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.     

Error assessment of climate variables for FAO-56 reference evapotranspiration

Meteorological stations, which measure all the required meteorological parameters to estimate reference evapotranspiration (ET_o) using the Food and Agriculture Organization Penman?CMonteith (FAO56-PM) method, are limited in Korea. In this study, alternative methods were applied to estimate these parameters, and the applicability of these methods for ET_o estimation was evaluated by comparison with a complete meteorological dataset collected in 2008 in Korea. Despite differences between the estimation and observation of radiation and wind speed, the comparison of ET_o showed small differences [i.e., mean bias error (MBE) varying ?0.22 to 0.25?mm?day^?1 and root-mean-square-error (RMSE) varying 0.06?C0.50?mm?day^?1]. The estimated vapor pressure differed considerably from the observed, resulting in a larger discrepancy in ET_o (i.e., MBE of ?0.50?mm?day^?1 and RMSE of 0.60?C0.73?mm?day^?1). Estimated ET_o showed different sensitivity to variations of the meteorological parameters??in order of vapor pressure?>?wind speed?>?radiation. It is clear that the FAO56-PM method is applicable for reasonable ET_o estimation at a daily time scale especially in data-limited regions in Korea.     

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.     

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example

Daily global solar irradiation (R _s) is one of the main inputs in environmental modeling. Because of the lack of its measuring facilities, high-quality and long-term data are limited. In this research, R _s values were estimated based on measured sunshine duration and cloud cover of our synoptic meteorological stations in central and southern Iran during 2008, 2009, and 2011. Clear sky solar irradiation was estimated from linear regression using extraterrestrial solar irradiation as the independent variable with normalized root mean square error (NRMSE) of 4.69 %. Daily R _s was calibrated using measured sunshine duration and cloud cover data under different sky conditions during 2008 and 2009. The 2011 data were used for model validation. According to the results, in the presence of clouds, the R _s model using sunshine duration data was more accurate when compared with the model using cloud cover data (NRMSE = 11. 69 %). In both models, with increasing sky cloudiness, the accuracy decreased. In the study region, more than 92 % of sunshine durations were clear or partly cloudy, which received close to 95 % of total solar irradiation. Hence, it was possible to estimate solar irradiation with a good accuracy in most days with the measurements of sunshine duration.     

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.     

Modeling the photosynthetically active radiation in South West Amazonia under all sky conditions

Accurate estimates of photosynthetically active radiation (PAR) are critical for the development of realistic models of plant productivity. However, in many areas such as the vast Amazon region of South America, there have been few empirical studies of PAR. Here, we analyzed the relationship between PAR and broadband solar irradiance (R _s) and formulated models to estimate PAR in two experimental sites (pasture and forest) in the Brazilian Amazon. Three different models of increasing complexity were developed based on information from R _s (model 1), R _s and clearness index (k _t; model 2), and R _s, k _t, and water vapor pressure (model 3). Estimates of PAR were generated for each season and for the entire year. All models had very high determination coefficients and indices of agreement for both pasture and forest sites. This strongly supports the use of R _s and k _t to produce robust estimates of PAR. The results obtained by annual models were close than that found by seasonal models, demonstrating that a single annual model is able to estimate PAR, albeit with lower accuracy.     

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.     

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.     

Modeling daily reference evapotranspiration (ET0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study

Estimation of reference evapotranspiration (ET₀) is needed to support irrigation design and scheduling, and watershed hydrology studies. There are many available methods to estimate evapotranspiration from a water surface, comprising both direct and indirect methods. In the first part of this study, the generalized regression neural networks model (GRNN) and radial basis function neural network (RBFNN) are developed and compared in order to estimate the reference ET₀ for the first time in Algeria. Various daily climatic data, that is, daily mean relative humidity, sunshine duration, maximum, minimum and mean air temperature, and wind speed from Dar El Beida, Algiers, Algeria, are used as inputs to the GRNN and RBFNN models to estimate the ET₀ obtained using the FAO-56 Penman-Monteith equation (PM56). The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. In the second part of the study, the empirical Hargreaves-Samani (HG) and Priestley-Taylor (PT) equations are also considered for the comparison. Based on the comparisons, the GRNN was found to perform better than the RBFNN, Priestley-Taylor and Hargreaves-Samani models. The RBFNN model is ranked as the second best model.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Precipitation drives interannual variation in summer soil respiration in a Mediterranean-climate, mixed-conifer forest

Predictions of future climate change rely on models of how both environmental conditions and disturbance impact carbon cycling at various temporal and spatial scales. Few multi-year studies, however, have examined how carbon efflux is affected by the interaction of disturbance and interannual climate variation. We measured daytime soil respiration (R _s) over five summers (June–September) in a Sierra Nevada mixed-conifer forest on undisturbed plots and plots manipulated with thinning, burning and their combination. We compared mean summer R _s by year with seasonal precipitation. On undisturbed plots we found that winter precipitation (PPT_w) explained between 77–96% of interannual variability in summer R _s. In contrast, spring and summer precipitation had no significant effect on summer R _s. PPT_w is an important influence on summer R _s in the Sierra Nevada because over 80% of annual precipitation falls as snow between October and April, thus greatly influencing the soil water conditions during the following growing season. Thinning and burning disrupted the relationship between PPT_w and R_s, possibly because of significant increases in soil moisture and temperature as tree density and canopy cover decreased. Our findings suggest that R _s in some moisture-limited ecosystems may be significantly influenced by annual snowpack and that management practices which reduce tree densities and soil moisture stress may offset, at least temporarily, the effect of predicted decreases in Sierran snowpack on R _s.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

In this study, unlike backpropagation algorithm which gets local best solutions, the usefulness of particle swarm optimization (PSO) algorithm, a population-based optimization technique with a global search feature, inspired by the behavior of bird flocks, in determination of parameters of support vector machines (SVM) and adaptive network-based fuzzy inference system (ANFIS) methods was investigated. For this purpose, the performances of hybrid PSO-ε support vector regression (PSO-εSVR) and PSO-ANFIS models were studied to estimate water level change of Lake Beysehir in Turkey. The change in water level was also estimated using generalized regression neural network (GRNN) method, an iterative training procedure. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R ²) were used to compare the obtained results. Efforts were made to estimate water level change (L) using different input combinations of monthly inflow-lost flow (I), precipitation (P), evaporation (E), and outflow (O). According to the obtained results, the other methods except PSO-ANN generally showed significantly similar performances to each other. PSO-εSVR method with the values of minMAE = 0.0052 m, maxMAE = 0.04 m, and medianMAE = 0.0198 m; minRMSE = 0.0070 m, maxRMSE = 0.0518 m, and medianRMSE = 0.0241 m; minR ² = 0.9169, maxR ² = 0.9995, medianR ² = 0.9909 for the I-P-E-O combination in testing period became superior in forecasting water level change of Lake Beysehir than the other methods. PSO-ANN models were the least successful models in all combinations.     

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)。     

Retrieving air humidity,global solar radiation,and reference evapotranspiration from daily temperatures: development and validation of new methods for Mexico. Part III: reference evapotranspiration

We evaluated two methods to estimate evapotranspiration (ETo) from minimal weather records (daily maximum and minimum temperatures) in Mexico: a modified reduced set FAO-Penman-Monteith method (Allen et al. 1998, Rome, Italy) and the Hargreaves and Samani (Appl Eng Agric 1(2): 96–99, 1985) method. In the reduced set method, the FAO-Penman-Monteith equation was applied with vapor pressure and radiation estimated from temperature data using two new models (see first and second articles in this series): mean temperature as the average of maximum and minimum temperature corrected for a constant bias and constant wind speed. The Hargreaves-Samani method combines two empirical relationships: one between diurnal temperature range ΔT and shortwave radiation Rs, and another one between average temperature and the ratio ETo/Rs: both relationships were evaluated and calibrated for Mexico. After performing a sensitivity analysis to evaluate the impact of different approximations on the estimation of Rs and ETo, several model combinations were tested to predict ETo from daily maximum and minimum temperature alone. The quality of fit of these models was evaluated on 786 weather stations covering most of the territory of Mexico. The best method was found to be a combination of the FAO-Penman-Monteith reduced set equation with the new radiation estimation and vapor pressure model. As an alternative, a recalibration of the Hargreaves-Samani equation is proposed.     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Analysis of photosynthetically active radiation and applied parameterization model for estimating of PAR in the North China Plain

As photosynthetically active radiation (PAR) variability and PAR estimating methods play an important role in climate change and ecological process research, PAR variation trends and broadband global solar radiation (R _s ) ratios (PAR/R _s ) in the North China Plain (NCP) are examined using in situ PAR and R _s observed data for 2005 to 2011. The annual average PAR value found in the NCP is 22.9 mol m^?2 d^?1. The highest and lowest values were recorded at Changwu and Luancheng sites, respectively. The highest PAR/R _s value was found in Jiaozhouwan due to large water vapor volumes present in this area. PAR/R _s levels have increased in the NCP due to a decrease in fine aerosols and increase in water vapor concentration. From these analysis results, a parameterization model that can be applied to all sky conditions was checked. Empirical estimation model comparisons for obtaining PAR values indicate that model was least accurate when R _s was used independently. When the model included R _s, the clearness index (K _s) and the solar zenith angle, the model estimated PAR values with acceptable accuracy. A parameterization model was constructed by considering K _s and attenuation factors of PAR under clear weather conditions (ρ _clear). The improved parameterization model more accurately predicts values for local sites and for various observation sites.     

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.     

Evapotranspiration and canopy resistance of grass in a Mediterranean region

Summary A simple method for estimating actual evapotranspiration (ET) could become a suitable tool for irrigation scheduling. Resistance models can be useful if data on canopy resistance to water vapor flow (rc) and on aerodynamic resistance (ra) are available. These parameters are complex and hard to obtain. In this studyrc is analysed for a reference crop (grass meadow). Canopy resistance is dependent on climate, weather (radiation, atmospheric vapor pressure deficit, aerodynamic resistance), agronomic practices (irrigation, grass cutting) and time scale (hour, day). Anrc model, proposed by Katerji and Perrier (KP model), using some meteorological parameters as inputs, is presented. Canopy resistance calculated according to the KP model was used to estimate a referenceET _ref on hourly and daily time scales.TheET _ref estimated using the KP model on a daily time scale was compared with a model proposed by Allen, Jensen, Wright and Burman (AJWB model) — in whichrc depends on leaf area index only — and with direct measurements from a weighing lysimeter. The results show an underestimation of 18% for the AJWB model against an underestimation of 2% for the KP model. Since the hypotheses are the same for both models and aerodynamic resistance plays a secondary role, the better results obtained by the KP model are due torc modelling.With 11 Figures