Assessing the ability of potential evaporation models to capture the sensitivity to temperature

Liu, Ziwei; Han, Juntai; Yang, Hanbo

doi:10.1016/j.agrformet.2022.108886

Cited by 9 publications

(7 citation statements)

References 45 publications

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“…According to its definition, E po is “ the evaporation that takes place from the same surface as the actual evaporation when the evaporating water is not a limiting factor and with the same solar radiative input ” (Brutsaert, 2015). However, E po estimated from conventional models (e.g., the Priestley‐Taylor model, which is most widely used in CR models) often do not conform to its definition (Aminzadeh et al., 2016; Z. Liu & Yang, 2021; Z. Liu et al., 2022; Szilagyi & Jozsa, 2008; Tu & Yang, 2022; Yang & Roderick, 2019), as the observed meteorological variables under real conditions do not necessarily represent the forcings when the underlying surface is “hypothetically” non‐water‐limited (Figure 3; Tu & Yang, 2022). Second, most CR models contain model parameters that do not have a clear physical meaning and require local calibration.…”

Section: Discussionmentioning

confidence: 99%

“…However, E po estimated from conventional models (e.g., the Priestley-Taylor model, which is most widely used in CR models) often do not conform to its definition (Aminzadeh et al, 2016;Z. Liu & Yang, 2021;Z. Liu et al, 2022;Szilagyi & Jozsa, 2008;, as the observed meteorological variables under real conditions do not necessarily represent the forcings when the underlying surface is "hypothetically" non-water-limited (Figure 3; .…”

Section: Discussionmentioning

confidence: 99%

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Potential Evaporation and the Complementary Relationship

Roderick

McVicar

2023

Water Resources Research

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The complementary relationship (CR) provides a framework for estimating land surface evaporation with basic meteorological observations by acknowledging the relationship between actual evaporation, apparent potential evaporation and potential evaporation (Epo). As a key variable in the CR, Epo estimates by conventional models have a long‐standing problem in practical applications. That is, the meteorological forcings (i.e., radiation and temperature) employed in conventional Epo models are observed under actual conditions that are generally not saturated. Hence, conventional Epo models do not conform to the original definition of Epo (i.e., the evaporation that would occur with an unlimited water supply). Here, we estimate Epo using the maximum evaporation approach (Epo_max) that does follow the original Epo definition. We find that adopting Epo_max considerably reduces the asymmetry of the CR compared to when the conventional Priestley‐Taylor Epo is used. We then employ Epo_max and develop a new physically based, calibration‐free CR model, which shows an overall good performance in estimating actual evaporation in 705 catchments at the mean annual scale and 64 flux sites at monthly and mean annual scales (R2 ranges from 0.73 to 0.75 and root‐mean‐squared error ranges from 9.8 to 18.8 W m−2).Both the 705 catchments and 64 flux sites cover a wide range of climates. More importantly, the use of Epo_max leads to a new physical interpretation of the CR.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Potential Evaporation and the Complementary Relationship

Roderick

McVicar

2023

Water Resources Research

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“…The effect of temperature increases on PET can be easily modelled with all available PET methods, as temperature is an input for all methods. The CO 2 stomatal effect, however, can only be directly accounted for with the Penman-Monteith method (Liu et al, 2022). Using a CO 2 -dependent stomatal resistance model implemented in PyEt (Yang et al, 2019), the effect of elevated CO 2 on stomatal resistance can be considered (see Eq.…”

Section: Examplementioning

confidence: 99%

PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration

Vremec,

Collenteur,

Birk

2024

Preprint

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Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change, and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of twenty different PET methods on both time series and gridded datasets. The majority of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt’s capabilities, including the estimation of PET with twenty PET methods for station, and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity/uncertainty analyses, and hind/forecasting studies, especially in data-scarce regions, is provided.

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“…commonly ignored in PET models employed for climate change studies, although excluding its stomatal effect may lead to an overestimation of PET (Kingston et al, 2009;Milly and Dunne, 2016;Vremec et al, 2022). The increase in temperature can be easily modelled with all available PET methods, as temperature is an input for all methods, while the CO 2 stomatal effect can only be directly accounted for with the Penman-Monteith method (Liu et al, 2022). Using a CO 2 -dependent stomatal resistance model implemented in PyEt (Yang et al, 2019), the effect of elevated CO 2 on stomatal resistance can be considered (see eq.…”

Section: Examplementioning

confidence: 99%

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Vremec

Collenteur²,

Birk³

2023

Preprint

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Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the used formula, adding uncertainty to the outcomes of hydrological models using ET. Climate change may cause additional uncertainty as each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change, and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of twenty different PET methods on both time series (Pandas) and gridded datasets (xarray). Most of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with twenty PET methods for station, and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity/uncertainty analyses, and hind/forecasting studies, especially in data-scarce regions, is provided.

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Assessing the ability of potential evaporation models to capture the sensitivity to temperature

Cited by 9 publications

References 45 publications

Potential Evaporation and the Complementary Relationship

Potential Evaporation and the Complementary Relationship

PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

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