Hargreaves Models Research Articles

Parametric calibration of Hargreaves–Samani (HS) reference evapotranspiration equation with different coefficient combinations under the humid environment

Evapotranspiration (ET) is a major hydrological component in irrigation system design, irrigation scheduling, and hydrologic and drainage design. Estimating crop ET requires accurate reference evapotranspiration (ET0) measurement. In this work, numerous modified Hargreaves models for ET0 were tested and calibrated against the standard FAO-56 Penman-Monteith eq. (F-56 PM) for further improvement in performance in the humid subtropical environment of Imphal West District, Manipur.The Martinez-Cob and Tejero-Juste (2004) equation resulted in best region-wide ET0 values of 3.536 mm d−1, mean absolute error (MAE) = 0.367 mm d−1, standard error of estimate (SEE) = 0.348 mm d−1, correlation coefficient (r) = 0.855, coefficient of determination (R2) =0.731, and index of agreement (d) = 0.891. Trajkovic (2007) equation has the second-best ET0 values of 3.383, MAE = 0.413 mm d−1, SEE = 0.3.77 mm d−1, r = 0.827, R2 = 0.685, and d = 0.852. The third-best equation, Ravazzani et al. (2012), with average ET0 values of 4.015 mm d−1, MAE = 0.506 mm d−1, SEE = 0.395 mm d−1, r = 0.855, R2 = 0.731, and d = 0.79.The observed wind speed data were sorted into five groups based on the average wind speed in different months in the region (1 m/s,2.5 m/s, 3.5 m/s, 4.5 m/s, and 5.5 m/s), and the estimated evapotranspiration was used to calibrate 14 selected combinations of the Hargreaves models. The calibration process significantly improved the performance of the different identified Hargreaves models.The analysis indicated that the improvement in estimation decreases with increasing wind speed. The revised coefficient predicted evapotranspiration is closer to F-56PM, with MAE (mm d−1) ranging from 0.125 at 1 m/s to 0.248 at 4.5 m/s and SEE(mm d−1) from 0.16 at 1 m/s to 0.245 at 5.5 m/s, as compared to the original Hargreaves equation, whose MAE is 0.543, and SEE is 0.400.More research must be conducted to extend the applicability of this equation to other regions of the State.

Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China.

The Universal Thermal Climate Index (UTCI) is believed to be a very powerful tool for providing information on human thermal perception in the domain of public health, but the solar radiation as an input variable is difficult to access. Thus, this study aimed to explore the optimal strategy on estimation of solar radiation to increase the accuracy in UTCI calculation, and to identify the spatial and temporal variation in UTCI over China. With daily meteorological data collected in 35 tourism cities in China from 1961 to 2020, two sunshine-based Angstrom and Ogelman models, and two temperature-based Bristow and Hargreaves models, together with neural network and support vector machine-learning methods, were tested against radiation measurements. The results indicated that temperature-based models performed the worst with the lowest NSE and highest RMSE. The machine-learning methods performed better in calibration, but the predictive ability decreased significantly in validation due to big data requirements. In contrast, the sunshine-based Angstrom model performed best with high NSE (Nash–Sutcliffe Efficiency) of 0.84 and low RMSE (Root Mean Square Error) of 35.4 J/m2 s in validation, which resulted in a small RMSE of about 1.2 °C in UTCI calculation. Thus, Angstrom model was selected as the optimal strategy on radiation estimation for UTCI calculation over China. The spatial distribution of UTCI showed that days under no thermal stress were high in tourism cities in central China within a range from 135 to 225 days, while the largest values occurred in Kunming and Lijiang in southwest China. In addition, days under no thermal stress during a year have decreased in most tourism cities of China, which could be attributed to the asymmetric changes in significant decrease in frost days and slightly increase in hot days. However, days under no thermal stress in summer time have indeed decreased, accompanying with increasing days under strong stress, especially in the developed regions such as Yangze River Delta and Zhujiang River Delta. Based on the study, we conclude that UTCI can successfully depict the overall spatial distribution and temporal change of the thermal environments in the tourism cities over China, and can be recommend as an efficient index in the operational services for assessing and predicting thermal perception for public health. However, extreme cold and heat stress in the tourism cities of China were not revealed by UTCI due to mismatch of the daily UTCI with category at hourly scale, which makes it an urgent task to redefine category at daily scale in the next research work.

Evaluation of 18 models for calculating potential evapotranspiration in different climatic zones of China

The accurate calculation of potential evapotranspiration (PET) is a critical step in researching evapotranspiration, hydrology and many other fields. Numerous models have been developed to quantify PET from standard meteorological observations, and combination methods are usually considered the most physically reasonable. However, adequate observations of meteorological variables for combination methods are unavailable in many locations, making it necessary to select alternative PET models with fewer data requirements. Here, a set of 18 models including four aerodynamic methods, five temperature-based methods, six radiation-based methods, and three combination methods were evaluated using meteorological measurements from 789 stations in four climatic zones in China: the mountain plateau zone (MPZ), temperate monsoon zone (TMZ), temperate continental zone (TCZ), and subtropical monsoon zone (SMZ). The annual PET calculated in each of the four climatic zones showed large discrepancies among the 18 models, and the largest disparity nationwide was 2.95-fold. The combination models performed best for calculating PET in all four climatic zones, followed by the radiation-based models, and both categories outperformed the aerodynamic and temperature-based methods. The Rohwer model was the only recommended aerodynamic method, and the Romanenko model was the only recommended temperature-based method for calculating PET in China. The Turc model was marginally the best radiation-based model in the SMZ, TMZ and TCZ, and the Hargreaves model in the MPZ, but both should be applied with caution in cold months. The Penman model was the recommended combination method in all four zones. Further comparison of the best models from each category showed that the Rohwer model might overestimate PET in the TMZ and TCZ, and underestimate it in the MPZ and SMZ. The Romanenko model overestimated PET, and the Turc and Hargreaves models both underestimated PET in all four zones, especially in the MPZ. The empirical coefficients of the five recommended models were regional calibrated to meet the requirements of PET calculation in different climatic zones.

Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

Applicability evaluation of different algorithms for daily reference evapotranspiration model in KBE system

The irrigation decision-making system based on knowledge-based engineering (KBE) is reported in this paper, and the basis of the KBE was knowledge of reference crop evapotranspiration (ET0). Therefore, the research examined the accuracy of the support vector machines (SVMs) in the model of ET0. In the first part of the study, the SVMs were compared with FAO-24, Hargreaves, McCloud, Priestley-Taylor and Makkink models. The results indicated that the SVMs performed better than other models. In the second part, the total ET0 estimation of the SVMs was compared with the other models in the validation. It was found that the SVMs models were superior to the others in terms of relative error. The further assessment of SVMs was conducted, and confirmed that the models could provide a powerful tool in KBE irrigation with a lack of meteorological data. It could provide a reference for accurate ET0 estimation in KBE irrigation systems.

Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China

Summary Reference evapotranspiration (ET 0 ) is an essential component in hydrological ecological processes and agricultural water management. Accurate estimation of ET 0 is of importance in improving irrigation efficiency, water reuse and irrigation scheduling. FAO-56 Penman–Monteith (P–M) model is recommended as the standard model to estimate ET 0 . Nevertheless, its application is limited due to the lack of required meteorological data. In this study, trained extreme learning machine (ELM), backpropagation neural networks optimized by genetic algorithm (GANN) and wavelet neural networks (WNN) models were developed to estimate ET 0 , and the performances of ELM, GANN, WNN, two temperature-based (Hargreaves and modified Hargreaves) and three radiation-based (Makkink, Priestley–Taylor and Ritchie) ET 0 models in estimating ET 0 were evaluated in a humid area of Southwest China. Results indicated that among the new proposed models, ELM and GANN models were much better than WNN model, and the temperature-based ELM and GANN models had better performance than Hargreaves and modified Hargreaves models, radiation-based ELM and GANN models had higher precision than Makkink, Priestley–Taylor and Ritchie models. Both of radiation-based ELM (RMSE ranging 0.312–0.332 mm d −1 , E ns ranging 0.918–0.931, MAE ranging 0.260–0.300 mm d −1 ) and GANN models (RMSE ranging 0.300–0.333 mm d −1 , E ns ranging 0.916–0.941, MAE ranging 0.2580–0.303 mm d −1 ) could estimate ET 0 at an acceptable accuracy level, and are highly recommended for estimating ET 0 without adequate meteorological data.

Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN)

Nonlinear complexity is a characteristic of hydrologic processes. Using fewer model parameters is recommended to reduce error. This study investigates, and compares, the ability of gene expression programming (GEP) and artificial neural network (ANN) techniques in modeling ET0 by using fewer meteorological parameters in the karst area of northwest Guangxi province, China. Over a 5-year period (2008–2012), meteorological data consisting of maximum and minimum air temperature, relative humidity, wind speed, and sunshine duration were collected from four weather stations: BaiSe, DuAn, HeChi, and RongAn. The ET0 calculated by the FAO-56 PM equation was used as a reference to evaluate results for GEP, ANN, and Hargreaves models. The coefficient of determination (R 2) and the root mean square error (RMSE) were used as statistical indicators. Evaluations revealed that GEP, and ANN, can be used to successfully model ET0. In most cases, when using the same input variables, ANN models were superior to GEP. We then established ET0 equations with fewer parameters under various conditions. GEP can produce simple explicit mathematical formulations which are easier to use than the ANN models.

A decision support system (GesCoN) for managing fertigation in open field vegetable crops. Part I-methodological approach and description of the software.

Reduced water availability and environmental pollution caused by nitrogen (N) losses have increased the need for rational management of irrigation and N fertilization in horticultural systems. Decision support systems (DSS) could be powerful tools to assist farmers to improve irrigation and N fertilization efficiency. Currently, fertilization by drip irrigation system (fertigation) is used for many vegetable crops around the world. The paper illustrates the theoretical basis, the methodological approach and the structure of a DSS called GesCoN for fertigation management in open field vegetable crops. The DSS is based on daily water and N balance, considering the water lost by evapotranspiration (ET) and the N content in the aerial part of the crop (N uptake) as subtraction and the availability of water and N in the wet soil volume most effected by roots as the positive part. For the water balance, reference ET can be estimated using the Penman–Monteith (PM) or the Priestley–Taylor and Hargreaves models, specifically calibrated under local conditions. Both single or dual Kc approach can be used to calculate crop ET. Rain runoff and deep percolation are considered to calculate the effective rainfall. The soil volume most affected by the roots, the wet soil under emitters and their interactions are modeled. Crop growth is modeled by a non-linear logistic function on the basis of thermal time, but the model takes into account thermal and water stresses and allows an in-season calibration through a dynamic adaptation of the growth rate to the specific genetic and environmental conditions. N crop demand is related to DM accumulation by the N critical curve. N mineralization from soil organic matter is daily estimated. The DSS helps users to evaluate the daily amount of water and N fertilizer that has to be applied in order to fulfill the water and N-crop requirements to achieve the maximum potential yield, while reducing the risk of nitrate outflows.

Evaluation of the FAO Penman–Montheith, Priestley–Taylor and Hargreaves models for estimating reference evapotranspiration in southern Malawi

This study evaluated the performance of the Food and Agriculture Organization (FAO) Penman–Monteith (PM) reference evapotranspiration (ET0) method for various limited data scenarios in southern Malawi. The study further evaluated the full data PM method against the radiation-based Priestley–Taylor (PT) and the temperature-based Hargreaves (HAG) methods, which are less data-intensive approaches commonly used to estimate ET0 in data-scarce situations. A comprehensive daily climate dataset observed at the Nchalo Sugar Estate in southern Malawi for the period 1971–2007 was the basis of the study. The results suggested that lack of data on wind speed and actual vapour pressure did not significantly affect the PM ET0 estimates. However, the estimation of radiation using various combinations of observed wind speed and relative humidity all resulted in significant deviations from the PM ET0. Further, the HAG and PT methods significantly underestimated the PM. However, the PM method computed with estimated climate variables instead of observed climate variables still outperformed both the PT and HAG methods if their original parameters and estimated radiation were used. Thus, new monthly parameters for the PT and the HAG methods are proposed for more accurate daily ET0 estimates.

Regional Estimation of Reference Evapotranspiration in Arid and Semiarid Regions

Evapotranspiration is critical to many applications including water resource management, irrigation scheduling, and environmental studies. Many models based on meteorological data have already been developed to estimate reference evapotranspiration ( ET0 ) in various climatic and geographical conditions. The main purpose of this study was to evaluate the performances of the Makkink, Priestley-Taylor, and Hargreaves models versus the Penman-Monteith FAO-56 (PMF-56) method in arid and semiarid regions of Iran during 1993–2005 and to identify the alternative ET0 model that presents results closest to the PMF-56 method. Additionally, a regional estimation of monthly ET0 with the best-performed model is presented by using the spatially distributed physical parameters and geographical information system. The results indicated that the Hargreaves model was the best model to estimate ET0 in eastern arid and semiarid regions of Iran. The spatial distribution maps of ET0 showed that ET0 values increased from north ...

Evaluation of Reference Crop Evapotranspiration Equations in Various Climates

Evapotranspiration is one of the most important elements for quantifying available water since it generally constitutes the largest component of the terrestrial water cycle. This study evaluated four models (Makkink, Turc, Priestley–Taylor and Hargreaves) commonly used to estimate monthly reference crop evapotranspiration (ETo) values. The main aim of this study was to determine the model used to estimate ETo with small data requirements and high accuracy for twelve synoptic stations in four climates of Iran. The results showed that the Turc model was the best suited model in estimating ETo for cold humid and arid climates. The Hargreaves model turned out to be the most precise model under warm humid and semi-arid climatic conditions. In contrast, the Makkink model presented the poorest estimates in all of the climates exception for cold humid environment. In cold humid climate, the Hargreaves model was the least accurate model in estimating ETo. In general, the results obtained from this study revealed very clearly that the Makkink and Priestley–Taylor models estimated ETo values less accurately than Turc and Hargreaves models for the all climates.

Evaluation of temperature-based global solar radiation models in China

Estimation of global solar radiation ( R s ) from the daily range of air temperature (Δ T) offers an important alternative in the absence of measured R s or sunshine duration because of the wide availability of air temperature data. In this paper, we assessed 16 R s models including modified versions of the Bristow and Campbell (B–C) and the Hargreaves (Harg) models across a wide range of agro-ecological conditions in China. Using long-term data from 15 sites in Northeast, North China Plain and Northwest China, we explored the main factors affecting model parameters and the predictive accuracy and proposed empirical equations to estimate these parameters. Two schemes in calculating Δ T were employed: Δ T 1 (based on single day T min) used in the Harg and Δ T 2 (based on 2-day average of T min) used in the B–C model. Results showed that the original B–C model performed similarly to the best performing modified Harg model, but significantly outperformed the original Harg model with a 4–7% higher accuracy. The common practice of fixing some parameters in the B–C model caused the most significant effect and resulted in a 3–9% lower accuracy than that of the original model. In contrast, modifications had the smallest effect and yielded little improvement and are thus unnecessary. The Δ T scheme had a moderate effect, with Δ T 1 generally resulting in a higher accuracy especially in high altitude areas. This indicates that the effect of cold or warm air advection is negligible in the B–C model even in a temperate climate. The accuracy of the temperature-based models was affected mainly by the magnitude of Δ T, with larger Δ T resulting in higher accuracy. Parameters of the B–C model correlated significantly with many commonly used geographical and meteorological factors, meaning that they are more easily obtainable without the need for calibration and consequently R s measurement. The parameter of the original Harg model correlated insignificantly with all the examined factors, meaning that it has to be calibrated. Main findings in this study are valuable in clarifying the relative impact of different approaches in applications on model accuracy, in demonstrating the advection effect, in identifying the dominant factors of model parameters and thus in increasing their availability.

Estimation of reference evapotranspiration and crop coefficient in wheat under semi-arid environment in India

The comparison of six standard reference evapotranspiration (ETo) estimate models was carried out with measured lysimeter evapotranspiration (ETc) in wheat crop in a semi-arid environment at Rahuri, India. The globally accepted reference evapotranspiration model of FAO 56 Penman Monteith underestimated the references ETo by 19.2% (420.5 mm) over lysimeter ET (520.7 mm). Out of the remaining five models, the Hargreaves model ranked first and was overestimated by 6.5% followed by the Blaney Criddle model (underestimated by −5.6%). The references ETo by pan evaporation model underestimated the reference ETo to the extent of −28.8%. The influence of statistical indicators like RMSE, MBE which was computed by considering Lysimeter ETc as standard, was quite low in the Hargreaves model compared to the rest of the models and hence, the Hargreaves method is quite acceptable for ETo estimates as this method requires much less climatic parameters (temperature and extraterrestrial radiation) than Penman Monteith (FAO56) and Modified Penman of FAO 24 as these models require aerodynamic and radiation terms, besides additional physical terms in former models of ETo estimates. The seasonal crop coefficients were 1.24, 1.13, 0.94, 0.85, 1.06 and 1.40 in Penman Monteith, Modified Penman, Hargreaves, Radiation balance, Blaney Criddle and Pan Evaporation models, respectively.

Assessment of evapotranspiration estimation models for use in semi-arid environments

Limited information exists on reliable estimates of evapotranspiration (ETo), to be used as forecasts, to achieve high irrigation water use efficiency in semi-arid environment, particularly under microirrigation (MI) system. The objectives of this study were to: (i) assess the estimates of ETo obtained using six models against experimentally determined values in a semi-arid environment, Karaj in Iran; (ii) assess the usefulness of short-term weather data in the computation of ETo estimates for forecasting purposes; and (iii) compare ETo computed for a semi-arid environment with that of a humid temperate environment, Tottori in Japan. In-field lysimeter experiments were conducted in 1993 and 1994 in Karaj to compute daily ETo from water-balance data and a similar experiment was conducted in 1972 and 1973 in Tottori. The ETo estimates were obtained using the Penman (PE), Penman–Monteith (PM), Wright–Penman (WP), Blaney–Criddle (BC), Radiation balance (RB), and Hargreaves (HG) models. The ETo forecasts for Karaj and Tottori were obtained using 5- or 8-year weather data and 1- and 2-year return period approach. Compared with the lysimeter values, over- and underestimations of ETo, by all six models, were a norm in Karaj. Nevertheless, the PM model produced the best ETo estimates as assessed by root mean square, mean bias error, and t-test statistics for the semi-arid Karaj, whereas the PE model performed best for the temperate Tottori. In general, the ETo forecasts obtained using 8-year weather data were better than 5-year weather data. The 2-year return period ETo forecasts were better than 1-year return period, regardless of the duration of weather data duration. The results show that: (i) PM model produced best ETo estimates for semi-arid environment whereas the PE model produced the best ETo estimates for humid temperate environment; and (ii) 8-year weather data and 2-year return period approach produced most reliable ETo estimates for forecasting purposes.