Determination of Reference Evapotranspiration Using Penman-Monteith Method in Case of Missing Wind Speed Data under Subhumid Climatic Condition in Hungary

Varga-Haszonits, Zoltán; Szalka, Éva; Szakál, Tamás

doi:10.4236/acs.2022.122014

Cited by 4 publications

(6 citation statements)

References 3 publications

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“…where ET ref is the reference evapotranspiration (MJ•m −2 •day −1 ), Δ is the slope of the temperature-vapour pressure curve (kPa•˚C −1 ), R n is the net radiation (MJ•m −2 •day −1 ), G is the heat conduction to the soil (MJ•m −2 •day −1 ), γ is the psychometric constant (kPa•˚C −1 ), T is the mean daily temperature at 2 m altitude (˚C), u 2 is the wind speed at 2 m altitude (m•s −1 ), e s is the saturation vapour pressure (kPa) and e a is the actual vapour pressure (kPa). 1) was verified on lysimeter data from the experimental station in Szarvas and the results showed a close relationship [5].…”

Section: The Penman-monteith Equationmentioning

confidence: 58%

“…The results obtained are shown in Table 1. In a previous study, we have already found that under the climatic conditions of Hungary, the effect of wind speed can be considered negligible or a constant average wind speed of 2 m/s can be used in the calculation of PM-ET ref [4]. Therefore, the relationship with wind speed was not investigated here.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…In our previous study, we have already made tests with the omission of the minor effects of wind speed under subhumid conditions in Hungary [4], and in this work, we have tested the accuracy of the formula simplified to a single effecting factor that has a strong effect on evapotranspiration and can be calculated easily when compared to the values calculated by the standardized Penman-Monteith equation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplified Penman-Monteith Equation Determined by Temperature-Based Global Radiation Data and Its Multilocal Validation under Subhumid Climatic Conditions in Hungary

Varga-Haszonits,

Szalka,

Vasas

et al. 2023

ACS

Self Cite

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The extent to which specific climatic factors influence evapotranspiration under subhumid conditions in Hungary was investigated. The reference evapotranspiration, calculated with the internationally accepted Penman-Monteith equation proposed by FAO, was considered. The results show that the influence of radiation, which provides energy for evaporation, is the strongest factor and that the influence of global radiation alone is very strong. Taking into account that radiation was measured under rather limited conditions in space and time, global radiation was calculated using the Hargreaves method based on temperature. Accordingly, we have defined a formula based on temperaturebased global radiation and verified the data obtained with the Penman-Monteith formula calculated for 14 meteorological stations. The verification gave good results, therefore the method can be used for practical purposes in the subhumid conditions of Hungary based on the data of the nearest meteorological station.

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Section: The Penman-monteith Equationmentioning

confidence: 58%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simplified Penman-Monteith Equation Determined by Temperature-Based Global Radiation Data and Its Multilocal Validation under Subhumid Climatic Conditions in Hungary

Varga-Haszonits,

Szalka,

Vasas

et al. 2023

ACS

Self Cite

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“…On the other hand, in the MSH zone, all three PMT methods showed an overestimate, with PMT 2 having the highest overestimation of 10% and 11% in the 2000-2005 and 2018-2022 periods, respectively. In a study conducted in Hungary in a subhumid climate, using a wind speed of 2 m s −1 resulted in good results [77].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Empirical Methods for Estimating Reference Evapotranspiration in Different Climatic Zones of Bosnia and Herzegovina

Srdić,

Srđević,

Stričević

et al. 2023

Water

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The study evaluated nine empirical methods for estimating reference evapotranspiration (ETo) in Bosnia and Herzegovina (BiH) across different climatic zones. The methods compared were the Hargreaves–Samani method (HS), the modified Hargreaves–Samani method (HM), the calibrated Hargreaves–Samani method (HC), the Priestley–Taylor method (PT), the Copais method (COP), the Makkink method (MAK), the Penman–Monteith method based on air temperature and overall average windspeed (PMT2), the Penman–Monteith method based on air temperature and regional average windspeed (PMT1.3), and the Penman–Monteith method based on air temperature and site-specific windspeed (PMTlok). These methods were tested against the “Food Agricultural Organization-Penman Monteith approach” (FAO-PM). The evaluation was performed using data from 20 meteorological stations in BiH, considering a common irrigation season (April–October) for two periods (2000–2005 and 2018–2022). The stations represented three climatic zones: semi-arid (SA), dry sub-humid (DSH), and moist sub-humid (MSH). The performance and ranking of the ETo methods were analyzed using the TOPSIS method. The trend of ETo during the common irrigation season for the period from 2018 to 2022 was determined using the Mann–Kendall test. The results of the study indicated that the HC method showed the best performance across all three climatic zones. The average root mean square error (RMSE) was 0.67 mm day−1, 0.49 mm day−1, and 0.50 mm day−1 for the SA, DSH, and MSH zones, respectively. As an alternative to the HC method, the PT method is recommended for its favorable results in both periods and in all zones. On the other hand, the HS method exhibited the highest average overestimation, particularly in the MSH zone, where ETo values were 18% higher compared with those of the FAO-PM method. The COP method also showed high overestimation and was not recommended for use. Regarding the MAK method, it resulted in underestimation during the period from 2000 to 2005, ranging from 17% in the DSH zone to 11% in the MSH zone. However, its performance improved during the period from 2018 to 2022, for which it ranked second place in the MSH zone. Among the PMT methods, the PMTlok, which utilized local average windspeed, yielded the best results. Despite performing well in the neighboring country of Serbia, the HM method showed poor overall performance in BiH. The findings of this study can serve as a foundation for further research in BiH to enhance irrigation practices in response to climate changes.

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“…Furthermore, all of our models suggested that wind speed was the least important feature. That might be due to the fact that we applied a constant wind speed that can be considered relatively low and can be neglected (Varga-Haszonits et al, 2022). The combination of low wind speed and the size of the greenhouse could lead to a decoupled microclimate.…”

Section: Using Machine-learning Algorithms To Predict Evaporation Und...mentioning

confidence: 99%

Evaluating Evapotranspiration in a Commercial Greenhouse: A Comparative Study of Microclimatic Factors and Machine-Learning Algorithms

Averbuch,

Moshelion

2024

Preprint

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The FAO-56 Penman-Monteith equation (FPME) is commonly used to calculate evapotranspiration and apply necessary irrigation, based on environmental data usually taken from a single measuring station. In this study, we hypothesized that the accuracy of the FPME is affected by microclimatic changes over time and space within the target area. Therefore, we tested the impact of numerous spatial and temporal environmental measurement points in a commercial greenhouse on the accuracy of the FPME, by comparing its evapotranspiration evaluation to the actual evaporation measured by dozens of weighing lysimeters throughout the year. Additionally, we harnessed the capabilities of machine-learning algorithms to utilize the extensive data acquired for predicting evapotranspiration. Our results revealed that the daily FPME exhibited a -22% to +22% discrepancy in accuracy, as compared to the lysimeters, with overestimation in the winter and underestimation in the summer. Interestingly, using more data points per day led to less accurate FPME evaluation. The conflict between an increased number of data points and a reduction in accuracy was explained by daily hysteresis. Machine-learning algorithms (Decision Tree, Random Forest, XGBoost and Neural Network) showed impressive accuracy in predicting evapotranspiration, when the model dataset contained temporal parameters (R^2 > 0.918). Furthermore, we demonstrated that spatial sampling had a stronger effect on the accuracy of predictions than the amount of the data collected. Specifically, when we used 10% of the original dataset (3.01e5 entries) with high consideration of spatial measurements, the best-performing models (Random Forest and XGBoost) were highly accurate (R2 = 0.913 and R2 = 0.935, respectively). The top three most influential features of all models were light, day and hour, underscoring the importance of the temporal dimension. This approach allowed us to explore the potential of leveraging advanced computational methods to improve the estimation of water loss under various environmental conditions.

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Determination of Reference Evapotranspiration Using Penman-Monteith Method in Case of Missing Wind Speed Data under Subhumid Climatic Condition in Hungary

Cited by 4 publications

References 3 publications

Simplified Penman-Monteith Equation Determined by Temperature-Based Global Radiation Data and Its Multilocal Validation under Subhumid Climatic Conditions in Hungary

Simplified Penman-Monteith Equation Determined by Temperature-Based Global Radiation Data and Its Multilocal Validation under Subhumid Climatic Conditions in Hungary

Assessment of Empirical Methods for Estimating Reference Evapotranspiration in Different Climatic Zones of Bosnia and Herzegovina

Evaluating Evapotranspiration in a Commercial Greenhouse: A Comparative Study of Microclimatic Factors and Machine-Learning Algorithms

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