FAO-56 PM Research Articles

Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach

Reference evapotranspiration (ETo) is a variable of great importance for several purposes, such as hydrological studies and irrigation scheduling. The FAO-56 Penman-Monteith (FAO-56 PM) equation is recommended to estimate ETo given its good accuracy. However, the estimation of ETo poses a challenge when the availability of meteorological data is limited since the FAO-56 PM equation requires data on temperature, relative humidity, solar radiation and wind speed. This study evaluates, for the first time, the performance of alternative equations, artificial neural network (ANN) and support vector machine (SVM), for the estimation of daily ETo across the entirety of Brazil using measured data on temperature and relative humidity or only temperature. Two strategies, not yet used in Brazil, were used to develop the ANN and SVM models: (i) the definition of groups of weather stations with similar climatic characteristics, using the K-means clustering algorithm, to develop models specific for each group; and (ii) the addition of previous meteorological data as input for the models. Data from 203 weather stations distributed across Brazil were used. The ANN and SVM models showed higher performances than the equations that were studied, even when they were calibrated. The evaluated strategies (clustering and previous days) provided considerable performance gains. For the temperature-based models, the best performance was obtained by the ANN developed with the strategy of clustering and the use of data from two previous days as input; however, due to the similar performance and greater generalization capacity, the ANN developed without clustering and with the use of data from four previous days is recommended. For the temperature- and relative humidity-based models, the ANN developed with data from four previous days was the best option.

Long-term trend analysis of climatic variables and reference evapotranspiration over different urban areas in Tunisia

In this study, the trend analysis of annual climatic variables including Tmax, Tmin, Tmean, RHmean, WS and SR as well as FAO-56 PM ET0 were investigated in three locations in Tunisia during 1984-2007. The Mann-Kendall Test, the Sen's Slope Estimator and linear regression tests were used for the analysis. The obtained results showed a significant increasing trends (a < 0.001) and (a < 0.01) in annual Tmax, Tmin and Tmean at all the considered locations. However, Tmin increase faster than Tmax with a slope of magnitudes ranging between 0.057 to 0.1 oC year-1. For RHmean, a non-significant tendency of decrease was observed in Chott-Mariem station. However, significantly increasing trends were found for Kelibia and Tunis Carthage. Concerning the WS variable, a tendency of decrease is observed during the study period for all the stations. Nevertheless, the statistical analysis of decreasing tendency of wind speed varied from non-significant for Tunis Carthage to highly significant (a <= 0.001) at Chott-Mariem and Kelibia. Despite the highly significant upward trend of temperature, the temporal pattern of mean annual FAO56 PM-ET0, over the different stations, did not exhibit any significant trend except for Kelibia station.

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET0)

Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET0) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the present paper, we propose a new evolving connectionist (ECoS) approaches for modeling daily reference evapotranspiration (ET0) in the Mediterranean region of Algeria. Three ECoS models, namely, (i) the off-line dynamic evolving neural-fuzzy inference system called DEFNIS_OF, (ii) the on-line dynamic evolving neural-fuzzy inference system called DEFNIS_ON, and (iii) the evolving fuzzy neural network called (EFuNN), were statistically compared using the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of correlation (R), and the Nash-Sutcliffe efficiency (NSE) indexes. The proposed approaches were applied for modeling daily ET0 using climatic variables from two weather stations: Algiers and Skikda, Algeria. Five well-known climatic variables were selected as inputs: daily maximum and minimum air temperatures (Tmax and Tmin), daily wind speed (WS), daily relative humidity (RH), and daily sunshine hours (SH). The effect of combining several climatic variables as inputs was evaluated, and at least six scenarios were developed and compared. The proposed ECoS models were compared against the reference Penman-Monteith model referred as "FAO-56 PM". According to the results obtained, the DEFNIS_OF1 model having Tmax, Tmin, WS, RH, and SH as inputs, is the best model, followed by the DEFNIS_ON1, and the EFuNN1 is the worst model. The R and NSE value calculated for the testing dataset for the Algiers and Skikda stations were (0.954, 0.910) and (0.954, 0.905), respectively. While both DEFNIS_OF1 and DEFNIS_ON1 showed good accuracy and high performances, the EFuNN1 was less accurate.

Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy

The occurrence of water shortages ascribed to projected climate change, especially in the Mediterranean region, fosters the interest in remote sensing (RS) applications to optimize water use in agriculture. Remote sensing evapotranspiration and water demand estimation over large cultivated areas were used to manage irrigation to minimize losses during the crop growing cycle. The research aimed to explore the potential of the MultiSpectral Instrument (MSI) sensor on board Sentinel-2A to estimate crop parameters, mainly surface albedo (α) and Leaf Area Index (LAI) that influence the dynamics of potential evapotranspiration (ETp) and Irrigation Water Requirements (IWR) of processing tomato crop (Solanum lycopersicum L.). Maximum tomato ETp was calculated according to the FAO Penman-Monteith equation (FAO-56 PM) using appropriate values of canopy parameters derived by processing Sentinel-2A data in combination with daily weather information. For comparison, we used the actual crop evapotranspiration (ETa) derived from the soil water balance (SWB) module in the Environmental Policy Integrated Climate (EPIC) model and calibrated with in-situ Root Zone Soil Moisture (RZSM). The experiment was set up in a privately-owned farm located in the Tarquinia irrigation district (Central Italy) during two growing seasons, within the framework of the EU Project FATIMA (FArming Tools for external nutrient Inputs and water Management). The results showed that canopy growth, maximum evapotranspiration (ETp) and IWR were accurately inferred from satellite observations following seasonal rainfall and air temperature patterns. The net estimated IWR from satellite observations for the two-growing seasons was about 272 and 338 mm in 2016 and 2017, respectively. Such estimated requirement was lower compared with the actual amount supplied by the farmer with sprinkler and drip micro-irrigation system in both growing seasons resulting in 364 (276 mm drip micro-irrigation, and 88 mm sprinkler) and 662 (574 mm drip micro-irrigation, and 88 mm sprinkler) mm, respectively. Our findings indicated the suitability of Sentinel-2A to predict tomato water demand at field level, providing useful information for optimizing the irrigation over extended farmland.

Evaluation of the Penman–Monteith reference evapotranspiration under limited data and its sensitivity to key climatic variables under humid and semiarid conditions

The objectives of this study were to assess the accuracy of FAO Penman Monteith equation (FAO PM) under limited data conditions and to perform sensitivity analysis to determine approximately the change in reference evapotranspiration (ETref) expected for a known change in one of the independent variables and derive the sensitivity coefficient. Meteorological data were collected from 8 weather stations under humid and semiarid condition in Cote d’Ivoire. The results showed good performance of FAO PM equation under missing solar radiation (Rs) in semiarid condition and under missing wind speed data (U2) and relative humidity (RH) in all locations with coefficient of determination (R2) ranging from 0.70 to 0.99 and regression slope from 0.99 to 1.05. Under missing Rs, RMSE varied from 0.45 to 0.48 mm/day and AME from 0.30 to 0.35 mm/day. The RMSE and AME vary respectively from 0.12 to 0.51 mm/day and from 0.09 to 0.30 mm/day under missing RH data, and respectively from 0.11 to 0.60 mm/day and 0.04 to 0.34 mm/day under missing wind speed data. The poor performance of FAO PM method to estimate ETo was observed when three climatic variables were missing with regression slope from 0.93 to 1.06 and R2 from − 0.06 to 0.26. The response of ETo to changes in all climate variables was linear, with high R2 values (≥ 0.99) in most cases. Any error in Rs, maximum temperature (Tmax) data would have contributed to significant change in ETo estimate. The effect of Rs on change in ETo estimates had the greatest slope (≥ 2.80) in Bouake, Daloa, Korhogo, Man, Seguela whereas it had the lowest slope in Ferkessedougou (slope = 2.74), Odienne (slope = 2.73), Yamoussoukro (slope = 2.77). The effect of Tmax in change in ETo was also important in all location except Daloa and Man with low regression slope values of 1.63, 1.74, respectively. All sensitivity coefficients showed a large degree of daily and seasonal fluctuations and revealed significant differences in northern and central study locations. The sensitivity coefficients of U2 and Tmax were greatest under semiarid condition while the one of Rs were very high in humid condition. Accurate measuring of U2, Tmax and Rs in estimating reference evapotranspiration using Penman–Monteith equation is required.

Evaluación de dos modelos para la estimación de la evapotranspiración de referencia con datos CERES

<p>Evapotranspiration is the most important variable in the Pampas plain. Information provided by sensors onboard satellite missions allows represent the spatial and temporal variability of evapotranspiration, which cannot be achieved using only measurements of weather stations. In this work, the Priestley and Taylor (PT) and FAO Penman Monteith (FAO PM) equations were adapted to estimate the reference evapotranspiration, ET<sub>0</sub> , using only CERES satellite products (SYN1 and CldTypHist). In order to evaluate the reference evapotranspiration from CERES, a comparison with in situ measurements was conducted. We used ET data provided by the Oficina de Riesgo Agropecuario, corresponding to 24 stations placed in the Pampean Region of Argentina (2001-2016). Results showed very good agreement between the estimates with CERES products and in situ values, with errors between ±0.8 and ±1.1 mm d–<sup>1 </sup>and r<sup>2</sup> greater than 0.75 at daily scale, and errors between ±14 and ±19 mm month<sup>–1</sup> and r<sup>2</sup> greater than 0.9, at monthly scale better results were obtained with adapted model FAO PM than PT. Finally, ET<sub>0</sub> monthly maps for the Pampean Region of Argentina were elaborated, which allowed knowing the temporal-spatial variation in the validation area. In conclusion, the methods presented here are a suitable alternative to estimate the reference evapotranspiration without requiring ground measurements.</p>

Calibration and evaluation of the FAO56-Penman-Monteith, FAO24-radiation, and Priestly-Taylor reference evapotranspiration models using the spatially measured solar radiation across a large arid and semi-arid area in southern Iran

Crop evapotranspiration (ET) is one of the main components in calculating the water balance in agricultural, hydrological, environmental, and climatological studies. Solar radiation (Rs) supplies the available energy for ET, and therefore, precise measurement of Rs is required for accurate ET estimation. However, measured Rs and ET and are not available in many areas and they should be estimated indirectly by the empirical methods. The Angstrom-Prescott (AP) is the most popular method for estimating Rs in areas where there are no measured data. In addition, the locally calibrated coefficients of AP are not yet available in many locations, and instead, the default coefficients are used. In this study, we investigated different approaches for Rs and ET calculations. The daily measured Rs values in 14 stations across arid and semi-arid areas of Fars province in south of Iran were used for calibrating the coefficients of the AP model. Results revealed that the calibrated AP coefficients were very different and higher than the default values. In addition, the reference ET (ETo) was estimated by the FAO56 Penman–Monteith (FAO56 PM) and FAO24-radiation methods by using the measured Rs and were then compared with the measured pan evaporation as an indication of the potential atmospheric demand. Interestingly and unlike many previous studies, which have suggested the FAO56 PM as the standard method in calculation of ETo, the FAO24-radiation with the measured Rs showed better agreement with the mean pan evaporation. Therefore, the FAO24-radiation with the measured Rs was used as the reference method for the study area, which was also confirmed by the previous studies based on the lysimeter data. Moreover, the accuracy of calibrated Rs in the estimation of ETo by the FAO56 PM and FAO24-radiation was investigated. Results showed that the calibrated Rs improved the accuracy of the estimated ETo by the FAO24-radiation compared with the FAO24-radiation using the measured Rs as the reference method, whereas there was no improvement in the estimation of ETo by the FAO56 PM method compared with the FAO24-radiation using the measured Rs. Moreover, the empirical coefficient (α) of the Priestley and Taylor (PT) ETo estimation method was calibrated against the reference method and results indicated ca. 2 or higher α values than the recommended α = 1.26 in all stations. An empirical equation was suggested based on yearly mean relative humidity for estimation of α in the study area. Overall, this study showed that (1) the FAO24-radiation method with the either measured or calibrated Rs is more accurate than the FAO56 PM, (2) the spatially calibrated AP coefficients are very different from each other over an arid and semi-arid area and are higher than those proposed by the FAO56, (3) the original PT model is not applicable in arid and semi-arid area and substantially underestimates the ETo, and (4) the coefficient of the PT should be locally calibrated for each station over an arid and semi-arid area.

Impact of climate change on potential evapotranspiration (case study: west and NW of Iran)

Potential evapotranspiration (ET0) is one of the main elements when computing agricultural irrigation requirements and scheduling. All climatic parameters as well as ET0 are influenced by climate change. The aim of this study is trend analysis of monthly and annual ET0 time series in the west and NW of Iran. Values of ET0 are estimated at 36 selected stations, using the FAO-56 Penman-Monteith (FAO-56 PM) method. Then, the non-parametric Mann-Kendall (MK) method was used to detect trends. The slopes of trend lines are estimated using Sen’s estimator approach. Results showed that about 86%of the monthly ET0 time series had upward trends of which 35.6 and 43% were significant at 0.01 and 0.05 levels, respectively, 47.4% exhibited a significant upward trend at the 10% level. In contrast, less than 0.7% of the whole monthly ET0 time series showed a significant downward trend (α < 0.01). Only 1.1% of the monthly ET0 time series had significant downward trends (α < 0.10). The strongest positive upward trend (significant at the 0.01 level) was detected in August at the Kermanshah station. However, the strongest negative downward trend belonged to the Khodabandeh station. The steepest upward and downward monthly ET0 slopes were observed at Maragheh and Khodabandeh stations. The magnitude of trends for these two stations are estimated as 2.33 and − 2.01 mm/year, respectively. On an annual timescale, above 94% of the stations had upward trend slopes. About 67% of the total stations exhibited significant trends at the 10% level. Very few sites (2.7%) showed downward annual ET0 trends (α < 0.10). At the annual scale, the first three strongest upward trends belonged to the Kermanshah, Urmia, and Tabriz stations, respectively (α < 0.01). It can be concluded that ET0 in the west and NW of Iran have an increasing trend for most of the stations. Therefore, it is important to use water in a prudent manner in this area.

A Committee Machine with Intelligent Systems for Estimating Monthly Mean Reference Evapotranspiration in an Arid Region

The aim of this research is to estimate the reference evapotranspiration ETo as given by FAO-56 PM equation in Basrah city, southern Iraq by using several climatic inputs data including maximum monthly mean air temperature, minimum monthly mean air temperature, monthly mean relative humidity and monthly mean wind speed. Three artificial intelligent systems (generalized regression neural network GRNN, multi-layer perceptron MLP and adaptive neuro-fuzzy inference systems ANFIS) were used for predicting reference evapotranspiration. Root mean squared error and coefficient of determination were used as comparison criteria for evaluation of performance of all the developed models. The results shown that the models performances of multi-layer perceptron models are better than adaptive neuro-fuzzy inference systems models and slightly better than generalized regression neural network models with different inputs combination. A Committee Machine with Intelligent Systems (CMIS) was constructed for estimation of ETo by integrating the results of predicting ETo from GRNN, MLP and ANFIS, each of them has a weight factor representing its contribution in overall estimation. The results illustrated that the performance of committee machine with intelligent systems is better than any one of the individual artificial intelligent systems for predicting ETo.

English

The performances of five empirical models, namely: Hargreaves-Samani, Makkink1 (1957), Makkink2 (1984), Priestley-Taylor and FAO 56-PM in estimating reference evapotranspiration (REF-ET) were separately compared with Epan data and FAO 56-PM, respectively. Based on statistical analysis, Hargreaves-Samani method compared best with daily and monthly Epan data, while Makkik2 (1984) ranked first with FAO 56-PM. In terms of regression analysis, Priestley-Taylor performed best with daily FAO 56-PM method while Hargreaves-Samani ranked first with daily Epan data. Hargreaves-Samani also correlated best with mean monthly Epan data. The quantitative evaluation of cumulative daily and monthly reference-evapotranspiration (RET-ET) values showed that Makkink (1984) produced the least overestimation and percent relative error against FAO 56-PM while Hargreaves-Samani performed best with Epan data with the least overestimation and percent relative error. In terms of cumulative monthly ETo totals for the farming season (Dec-April) over the study period, Hargreaves-Samani ranked best with Epan data with the least overestimation and percent relative error while Priestley–Taylor ranked best with FAO 56-PM producing the least overestimation. Overall, Hargreaves-Samani with its original coefficient was adjudged best, capable of approximating FAO 56-PM and Epan data in the Lower Niger River Basin, followed by Makkink (1984) and Priestley-Taylor. Penman-Monteith estimates were used to develop monthly correction factors for adjusting Empirical models for their potential use in Lower Niger Basin. A comparative study such as this has not been undertaken in the Lower Niger River Basin. The models recommended in this study are economical, lesser-data demanding and can be applied to predicting REF-ET in remote agricultural areas. Key words: Reference-evapotranspiration (RET-ET), empirical models, radiation-based methods, temperature-based methods, FAO 56 –PM, Lower Niger River Basin.

Assessing the suitability of American National Aeronautics and Space Administration (NASA) agro-climatology archive to predict daily meteorological variables and reference evapotranspiration in Sicily, Italy

For decades, the importance of evapotranspiration processes has been recognized in many disciplines, including hydrologic and drainage studies, irrigation systems design and management.In this research, the suitability of the Prediction Of Worldwide Energy Resource database published by the American National Aeronautics and Space Administration (POWER-NASA), to estimate daily meteorological variables and ET0 was assessed in Sicily, Italy, for the period 2006–2014, based on ground data measured by a network of climate stations belonging to the regional Agro-meteorological Information Service (SIAS). After comparing the climate data (minimum, Tmin, maximum, Tmax, and average, Tavg, air temperature, relative air humidity, RH, global solar radiation, Rs and wind speed, u) available in both databases, a statistical comparison was carried out on ET0 values estimated according to the Penman Monteith equation in the version proposed by the Food and Agriculture Organization (FAO-56 PM).The analysis showed that correlations between air temperature and relative air humidity from both databases are affected by elevation of the weather stations and the distance from the sea. In addition, ET0 values estimated with POWER-NASA database were generally comparable to those obtained by using the SIAS records, with RMSE values ranging between 0.68 and 1.27mmd−1 and MBE varying between −0.39 and 0.73mmd−1. The greatest differences in ET0 values are due to the resolution of POWER-NASA archive (1° latitude by 1° longitude), that cannot detect the actual spatial variability observed on ground, as well as to inaccurate estimations of relative air humidity occurring for the coastal weather stations as well as of the air temperature for those inland stations characterized by high elevations. However, the achieved results support the possibility of obtaining suitable estimates of daily ET0 based on the POWER-NASA agro-climatology archive, even to other Mediterranean countries where most of the climate variables are not measured.

Generalized wavelet neural networks for evapotranspiration modeling in India

This study focuses on the application of generalized wavelet neural network (GWNN) models corresponding to the FAO-56 Penman Monteith (FAO-56 PM), Turc, and Hargreaves (HG) methods for estimating daily reference evapotranspiration (ETo). The daily pooled climate data from 15 different locations under 4 different agro-ecological regions (AERs: semi-arid, arid, sub-humid, and humid) in India are used as an input to GWNN models. The inputs include three combinations of climate data (minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation) and the target consists of the FAO-56 PM estimated ETo. Further, the GWNN models were applied to 15 individual model development locations and 10 different model testing locations to test the generalizing capability. Comparison of developed GWNN models was made with the classic generalized artificial neural network (GANN), generalized linear regression (GLR), generalized wavelet regression (GWR), and corresponding conventional methods to test the superiority of one model over the other. Results reveal that the GWNN models followed by GANN models performed better than GWR and GLR models for four AERs. The testing results suggest that the GWNN and GANN models have better generalizing capabilities than the GWR and GLR for almost all region locations.

ESTIMATIVA DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA POR PENMAN - MONTEITH FAO 56 USANDO DADOS METEOROLÓGICOS LIMITADOS EM ARACAJU, SERGIPE

The method Penman-Monteith (FAO-56 PM) is recommended as standard for estimation of reference evapotranspiration (ETo). However, this method requires a large number of input data such as solar radiation, air temperature, relative humidity and wind speed, which has limited its use in many locations. Based on this, the objective of this work was to evaluate the estimation of ETo FAO-56 PM using limited weather data in Aracaju, Sergipe State, Brazil. The study was conducted from daily data of maximum and minimum air temperatures, relative humidity, wind speed and sunshine. Were evaluated three scenarios for estimation ETo FAO-56 PM with limited data: absence of solar radiation, relative humidity and wind speed; absence of solar radiation and wind speed and absence of relative humidity and wind speed. The values of ETo estimated by FAO-56 PM with complete data and missing data were compared considered the coefficient of determination (R2), MBE (mean bias error) and RMSE (root mean square error). The best estimates of ETo FAO-56 PM was when solar radiation was available, with missing data of relative humidity (estimated from the minimum temperature) and constant values of wind speed between 1 and 3 m s-1, however only when air temperatures are available is not recommended for estimation ETo FAO PM on local weather conditions.

Performance of radiation-based reference evapotranspiration equation developed for Indian sub-humid conditions

In this study, asite-specific radiation based equation for estimating reference evapotranspiration (ETo) was developed and its performance was statistically analysed in comparison to widely accepted FAO Penman-Monteith (FAO-56 PM) model and four radiation-based ETo methods for sub-humid Hazaribagh region of Jharkhand state. The equation was developed with daily values of incoming solar radiation in conjunction with air temperature (minimum and maximum) by considering daily FAO-56 PM ET0 values as index with weather dataset of 15 years (1990-2004). The performance of developed equation validated with eight years (2005-2012) daily weather dataset revealed that it estimated ETo values better than other radiation-based methods. The respective higher and lower values of agreement index and root mean square error with FAO-56 PM ET0 values during validation period confirms efficacy of developed equation whose performance tested at another Indian sub-humid location (Pantnagar) confirmed its suitability as well. Considering the limitations associated with reliability and availability of weather data especially in developing countries, developed equation is recommended as practical one to estimate ETo in sub-humid climatic conditions if FAO-56 PM model cannot be used due to non-availability of required weather parameters at a location.

Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey

The Food and Agriculture Organization advocates the Penman–Monteith (FAO-56 PM) equation as the standard model for estimation of the reference evapotranspiration (ET0) because it is considered to have better accuracy. However, in regions where meteorological variables such as solar radiation, wind speed, and relative humidity are not gauged, the Hargreaves–Samani (HS) equation is resorted to as an alternative simply because it needs minimum and maximum air temperatures only as the explanatory variables. In this study, first the HS equation is applied to the monthly means of measured temperature data recorded at 275 meteorology stations in Turkey. Next, the coefficients of the HS equation are calibrated using the ET0 values given by the FAO-56 PM equation at all these stations. Next, the HS equation is modified by adding the wind speed as an extra explanatory variable, separately in each one of seven geographical regions of Turkey, which is observed to yield smaller error statistics as compared to the original HS equation. It is concluded that for estimation of the ET0 in regions where meteorological measurements are scarce, the HS equation modified in a similar manner can be used with better precision.

Effect of solar radiation models on evapotranspiration estimation

The crop evapotranspiration has the great effect on defining and planning of water resources. The estimation of evapotranspiration depends on various climatic parameters. In this study, the analysis of the effect of solar radiation (Rs) on daily reference evapotranspiration (ET0) was conducted. The FAO-56 Penman-Monteith method (FAO-56 PM) was used for the estimation of ET0 in Serbia at eight meteorological stations for the period 1980-2010. The Rs was estimated using the ten one-parameter global solar radiation models. The ET0 with Almorox and Hontoria model 1 had the smallest deviation related to the ET0 with Angstr?m-Prescott (AP) model, and ET0 with To?rul et al. model 2 and Rietveld model had the greatest deviation from ET0 with AP model.

Comparison of Different ET0 Formula in Tibetan Pasturing Area

It is difficult to calculate ET0 by FA056 PM by the shortage of meteorological data in Tibetan pasturing area. A simple?accurate and less of meteorological data formula is very important to calculate water demand of crop in Tibet. In this paper?FAO17 Penman formula?Priestley-Taylor formula?Hargreaves-Samani formula, Irmark-Allen(I-A) fitting formula were used to calculate the ET0 in three typical meteorological stations?Dangxiong county?Gaize county?Naqu county? in Tibetan pasturing area from 1983-2012. At the same time, taking FAO56 PM formula as the standard method. The results show that? (1) The calculation results of FAO17 PM?PT formula?HS formula, I-A fitting formula are higher than the value calculated by FAO56 PM formula because of the intense radiation, great difference in temperature and strong wind on highaltitudelocalities; (2) The calculation results by I-A fitting formula was the most closest to FAO56 PM formula and the error was relatively small(relative error?20%?coefficient of determination?0.94); (3) Because of less of meteorological data and relative accuracy, I-A fitting formula can be considered to instead of the FAO56 PM formula to calculate the ET0 in Tibetan pasturing area where the meteorological data is lack.

Comparison of Different ET0 Formula in Tibetan Pasturing Area

It is difficult to calculate ET0 by FA056 PM by the shortage of meteorological data in Tibetan pasturing area. A simple?accurate and less of meteorological data formula is very important to calculate water demand of crop in Tibet. In this paper?FAO17 Penman formula?Priestley-Taylor formula?Hargreaves-Samani formula, Irmark-Allen(I-A) fitting formula were used to calculate the ET0 in three typical meteorological stations?Dangxiong county?Gaize county?Naqu county? in Tibetan pasturing area from 1983-2012. At the same time, taking FAO56 PM formula as the standard method. The results show that? (1) The calculation results of FAO17 PM?PT formula?HS formula, I-A fitting formula are higher than the value calculated by FAO56 PM formula because of the intense radiation, great difference in temperature and strong wind on highaltitudelocalities; (2) The calculation results by I-A fitting formula was the most closest to FAO56 PM formula and the error was relatively small(relative error?20%?coefficient of determination?0.94); (3) Because of less of meteorological data and relative accuracy, I-A fitting formula can be considered to instead of the FAO56 PM formula to calculate the ET0 in Tibetan pasturing area where the meteorological data is lack.

Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages

Near-future irrigation demand forecasting is important information for anticipating decisions on crop irrigation scheduling and planning water allocation in large irrigation command areas of Texas. The key determinant that is required for estimating irrigation demand in advance is toward the evapotranspiration forecast. Normally, in rich data environment, current reference evapotranspiration (ETo) is estimated by the well-known FAO56 PM method which requires bunch of observed climatic data. In poor data environment for either current or future estimation, this well-known method application is restricted. Indeed, the correctness of ETo forecast remains a challenging computational task, since inaccurate weather variables can alter the forecast accuracy. Therefore, this study aims to employ artificial neural network (ANN) methodology for forecasting near future ETo values by using restricted climate information messages retrieved from public weather forecast source. Four ANNs learning algorithms including the Generalized Feedforward (GFF), Linear Regression (LR), Multilayer Perceptron (MLP) and Probabilistic Neural Network (PNN) are applied with three sets of inputs combination composed of minimum (Tmin) and maximum (Tmax) daily air temperatures, extraterrestrial radiation (Ra) and net solar radiation (Rs) to forecast ETo in Dallas. The coefficient of correlation (CC), mean square error (MSE), normalized mean square error (NMSE), mean absolute error (MAE) and mean square error skill score (MSESS) were used for the models evaluation. Statistically, in comparison with FAO56 PM, the performances of ANNs models using only Tmax and Tmin predictors were inferior to those of Tmax, Tmin and Ra. With Tmax, Tmin and Rs input-sets, MLP yielded the highest accuracies (CC=0.926; MSE=0.770mm/day, NMSE=0.143mm/day; MAE=0.708mm/day). Tmax is an important ETo forecast predictor, and the performance improvement relies mostly on Rs accuracy. With precise weather forecast information, ANN made ETo forecast possible (Average CC=0.860, MSESS=0.738). These results can assist irrigation districts to accommodate in advance their crop water demand to near-future irrigation requirement.

Sensitivity Analysis of FAO-56 Penman-Monteith Method for Different Agro-ecological Regions of India

This paper analyzes the sensitivity of reference evapotranspiration (ETo) to climatic variables for different agro-ecological regions of India: semi-arid (Kovilpatti and Parbhani), humid (Mohanpur), and sub-humid (Ludhiana and Ranichauri). The FAO-56 Penman-Monteith (FAO-56 PM) method is used to estimate ETo, and sensitivity of ETo has been studied in terms of change in maximum air temperature (T max ), minimum air temperature (T min ), solar radiation (R s ), average relative humidity (RH avg ), and wind speed (W s ). Sensitivity analysis is performed by increasing and decreasing the climate variables such as T max , T min , R s , and RH avg by one unit of increment and decrement, respectively, up to five units (except for W s ) while keeping the other variables and parameters constant. However, wind speed W s (km h−1) is only increased with an increment of one km h−1 up to five km h−1. The results showed that the change in ETo is linearly related to change in all climate variables (r 2 = 0.97 in most cases) at all sites. Further, ETo is most sensitive to R s at Kovilpatti, Mohanpur and Ranichauri, and to W s at Ludhiana and Parbhani. However, the sensitivity of ETo to the same variable shows considerable variation from site to site and at the same site within the year. ETo is less sensitive to RH avg followed by T min at all sites.