Estimation Of Reference Evapotranspiration Research Articles

Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates in the Peruvian Altiplano

The FAO 56 Penman–Monteith equation (PM) is considered the most accurate method for estimating reference evapotranspiration (ETo). However, PM requires a large amount of data that is not always available. Thus, the objective of this study is to improve the Hargreaves–Samani (HS) reference evapotranspiration estimates in the Peruvian Altiplano (PA) by calibrating the radiation coefficient KRS. The results show modified HS (HSM) ETo estimates at validation after KRS calibration, revealing evident improvements in accuracy with Nash–Sutcliffe efficiency (NSE) between 0.58 and 0.93, percentage bias (PBIAS) between −0.58 and 1.34%, mean absolute error (MAE) between −0.02 and 0.05 mm/d, and root mean square error (RMSE) between 0.14 and 0.25 mm/d. Consequently, the multiple linear regression (MLR) model was used to regionalize the KRS for the PA. It is concluded that, in the absence of meteorological data, the HSM equation can be used with the new values of KRS instead of HS for the PA.

Comparison of machine learning techniques and spatial distribution of daily reference evapotranspiration in Türkiye

Reference evapotranspiration (ET0) estimates are commonly used in hydrologic planning for water resources and agricultural applications. Last 2 decades, machine learning (ML) techniques have enabled scientists to develop powerful tools to study ET0 patterns in the ecosystem. This study investigated the feasibility and effectiveness of three ML techniques, including the k-nearest neighbor algorithm, multigene genetic programming, and support vector regression (SVR), to estimate daily ET0 in Türkiye. In addition, different interpolation techniques, including ordinary kriging (OK), co-kriging, inverse distance weighted, and radial basis function, were compared to develop the most appropriate ET0 maps for Türkiye. All developed models were evaluated according to the performance indices such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Taylor, violin, and scatter plots were also generated. Among the applied ML models, the SVR model provided the best results in determining ET0 with the performance indices of R2 = 0.961, RMSE = 0.327 mm, and MAE = 0.232 mm. The SVR model’s input variables were selected as solar radiation, temperature, and relative humidity. Similarly, the maps of the spatial distribution of ET0 were produced with the OK interpolation method, which provided the best estimates.

Projection of net irrigation requirement over India using a bias-corrected spatially downscaled dataset

Abstract With the country's economy directly dependent on food grain production, accurate estimation of the net irrigation requirement (NIR) of crops is essential for maximizing production. The daily IMD precipitation (0.25° × 0.25°) and temperature (1° × 1°) gridded datasets (1971–2010) were considered for the present scenario. Two satellite-based crop coefficient layers were prepared for the study: (i) kc_EEFlux, the evaporative fraction layer from earth engine evapotranspiration flux (EEFlux) and (ii) kc_FEWS, the ratio of the famine early warning system network (FEWSNET) monthly actual evapotranspiration (ET) (ETa) to ET0_FEWSNET layers. The NIR was estimated for three different cropping pixels derived from the land-use land cover (LULC) map of India, namely, Kharif-only, Rabi-only, and double/triple pixels using ‘ET0 and NIR toolbar’. The National Aeronautics and Space Administration (NASA)'s Earth Exchange Global Daily Downscaled Projections dataset were employed for projecting the future scenario. Results showed discrepancies in the extreme values of FAO56-Penman–Monteith (PM) and Hargreaves–Samani's estimated reference ET (ET0) but followed a similar trend in all months (R2 = 0.88). Results also suggested that FEWSNET offered more reliable estimates of NIR over EEFlux. The projected ensemble NIR indicated a probable negative relative change during the Kharif season (RCP4.5: from −14.47 to −7.25%; RCP8.5: from −11.45 to −5.59%) while a probable positive relative change during the Rabi season (RCP4.5: 3.30–5.21%; RCP8.5: 2.34–8.83%).

Estimation of reference evapotranspiration by three methods, for the region of Ilhéus/BA

Knowledge of evapotranspiration at various stages of plant development allows irrigation management to be more efficiently carried out according to the actual crop demand. There are a variety of methods that can be used to determine reference evapotranspiration, and due to the high variability of parameters influencing this phenomenon, it is common to have studies that compare different methods to evaluate which one has the greatest applicability to the study location. Monthly climatological data from the National Institute of Meteorology - INMET were collected for the municipality of Ilhéus/BA. Reference evapotranspiration was estimated using the Thornthwaite, Camargo, and Priestley & Taylor methods, and their results were compared with the standard Penman-Monteith method (FAO-56) using statistical indices, the Wilmott agreement index, Pearson correlation coefficient, and confidence index. The Priestley-Taylor method was classified as "Excellent" (c > 0.85). The Thornthwaite and Camargo methodologies were classified as "Fair" (0.61 < c < 0.65) and "Good" (0.66 < c < 0.75), respectively. The Priestley-Taylor method overestimated the values of the PMFAO method, but obtained a higher coefficient of determination with values close to those found by the standard method, and can be used as an alternative to the Penman-Monteith (FAO-56) reference method for the Ilhéus-BA region. The Thornthwaite and Camargo methods had low coefficients of determination and obtained the worst statistical performance, and therefore should be used with caution.

Estimating reference evapotranspiration using Penman-Monteith equation integrated with optimized solar radiation models

Accurate estimates of reference evapotranspiration (ET0) are of great significance to water resources planning and management, but the actual solar radiation (Rs), as the primary parameter for ET0 estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (Rs) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligence algorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing Rs models to improve ET0 estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical Rs models, comprising three sunshine-based models (Angstrom, Ögelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET0 estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate Rs estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimization algorithm, this study found that GSA performed better for the Ögelman model, Fan model, and Chen model when integrating into the PM equation for ET0 estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PMGSA–Fan obtained the most accurate estimates of ET0 at four climatic zones, with a regional spatial gradient in estimates accuracy of Rs from north to south of China. In terms of sunshine-based models, the PMMEA–Angstrom performed better in TMZ, TCZ, and MPZ, whereas the PMGSA–Ögelman performed better in SMZ, respectively; in terms of temperature-based models, the PMMEA–Hunt performed better in TMZ and SMZ, whereas the PMLSM–Hargreaves performed better in TCZ and MPZ, respectively. Overall, this study identifies the optimal Rs estimation model and optimization algorithm for ET0 estimation at four climatic zones, which provide regionally and nationally accurate water consumption information without actual measured Rs in China.

Forecasting Long-Series Daily Reference Evapotranspiration Based on Best Subset Regression and Machine Learning in Egypt

The estimation of reference evapotranspiration (ETo), a crucial step in the hydrologic cycle, is essential for system design and management, including the balancing, planning, and scheduling of agricultural water supply and water resources. When climates vary from arid to semi-arid, and there are problems with a lack of meteorological data and a lack of future information on ETo, as is the case in Egypt, it is more important to estimate ETo precisely. To address this, the current study aimed to model ETo for Egypt’s most important agricultural governorates (Al Buhayrah, Alexandria, Ismailiyah, and Minufiyah) using four machine learning (ML) algorithms: linear regression (LR), random subspace (RSS), additive regression (AR), and reduced error pruning tree (REPTree). The Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) was used to gather daily climate data variables from 1979 to 2014. The datasets were split into two sections: the training phase, i.e., 1979–2006, and the testing phase, i.e., 2007–2014. Maximum temperature (Tmax), minimum temperature (Tmin), and solar radiation (SR) were found to be the three input variables that had the most influence on the outcome of subset regression and sensitivity analysis. A comparative analysis of ML models revealed that REPTree outperformed competitors by achieving the best values for various performance matrices during the training and testing phases. The study’s novelty lies in the use of REPTree to estimate and predict ETo, as this algorithm has not been commonly used for this purpose. Given the sparse attempts to use this model for such research, the remarkable accuracy of the REPTree model in predicting ETo highlighted the rarity of this study. In order to combat the effects of aridity through better water resource management, the study also cautions Egypt’s authorities to concentrate their policymaking on climate adaptation.

Sensitive analysis of meteorological data and selecting appropriate machine learning model for estimation of reference evapotranspiration

This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz and Dezful in the southwest of Iran. Comparison between results of the FAO Penman-Monteith (FPM) method and the mentioned three methods shows that ANN-GA with the Levenberg-Marquardt training method is the best method and the M5T model is the second appropriate method for estimation of reference evapotranspiration. In Ahvaz, R2 and RMSE of ANN-GA method are 0.996, 0.184 mm/day. For M5T method, these values are 0.997 and 0259 mm/day, and for GEP method, they are 0.979 and 0.521 mm/day. In Dezful, R2 and RMSE of ANN-GA method are 0.994, 0.235 mm/day. For M5T method, these values are 0.992 and 0265 mm/day, and for GEP method, they are 0.963 and 0.544 mm/day. In addition, sensitivity analysis shows that the maximum temperature is the most effective parameter, and the wind speed is second effective parameter. In Dezful, the effect of the maximum temperature is more than those of Ahvaz but the effect of wind speed is less than those of Ahvaz. Because Ahvaz is more flatter than Dezful (the movement of wind in Ahvaz is freer than those of Dezful). The third effective meteorological parameter is the average relative humidity in Ahvaz and the sunny hours in Dezful. The reason for this subject is the less distant of Ahvaz from the Persian Gulf (it is source of moisture).

Estimation of reference evapotranspiration using artificial neural network models for semi-arid region of Haryana

The study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) for semi-arid region of Haryana state. Ten years (2011-2020) daily weather data of maximum and minimum temperature, relative humidity, wind speed and sun shine hours was collected from the meteorological observatory at CCS HAU, Hisar. Multilayer perceptron feed forward back propagation ANN models were evaluated for different training algorithms (10), number of hidden layers (1-3) and number of neurons in hidden layers (1-30). Training algorithms compared in the study were heuristic techniques (GDA, GDX, RP), conjugate gradient (CGF, CGP, CGB, SCG), quasi-Newton (BFG, OSS) and Levenberg-Marquardt (LM). Results were compared against standard FAO Penman-Monteith method. The study revealed that best performance for ANN was found with LM algorithm in single layer of 13 neurons exhibiting RMSE, R, ME and RPD values 0.306, 0.986, 0.976 and 6.63, respectively. ANN models showed good performance in prediction of reference evapotranspiration.

Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia

Evapotranspiration is one of the hydrological cycle’s most important elements in water management across economic sectors. Critical applications in the agriculture domain include irrigation practice improvement and efficiency, as well as water resource preservation. The main objective of this research is to forecast reference evapotranspiration using the vector autoregression (VAR) model and investigate the meteorological variables’ causal relationship with reference evapotranspiration using a statistical approach. The acquired 20-year, 1-year, and 2-month research climate datasets from Penang, Malaysia, were split into 80% training data and 20% validation data. Public weather data are used to train the initial VAR model. A Raspberry Pi IoT device connected to a DHT11 temperature sensor was outfitted at the designated experimental crop site. In situ data acquisition was done using DHT11 temperature sensors to measure the ambient temperature and humidity. The collected temperature and humidity data were used in conjunction with the vector autoregression (VAR) model to calculate the reference evapotranspiration forecast. The results demonstrated that the 20-year dataset showed better performance and consistent results in forecasting general reference evapotranspiration, derived using root mean square error (RMSE) and correlation coefficient (CORR) of 1.1663 and −0.0048, respectively. As for the 1-year dataset model, RMSE and CORR were recorded at 1.571 and −0.3932, respectively. However, the 2-month dataset model demonstrated both positive and negative performance due to seasonal effects in Penang. The RMSE ranged between 0.5297 to 2.3562 in 2020, 0.8022 to 1.8539 in 2019, and 0.8022 to 2.0921 in 2018. As for CORR, it ranged between −0.5803 to 0.2825 in 2020, −0.3817 to 0.2714 in 2019, and −0.3817 to 0.2714 in 2018. In conclusion, the model tested using 20-year, 1-year, and 2-month meteorological datasets for estimating reference evapotranspiration (ET0) based on smaller RMSEs demonstrates better performance at predicting the true values, as well as producing both positive and negative CORR performance due to seasonal variations in Penang.

Evaluation of the performance of CFSR reanalysis data set for estimating reference evapotranspiration (ET0) in Turkey

Evapotranspiration is a key process and a necessary parameter for hydrological, meteorological, and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, reference evapotranspiration (ET0) calculated using meteorological data is generally preferred over actual evapotranspiration. However, it is challenging to get complete and accurate data from meteorology stations in rural and mountainous regions. This study examined the suitability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations to compute seasonal reference evapotranspiration for seven different climatic regions of Turkey. The ET0 calculations using the CFSR reanalysis dataset for 1987-2017 were compared to data at 259 weather stations observed in Turkey. As a result of statistical evaluations, it has been determined that the most successful predicted season is winter (C’ = 0.64-0.89, SPAEF= 0.63-0.81). The most successful estimations for this season were obtained from coastal areas with low elevations. The weakest estimations were obtained for the summer season (C’ = 0.52-0.85, SPAEF= 0.59-0.77). These results show that the ET0 estimation ability of the CFSR reanalysis dataset is satisfactory for the study area. In addition, it has been observed that CFSR tends to overestimate the observation data, especially in the southern and western regions. These findings indicate that the results of the ET0 calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used, especially in the summer models.

Investigating the roles of different extracted parameters from satellite images in improving the accuracy of daily reference evapotranspiration estimation

Agricultural water management, crop modeling, and irrigation scheduling are all dependent on the accurate estimation of reference evapotranspiration (ET0). A satellite image can also compensate for the lack of reliable weather information. So, in this study, stochastic gradient descent (SGD) has been implemented for optimizing multilayer perceptron (MLP) and developing SGD-MLP to estimate daily ET0 in Tabriz (semi-arid climate) and Babolsar (humid climate) stations, Iran, using extracted data from satellite images. The estimated ET0 values were compared to the determined ET0 based on the FAO-Penman–Monteith equation. Based on satellite image data collected from 2003 to 2021, the database was constructed. During the development of the abovementioned models, data from 2003 to 2016 (70%) were used for training purposes, and residual data (30%) were used for testing purposes. Additionally, the input variables, including land surface temperature (LST) day and night, normalized difference vegetation index (NDVI), leaf area index (LAI), and a fraction of photosynthetically active radiation (FPAR) from MODIS sensor, were utilized to estimate the daily ET0. Thus, there are three studied models; first is based on the LST, second on the vegetation indices, and third on the combination of the LST and the vegetation indices. Additionally, four performance indexes, including the coefficient of determination (R2), the root-mean-square error (RMSE), Willmott’s index of agreement (WI), and Nash–Sutcliffe efficiency, were utilized in order to measure the implemented model’s accuracy. According to the obtained results, the SGD-MLP-3 with input parameters of LSTday&night, LSTmean, LAI, NDVI, and FPAR gave the most accurate results with RMSE and WI values of as 0.417 mm/day, 0.973, for Tabriz and 0.754 mm/day, 0.922 for Babolsar stations, respectively. Conclusively, LST of daytime, nighttime, and average may be suggested as the most influential parameter for ET0 estimation.

Reference evapotranspiration estimation using machine learning approaches for arid and semi-arid regions of India

Accurate estimation of reference evapotranspiration (ET0) is vital for hydrological studies and irrigation scheduling. This study aimed to estimate ET0 using 4 machine learning algorithms: random forest, support vector machine, light gradient boosting decision trees and extreme gradient decision trees. Daily data for 2001 to 2020 at 11 (arid and semi-arid) stations were used for modelling. Eighteen scenarios with different input combinations were evaluated using the data of maximum and minimum air temperature, mean relative humidity and wind speed, number of sunshine hours, solar radiation, and extra-terrestrial radiation data at these stations. The ET0 estimated using the FAO 56 Penman-Monteith equation was chosen as the target value for model fitting. The best input combination was found in the models that used all inputs, while the least accurate were the models that used temperature data only. The results showed that the support vector machine models outperformed the other models at most stations. The application of various input combinations indicated that the use of fewer inputs also gave reasonable accuracy in the modelling. In addition, wind speed and solar radiation were found to be important parameters for precise estimation.

Estimation of daily reference evapotranspiration by hybrid singular spectrum analysis-based stochastic gradient boosting

In this study, stochastic gradient boosting (SGB), a commonly-adopted soft computing method, was used to estimate reference evapotranspiration (ETo) for the Adiyaman region of southeastern Türkiye. The FAO-56-Penman-Monteith method was used to calculate ETo, which we then estimated using SGB with maximum temperature, minimum temperature, relative humidity, wind speed, and solar radiation obtained from a meteorological station.•The calculated ETo time series values were decomposed into sub-series using Singular Spectrum Analysis (SSA) to enhance prediction accuracy.•Each sub-series was trained with the first 70% of observations and tested with the remaining 30% via SGB. Final prediction values were obtained by collecting all series predictions.•Three lag times were taken into account during the predictions, and both short-term and long-term ETo values were estimated using the proposed framework. The results were tested with respect to root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) indicators for ensuring whether the model produced statically acceptable outcomes.

Hybrid Neural Network Based Models for Evapotranspiration Prediction Over Limited Weather Parameters

Evapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> ) estimation models, having as input a limited set of meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> , SR estimation models are also developed. These <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN, and GRU-ANN. Two main approaches were taken for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> estimation: (i) directly use those algorithms to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> , and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> . For the latter case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of the Penman-Monteith method (a.k.a. FAO-56PM method, adopted by the United Nations Food and Agriculture Organization) to compute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ET_{o}$ </tex-math></inline-formula> , which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(R^{2})$ </tex-math></inline-formula> of 0.977. The developed model was then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar results to the ones for which the models were initially designed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}&gt;0.95$ </tex-math></inline-formula> ), proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.

Dados da Nasa Power e de estações meteorológicas de superfície em diferentes climas na estimativa da evapotranspiração de referência

Abstract The objective of this work was to evaluate the data estimated by NASA Power in relation to that measured at surface weather stations under different climates, and to verify the effects of these data on reference evapotranspiration (ETo) estimation. For comparison, data measured at 21 surface weather stations, located in Brazil, Israel, Australia, Portugal, and the United States of America were used, representing different Köppen climate types. The following climatic variables were analyzed daily: maximum (Tmax), mean (Tmean), and minimum (Tmin) air temperatures; wind speed; incident solar radiation; and mean relative humidity (RHmean). Wind speed showed the highest variations and was overestimated in the Cfb, BWh, BSh, and Cfa climates. Tmean and mean wind speed were estimated accurately in the Csa and BWh climates, whereas Tmax and Tmin were underestimated in 13 and 9 climates, respectively; Tmin did not show adequate results in tropical climates. Incident solar radiation was overestimated in all climates, except in BSh, but presented the best statistical indicators among the analyzed variables. The scenarios in which ETo was estimated using the Penman-Monteith method and data from NASA Power were consistent even for the climate type that presented the worst association between measured and estimated data.

Developing Turfgrass Water Response Function and Assessing Visual Quality, Soil Moisture and NDVI Dynamics of Tall Fescue Under Varying Irrigation Scenarios in Inland Southern California

Highlights A turfgrass water response function was developed for 'Westcoaster' tall fescue. The Weathermatic smart controller demonstrated reliable estimations of reference evapotranspiration. Restricting irrigation frequency did not yield water savings or lead to improvements in turfgrass quality. Abstract. Efficient landscape irrigation is an important water conservation strategy and reduces strain on limited water resources in inland Southern California. A three-year field irrigation trial was conducted in Riverside, California, to evaluate the response of 'Westcoaster' tall fescue (Festuca arundinacea Schreb.) to a wide range of autonomous irrigation scenarios implemented using a Weathermatic smart controller. The response of the turfgrass was assessed using visual rating (VR) and normalized difference vegetation index (NDVI) data collected with a handheld sensor. These experimental data were then used to develop a regression-based turfgrass water response function (TWRF), which was further employed to estimate the response of tall fescue to different irrigation levels during years with varying atmospheric evaporation demands, including extreme conditions. A strong correlation was observed between NDVI and VR (r = 0.88). A minimum NDVI threshold of 0.7 was identified to maintain an acceptable level of tall fescue quality for residential areas. During the trial periods, irrigation application rates below 100% of reference evapotranspiration (ETo) were insufficient to sustain the desired quality of tall fescue. The TWRF estimations suggested that 60% ETo can be applied for short periods (7 to 43 days, depending on the atmospheric evaporative demands) during the summer before the visual quality drops below the minimum threshold. Comparing different irrigation strategies, it was observed that on-demand irrigation, which adjusts irrigation frequency based on ETo demand, resulted in higher NDVI and VR values compared to a limited watering schedule of only a few days per week. Analysis of soil moisture data revealed that the primary root water uptake occurred mainly in the topsoil layer (&amp;lt;33 cm), and the applied irrigation water primarily replenished the shallow soil layer (&amp;lt;20 cm) due to the coarse sandy loam soil characteristics. The Weathermatic controller, equipped with on-site air temperature measurements, provided reliable ETo estimations and can be used for autonomous landscape irrigation scheduling in semiarid regions. Keywords: Keywords., Evapotranspiration, Remote sensing, Smart irrigation controller, Urban irrigation, Water conservation.

Station Aridity in Weather Monitoring Networks: Evidence from the Oklahoma Mesonet

HighlightsStation aridity can cause overestimation of ETref at weather monitoring networks in irrigated areas.Station aridity was demonstrated in a mesoscale weather monitoring network.Station aridity is amplified in water-scarce irrigated areas during droughts.Station aridity should be accounted for to achieve water conservation through weather-informed irrigation.Abstract. Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. Keywords: Evapotranspiration, Irrigation requirement, Reference condition, Station aridity, Weather station.

Comparison of Different Methods in Estimating Reference Evapotranspiration at Gaya District of Bihar, India

Evapotranspiration (ET), a complex mechanism in the hydrological cycle, influences runoff, which in turn influences how much water is required for irrigation. There are many techniques available to calculate of the reference evapotranspiration (ETo). Unfortunately, most efficient reference evapotranspiration approaches are parameter-rich models, making them unsuitable for use in data-scarce locations. On the other hand, accuracy and reliability of simple ETo models vary widely according to regional climate conditions. The purpose of this study was to assess the effectiveness of two temperature-based and three radiation-based simple ET methods in estimating ET algorithms used to forecast the wheat crop for the years 2017–2018 in Gaya District, Bihar. The performance was measured by comparing the temperature and radiation-based method with the parameter intensive Penman-Monteith method. Comparative evaluation of two method (radiation and temperature) was performed through statistical tests, and it was found that the (R2 = 1 and R2 = 0.98) and RMSE = 0.84. The study found that ETo values calculated from the six methods were highly correlated (Pearson correlation coefficient 0.84 to 0.99). Through data analysis for model comparison, it was found that the Radiation method performed satisfactorily when compared to the standard Penman-Monteith model estimate (R2 = 0.94). However, multivariate statistical testing revealed that the ETo readings obtained using various techniques differed significantly from one another. In comparison to radiation-based ETo approaches, temperature-based ETo techniques showed greater disparities. The Priestley-Taylor, Turc, and Jensen-Haise methods all outperformed the other ETo methods in general. When estimating ETo in this study region, it was discovered that radiation-based approaches performed better than temperature-based methods.

Estimation of Crop Water Requirement of Kharif Maize Crop using CROPWAT 8.0 Model in Bilaspur District

Water is one of the most important elements in this world. No life will exist without water. The main consumer of water is agriculture but due to mismanagement, overpopulation, and climatic changes this valuable natural resource is getting scarce position throughout the world. It has become very important to define appropriate strategies for planning, development, and management of water resources. The main objective of this paper is to the estimation of crop water requirement of Kharif maize crop for effective irrigation management. For proper irrigation management, CROPWAT is the best model suggested by FAO. 16-year climatic data were analyzed with the CROPWAT model, which is based on the United Nations Food and Agriculture Organization (FAO) paper number 56 (FAO 56). The Penman-Monteith method was used to estimate reference evapotranspiration (ET0). The model predicted the daily, decadal as well as monthly crop water requirement at different growing stages of maize crop. The average reference crop evapotranspiration (ETc­) during the 16 years was found to be 313.21 mm. It varies from 222.8 mm to 515.9 mm. The average irrigation requirement for maize crops during the period from 25th June to 17th September was 42.01 mm.

Calibration and evaluation of Hargreaves-Samani equation for estimating reference evapotranspiration: A case study in the Amu Darya River Basin, Central Asia

Study regionAmu Darya River Basin (ADRB), Central Asia. Study focusReference-evapotranspiration (ETo) is the most prominent variable for agriculture water-resource management. The Penman-Monteith (PM) equation has been accepted as universal-standard for estimating ETo but it requires extensive climatic-variables. The PM alternative Hargreaves-Samani (HS) equation inadequately estimates ETo with an average error of RMSE 1.84 mm/day, NSE 0.27, and MAE 0.61 mm/day, respectively, in the ADRB. New hydrological insight for the regionThe present study proposes the calibration of original HS-equation based on actual vapor pressure and linear regression to estimate daily ETo at 114 meteorological stations in the ADRB. The locally-calibrated HS-equation coefficient (C) was found variated-widely across the different climatic-conditions and months, ranging from 0.0025 to 0.0186, due to hyper-aridity conditions in the study area. Compared with proposed methods by other studies, the modified-HS equation and empirical-coefficient using newly-proposed variable leads to a significant reduction of the average root means square error and mean absolute error by about 55 % and 52 %, respectively, demonstrates their feasibility and accuracy in the ADRB, Central Asia. In-addition, the modified-HS-equation based on climatic zones were found to be more accurate and comprehensive than geographical localizations. The modified-HS equation and empirical-coefficient using newly-proposed variable and approach is suitable to estimate ETo in a data scarce region (e.g., ADRB), when only temperatures, solar radiation and vapor pressure data are available.