Estimation Of Reference Evapotranspiration Research Articles

An improved remote sensing reference evapotranspiration estimation by simple satellite data and machine learning

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (e.g., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies

Improved remote sensing reference evapotranspiration estimation using simple satellite data and machine learning

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (i.e., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies

Differences in sum-of-hourly and daily reference evapotranspiration for a rice–wheat cropping system in Ganga Basin, India

ABSTRACT Estimating reference evapotranspiration (ETo) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures in ETo computation. Hourly timesteps can improve the accuracy of ETo estimates, as data averaged daily may misrepresent evaporative power during parts of the day. This study investigates the differences between daily ETo computations performed at 24 h (ETo,d) and sum of hourly (ETo,h) for rice–wheat cropping systems in the Ganga Basin, India. The meteorological data were collected from an automatic weather station located in an experimental plot at IIT Kanpur, India. Daily and sum-of-hourly ETo computations were performed according to the FAO-PM equation for rice and wheat cropping seasons. Diurnal variations of meteorological variables resulted in an underestimation of ETo when the daily timestep was considered. No significant difference was observed during wet periods. The sum-of-hourly estimates of ETo were able to capture the abrupt changes in climate variables, while the daily ETo failed to represent it as it considered the average values only. As a result, the sums of hourly ETo estimates are more reliable in the Ganga Plains.

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Evaluation and calibration of alternative methods for estimating reference evapotranspiration in the main hydrosystems of Senegal: Senegal, Gambia and Casamance River Basins

Evaluation and calibration of alternative methods for estimating reference evapotranspiration in the main hydrosystems of Senegal: Senegal, Gambia and Casamance River Basins

A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain

The reference evapotranspiration (ETo) is a key parameter in achieving sustainable use of agricultural water resources. To accurately acquire ETo under limited conditions, this study combined the northern goshawk optimization algorithm (NGO) with the extreme gradient boosting (XGBoost) model to propose a novel NGO-XGBoost model. The performance of this model was evaluated using meteorological data from 30 stations in the North China Plain and compared with XGBoost, random forest (RF), and k nearest neighbor (KNN) models. An ensemble embedded feature selection (EEFS) method combined with the results from RF, XGBoost, adaptive boosting (AdaBoost), and categorical boosting (CatBoost) models is used to obtain the importance of meteorological factors in estimating ETo, and thereby determine the optimal combination of inputs to the model. The results indicated that by using the top 3, 4, and 5 important factors as input combinations, all models achieved high ETo estimation accuracy. It is worth noting that there were significant spatial differences in the estimation precisions of the four models, but the NGO-XGBoost model exhibited consistently high estimation precisions, with global performance indicator (GPI) rankings of 1st, and the range of coefficient of determination (R2), nash efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and mean bias error (MBE) were 0.920–0.998, 0.902–0.998, 0.078–0.623 mm d−1, 0.058–0.430 mm d−1, and −0.254–0.062 mm d−1, respectively. Furthermore, the accuracy of the NGO-XGBoost model in estimating ETo varied across different seasons, which was more significantly affected by humidity and wind speed in winter. When the target station data was insufficient, the NGO-XGBoost model was trained by using the historical data from neighboring stations and still maintained a high precision. Overall, this study recommends a reliable method for estimating ETo, which provides a reference for accurately calculating ETo in the North China Plain in the absence of meteorological data.

Selection of best wavelet functions and decomposition levels for coupling with soft computing methods in estimating ETo in coastal and island regions

ABSTRACT Mass transfer-based models are among the extensively applied reference evapotranspiration (ET o ) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ET o values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ET o ). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions

A correct determination of irrigation water requirements necessitates an adequate estimation of reference evapotranspiration (ETo). In this study, monthly ETo is estimated using artificial neural network (ANN) models. Eleven combinations of long-term average monthly climatic data of air temperature (min and max), wind speed (WS), relative humidity (RH), and solar radiation (SR) recorded at nine different weather stations in Tunisia are used as inputs to the ANN models to calculate ETo given by the FAO-56 PM (Penman–Monteith) equation. This research study proposes to: (i) compare the FAO-24 BC, Riou, and Turc equations with the universal PM equation for estimating ETo; (ii) compare the PM method with the ANN technique; (iii) determine the meteorological parameters with the greatest impact on ETo prediction; and (iv) determine how accurate the ANN technique is in estimating ETo using data from nearby weather stations and compare it to the PM method. Four statistical criteria were used to evaluate the model’s predictive quality: the determination coefficient (R2), the index of agreement (d), the root mean square error (RMSE), and the mean absolute error (MAE). It is quite evident that the Blaney–Criddle, Riou, and Turc equations underestimate or overestimate the ETo values when compared to the PM method. Values of ETo underestimation ranged from 1.9% to 66.1%, while values of overestimation varied from 0.9% to 25.0%. The comparisons revealed that the ANN technique could be adeptly utilized to model ETo using the available meteorological data. Generally, the ANN technique performs better on the estimates of ETo than the conventional equations studied. Among the meteorological parameters considered, maximum temperature was identified as the most significant climatic parameter in ETo modeling, reaching values of R and d of 0.936 and 0.983, respectively. The research showed that trained ANNs could be used to yield ETo estimates using new data from nearby stations not included in the training process, reaching high average values of R and d values of 0.992 and 0.997, respectively. Very low values of MAE (0.233 mm day−1) and RMSE (0.326 mm day−1) were also obtained.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Reference evapotranspiration estimation using reanalysis and WaPOR products in dryland Croplands

Accurate estimation of the reference evapotranspiration (ETo) is crucial for determining crop water requirements. However, the lack of appropriate weather stations representing croplands, particularly in drylands, may adversely influence the accuracy of ETo estimates. To overcome this issue, a promising approach is to use meteorological stations in cropland areas to collect weather data that are representative of actual conditions. However, the number of agrometeorological stations in these areas is limited. Therefore, this study aims to assess the effectiveness of three datasets, including ERA5 and ERA5-Land, and WaPOR (Water Productivity Open-access portal), for estimating ETo in cropland areas on a basin scale. The land use/land cover (LULC) of the European Space Agency (ESA) was used to identify the sites resembling agrometeorological stations. Data were collected from 2009 to 2022, and the FAO-Penman-Monteith method was used to estimate daily and monthly ETo. The accuracy and reliability of ETo estimates with the three datasets were evaluated by comparing them with ETo estimated by ground measurements. Statistical analysis metrics, normalized root mean squared error (nRMSE), and relative mean bias error (rMBE) were used to assess the performance of the datasets. This study highlights that ERA5 exhibited superior overall performance compared to other datasets in estimating ETo. However, WaPOR performed better at high-altitude stations with inhomogeneous topography than ECMWF reanalysis (i.e., ERA5 and ERA5-L). Thus, none of the datasets could provide accurate ETo estimates for all the stations within the basin. Therefore, applying the best-performing data source yielded better results than using a single dataset. These findings are valuable for improving irrigation scheduling and water management practices on a large scale, particularly in regions facing data scarcity challenges.

Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

AbstractAccurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements.

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman–Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman–Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

Estimation of reference evapotranspiration using some class-A pan evaporimeter pan coefficient estimation models in Mediterranean-Southeastern Anatolian transitional zone conditions of Turkey.

Reference evapotranspiration (ETo), which is used as the basic data in many studies within the scope of hydrology, meteorology, irrigation and soil sciences, can be estimated by using the evaporation (Epan) measured from the class-A pan evaporimeter. However, this method requires reliable pan coefficients (Kp). Many empirical models are used to estimate Kp coefficients. The reliability of these models varies depending on climatic and environmental conditions. Therefore, they need to be tested in the local conditions where they will be used. In this study, conducted in Kahramanmaraş, which has a semi-arid Mediterranean climate in Turkey during the July-October periods of 2020 and 2021, aimed to determine the usability levels of six Kp models in estimating daily and monthly average ETo. The Kp coefficients estimated by the models were multiplied with the daily Epan values, and the daily average ETo values were estimated on the basis of the model. The daily Epan values were measured using an ultrasonic sensor sensitive to the water surface placed on the class-A pan evaporimeter. The ultrasonic sensor was managed by a programmable logic controller (PLC). To enable the sensor to be managed by PLC, a software was prepared using the CODESYS programming language and uploaded to the PLC. The daily average ETo values determined by the FAO-56 Penman-Monteith equation were accepted as actual values. The ETo values estimated by the Kp models were compared with the actual ETo values using the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2) statistical approaches. The Wahed & Snyder outperformed the other models in estimating daily (MAE = 0.78 mm day-1, MAPE = 14.40%, RMSE = 0.97 mm day-1, R2 = 0.82) and monthly (MAE = 0.32 mm day-1, MAPE = 5.88%, RMSE = 0.32 mm day-1, R2 = 0.99) average ETo. FAO-56 showed the nearest performance to Wahed & Snyder. The Snyder model presented the worst performance in estimating daily (MAE = 2.09 mm day-1, MAPE = 37.53%, RMSE = 2.36 mm day-1, R2 = 0.82) and monthly (MAE = 1.83 mm day-1, MAPE = 31.82%, RMSE = 1.87 mm day-1, R2 = 0.99) average ETo. It has been concluded that none of the six Kp models can be used to estimate the daily ETo in Kahramanmaraş located in the Mediterranean-Southeastern Anatolian transitional zone, and only Wahed & Snyder and FAO-56 can be used to estimate the monthly ETo without calibration.

Improvement the estimation of reference evapotranspiration by combining different types of meteorological data Using machine learning models

Irrigation and the strategic planning thereof play a pivotal role in diverse hydrological inquiries, with reference evapotranspiration (ETo) standing as a paramount variable within this domain. While the equation (FAO-56 PM)is extensively employed for (ETo)estimation, its dependence on numerous weather datas such as solar radiation, temperature, relative humidity, extraterrestrial radiation and wind speed, introduces inherent constraints, the remote computation necessitates a substantial array of sensors, thereby incurring considerable expenses. To surmount this challenge, artificial intelligence methodologies, encompassing various machine learning (ML) models, are harnessed for ETo estimation, requiring only minimal parameters.This investigation scrutinizes the effectiveness of alternative equations (Hargreaves-Samani, Romannenko, Jensen-Haise, ASCE_PM) vis-à-vis (ML) models such as Xgboost,Support Vector Machine (SVM), and Random Forest (RF) in the estimation of ETo across the Meknes region, utilizing diverse permutations of the four measured variables. The study employs an extensive array of hyperparameters in two distinct scenarios: (i) randomization of all data, and (ii) training on one station while validating on another. All methodologies employed in this study yield satisfactory outcomes when juxtaposed against empirical models reliant on minimal meteorological data.

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration.

Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

Balanço de Radiação sobre água de Baixa Turbidez Purificada Artificialmente para Irrigação de Fumo Cultivado sob Sombreamento. II. Análise do Albedo da água e Modelagem da Radiação Líquida

Abstract The albedo of a water surface and the energy available for evaporation are strongly correlated and studying such processes is of paramount importance for water security at farm and regional levels. In this paper, water albedo (αw) and net all-wave radiation (Rn) were analyzed after being measured above the surface of an artificially cleaned and low-turbidity water used for tobacco irrigation. It was observed that αw decreased as the sun elevation (θ) increased, especially for clear and near clear skies. The results showed that αw can be reasonably predicted with a power law model either in terms of θ or Sg (incoming solar radiation) across different cloud cover conditions. From this study, a mean daily albedo of 0.05 is recommended. Three approaches were considered for estimation of daily Rn. In the first, a linear regression model strongly fitted Rn data in terms of Sg solely. The second option based on the definition of Rn was [0.95Sg - Lnet(56)], where Lnet(56) is net longwave (LW) radiation as used in the FAO56 model for reference evapotranspiration estimation, and the third was [0.95Sg - Lnet(MLR)], where MLR stands for multiple linear regression. The disadvantages of approaches (1) and (3), based on regressions, is that they are constrained to the type of water stored in the farm and the climatic conditions of the region. The performance of approach (2), where Lnet(56) is a widely used model, was comparable to the others can potentially be improved with a site-specific calibration. All three approaches for estimating daily Rn proposed in this study can possibily be extended to clear water that did not go through any filtration process.

Daily reference evapotranspiration estimation utilizing deep learning models with varied combinations of weather data

Effective irrigation planning pivots on the meticulous monitoring of ETo (the reference evapotranspiration), a fundamental variable in diverse studies. The go-to method for approximate ETo, the FAO-56 Penman-Monteith (FAO-56 PM) equation, demands an array of weather data, encompassing relative humidity, temperature, solar radiation, and wind speed. However, this data-intensive requirement presents challenges in situations where such information is limited, and artificial intelligence is being used to address this challenge, come into play to estimate ET0 with a streamlined set of parameters. The study begins with a comprehensive analysis, comparing the performance of Penman-Monteith (FAO-56 PM) and (ASCE_PM) with deep learning models such as artificial neural networks (ANN) and one-dimensional convolutional neural networks (CNN 1d).The principal aim is to estimate daily reference evapotranspiration (ETo) in the region of Morocco, specifically Meknes, employing a minimal set of meteorological variables across various combinations of measured data on the fundamental variables that constitute ETo. These combinations encompass scenarios involving all four variables, different combinations of three, two, and each variable in isolation. Two implementation scenarios are considered: (i) cross-validation across all datasets and (ii) training with one station and validating with another. Across these varied techniques, commendable results emerge, portraying a favourable comparison against empirical models reliant on minimal meteorological data.