Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

Kobayashi, Kenichiro; Duc, Le; Apip,; Oizumi, Tsutao; Saito, Kazuo

doi:https://doi.org/10.5194/nhess-20-755-2020

Articles | Volume 20, issue 3

https://doi.org/10.5194/nhess-20-755-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-20-755-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 3

Research article

|

23 Mar 2020

Research article |

| 23 Mar 2020

Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

Kenichiro Kobayashi, Le Duc, Apip, Tsutao Oizumi, and Kazuo Saito

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Final revised paper (published on 23 Mar 2020)
Preprint (discussion started on 09 Jan 2019)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'hydrological prediction using mesoscale ensemble', Alan Seed, 14 Jan 2019
- AC1: 'Reply to Dr. Seed', Kenichiro Kobayashi, 28 Jan 2019
RC2: 'Review of “Ensemble flood simulation for a small dam catchment: Part 2”', Anonymous Referee #2, 11 Mar 2019
- AC2: 'Reply to Anonymous Referee #2', Kenichiro Kobayashi, 19 Apr 2019

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (05 May 2019) by Kai Schröter

Dear Kenichiro Kobayashi, Apip, Le Duc, Tsutao Oizumi, and Kazuo Saito,

thank you very much again for your submission 'Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls. Part 2: Flood forecasting using 1600 member 4D-EnVAR predicted rainfalls' and your responses to the reviewers' comments.

Both referees acknowledge that you address a relevant topic which is of interest to the readers of NHESS. However, both reviewers raise concerns regarding the novelty of your approach and the general scientific insights that are achieved. Therefore, I decided for major revisions.

Important comments seem to arise from a lack of clarity of the objectives of this study, in particular regarding differences and additions to your 'Part 1' paper.
Is it about flood forecasting using a huge number of ensembles of rainfall forecasts or on the usefulness of this input for the operation of the Kasahori dam?
I strongly recommend to make this point unambiguously clear (see also the first sentence of your abstract).
Further, for the newly introduced paragraph on the theory of selecting best ensemble members, I suggest to also add the key references from the literature.

Both reviewers provided detailed reports highlighting all the points you should address in the revisions. According to your responses, I am positive about the new version of the manuscript and that you will take up their remarks and implement the necessary changes.

Please provide a joint revised marked up (track changes) version of your initial manuscript to make clear how you include the changes in response to both referees. Please ensure consistency in e.g. figure numbering in this version. Additionally, you have to upload the new version of the manuscript.

If you have any question regarding the procedure or interpretation of the comments, please do not hesitate to contact me for clarification.

Please also note that this decision does not necessarily imply acceptance of the manuscript in the journal NHESS, and it still will depend on your reply (and subsequent edits to your manuscript) to referees comments, as well as on the reviewer comments of the revised version.

I look forward to receiving the revised version of your manuscript.

Best regards
Kai Schröter

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AR by Kenichiro Kobayashi on behalf of the Authors (11 Jun 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (26 Jun 2019) by Kai Schröter

RR by Alan Seed (12 Jul 2019)

RR by Anonymous Referee #2 (02 Aug 2019)

Suggestions for revision or reasons for rejection

I express my gratitude to the authors for their detailed response and the revised manuscript. I carefully read them to provide my suggestion. The main reasons for suggesting reject & resubmit in the previous review include 1) that apparently probabilistic discharge forecasts by a large set of ensemble cannot be seen as an improved prediction and 2) the proposed ensemble selection method has a significant potential flaw because the method is valid only for one location and not feasible for distributed modeling. To resolve these issues, I suggested validation of discharge simulation at multiple gaging locations within the study catchment and rigorous evaluation with probabilistic measures. Despite the efforts of the authors, I don’t think these two main issues are properly addressed in the revised manuscript. Therefore, I do not recommend the publication of this manuscript in NHESS.

If this article is published despite my opposition, I suggest the readers and editors take a close look at 1600 ensemble discharge traces shown in gray (Fig 6) which fill a whole range of discharge values from 0 to 2,000 cms. I don’t think any expert can make a meaningful decision on the risk management with these highly uncertain forecasts even though the mean values are close to the observed. Unfortunately, in my view, this is one of the typical cases which should be avoided in the probabilistic forecasts. However, in this study, rigorous statistical evaluations (e.g. calculation of probabilistic scores per each forecast lead time throughout the simulation) are lacking and failed to demonstrate the adequacy of the probabilistic discharge forecasts.

As a remedy for too dispersed ensemble forecasts, an ensemble selection method adapted from particle filtering was applied in the latter part of the revised manuscript. However, in addition to the fact that the performance of the selected ensemble deteriorates quickly, there is a critical flaw that this selection may be valid only for one location. To resolve this issue, the evaluation is required at multiple locations, which is also lacking in the manuscript.

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RR by Anonymous Referee #3 (08 Oct 2019)

Suggestions for revision or reasons for rejection

General comments:
The authors provide an interesting study about using ensemble forecasts with a very large number of members and subsequent reduction of the members for a more “practical” application in real time flood forecasting. The temporal and spatial error of NWP is less investigated than the error in the predictor. This paper addresses such issues and is of scientific interest.
The study is based on only one flood event, which makes conclusions very difficult. It is a major drawback, but an inherent problem in using a relatively new technique for forecasting very rare events. Therefore, I consider studies about single events useful for the flood forecasting community even if there should not be a significant advance of science. However, this must be made clearer.
Other authors have already done a lot of research in ensemble calibration or member reduction techniques for application in forecasting. E.g., methods based on Bayesian theory and regression techniques were used to assign weights to the members of an ensemble forecast based on prior information. Here, the authors should extend their literature study in their introduction (Reich and Cotter is “just” a text book citation). They could also add this aspect to the objectives of their work.
How stable is the selection of members with time? It seems to be relatively stable for their case study, but might happen that the selection must be updated very often. I have doubts if a single event study can provide a solution ready for operational forecasting. In the discussion, they should give more information or judgement if the proposed solution is transferrable to other events and make limitations clearer.
Specific comments:
Abstract: when introducing the „dynamical selection“ of the best ensemble members (a kind of sub-ensemble), the authors should not refer to the criterion (NSE), which can be questioned, but mention the techniques applied. Furthermore, they should make clear that only a single extreme event was used for the study.
P 1 L 25ff: ensemble forecasts do not necessarily give the probability of occurrence of a flood – that is a common misunderstanding. A good ensemble gives information about the range of uncertainty (“frames the future development”), i.e. the observation should be within the uncertainty band. Most ensemble forecasts assume the same probability for each member and use frequency evaluations. Probability is obtained by data assimilation and prost-processing, as the authors have added in their revision. The authors could carefully revise their usage of probability in the text and check where frequency is a more appropriate term.
P 3 L 27: “The main theme of this Part 2 paper is that the 1600 ensemble rainfall forecasts can significantly improve the rainfall forecast over the large area around Kasahori dam”: They should not give a theme with statements of their result, but first put the research question and objectives here. Also, after the revision, they have added work regarding the selection of best members in an operational case. It could be mentioned now in the objectives if not the title.
Section 2 is a very short – the content might be moved to section 1.
P 5 L 31: The rationale of the FSS should be briefly explained. Please explain the meaning of high and low values (just add sth. like “can have values between x and y, where y indicates the best possible score…”). Equations can be referred to by the citation. The reference (Duc et al., 2013) is not appropriate. The FSS is relatively new, so the original source must be cited here instead of own work of the authors using the FSS, which others proposed earlier.
P 5 L 34 “Note that an additional experiment with 4DEnVAR-NHM using 50 ensemble members”: how were the 50 members produced? Please give information (or a citation) about the differences in the ensemble generation mechanism of the 50- and 1600-member ensembles.
P 6 L 26: “but the ensemble mean precipitation is smeared out as a side effect of the averaging procedure”: then, the averaging procedure is maybe not a good solution. This is a well-known effect of ensembles of large size. Instead of averaging, other authors have used ensemble size reduction techniques. Finally, the authors did that but do not introduce at this stage of the manuscript (see comment above).
P 7 l 26 ff: The calibration of a hydrological model for a single event is questionable. Furthermore, using radar data instead of observations rises questions about the quality of the radar data, as can be seen later (fig. 13). Calibrating against “wrong” inputs produces higher uncertainty of the hydrological model, because it does not represent the physical processes well. The observed runoff is not a product of the radar data but a product of the observed rain. Parameters could get non-behavioral values in order to fit with the wrong rain input. As the authors assume a perfect hydrological model (without considering its uncertainty), it should be calibrated against the most perfect input data available. I think that the hydrological model is not valid here. However, the calibration and their discussion could be updated with a reasonable effort and the overall study is not about hydrology. If the radar data are used in operational service, but an error is known, the input data must be improved or post-processing can be applied, e.g. bias corrections. Research went a lot further in these topics.
P 8 L 7: In figure 4, it can be seen that the observation captured all three peaks quite well. Observed data show a consistent behavior. The hydrological model shows weakness in simulating double-peak flood waves. This would not necessarily prohibit it’s use for the study. The authors could add observed areal rainfall in Fig. 5 for better interpretation. From fig. 13, it is clear that there is an underestimation of rainfall by the radar products compared to the gauge stations. It would be good to use observed rain input to simulate stream flow as the reference for comparisons.
P 8 L 29: “observed rainfall within the range” – I think that Fig. 6 only shows runoff ensembles, so instead of “rainfall” they should use “runoff” here.
P 9 L 2 ff: see comment for fig. 7.
P 10 L 17 ff.: The authors should add if the NSE is of stream flow. Why not choose the best 50 members of the rain forecast?
Fig. 1: what is the meaning of the spatial scale? I did not find an explanation in the text or the caption.
Fig. 2: Y axis is “Observed Relative Frequency” and should be labelled accordingly. The reliability diagram is not intuitive, in my opinion not even appropriate for a single event situation – even if recommended by one reviewer. Usually, there are not such pairs like 90 % forecast probability and 0% observed frequency. For small data sets, other authors have used bootstrapping to refine the probability distributions. However, from a single event, one cannot conclude the reliability of a certain forecast technique. The authors may re-consider their usage of reliability diagrams in that context. Fig. 1 gives a good idea of the performance of the different systems for different rain intensities. I propose to leave out the reliability diagrams here, or replace with a more suitable performance measure for the single event. Maybe just give the Brier score or other metrics, as mentioned by reviewer #2.
Fig. 4: does R/A stand for “observation”? Is that radar composite or ground based? The plot is not easy to understand. It would be better to draw the 5-95 percentiles as light gray, the iqr as darker gray areas and the observed/derived lines as such, in colors. As done in fig. 8.
Fig. 6: Using the same style as figure 8 would be more informative. Then, figure 8 could be left out.
Fig. 7: the NSE is not good in characterizing the performance of stream flow ensembles of a single event. It is usually applied for calibrating hydrological models against observations, and more common for long time series. I propose to leave out fig. 7, and remove the corresponding text. It does not add to the findings but rises questions.
Fig. 10: again, the style of fig. 8 would be better here.

Referee Report: PDF

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ED: Reconsider after major revisions (further review by editor and referees) (17 Oct 2019) by Kai Schröter

AR by Kenichiro Kobayashi on behalf of the Authors (25 Nov 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (04 Dec 2019) by Kai Schröter

RR by Anonymous Referee #3 (05 Dec 2019)

RR by Anonymous Referee #2 (18 Jan 2020)

ED: Publish as is (04 Feb 2020) by Kai Schröter

Short summary

The feasibility of flood forecasting with 1600 rainfall forecasts was investigated. The rainfall forecasts were obtained from an advanced data assimilation system. The high probability of flood occurrence was predicted, which is not possible by the single deterministic forecast. The necessity of emergency flood operation was shown with a long leading time. This suggests that it is worth investing in increasing numbers of meteorological ensembles to improve flood forecasting.