Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions
A classic method for assessing the reference crop evapotranspiration (ET<sub>o</sub>) is the pan evaporation (E<sub>pan</sub>) method that uses E<sub>pan</sub> measurements and pan coefficient (k<sub>p</sub>) models, which can be functions of relative...
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MDPI AG
2020-09-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/7/3/62 |
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| author | Konstantinos Babakos Dimitris Papamichail Panagiotis Tziachris Vassilios Pisinaras Kleoniki Demertzi Vassilis Aschonitis |
| author_facet | Konstantinos Babakos Dimitris Papamichail Panagiotis Tziachris Vassilios Pisinaras Kleoniki Demertzi Vassilis Aschonitis |
| author_sort | Konstantinos Babakos |
| collection | DOAJ |
| description | A classic method for assessing the reference crop evapotranspiration (ET<sub>o</sub>) is the pan evaporation (E<sub>pan</sub>) method that uses E<sub>pan</sub> measurements and pan coefficient (k<sub>p</sub>) models, which can be functions of relative humidity (RH), wind speed (u<sub>2</sub>), and temperature (T). The aim of this study is to present a methodology for evaluating the robustness of regression coefficients associated to climate parameters (RH, u<sub>2</sub>, and T) in pan method models during recalibration at local conditions. Two years of daily data from April to October (warm season) of meteorological parameters, E<sub>pan</sub> measurements from class A pan evaporimeter and ET<sub>o</sub> estimated by ASCE-standardized method for the climatic conditions of Thessaloniki (Greece, semi-arid environment), were used. The regression coefficients of six general nonlinear (NLR) regression E<sub>pan</sub> models were analyzed through recalibration using a technique called “random cross-validation nonlinear regression RCV-NLR” that produced 1000 random splits of the initial dataset into calibration and validation sets using a constant proportion (70% and 30%, respectively). The variance of the regression coefficients was analyzed based on the 95% interval of the highest posterior density distribution. NLR models that included coefficients with a 95% HPD interval that fluctuates in both positive and negative values were considered nonrobust. The machine-learning technique of random forests (RF) was also used to build a RF model that includes E<sub>pan</sub>, u<sub>2</sub>, RH, and T parameters. This model was used as a benchmark for evaluating the predictive accuracy of NLR models but, also, for assessing the relative importance of the predictor climate variables if they were all included in one NLR model. The findings of this study indicated that locally calibrated NLR functions that use only the E<sub>pan</sub> parameter presented better results, while the inclusion of additional climate parameters was redundant and led to underfitting. |
| first_indexed | 2024-03-10T16:38:57Z |
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| id | doaj.art-a1305092ebd746e0b6e0edbd3d3de6cd |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-10T16:38:57Z |
| publishDate | 2020-09-01 |
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| series | Hydrology |
| spelling | doaj.art-a1305092ebd746e0b6e0edbd3d3de6cd2023-11-20T12:09:50ZengMDPI AGHydrology2306-53382020-09-01736210.3390/hydrology7030062Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local ConditionsKonstantinos Babakos0Dimitris Papamichail1Panagiotis Tziachris2Vassilios Pisinaras3Kleoniki Demertzi4Vassilis Aschonitis5Department of Hydraulics, Soil Science and Agricultural Engineering, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Hydraulics, Soil Science and Agricultural Engineering, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSoil and Water Resources Institute, Hellenic Agricultural Organization-Demeter, 57001 Thermi-Thessaloniki, GreeceSoil and Water Resources Institute, Hellenic Agricultural Organization-Demeter, 57001 Thermi-Thessaloniki, GreeceGoulandris Natural History Museum, Greek Biotope/Wetland Centre, 57001 Thermi-Thessaloniki, GreeceSoil and Water Resources Institute, Hellenic Agricultural Organization-Demeter, 57001 Thermi-Thessaloniki, GreeceA classic method for assessing the reference crop evapotranspiration (ET<sub>o</sub>) is the pan evaporation (E<sub>pan</sub>) method that uses E<sub>pan</sub> measurements and pan coefficient (k<sub>p</sub>) models, which can be functions of relative humidity (RH), wind speed (u<sub>2</sub>), and temperature (T). The aim of this study is to present a methodology for evaluating the robustness of regression coefficients associated to climate parameters (RH, u<sub>2</sub>, and T) in pan method models during recalibration at local conditions. Two years of daily data from April to October (warm season) of meteorological parameters, E<sub>pan</sub> measurements from class A pan evaporimeter and ET<sub>o</sub> estimated by ASCE-standardized method for the climatic conditions of Thessaloniki (Greece, semi-arid environment), were used. The regression coefficients of six general nonlinear (NLR) regression E<sub>pan</sub> models were analyzed through recalibration using a technique called “random cross-validation nonlinear regression RCV-NLR” that produced 1000 random splits of the initial dataset into calibration and validation sets using a constant proportion (70% and 30%, respectively). The variance of the regression coefficients was analyzed based on the 95% interval of the highest posterior density distribution. NLR models that included coefficients with a 95% HPD interval that fluctuates in both positive and negative values were considered nonrobust. The machine-learning technique of random forests (RF) was also used to build a RF model that includes E<sub>pan</sub>, u<sub>2</sub>, RH, and T parameters. This model was used as a benchmark for evaluating the predictive accuracy of NLR models but, also, for assessing the relative importance of the predictor climate variables if they were all included in one NLR model. The findings of this study indicated that locally calibrated NLR functions that use only the E<sub>pan</sub> parameter presented better results, while the inclusion of additional climate parameters was redundant and led to underfitting.https://www.mdpi.com/2306-5338/7/3/62class A pan evaporationreference evapotranspirationpan models’ recalibration at local conditionseffects of climate parameters in pan evaporation models |
| spellingShingle | Konstantinos Babakos Dimitris Papamichail Panagiotis Tziachris Vassilios Pisinaras Kleoniki Demertzi Vassilis Aschonitis Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions Hydrology class A pan evaporation reference evapotranspiration pan models’ recalibration at local conditions effects of climate parameters in pan evaporation models |
| title | Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions |
| title_full | Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions |
| title_fullStr | Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions |
| title_full_unstemmed | Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions |
| title_short | Assessing the Robustness of Pan Evaporation Models for Estimating Reference Crop Evapotranspiration during Recalibration at Local Conditions |
| title_sort | assessing the robustness of pan evaporation models for estimating reference crop evapotranspiration during recalibration at local conditions |
| topic | class A pan evaporation reference evapotranspiration pan models’ recalibration at local conditions effects of climate parameters in pan evaporation models |
| url | https://www.mdpi.com/2306-5338/7/3/62 |
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