Reference Crop Evapotranspiration Research Articles

Spatiotemporal changes and driving factors of reference evapotranspiration and crop evapotranspiration for cotton production in China from 1960 to 2019

Introduction: Understanding and tracking changes in crop water requirements is crucial for effective irrigation, water planning, and future decisions. Determining the reference evapotranspiration (ETO) and crop evapotranspiration (ETC) of China cotton is essential for water resource management.Methods: This study analyzed the spatiotemporal changes in ETO and ETC at 248 standard stations in cotton production regions of China from 1960 to 2019, and the ETO and ETC of each station were quantified by using the CropWat 8.0 and non-parametric Mann-Kendall test. The impacts of climate change on ETO and ETC were evaluated by analyzing the contribution rate and sensitivity coefficient of climate change.Discussion: The results revealed distinct distributions of ETO and ETC across various growth stages and spatial scales in the cotton production regions of China. In the Huanghe Valley, the rate of decline for ETO decreased from 787.23 mm to 769.84 mm, while in the Yangtze Valley cotton region, it decreased from 749.19 mm to 735.01 mm. Similarly, in the Northwest inland cotton regions, the rate of decline for ETO reduced from 991.19 mm to 982.70 mm. As for ETC, the rate of decline decreased from 677.62 mm to 654.33 mm in the Huanghe Valley, from 653.02 mm to 625.50 mm in the Yangtze Valley, and from 916.25 mm to 886.74 mm in the Northwest inland cotton regions. ETO was highly sensitive to maximum air temperature (Tmax), followed by relative humidity (RH), sunshine duration (SD), wind speed at 2 m height (WS), and minimum air temperature (Tmin). WS was the most influential climate variable associated with ETO change, followed by Tmax, SD, RH, and Tmin. Significant declines in WS and SD were indicated in the decrease in ETO in the Huanghe Valley and Yangtze Valley cotton regions. WS showed a significant decrease in ETO in the northwestern inland cotton region. However, decreased RH and increased temperature commonly reversed the trend of ETO from 2000 to 2019, and the northwestern inland cotton region had the most significant upward trend. Amidst high temperatures and drought stress, the irrigation needs of cotton were rising, posing a significant threat to both cotton production and water resources.

Assessment of the Correction of the Reference Evapotranspiration at Nonirrigated Weather Stations Affected by Aridity and Delimitation of the Meteorological Conditions that Limit its Implementation

As most reference crop evapotranspiration (ETref) estimates are computed from weather stations located outside irrigated plots, the site aridity can produce ETref overestimation. To obtain more reliable ETref estimates, the potential of using the methodology that corrects the observed air and dew point temperatures was first analyzed in this study, and the meteorological conditions that limit this methodology implementation were subsequently assessed. A statistical analysis was conducted of pairwise comparisons between a station under reference conditions (Montañana) and four nearby stations affected by aridity in Spain for 2020. The daily reference evapotranspiration was calculated with the FAO56 Penman–Monteith equation (ETo PM), and afterward, the corrected daily reference evapotranspiration (ETo PMcor) was obtained using the previously mentioned correction methodology. The statistical analysis showed a greater connection between ETref and ETo PMcor in all pairwise comparisons, with the strongest connection reached between ETref Montañana station and ETo PMcor Pastríz station (coefficient of determination r2 = 0.98 and root mean square error RMSE = 0.34 mm/day.). This ETo PM correction methodology was established considering a difference between the minimum temperature (Tmin) and the dew point temperature (Tdew) greater than 2 °C, as this value is considered the benchmark of aridity, but this research found that high aridity (P/ETo < 0.5) inland locations and high aridity coastal locations with high relative humidity (RH), markedly as of 61.5% and 68%, respectively, could exhibit days where Tmin-Tdew < 2 °C does not reflect well-watered soil and healthy grass but rather high RH levels (r2 = 0.77 and 0.57, respectively). As this correction methodology was not established considering Tmin-Tdew values smaller than 2 °C, the application scope of this methodology is limited under the above scenarios.

Management of irrigation water in Al-Baha, Saudi Arabia, using a simple alternative to the Penman–Monteith equation

Estimating reference crop evapotranspiration (ETo) is very important in the planning and scheduling of irrigation water. A simple method for the estimation of ETo is needed, with fewer meteorological factors that are required for estimation using the Penmen–Monteith equation (PME). The aim of this study was to manage irrigation water in the Al-Baha region of Saudi Arabia using a simple equation to estimate ETo based on air temperature as an effective alternative to the PME. Four simple temperature-dependent equations proposed by Thornthwaite, Blaney-Criddle, Hamon and Linacre were selected for analysis. Of these four equations, the Linacre equation (LE) was found to be best for estimating ETo. Although the ETo rate was overestimated when using the LE compared with the PME, it had the lowest error percentage (9.55%) and the highest R2 (0.97). After deducing a new and accurate equation based on the LE with the ability to estimate the ETo rate, the modified LE produced values closest to the results obtained using the PME for the Al-Baha region. It can thus be relied upon for estimating irrigation needs in the Al-Baha region with the irrigation systems used in the region.

Performance evaluation of simpler reference crop evapotranspiration estimation equations with and without local calibration

The present study was taken up to demonstrate an overall methodology for local calibration of simpler reference crop evapotranspiration (ET0) estimation equations relative to the physically-based, albeit more data-intensive Penman-Monteith (PM) method. Climate records for the historical period 2006-2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used for this purpose. The numerical coefficients in the original forms of the simpler equations namely, Blaney-Criddle (BC), Priestley-Taylor (PT), Radiation (RAD), and Turc (TC) were treated as unknown parameters and optimized for each station using PM ET0 estimates as reference. Results of local calibration indicated significant improvement in prediction accuracies and bias reduction for all the equations with the calibrated PT equation exhibiting the best performance. Spatial maps depicting the variations in the optimal values of parameters were developed to aid the accurate estimation of monthly ET0 values in the study area using only minimal climate inputs.

Hybrid COOT-ANN: a novel optimization algorithm for prediction of daily crop reference evapotranspiration in Australia

Hybrid COOT-ANN: a novel optimization algorithm for prediction of daily crop reference evapotranspiration in Australia

A Method for Identifying the Dominant Meteorological Factors of Atmospheric Evaporative Demand in Mid‐Long Term

AbstractTo better understand the changes in the atmospheric evaporative demand (AED) in the context of global warming, the anomaly contribution analysis is presented to estimate mid‐long term contributions of meteorological factors to the AED. The Pearson correlation coefficient (RP) between the total contribution (ψ) of meteorological factors and the relative variation (ϕ) of the AED, and the Sen's trend slope (βS) of (ϕ, ψ) scatters are used to evaluate the applicability of the method. The smaller the values of |1 − RP| and |1 − βS|, the more applicable the method is. To validate the method, the reference crop evapotranspiration is employed as a proxy for the AED. The multi‐year contribution analysis is used as a comparison approach, which can only investigate the dominant meteorological factors of the AED in long term. Moreover, the Huaihe River basin of China is taken as a case study. Results show that (a) the values of |1 − RP| and |1 − βS| in mid‐long term are less than 0.1 in most cases when applying the anomaly contribution analysis, and the mid‐long term contribution processes of meteorological factors to the AED are clearly demonstrated; and (b) the wind speed and sunshine hours are the two most dominant factors (the total absolute contribution exceeds 60%) in long term, but they are not always the dominant factors in mid‐long term (e.g., wind speed in summer, and sunshine hours in winter). Therefore, the anomaly contribution analysis is a reasonable and effective method, which can help to gain insights into the changes in the AED.

Crop coefficient determination and evapotranspiration estimation of watermelon under water deficit in a cold and arid environment.

To investigate the evapotranspiration and crop coefficient of oasis watermelon under water deficit (WD), mild (60%-70% field capacity, FC)and moderate (50%-60% FC) WD levels were set up at the various growth stages of watermelon, including seedling stage (SS), vine stage (VS), flowering and fruiting stage (FS), expansion stage (ES), and maturity stage (MS), with adequate water supply (70%-80% FC) during the growing season as a control. A two-year (2020-2021) field trial was carried out in the Hexi oasis area of China to explore the effect of WD on watermelon evapotranspiration characteristics and crop coefficient under sub-membrane drip irrigation. The results indicated that the daily reference crop evapotranspiration showed a sawtooth fluctuation which was extremely significantly and positively correlated with temperature, sunshine hours, and wind speed. The water consumption during the entire growing season of watermelon varied from 281-323 mm (2020) and 290-334 mm (2021), among which the phasic evapotranspiration valued the maximum during ES, accounting for 37.85% (2020) and 38.94% (2021) in total, followed in the order of VS, SS, MS, and FS. The evapotranspiration intensity of watermelon increased rapidly from SS to VS, reaching the maximum with 5.82 mm·d-1 at ES, after which it gradually decreased. The crop coefficient at SS, VS, FS, ES, and MS varied from 0.400 to 0.477, from 0.550 to 0.771, from 0.824 to 1.168, from 0.910 to 1.247, and from 0.541 to 0.803, respectively. Any period of WD reduced the crop coefficient and evapotranspiration intensity of watermelon at that stage. And then the relationship between LAI and crop coefficient can be characterized better by an exponential regression, thereby establishing a model for estimating the evapotranspiration of watermelon with a Nash efficiency coefficient of 0.9 or more. Hence, the water demand characteristics of oasis watermelon differ significantly during different growth stages, and reasonable irrigation and water control management measures need to be conducted in conjunction with the water requirements features of each growth stage. Also, this work aims to provide a theoretical basis for the irrigation management of watermelon under sub-membrane drip irrigation in desert oases of cold and arid environments.

Determination of Crop water Requirement and Irrigation Scheduling for Major Crops of Surendranagar, Gujarat Using CROPWAT 8.0

Abstract: Due to the growing need for water for uses such as hydropower, farming, and drinking purpose among others, water is becoming an endangered asset. Water is increasingly needed for a variety of reasons as the population grows. On the contrary, the accessibility of water resources is limited. To determine crop water requirement for any crop and evaluate irrigation scheduling for effective water management, crop coefficients and reference crop evapotranspiration must be calculated. The purpose of this research is to determine crop water requirement of Cotton, Wheat and Sesame for Surendranagar Region and Prepare Irrigation Scheduling using the FAO-56 Penman-Monteith Method using FAO CROPWAT 8.0 Software. Meteorological data for Surendranagar region such as maximum and minimum temperatures, mean relative humidity, sunshine hours, and wind speed, are obtained from Wadhwan taluka of Surendranagar used to calculate reference crop evapotranspiration. Crop water requirement for Cotton using CROPWAT 8.0 model is obtained 876.3 mm/dec Crop water Requirement for Wheat is obtained 543.9 mm/dec and Crop water Requirement for Sesame is obtained 529.6 mm/dec This finding may be helpful for agricultural planning and effective irrigation control in the growing of cotton, Wheat and Sesame.

Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a Random Forest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves–Samani, Modified Penman and modified Hargreaves–Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990–2010) and nine years (2011–2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r2 value of 0.92. The seasonal ET0 estimated by modified Hargreaves–Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop when compared to irrigation scheduling by the FAO PM model, demonstrating the applicability of the developed model for irrigation in the semiarid region of the Punjab in India.

An Integrated Approach to Evaluating Crop Water Requirements and Irrigation Schedule for Optimizing Furrow Irrigation Design Parameters in Kurnool District, India

In Kurnool district, due to the hot, arid climatic conditions, proper study of crop evapotranspiration and its effect on crop water demand for various crops is an extremely important issue. More focus will be given to the design and development of surface irrigation systems based mostly on furrow irrigation, as the rainfall pattern for this district was irregular in 2005. The crop water requirement and furrow irrigation design parameters for optimising the beneficial utilisation of available water resources and field performances are studied in this research. Some major crops, such as cotton, sugarcane, sorghum, maize, and sunflower, are considered when adopting a suitable furrow irrigation system for soil conditions of the types black clay, red loamy, and medium loam. The reference crop evapotranspiration was estimated for 2005 based on the Penman–Monteith equation as per FAO guidelines by the CROPWAT 8.0 model. Crop Water Requirement (CWR), Net Irrigation Requirement (NIR), and Gross Irrigation Requirement (GIR) were determined for available climatic and soil conditions. Soil surface characteristics were studied using AQUACROP 6.1 and compared with CROPWAT 8.0 for model accuracy. Various conservative and non-conservative crop characteristics were studied under limited set conditions, and correlation equations were developed between different parameters. Moreover, different furrow irrigation design parameters were considered by FURDEV (a module of the SURDEV model) for three modes of operation (i.e., fixed flow, cutback flow, and tailwater reuse method) under modified SCS (Soil Conservation Service) families of soil infiltration characteristics. NIR values were 200 mm, 1423.2 mm, 220 mm, 150 mm, and 150 mm for cotton, sugarcane, maize, sorghum, and sunflower, respectively. A maximum significant dry yield was observed for maize (13.586 tonnes/ha). Additionally, the maximum application efficiency and storage efficiency were obtained at 95.5% for sorghum and 99.3% for sunflower, which would be a beneficial outcome of this research. In general, the results of this research might be very effective for the irrigation authority of Kurnool District to plan suitable approaches for designing and developing proper water management systems.

A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

Crop Water Requirement for Cotton Crop in Unchadi Section Command Area of Shetrunji Right Bank Main Canal using WEAP Model

Abstract: Crop coefficients and reference crop evapotranspiration must be determined in order to estimate irrigation water requirements for any crop and improve irrigation scheduling and water management. The objective of this study is to evaluate the crop water requirements of cotton using the FAO-56 Penman-Monteith Method with dual crop coefficients. Meteorological data for the Unchadi Section Command Area of Shetrunji Right Bank Main Canal, such as maximum and minimum temperatures, mean relative humidity, sunlight hours, and wind speed, are used to calculate reference crop evapotranspiration. The MABIA method in WEAP (Water Evaluation And Planning) software uses the dual-Kc approach to calculate crop evapotranspiration or crop water requirement, which is described in FAO Irrigation and Drainage Paper No. 56, whereby the Kc value is divided into a ‘basal’ crop coefficient (Kcb) and a separate component (Ke) representing evaporation from the soil surface. The basal crop coefficient represents actual ET conditions when the soil surface is dry but sufficient root zone moisture is present to support full transpiration. Cotton crop water requirement (ETc) using is found about 940.93 mm during the growing season in region. This result can be useful for agricultural planning and efficient irrigation management for cotton cultivation in Unchadi Section Command Area of Shetrunji Right Bank Main Canal.

Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model.

The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation water use efficiency, it is necessary to predict the water requirements for crops in advance. Reference evapotranspiration (ETo) is a hypothetical standard reference crop evapotranspiration, many types of artificial intelligence models have been applied to predict ETo; However, there are still few in the literature regarding the application of hybrid models for deep learning model parameters optimization. This paper proposes two hybrid models based on particle swarm optimization (PSO) and long-short-term memory (LSTM) neural network, used to predict ETo at the four climate stations, Shaanxi province, China. These two hybrid models were trained using 40 years of historical data, and the PSO was used to optimize the hyperparameters in the LSTM network. We applied the optimized model to predict the daily ETo in 2019 under different datasets, the result showed that the optimized model has good prediction accuracy. The optimized hybrid models can help farmers and irrigation planners to make plan earlier and precisely, and can provide valuable information to improve tasks such as irrigation planning.

Estimation of Reference Crop Evapotranspiration with Three Different Machine Learning Models and Limited Meteorological Variables

Precise reference crop evapotranspiration (ET0) estimation plays a key role in agricultural fields as it aids in the proper operation and management of irrigation scheduling. However, reliable ET0 estimation poses a challenge when there is insufficient or incomplete long-term meteorological data at the East Coast Economic Region (ECER), Malaysia, where the economy is highly dependent on agricultural crop production. This study evaluated the performances of different standalone machine learning (ML) models, namely, the light gradient boosting machine (LGBM), decision forest regression (DFR), and artificial neural network (ANN) models using four different combinations of meteorological variables. The incorporation of solar radiation enhanced the accuracy of the standalone ML models, demonstrating the role of energetic factors in the evapotranspiration mechanism. Additionally, both the ANN and LGBM models showed overall satisfactory performances, and were thus recommended them as alternate models for ET0 estimation. This was owing to their good capability in capturing the non-linearity and interaction process among the meteorological variables. The outcomes of this study will be advantageous to farmers and policymakers in determining the actual crop water demands to maximize crop productivity in data-scarce tropical regions.

Local and global sensitivity analysis and its contributing factors in reference crop evapotranspiration

Abstract Sensitivity analysis (SA) intends to identify the key meteorological variables that affect the performance of reference crop evapotranspiration (ET0) models. It is of importance in assessing the variability of meteorological variables and ET0, especially in the face of increasing climate uncertainties. However, the surging of inconsistencies resulting from global changes in meteorological conditions due to climate change have impacted the ET0 model estimation in different regions, with detrimental effects on water resources and crop production. Therefore, efficient SA is necessary to evaluate the impact of changes in meteorological variables that influence ET0 model estimation. This mini review analyses the various SA methods applied in the field of ET0, based on a comprehensive and comparative analysis of existing SA methods from all around the world. The study discusses the advantages and disadvantages of each SA method, as well as the factors affecting the SA of ET0. The study also provides future prospects that may contribute to more solid and powerful analysis for ET0 model estimations and conclusions.

Construction, calibration and performance evaluation of a lysimeter, with a double weighing system: Hydraulic and mechanical

According to the literature, hydraulic and mechanical weighing lysimeters, which use electronic load cells, are considered the most accurate equipment to directly determine reference evapotranspiration (ETo) and crop evapotranspiration (ETc). However, the lack of comparative performance studies between these systems makes it impossible to make accurate statements about which is the most stable system and has the highest accuracy measurement. To evaluate the performance, it is important to consider parameters such as calibration, error analysis, accuracy test, hysteresis, and also the susceptibility to interference from external factors. Therefore, this study aimed to compare hydraulic and mechanical weighing systems through the construction, calibration and performance evaluation of a lysimeter with a dual weighing system operating simultaneously under the same environmental conditions. This study aimed to fill in the gaps found in the literature and help to solve questions related to the performance of these systems. Both systems have a high degree of linearity, with R² above 0.999, responding proportionally to mass variations in the lysimeter. However, the mechanical weighing system performed better with lower hysteresis, higher accuracy measurement, and more stable behavior, being less susceptible to environmental interference than the hydraulic weighing system. Both systems are suitable for instantaneous measurements of mass and determination of ETo and ETc, but for purposes that require greater accuracy measurement, it is recommended to opt for the mechanical weighing system.

Water Requirements of Wheat Crops in a Small Watershed by Using CROPWAT

A study was conducted to determine the water demand of wheat crops in the area around the Tonna Pumping Canal. It used 2017-2018 climate data to determine the need for wheat crops in water in CROPWAT. Reference crop evapotranspiration (ETo) was determined using the FAO Penman Monteith method. When considering wheat crops, four stages of crop growth were considered: early stage, development stage, mid-maturity, and late-maturity stages. Crops were planted in the 2017 rabi season and yields were determined for each.This study shows that baseline evapotranspiration (ETo) in the study area ranges from 1.48 to 3.98 mm/day. Crop evapotranspiration (ETc) and water demand for wheat crops ranged from 0.44 to 3.75 mm/day and 1.9 to 39.8 mm/day. The maximum water demand was 0.13 l/s/ha or 1.1 mm/day with 65% efficiency. The irrigation water demand was estimated at about 27.93 mm/day, and the actual water intake was 81,000.29 m3. The canal can therefore provide a convenient supply of the water needed to irrigate the area.

Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China

This paper established hybrid prediction models based on variational mode decomposition (VMD), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) combined with the backpropagation neural network model (BPNN) to improve the prediction accuracy of reference crop evapotranspiration (ET0) time series with the characteristics of nonlinearity and instability. The daily ET0 data of 11 representative stations in Xinjiang from 1993 to 2016 were selected for model training and testing and compared with the results of support vector regression (SVR) and gradient boosting regression tree (GBRT) as the two empirical machine learning models. The results indicated the superiority of the VMD-BPNN hybrid prediction model to EMD-BPNN and EEMD-BPNN in terms of accuracy and stability, with root mean square error (RMSE) = 0.405 mm/d, mean absolute error (MAE) = 0.268 mm/d, and coefficient of determination (R2) = 0.979. When employing the VMD-BPNN model to forecast ET0 for seven days, the RMSE and Nash-Sutcliffe efficiency coefficient (NSE) of the VMD-BPNN model were 0.588 mm/d and 0.952, respectively, and the prediction results indicated high precision and reliability. The prediction accuracy of the VMD-BPNN model was significantly higher than that of single machine learning models, such as BPNN, SVR, and GBRT. The RMSE and MAE values of the VMD-BPNN model were more than 60% smaller than BPNN, SVR, and GBRT models, and R2 and NSE were approximately 18% higher than BPNN, SVR, and GBRT models, respectively. This demonstrates the effectiveness of the VMD method in reducing the non-stationarity of the original daily ET0 data. The BPNN model predicted the decomposed data series, and the prediction accuracy and stability were significantly enhanced. This indicates the high reliability of the VMD-BPNN model and its capability for ET0 prediction in Xinjiang.

Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations; the role of T, n, RH, wind speed (U2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.

Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

The increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET0 estimation based on the users’ interests.