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Background: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results: Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R²) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R² values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. [ABSTRACT FROM AUTHOR]

Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas. [ABSTRACT FROM AUTHOR]

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman–Monteith, Priestley–Taylor, and Shuttleworth–Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016–2017 and 2019–2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse. [ABSTRACT FROM AUTHOR]

We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed for the equation (i.e., minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were obtained from the North American–CORDEX initiative. We used dynamically downscaled projections of three CMIP5 GCMs for RCP8.5 emission scenarios (i.e., HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), and each was dynamically downscaled to ~25 km by two RCMs (i.e., WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing the historical period (1986–2005) to future projections (2020–2039 and 2040–2059). The regional spatial average of the six GCM-RCM combinations projected an increase in the annual PET of about +4% and +8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range for the two projection periods were +1.4%–+8.7% and +3%–+14.2%, respectively. The projected annual PET increase trends are consistent across the entire region and for the six GCM-RCM combinations. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Quantitative assessments of the projected PET changes provided by this study should be considered in upcoming studies to develop resilience plans and adaptation strategies for mitigating the projected future changes. [ABSTRACT FROM AUTHOR]

This study extensively examines the estimation of irrigation water requirements using different methodologies based on Earth Observation data. Specifically, two distinct methods inspired by recent remote sensing and satellite technology developments are examined and compared. The first methodology, as outlined by Maselli et al. (2020), focuses on using Sentinel-2 MSI data and a water stress scalar to estimate the levels of actual evapotranspiration and net irrigation water (NIW). The second methodology derives from the work of D'Urso et al. (2021), which includes the application of the Penman–Monteith equation in conjunction with Sentinel-2 data for estimating key parameters, such as crop evapotranspiration and NIW. In the context of the Bekaa Valley in Lebanon, this study explores the suitability of both methodologies for irrigated potato crops (nine potato fields for the early season and eight for the late season). The obtained NIW value was compared with measured field data, and the root mean square errors were calculated. The results of the comparison showed that the effectiveness of these methods varies depending on the growing season. Notably, the Maselli method exhibited better performance during the late season, while the D'Urso method proved more accurate during the early season. This comparative assessment provided valuable insights for effective agricultural water management in the Bekaa Valley when estimating NIW in potato cultivation. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Determination of reference evapotranspiration (ETo) in required for design, management and scheduling of irrigation water in fan and pad greenhouses. In actual practice estimation of (ETo) in fan and pad greenhouses is often made using the Penman-Monteith FAO-56-PM; method from external meteorological data. This requires availability of accurate meteorological input data (temperature, relative humidity, wind speed, and solar radiation). This is constrained by lack of such data which is a common problem in developing countries. In this study the proposed procedure to estimate ETo is based on using limited data of outdoor historically recorded climate elements of only temperature wind speed, and site characteristics (altitude, latitude and sun shine hours). In the proposed method radiation is to be predicted from data of air temperature difference rather than its direct measurement. This because radiation measurement using pyranometers and net radiometers is borne to errors calibration errors commonly plagued by hysteresis, and nonlinearity. The obtained results of the proposed alternative procedure were statistically validated in comparison with the standard method (FAO 56 PM) using unlimited input data measured inside the greenhouse and in reference to a directly measured ETo values by class-A-evaporation pan. The performance of the developed model was evaluated by the determination coefficient of the regression "R² for goodness-of-fit" and by using the Root Mean Square Error (RMSE). The needed data is collected during three years in three sites in Khartoum North-Sudan El Alafoon, Halfaya, and Shambat. In each site three greenhouses were employed, and data is taken every three days for three months in each year. The obtained result reveals that the proposed limited data procedure to estimate the ETo inside greenhouses agree on statistical basis well with both pan measurement and PM estimation from measured indoor climate variables. The study reveals importance of temperature data for estimating ETo in greenhouses and calls for insuring high quality temperature data for calculating ETo in fan and pad greenhouses. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) estimates are often unavailable at fine spatial resolutions due to the lack of dense climatic networks. The present study was taken up to develop a methodology for obtaining a gridded Penman-Monteith (PM) ET0 dataset for Karnataka State, India, through spatial interpolation of estimates obtained at climate stations. Three interpolation techniques, Inverse Distance Weighted (IDW), Kriging, and Point-Based Sentinel Hospitals Area Distance (P-BSHADE), were employed to derive PM ET0 estimates. Considering the historical climate records of 67 stations located in Karnataka State for the period of 2006–2016, the interpolation methodologies were evaluated using a cross-validation procedure. Results showed that the performance of IDW was better with significant error reduction at validation sites. Subsequently, the IDW algorithm was used to derive daily PM ET0 values at 0.25° × 0.25° grids over the Karnataka State, India. Finally, the comparative analysis of developed PM ET0 gridded data product against three global ET0 products indicated the developed gridded ET0 to be most accurate across different agroclimatic zones. Web links of the developed product have been created in an effort to share the data on ET0, which is a critical input in a variety of studies in earth sciences. [ABSTRACT FROM AUTHOR]

The hydrological cycle should be scrutinized and investigated under recent climate change scenarios to ensure global water management and to increase its utilization. Although the FAO proposed the use of the Penman–Monteith (PM) equation worldwide to predict evapotranspiration (ET), which is one of the most crucial components of the hydrological cycle, its complexity and time-consuming nature, have led researchers to examine alternative methods. In this study, the performances of numerous temperature-driven ET methods were examined relative to the PM using daily climatic parameters from central stations in 11 districts of the Kahramanmaras province. Owing to its geographical location and other influencing factors, the city has a degraded Mediterranean climate with varying elevation gradients, while its meteorological patterns (i.e., temperature and precipitation) deviate from those of the main Mediterranean climate. A separate evaluation was performed via ten different statistical metrics, and spatiotemporal ET variability was reported for the districts. This study revealed that factors such as altitude, terrain features, slope, aspect geography, solar radiation, and climatic conditions significantly impact capturing reference values, in addition to temperature. Moreover, an assessment was conducted in the region to evaluate the effect of modified ET formulae on simulations. It can be drawn as a general conclusion that the Hargreaves–Samani and modified Blaney–Criddle techniques can be utilized as alternatives to PM in estimating ET, while the Schendel method exhibited the lowest performance throughout Kahramanmaras. [ABSTRACT FROM AUTHOR]

The objective of this study is to examine changes in atmospheric water demand in the central semi-arid Pampas region over the last 30 years through evaporation and reference evapotranspiration (ETo). Data were obtained from the agrometeorological station of the College of Agronomy at UNLPam located in Santa Rosa, La Pampa. Evaporation was measured using an American-type A pan (E-T), and ETo was estimated using the Penman-Monteith equation (ETo-PM) for the period 1988-2019. Results showed that annual E-T ranged from 1850.0 to 2940.0 mm, while annual ETo-PM varied from 1130.0 to 1620.0 mm. The average annual E-T was 2332.7 mm ± 320.8 mm, and the average annual ETo-PM was 1346.4 mm ± 145.8 mm. Despite differences in absolute values, E-T and ETo-PM were directly associated (p<0.01), consistent with previous studies in other parts of the world. The annual tank coefficient (kt), defined as the slope of the regression line between ET and ETo, was 0.5713. According to this kt, annual E-T overestimated ETo-PM by 75 %. Seasonally (spring, summer, autumn, and winter), ET overestimated ETo between 66 % and 100 %. Both annual E-T and ETo showed an increasing trend (p<0.05). Considering that E-T is a direct measurement and ETo-PM is an estimation, and given the substantial differences in absolute values, there is a need for a lysimeter to precisely measure atmospheric and crop water demand. [ABSTRACT FROM AUTHOR]

Study region: The state of Oklahoma located in the Southern Plains region of the United States. Study focus: The standardized precipitation evapotranspiration index (SPEI) is a widely used meteorological drought index that incorporates potential evapotranspiration (PET) into a precipitation-based index. However, the understanding of the appropriate PET method for SPEI across different temporal scales in non-arid climate conditions remains limited. We compared Thornthwaite (TW), Hargreaves (HG), and Penman-Monteith (PM) equations for SPEI at various accumulations, considering three temporal scales: 1) long-term (25 years), 2) event-based, and 3) monthly. Also, we examined the log-logistic and generalized extreme value distributions to test the normality of SPEI computed from the three PET methods. To do this, we utilized high-quality climate datasets measured at 107 stations across the state of Oklahoma, United States, which has a diverse climate ranging from semi-arid to humid subtropical. New hydrological insights for the region: The log-logistic distribution was found to be suitable for SPEI. The SPEI-HG showed better agreement with SPEI-PM than SPEI-TW in this region for the analyses of three temporal scales. However, for accumulations of SPEI longer than one year, both TW and HG equations showed no significant differences with SPEI-PM. The findings provide practical guidance for selecting an appropriate PET equation in the Southern Plains region depending on the purpose of study without resorting to data-intensive methods for PET estimation.

El objetivo del presente trabajo es estudiar los cambios en la demanda de agua de la atmósfera en la región semiárida pampeana central en los últimos 30 años a través de la evaporación y la evapotranspiración de referencia (ETo). La información para alcanzar el objetivo se obtuvo de la estación agrometeorológica de la Facultad de Agronomía de la UNLPam. ubicada en Santa Rosa, La Pampa. La evaporación fue mediada con un tanque americano tipo A (E-T) y la ETo estimada con la ecuación de Penman-Monteith (ETo-PM) para el periodo 1988-2019. Los resultados mostraron que la E-T anual osciló entre 1850,0 y 2940,0 mm, mientras que la ETo-PM anual varió de 1130,0 a 1620,0 mm. La E-T anual promedio fue de 2332,7 mm ± 320,8 mm, y la ETo-PM anual promedio fue de 1346,4 mm ± 145,8 mm. A pesar de las diferencias en los valores absolutos la E-T y ETo-PM se encontraron asociadas de manera directa (p

Based on spot-scale measurements of lysimeter-based water fluxes and chamber-based carbon fluxes (ETWML or FSTC), and field- scale measurements of eddy covariance-based water and carbon fluxes (ETEC or FEC), a coupled water-carbon flux model was developed to predicted simultaneously hourly water and carbon fluxes at either spot or field scale in the water-saving irrigated paddy in Tai Lake Region of China. The coupled model integrated Penman-Monteith and Farquhar-von Caemmerer-Berry (FvCB) sub-models with the Jarvis sub-model, and was calibrated based on measured fluxes in 2015 and validated using data in 2016–2017. The performance of the coupled model was assessed by comparing simulated values with measured hourly ETWML, FSTC, ETEC and FEC under various conditions, including day/night/all day periods, different canopy coverage levels, and seasonal variations during rice seasons. The results revealed notable differences in the parameters of the Jarvis sub-model between plot and field scales. The ratio of leaf dark respiration to leaf maximum carboxylation rate were respectively calibrated as 0.041 and 0.047 to reflect the respiration of soil-vegetation system at plot and field scales. The maximum carboxylation rate (Vm) reached its peak during the jointing-booting stage and then decreased until the end of the rice season, and the Vm values at the plot scale were considerably higher than those at the field scale. The coupled model respectively exhibited poor and acceptable performance in estimating water and carbon fluxes during nighttime, and performed well during daytime or throughout the day. Additionally, the coupled model performed much better in estimating diurnal and seasonal variations in fluxes under high leaf area index conditions compared to low leaf area index conditions.

Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas.

We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed for the equation (i.e., minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were obtained from the North American–CORDEX initiative. We used dynamically downscaled projections of three CMIP5 GCMs for RCP8.5 emission scenarios (i.e., HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), and each was dynamically downscaled to ~25 km by two RCMs (i.e., WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing the historical period (1986–2005) to future projections (2020–2039 and 2040–2059). The regional spatial average of the six GCM-RCM combinations projected an increase in the annual PET of about +4% and +8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range for the two projection periods were +1.4%–+8.7% and +3%–+14.2%, respectively. The projected annual PET increase trends are consistent across the entire region and for the six GCM-RCM combinations. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Quantitative assessments of the projected PET changes provided by this study should be considered in upcoming studies to develop resilience plans and adaptation strategies for mitigating the projected future changes.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman–Monteith, Priestley–Taylor, and Shuttleworth–Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016–2017 and 2019–2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.

Reference evapotranspiration (ET0) is a crucial parameter in hydrology that is used to estimate the amount of water lost through evaporation and transpiration from a standard reference crop under specified climatic conditions. ET0 is calculated based on several meteorological variables such as temperature, humidity, wind speed, and solar radiation. However, in many regions, there may be limited availability of these meteorological data, making it difficult to estimate ET0 using conventional methods. This study aimed at using machine learning (ML) techniques to estimate ET0 with minimal climatic inputs, using the Food and Agriculture Organization (FAO)-56 Penman–Monteith model as the standard reference method. Different ML models, including Long Short-Term Memory (LSTM) neural networks, Gradient Boosting Regressor (GBR), Random Forest (RF), and Support Vector Regression (SVR), were developed with climatic variables as input parameters. To summarize, the study evaluated different machine learning models to estimate ET0 with minimal climatic inputs and compared their performance with the standard Penman–Monteith model for Hawalbagh experimental farm observatory. The results showed that the LSTM model performed better than other ML models, followed by SVR and RF, in estimating ET0 with minimal climate data. The study concluded that LSTM and SVR models are the most robust ML models for estimating ET0 in such scenarios. The study found that even with limited climatic data, such as a two-parameter combination (maximum temperature and relative humidity, relative humidity and sunshine hours) or a three-parameter combination (minimum temperature, relative humidity, and precipitation, minimum temperature, maximum temperature, and precipitation), promising results in ET0 estimation can be achieved. The study's findings are significant for estimating ET0 in data-scarce regions, especially for agricultural water management in semi-arid climates. [ABSTRACT FROM AUTHOR]

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic understanding of how SIF relates to T dynamics is key for the future development and implementation of robust and reliable SIF-based T products. This study aims to contribute insights by experimentally assessing the sensitivity of several SIF-based T estimation strategies for evolving soil water limitation. We investigated extensive in situ and airborne data acquired during a water limitation experiment in a maize canopy in northern Italy. We evaluated five empirical strategies to integrate SIF in a T modelling framework based on the Penman-Monteith (PM) and the Ball-Berry-Leuning (BBL) concepts. Our results indicate that replacing model parameters sensitive to canopy conductance with SIF results in the best agreement between modelled and measured T under evolving water limitation. Our study contributes expanding existing knowledge with empirical insights on the sensitivity of SIF based T approaches under increasing soil water limitation at short time scales. [ABSTRACT FROM AUTHOR]

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Evapotranspiration (ET) is an important factor of water cycle and energy exchange between terrestrial and atmospheric systems. It aims to improve the accuracy of regional scale ET estimation and provide more accurate data and theoretical support for climate change research, water resources management and environmental protection, based on the Landsat8 OLI remote sensing image and weather station data from 2017 to 2018. The results showed that: (1) the average relative errors of SEBS model and Penman formula were 15.07%, 17.94%, 16.17%, 9.93% and 13.18%, indicating that the estimation of daily evapotranspiration in the study area could be realized based on SEBS model, and the estimation effect was good. (2) In the study area, the ET space showed a decreasing trend of latitude, and the ET increased with the decrease of latitude. (3) Based on SEBS model, the fitting coefficients of daily evapotranspiration with NDVI, surface albedo and surface temperature were ranked as surface temperature > surface albedo > NDVI. (4) Principal component analysis showed that the driving factor closely related to the first principal component was air temperature, and the variance contribution rate was 40.434%. It can be seen that SEBS model can well simulate the evapotranspiration of Hetao Irrigation District, and the simulated potential evapotranspiration has obvious annual distribution characteristics, which first increases and then decreases from April to October. The highest value of ET appears in July, and the main characteristic influencing factor of ET is temperature. [ABSTRACT FROM AUTHOR]

The degree of coupling between canopy and atmosphere, through the decoupling factor Ω, well describes the behaviour of a crop concerning its water use and carbon dioxide exchange. Super high-density hedgerow olive orchard system is in great expansion all over the world and, since it has a complex field structure in rows of adjacent trees, investigations are necessary to assess the Ω patterns, as well as aerodynamic (ga) and canopy (gc) conductances in different water conditions. In this study, in a hedgerow olive orchard (cv. "Arbosana") submitted to full (FI) and regulated deficit irrigation (RDI), cropped under a Mediterranean semi-arid climate (southern Italy), Ω has been determined using gc, as deduced by inverting the Penman-Monteith equation, and ga, by upscaling the wind speed measured in a close station to the canopy; the transpiration has been measured by sap flow thermal dissipation method. The results showed that this olive orchard results very well coupled to the atmosphere, in any soil water conditions; Ω is generally very low, being during daytime equal in mean to 0.021±0.003 ms-1 and 0.018±0.004 ms-1 for RDI and FI, respectively. This condition is linked to ga and gc values; in fact, canopy conductance is much smaller than the aerodynamic one in any water and climatic conditions, except when all canopy surfaces are saturated in water. In this latter case, the gc assumes the highest values due to the contribution of the part of conductance attributable to the structure of the orchard. [ABSTRACT FROM AUTHOR]

Copyright of Ege Üniversitesi Ziraat Fakültesi Dergisi is the property of Ege Universitesi, Ziraat Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The hydrological cycle should be scrutinized and investigated under recent climate change scenarios to ensure global water management and to increase its utilization. Although the FAO proposed the use of the Penman–Monteith (PM) equation worldwide to predict evapotranspiration (ET), which is one of the most crucial components of the hydrological cycle, its complexity and time-consuming nature, have led researchers to examine alternative methods. In this study, the performances of numerous temperature-driven ET methods were examined relative to the PM using daily climatic parameters from central stations in 11 districts of the Kahramanmaras province. Owing to its geographical location and other influencing factors, the city has a degraded Mediterranean climate with varying elevation gradients, while its meteorological patterns (i.e., temperature and precipitation) deviate from those of the main Mediterranean climate. A separate evaluation was performed via ten different statistical metrics, and spatiotemporal ET variability was reported for the districts. This study revealed that factors such as altitude, terrain features, slope, aspect geography, solar radiation, and climatic conditions significantly impact capturing reference values, in addition to temperature. Moreover, an assessment was conducted in the region to evaluate the effect of modified ET formulae on simulations. It can be drawn as a general conclusion that the Hargreaves–Samani and modified Blaney–Criddle techniques can be utilized as alternatives to PM in estimating ET, while the Schendel method exhibited the lowest performance throughout Kahramanmaras.

This study uses the Food and Agricultural Organization (FAO) Penman-Monteith equation to develop a crop water algorithm needed to automate the supply of specific amount of water to crops, depending on their different crop water requirements. This was done to deviate from the practice of supplying the same amount of water to different crops during irrigation practices which could lead to over-irrigation or under-irrigation resulting in pest infestation and eventually low yield. The crop water requirement for cocoyam, spinach and tomatoes were estimated using data from FAO. A microcontroller-based smart irrigation device incorporated with real-time clock was developed to supply the right amount of water to crops at the right time and duration daily. The implementation was done using a laboratory-scale irrigation test bed and experimental results reveal the effectiveness of the developed system in the automation of crop-specific irrigation systems and in line with their Crop Water Requirement (CWR). Possible applications include greenhouses where researchers have to apply a specific amount of water to crops for experiments; horticultural gardens and nurseries to mention a few. [ABSTRACT FROM AUTHOR]

The páramos are Neotropical alpine tundra‐like ecosystems that play a crucial role as biodiversity hotspots and also act as water sources for the inter‐Andean regions and cities. Improving our understanding of hydrological processes, here evapotranspiration, is crucial, especially in the context of global changes. In páramos, most research have focused on estimating potential evapotranspiration (ETo) using the Penman–Monteith method. Only a few studies have quantified actual evapotranspiration using mostly the Eddy covariance method (EC) or volumetric lysimeters. Importantly, these studies focused only on tussock grass communities, and none have addressed the effect of other plant communities specific to páramos on the actual evapotranspiration of this ecosystem. In this research, portable closed chambers were installed for the first time in a páramo (in Los Nevados National Park, between 3900 and 4100 masl) to quantify actual evapotranspration (ETa) in March and May 2019 in three representative plant communities of the páramo (giant rosettes, shrubs and tussock grasses). The ETa rates measured were then compared with ETo estimated using the Penman–Monteith method. Also, environmental factors of solar radiation, temperature and relative humidity were recorded and their influence on ETa variation was analysed. Our results indicate that ETa daily rates were very low with a high daily variation (0.290 ± 0.266 mm day−1). The shrubs showed higher ETa rates even though differences among communities were not significant. ETo rates calculated via the Penman–Monteith method were significantly higher (1.017 ± 0.468 mm day−1) than those measured using the portable chambers. Portable closed chamber is a promising method to assess ETa at small spatial and time scales and under controlled environment; however, they should be improved to enable ETa measurements on longer time periods. This study confirms the highly variable and low evapotranspiration rate of the páramo vegetation, here confirmed across different plant communities and underlines the importance of solar radiation and temperature, which were positively correlated with evapotranspiration rates. [ABSTRACT FROM AUTHOR]

A multi-technology study of evapotranspiration was conducted on the tropical seasonal forest in Nonggang Karst of Guangxi. From January 2019 to June 2020, three independent methods, including the eddy covariance method (EC), resistance method and Penman–Monteith method (PM), were used to estimate the annual evapotranspiration (ET). We found that the estimated annual ET varied dramatically: with values of 456.66 mm (EC), 292.24 mm (resistance method) and 699.59 mm (PM), respectively. The values were all lower than the reference evapotranspiration (853.26 mm year−1) and potential evapotranspiration (1030.61 mm year−1). The EC method had an energy imbalance problem, with an annual energy closure of 46% at the annual scale. The annual estimate of evapotranspiration after a 100% energy closure correction was 915.03 mm, which was higher than the reference evapotranspiration (853.26 mm), so the corrected annual estimates were considered to be unreasonable. Comparing the resistance method with the EC method, it was found that not only is the annual evapotranspiration (ET) lower in the EC method, but the sensible heat flux is also lower, indicating that the resistivity method has lower energy closure than the EC method, suggesting that this method is not suitable for use in karst forests. When comparing the PM method with the EC method, surface conductivity is the most critical parameter. As the most difficult parameter to quantify in the Penman–Monteith equation, the key influencing factor, maximum stomatal conductance, was carefully explored. In the selection of maximum stomatal conductance, the sensitivity of annual evapotranspiration to maximum stomatal conductance values was first analyzed. It was found that the sensitivity is strong before 0.018 m s−1. When gsmax is 0.0025 m s−1, the annual evapotranspiration (456 mm) is equivalent to that of the EC method, and it slowly decreases after reaching 0.018 ms−1. This indicates that when gsmax is 0.0025 m s−1, the annual evapotranspiration is lower or higher than the critical value of the EC method. Therefore, different maximum stomatal conductance values will result in annual evapotranspiration based on the PM method being higher or lower than the annual evapotranspiration measured by the EC method. In order to obtain a more accurate maximum stomatal conductance, the surface conductance was calculated based on the PM equation, using the maximum stomatal conductance of four key tree species in the study area. The FAO universal fixed surface conductance of 1/70 m s−1 was used to constrain the calculation. The reason for this treatment is that the reference underlying surface of FAO is a uniformly flat and well-watered grassland, with a larger surface conductance than forests. The results showed that the selected maximum stomatal conductance values were all within a reasonable range, and the calculated annual evapotranspiration values were 267.28 mm, 596.42 mm, 699.59 mm and 736.90 mm, respectively. Considering the EC method as the lower limit (456.66 mm), the reference evapotranspiration as the upper limit (853.26 mm) and the specific vegetation in the study area, the estimated annual evapotranspiration of the primary forest in the Nonggang karst area of Guangxi (PM method) falls within the range of 596.42 mm to 736.90 mm, which is relatively reasonable. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to estimate the water requirement of crops such as wheat, barley, alfalfa, lentils, and chickpeas in the northern half of Ardabil province, Iran. For this purpose, remote sensing methods such as SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) were used, and the results were compared with Penman-Monteith, Hargreaves-Samani, and pan evaporation methods. The results showed that wheat yield at 2018.11.30 with numerical values of 1.52 in SEBAL method and 1.09 mm/day in SEBS method had the lowest and lentil product at 2013.7.3 with numerical values of 7.86 and 8.14 mm/day had the highest water requirement among the studied products. Also, based on the results, the SEBAL method has the lowest error with RMSE of 0.893 and MAD of 0.843 compared to Penman-Monteith and Hargreaves-Samani methods; it has the highest error rate with RMSE of 1.112 and MAD of 1.215. The SEBS method also has the lowest error rate with RMSE of 0.930 and MAD of 0.833 compared to Hargreaves and the highest error rate with RMSE of 1.691 and MAD of 1.49 compared to the Penman-Monteith method. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) estimation at different spatial and temporal scales with a paucity of climatic parameters in a river basin is becoming a challenging task. Accurate estimation of ET is necessary for efficient water resource management and improving water efficiency at the field scale. Therefore, this study attempts to indirectly estimate actual ET from version 006 of MODIS-Terra product (MOD16A2.006), Sentinel-2A and Variable infiltration capacity (VIC-3L) model using survey information collected from a traditional paddy field in Kangsabati river basin. Further, this study is undertaken to standardize raw MODIS-Terra ET product (MOD16A2.06) using a genetic-based algorithm for better field applicability at local condition. The MODIS-standardized ET and ET estimated using different methods along with raw MODIS-Terra ET product were evaluated against observed ET estimated using globally recommended FAO-56 Penman–Monteith (PM) equation coupled with a crop coefficient. MODIS-Terra ET estimates were standardized using a genetic-based algorithm to enhance the efficacy of MODIS-Terra ET (MODIS-raw ET) for better field applicability. The result revealed that the genetic-based algorithm (MODIS-standardized ET) improved significantly with the NSE and RMSE from approximately − 0.03 to 0.86 and 13.89 to 2.56 (mm/8 day). Of various ET models Sentinel-2A ET performed best followed by MODIS-standardized ET, VIC-3L ET and MODIS-raw ET with R2 = 0.92, NSE = 0.89, RMSE = 1.89 (mm/8 day), R2 = 0.88, NSE = 0.86, RMSE = 2.47 (mm/8 day), R2 = 0.77, NSE = 0.76, RMSE = 3.02 (mm/8 day) and R2 = 0.41, NSE = − 0.03, RMSE = 7.31 (mm/8 day), respectively. The result showed that Sentinel 2A and MODIS-standardized-based ET can be used under data scarce conditions for better field applicability and water management practices. [ABSTRACT FROM AUTHOR]

The existing researches for leaf area index (LAI)-based Penman–Monteith evapotranspiration (ET) model (PML) are mostly carried out at yearly scale and the analysis of effects of seasonal change and different underlying surface conditions on model parameters is scarce. This study emphasizes on the influences of seasonal change and diverse land surface conditions on ET by optimizing the sensitive parameters, namely soil evaporation coefficient f and maximum stomatal conductance gsx, with particle swarm algorithm. This analysis is based on the observations of eight flux stations in China. The model performance is reasonable with a best Pearson r of 0.87. The seasonal calibration results indicate parameters change evidently in different seasons and have obvious spatial heterogeneity. The seasonal calibration method has an obvious effect on improving the ET accuracy in spring, which is mostly influenced by regional temperature and relative humidity. This study further demonstrates the need to dynamically adjust model parameters over time with PML model for evapotranspiration simulations, rather than simply setting these parameters to constants depended on subsurface conditions such as land use type. [ABSTRACT FROM AUTHOR]

Most of the current parameterization schemes for the Penman–Monteith–Leuning evapotranspiration (ET) model (PML) consider meteorological and energy factors and land use types, but the analysis of the effect of soil moisture (SM) changes on ET processes lacks sufficient attention. This paper proposes a parameterization scheme for the sensitive parameters of the PML model considering soil water content, i.e., coupling the land surface SM in the calculation of soil evaporation coefficient f and coupling the SM of the root zone layer in the calculation of maximum stomatal conductance g s x , respectively. The new parameterization scheme is validated at 13 flux sites worldwide and showed significant improvements in improving the correlation with measured values. Moreover, based on the analysis of the spatial distribution of soil evaporation and vegetation transpiration, and the correlation between SM and ET, the regional characteristics of the effect of SM on ET are further revealed. This study provides a new idea for conducting the fusion simulation of SM based on a PML model, which is useful for the subsequent development of the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate change and the related temperature rise can cause an increase in evapotranspiration. Thus, the assessment of potential evapotranspiration (PET) trends is important to identify possible ongoing signals of climate change, in order to develop adaptation measures for water resource management and improve irrigation efficiency. In this study, we capitalize on the data available from a network of 46 complete meteorological stations in Sicily that cover a period of about 21 years (2002–2022) to estimate PET by the Food and Agriculture Organization (FAO) using the Penman–Monteith method at the daily time scale in Sicily (southern Italy). We then analyse the trends of PET and assess their significance by Sen's Slope and the Mann–Kendall test at multiple temporal scales (monthly, seasonal, and annual). Most of the locations do not show significant trends. For instance, at the annual timescale, only five locations have a significantly increasing trend. However, there are many locations where the monthly trend is statistically significant. The number of locations where monthly trend is significant is maximum for August, where 18 out of these 46 stations have an increasing trend. In contrast, in March, there are no locations with a significant trend. The location with the highest increasing trend of PET indicates trend slopes of 1.73, 3.42, and 10.68 mm/year at monthly (August), seasonal (summer), and annual timescales, respectively. In contrast, decreasing PET trends are present only at the monthly and seasonal scales, with a maximum of, respectively, −1.82 (July) and −3.28 (summer) mm/year. Overall, the findings of this study are useful for climate change adaptation strategies to be pursued in the region. [ABSTRACT FROM AUTHOR]

Background and Objectives One of the main elements of hydrological cycle is evapotranspiration. Accurate estimate of this element is so crucial in agricultural water management. The reference crop potential evapotranspiration (ET0) is defined as the rate of evapotranspiration from a clipped green grass with a height of 8-12 cm, well irrigated and without nutrition deficiency in a given time (mainly day). In such conditions the hypothetical effective grass leaf area index is 2.88 m²m-2 (Allen et al. 1998). The aim of this study is to find the most suitable method among the three empirical ET0 models that needs less meteorological parameters in Urmia Lake basin. For this purpose, daily climatic data of the nine weather stations selected across the Urmia Lake basin were used. Methodology The study area is the Urmia Lake basin located in north west part of Iran with an area of about 52000 square km. This basin faced with water deficiencies problem in recent years that led to shrinkage the water surface of the lake dramatically. The water in the lake is hypersaline and was found as the second saline lake after the Dead Sea in middle east. The three candidate models used here are the Turk, Doorenbos and Pruitt (DP), and Blaney-Criddle (BC) models. Their accuracies were evaluated with the FAO-56 Penman-Monteith (FAO56PM) model (Allen et al. 1998). The main reason in selecting the FAO56PM method as a bench mark to evaluate other three methods is that this approach is physically based and explicitly incorporates both physiological and aerodynamic parameters (Sentelhaset al. 2010). Also, this method was used as the bench mark method in previous published papers (Allen et al. 1998, Sentelhaset al. 2010, Tabari 2010). Data in daily scale were collected from Islamic Republic of Iran Meteorological Organization (IRIMO). The data are the wind speed in 10 m height (U10), maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH) and actual sunshine hours (n). The U10 converted to the two m height wind speed (U2) using the equation presented in Nandagiri and kovoor (2005). The mean daily air temperature (Tmean) evaluated using the arithmetic mean of Tmax and Tmin. All the equations used in estimation of other parameters are included in the work of Dinpashoh et al. (2011). The quality of data inspected visually and few missing values constructed using the most suitable method for each of the climatic parameters (Hasanpour Kashani and Dinpashoh. 2012). The performances of the candidate models wereevaluated. using the three criteria which are the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Findings Results showed that the medians of R² obtained for the stations in the two models namely Turk and BC were equal to 0.9, but it was about 0.75 in the case of DP model. The median of RMSE obtained for Turk model was about 2 (mm day-1), however, it was about 2.2 and 3.5 (mm day-1) for the BC and DP models, respectively. The median of MAE for the selected stations was less than 1 (mm day-1) obtained for Turk, however, it was 1.9 and 2.5 (mm day-1) in the case of BC and DP, respectively. Both the two empirical models namely Turk and BC had similar performances which can be used as the alternative method to FAO54PM in Urmia Lake basin. In order to solve the water crisis in Urmia Lake basin it can be suggest to modification of crop pattern, i.e. cultivation of those crops that need less water, improving water efficiency by using modern systems such as drip irrigation, sprinkler irrigation and so on. Restriction of digging new deep wells in this basin is recommended for sustainable water use. Finally, some serious and quick works should be done in this basin to restrict pollution of both surface and groundwater. It can be claimed that the findings of this study can help water authority managers in better management of water resources across the basin. Conclusion In this research potential reference crop evapotranspiration estimated in the nine stations selected in (and/or the adjacent borders of) Urmia Lake basin. Three candidate models namely Turk, Doorenbos and Pruitt (DP), and Blaney-Criddle (BC) models compared with the FAO56PM output in this study. Based on the two performance criteria namely R²and RMSE it can be concluded that the Turk model having the R²equal to 0.9 and RMSE equal to 2 (mm day-1) is the preferable one among the others for the region under study. It can be suggested that this conclusion is valid when the candidate empirical models limited to the aforementioned three models. It is recommended to use other empirical models for better estimation of ET0 in the Urmia Lake basin. It can be claimed that the findings of this study can help water authority managers in better management of water resources across the basin. [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important parameter to know in the management of agricultural water resources. There are several methods for estimating evapotranspiration (ETo). The FAO Penman-Monteith method is considered the universal standard for estimating ETo, however, the climate parameter data needed to calculate the ETo of the FAO Penman-Monteith method are not always available, so it is necessary to evaluate performance using several ETo equations as a comparison in estimating evapotranspiration. This study aims to find the performance of the ETo 38 empirical method against the FAO Penman Monteith method and the climatological data parameters that most influence the accuracy of the calculations. The data used is in the form of climatological data for Bora Station which represents land and settlement areas, Boladangko Station representing mountainous areas and Singkoyo Station representing coastal areas with an observation period of 32 years (1986-2017). The performance of the 38 empirical methods was evaluated using the PE, RMSE, MAE, MR and slope methods. The results of this study indicate that: the Makkink method, Abtew1, Irmak et al., Tabari & Talaee1, Valiantzas1 and Abtew3 showed excellent performance, while the WMO and Mahringer methods performed poorly in the three research locations; the number of climatological parameters used had no significant effect on the ETo results obtained. The use of climatological parameters in the form of temperature and solar radiation in the ETo calculation has the greatest effect on the accuracy of the ETo value. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Priestley‐Taylor (PT) method is widely used in hydrologic and crop models to calculate the atmospheric demand for water or reference evapotranspiration (ETo). However, the default value of the PT coefficient PTa=1.26 $\left({\mathrm{P}\mathrm{T}}_{a}=1.26\right)$ cannot assure reliability under different climatic regions due to the inadequate consideration of the aerodynamic components. In addition, the spatial and temporal evolution of PTa at global scales and how meteorological parameters affect its change have not been elucidated to date. Here, we derived an analytical expression of PTa following the Penman‐Monteith method, i.e., PTa=1SA1+SU2 ${\mathrm{P}\mathrm{T}}_{a}=\frac{1}{{S}_{A}}\,\left(1+{S}_{{U}_{2}}\right)$, where SA and SU2 ${S}_{{U}_{2}}$ represent the available energy and wind speed impacts on ETo, respectively. The daily Princeton Global Forcing data set (0.25° × 0.25°, 1948–2016) was used to validate PTa. The calibration‐free PTa improved the simulation of aerodynamic components and, consequently, the accuracy of ETo estimation. The gridded global monthly PTa data set is fully open‐source and can be incorporated into hydrologic and crop models. We also demonstrated that PTa under equilibrium or quasi‐equilibrium evaporation conditions (1.26 ± 0.04) was very close to the default value. The simplified PTa, with relative humidity as the only input, still performed better than the original method. Results indicated that the radiative component was the driving factor of the global ETo changes from 1948 to 2016. The impacts of available energy and wind speed on global ETo variations tend to intensify in a warming climate. Our findings have important implications for understanding the roles of radiative and aerodynamic components in the global ETo changes. Key Points: An analytical expression for the Priestley‐Taylor (PT) coefficient following the Penman‐Monteith method is derivedThe derived PT coefficient well captures the temporal and spatial distribution of global ETo, as well as its long‐term trendThe simplified PT coefficients with relative humidity as the only input still perform better than the original PT method [ABSTRACT FROM AUTHOR]

Evapotranspiration(ET) is an important component of farmland water cycle, and its accurate estimation is of great significance for precision irrigation and water-saving agriculture. The Penman-Monteith (P-M) model is one of the most commonly used estimation methods, but reliable representation of canopy resistance (rs) has been a difficult problem in applying the P-M. In this paper, seven commonly used rs models were selected to assess if their simulated rs could be used with P-M to directly estimate winter wheat ET. The P-M simulated ET was compared with measured values by Bowen ratio energy balance (BREB) system in Shunyi, Beijing for two years (2020 and 2021), and the main factors affecting wheat rs were analyzed. The results showed that the seven models generally underestimated wheat canopy resistance and overestimated evapotranspiration. Overall, the Todorovic model (TD) performed the best, and the R² for simulated rs and ET were >0.605, mean bias error (MBE) being -82.8s·m-1 and 10.4W·m-2, respectively, with root mean square error (RMSE) of 254.4s·m-1and 33.5W·m-2; the other six models performed poor, and the R² for simulated rs was between 0.113-0.241, MBE and RMSE, were between -236.4 to -61.3s·m-1 and 277.2 to 373.8s·m-1, respectively. The R² for simulated ET was between 0.046-0.184, MBE and RMSE were between 44.5-97.4W·m-2 and 81.4-147.9W·m-2, respectively. On basis of RMSE, the performing order was TD>FAO56-PM>Katerji-Perrier (KP)>Garcıá-Santos (GA)>idso (IS)>Jarvis (JA)>CO. The correlation between canopy resistance and various factors suggested that net radiation (Rn) affected the most on wheat rs, while air temperature (Ta) and canopy temperature (Tc) affected the least with the following specific order: Rn>leaf area index (LAI)>relative humidity (RH)>vapor pressure deficit (VPD)>soil moisture (θ)> canopy-air temperature difference (ΔT)>Tc>Ta. This results better explained the good performance of TD model, and it considers the key factors affecting canopy resistance such as Rn, VPD and ΔT. In addition, it has no parameters to be calibrated, which makes it easy to use. The results of this paper provided a scientific basis for applying one-step approach to calculate the water consumption of winter wheat. [ABSTRACT FROM AUTHOR]

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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.

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Climate change refers to the statistically significant changes in the mean and dispersion values of meteorological factors. Characterizing potential evapotranspiration (ET0) and its climatic causes will contribute to the estimation of the atmospheric water cycle under climate change. In this study, based on daily meteorological data from 26 meteorological stations in Heilongjiang Province from 1960 to 2019, ET0 was calculated by the Penman–Monteith formula, linear regression method and the Mann–Kendall trend test were used to reveal the seasonal and inter-annual changing trend of ET0. The sensitivity-contribution rate method was used to clarify the climatic factors affecting ET0. The results showed that: (1) From 1960 to 2019, the maximum temperature (Tmax), minimum temperature (Tmin) and average temperature (Tmean) showed an increasing trend, with climate tendency rate of 0.22 °C per decade (10a), 0.49 °C/(10a), 0.36 °C/(10a), respectively. The relative humidity (RH), wind speed (U) and net radiation (Rn) showed a decreasing trend, with a climate tendency rate of −0.42%/(10a), −0.18 m/s/(10a), −0.08 MJ/m2/(10a), respectively. (2) ET0 showed a decreasing trend on seasonal and inter-annual scales. Inter-annually, the average climate tendency rate of ET0 was −8.69 mm/(10a). seasonally, the lowest climate tendency rate was −6.33 mm/(10a) in spring. (3) ET0 was negatively sensitive to Tmin, and RH, while positively sensitive to Tmax, TmeanU and Rn, its sensitivity coefficient of U was the highest, which was 1.22. (4) The contribution rate of U to ET0 was the highest on an inter-annual scale as well as in spring and autumn, which were −8.96%, −9.79% and −13.14%, respectively, and the highest contribution rate to ET0 were Rn and Tmin in summer and winter, whose contribution rates were −4.37% and −11.46%, respectively. This study provides an understanding on the response of evapotranspiration to climatic change and further provides support on the optimal allocation of regional water resource and agricultural water management under climate change. [ABSTRACT FROM AUTHOR]

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This study estimated global potential evapotranspiration (PET) using six climate variables of 14 CMIP6 GCMs at different latitudes of both hemispheres. Hargreaves-Samani (HS) and Penman-Monteith (PM) were used to estimate historical and future PETs. To evaluate the historical reproducibility of the six climate variables of the CMIP6 GCMs, five evaluation indicators were used and compared with the NCEP/NCAR reanalysis data. Based on the evaluation metrics, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was used to calculate the weights for Multi-Model Ensemble (MME) for each band of latitude. After that, the projected annual and seasonal PETs were estimated for the near (2031–2065) and far (2066–2100) futures. The change rate of PET for five timeframes (Annual, Spring, Summer, Fall, and Winter) was calculated for four socioeconomic shared pathways (SSPs). As a result, the PM's PET and HS's PET for all scenarios showed the highest increased signals at 75.5° ∼ 90° in the northern hemisphere (NH) compared to the other latitudes. Especially, the change in the HS of SSP5–8.5 was increased by 26.4% in NH latitude 45.5° to 60° (NL4) compared to the base period. Furthermore, the changes in PET of PM and HS for December, January, and February (DJF) and September, October, and November (SON), and June, July, and August (JJA) lead to remarkably high increases in both hemispheres, irrespective of the level of the emission scenario, completely ignoring the seasonal water cycle in the historical period (1950–2014). • Since climate variables of CMIP6 GCM differ significantly from each other, MME should be used. • The PET trends of HS have been globally similar to PET of PM in the historical and future periods. • In the far future, the HS and PM trends showed the water cycle in SSP2–4.5 and 3–7.0. • In all scenarios except SSP1–2.6, the high energy for the water cycle was maintained at all latitudes. [ABSTRACT FROM AUTHOR]