the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Apr 2024
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
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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Status: closed
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RC1: 'Comment on gmd-2024-63', Anonymous Referee #1, 13 May 2024
This paper presents a python package to implement twenty different PET methods. This package is going to be quite useful in hydrological modeling. The paper is quite well written and is easy to read. Therefore, I don’t have any major suggestions. There are a few things that the authors can add to further improve the paper:
- Add the equations of each of the 20 models in the paper or in the Appendix. Importantly, specify the units of each of the variables along with that of PET. I see that the units have been provided on GitHub, but providing the units to the paper would also be useful.
- Include the plausible ranges of ET model parameters that can be treated as free parameters. This will be very helpful for the modelers who are new to these models.
- Re-check the performance measures in the Figure 5. The R2 does not change between default and calibrated scenarios. In the lines 236, you mention that R2 increases. But it does not.
Citation: https://doi.org/10.5194/gmd-2024-63-RC1 -
AC2: 'Reply on RC1', Matevž Vremec, 31 May 2024
Thank you very much for your positive feedback on our manuscript. We appreciate your valuable suggestions and will address them in the revised manuscript. Specifically, we will add the equations for the 20 models to the Appendix with detailed units for each variable and the plausible parameter ranges. We will also update GitHub accordingly. Additionally, we will review the performance measures in Figure 5 as you recommended.
Best regards!
Citation: https://doi.org/10.5194/gmd-2024-63-AC2
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RC2: 'Comment on gmd-2024-63', Anonymous Referee #2, 18 May 2024
This paper presents a Python package for Potential Evapotranspiration (PET) estimation, integrating 20 different methods within a single framework. I appreciate the effort to consolidate these methods into one publicly available platform, facilitating an understanding of various PET estimation approaches for users, which aids practical applications. However, the paper lacks novel scientific contributions or innovations, particularly given the existence of R packages for PET estimation and numerous established publications in this area.
Specific comments:
Although the authors acknowledge the uncertainties associated with different PET estimation methods and the additional uncertainties introduced by climate change, no uncertainty analysis is provided. The Penman-Monteith method is regarded as the standard. If one method is indeed the standard, the necessity of including other methods must be clearly justified.While acknowledging the Penman-Monteith method as a standard reference, the manuscript should objectively evaluate the potential value of other empirical approaches, especially for applications in data-sparse regions or for quantifying uncertainties across an ensemble of methods.
The introduction lacks a compelling motivation for the research. It should clearly identify key remaining scientific gaps or problems in PET estimation that PyET aims to solve through new research or technical capabilities.
A detailed comparison with existing R packages for PET estimation is essential. Highlight the significant advantages of your Python package over these existing tools. Clarify the scientific contributions of PyET beyond merely including more methods.
The model description section should provide detailed information on the included PET methods, provide an in-depth analysis of the fundamental assumptions, limitations, and suitability of each PET approach for different hydroclimatic regimes and data availabilities. Clearly articulate the significant advantages offered by PyET in terms of new scientific insights, functionality, performance, or other technical merits.Citation: https://doi.org/10.5194/gmd-2024-63-RC2 -
AC1: 'Reply on RC2', Matevž Vremec, 31 May 2024
Thank you very much for your constructive feedback! We acknowledge that our initial manuscript did not sufficiently emphasize how PyET extends beyond existing scientific contributions. In the revised manuscript, we will highlight the following scientific advancements and unique features of PyET:
- Direct estimation from gridded data: All methods in PyET uniquely support direct estimation of PET from gridded data, addressing a critical gap where existing packages focus on station-time series data or only specific regions. This capability allows for the direct use of regional and global meteorological data and climate projection data typically available in NetCDF formats.
- Healthy scientific ecosystem: Although we are aware that an R-package with similar purposes for station data exists, we argue that having multiple packages (although not too many) with the same aim is vital for a healthy ecosystem. The availability of many methods across multiple programming languages fosters a constructive 'competition' that likely benefits scientific progress.
- Python ecosystem integration: By developing PyET within the Python ecosystem—one of the most widely used programming languages—we fully leverage the functionalities of this ecosystem. Python’s simplicity and its extensive range of compatible packages make it accessible for even the most novice programmers to easily apply PyET to their projects.
- Flexible inputs and parameters: PyET allows for the use of diverse meteorological and crop inputs, such as leaf area index and crop height, and provides the flexibility to adjust other model parameters. All of these capabilities are not available in other packages.
Best regards!
Citation: https://doi.org/10.5194/gmd-2024-63-AC1
-
AC1: 'Reply on RC2', Matevž Vremec, 31 May 2024
Status: closed
-
RC1: 'Comment on gmd-2024-63', Anonymous Referee #1, 13 May 2024
This paper presents a python package to implement twenty different PET methods. This package is going to be quite useful in hydrological modeling. The paper is quite well written and is easy to read. Therefore, I don’t have any major suggestions. There are a few things that the authors can add to further improve the paper:
- Add the equations of each of the 20 models in the paper or in the Appendix. Importantly, specify the units of each of the variables along with that of PET. I see that the units have been provided on GitHub, but providing the units to the paper would also be useful.
- Include the plausible ranges of ET model parameters that can be treated as free parameters. This will be very helpful for the modelers who are new to these models.
- Re-check the performance measures in the Figure 5. The R2 does not change between default and calibrated scenarios. In the lines 236, you mention that R2 increases. But it does not.
Citation: https://doi.org/10.5194/gmd-2024-63-RC1 -
AC2: 'Reply on RC1', Matevž Vremec, 31 May 2024
Thank you very much for your positive feedback on our manuscript. We appreciate your valuable suggestions and will address them in the revised manuscript. Specifically, we will add the equations for the 20 models to the Appendix with detailed units for each variable and the plausible parameter ranges. We will also update GitHub accordingly. Additionally, we will review the performance measures in Figure 5 as you recommended.
Best regards!
Citation: https://doi.org/10.5194/gmd-2024-63-AC2
-
RC2: 'Comment on gmd-2024-63', Anonymous Referee #2, 18 May 2024
This paper presents a Python package for Potential Evapotranspiration (PET) estimation, integrating 20 different methods within a single framework. I appreciate the effort to consolidate these methods into one publicly available platform, facilitating an understanding of various PET estimation approaches for users, which aids practical applications. However, the paper lacks novel scientific contributions or innovations, particularly given the existence of R packages for PET estimation and numerous established publications in this area.
Specific comments:
Although the authors acknowledge the uncertainties associated with different PET estimation methods and the additional uncertainties introduced by climate change, no uncertainty analysis is provided. The Penman-Monteith method is regarded as the standard. If one method is indeed the standard, the necessity of including other methods must be clearly justified.While acknowledging the Penman-Monteith method as a standard reference, the manuscript should objectively evaluate the potential value of other empirical approaches, especially for applications in data-sparse regions or for quantifying uncertainties across an ensemble of methods.
The introduction lacks a compelling motivation for the research. It should clearly identify key remaining scientific gaps or problems in PET estimation that PyET aims to solve through new research or technical capabilities.
A detailed comparison with existing R packages for PET estimation is essential. Highlight the significant advantages of your Python package over these existing tools. Clarify the scientific contributions of PyET beyond merely including more methods.
The model description section should provide detailed information on the included PET methods, provide an in-depth analysis of the fundamental assumptions, limitations, and suitability of each PET approach for different hydroclimatic regimes and data availabilities. Clearly articulate the significant advantages offered by PyET in terms of new scientific insights, functionality, performance, or other technical merits.Citation: https://doi.org/10.5194/gmd-2024-63-RC2 -
AC1: 'Reply on RC2', Matevž Vremec, 31 May 2024
Thank you very much for your constructive feedback! We acknowledge that our initial manuscript did not sufficiently emphasize how PyET extends beyond existing scientific contributions. In the revised manuscript, we will highlight the following scientific advancements and unique features of PyET:
- Direct estimation from gridded data: All methods in PyET uniquely support direct estimation of PET from gridded data, addressing a critical gap where existing packages focus on station-time series data or only specific regions. This capability allows for the direct use of regional and global meteorological data and climate projection data typically available in NetCDF formats.
- Healthy scientific ecosystem: Although we are aware that an R-package with similar purposes for station data exists, we argue that having multiple packages (although not too many) with the same aim is vital for a healthy ecosystem. The availability of many methods across multiple programming languages fosters a constructive 'competition' that likely benefits scientific progress.
- Python ecosystem integration: By developing PyET within the Python ecosystem—one of the most widely used programming languages—we fully leverage the functionalities of this ecosystem. Python’s simplicity and its extensive range of compatible packages make it accessible for even the most novice programmers to easily apply PyET to their projects.
- Flexible inputs and parameters: PyET allows for the use of diverse meteorological and crop inputs, such as leaf area index and crop height, and provides the flexibility to adjust other model parameters. All of these capabilities are not available in other packages.
Best regards!
Citation: https://doi.org/10.5194/gmd-2024-63-AC1
-
AC1: 'Reply on RC2', Matevž Vremec, 31 May 2024
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