. The results showed that use of delays longer than four days have little effect on the accuracy of models and in some cases can result in reduced accuracy. The results of similar studies that have used wavelet transform to preprocessing data are in correspondence with our findings (1, 4, 5 and 12). Results of GMDH neural network showed that number of delays did not affect the accuracy of model. This study evaluates the accuracy of the wavelet-artificial neural network hybrid model for different mother wavelets. Results showed that Meyer mother wavelet due to its complexity and its shape improved the accuracy of models. These findings correspond with the findings of Rajaee and Ebrahimi(2014), Shoaib et al(2015) and Toofani et al (2012) (13, 15 and 17).
Results of this study showed that, at the forecasting of one day ahead reference evapotranspiration Wavelet-GMDH model (MAPE=5.53%) has significant superiority to GMDH model (MAPE=22.11%). The results of this study can be used in the planning of irrigation water in this area. At the end it is recommended that proposed models has been evaluated in different climate conditions of Iran.

Correct estimation of reference evapotranspiration (ETo) needs to estimate accurate solar radiation values. There are many models to estimate solar radiation so that one of these relationships is Angstrom-Prescott (A-P) equation. The accuracy of Angstrom-Prescott equation in estimating solar radiation value depends on the estimation accuracy of a and b coefficients of the mentioned equation. The purpose of the present research was to investigate the accuracy of models for estimating coefficients in A-P equation and their effects in the solar radiation and reference evapotranspiration values. To do this purpose, the meteorological data of four synoptic stations including Urmia, Takab, Mahabad and Salmas in the Urmia lake watershed in the daily scale from 1998 to 2008 were used. The models which used for the determination of Angstrom-Prescott equation coefficients were included models based on the annual average of relative sunshine hours , elevation and , elevation and latitude and , elevation and latitude, annual average of air temperature, elevation. The obtained coefficients were used to compute the solar radiation values in Angstrom-Prescott equation and then reference evapotranspiration was calculated by FAO-Penman-Monteith equation. To evaluate the models accuracy, statistical indices including RMSE, MBE, MPE and R2 were used. The evaluation results of the models showed that the model based on annual average of relative sunshine hours (Rietveld model) had the best performance in determining the coefficients of AngstromPrescott equation. The average values of RMSE, MBE, MPE and R2 indices for actual reference evapotranspiration and reference evapotranspiration obtained from coefficients of mentioned model were found 0.33 and 0.25 mm/day, 6.27% and 0.9977, respectively. For AngstromPrescott equation coefficients from selected model, a=0.26 and b=0.43 were suggested in the studied stations in the Urmia lake watershed.

Evapotranspiration is one of the important factors in water resources consumption in the agriculture part. Therefore, presenting a method which gives suitable and accurate prediction of reference evapotranspiration can help to take optimum decision for water resource programing. In this research, time series and artificial neural networks methods were compared each other in order to predict the monthly reference evapotranspiration in Urmia synoptic station. To achieve this goal, at the first step, the best time series model between AR and ARMA models and the best artificial neural networks model between radial basis function (RBF) and multilayer perceptron (MLP) neural networks were selected. In the second step, the two models chosen were compared each other. In the mentioned artificial neural networks, the deferent monthly lags of reference evapotranspiration were used as network input. In this process, the monthly reference evapotranspirations were computed from 1971 to 2010 using FAO Penman-Monteith method. The mentioned dates from 1971 to 2005 were used to select the best time series model and the best structure of networks and the dates from 2006 to 2010 were utilized to compare the methods used. The results showed that the AR(11) model has the best performance among other time series models and the RBF model has the lower error than the MLP model. The comparison of the best time series model (AR(11) model) with the best artificial neural networks model (RBF model) showed that the RBF model could predict the reference evapotranspiration by the lowest error from 1971 to 2010 period. The root mean square error in AR(11) and RBF models was obtained 1.85 and 0.999 mm/month respectively.

Different equations have been introduced for estimating grass reference evapotranspiration (ETo) that are in the range of easy to very complex equation of energy balance. Empirical ETo equations have remained in popular use because of simplicity and the smaller number of input parameters needed for computation. The Hargreaves−Samani (1985) equation is one of the empirical equations which widely used by researchers for estimating the ETo. The most important parameters in estimating ETo are temperature and solar radiation (Rs). The RS has a key role in energy balance in the ground-atmosphere system and is a key parameter in calculating ETo. Hargreaves and Samani (1982) recommended a simple equation to estimate solar radiation (Rs): R_S=(KT)(R_a) 〖(TD)〗^0.5 (1) Combining equation 1 with the original Hargreaves equation (Hansen et al, 1979) resulted in a simplified equation which requires only temperature and latitude (Hargreaves and Samani, 1982, 1985). The simplified equation is as:ET_o=0.0135 (KT)(R_a) (TD)^0.5 (T+17.8) (2)In the above equations, TD is the difference between maximum and minimum daily air temperature (Tmax-Tmin) (oC) for weekly or monthly periods; Ra is extraterrestrial radiation (mm/day); KT is empirical coefficient and T is the average daily air temperature (oC). The equation 2 and its empirical coefficient (KT) were investigated in various studies and its application in ETo proper estimation was validated. Ever now it was not done any study about the effect of solar radiation estimation methods on the results of Hargreaves-Samani ETo equation in Shahrekord plain. In the present study, the effect of 8 radiation estimation models on estimating ETo by Hargreaves-Samani model based on lysimeter measurements was investigated. This study was carried out in the agricultural station of Shahrekord in Charmahal-va-Bakhtiari province in Karoon basin, Iran. The experimental site is characterized with cold semi humid climate, altitude 2070 m above sea level, mean annual air temperature equals to 12.02 oC and average annual precipitation of 321.5 mm (Mahdavi et al, 2011). The present study was done by using a volumetric lysimeter planted with alfalfa in the Agricultural Research Center of Charmahal-va-Bakhtiari province in 2011. The date of the beginning and end of the measurement was on 21 April and 22 October 2011, respectively. In the next of the lysimeter, a drainage water measuring hole was constructed. The time of irrigation was based on 50% of maximum allowable depletion (MAD) of soil moisture and this amount was applied based on measuring the soil moisture in depth of 180 centimeters of the lysimeter. Meteorological data used in this study were collected from the weather station of Agricultural Research Center of Shahrekord in 2011. The used meteorological variables were maximum, minimum and average daily air temperature, solar radiation (RS), precipitation, wind speed, soil temperature, dew point temperature, sunshine hours, relative humidity, cloudiness ratio and evaporation from pan. The amount of Rs was calculated from 8 estimation models including Hargreaves-Samani (HS), Doorenbos- Pruitt, Annandale et al, Allen, Ertekin-Yaldiz, Samani, Goodin et al and Mahmood-Hubbard. Then ETo calculated from HS equation based on the Rs resulted from the radiation models was compared with ETo measured by lysimeter. For evaluating model accuracy, some indices include mean bias error (MBE), root mean square error (RMSE), relative error (RE), coefficient of determination (R2), index of agreement (d) and model efficiency (ME) were used.The results showed that the Hargreaves-Samani equation had underestimation based on more Rs models. With respect to differences related to lysimeter data, the Goodin et al and Ertekin-Yaldiz models had the poorest results. Also ETo calculated from Mahmood-Hubbard and Annandale et al models had the best results with 8% and 10% underestimation, respectively. With respect to all the evaluation criteria, it can be concluded that the Mahmood-Hubbard is the best solar radiation model with respect to effecting on calculating ETo in the climate of the present study and the Annandale et al model is in the next order. Also the Goodin et al and Samani are the poorest models in the solar radiation estimation. The Mahmood-Hubbard model generally has more physically base and obtained under various conditions and 9 years data. For this reason, probably the model had the best results in calculating ETo. Also the Annandale et al model takes into account the effect of elevation and reduced thickness of the atmosphere on the primary Hargreaves-Samani model and because the area of study (Shahrekord plain) is a region of high elevation, therefore the Annandale et al model has been a proper model in second order in estimating ETo. The poor result of the Goodin et al model was probably because of obtaining the coefficients of this model under limited conditions.

Climate change via variations in temperature pattern، precipitation and other climate variables patterns affects all the hydrologic processes. One of the main consequences of the climate change is its impacts on agricultural water use، which can cause serious challenges for water resources management. The purpose of this research was to investigate the global warming impacts on reference evapotranspiration، precipitation deficit (PD) and vapor pressure deficit (VPD) in Urmia synoptic station. To achieve this goal، using the HadCM3 global circulation model and LARS-WG5 weather generator، the minimum and maximum temperature and precipitation values for 2011-2030، 2046-2065 and 2080-2099 periods based on A1B، A2 and B1 scenarios were simulated. Then، using the forecasted parameters، the reference evapotranspiration، PD and VPD were calculated and compared with the corresponding base data (1971-2010). The results show that in each of the three periods the precipitation in the spring will be decreased but the autumn precipitation will be increased in comparison with the base period. Meanwhile، the maximum event of the precipitation will be shifted from the spring season in the base period to the autumn season in 2090. Moreover، similar to air temperature، the reference evapotranspiration، PD and VPD will be increased in 2090 and such increment will be more in the warm months than the cold months. The amounts of these parameters’ increments in the A2 scenario are more than those in the A1B scenario and also in the A1B scenario are more than those in the B1 scenario.

Evapotranspiration as an important element of hydrologic cycle plays a crucial role in the assessment of watershed balance. To calculate plant water requirements، first reference evapotranspiration (ET0) must be computed، then on this basis this calculation can be applied for any other plants. In the present study the precise assessment of daily reference evapotranspiration was carried out in Bonab station by standard FAO-Penman-Monteith then the combination of daily climatic parameters such as average، minimum and maximum of air temperature، average، minimum and maximum of relative humidity، rainfall، wind speed and sunlight hours were all considered as an input of Multi-Layered-Perceptrons Feed Forward Back Propagation Neural Networks and M5 model tree were both applied to achieve better results. It can be inferred that though the Neural Network approach may render more exact result than M5 model tree، however، M5 model tree provides a more understandable، applicable and simple linear relation in predicting evapotranspiration.

Potential evapotranspiration is one of basic parameter in hydrologic cycle that should be estimate in irrigation design and scheduling, watershed hydrology studies. Many applications in diverse disciplines require estimates of evapotranspiration (ET) at hourly or smaller time steps. The primary objectives of this study were to compare the American Society of Civil Engineers (ASCE) and FAO-56 Penman–Monteith equations for hourly ET0 (ET0,hourly,ASCE and ET0, hourly,FAO) estimations for semiarid climate conditions and to compare the 24 h sum of ASCE (ET0,24 h,ASCE) and FAO-56 hourly ET0 (ET0,24 h,FAO) with the daily ET0 (ET0,d,FAO) computed from the daily FAO-56 equation, which is identical to ASCE daily ET0 equation. 278-days, i.e., 2008–2009 continuous hourly and daily weather data from the automated internet weather station where placed in private Farm in fariman khorasan razavi province were used. It was evident that during the day, ET0,hourly,ASCE was higher than ET0, hourly, FAO due to a lower surface resistance parameter value, while at night ET0, hourly,ASCE was lower than ET0, hourly,FAO due to a higher surface resistance parameter value. The ET0, hourly,FAO was about 18% less than ET0, hourly,ASCE and ET0,24 h,FAO was about 14% lower than ET0,24 h,ASCE. The difference between ET0, hourly,ASCE and ET0, hourly,FAO during the day and night was highly dependent on wind speed. For the entire year, ET0,24 h,FAO was 2.6% higher than ET0,d,FAO while ET0,24 h,ASCE was 17% higher than ET0,d,FAO. These results demonstrated that for applications that require hourly time steps or daily ET0 for the entire year, the use of ET0, hourly, FAO and ET0,24 h,FAO, respectively, will yield more consistent outcomes.

Accurate estimation of crop water requirement is the basic step for designing and scheduling of irrigation and drainage projects. Evapotranspiration (ET) is a stochastic variable, therefore to reduce the risk level, it is needed to select and use proper evapotranspiration probability levels. In this research, 24 years meteorology data of Orumieh synoptic station have been used to estimate ET0. Also for investigation the effect of ET0 calculation methods on its value, FAO Penman-Monteith (FP-M) and Hargreaves-Samani (H-S) models were selected. Daily ET0 distribution curves with different probability levels for each method were extracted. Differences between maximum daily ET0 values (calculated by FP-M method) with 75% and 50% probability level was 1.0 mm/day (equivalent to 13%) and between 90% and 50% was 1.9 mm/day (24%). Similarly for H-S method the difference was 0.5 and 0.9 mm/day (8% and 15%), respectively. Also at a particular probability level, there was noticeable difference between values of FP-M and H-S methods. In order to understand the impacts of calculation period length on mean daily ET0 value, daily ET0 with 99% probability, and moving average of daily ET0 were calculated for 1, 3, 5, 7, 10, 15, 20, 25 and 30 day perrids. After determination of occurrence date of average maximum daily ET0 for the above periods, average of maximum daily ET0 with different probability levels for the periods of 1 to 30 days were determined. The occurrence of peak dates within 1 to 20 day periods, for FP-M method was 2 weeks after H-S method but for 25 and 30 day periods the dates were the same. The results showed that the peak mean daily ET0 of FP-M method for 5 day periods at 50% and 75% probability levels were 7.8 and 8.7 mm/day (12% difference) and for H-S method they were 6.1 and 6.6 mm/day (8% difference).

Reference evapotranspiration (ETo) is an essential parameter required for proper management of agricultural crop irrigation. ETo is influenced by many different hydrological variables and as a result is a very complex procces. ETo is usually estimated by empirical or process-orinented models (mathematical relationships) from historical weather data. The need for accurate estimates of ETo and the complexity of developing models to describe such complex process magnifies the need for developing new data mining methods. In this paper, the possibility of using a combined method of multiple linear regressions with principal componenets analysis (MLR-PCA) for estimating reference evapotranaspiration was investigated. In this analysis, measured daily meteorological data of Kerman synoptic weather station recorded from 1996 to 2005 were used. Three principal componenets that explained 80% of the total variance of the data were recognized as the principle componenets and others as disorder. Using the extracted principle componenets, a multiple linear regression model was developed to estimate ETo. The statistic index of t for assessing the results of a fixed constant and each componenets of PC1 and PC2 were determined. According to the results, all coefficients were significant at the level of 95% and PC1 had more importance than the other component namely PC2. This revealed that the variables of radiation intensity, relative humidity, sunshine hours, minimum temperature and maximum temperature had more importance in estimating reference evapotranspiration than other climatological parameters. Comparison of MLR-PCA model with Penman-Monteith results showed that about 82% of the total amount of the ETo variance is defined by the three aformenstioned principle componenets.

Climate variability empowers critical consequences on sustainability of soil and water resources. In this paper the trend of annual and seasonal time scale of temperature (minimum, maximum, average), relative humidity (minimum, maximum, average), precipitation, wind speed, extreme events, cloudiness, reference evapotranspiration employing Mann-kendall and least square errors were studied. These parameters showed direct or indirect effect on climate variability in northeast of Iran. The results, for example in Mashhad station, showed an increasing trend in temperature, decreasing trend in humidity and no trend in precipitation. In addition, there were an increasing trend in the number of clear days (no cloud) and a decreasing trend in number of cloudy days across all study stations but Mashhad.

In this study, the Penman-Monteith methods proposed by the Food and Agriculture Organization (FAO-56) and American Society of Civil Engineers (ASCE) were used for hourly ETo estimation under the semiarid climate of Kerman, Iran. Hourly ETo estimations obtained from the proposed methods were compared with measured ETo values by using a large weighing electronic lysimeter during April to September 2005 (totally 3352 hourly ETo data cases). Simple linear regression and statistical factors such as root mean square error and index of agreement were used for estimated and observed value comparison. The average of measured and estimated hourly ETo values using these methods for integrated data were 0.28 and 0.23 mm hr-1, respectively, which means that average estimated ETo values were approximately 21 percent less than the measured ETo values. This analysis was also performed for hourly data of each month during the study period. The results showed that FAO-56 Penman-Monteith underestimated ETo values by 18.4, 19.3, 26.3, 20.4, 21.4 and 22.1 percent for April to September, respectively, when compared with the measured values. Similarly, the ASCE Penman-Monteith underestimated ETo values by 17, 19.6, 18.4, 18.2, 19.7 and 20.9 percent for the same period, respectively, when compared with the lysimetric data. Finally, a set of regression equation for transformation of the estimated hourly data into the measured hourly ETo values has been presented for each month.

Under different climatic conditions of Iran, the evaluation of evapotranspiration (ETo) models sensitivity to meteorological parameters, prior to introducing the superior performance model, seems quite necessary. Using a 35-year (1971-2005) climatological observations in Hamedan, this study compares the sensitivity of different commonly used evapotranspiration models to different meteorological parameters within the IPCC recommended variability range of 10 to 20% during the growing season (April-October). The radiation and temperature-based ETo models include: Penman-Monteith -FAO56 [PMF56], Jensen-Haise [JH1,2], Humid Turc [TH], Arid (semi) arid Turc [TA], Makkink [MK], Hansen [HN], and Hargreaves-Samani [HS]. Results indicate that all the above-mentioned ETo models show the highest sensitivity to radiation and temperature parameters. This implies that special care is required when we apply model-generated radiation and albedo parameters in such ETo models. It is predicted that by 2050, as a result of global warming, the cold semi-arid climates of Iran will cause an average evapotranspiration rise of about 8.5% in crop reference during the growing season.