Hargreaves Model Research Articles

Potential dynamic of irrigation water requirement for rice across Northeast China

Sufficient water is essential for maintaining rice production yields, but precipitation and ground water generally do not meet the requirements for rice growth. Irrigation is therefore necessary and the quantity of irrigation water requirement (IWR) is also highly dependent on climatic alterations. We utilized an ensemble of 20 fine-resolution downscaled global climate models to characterize the future dynamics of IWR across Northeast China, under two representative concentration pathway scenarios (RCP4.5 and RCP8.5). Crop evapotranspiration was a critical factor in IWR determinations and was estimated through the Hargreaves model. The model was recalibrated to optimize its performance and this resulted in normalized root mean squared errors of < 10%. Based on reliable crop evapotranspiration and effective precipitation data in baseline (1976–2005) and future periods (2036–2065 and 2070–2099), IWR decreased from southwestern Heilongjiang and western Jilin to the southeastern and northeastern areas. The IWR displayed a general increasing trend but overall the tendency decreased from west to east. The western areas were exposed to higher magnitudes of IWR increases, indicating that the water deficit for rice would be more severe in these regions. IWR levels increased with time slice under RCP8.5 relative to RCP4.5. The predicted IWR changes in future periods were greatest for Heilongjiang, followed by Jilin and Liaoning. In addition, Heilongjiang was predicted to have the most stable IWR in the future. These predictions of IWR dynamics highlight sensitive areas prone to water deficits and can serve as guides for specific irrigation schedules in the different rice growing regions across Northeast China.

Estimating future daily pan evaporation for Qatar using the Hargreaves model and statistically downscaled global climate model projections under RCP climate change scenarios

Evaporation plays a significant role in the hydrologic cycle, and thus the estimation of evaporative demand is crucial for water resources management and irrigation scheduling. Evaporation is subjected to the effects of global climate change, which can be quantified by the representative concentration pathway (RCP) scenarios. However, forecasts of future daily pan evaporation under RCP climate change scenarios using a temperature-based pan evaporation model and downscaled temperature forecasts for an arid region (e.g., Qatar) have rarely been reported in the literature. The present study provides estimations of future daily pan evaporation in Qatar under four RCP climate change scenarios for the period January 1, 2018 to December 31, 2050. Modelled maximum and minimum daily temperature data from a general circulation model (GCM) within the Coupled Model Intercomparison Project Phase 5 (CMIP5) experimental framework are extracted and downscaled using the Quantiles-matching approach. A pan evaporation model coupling the Hargreaves potential evapotranspiration model and a Class A pan coefficient model is calibrated against observed pan evaporation data and utilized to estimate future daily pan evaporation in the study area using downscaled daily temperature. Various measures of fit are employed to evaluate the performance of the downscaling approach and pan evaporation model, and the results show that the performance of the downscaling approach and pan evaporation method are both satisfactory. The study also finds that the pan evaporation increased fastest under the RCP60 scenario, and the average increase in pan evaporation was roughly 0.01095 mm/day per year. The study provides datasets for future pan evaporation for the period between 2018 and 2050 that can be useful for the development of numerical hydrologic models, management of water resources, and irrigation scheduling.

Potential dry/wet dynamic in China under RCP scenarios

Dry and wet division is one of the most basic contents in climate classifications. In order to explore regional potential features, 20 global climate models (GCMs) were statistically downscaled to reproduce temperature and precipitation at a resolution of 0.25° × 0.25° across China, which illustrated agreeable performance in comparison with observation. Taking temperatures as inputs, the Hargreaves model was implemented to estimate potential evapotranspiration (PET). This model was typically recalibrated to keep accuracy in further usage, which resulted in normalized root mean squared error being less than 5%. The indicator defined by the ratio of annual precipitation to annual PET, namely dry/wet index (IDW), was projected in potential dynamic of dry/wet division. IDW was consistently expected in an increasing trend under RCP4.5 relative to the baseline period of 1986–2005. Regards RCP8.5, IDW was diagnosed in a decreasing trend in northern Xinjiang and most central-southern regions, but an increasing trend in most northern regions, implying dry tendency in current wet condition in southern parts while wet tendency in dry condition in northern parts. The possible contribution of precipitation and PET could unveil regional differential change in IDW. It was highlighted that the area exposed to extreme dry, and dry was likely to decrease, but the exposure to wet and extreme wet tended to increase in the future. These can provide a better knowledge of potential change of climate and water resource, supporting adaptive strategies in response to climate change.

Comparison of reference evapotranspiration models for the agro-ecological zones of Nigeria

Irrigation practices are best done by estimating the crop water requirement in order to avoid over or under irrigation which may negatively affect crop yields. In this study, weather data (2004-2015) were collected and analyzed. The weather data include; minimum and maximum temperature (°C), relative humidity (%), wind speed (km/day), sunshine (hr/day) and radiation (MJ/m2/day). FAO Penman-Monteith model is a universal standard model used with other five evapotranspiration models such as; Priestley-Taylor model, Thornth-Waite model, Hargreaves model, ASCE-Penman Monteith model and Blaney-Criddle model to compute the mean monthly reference evapotranspiration (ET°) for the six agro-ecological zones of Nigeria. Statistic regressions were performed to examine the relationship of the reference ET° estimates from the five models with the estimates by FAO Penman-Monteith model. The results of the analyses show that the mean monthly average ETo estimates by the FAO Penman-Monteith model, Priestley-Taylor model, Thornth-Waite model, Hargreaves model, ASCE-Penman Monteith model and Blaney-Criddle model across the six weather stations are; 6.48, 7.66, 14.14, 11.16, 5.57, and 3.70 mm/day, respectively. The best predictor is the ASCE-Penman Monteith model which correlated well with the FAO Penman-Monteith model while, the Priestley-Taylor model is the second-best model. Thornth-Waite model and Hargreaves model produced underestimated ET values while, Blaney-Criddle model greatly over-estimated the FAO Penman-Monteith model. Therefore, this study is useful to the precise agricultural water management and regional water resources planning.

Study of Different Evapotranspiration Models under Polyhouse and Open Environment in Tarai Region of Uttarakhand

The average of mean monthly ET0 estimated under polyhouse by FAO PM (benchmark) model was 39.44 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 38.37, 18.18, 37.80, 48.17 and 53.87 mm respectively. Whereas, the average of mean monthly ETo estimated under open environment by FAO PM (benchmark) model was 116.34 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 119.33, 133, 126.41, 113.17 and 117.37 mm respectively. The FAO Penman and Hargreaves model are found to be most and least appropriate models for estimating daily ET0 under polyhouse. Whereas, FAO Radiation and Stanghellini model observed to be most and the least appropriate models in an open environment for estimating daily ET0 under polyhouse for the Pantnagar Tarai condition of Uttarakhand.

Estimation of Global Solar Radiation from Temperature Extremes: A Case Study of Hebron City, Palestine

Solar radiation is the main energy source for mankind and an accurate data of solar radiation levels for a particular location is vital for the optimum operation of solar energy transducers such as photovoltaic cells and solar thermal collectors. This study aimed to calibrate some of the existing models in the literature for estimating daily global solar radiation parameter using available measured records of air temperature extremes and new models were developed based on maximum and minimum air temperatures. Applicability of the Hargreaves model, Allen model, Bristow-Campbell model and Chen et al. model were evaluated for computing the global solar radiation for Hebron city in Palestine. Estimated values were compared with measured values in terms of the coefficient of determination (R 2 ) and root mean square error (RMSE). All models provide good estimates when compared to the accurate values with R 2 0.9226 (Bristow-Campbell model) to 0.9547 (Chen et al. model), while the proposed model provides a value of 0.9632. The RMSE value ranges from 0.7632 for Chen et al. model to 0.9211 for Bristow-Campbell model, however a lower value (0.7118) for the proposed model. This study found that the proposed model estimates global solar radiation at the location of study better than the other models.

Estimating reference crop evapotranspiration with elevation based on an improved HS model

Abstract Estimation of ET0 in high-elevation (above sea level, ASL) areas of Tibet presents unique challenges: scarcity of monitoring stations, short-time coverage of meteorological data, low-oxygen and low-pressure environment, and strong solar radiation. In this study, altitude factors and modified temperature constants are utilized to improve the Hargreaves (HS) model based on 30-year daily meteorological data from 20 typical sites in Tibet. The improved model, Hargreaves-Elevation (HS-E) improved model, could provide better results at different time scales. Negative ET0 values were unavoidable in the HS model when applied to high-elevation areas. However, the HS-E model solved this problem and improved the accuracy of estimated ET0. In particular, the HS-E model performs better when the time scale becomes larger. Therefore, the HS-E model is highly recommended to estimate ET0 in high-elevation areas where the meteorological data are scarce, for example, in Tibet above 2,000 m.

An inexact irrigation water allocation optimization model under future climate change

Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers.

Efecto de la subdivisión de cuencas y la estimación de variables climáticas en la simulación de componentes de balance hídrico en cuencas semiáridas mediterráneas

Los resultados de cualquier intento de modelización hidrológica dependen de la calidad y discretización de los datos de entrada. En este trabajo se evalúa cómo la utilización de diferentes capas de variables climáticas de entrada (precipitación y ETP) y diferentes esquemas de discretización de la cuenca influyen en los resultados de un modelo hidrológico bien conocido (SWAT). En concreto se prueban 4 conjuntos de variables de entrada y discretización. Los estadísticos utilizados para evaluar la exactitud obtenida con estos conjuntos son RMSE, PBIAS, NSE y r2. Los resultados muestran que el uso de mejores modelos para obtener series de ETP (Penman Monteith FAO o Hargreaves calibrado) mejora considerablemente la exactitud del modelo respecto a las series obtenidas con el modelo de Hargreaves sin calibrar (opción por defecto en SWAT). Sin embargo, no se aprecian diferencias entre los resultados obtenidos con Penman Monteith FAO y Hargreaves calibrado. Por otro lado, el uso de información climática distribuida mejora notablemente los resultados obtenidos con información agregada. Se observa también la necesidad de calibrar los parámetros de SWAT ya que los valores por defecto están optimizados para ambientes templados de EEUU. The results of hydrological modelling depend on the quality and spatial resolution of the input data. This paper evaluates how the use of different estimations of climatic variables input layers (precipitation and ET P ) and different basin discretization schemes influence affect the results of a contrasted hydrological model (SWAT). Specifically, 4 se ts of input and discretization variables are tested. The statistics used to evaluate the accuracy obtained with these sets are RMSE, PBIAS, NSE and r 2 . The results show that the use of better models to obtain ET P series (Penman Monteith FAO or calibrated Hargreaves) considerably improves the accuracy of the model compared to the series obtained with the uncalibrated Hargreaves model (default option in SWAT). However, there is no difference between the results obtained with Penman Monteith FAO and calibrate d Hargreaves. On the other hand, the use of distributed climate information significantly improves the results obtained with aggregated information. There is also a need to calibrate SWAT parameters as the default values are optimized for temperate environ ments in the USA.

Predictive Performance of Hargreaves Model in Estimating Solar Radiation in Yola, Adamawa State Nigeria Using Minimum Climatological Data

Mathematical models have been very useful in reducing challenges encountered by researchers due to the inability of having solar radiation data or lack of instrumental sites at every point on the Earth. This work aimed at investigating the prediction performance of Hargreaves-Samani’s model in estimating global solar radiation (GSR) out of the many other empirical models so far formulated for this purpose. This model basically uses maximum and minimum temperature data and basically used in mid-latitudes. The paper attempts to assess the predictive performance of Hargreaves-Samani’s model in the Savanna region using Yola as a case study. Estimated values of GSR from one month data adopted from the Meteorological station of the Department of Geography, Federal University of Technology, Yola, Nigeria was used for this purpose. Using this model shows a 95% index of agreement (IA) with the observed values; which suggests a good model performance and can also be used in estimating global solar radiation in the Savanna region particularly in areas with little or no such climatic data.

Measurement Uncertainty Analysis of Temperature Based Solar Radiation Estimation Models

Solar radiation is the main energy source for activities in the earth. It is important that the solar radiation values are known accurately. In cases where parameters about solar radiation cannot be measured, solar radiation estimation models are used. These are mathematical functions derived from the measured meteorological parameters. In this study, temperature-based estimation models that commonly used in the literature were examined, and uncertainty analysis of the models were applied. These solar radiation estimation models are Allen model, Hargreaves model, Chen model and Bristow–Campbell model. These models calculate the total global solar radiation with the difference between the maximum and minimum air temperatures. Measurement uncertainty budgets of the models and an example calculation can be found in the study.

Enhanced Delivery of Galanin Conjugates to the Brain through Bioengineering of the Anti-Transferrin Receptor Antibody OX26.

The blood-brain barrier (BBB) is a formidable obstacle for brain delivery of therapeutic antibodies. However, antibodies against the transferrin receptor (TfR), enriched in brain endothelial cells, have been developed as delivery carriers of therapeutic cargoes into the brain via a receptor-mediated transcytosis pathway. In vitro and in vivo studies demonstrated that either a low-affinity or monovalent binding of these antibodies to the TfR improves their release on the abluminal side of the BBB and target engagement in brain parenchyma. However, these studies have been performed with mouse-selective TfR antibodies that recognize different TfR epitopes and have varied binding characteristics. In this study, we evaluated serum pharmacokinetics and brain and CSF exposure of the rat TfR-binding antibody OX26 affinity variants, having KDs of 5 nM, 76 nM, 108 nM, and 174 nM, all binding the same epitope in bivalent format. Pharmacodynamic responses were tested in the Hargreaves chronic pain model after conjugation of OX26 affinity variants with the analgesic and antiepileptic peptide, galanin. OX26 variants with affinities of 76 nM and 108 nM showed enhanced brain and cerebrospinal fluid(CSF) exposure and higher potency in the Hargreaves model, compared to a 5 nM affinity variant; lowering affinity to 174 nM resulted in prolonged serum pharmacokinetics, but reduced brain and CSF exposure. The study demonstrates that binding affinity optimization of TfR-binding antibodies could improve their brain and CSF exposure even in the absence of monovalent TfR engagement.

Models for forecasting monthly mean daily global solar radiation from in-situ measurements: Application in Tropical Climate, India

Radiation availability varies with reference to the geometrical location. It is necessary to develop the models to predict the daily global solar radiation (GSR) for the locations where there is a huge potential of radiation exists and to improve the utilization of energy. This study aimed to develop the solar radiation models with readily available input parameters for Tiruchirappalli, which covers an annual mean daily global solar radiation of 4.50kWh/m2-day.Exponential model (EM), modified exponential model (MEM), power series model (PSM), Hargreaves and Samani model (HSM), Hargreaves-model (HGM) and Hunt models (HUM) which uses air temperature and extra-terrestrial radiation on a horizontal surface as input parameters were developed and validated. Dumas model which uses air temperature and maximum possible sunshine duration (So) as input parameters was also verified. Dumas model was modified as hybrid models to check for their applicability. The performance of the models was analyzed by using the statistical tools. Out of all the models, HSM and PSM had shown an accurate estimation of daily GSR (errors: 6.62% & 7.95% respectively) for the selected location. Dumas model's performance was also found satisfactory (error: 11.02%).

A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico

This work presents a modification to the model proposed by Hargreaves and Samani, based on daily temperature difference, to estimate daily global solar radiation. This is done by analyzing the relation between daily temperature increments and the fitting parameter in the model. Six different climates and four day types have been considered. Day classification was done in accordance to the daily clearness index. The proposed model was validated on a database that comprises 47 stations distributed in Mexico.The proposed methodology to estimate daily solar global radiation is compared against the original Hargreaves model, using fixed reported values for the fitting parameter. The performance of the proposed model is done based on four metrics commonly used. The proposed model reduces the overall errors and bias from the original model, and thus its validity on different climate types is extended. It was obtained a reduction of the RMSE of at least 15% and up to 30%, depending on climate, when compared with the Hargreaves-Samani model. Obtained RMSE are below the mean RMSE reported in literature. Similar improvements were observed with other metrics. In addition, average daily clearness index for each station and their distribution are presented for all stations.

Comparison Model Hargreaves, Annandale and New Model for Estimation of Solar Radiation in Perlis, Malaysia

&lt;p&gt;Sunlight is a potential source of electrical energy in the universe. Utilization of solar radiation is not optimal to be a source of electricity generation. Before harnessed solar radiation in order to know in advance the estimated potential energy. Modelling estimates of solar radiation in three models: models Hargreaves, Annandale and the proposed new model. The new model proposed solar radiation estimates based on models Hargreaves and Annandale. Some models for estimating solar radiation is discussed in this paper which aims to determine the potential of solar radiation into electrical energy potential. A new model is proposed to estimate solar radiation. Comparison of the three models are estimated using statistical analysis e (%), CRM, and RMSE is obtained that the proposed new model is better, when compared to other models.&lt;/p&gt;

Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data

Accurate estimation of reference evapotranspiration (ET0) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET0 as benchmark. Two data management scenarios were evaluated for estimation of ET0: (1) the models were trained/calibrated and tested using the local data of each station; and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267mm/d and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling.

Performance Assessment Of Hargreaves Model In Estimating Global Solar Radiation In Sokoto, Nigeria.

This paper presents, an estimation of daily global solar radiation in Sokoto (latitude and longitude)for the year 2009 using Hargreaves model. This estimation was based on latitude and daily minimum and maximum temperature in Sokoto State. The measured and estimated solar radiation data were compared forand analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), coefficient of determination (R2 and percentage error (e). The results showed that the value of CRM is 0.04, which indicates the tendency of the estimation model to under-estimate the measure solar radiation. Meanwhile, the value of RMSE was 1.63% and the value of R2 is 0.8260 which is very closed to 1, indicating that about 82.60% of the total variation is explained in the data. For the percentage error, the value was -3.16%, which indicates that the model estimation is good. The model has shown itself to be a robust and reasonably accurate method for estimating global solar radiation in Sokoto andits environment. However, considering the uniqueness of climatological data in each month of a year, it is recommended that the model should be subjected to further validation involving months of data running over five years.

Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100

The spatiotemporal change and trend of annual potential evapotranspiration (PET) over the Loess Plateau of China from 2011 to 2100 are assessed in this work. PET is calculated using the Hargreaves model with monthly mean, maximum, and minimum temperatures and a 1km spatial resolution, which are generated using the Delta downscaling method and general circulation models (GCMs) with four representative concentration pathway (RCP) scenarios. The PET trend is detected via Mann–Kendall and Sen’s slope estimator tests. The following results are drawn: (1) the Delta downscaling method shows a favorable performance in detecting GCM monthly temperatures based on the mean absolute error and regression analysis between downscaled data and independent surface observations. Among the 28 GCMs, the NorESM1-M and GISS-E2-R models show the best performance in reproducing the monthly mean/maximum and minimum temperatures over the Loess Plateau, respectively; (2) the average annual PET over this region will increase by 12.7%–23.9% from 1961 to 1990 to the end of this century (2071–2100). However, these increments show strong spatial variations; (3) the annual PET during the 2011–2100 period at each grid of the region demonstrates a significantly increasing trend under each RCP scenario, while RCP2.6, RCP4.5, RCP6.0, and RCP8.5 have average magnitudes of trend of 10.4mm/10yr, 17.7mm/10yr, 21mm/10yr, and 29.7mm/10yr, respectively; (4) the annual PET with significant trends during the other three periods (2011–2040, 2041–2070, and 2071–2100) present various spatial distributions in their magnitudes of trend under the aforementioned RCP scenarios. RCP2.6 showed a significant decrease during 2041–2070 and 2071–2100, although such trends are only observed at 0.3% and 1.2% of the Loess Plateau, respectively; and (5) the spatial results provide some information, such as locations and area ratios, which are valuable in assessing future PET changes and trends. These spatiotemporal results represent the PET changes and trends in detail and provide insights for developing flexible adaptation and mitigation strategies to combat the effects of global warming in this region.

Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China

Accurate estimation of reference evapotranspiration is vital to hydrological and ecological processes. The FAO-56 Penman–Monteith (PM) model has the higher accuracy for ET0 estimation, but it requires many meteorological inputs that are not commonly available. Therefore an ideal method is needed requiring as minimal as possible input data variables without affecting the accuracy of estimation. The temperature-based models are especially interesting due to its input data, air temperature, can be monitored easily and is one of the commonly available climatic inputs. Among the temperature-based models, the Hargreaves (HG) model requiring maximum and minimum air temperature as the inputs is considered as one of the simplest and accurate ET0 methods, but this method needed a local calibration. The present study calibrated the HG model using Bayesian theory at 19 meteorological stations in Sichuan basin of southwest China. Meteorological data during 1961–1990 were used for the calibration and data during 1991–2014 were used for the validation. The results confirmed that the locally calibrated HG model (with average RRMSE, MAE and NS of 0.284, 0.433mm/d and 0.783) performed better than the original HG model (with average RRMSE, MAE and NS of 0.567, 0.959mm/d and 0.134). Both of the calibrated and original HG models overestimated ET0 at daily, monthly and annual timescale, but the calibrated HG model provided closer average values with PM ET0, which could confirm the good performances of the calibrated HG model. Therefore, the calibrated HG model could be highly recommended for estimating ET0 when only temperature data are available.

Comparative study of reference evapotranspiration estimation methods including Artificial Neural Network for dry sub-humid agro-ecological region

In the present study, an attempt has been made to compare the reference evapotranspiration (ETo), computed by eight different methods, namely, Penman-Monteith, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas, ANN and FAO(24) model for the dry subhumid agro-ecological region (Varanasi). An attempt was also made to find out utility of artificial neural networks (ANN) for estimation of ET0 with minimum input. Feed forward network has been used for prediction of ETo using resilient back-propagation method and the architecture 2–21(having parameters Tmean and solar radiation) was found to be the best one. The average annual evapotranspiration (by Penman-Monteith method) for Varanasi was found to be 1447.4 mm. When compared among the different methods for estimation of reference evapotranspiration with Penman-Monteith method, the FAO-24 and Hargraves-Samani (3) under estimate, however, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas over-estimate and ANN closely estimates reference evapotranspiration.