the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jul 2024
Modelling rainfall with a Bartlett-Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
Abstract. The Bartlett-Lewis (BL) model is a stochastic framework for representing rainfall based upon Poisson cluster point process theory. This model has been used for over 30 years in the stochastic modelling of daily and hourly rainfall time series. Historically, the BL model was known to underestimate sub-daily rainfall extremes, but recent advancements have addressed this issue, making it a viable alternative to traditional rainfall frequency analysis methods, such as those based on annual maxima time series. Despite its potential, calibrating the BL model is a not a trivial task. The model's formulation is complex, and calibrating it involves a nonlinear optimisation process that can be numerically unstable, which has limited its broader application. To promote the use of the BL model and demonstrate its capabilities in modeling sub-hourly rainfall –both standard and extreme statistics– we have developed an open-source Python package called pyBL. This paper details the design of the BL model and summarises the key features of the pyBL package. It includes a brief explanation of how to use the package in selected user scenarios. In addition, we report upon scientific experiments that resemble real-world situations to showcase pyBL's ability to model sub-hourly rainfall extremes with short records and its flexibility in utilising records of various timescales and lengths.
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Status: open (until 16 Sep 2024)
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RC1: 'Comment on egusphere-2024-1918', Nadav Peleg, 03 Aug 2024
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I have been familiar with the Bartlett-Lewis model for many years, and I am pleased to see that the authors have provided a Python version of the model. Overall, I find the manuscript to be well-written and structured, with only a few points I would like the authors to address. I have included my specific comments below.
Sincerely,
Nadav Peleg
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The readers will benefit from seeing all formulations of the BL model - either in the section on the model structure (Section 2.1) or in the supplementary material if you do not wish to lengthen the paper. The lack of equations in a paper that describes a model is somewhat unexpected.
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In the case study presented, you address the issue of sample size. As part of the discussion of model calibration, I would present this information in advance to the reader.
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I suggest adding a short section to provide readers with a more comprehensive understanding of the sensitivity of the model parameters. You may list all model parameters in a table (this would be very useful for readers to gain a better understanding of the model engine) and present the local or global sensitivity.
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It would be useful to begin by presenting several box plots demonstrating the model's ability to reproduce the interannual variability, monthly and daily rainfall statistics before presenting the results of the extreme rainfall (e.g., Figure 3). Currently, it appears that the model is only calibrated to simulate extreme events correctly.
- The font size in Figure 2 is too small.
Citation: https://doi.org/10.5194/egusphere-2024-1918-RC1 -
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RC2: 'Comment on egusphere-2024-1918', Anonymous Referee #2, 12 Aug 2024
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This paper discusses the development of pyBL, a software package implemented in Python for generating realistic synthetic rainfall time series using the Bartlett-Lewis model. Given the current need for future rainfall time series to formulate climate change mitigation strategies, this paper is highly suitable for the Geoscientific Model Development journal in terms of practicality. However, I would like to suggest solutions for the following issues:
Line 110: As a reviewer with a personal interest in the practical application of this model, I have applied it across various fields. Based on that experience, although Equation 2 has a solid theoretical foundation (as cited in Kaczmarska et al., 2014, which states that statistics with greater interannual variability should be given less weight, and vice versa), it has shown problems such as underestimation of extreme values in real-world applications. The most significant reason, I speculate, is that interannual variability, as mentioned by Marani (2003) and Kim and Onof (2020), is a large-scale variability that the Poisson cluster rainfall model cannot replicate. This large-scale variability is related to extreme values that pose real-world problems. For example, if a time series shows high interannual variability in 1-hour variance, the year that contributed to this high variability is likely to contain extreme values. Therefore, I believe it would be more appropriate to apply greater weight to statistics with large interannual variability. Additionally, the magnitude of each MMM in this equation varies significantly. Thus, the weight factor should be adjusted to account for these relative differences, which could introduce confusion. Therefore, I recommend adopting a method of determining the weight factor based on the application field of the generated rainfall, as suggested by Kim and Olivera (2012). Moreover, I suggest using a normalized form of the function, such as Sigma(w_i \times (1 - f_k / f'_k)), instead of Equation 2. At the very least, users should have the option to choose such a method.
Section 2.4: The Bartlett-Lewis model is likely to produce different parameters corresponding to different local minima with each calibration attempt. However, there is no way to discern whether the variability of the parameters derived from the method presented here is due to parameter calibration or sampling. To demonstrate the validity of the method proposed in this section, you must show that calibration consistently produces the same parameters for the same rainfall statistics.
Reference Kim, D., & Olivera, F. (2012). Relative importance of the different rainfall statistics in the calibration of stochastic rainfall generation models. Journal of Hydrologic Engineering, 17(3), 368-376.
Other parts of the paper is very well organized. So, please clearly take care of the above two issues.
Citation: https://doi.org/10.5194/egusphere-2024-1918-RC2
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