the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand's Chi Watershed: Implications for Flood Management
Tossapol Phoophiwfa
Prapawan Chomphuwiset
Thanawan Prahadchai
Jeong-Soo Park
Arthit Apichottanakul
Watchara Theppang
Piyapatr Busababodhin
Abstract. Extreme rainfall events in the Chi watershed of Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the Chi watershed, Northeast Thailand, and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2018. The Generalized Pareto Distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using Maximum Likelihood Estimator (MLE) and Linear Moment Estimator (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p-values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet, Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2-day (CONS-2) and 3-day (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.
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Tossapol Phoophiwfa et al.
Status: open (extended)
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RC1: 'Comment on hess-2023-167', Anonymous Referee #1, 25 Sep 2023
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Employing the Generalized Pareto Distribution to Analyze Extreme Rainfall Events on Consecutive Rainy Days in Thailand’s Chi Watershed: Implications for Flood Management
In their study, the authors have put forth the application of the Generalized Pareto Distribution (GPD) as a means to characterize the extreme rainfall data. They have estimated the distribution's parameters through both maximum likelihood and linear moment estimation methods. However, it is worth noting that relying solely on the GPD may be insufficient. It would be beneficial to incorporate additional distributions for comparative analysis. Furthermore, enhancing the analysis by taking into account spatial and temporal dependencies within the model could prove valuable.
Comments:
- Regarding the selection of the threshold "u," is it possible to treat it as a model parameter and estimate it directly from the data?
- There appears to be some confusion regarding the notations used in equation (2) and in the negative log likelihood function, particularly on line 150 of page 9, and in several other instances.
- How can you ensure the convergence of estimates when using Maximum Likelihood Estimation (MLE)? Have you experimented with different initial values in the R functions employed for estimation?
- While the authors have conducted model diagnostics and assessed goodness-of-fit, it would be advisable to include information criteria such as AIC and BIC to facilitate the selection of a more suitable model. Additionally, providing insights into prediction accuracy for each model would be beneficial.
- How have you verified the stationarity of the data, and what measures are taken if it is found to be non-stationary? Have you considered incorporating external variables, such as temperature or others, into the model to enhance the accuracy of the analysis?
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Citation: https://doi.org/10.5194/hess-2023-167-RC1 -
AC1: 'Reply on RC1', Piyapatr Busababodhin, 27 Sep 2023
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Dear Reader,
I would like to express my sincere gratitude to you and the reviewer(s) for the time and effort spent reviewing my manuscript. I have carefully considered all the comments and suggestions provided and have made the necessary revisions to address them. Below is a summary of the major changes made in response to the reviewer's comments:
- Threshold "u" as a Model Parameter: I have incorporated methods like the "mean residual life plot" to determine an appropriate threshold and treated it as a parameter estimated from the data. This approach has been detailed in the revised manuscript.
- Confusion in Notations: I have reviewed and rectified the inconsistencies in notations, particularly in equation (2) and the mentioned line on page 9. The notations are now consistent and clearly defined throughout the manuscript.
- Convergence of MLE Estimates: I have tested the convergence with different initial values and found consistent results. This process and its outcomes have been elaborated upon in the revised manuscript.
- Model Selection Criteria: I have included the AIC and BIC criteria in the results section, which will aid in model comparison and selection. Additionally, prediction accuracy metrics like RMSE have been added to further strengthen the model evaluation.
- Data Stationarity: I have clarified the methods used to test for stationarity in the manuscript. The steps taken to ensure the data's stationarity, such as differencing, have been detailed.
- Incorporation of External Variables: I have discussed the feasibility and implications of incorporating external variables like temperature. While they have not been included in the current analysis, their potential benefits and limitations for future studies have been addressed.
I believe that these revisions have significantly improved the quality of the manuscript and addressed the concerns raised by the reader and reviewer.
Thank you for your time and consideration. I look forward to hearing from you soon.
Sincerely,
Piyapatr
Tossapol Phoophiwfa et al.
Tossapol Phoophiwfa et al.
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