Prediction Of Daily Evapotranspiration Research Articles

Daily Evapotranspiration Prediction at Arid and Semiarid Regions by Using Multiple Linear Regression Technique at Ramadi City in Iraq Region

For many decades, because of the climate change, the interest in water resources management have been increased. The researchers curried out huge efforts to investigate the factors that influence the water resources quantities. Many of these efforts focused on studying the water losses by evapotranspiration. In the current study, a new mathematical model is introduced. It was built based on Multiple linear regression method (MLR). This model was utilized to estimate the daily evapotranspiration in Ramadi city which characterized with arid and semiarid environment and to investigate the influence of different parameters on the evapotranspiration process. The field data were collected from the digital meteorological station at the Upper Euphrates basin Developing Centre during the period from 23/11/2020 to 1/10/2022. These data include the evapotranspiration, maximum temperature, the minimum temperature, the average temperature, wind speed, relative humidity, and solar radiation. The model was calibrated using 80% of the data set, while 20% of the data set were used for validation. The solar radiation shows the highest impact on ET, while the lowest was recorded by relative humidity. High performance of the model was proved by testing it using Performance Indicators such as RMSE, NAE, MAPE, NSE, and R2.

Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons

This study forecasts multi-step-ahead Potential Evapotranspiration (ETO) in India using globally available fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) gridded climate reanalysis products (ERA5). For this purpose, the potential of six machine learning approaches are examined across different agro climatic zones and cropping seasons. Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Multi-Layer Perceptron (MLP), one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are first evaluated at the station level for up to 28-day-ahead prediction of daily ETO using meteorological station data as well as gridded ERA5 products. Using meteorological observations, at all three stations, SVR performs the best for a prediction horizon (PH) up to 2-days whereas LSTM performs the best for PH of 7-days. Using ERA5 datasets as input, among the three stations, the best performance is observed in Nagpur, where the best models for real-time and 28-day ahead prediction are LSTM (R2 = 0.847 and MAE = 0.474 mm/day) and RF (R2 = 0.722 and MAE = 0.635 mm/day) respectively. As the PH increases from 2-day to 28-day-ahead, models using ERA5 datasets performs better than those using station observations for most of the cases. Although the prediction performance drops initially with the increase in lead time, the drop in performance between 7-day and 28-day-ahead prediction is negligible. Evaluation of gridwise ETO prediction across entire India, using Global Land Evaporation Amsterdam Model (GLEAM) dataset as a reference, indicates MLP and CNN as the top performing models. Considering the crop seasons, model performance during Rabi season (October-March) ranged from 0.103 to 0.145 mm/day (MAE) and 0.977 to 0.988 (R2), which is better than the Kharif season (June-September) where MAE ranged from 0.140 to 0.234 mm/day and R2 ranged from 0.906 to 0.962. During the Rabi season, the ETO prediction performance of the arid agro-climatic zone is found to be superior to the other three agro-climatic zones, with the highest range of R2 (0.939 to 0.955) and lowest range of MAE (0.146 to 0.182 mm/day). Even the worst prediction performance, which is observed in the Humid region during the Rabi season, is also reasonably good (R2 = 0.656 to 0.79); thereby establishing the potential of the proposed models in multi-step ahead ETO prediction across various agro-climatic zones and cropping seasons.

Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China

Reliable and accurate prediction of reference evapotranspiration (ETo) is a precondition for the efficient management and planning of agricultural water resources as well as the optimal design of irrigation scheduling. This study evaluated the performances of four bio-inspired algorithm optimized extreme learning machine (ELM) models, i.e. ELM with genetic algorithm (ELM-GA), ELM with ant colony optimization (ELM-ACO), ELM with cuckoo search algorithm (CSA) and ELM with flower pollination algorithm (ELM-FPA), for predicting daily ETo across China by using a five-fold cross-validation approach. These models were further compared with the classical ELM model parameterized by the grid search method to demonstrate their capability and efficiency. Daily maximum and minimum ambient temperatures, wind speed, relative humidity and global solar radiation data during 2001–2015 collected from eight meteorological stations in contrasting climates of China were utilized to train, validate and test the models. The results showed that ETo values predicted by all ELM models agreed well with the corresponding FAO-56 Penman–Monteith values, with R2, RMSE, NRMSE and MAE ranging 0.9766–0.9967, 0.0896–0.2883 mm day−1, 3.2910%-11.7653% and 0.0708–0.1998 mm day−1, respectively. The ELM-FPA model (R2 = 0.9930, RMSE = 0.1589 mm day−1, NRMSE = 5.5406% and MAE = 0.1188 mm day−1) slightly outperformed the ELM-CSA model (R2 = 0.9922, RMSE = 0.1619 mm day−1, NRMSE = 5.6864% and MAE = 0.1200 mm day−1) during testing, both of which were superior to the ELM-ACO (R2 = 0.9912, RMSE = 0.1730 mm day−1, NRMSE = 6.0816% and MAE = 0.1254 mm day−1) and ELM-GA (R2 = 0.9889, RMSE = 0.1895 mm day−1, NRMSE = 6.7197% and MAE = 0.1310 mm day−1) models, followed by the standalone ELM model (R2 = 0.9856, RMSE = 0.2104 mm day−1, NRMSE = 7.1693% and MAE = 0.1408 mm day−1). The four hybrid ELM models exhibited higher improvements in daily ETo prediction in the temperate monsoon and (sub)tropical monsoon climates (with average decrease in RMSE of 14.0%, 25.1%, 31.4% and 33.1%, respectively), compared with those in the temperate continental and mountain plateau climates (with average decrease in RMSE of 5.2%, 9.8%, 12.9% and 14.1%, respectively). The results advocated the capability of bio-inspired optimization algorithms, especially the FPA and CSA algorithms, for improving the performance of the conventional ELM model in daily ETo prediction in contrasting climates of China.

Daily Evapotranspiration Prediction from Louisiana Flooded Rice Field

During 1995, daily evapotranspiration (Et) from a flooded rice field was calculated with a water balance equation using measured values of water level, precipitation, irrigation, seepage, and tailwater runoff. Stagewise Et was 6.3, 8.1, and 6.8 mm/d for the vegetative, flowering, and yield formation stages, respectively; average daily Et was 6.8 mm/d. Grass reference Et (Etr) combination models Penman-Monteith, FAO-Penman, and 1963 Penman were evaluated for their capabilities to predict rice Et using daily weather data. Daily Etr was also calculated by summing up hourly values with the Penman-Monteith method. The Penman-Monteith (daily) method had a coefficient of determination (R2) of 63.7%, as compared with 62.9, 60.0, and 61.7% for Penman-Monteith (hourly), FAO-Penman, and 1963 Penman methods, respectively. Crop coefficients (Kc) using the Penman-Monteith (daily) model were 1.39, 1.51, and 1.43 for the vegetative, flowering, and yield formation stages, respectively. Developed Kc values were verified using limited rice Et data in 1996. The Kc values underpredicted rice Et by 3.7%, which was acceptable for water management.