the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Socio-meteorology: flood prediction, social preparedness, and cry wolf effects
- 1Institute of Engineering Innovation, the University of Tokyo, Tokyo, Japan
- 2Department of Civil Engineering, the University of Tokyo, Tokyo, Japan
- 3Department of Urban Management, Kyoto University, Kyoto, Japan
- 1Institute of Engineering Innovation, the University of Tokyo, Tokyo, Japan
- 2Department of Civil Engineering, the University of Tokyo, Tokyo, Japan
- 3Department of Urban Management, Kyoto University, Kyoto, Japan
Abstract. To improve the efficiency of flood early warning systems (FEWS), it is important to understand the interactions between natural and social systems. The high level of trust in authorities and experts is necessary to improve the likeliness of individuals to take preparedness actions responding to warnings. Despite a lot of efforts to develop the dynamic model of human and water in socio-hydrology, no socio-hydrological models explicitly simulate social collective trust in FEWS. Here we develop the stylized model to simulate the interactions of flood, social collective memory, social collective trust in FEWS, and preparedness actions responding to warnings by extending the existing socio-hydrological model. We realistically simulate the cry wolf effect, in which many false alarms undermine the credibility of the early warning systems and make it difficult to induce preparedness actions. We found (1) considering the dynamics of social collective trust in FEWS is more important in the technological society with infrequent flood events than in the green society with frequent flood events; (2) as the natural scientific skill to predict flood events is improved, the efficiency of FEWS gets more sensitive to the behavior of social collective trust, so that forecasters need to determine their warning threshold by considering the social aspects.
Yohei Sawada et al.
Status: final response (author comments only)
-
RC1: 'Comment on hess-2021-497', Anonymous Referee #1, 07 Dec 2021
This paper presents the improvement of an existing socio-hydrological model on the interactions between flood forecasting and flood loss by including social collective trust. The manuscript uses the model to investigate the cry wolf effect (where individuals may be less likely to implement protective measures if they have experienced false alarms). I believe including trust and investigating its role is a relevant contribution to the socio-hydrological literature. The manuscript shows an interesting analysis of the (potential) role of social collective trust and its implications for early warning systems.
However, I believe a major limitation of the work is the lack of comparison between model results and data or empirical evidence. I appreciate that there may not be enough data available to actually compare the model results to data, but given this limitation I believe the model equations and parameter choices should be much better substantiated with evidence from the literature. In addition, one could, in a descriptive way, compare the results with findings in the literature related to the cry wolf effect rather than only compare the results to the results of another model. In the current state, the manuscript does not provide enough evidence for the model assumptions and their relevance. This means that it is impossible to draw any useful conclusions from the results of the analysis, since it is unclear how well the model represents reality.
Some other remarks:
- The authors use socio-meteorology in their title and in the final paragraph of the discussion and conclusion they call for a new field called socio-meteorology. However, it is not clear to me why this work is so different that it does not fit within the field of socio-hydrology (the authors are only using discharge and forecasts of discharge in their model, to me this is hydrology, not meteorology). I would suggest to choose a different title, and stick to using socio-hydrology, as the authors do throughout the entire manuscript (the socio-meteorology is in fact only mentioned as an afterthought in the final paragraph of the manuscript).
- Introduction, lines 43 to 80: after reading the introduction for the first time I had the impression that there was actually no evidence for the cry wolf effect and for a relationship between the false alarm ratio and the implementation of measures. This made me wonder what the relevance of the presented model and manuscript is. However, after re-reading I see that I misinterpreted and there are studies that do find evidence in support of the cry wolf effect, but also some that do not. I would suggest the authors rewrite this part of the introduction to better present the argument for why their study is important.
- In the model description in line 148 (and after) the authors talk about preparedness actions (and mitigation and protection actions), please elaborate and explain what these actions are. There are many preparedness actions that do not depend on a flood warning to be implemented, what about those actions? These kind of measures may actually be implemented when experience of damage is high and trust in flood warning is low (which is the opposite of the cry wolf effect).
- Equation 6 models the cost of mitigation and protection actions, why is this relevant? Please discuss why you calculate this. Later, in section 3.1, I see that the total loss is calculated as D + C. I suggest to move this to section 2, since it is quite important and now it is a bit hidden away, which means the importance of C is unclear. Also how are the costs of protection actions determined? What is this based on? Also, why is the loss calculated as D+C, please explain this.
- In lines 177 to 179, the authors state that it is reasonable to assume that trust in FEWS increases (decreases) when prediction succeeds (fails). Please elaborate, this is the main contribution of the manuscript and this claim should be substantiated more. (The authors reference Wachinger et al. (2013), but Wachiger et al. (2013) actually hypothesise that the cry wolf effect may be an explanation for the risk perception paradox and do not provide the evidence to support this hypothesis.)
- In lines 200 to 202 the authors state: “In our proposed model, high social collective trust in FEWS can maintain the high level of social preparedness even if a community completely loses past flood experiences (equation (7)).” To me it seems unlikely that preparedness stays high solely based on trust while people have forgotten about floods. Is there any evidence from the literature that supports this assumption?
- For all variables and parameters: what are the units?
- For all equations and values the authors choose: please provide more evidence from the literature as to why this is a good representation of reality. This is especially important given the lack of data for comparison with model results, as mentioned in my main point.
- Table 2 and lines 207- 208: why are those parameters fixed and why do they have those values? Are they based on anything?
- For the parameters that are varied, why those values?
- Figure 1: what does half of social collective trust and social collective memory mean? Why half?
- In line 289 it is stated that figure 2 shows predefined warning threshold, but the figure axis title is predefined probability threshold. Same for figures 3 and 4.
- AC1: 'Reply on RC1', Yohei Sawada, 27 Jan 2022
-
RC2: 'Comment on hess-2021-497', Anonymous Referee #2, 19 Dec 2021
This paper presents an advanced modelling approach to studying the relationship between society and natural extremes. The authors developed an older model by Girons Lopez et al. (2017) by adding the "cry wolf effect" phenomenon. I find this a stimulating extension.
As mathematical modelling is not my field of expertise, I cannot assess the modelling section (which is the core of the paper). Therefore, my review is more focused on the context and overall results.
The paper is well-written and readable. However, some aspects should be explained better (see below). I also pose several questions for the discussion.
Questions and comments:
Title: Please consider changing the title to more reflect the paper's topic. E.g. "Possibilities of mathematical modelling in socio-meteorology: flood prediction etc." or "Mathematical modelling of the cry wolf effect".
Line 68: You mention that some studies claim that the cry wolf effect does not exist. How do your results affect this debate? Why do you think some authors have found the cry wolf effect problematic? Please discuss these questions.
Lines 78-80: I agree with this sentence. However, it would be nice to give a practical recommendation – what does your research imply? How should we consider the social aspects?
Line 139: You mention that the "trust in flood warnings" is based on the accuracy of warnings. Are there any other possibilities how to increase public trust? E.g. by social activities, education etc.? I understand that the mathematical model must be simple, but please discuss this.
Lines 163-164: Please define the terms "social collective memory" and "social collective trust".
Line 239: Why did you set gamma = 0.5? Why exactly 0.5? What does it mean?
Line 257: Please explain the terms "green society" and "technological society".
Tables 2, 3, 4, 5, 6: Please explain how you got the values of the parameters. Are these values based on empirical knowledge or literature review? Or are they just selected arbitrarily? What do these values mean?
Figure 1: Why do you show precisely the time range you show? Is it a random selection? What does mean the height of the colour bars? Is it the flood intensity or damage level? Would you please add a description of the y-axis?
Discussion and conclusion: According to your findings, it is possible to give a practical recommendation to FEWS strategy? I.e. do you suggest issuing fewer warnings (to reduce the cry wolf effect but risk the damages of flooding) or more warnings (to be safer but risk the cry wolf effect)? Please discuss.
Discussion and conclusion: Please also discuss your findings in the context of papers on the cry wolf effect you mentioned in the Introduction section.
Final remarks: Your paper is based on the modelling approach only. Would you please suggest how it would be possible to validate your findings on real data?
- AC2: 'Reply on RC2', Yohei Sawada, 27 Jan 2022
Yohei Sawada et al.
Yohei Sawada et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 403 | 95 | 13 | 511 | 5 | 5 |
- HTML: 403
- PDF: 95
- XML: 13
- Total: 511
- BibTeX: 5
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1