The influence of antecedent conditions on flood risk in sub-Saharan Africa

Bischiniotis, Konstantinos; van den Hurk, Bart; Jongman, Brenden; Coughlan de Perez, Erin; Veldkamp, Ted; de Moel, Hans; Aerts, Jeroen

doi:https://doi.org/10.5194/nhess-18-271-2018

Articles | Volume 18, issue 1

https://doi.org/10.5194/nhess-18-271-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/nhess-18-271-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 18, issue 1

Research article

|

19 Jan 2018

Research article |

| 19 Jan 2018

The influence of antecedent conditions on flood risk in sub-Saharan Africa

Konstantinos Bischiniotis, Bart van den Hurk, Brenden Jongman, Erin Coughlan de Perez, Ted Veldkamp, Hans de Moel, and Jeroen Aerts

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Final revised paper (published on 19 Jan 2018)
Preprint (discussion started on 17 Feb 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review: „The influence of antecedent conditions on sub-Saharan Africa“ by Bischiniotis et al.', Anonymous Referee #1, 24 Feb 2017
- AC1: 'Response to Reviewer 1', Konstantinos Bischiniotis, 07 Jun 2017
RC2: 'Approach of potential interest but currently lacking significant statisitcal evidence', Anonymous Referee #2, 26 Apr 2017
- AC2: 'Response to Reviewer 2', Konstantinos Bischiniotis, 07 Jun 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by Editor and Referees) (13 Jun 2017) by Bruno Merz

AR by Konstantinos Bischiniotis on behalf of the Authors (23 Jul 2017) Author's response

ED: Referee Nomination & Report Request started (25 Jul 2017) by Bruno Merz

RR by Anonymous Referee #1 (03 Aug 2017)

Suggestions for revision or reasons for rejection

Dear authors,

I reviewed the revised manuscript „Influence of antecedent conditions on flood risk in sub-Saharan Africa“. The authors tried to extent their statistical analysis and compare the antecedent short- and seasonal scale conditions of reported floods by MunicRe with the conditions in years, where no flood occurred. Simple statistical tests are conducted in order to test the significance of precipitation anomalies at different temporal scales.

I appreciate the effort and I believe, that the manuscript has improved from a methodological point of view. However, I am unfortunately not convinced that the findings are yet sufficient for publication. The major conclusion, that floods are triggered by a combination of the catchment state (as represented by SPEI) and high precipitation during the build-up period is rather trivial and has been investigated in various studies, mostly by means of more complex methods. The differences reported in the manuscript are mostly not very clear and often not statistically significant. Further the shortcomings of the datasets and methods impede the interpretation of the results.

Thus, as much as I regret it, I cannot recommend publication at the current state. I would like to encourage the authors to further advance their methods (particularly to use more complex inferential methods, which are suitable for the data) and re-submit a new-version, if more robust results could be achieved.
In the following I will raise my three major concerns:

1) Introduction, climatic and hydrological setting:
The study covers a large target region with various different climatic settings. No information is provided on climate-variability, large scale climatic circulation modes and on typical flood generation processes in different parts of the region. The Köppen classificastion (Fig.4) is not sufficient to describe the climate of the region (especially the very basic version, which classifies the center of the continent as oceanic?). It might be a step into the right direction to analyse the spatial climatic variability (SPEI). Maybe one could then find different flood types in different regions, which are characterized by different SPEI-flood relationships.

2) Data-Quality:
One major problem of the study remains the quality of the MunicRe data set. The comparison of observed and non-observed floods assumes, that the data set is a) somehow complete and b) that the climate-flood link is stationary. However, as the authors admit, there is a strong trend in the data, which points to increasing settlements in flood-prone areas or an increase of flood reports. Those problems might blur important statistical relationship.
In their recent publication (“Should seasonal rainfall forecasts be used for flood preparedness?”) the authors show (based on modelling results), that the precipitation-flood link varies over the target region and is highly dependent on the climatic conditions. In the presented manuscript, all floods are pooled, which also might blur clear results. I fear, that the quality MunicRe data set alone might be too poor and the number of reported floods too low to draw statistically significant conclusions and to derive interesting/new results.

3) Statistical Methods:
There are quite a few methodological problems. The assumptions of statistical tests are often violated by data sets. E.g. event-precipitation (Pre7 and Max7) have been z-normalized, although short-term precipitation is certainly not normal-distributed. Likewise the significance of different mean SPI values for flood and non-flood are based on a z-test and results are questionable. The 75%-quantiles in Fig. 6 clearly overlap, doesn’t that actually indicate, that differences are not significant?
Also the comparison of “flood probabilities” (Fig. 7) is not very robust, since it includes classes with different numbers of cases. Thus, single floods with anomalous pre-conditions can change the entire plot (and thus the interpretation of results). E.g. the sudden drop of SPEI0 is certainly a statistical artefact. Again I would rather use boxplots or similar methods, which include the range of values and some measure of significance. Further I wonder, why flood-likelyhood-ratio is always >1, even if SPEI is clearly negative. This might indicate either a data problem or a problem of the method.

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RR by Anonymous Referee #2 (14 Aug 2017)

ED: Reconsider after major revisions (further review by Editor and Referees) (25 Aug 2017) by Bruno Merz

Dear authors,

Thanks a lot for your revised manuscript. The 2 referees of the original version have looked at the revisions and have come to conflicting recommendations. Reviewer #2 is happy with your revisions, whereas Reviewer #1 recommends to reject the paper, although he/she acknowledges that the manuscript has improved compared to the original version. Given this situation, I have had a close look at the manuscript and decided on major revisions. I feel that, on the one hand, the concerns of Reviewer #1 are valid, but on the other hand, I think that the paper can hopefully be published after further revisions. I comment the 3 major concerns that reviewer #1 has raised:

(1) I agree with the reviewer that there is little information on the climatic and hydrological setting, and the Köppen classification used does not seem to bring further insight to the topic. It would be great if the variation of flood generation across your study area could be discussed – but on the other hand I would understand that, given the coarseness of the data and the study design (pooling of all data across the study area), the actual benefit of such a discussion might be small and might not be worth a large effort.

(2) Concerning the ‘data quality’ issue, the reviewer might be right that the data quality is so low, and the confounding factors so various, that a statistical analysis always contains weak signals and much noise. My recommendation is that you check whether all your statements on the use of the results and the potential for improving/implementing early warning and preparedness schemes are valid. I feel that there would be a very long way to go, and in the end it might not be possible to gain much along this path.

(3) I feel that the concerns of the referee on ‘statistical methods’ should be taken very seriously. To use simple, pragmatic methods is not a problem in case they are appropriate and robust, but I ask you to really consider his/her questions about the methodology, in particular his/her point about the robustness of the results. For example, would it be possible to provide uncertainty intervals for the ‘relative odds of floods vs NF’? Figures 7 and 8 show a strange behaviour for large SPEI values which somehow undermines the confidence in the analysis. Further, the statement (based on Fig. 5b) that max-7-day-precip is significantly different between floods and non-floods should be carefully interpreted: The difference might be statistically significant (partly because statistical tests tend to find significance when the sample size gets large), but maybe not relevant given the variability in both samples.

Best regards, Bruno Merz

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AR by Konstantinos Bischiniotis on behalf of the Authors (06 Oct 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (15 Oct 2017) by Bruno Merz

RR by Anonymous Referee #1 (24 Oct 2017)

Suggestions for revision or reasons for rejection

I once again reviewed the revised version of the manuscript of Bischiniotis at al. Personally I am unfortunately still not very confident with the quality of the data and particularly with the robustness of the applied methods. However I acknowledge the fact the reviews of the manuscript diverge a lot (the other expert seems to be satisfied) and thus would like to hand over the decision to the editor.

Please find my major concerns below:

1) Climatic settings:
The authors still refer to the very basic Köppen climate classification, which assigns some parts of Central Africa to "oceanic climates". I think the authors mean "summer moist subtropical climates with a pronounced seasonality". In general I do not see any benefit from the Köppen climate map and a source is still missing.

2) Statistical Methods:
As stated in the former review, I am not convinced that the methods applied are robust and I feel that statistical assumptions of parametric methods are often violated.
The normalization of PRE7 is now done following the SPI methodology. Therefore 31 data points (including one flood and 30 non-flood cases) are fitted by means of a gamma distribution. Since different types of cases are combined (f, nf cases), I do not think that the application of a parametric method is feasible. In general the applicability of the distribution should be tested.

The same applies to the generation of confidence intervals of the RR: Here the authors state, that the logarithm of the sample is normally distributed. This is not trivial to me and has to be shown or justified.

Concerning the illustration of the results: The uncertainty of the results should be well communicated. Thus confidence intervals should always be shown in the manuscript (not only in the appendix). I would suggest to show (light colored) polygons behind each line in the original figures and delete all the confidence plots of the supplementary material.
Further, the entire range (including the rather strange behavior for SPI0=3) should be shown in order to communicate the high uncertainty of the results.

3) Conclusions
The conclusions and the discussion are almost congruent. I think one can delete the latter.

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ED: Publish subject to minor revisions (review by editor) (02 Nov 2017) by Bruno Merz

AR by Konstantinos Bischiniotis on behalf of the Authors (16 Nov 2017) Author's response Manuscript

ED: Publish subject to technical corrections (28 Nov 2017) by Bruno Merz

AR by Konstantinos Bischiniotis on behalf of the Authors (30 Nov 2017) Manuscript

Short summary

Preparedness activities and flood forecasting have received increasing attention and have led towards new science-based early warning systems. Understanding the flood triggering mechanisms will result in increasing warning lead times, providing sufficient time for early action. Findings of this study indicate that the consideration of short- and long-term antecedent conditions can be used by humanitarian organizations and decision makers for improved flood risk management.