the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A more comprehensive uncertainty framework for historical flood frequency analysis: a 500-year long case study
Abstract. The value of historical data for flood frequency analysis has been acknowledged and studied for a long time. A specific statistical framework must be used to comply with the censored nature of historical data. Indeed, it is assumed that all floods having exceeded a given perception threshold were recorded as written testimonies or flood marks. Conversely, all years without a flood record in the historical period are assumed to have a maximum discharge below the perception threshold. This paper proposes a Binomial model which explicitly recognizes the uncertain nature of both the perception threshold and the starting date of the historical period. This model is applied to a case study for the Rhône River at Beaucaire, France, where a long (1816–2020) systematic series of annual maximum discharges is available along with a collection of 13 historical floods from documentary evidences over three centuries (1500–1815). Results indicate that the inclusion of historical floods reduces the uncertainty of 100- or 1000-year flood quantiles, even when only the number of perception threshold exceedances is known. However, ignoring the uncertainty around the perception threshold leads to a noticeable underestimation of flood quantiles uncertainty. A qualitatively similar conclusion is found when ignoring the uncertainty around the historical period length. However, its impact on flood quantiles uncertainty appears to be much smaller than that of the perception threshold.
- Preprint
(1105 KB) - Metadata XML
- BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on hess-2024-50', Neil Macdonald, 27 Mar 2024
I really enjoyed reviewing this paper, it provides an excellent examination on the value and importance of using historical records to improve precision and accuracy in flood frequency analysis, particularly for the largest flood events. The authors present a compelling case for the utility of historically augmented records within flood frequency analysis. I felt the case study was particularly valuable as it demonstrates how such information can be embedded within and used to support and question conventional flood risk assessments. The authors cover all the key areas, including non-stationarity of the series, whilst providing a detailed case study which adds to the growing evidence base demonstrating the importance of historical flood records in FFA globally.
I have provided an annotated copy of the manuscript with comments, minor amendments and thoughts, I hope that the authors find this helpful in making their revisions.
My only more substantive comment relates to some of the image quality, whilst the figure content is fine, several of the figures may need to be presented at a higher resolution (e.g. Fig 4), I do though recognise that this may simply be a presentation issue with the review manuscript file size.
Neil Macdonald
University of Liverpool
- AC2: 'Reply on RC1', Michel Lang, 07 May 2024
-
RC2: 'Comment on hess-2024-50', Helen Hooker, 25 Apr 2024
The article presents a probabilistic model for flood frequency analysis that uses the number of times a perception threshold is exceeded over a historical period and considers the uncertainty of discharges during the systematic period. The experiments are interesting and well planned. A key novelty of the model is to recognise the imperfect nature of both the perception threshold and the length of the historical period by making them parameters of the probabilistic model.
The five model variations are clearly explained; however, their differences could be made clearer by presenting these in a table so that readers can refer to them more easily. Uncertainty in the results is considered throughout, which is a strength of the article.
The results section is lengthy with 13 figures, several of these are multi-plot. The paper would benefit from choosing the key results to present and focussing the discussion on these.
The conclusion summarises the main findings well but could include more recommendations for use of the model in practice and linking back to the wider picture of flooding.
The structure of the article is clear and generally well written. Please see the supplementary attachment for minor comments.
- AC1: 'Reply on RC2', Michel Lang, 07 May 2024
Status: closed
-
RC1: 'Comment on hess-2024-50', Neil Macdonald, 27 Mar 2024
I really enjoyed reviewing this paper, it provides an excellent examination on the value and importance of using historical records to improve precision and accuracy in flood frequency analysis, particularly for the largest flood events. The authors present a compelling case for the utility of historically augmented records within flood frequency analysis. I felt the case study was particularly valuable as it demonstrates how such information can be embedded within and used to support and question conventional flood risk assessments. The authors cover all the key areas, including non-stationarity of the series, whilst providing a detailed case study which adds to the growing evidence base demonstrating the importance of historical flood records in FFA globally.
I have provided an annotated copy of the manuscript with comments, minor amendments and thoughts, I hope that the authors find this helpful in making their revisions.
My only more substantive comment relates to some of the image quality, whilst the figure content is fine, several of the figures may need to be presented at a higher resolution (e.g. Fig 4), I do though recognise that this may simply be a presentation issue with the review manuscript file size.
Neil Macdonald
University of Liverpool
- AC2: 'Reply on RC1', Michel Lang, 07 May 2024
-
RC2: 'Comment on hess-2024-50', Helen Hooker, 25 Apr 2024
The article presents a probabilistic model for flood frequency analysis that uses the number of times a perception threshold is exceeded over a historical period and considers the uncertainty of discharges during the systematic period. The experiments are interesting and well planned. A key novelty of the model is to recognise the imperfect nature of both the perception threshold and the length of the historical period by making them parameters of the probabilistic model.
The five model variations are clearly explained; however, their differences could be made clearer by presenting these in a table so that readers can refer to them more easily. Uncertainty in the results is considered throughout, which is a strength of the article.
The results section is lengthy with 13 figures, several of these are multi-plot. The paper would benefit from choosing the key results to present and focussing the discussion on these.
The conclusion summarises the main findings well but could include more recommendations for use of the model in practice and linking back to the wider picture of flooding.
The structure of the article is clear and generally well written. Please see the supplementary attachment for minor comments.
- AC1: 'Reply on RC2', Michel Lang, 07 May 2024
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
344 | 89 | 34 | 467 | 34 | 30 |
- HTML: 344
- PDF: 89
- XML: 34
- Total: 467
- BibTeX: 34
- EndNote: 30
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1