A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region

In this study, the performance of M5 model tree and conventional method for converting pan evaporation data (E_p) to reference evapotranspiration (ET₀) were assessed in semi-arid regions. Conventional method uses pan coefficient (K_p) as a factor to convert E_p to ET₀. Two common K_p equations for pans with dry fetch (Allen et al. 1998; Abdel-Wahed and Snyder in J Irrig Drain Eng 134(4):425–429, 2008) were considered for the comparison. The values of ET₀ derived using these three methods were compared to those estimated using the reference FAO Penmane Monteith (FAO-PM) method under semi-arid conditions of the Khuzestan plain (Southwest Iran). The results showed that the M5 model is the best one to estimate ET₀ over test sites (0.5 mm d^?1 of root mean square error (RMSE) and 0.98 of coefficient of determination (R ²). Conversely, the performance of the two K_p equations was poor.     

Potential Evapotranspiration Model for Muda Irrigation Project, Malaysia

Methods to estimate free-surface evaporation E _p and potential evapotranspiration ET _p using well known models, namely, Penman–Monteith model, modified Penman method, Food and Agriculture Organization Penman–Monteith (FAO-PM) method, Blaney–Criddle method, and Christiansen method, without calibrating any model calibration parameters, for monthly time series are presented. The long-term monthly averaged daily models results are calculated using recorded average historic meteorological data (1980–1997) and compared with the USBR Class-A black pan evaporation data obtained from the Muda Agricultural Development Authority, Malaysia. The comparison results of the long-term monthly averaged daily estimates of E _p using these models show that the E _p values for the Penman–Monteith model and FAO-PM method for different months are found to be compared satisfactorily with the recorded pan evaporation data. The results of the estimated E _p values for different months, the variations of relative errors in different months, the values of mean monthly differences between recorded and estimated E _p , and the comparison between the models annual E _p with the recorded annual E _p , using these models suggest that the Penman–Monteith model can be selected as the best model in E _p estimation in the Muda Irrigation Project, Malaysia. The FAO-PM method can be considered as the second best model, successively followed by the Blaney–Criddle method, modified Penman method, and Christiansen method. Thus, the results of the Penman–Monteith model can be interpreted as the validation of the E _p model and can safely be used in ET _p estimation in the Muda Irrigation Project, Malaysia.     

Framework for Standardizing Less Data-Intensive Methods of Reference Evapotranspiration Estimation

Evapotranspiration is one of the vital components of water cycle and its accurate estimation is the key to sustainable management of irrigation water. The FAO Penman-Monteith (FAO-PM) method is recommended as the standard method for computing reference evapotranspiration (ET_o) as well as for evaluating other indirect methods. However, due to the lack of weather data such as radiation, relative humidity and wind speed in many regions of the world, especially in developing countries, the FAO-PM method is difficult to use. To address this issue, a fairly robust methodology is proposed in this study to standardize two popular less data-intensive (temperature-based) ET_o methods, viz., Hargreaves-Samani (HS) and Penman-Monteith Temperature (PMT) against the FAO-PM method. To achieve this goal, the daily and monthly biases of these two methods were adjusted using the weather data of 14 locations for the 1979–2003 period. Subsequently, the performance of the standardized (de-biased) less data-intensive methods were verified using salient statistical and graphical indicators for the 2004–2013 period. The results indicated that the HS and PMT methods underestimate ET_o on a monthly time step by 9.62 and 14.77%, respectively. However, the performances of these methods significantly improve after the standardization. The estimates of ET_o by the standardized less data-intensive methods were found to be in close agreement with those by the standard FAO-PM method, thereby suggesting the usefulness and applicability of the proposed framework in data-scarce situations irrespective of agro-climatic conditions.     

Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates

Evapotranspiration and evaporation measurements are important parameters for many agricultural activities such as water resource management and environmental studies. There are several models which can determine pan coefficient (K _Pan), using wind speed, relative humidity and fetch length conditions. This paper analyses seven exiting pan models to estimate K _Pan values for two different climates of Iran. Monthly mean reference crop evapotranspiration (ET₀) was calculated according to the pan-ET₀ model. The results showed that estimated pan coefficients by majority of the suggested models were not statistically accurate to be used in the pan-ET₀ conversion method. However, for the cold semi-arid climate condition, the best K _Pan models for estimation of ET₀ were Orang and Raghuwanshi–Wallender, respectively. Also, the Snyder and Orang models were best fitted models for warm arid climate, respectively. The mean annual value of K _Pan, determined by Penman–Monteith FAO 56 (PMF-56) standard model for warm arid sites, was approximately 32% higher than the corresponding value in the cold semi-arid climate. Similarly, the mean annual ET₀ in the warm arid sites was 66% higher, compared to the ET₀ of the cold semi-arid sites. These types of warm arid and semi-arid climates are found widely throughout the world.     

Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure

Potential evapotranspiration (ET_o) is an essential hydrologic parameter for having better understanding for hydrologic cycle in certain catchment area. In addition, ET_o is vital for calculating the agricultural demand. In fact, Penman-Monteith (PM) method is considered as reference method for estimating (ET_o), however, this method required a lot of data to be used which is not usually available in many catchment areas. Furthermore, there are several efforts that have been performed as competitor to reach accurate estimation of (ET_o) when there is lack of data to utilize (PM) method, but still required numerous research. Recently, methods based on Artificial Intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering and especially for hydrological process. However, time series prediction based on Artificial Neural Network (ANN) learning algorithms is fundamentally difficult and faces problem. One of the major shortcomings is that the ANN model experiences over-fitting problem during training session and also occurs when a neural network loses its generalization. In this research a modification for the classical Multi Layer Preceptron- Artificial Neural Network (MLP-ANN) modeling namely; Ensemble Neural Network (ENN) is proposed and applied for predicting daily ET_o. The proposed model applied at two different region with two different climatic conditions, Rasht city located north part of Iran and Johor Bahru City, Johor, Malaysia using maximum and minimum daily temperature collected from 1975 to 2005. The result showed that the ENN outperformed the classical MLP-ANN method and successfully predict daily ETo utilizing maximum and minimum temperature only with satisfactory level of accuracy. In addition, the proposed model could achieve accuracy level better than the traditional competitor methods for ET_o.     

Performance of Recalibrated Equations for the Estimation of Daily Reference Evapotranspiration

An accurate estimation of reference evapotranspiration (ET₀) is of paramount importance for many studies such as hydrologic water balance, irrigation system design and management, crop yield simulation, and water resources planning and management. In the present study, Blaney-Criddle, Jensen-Haise and Hargreaves (temperature based), Priestley-Taylor, Radiation and Makkink (radiation based) and, Pan Evaporation and Christiansen (pan evaporation based) methods have been evaluated and recalibrated with respect to FAO-56 Penman-Monteith method for estimating daily ET₀ in the semi-arid Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar sites of Andhra Pradesh, India. Recalibrated Blaney-Criddle (temperature based), Radiation (Radiation based) and Christiansen (Pan evaporation based) methods showed a satisfactory performance at the sites. Further, recalibrated Blaney- Criddle method showed relatively better performance than Radiation and Christiansen methods in the daily ET₀ estimation. Recalibrated Blaney- Criddle method may therefore be adopted at the sites selected for the present study and also at the sites with similar climatic conditions for satisfactory daily ET₀ estimation.     

Performance Evaluation of Reference Evapotranspiration Estimation Using Climate Based Methods and Artificial Neural Networks

This study is an attempt to find best alternative method to estimate reference evapotranspiration (ET_o) for the Mahanadi reservoir project (MRP) command area located at Raipur (Chhattisgarh) in India, when input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ET_o in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the backpropagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ET_o can be estimated from maximum and minimum temperature using ANN approach in MPR area.     

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (E_pan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R²), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling E_pan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.     

Assessment of the Performance of a Distributed Code in Relation to the ETp Estimates

This article is a preliminary report of a research activityfocusing on the effects of the crop potentialevapotranspiration (ET_p) generation methods on boththe performance of a distributed catchment model as well ason the magnitude of its main effective parameters. Thereferred effects were assessed on the basis of theindependent Multi-Calibration (MC) of the distributed modelfor which three different ET_p scenarios were generatedas follows: (A) FAO-24 standard approach;(B) FAO-24 approach but used with coefficients of the wind function and of the Stefan-Boltzmann equation selected according to local conditions; and (C) FAO-56 standard approach. ET_p was estimated as a function of the reference crop evapotranspiration (ET_o) by means of the k _c-ET_o approach. For assessing the performance of theestimation approach, the ET_o outputs of the three scenarioswere compared to local ET_o constraints derived from a previous research. The outputs of scenario B and scenario C showed a close agreement with the local ET_o constraints. Their effect on the performance of the hydrological model alsoseemed to be comparable. The output of scenario A producedhigher estimates than the outputs of the other two scenarios, inthe order of 200 mm per year. The best model performance was obtained by using the estimates of scenario A. The research alsorevealed the weak dependency of the model effective parametersinspected in this preliminary research on the ET_pestimating methods.     

应用双作物系数模型估算温室番茄耗水量

准确估算作物耗水量对于合理利用有限的水资源和制定合理的灌溉制度至关重要。本文利用3个生长季的西北地区日光温室番茄水量平衡计算耗水资料,率定和验证双作物系数模型SIMDual Kc在日光温室条件下的适用性。结果表明耗水模拟值与实测值有较好的一致性。模型估算的平均标准误差为0.55 mm·d-1,平均绝对误差为0.44 mm·d-1。模型估算的番茄初期、中期和后期的基础作物系数分别为0.50、0.85和0.55。番茄生育初期蒸发占耗水的比例最大为22.8%;发育期最小,仅为3.2%。3年全生育期总蒸发量占总耗水量的比例平均为5.9%,表明温室生产中植株蒸腾为耗水最主要部分。     

Daily Reference Evapotranspiration for Hyper-Arid to Moist Sub-Humid Climates in Inner Mongolia,China: II. Trends of ET<Subscript>o</Subscript> and Weather Variables and Related Spatial Patterns

This study focuses on assessing trends of reference evapotranspiration (ET_o) considering aridity. Weather data sets of 54–62 years of Inner Mongolia, a Chinese Province where climate varies from hyper-arid in the West to wet sub-humid in the East, were used. Trends were analyzed for ET_o computed with the FAO Penman-Monteith method (PM-ET_o) using full data sets of maximum and minimum temperature (T_max and T_min), sunshine duration (SD) used to compute net radiation, relative humidity (RH) and wind speed (WS). Trends were also assessed for ET_o computed with the Hargreaves-Samani temperature eq. (ET_{o HS}) and the Penman-Monteith equation with temperature estimates of solar radiation and actual vapour pressure (ET_{o PMT}). In addition, trends relative to T_max, T_min, SD, RH and WS were assessed. Trends for PM-ET_o show to vary with aridity, with decreasing trends in the areas marked by aridity in the West and increased trends in less arid and sub-humid areas in the East. The detected trends are well explained by the trends in weather variables which consist of large increasing trends of T_max and T_min and of decreasing trends for SD, RH and WS. Therefore, negative trends of ET_o occur where impacts of increases in temperature and decreases in RH are smaller than impacts of declining SD and WS; otherwise, when warming influences are larger it results a positive trend for ET_o. Trends were coherent when considering seasonality influences. Contrarily, results for the temperature methods, ET_{o PMT} and ET_{o HS,} always identified increased trends for ET_o due to warming effects. These results show that it is inappropriate to assess ET_o trends when using simplified temperature methods.     

Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration

An accurate estimation of reference evapotranspiration (ET₀) is of paramount importance for many studies such as hydrologic water balance, irrigation system design and management, crop yield simulation, and water resources planning and management. Simple regression techniques, may sometimes, provide adequate estimation of ET₀. Implementation of regression methods considering all the predictor variables may, however, lead to overfit and consequent reduction in the predictive capability. The regression models for ET₀ have been developed in the present study for Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh, India by following step-wise procedure, eliminating superfluous predictor variables based on statistical criteria. The sunshine hours, wind velocity, temperature and relative humidity influenced ET₀ in the study area. The linear regression models developed in terms of predictor variables may conveniently be applied in the regions selected for the present study and, in the regions with similar climatic conditions for satisfactory ET₀ estimation.     

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 (ET_o) values. The main aim of this study was to determine the model used to estimate ET_o 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 ET_o 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 ET_o. In general, the results obtained from this study revealed very clearly that the Makkink and Priestley–Taylor models estimated ET_o values less accurately than Turc and Hargreaves models for the all climates.     

Spatio-temporal Calibration of Blaney–Criddle Equation in Arid and Semiarid Environment

Estimates of reference evapotranspiration (ET_o) are widely used in irrigation engineering to define crop water requirements. A major drawback to application of the FAO Penman-Monteith is the relatively high data demand which unfortunately, for many locations; such meteorological variables are often incomplete and/or not available. Alternatively, the Blaney–Criddle (BC) equation is a simpler method for ET_o estimation. In this study, the BC equation was calibrated using three methods: spatially calibration at each station for the whole period (ET_o-BC_S); two periods calibration (ET_o-BC_S2); and spatial and temporal calibration at each station for each month (ET_o-BC_S,T) using twelve stations a cross Jordan. The calibration coefficient of BC equation (a, b) were determined at all stations. The results of the calibration methods showed that: (1) the spatial calibration of BC had the highest RMSE, and ME and Lower R² comparing to spatial and temporal calibration and two periods calibration. (2) Improvement was achieved for the BC equation when considering the spatial and temporal calibration for all months at each station. The values of a were negative for all months of any station. The higher values of a are coincided with cold or low temperature months while the high values coincided with high month temperature. The b values were positive for the whole stations and months. As the a values, it seems that b values had higher values in warm months than the cold one. A relatively good improvement could be obtained using two periods calibration instead of one period. The maps of a and b clearly show that a and b varied considerably in the study area and being aware of the spatial temporal variations of climatologically parameters is important in managing the limited water resources. Knowing the spatial temporal changes of such parameters, accurate calculations of ET_o can be achieved which will lead to precise and elevate water resources management in the arid region such as Jordan.     

A Modified SEBAL Modeling Approach for Estimating Crop Evapotranspiration in Semi-arid Conditions

Remote sensing methods are becoming attractive to estimate crop evapotranspiration, as they cover large areas and can provide accurate and reliable estimations; intensive field monitoring is also not required, although some ground-truth measurements can be helpful in interpreting satellite images. For the purposes of this paper, modeling and remote sensing techniques were integrated for estimating actual evapotranspiration of groundnuts (Arachishypogaea, L.) that is cultivated near Mandria Village in Paphos District of Cyprus. The Surface Energy Balance Algorithm for Land (SEBAL) was adopted for the first time in Cyprus, employing the essential adaptations for local soil and meteorological conditions. Landsat-5 TM and 7 ETM+ images were used to retrieve the needed spectral data. The SEBAL model is enhanced with empirical equations determined as part of the present study, regarding crop canopy factors, in order to increase its accuracy. Maps of ET_a were created using the SEBAL modified model (CYSEBAL) for the area of interest. The results have been compared to the measurements from an evaporation pan (which was used as a reference) and those of the original SEBAL model. The statistical comparison has shown that the modified SEBAL yields results that are comparable to those of the evaporation pan. T-test application has revealed that the statistical difference between SEBAL and CYSEBAL is significant and quite crucial, especially in a place with limited surface and underground water resources.     

西藏高海拔地区气象数据缺失条件下的ET0计算研究

西藏高海拔地区低氧低压(平均不足海平面的2/3)、太阳辐射强(年太阳辐射6 000~8 000 MJ/m2)、近地层空气湿度变化大,加之西藏地区气象资料系列短、站点少,该地区ET_0计算具有特殊性及不便性。本研究基于西藏地区9个典型站点20年逐日气象资料,通过引入海拔因子与修正温度常数对Hargreaves(HS)模型进行改进,旨在得到一种少参、准确的高海拔地区ET_0简易计算方法。结果表明,海拔2 000 m以上地区Hargreaves-Elevation(HS-E)改进模型在不同时间尺度条件下的修正结果均明显优于HS模型且避免了原HS模型在高海拔地区ET_0计算出现负值的情况,提升了ET_0计算值的实用性与精度。以PM模型ET_0计算值为标准进行误差分析,HS-E模型逐日ET_0计算的纳什效率系数(NSE)、均方根误差(RMSE)和平均相对误差(MRE)分别为0.8、0.53mm/d和13.80%,逐月ET_0计算的NSE、RMSE和MRE分别为0.84、11.90 mm/month和12.50%;对比不同时间尺度条件下(日、月)误差分析结果可知,计算时间尺度越大HS-E模型结果越优。HS-E改进模型在高海拔地区适应性较强,具有较高的计算精度,可作为西藏海拔2000 m以上地区气象数据缺失条件下ET_0计算的推荐模型。     

Modeling Nonlinear Monthly Evapotranspiration Using Soft Computing and Data Reconstruction Techniques

The objective of this study is to develop soft computing and data reconstruction techniques for modeling monthly California Irrigation Management Information System (CIMIS) evapotranspiration (ET_o) at two stations, U.C. Riverside and Durham, in California. The nonlinear dynamics of monthly CIMIS ET_o is examined using autocorrelation function, phase space reconstruction, and close returns plot. The generalized regression neural networks and genetic algorithm (GRNN-GA) conjunction model is developed for modeling monthly CIMIS ET_o. Among different input variables considered, solar radiation (RAD) is found to be the most effective variable for modeling monthly CIMIS ET_o using GRNN-GA for both stations. Adding other input variables to the best 1-input combination improves the model performance. The generalized regression neural networks and backpropagation algorithm (GRNN-BP) conjunction model is compared with the results of GRNN-GA for modeling monthly CIMIS ET_o. Two bootstrap resampling methods are implemented to reconstruct the training data. Method 1 (1-BGRNN-GA) employs simple extensions of training data using the bootstrap resampling method. For each training data, method 2 (2-BGRNN-GA) uses individual bootstrap resampling of original training data. Results indicate that Method 2 (2-BGRNN-GA) improves modeling of monthly CIMIS ET_o and is more stable and reliable than are GRNN-GA, GRNN-BP, and Method 1 (1-BGRNN-GA).     

Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment

This paper describes a detailed evaluation of the performance and characteristic behaviour of feed-forward artificial neural network (ANN) and M5 model tree for estimating reference evapotranspiration (ET₀) at four meteorological sites in an arid climate. The input variables for these models were the maximum and minimum air temperature, air humidity and extraterrestrial radiation. 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 the ANN estimated ET₀ better than the M5 model tree but both models performed well for the study area and yielded results close to the FAO56-PM method. Root mean square error and R² for the comparison between reference and estimated ET₀ for the tested data using the proposed ANN model are 5.6 % and 0.98, respectively. For the M5 model tree method these values are 8.9 % and 0.98, respectively. The overall results are of significant practical use because the temperature and Humidity-based model can be used when radiation and wind speed data are not available.     

Experimental Analyses of the Evaporation Dynamics in Bare Soils under Natural Conditions

In spite of recent progresses in evaporation estimate through advanced models and laboratory experiments, the drying process of bare soils through its successive stages remains difficult to predict. A study which addresses evaporation modeling in natural bare soils is presented. It relies upon hydro-meteorological measurements performed in a plot with a bare silt loam soil maintained in natural conditions. The following steps are involved: estimate of daily actual evaporation, E_a, through the hydrological balance, scaling with pan evaporation measurements, E_pan, and analysis of the relation of E_a/E_pan with the soil moisture vertical profile. The results enable us (1) to check the occurrence of the first stage of the evaporation process, characterized by not limited-soil water supply and high evaporative rates, and (2) to identify the transition from the first to the second stage, with decreasing soil water-limited E_a values. The last point requires the introduction of a soil water content threshold at 5 cm depth, that is associated with the soil field capacity. The adopted procedure provides insights on the soil water dynamics at depths differently involved through the successive stages of the evaporative process. Finally, indications on the use of pan evaporation measurements in evaporative rate estimate at least during the first stage of the process are also given.     

SIMETAW# - a Model for Agricultural Water Demand Planning

A successful water management scheme for irrigated crops requires an integrated approach, which accounts for water, soil, and crop management. SIMETAW# is a user friendly soil water balance model that assesses crop water use, irrigation requirements, and generates hypothetical irrigation schedules for a wide range of crops experiencing full or deficit irrigation. SIMETAW# calculates reference evapotranspiration (ET_o), and it computes potential crop evapotranspiration (ET_c), and the evapotranspiration of applied water (ET_aw), which is the amount of irrigation water needed to match losses from the effective soil root zone due to ET_c that are not replaced by precipitation and other sources. Using input information on crop and soil characteristics and the distribution uniformity of infiltrated irrigation applications in full or deficit conditions, the model estimates the mean depth of infiltrated water (IW) into each quarter of the field. The impact of deficit irrigation on the actual crop evapotranspiration (ET_a) is computed separately for each of the four quarters of the cropped field. SIMETAW# simulation adjusts ET_o estimates for projected future CO₂ concentration, and hence the model can assess climate change impacts on future irrigation demand allowing the user to propose adaptation strategies that potentially lead to a more sustainable water use. This paper discusses the SIMETAW# model and evaluates its performance on estimating ET_c, ET_a, and ET_aw for three case studies.