Evapotranspiration Estimation Methods Research Articles

Methodology for Obtaining ETo Data for Climate Change Studies: Quality Analysis and Calibration of the Hargreaves–Samani Equation

Reference evapotranspiration (ETo) is an important part of the water cycle, essential for climate studies, water resource management, and agricultural planning. However, accurate estimation of ETo is challenging when meteorological data are insufficient or of low quality. Furthermore, in climate change studies where large amounts of data need to be managed, it is important to minimize the complexity of the ETo calculation. This study presents a comprehensive approach that integrates data quality analysis with two calibration methods—annual and cluster-based—to improve ETo estimates based solely on temperature data from a set of weather stations (WS). First, the quality and integrity of meteorological data from several WS were analyzed to reduce uncertainty. Second, the Hargreaves–Samani equation (HS) is site calibrated using two approaches: (a) annual calibration, where the radiation coefficient (kRs) is adjusted using a data set covering the entire year; (b) cluster-based calibration, where independent radiation coefficients are adjusted for clusters of years and months. The methodology was evaluated for the Alentejo region in Southern Portugal, using data from 1996 to 2023. When using the original HS equation with a kRs = 0.17 °C−0.5, ETo was estimated with errors from 14.9% to 22.9% with bias ranging from −9.0% to 8.8%. The annual calibration resulted in kRs values between 0.157 and 0.165 °C−0.5 with estimation errors between 13.3% and 20.6% and bias ranging from −1.5% to 1.0% across the different weather stations. Calibration based on clusters of months and years produced unclear results. Dry season months showed better results using cluster-based calibration, while wet season months performed poorly regardless of the calibration approach. The results highlight the importance of meteorological data quality and site-specific calibration for refining temperature-based ETo estimation methods, and for the region studied, the gains do not justify the increased complexity of the cluster-based approach.

How to Measure Evapotranspiration in Landscape-Ecological Studies? Overview of Concepts and Methods

Abstract Evapotranspiration (ET) is a key component of the hydrological cycle, encompassing evaporation processes from soil and water surfaces and plant transpiration (Sun et al., 2017). Accurate estimation of ET is vital for effective water resource management, agricultural planning, and environmental monitoring (Gowda et al., 2008). However, the complex interactions between land surface conditions, vegetation, and atmospheric factors make direct measurement of ET challenging, leading to the development of various estimation methods. Remote sensing has become a widely used approach for estimating ET over large areas because it provides spatially comprehensive data (Xiao et al., 2024). Methods like the Surface Energy Balance Algorithm for Land and the Surface Energy Balance System utilise satellite-derived thermal imagery and meteorological inputs to calculate ET by analysing the energy exchanges between the land surface and the atmosphere. These methods are advantageous for their broad spatial coverage, making them particularly useful for regional to global scale studies. However, they require careful calibration and validation, and their accuracy can be affected by the spatial resolution of the satellite data and the quality of meteorological inputs. In addition to remote sensing, several other ET estimation methods are commonly employed. The Penman-Monteith equation is one of the most widely accepted methods, integrating meteorological data—such as air temperature, humidity, wind speed, and solar radiation— with biophysical properties of vegetation to estimate ET. This method has been validated extensively, making it a standard reference in ET studies. Empirical methods like the Hargreaves-Samani equation provide simpler alternatives that require fewer data inputs, making them suitable for regions with limited meteorological information but with a trade-off in accuracy. Direct measurement techniques offer highly accurate ET data, including lysimeters and eddy covariance systems. Lysimeters measure water loss directly from a soil column, while eddy covariance systems assess the exchange of water vapour and energy between the surface and the atmosphere. Despite their precision, these methods are limited by high costs, maintenance requirements, and their applicability to small-scale, homogeneous areas (Howell, 2005). Choosing the appropriate ET estimation method depends on the scale of the study, data availability, and the specific application. Remote sensing and models like Penman-Monteith offer scalability and broad applicability, while direct measurements provide precise data at localised scales. Integrating these methods can improve the reliability of ET estimates, enhance water resource management, and aid in climate adaptation efforts.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Comparative analysis of FAO dual Kc, Priestley‐Taylor and flux variance similarity methods in partitioning evapotranspiration from flood‐irrigated rice fields

AbstractAccurate quantification of evapotranspiration (ET) and its bifurcation into productive transpiration (T) and unproductive evaporation (E) are essential for the successful implementation of sustainable irrigation practices. This study compares the performances of three distinct methods for partitioning ET into E and T fluxes from flood‐irrigated rice fields. These methods include: i) the FAO dual Kc‐ETo model, which utilizes Penman–Monteith (PM) estimates of reference ET and crop‐specific scaling factors; ii) the Priestley‐Taylor (PT) method, which relies on radiation‐driven energy balance at the canopy surface; and iii) the flux variance similarity (FVS) method, which utilizes high‐frequency eddy covariance (EC) measurements of carbon and water vapour fluxes. Meteorological, phenological and hydrological parameters were monitored over two winter seasons in a paddy field, replicating site‐specific management strategies. The cumulative ETs obtained from the PM, PT and FVS methods were 445 mm, 300 mm and 356 mm, respectively, for season 1 and 428 mm, 321 mm and 375 mm, respectively, for season 2. The accuracy of the ET estimation and partition methods was assessed against independent measurements of ET and E using a lysimeter and a microlysimeter. The results demonstrated that the EC‐based FVS method offers superior accuracy in both quantifying (R2 = 0.67, RMSE = 0.70 mm) and partitioning (R2 = 0.51, RMSE = 0.76 mm) ET fluxes, followed by the PM‐based FAO dual Kc‐ETo and PT methods. Evaporation accounted for 29 ± 5% of consumptive water use, highlighting an opportunity for the implementation of water‐saving strategies, particularly during vegetative and transplantation stages. The findings of the path analysis indicate that vegetative and radiation factors influence ET variation, while meteorological and soil factors significantly impact T variation.

Calibration and evaluation of various reference evapotranspiration estimation methods in a humid subtropical climate: A case study in Samsun Province, Türkiye

Reference evapotranspiration is crucial for estimating plant water needs and managing resources. Simplifying the Penman-Monteith model by reducing input parameters and local calibration can enhance efficiency and reliability. In this study, three empirical formulas—Hargreaves and Samani (HS, 1985), Priestley and Taylor (PT, 1972), and the World Meteorological Organization (WMO, 1966)—representing temperature-, solar radiation-, and mass-transfer-based approaches, respectively, were evaluated. To achieve this objective, meteorological data from five synoptic stations situated in the Samsun province in the Black Sea region of Türkiye, were utilized. Three methods, namely the traditional method, regression analysis, and genetic algorithm, were employed to estimate the local calibration coefficients of empirical equations based on the Penman-Monteith 56 equation. Finally, the outcomes were evaluated based on four criteria: root-mean-square error, coefficient of determination, mean bias error, and percentage error of estimate. The results of empirical formulas both before and after calibration were analyzed. Prior to calibration, HS and PT exhibited greater accuracy for the case study. This accuracy trend was also observed in the calibrated results. Additionally, among the three employed calibration methods, regression analysis and traditional methods demonstrated a higher level of accuracy.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

A universal triangle method for evapotranspiration estimation with MODIS products and routine meteorological observations: Algorithm development and global validation

The surface temperature (Ts) and vegetation index (VI) triangle method is one of the most famous and widely applied methods for regional evapotranspiration (ET) estimation. However, no robust validation has been performed yet to evaluate its accuracy on a global scale, making its accuracy unclear when compared with other global ET models. The main reason behind it is that most traditional triangle models depend on the Ts-VI feature space within a certain spatial domain to parameterize their boundary conditions. The domain dependency makes it impossible to derive universal boundaries for continental or global application. Under this background, this study presented a universal triangle method for ET estimation with Moderate Resolution Imaging Spectroradiometer (MODIS) products and routine meteorological observations. The solution for boundary conditions was performed pixel by pixel without the consideration of spatial domain based configuration. Specifically, the dry boundary was solved theoretically based on the surface energy balance equation, and the wet boundary was substituted by wet-bulb temperature estimated from dew point temperature and air temperature. The validation against ground observations collected from 33 flux towers worldwide shows that this universal triangle method achieved evaporative fraction (EF) estimation with the correlation coefficient of 0.65, the mean absolute error of 0.17, the root-mean-square-error of 0.20, and the bias of 0.13. These four metrics of daily ET estimation were 0.83, 0.71 mm/d, 0.90 mm/d, and 0.41 mm/d, respectively. This analysis is the first comprehensive validation of the triangle method on a global scale. The comparison with early work suggests that our universal triangle method on a global scale has reached a comparable accuracy to traditional triangle models at regional scale. However, the universal triangle method has the capacity to monitor ET continuously pixel by pixel without domain dependency. This is just what most traditional triangle methods do not possess.

Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

Study regionSenegal River Basin in West Africa Study focusThis paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984–1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the regionGood consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

AbstractIn this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.

Observed interannual variability and projected scenarios of drought in the Chorotega region, Costa Rica

The observation-based analysis of drought development in the Chorotega region showed that, despite the area being relatively small, agricultural drought exhibits high spatial variability across the region. However, the lack of net radiation data hinders the capacity to provide reliable estimates of evapotranspiration (ET), affecting the assessment of drought occurrence, since its propagation across the hydrological system is very sensitive to the ET estimation method. The coarse resolution of satellite-derived Normalized Difference Vegetation Index (NDVI) products and the lack of information on irrigation in agricultural areas limits the ability to properly establish a relationship between drought and vegetation response. Based on the observations, the most prominent precipitation deficits occur between September and October (–100 mm on average), showing that changes in the large-scale circulation are responsible for the impact of severe drought in the region. In agreement with previous studies, El Niño-Southern Oscillation (ENSO) is the main modulator of the drought severity, with the warm ENSO phase favoring an enhanced drought development and its influence being more significant between August and October, displaying correlations greater than –0.6. The climate change projections under RCP4.5 and RCP8.5 scenarios suggest the intensification of drought events in the Chorotega region at mid-century, with the Tempisque-Bebedero basin being the most affected area in terms of precipitation decrease and warming. The projected scenarios correspond to an increase of 1 oC for mean temperature and more of 2 oC for minimum and maximum temperature in the 2050 horizon, as well as a decrease of 400 to 800 mm for annual precipitation under both RCPs.

Assessment of crop water requirement and irrigation scheduling of potato using weather models under sub temperate climatic condition of North-Western Himalaya

To optimize irrigation water and enhance yield, appropriate amount of irrigation water and its scheduling according to real time weather data can help in reducing crop water foot prints. The evapotranspiration (ET) based irrigation scheduling using present weather conditions reflecting changed climatic scenarios in the study regions in the past few decades has proved to be sagacious use of water. To study the ET based irrigation scheduling, field experiment conducted with the treatments comprising four ET based weather models irrigation scheduling methods viz. no irrigation or rainfed, Thornthwaite, Hargreaves method, Temperature and modified Penman Monteith under sowing windows of 20th December and 20th January were conducted at research farm, of Department of Agronomy, CSK HPKV, Palampur, Himachal Pradesh during Rabi season of 2015-16. The estimated crop evapo-transpiration (ETc) with different ET estimation methods under North Western Himalayas showed variation during crop season. The highest estimated ET was observed in December as compared to January sown potato crop with Hargreaves method (376.8 mm) followed by Temperature (354 mm), Thornthwaite (329.5 mm) and the lowest in modified Penman Monteith methods (241.9 mm). The estimated ETc from pan evaporation observed to be the highest to the tune of (382.3 mm) compared to other methods of ETc estimation. The crop coefficients modeled at different pheno-phases were used for estimating water requirement of potato crop. It was found that irrigation scheduling based on modified Penman Monteith of ET estimation saved one irrigation to the tune of 60 mm during crop period without affecting significantly growth, and tuber yield of the potato as compared to other methods, viz., However, the irrigation schedule based on Thornthwaite method has recorded highest total potato tuber yield (171.76 q ha-1) being at par with irrigation schedules worked out from other ET estimation methods. The total water footprints were the lowest in modified Penman Monteith to the tune of 300 liters per kg of potato yield compared to other methods.

Irrigation Scheduling Based on Evapotranspiration of Tomato (Solanumm Lycopersicum) Using Atmometer in The Upland Rolling Production Area

Objective : The goal is to provide the right amount of water to the crop, at the right time, to meet the crop's needs. It was conducted to estimate evapotranspiration using atmometers for irrigation scheduling that may be highly beneficial in soil water balance, water allocation and budgeting, irrigation management, and improving crop yield and production for greater income. Methods: This study was conducted at the Organic Agricultural Research and Development Innovation Center at the Bataan Peninsula State University – Abucay Campus, Bangkal, Abucay, Bataan, Philippines (North 14°44'28” East 120°27'04”). The effects of using different evapotranspiration estimation methods on the timing and amounts of water application were then evaluated by using a computed irrigation scheduling model. Findings: The atmometer ( and Penman-Monteith ( values were statistically analyzed using linear regression (y=0.8573x + 1.586), coefficients of determination (R² = 0.7236), root mean – squared error (RMSD = 0.73), mean bias error (MBE = 0.03), and the t-statistics (significant at 5% level). The study revealed that the ETo-A data strongly correlated with the ETo-PM data. Using an atmometer for scheduling irrigation on tomatoes would have resulted in an equally similar distribution of irrigation events, more water would have been provided throughout the season compared to using ETo-PM data. Novelty : The calibrated evapotranspiration data from the atmometer may guide appropriate irrigation depth and schedule for tomatoes. By aligning irrigation intervals with other crops’ water requirements, farmers can optimize water usage, minimize water stress, and achieve higher crop yields. Keywords: Atmometer, Bataan Philippines, Evapotranspiration, Tomato, Penman-­Montheith Equation

A COMPARATIVE RELATION OF DISTINCT REFERENCE CROP EVAPOTRANSPIRATION MODELS FOR SOUTHERN BRAZIL

Water is one of the main limiting factors for achieving high productivity in agriculture. The hydric requirement of plants is fundamental for the dimensioning of the irrigation system and contributes to the better use of hydric resources. Moreover, the accurate computation of this element is essential for water management in agricultural systems. Nonetheless, due to the heterogeneity of different evapotranspiration estimation methods, the performance of its calculation can be considerably compromised. Accordingly, the aim of this study was to compare the methods for estimating reference evapotranspiration (ETo) by Benevides & Lopes, Camargo, Hargreaves & Samani, Jensen & Haise, Linacre, Makkink, Penman, Priestley & Taylor, Tanner & Pelton, and Turc, with the FAO-56 Penman-Monteith standard method, to evaluate the performance and accuracy of equational models. Furthermore, data from an automatic weather station belonging to the Brazilian National Institute of Meteorology (INMET), located in Palmeira das Missões, Rio Grande do Sul, Brazil, from January 1, 2020, to January 1, 2021, were used. Comparative statistical methods were utilized to express the accuracy of the models and indicate the most appropriate equations for the conditions of the selected location. Cluster analysis and Principal Component Analysis (PCA) were applied. For Palmeira das Missões, the model proposed by Hargreaves & Samani indicated the best results and was characterized as the most appropriate alternative to estimate the ETo more accurately. The method indicated the most favorable results for R2 (0.9890), d (0.9253), and r (0.9944). Furthermore, cluster and PCA analyses expressed the behavior of relationships between different mathematical models and meteorological parameters in relation to the ETo determination.

A thermodynamics-based versatile evapotranspiration estimation method of minimum data requirement for water resources investigations

A recent, two-parameter version of the thermodynamically derived complementary relationship (CR) of evaporation has been tested on a monthly basis at 124 FLUXNET stations around the globe. Local, station-by-station calibration explained 91% (R2) of the variance in eddy-covariance (EC) obtained latent-heat fluxes with the same Nash-Sutcliffe efficiency (NSE) value. When the dimensionless Priestley-Taylor parameter (α) was expressed as a universal function (f) of the estimated wet-environment air temperature (Tw), station-by-station calibration of the single dimensionless parameter, b (accounting for moisture advection), yielded an R2 value of 87% and NSE of 86%. Global calibration (all stations at once) of the two-parameter CR version yielded R2 = 82% and NSE = 81%, while the single-parameter version produced R2 = 81% and NSE = 79%. With a representative value (between the locally calibrated mode and mean) of b set equal to two, the thus, calibration-free CR still maintained an R2 of 80% and NSE of 78%, which is significantly better than Morton’s calibration-free WREVAP model (i.e., 71% and 55%, respectively). The advantage of the current CR model is that it can be employed in a fully calibration-free mode, similar to WREVAP, yet with available EC measurements or water-balance derived latent-heat fluxes the single [b, when α = f(Tw) is chosen] or two parameters (α and b) of the model can be easily calibrated within the respective 1 – 1.32 and 1 – 10 intervals, for further improved performance.

Evaluation of machine learning models for prediction of daily reference evapotranspiration in semi-arid India

Reference evapotranspiration (ET ) is controlled by climatic factors; hence, its estimation provides an idea 0 about the atmospheric demand of water. Machine learning techniques like elastic net (ELNET), K-nearest neighbours (KNN), multivariate adaptive regression splines (MARS), partial least squares regression (PSLR), random forest (RF), support vector regression (SVR), XGBoost and cubist were employed to predict daily reference evapotranspiration based on daily weather parameters of twenty years. Penman-Monteith method was used as the reference method for ET estimation. All models performed well during calibration showing 0 higher coefficient of determination (R2) which ranged from 0.97 (for PLSR) to 1 (for cubist models). Mean absolute error during calibration ranges from 0.027 mm d-1 for cubist to 0.607 mm d-1 for ELNET. Cubist model (R2 = 1, MAE = 0.017 mm d-1, RMSE = 0.027 mm d-1) outperformed other models during the calibration. During validation, the coefficient of determination (R2) for the machine learning models varied from 0.819 to 1, RMSE varied from 0.06 to 0.60 mm d-1 and MAE varied from 0.031 to 0.38 mm d-1. Based on statistical parameters, best performance was observed for cubist model (R2 = 1, RMSE = 0.06 mm d-1, MAE = 0.031 mm d-1) among the studied machine learning models for the prediction of reference evapotranspiration. Hence, the cubist model may be used to estimate daily reference evapotranspiration for the studied region.

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 Urban Evapotranspiration at High Spatiotemporal Resolution and Considering Flux Footprints

Evapotranspiration (ET) estimations at high spatiotemporal resolutions in urban areas are crucial for extreme weather forecasting and water management. However, urban ET estimation remains a major challenge in current urban hydrology and regional climate research due to highly heterogeneous environments, human interference, and a lack of observations. In this study, an urban ET model, called the PT-Urban model, was proposed for half-hourly ET estimations at a 10 m resolution. The PT-Urban model was validated using observations from the Hotel Torni urban flux site during the 2018 growing season. The results showed that the PT-Urban model performed satisfactorily, with an R2 and root-mean-square error of 0.59 and 14.67 W m−2, respectively. Further analysis demonstrated that urban canopy heat storage and shading effects are essential for the half-hourly urban energy balance. Ignoring the shading effects led to a 38.7% urban ET overestimation. Modeling experiments further proved that flux footprint variations were critical for the accurate estimation of urban ET. The setting source areas either as an invariant 70% historical footprint or as a circle with a 1 km radius both resulted in poor performances. This study presents a practical method for the accurate estimation of urban ET with high spatiotemporal resolution and highlights the importance of real-time footprints in urban ET estimations.

Effect of different methods for estimation of evapotranspiration and water deficit in soil on the growth rates of conilon clonal coffee

The water deficit is considered one of the main problems in coffee culture around the world. As a way to get around this problem, producers all over the world adopt the practice of irrigation. However, in the most cultivated areas, a rational irrigation management is not carried out, resulting in excessive or insufficient applications. Based on this, the present work aimed to evaluate the effect of different methods of evapotranspiration estimative and water deficit levels on the growth rates of the clonal conilon coffee. The experiment was conducted in a greenhouse, in a 2 x 3 factorial scheme with the factor evapotranspiration in 2 levels (Penman-Monteith (PM) and Hargreaves & Samani (HS), and the factor water deficit in 3 levels (80%, 50% and 30% of Evapotranspiration of Reference (ETo)) in a completely randomized design. From the results obtained, it was observed that the evapotranspiration estimation method and the water deficit levels had an effect on the growth rates and free assimilation rate of the clonal conilon coffee tree, with the highest growth values in plants irrigated with 80% of ETo by the HS method, and higher assimilation values in plants irrigated with 80% of ETo by the PM method.

Development and Application of ETCalc, a Unique Online Tool for Estimation of Daily Evapotranspiration

ABSTRACT Various empirical methods that use meteorological data have been developed for estimating evapotranspiration. However, there are currently no online tools available for the estimation of daily evapotranspiration based on user-provided daily data. Here, we introduce ETCalc (https://etcalc.hydrotools.tech), a free, unique online tool that integrates eight methods (i.e. Penman-Monteith, Thornthwaite, Blaney – Criddle, Turc, Priestley – Taylor, Hargreaves, Jensen – Haise and Abtew) for estimation of daily potential evapotranspiration, reference evapotranspiration and, by employing user-defined crop (or cover) coefficients, daily actual evapotranspiration, based on user-provided daily meteorological data. ETCalc has been developed in response to the effort of the Canadian federal government to encourage easier and open access to science and is applicable to any area for which basic meteorological data are available and hence, its suitability is not restricted to particular geographical areas. Through a streamlined interface, ETCalc allows for uploading of user-provided data, tabular and graphical inspection of the input and output data, as well as export of the output data and of the associated metadata. The use of ETCalc is exemplified using 10-year daily meteorological data from Charlottetown, Prince Edward Island, Canada for comparing the output from each of the ETCalc methods and for the calculation of the precipitation deficit.