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The United Nations Food and Agricultural Organization (FAO) approved the Hargreaves‐Samani formula (HAR‐85) as an alternative to the standard Penman‐Monteith method (FAO‐PM) for estimating grass reference evapotranspiration (ETo). With much less data demand, HAR‐85 is unequivocally useful where meteorological variables are often scarce, incomplete or unavailable. Herein, we evaluate HAR‐85 against FAO‐PM across 2.505 million km2, representing Sudan and South Sudan and encompassing wide hydroclimate domains including the Nile River. We further propose simple year‐round and seasonal adjustment models to correcting HAR‐85 across the entire study area. The models express HAR‐85's error in multiple linear regressions in terms of latitude, longitude, altitude and/or monthly rainfall. Varying data periods, including odd, even and all years, are used in the evaluation and the adjustment models development and validation processes to investigate the influence of changing data period. A suit of eight performance indicators shows dependency of the original bias of HAR‐85 on the geographical location, monthly rainfall amount, season of the year and data period. All error indicators amplify southward from the hyper‐arid region to the dry sub‐humid zone. For example, the mean bias error (MBE) ranges from −0.51 to 1.29 mm/day, respectively. Study area‐wide, HAR‐85 least represents FAO‐PM during the hottest month and the transitional month (between the wet and dry‐cool seasons) with MBE of 0.65 and 0.70 mm/day, respectively. Conversely, it represents FAO‐PM the most in the wettest month, with smallest MBE of 0.32 mm/day. Beholding this spatiotemporal trait, the final yearly and seasonal adjustment models developed herein enormously moderate the predominant overestimation of the original HAR‐85. The former model explains 46.7% of the error variance whereas 36.9% to 62.3% of the variation in the error is explainable by the latter models. These adjustment models narrow the monthly MBE among the stations from −0.71‐2.17 to −0.80‐1.20 and −0.65‐0.99 mm/day, respectively. Without undermining the accuracy, the year‐round adjustment model can still be feasibly recommended for general use across the study area. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration has great importance in irrigation scheduling. Moreover, the lack of availability of lysimetric data has led researchers to use indirect methods, including data-driven approaches. In the present study, the ability of Gaussian process regression (GPR), support vector regression (SVR) and random forest (RF) data-driven methods was investigated to estimate the evapotranspiration of the reference plant. For this purpose, meteorological data on average temperature, wind speed, relative humidity and sunny hours in the period 2013-18 were collected in nine northern stations of Iran including Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr, Sari, Turkmen port, Gorgan, and Gonbad Kavous. Evapotranspiration calculated using FAO-Penman-Montith method was considered as the target output and four combined scenarios of meteorological parameters were considered to calibrate and validate the studied methods. The accuracy of the mentioned methods was compared using the statistical parameters of correlation coefficient, scatter index, and Wilmott’s coefficient. The results showed that GPR4 model with scatter index in the range of 0.132 to 0.179 in Astara, Bandar Anzali, Rasht, Ramsar, Nowshahr and Sari stations, SVR4 model with dispersion index of 0.116 to 0.120 in Turkmen and Gonbad Kavous stations and the Hargreaves-Samani method with a scatter index of 0.509 at Gorgan station had much more accurate estimates of the evapotranspiration of the reference plant.

The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.

Long-term and reliable gridded estimates of potential evapotranspiration (PET) are often dearth. Being the longest available dataset with no validation, this work makes an effort to answer two questions: First, how well does the Climatic Research Unit (CRU) Time-Series (TS) Version 4.05 PET (CRU-PET) dataset capture FAO Penman-Monteith grass reference evapotranspiration (ET o) across a range of environments and, second, can a simple adjustment approach be devised to curb the error, if any? The CRU-PET dataset is evaluated against ET o in two data-scarce countries, namely Sudan and South Sudan. Monthly station data measured at 12 stations spread over hyper-arid, arid, semi-arid and dry sub-humid zones are used. It is shown generally that CRU-PET tends to perform better in dry than wet conditions, and in arid than in humid locations. In this regard, CRU-PET has a tendency to overestimate (underestimate) the stations' values in the arid (humid) zone, with best performance characteristics achieved north of latitude 15° N. Rainfall plays a key role in determining the bias and has a non-linear effect. This bias is, however, unclear in rainless months when both augmented overestimates and underestimates are observed. Incorporating geographical coordinates (latitude, longitude and altitude) as co-variables with monthly rainfall in a multiple linear regression explained 40.7% of the variations in the bias. In this data-scarce study area (>2.5 million km2), this simple adjustment significantly improves the stations' CRU-PET as indicated by eight performance metrics. Furthermore, validation analysis showed a reduction of the overall mean bias error based on the 12 stations from −0.255 to 0.086 mm/day. To apply this correction method on a spatial domain, gridded precipitation data are needed. We used the Global Precipitation Climatology Centre (GPCC 8) dataset, with which 41.9% of the CRU-PET bias is explained. In conclusion, this study cautions the use of CRU-PET dataset without prior evaluation in areas with similar geographical boundaries, climatic conditions and limited availability of data. [Display omitted] • We evaluate PET from CRU TS4.05 (CRU-PET) across diverse environments in Sudan and South Sudan. • Monthly CRU-PET shows underestimation (overestimation) in wetter (drier) localities and seasons. • Adjustment regression formula incorporating monthly rainfall, latitude, longitude and altitude is developed. • The simplicity of the adjustment approach opens venues for application elsewhere. • Previous results obtained using uncorrected CRU-PET should be treated with caution. [ABSTRACT FROM AUTHOR]

FAO Penman-Monteith (FAO-PM) is deemed as a sole standard method for estimating reference evapotranspiration (ET ). However, limited availability of meteorological data at spatial and temporal o scales restricts the application of this method. To address this issue, the FAO 56 experts suggested three methods when only maximum and minimum temperature data are available: (i) Temperature-based Penman-Monteith (PMT-1) method wherein T ≈ T (ii) PMT-2 wherein T ≈ T -2.5, and (iii) dew min dew min Hargreaves method. These ET methods were assessed for a semi-arid basin of Western India which lacks adequate climatic data. The performances of the ET methods were evaluated against the standard FAO-PM method using salient statistical and graphical indicators, together with the sensitivity analysis. The results of the three temperature-based methods had a tendency of over-predication of ET in the study area. The PMT-1 method, however, provided superior ET estimates compared to PMT-2 and Hargreaves methods. For estimating monthly ET , the FAO-PM method was most sensitive to temperature. Further, ET of the monsoon season over the study area increased from 5 to 12% during 'drought' years compared to 'normal' years. It was concluded that PMT-1 method is the most suitable temperature-based method for estimating ET in semi-arid regions under limited climatic condition.

FAO Penman-Monteith (FAO-PM) is deemed as a sole standard method for estimating reference evapotranspiration (ETo). However, limited availability of meteorological data at spatial and temporal scales restricts the application of this method. To address this issue, the FAO 56 experts suggested three methods when only maximum and minimum temperature data are available: (i) Temperature-based Penman-Monteith (PMT-1) method wherein Tdew ≈ Tmin (ii) PMT-2 wherein Tdew ≈ Tmin-2.5, and (iii) Hargreaves method. These ETo methods were assessed for a semi-arid basin of Western India which lacks adequate climatic data. The performances of the ETo methods were evaluated against the standard FAO-PM method using salient statistical and graphical indicators, together with the sensitivity analysis. The results of the three temperature-based methods had a tendency of over-predication of ETo in the study area. The PMT-1 method, however, provided superior ETo estimates compared to PMT-2 and Hargreaves methods. For estimating monthly ETo, the FAO-PM method was most sensitive to temperature. Further, ETo of the monsoon season over the study area increased from 5 to 12% during 'drought' years compared to 'normal' years. It was concluded that PMT-1 method is the most suitable temperature-based method for estimating ETo in semi-arid regions under limited climatic condition. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration is a significant agrometeorological parameter used for estimation of crop water requirement and irrigation scheduling. The present study was undertaken to determine the reference evapotranspiration and crop water requirement for apple cultivation in the Kashmir valley. Reference evapotranspiration (ET0) was determined for seven major apple producing districts of Kashmir valley, viz. Srinagar, Budgam, Kupwara, Pulwama, Baramulla, Anantnag and Shopian. The average ET0 for apples cultivation in Kashmir Valley was 912 mm. The mean water requirement (ETc) was minimum during the initial stage being 69 mm and maximum during the mid-season stage being 668 mm. The mean water requirement during the late-season stage was 175 mm. The minimum annual ETc was observed at Baramulla (846 mm) and the maximum annual ETc at Srinagar (953 mm). Different stations showed variations in water requirement due to differences in altitude and local weather conditions.

Agricultural expansion, population growth, rapid urbanization, and climate change have all significantly impacted global water supply and demand and have led to a number of negative consequences including ecological degradation and decreases in biodiversity, especially in arid and semi-arid areas. The agricultural sector consumes the most water globally; crop irrigation alone uses up more than 80% of available agricultural water. Thus, to maintain sustainable development of the global economy and ecosystems, it is crucial to effectively manage crop irrigation water. We focus on the arid and semi-arid Heihe River Basin (HRB), China, as a case study in this paper, extracting spatiotemporal information on the distribution of crop planting using multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) remote sensing (RS) images. We estimate the spatiotemporal crop irrigation water requirements (IWRc) using the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method and reveal variations in IWRc. We also analyze the impact of changes in crop planting structure on IWRc and discuss strategies for the rational allocation of irrigation water as well as policies to alleviate imbalance between water supply and demand. The results of this study show that effective rainfall (ER) decreases upstream-to-downstream within the HRB, while crop evapotranspiration under standard conditions (ETc) increases, leading to increasing spatial variation in IWRc from zero up to 150 mm and between 300 and 450 mm. Data show that between 2007 and 2012, annual mean ER decreased from 139.49 to 106.29 mm, while annual mean ETc increased from 483.87 to 500.38 mm, and annual mean IWRc increased from 339.95 to 370.11 mm. Data show that monthly mean IWRc initially increased before decreasing in concert with crop growth. The largest values for this index were recorded during the month of June; results show that IWRc for May and June decreased by 8.14 and 11.67 mm, respectively, while values for July increased by 5.75 mm between 2007 and 2012. These variations have helped to ease the temporal imbalance between water supply and demand. Mean IWRc values for oilseed rape, corn, barley, and other crops all increased over the study period, from 208.43, 349.35, 229.26, and 352.85 mm, respectively, in 2007, to 241.81, 393.10, 251.17, and 378.86 mm, respectively, in 2012. At the same time, the mean IWRc of wheat decreased from 281.53 mm in 2007 to 266.69 mm in 2012. Mainly because of changes in planting structure, the total IWRc for the HRB in 2012 reached 2692.58 × 106 m³, an increase of 332.16 × 106 m³ (14.07%) compared to 2007. Data show that 23.11% (76.77 × 106 m³) of this increase is due to crop transfers, while the remaining 76.89% (255.39 × 106 m³) is the result of the rapid expansion of cultivated land. Thus, to maintain both the sustainable development and ecological security of the HRB, it is crucial to efficiently manage and utilize agricultural water in light of spatiotemporal patterns in IWRc changes as well as IWRc variations between different crops. The cultivation of water-demanding crops and the further expansion of agricultural land should also be avoided. [ABSTRACT FROM AUTHOR]

Abstract Evapotranspiration (ET) is an important component of the surface energy balance and water cycle, especially in arid and semiarid regions. The characteristics of the actual evapotranspiration (ETa), which was calculated using the eddy covariance method, and the reference evapotranspiration (ET0), which was estimated using the Food and Agriculture Organisation (FAO) Penman–Monteith method, were analysed. This work focussed on the seasonal variations in evapotranspiration and crop coefficient (Kc) above the heterogeneous canopy of an arid oasis ecosystem in a cornfield of the Zhangye oasis in northwestern China. The results showed that in 2008, the total net radiation (Rn) was 2457.73 MJ∙m−2 and that the rainfall was 117 mm. The average wind velocity, air temperature, and specific humidity, which were observed 2 m above the ground surface, were 1.23 m∙s−1, 7.07 °C, and 3.66 g∙kg−1, respectively. The total ETa and ET0 were 654.69 mm and 1039.92 mm, respectively; thus, the ET0 was higher than the ETa. The difference between the ET0 and ETa was high in summer and autumn, and low in winter and spring. The ETa was greatly influenced by irrigation events, whereas the ET0 was not influenced by irrigation. The ETa and ET0 were both greatly influenced by meteorological elements. The Kc values were less than 0.5 outside of the maize-growing stage and greater than 0.5 during the entire maize-growing stage (from 20 April to 22 September 2008). The Kc values were 0.63, 0.75, 0.78, 0.76, 0.61 and 0.71 at the seedling, shooting, heading, filling, and maturity stages and the entire growth stage, respectively. [ABSTRACT FROM AUTHOR]

The reference evapotranspiration (ETo) is an important agrometeorological parameter which has been used in a number of applications. In present study daily ETo was determined for 16 stations of Gujarat having long period (10-20 years) weather data following P-M approach. The Kc values for maize and wheat as given in FAO-56 was used in which Kcmid and Kcend were corrected for climatic conditions of stations. The corrected Kc values were used to calculate the daily crop water requirement (ETc) for wheat and rabi maize crops grown at different locations of Gujarat. The results revealed that during winter season (Nov. 15 to March 13) the mean daily (ETo) varies from 4.2 to 7.6 mm day-1. However the large variation in ETo across the locations (2.9 to 9.8 mm day-1) was observed, the lowest being at Khedbrahma and highest being at Targadia. The correction applied in Kcmid and Kcend suggested that at most of the stations Kcmid and Kcend for wheat crop were higher than that of FAO values while the corrected Kc values for maize were found to be less than that given by FAO. The mean water requirement (ETc) of wheat during its initial stage was found to be lower and almost constant and it increased continuously during developmental stage (from 1.9 to 5.2 mm day-1) and during the mid season stage (from 5.6 to 7.5 mm day-1) and decreased during the late-season stage (from 7.3 to 3.6 mm day-1). The seasonal water requirement across the locations varies between 400.5 mm (Khedbrahma) to 684.0 mm (Arnej). The mean water requirement of maize during initial stage is 1.3 mm day-1, during developmental stage 1.4 to 5.0 mm day-1, during the mid season stage ETc varies between 5.0 to 6.6 mm day-1 and during lateseason stage it decreases from 6.4 to 2.5 mm day-1 . The seasonal water requirement of rabi maize varies between 330.7 mm (Khedbrahma) to 520.5 mm (Bharuch).

This study analyzed the correlations between the meteorological/substrate-moisture variables and the evapotranspiration (ET) to improve the understanding of the factors influencing the thermal performance of green roofs, including cooling the surface and interior temperature of the building, in a subtropical climate. Daily ET of the green roof had been estimated using the FAO Penman-Monteith method to increase the number of ET data and the Nash-Sutcliffe coefficient is 0.51–0.62 compared to the observed daily ET from data of green roof experiments in Taipei during December 2012 and September 2013. Based on correlation analysis, this study then suggests that ET may have a higher influence on T diff , i.e. the difference in surface temperatures of green and original roofs, than the meteorological variables or substrate water content (SWC), although the radiative heating is sometimes more influential to T diff than the cooling caused by ET of the green roof in thin substrate condition. This study also found that under wet and dry substrates, daily ET is highly correlated with weather-related variables and with SWC, respectively. Furthermore, because of the low insulation of wet substrate, more downward fluxes are sometimes found under high than low SWC conditions. On the contrary, under the dry substrate, increasing SWC helps reduce the downward fluxes. This study, therefore, indicates that an optimal range of substrate moisture appears to maximize the function of a green roof in reducing the surface temperature and the downward substrate-bottom flux. Considering that a counterbalance exists between SWC and ET as well as irrigation can be applied to control SWC, future studies could be aimed at the optimal range of SWC to execute informed irrigation concerning the thermal performance, water-saving irrigation, and storm-water-retention function of the green roofs either in dry period or before storm events. • FAO Penman-Monteith method has fair performance in estimating the daily evapotranspiration of green roofs. • Evapotranspiration is more essential in green-roof-surface cooling than the meteorological factors and substrate moisture. • The meteorology (substrate moisture) is highly correlated with daily evapotranspiration under the wet (dry) substrate. • The optimal range of substrate moisture helps green roofs to reducing the surface temperature and the downward heat flux. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration is a significant agrometeorological parameter used for estimation of crop water requirement and irrigation scheduling. The present study was undertaken to determine the reference evapotranspiration and crop water requirement for apple cultivation in the Kashmir valley. Reference evapotranspiration (ET0) was determined for seven major apple producing districts of Kashmir valley, viz. Srinagar, Budgam, Kupwara, Pulwama, Baramulla, Anantnag and Shopian. The average ET0 for apples cultivation in Kashmir Valley was 912 mm. The mean water requirement (ETc) was minimum during the initial stage being 69 mm and maximum during the mid-season stage being 668 mm. The mean water requirement during the late-season stage was 175 mm. The minimum annual ETc was observed at Baramulla (846 mm) and the maximum annual ETc at Srinagar (953 mm). Different stations showed variations in water requirement due to differences in altitude and local weather conditions. [ABSTRACT FROM AUTHOR]

Abstract: Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g. the Penman–Monteith benchmark model, are extremely non-linear and require weather input data that are not routinely monitored. In such situations, artificial intelligence (AI), neural computing techniques that are able to accurately map complex, non-linear input-output relationships offer a useful alternative. This paper has used the Kohonen Self-Organizing Map (SOM), unsupervised artificial neural networks, to develop prediction models for the ETo. This was achieved by using the powerful clustering capability of the SOM to analyze the multi-dimensional data array comprising the estimated ETo (based on the FAO Penman–Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the SOM-based ETo estimates, even when forced with fewer input data variables, were in good agreement with those obtained using the conventional FAO Penman–Monteith formulation employing the full complement of weather data. Further comparisons were carried out between the SOM model estimates of the ETo and those based on the use of feed-forward back propagation supervised artificial neural networks and the results showed that the SOM estimates were superior. Finally, the SOM-based estimates were also found to be significantly superior to those estimated using established empirical ETo methods recommended in the literature for situations where the full complement of input weather needed to drive the Penman–Monteith model are unavailable. This offers significant potential for more accurate estimation of the ETo in data scarce regions of the world. [Copyright &y& Elsevier]

Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman-Monteith benchmark model, are time-consuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self-organizing map (KSOM), unsupervised artificial neural networks, to predict the ETo. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ETo (based on the Food and Agricultural Organization (FAO) Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM-based ETo estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM-based estimates were also found to be significantly superior to those estimated using currently recommended empirical ETo methods for data scarce situations such as those in developing countries. [ABSTRACT FROM AUTHOR]