Single and dual crop coefficients and crop evapotranspiration for wheat and maize in a semi-arid region

In this study, weighing lysimeters were used to investigate the daily crop coefficient and evapotranspiration of wheat and maize in the Fars province, Iran. The locally calibrated Food and Agriculture Organization (FAO) Penman–Monteith equation was used to calculate the reference crop evapotranspiration (ET_o). Micro-lysimetry was used to measure soil evaporation (E). Transpiration (T) was estimated by the difference between crop evapotranspiration (ET_c) and E. The single crop coefficient (K _c) was calculated by the ratio of ET_c to ET_o. Furthermore, the dual crop coefficient is composed of the soil evaporation coefficient (K _e) and the basal crop coefficients (K _cb) calculated from the ratio of E and T to ET_o, respectively. The maximum measured evapotranspiration rate for wheat was 9.9 mm?day^?1 and for maize was 10 mm?day^?1. The total evaporation from the soil surface was about 30 % of the total wheat ET_c and 29.8 % of total maize ET_c. The single crop coefficient (K _c) values for the initial, mid-, and end-season growth stages of maize were 0.48, 1.40, and 0.31 and those of wheat were 0.77, 1.35, and 0.26, respectively. The measured K _c values for the initial and mid-season stages were different from the FAO recommended values. Therefore, the FAO standard equation for K _c-_mid was calibrated locally for wheat and maize. The K _cb values for the initial, mid-, and end-season growth stages were 0.23, 1.14, and 0.13 for wheat and 0.10, 1.07, and 0.06 for maize, respectively. Furthermore, the FAO procedure for single crop coefficient showed better predictions on a daily basis, although the dual crop coefficient method was more accurate on seasonal scale.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

Estimation of pan evaporation (E _pan) using black-box models has received a great deal of attention in developing countries where measurements of E _pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E _pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E _pan predictions (r?=?0.97, RMSE?=?0.81?mm?day^?1, MAE?=?0.63?mm?day^?1 and PE?=?0.58?%). The results also showed that the most accurate E _pan predictions with a CANFIS model can be achieved with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E _pan process.     

Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada

The accuracy of nine solar radiation (R _s ) estimation models and their effects on reference evapotranspiration (ET _o ) were evaluated using data from eight meteorological stations in Canada. The R _s estimation models were FAO recommended Angstrom-Prescott (A-P) coefficients, locally calibrated A-P coefficients, Hargreaves and Samani (H-S) (1982), Annandale et al., (2002), Allen (1995), Self-Calibrating (S-C, Allen, 1997), Samani (2000), Mahmood and Hubbard (M-H) (2002), and Bristow and Campbell (B-C) (1984). The estimated R _s values were then compared to measured R _s to check the appropriateness of these models at the study locations. Based on root mean square error (RMSE), mean bias error (MBE) and modelling efficiency (ME) ranking, calibrated A-P coefficients performed better than all other methods. The calibrated H-S method (using new K _RS 0.15) estimated R _s more accurately than FAO-56 recommended A-P in Elora, and Winnipeg. The RMSE of the calibrated H-S method ranged between 1-6% and the RMSE of the calibrated and FAO recommended Angstrom-Prescott (A-P) methods ranged between 1-9%. The models with the least accuracy at the eight locations are the Mahmood & Hubbard (2002) and Self-Calibrating models. The percent deviation in ET _o calculated with estimated R _s was reduced by about 50% as compared to deviation in measured versus estimated R _s .     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

Attribution analyses of potential evapotranspiration changes in China since the 1960s

This paper focuses on the primary causes of changes in potential evapotranspiration (ET_o) in order to comprehensively understand climate change and its impact on hydrological cycle. Based on modified Penman-Monteith model, ET_o is simulated, and its changes are attributed by analyzing the sensitivity of ET_o to influence meteorological variables together with their changes for 595 meteorological stations across China during the period 1961–2008. Results show the decreasing trends of ET_o in the whole country and in most climate regions except the cold temperate humid region in Northeast China. For China as a whole, the decreasing trend of ET_o is primarily attributed to wind speed due to its significant decreasing trend and high sensitivity. Relative humidity is the highest sensitive variable; however, it has negligible effect on ET_o for its insignificant trend. The positive contribution of temperature rising to ET_o is offset by the effect of wind speed and sunshine duration. In addition, primary causes to ET_o changes are varied for differing climate regions. ET_o changes are attributed to decreased wind speed in most climate regions mainly distributed in West China and North China, to declined sunshine duration in subtropical and tropical humid regions in South China, and to increased maximum temperature in the cold temperate humid region.     

Performance evaluation of modified versions of Hargreaves equation across a wide range of Iranian climates

Reference evapotranspiration (ET_o) is significant for water resources planning and environmental studies. Many equations have been developed for ET_o estimation in various geographic and climatic conditions, of which, the Penman–Monteith FAO 56 (PMF-56) equation was accepted as reference method. A major complication in estimating ET_o by the PMF-56 model is the requirement for meteorological data that may not be readily available from typical weather stations in many areas of the globe. This restriction necessitates use of simpler models which require less input data. In this study, the original and five modified versions of the Hargreaves equation that require only temperature and rainfall were evaluated in humid, semi-humid, semi-arid and arid climates in Iran. The results showed that the original and modified versions of the Hargreaves equation had the poorest performance in semi-humid climate and the best performance in windy humid environment. Further, the ET_o estimations with the Hargreaves equations having rainfall parameter were poor as compared to those from the PMF-56 method under majority of the climatic situations studied.     

Estimating net radiation at surface using artificial neural networks: a new approach

This study describes the results of artificial neural network (ANN) models to estimate net radiation (R _n), at surface. Three ANN models were developed based on meteorological data such as wind velocity and direction, surface and air temperature, relative humidity, and soil moisture and temperature. A comparison has been made between the R _n estimates provided by the neural models and two linear models (LM) that need solar incoming shortwave radiation measurements as input parameter. Both ANN and LM results were tested against in situ measured R _n. For the LM ones, the estimations showed a root mean square error (RMSE) between 34.10 and 39.48?W?m^?2 and correlation coefficient (R ²) between 0.96 and 0.97 considering both the developing and the testing phases of calculations. The estimates obtained by the ANN models showed RMSEs between 6.54 and 48.75?W?m^?2 and R ² between 0.92 and 0.98 considering both the training and the testing phases. The ANN estimates are shown to be similar or even better, in some cases, than those given by the LMs. According to the authors?? knowledge, the use of ANNs to estimate R _n has not been discussed earlier, and based on the results obtained, it represents a formidable potential tool for R _n prediction using commonly measured meteorological parameters.     

The Influence of Selected Meteorological Parameters on the Concentration of Surface Ozone in the Central Region of Poland

Abstract?This paper presents the results of measurements of the concentration of surface ozone and concurrent standard meteorological parameters: total solar radiation, temperature, relative humidity, pressure, wind speed, and vertical and horizontal components of the wind. The data were collected from 2005 to 2010 at stations located in central Poland (Mazowieckie voivodeship): Warszawa (urban), Legionowo (suburban), Granica and Belsk (rural). Furthermore, Granica is situated in the forested area of Kampinoski National Park. Continuously measured surface ozone concentrations demonstrated the well-known diurnal cycle of surface ozone concentration with a maximum in the afternoon and a minimum in the early morning hours. The averaged diurnal variations over six years reveal that the highest concentrations appear at rural stations (Belsk: 55?µg?m^?3 and Granica: 50?µg?m^?3) and the lowest at the urban station (Warszawa: 41?µg?m^?3). The threshold for high levels of surface ozone (120?µg?m^?3 per 8?h) was exceeded most often at Granica and Belsk. The occurrence of the ozone “weekend effect,” especially at urban stations, has been identified. The difference between weekend and weekday surface ozone concentrations at urban and rural stations was as high as 6.5?µg?m^?3 and approximately 2?µg?m^?3, respectively. Using appropriate statistical tools, it has been shown that meteorological conditions have a significant influence on ozone concentration. High correlation coefficients were found between ozone concentration and solar radiation, temperature, relative humidity, and wind speed. The forward stepwise regression model explains up to 75% of the variations in daily surface ozone concentration in terms of meteorological variability in summer and up to 70% in winter. At the same time, a multilayer perceptron neural network model was used to reconstruct the concentration of surface ozone. High correlation coefficients (up to 0.89) indicate that, on the basis of standard meteorological parameters and NO₂ concentration, we can determine ozone concentration with high accuracy.     

基于温度的参考作物蒸散量计算方法的适用性评价

参考作物蒸散量是表征气候干湿程度、植被耗水量、生产潜力及水资源供需平衡的重要指标之一。以海口和敦煌两个气候相差较大的站点为例,利用Irmark-Allen、Hargreaves、Jensen-Haise 3种基于温度的ET 0计算方法,计算了 2013 2015 年两个站点的参考作物蒸散量,以FAO98 Penman-Monteith方法计算所得结果为标准,依据相关系数(R)及其显著性(P)、均方根误差(RMSE)和平均偏差(MBE)等量化指标,分别对3种方法计算结果在两个站点月和日序列的适用性进行评价,并对这3种方法进行本地化修正优化和检验。结果表明:本地化前,Irmark-Allen方法在海口的计算与Penman-Monteith的偏差最小且相关性好( R =0.97, P <0.01,RMSE=0.38 mm/d,MBE=-0.01 mm/d),其他两种方法均高估。3种基于温度的ET 0方法在敦煌都有很大的误差,其中Irmark-Allen方法在夏季偏低,在冬季偏高;Hargreaves方法整体偏高;Jensen-Haise方法在冬季不适用,出现无效负值,而在其他时段偏高。本地化后,3种基于温度的ET 0方法在两个地区都得到明显改善,其中Jensen-Haise方法在海口效果最好( R =0.96, P< 0.01,RMSE=0.61 mm/d,MBE=0.003 mm/d),在敦煌效果也是最好的( R =0.96, P <0.01,RMSE=0.69 mm/d, MBE=-0.02 mm/d)。     

Evapotranspiration of deforested areas in central and southwestern Amazonia

Considering the high rates of evapotranspiration of Amazonian forests, understanding the impacts of deforestation on water loss rates is important for assessing those impacts on a regional and global scale. This paper quantifies evapotranspiration rates in two different pasture sites in Amazonia and evaluates the differences between the sites. In both places, measured evapotranspiration varies seasonally, decreasing during the dry season. The decrease is higher at the southwestern Amazonia site, while at the central Amazonia site, the decrease is less pronounced. During the dry season, average values of evapotranspiration are around 2.2?±?0.6?mm?day^?1 in central Amazonia and 2.4?±?0.6?mm?day^?1 in southwestern Amazonia, while during the wet season, those values are 2.1?±?0.6?mm?day^?1 in central Amazonia and 3.5?±?0.8?mm?day^?1 in southwestern Amazonia. On an annual basis, the pasture in southwestern Amazonia has higher evapotranspiration than in central Amazonia. We conclude that the main reason for this difference is the lower available energy in the wet season at the central Amazonian site, combined with a lower leaf area index at this site during the whole year. Still, the evapotranspiration is significantly controlled by the vegetation, which is well coupled with the local moisture conditions in the dry season.     

Detection of a katabatic wind event with GPS meteorology measurements at Scott Base Antarctica

A katabatic wind event which dramatically affects the polar climate has been detected using GPS meteorology measurements. GPS-derived precipitable water vapor (GPS PWV) variability and its relation to a katabatic event at Scott Base station, Antarctica was investigated. The investigations using the data gathered from 21 to 30 November of 2002. They showed that the water vapor profile exhibited an irregular pattern with a maximum PWV of 7.38?mm (~6?mm on average). This event was strongly influenced by relative humidity than by wind speed activity. The dominant wind flow during this period was from the North to Northeast (blowing from the Ross Sea) with a mean speed of 3.79?ms^?1. The PWV increased when the temperature was between ?15 and ?11°C. During the katabatic event identified between 21:30 UT of 28 November and 18:40 UT on 29 November, the wind blew from the Southeast to South direction (from the Ross Ice Shelf) with a maximum speed of 10.92?ms^?1. The PWV increased ~1.0?mm (23%) from the mean value accompanied by severe wind had pronounced effect on GPS observations.     

Mesoscale simulations of multi-decadal variability in the wind resource over Korea

This study investigated multi-decadal variability in the wind resource over the Republic of Korea using the Weather Research and Forecasting (WRF) mesoscale meteorological model. Mesoscale simulations were performed for the period from November 1981 to November 2010. The typical wind climatology over the Korean Peninsula, which is influenced by both continental and oceanic features, was represented by the physics-based mesoscale simulations. Winter had windier conditions with northwesterly flows, whereas less windy with southwesterly flows appeared in summer. The annual mean wind speeds over the Republic of Korea were approximately 2 m s^?1 with strong wind in mountainous areas, coastal areas, and islands. The multi-decadal variability in wind speed during the study period was characterized by significant increases (positive trend) over many parts of the study area, even though the various local trends appeared depending on the station locations. The longterm trend in the spatially averaged wind speed was approximately 0.002 m s^?1 yr^?1. The annual frequency of daily mean wind speeds over 5 m s^?1 at the turbine hub height also increased during the study period throughout the Republic of Korea. The present study demonstrates that multi-decadal mesoscale simulations can be useful for climatological assessment of wind energy potential.     

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s^?1, considerably smaller than the result (1.29 m s^?1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.     

地面有效辐射气候学模型评估和参数优化

基于中国19个辐射站1993-2012年的地面辐射平衡资料和气象资料,分析评估了布朗特法、彭曼法、别尔良德法、FAO24法、FAO56-PM法、邓根云法和童宏良法7种参数化方案计算中国地面有效辐射的适用性;并以均方根误差最小为目标函数,利用步长加速法和多元回归法迭代求解最优参数,建立适合于中国的最优参数化逐日有效辐射估算方法。结果表明:参与评估的7种方案都不同程度低估了中国的有效辐射;从全中国总体误差水平看,童宏良法的平均绝对百分比误差和均方根误差小于其他6种方案,分别为27.0%和24.5 W/m2,估算效果较好;其次是彭曼法和邓根云法;FAO56-PM法精度较低,不适用于中国的有效辐射估算。针对单站来说,邓根云法在东部平原地区的精度最高,童宏良法由于考虑了海拔高度的订正,适用于西部高原地区。相关分析表明水汽压是影响有效辐射估算误差的最关键因素,因此根据水汽压的地理分布规律,分东部区和西部区建立分区方案。基于观测资料建立的全中国方案和分区方案的均方根误差分别为20.8和21.4 W/m2,精度均高于已有参与评估的7种方案;而且在绝大多数站点,分区方案的误差小于全中国方案,所以划分东部区和西部区进行有效辐射模型参数化很有必要。同时发现,分区方案在西部区明显优于邓根云法,在东部区明显优于童宏良法,因此推荐其作为中国有效辐射的计算方法。      

Influence of weather conditions and spatial variability on glacier surface melt in Chilean Patagonia

In order to clarify how differences in weather conditions affect the surface heat balance of a large maritime glacier, meteorological observations were carried out in the ablation area of Glaciar Exploradores in the Chilean Patagonia during the austral summer of 2006/2007. Under cloudy/rainy weather, when the air temperature and wind speed were high due to advection, the average melting heat was 18.8 MJ m^?2 day^?1 and the turbulent heat fluxes contributed 35% of the total melt. During clear weather, the average melting heat was 16.9 MJ m^?2 day^?1 and 13% of the total was the turbulent heat fluxes. A decrease in air temperature due to the development of the glacier boundary layer on clear days will lead to an overestimation of the melt using the air temperature at a weather station outside of the glacier.     

Pan coefficient (K p) estimation under uncertainty on fetch

The FAO Penman–Monteith (F-PM) method is a frequently applied approach for calculating the daily reference evapotranspiration (ET₀). This method requires long records of meteorological data, which makes it quite hard to employ in locations with no or limited available data. Evaporation pans are widely used to estimate the reference ET₀, but this method requires reliable estimates of the pan coefficient (K _p). The objectives of this study were to determine the proper values of monthly and annual K _p, as well as the best method among those available for the estimation of K _p values in the study area. Measured weather data from 1992 to 2006 were obtained from 18 stations in the North and Northwest of Iran. Daily ET₀ calculated using methods by Bernardo et al. and Pereira et al. were compared with those calculated by the F-PM method. The employed methods at all stations, except those located in the north of the study area with high relative humidity, overestimated the ET₀ compared to the F-PM method. The constant parameters of these methods were optimized by a trial and error scheme to minimize the root mean square error. The results indicated that modified K _p coefficients from Bernardo et al.’s method ranged between 0.41 and 0.87 and the optimal coefficient of Pereira et al.’s method ranged between 0.49 and 0.95. Modified monthly K _p from Bernardo et al.’s method ranged between 0.3 and 1.07 and those from Pereira et al.’s method ranged between 0.4 and 1.18. Modified K _p of the methods by Bernardo et al. and Pereira et al. showed the higher estimation accuracy of daily ET₀ values. In general, the performance of the modified K _p of Bernardo et al.’s method was higher than Pereira et al.’s method for all stations. Thus, in the study region and under the same climatic conditions [in areas with only pan evaporation (E _p) records], the use of climatic monthly modified K _p to calculate ET₀ based on class A E _p is recommended.     

Dry-wet variations and cause analysis in Northeast China at multi-time scales

Global warming has caused unevenly distributed changes in precipitation and evapotranspiration, which has and will certainly impact on the wet-dry variations. Based on daily meteorological data collected at 91 weather stations in Northeast China (NEC), the spatiotemporal characteristics of dry and wet climatic variables (precipitation, crop reference evapotranspiration (ET₀), and humid index (HI)) are analyzed, and the probable reasons causing the changes in these variables are discussed during the period of 1961–2014. Precipitation showed non-significant trend over the period of 1961–2014, while ET₀ showed a significant decreasing trend, which led to climate wetting in NEC. The period of 2001–2012 exhibited smaller semiarid area and larger humid area compared to the period of 1961–1980, indicating NEC has experienced wetting process at decadal scale. ET₀ was most sensitive to relative humidity, and wind speed was the second most sensitive variable. Sunshine hours and temperature were found to be less influential to ET₀ in the study area. The changes in wind speed in the recent 54 years have caused the greatest influence on ET₀, followed by temperature. For each month, wind speed was the most significant variable causing ET₀ reduction in all months except July. Temperature, as a dominant factor, made a positive contribution to ET₀ in February and March, as well as sunshine hours in June and July, and relative humidity in August and September. In summary, NEC has experienced noticeable climate wetting due to the significantly decreasing ET₀, and the decrease in wind speed was the biggest contributor for the ET₀ reduction. Although agricultural drought crisis is expected to be partly alleviated, regional water resources management and planning in Northeast China should consider the potential water shortage and water conflict in the future because of spatiotemporal dry-wet variations in NEC.     

New alternatives for reference evapotranspiration estimation in West Africa using limited weather data and ancillary data supply strategies.

Accurate estimation of reference evapotranspiration (ET ₀ ) is essential for the computation of crop water requirements, irrigation scheduling, and water resources management. In this context, having a battery of alternative local calibrated ET ₀ estimation methods is of great interest for any irrigation advisory service. The development of irrigation advisory services will be a major breakthrough for West African agriculture. In the case of many West African countries, the high number of meteorological inputs required by the Penman-Monteith equation has been indicated as constraining. The present paper investigates for the first time in Ghana, the estimation ability of artificial intelligence-based models (Artificial Neural Networks (ANNs) and Gene Expression Programing (GEPs)), and ancillary/external approaches for modeling reference evapotranspiration (ET ₀ ) using limited weather data. According to the results of this study, GEPs have emerged as a very interesting alternative for ET ₀ estimation at all the locations of Ghana which have been evaluated in this study under different scenarios of meteorological data availability. The adoption of ancillary/external approaches has been also successful, moreover in the southern locations. The interesting results obtained in this study using GEPs and some ancillary approaches could be a reference for future studies about ET ₀ estimation in West Africa.