Thank you for the comment. I would like to elucidate on this subject that the integration of vulnerability presented in the study is not limited to the indicator-based method of vulnerability assessment used in the study. We selected the indicator-based method since it provides a range of perspectives and dimensions based on different indicators. Also, it well differentiates between the overall 'vulnerability' and 'exposure' and allows for a comparative understanding between the two concepts. However, any other method of vulnerability assessment can be used to achieve its integration. 

Response to the comments of Reviewer #1

Overview:

This paper reviews use of a 'serious game' in exploring how different types of information influences flood management decisions, here tested in the city of Mumbai, India. I comment here as someone who is familiar with urban flood management, but less familiar with use of 'serious games.' Overall, I think this is an important concept to explore how including information about exposure and vulnerability (rather than just the hazard itself) could improve decision making. However, as articulated below, I do think multiple aspects need to be clarified or explained more.

General Comments:

Comment 1: It seems like there could be some more description of previous work in this realm, including adding this helpful recent review of serious games and flood risk (https://wires.onlinelibrary.wiley.com/doi/full/10.1002/wat2.1589).

Response: Thank you for the suggestion. We would certainly include more descriptions of previous work in the revised manuscript including the review paper that the Reviewer has suggested. Following is the description which we finalized to include in the revised manuscript.

Serious games in hydrological hazards

Specifically, in the context of hydrological hazards such as extreme precipitation events and floods, various applications of serious games exist in the literature. In the revised version of the manuscript, we will add a comprehensive understanding of the existing literature on serious games and flood risk, here we present few examples. For instance, Arnal et al. (2016) and Crochemore et al. (2016) designed risk-based games to understand better the perception of probabilistic forecasts for flood mitigation in the decision-making process. Terti et al. (2019) created a role-playing game called ANYCaRE to investigate crisis decision-making in simulations of floods or strong winds in Europe. Sermet et al. (2020) developed a web-based decision support tool for multiple hydrological hazards, such as floods and droughts, to discuss the decision-making process regarding budget, technicality, preparedness and response. For more information regarding the application of serious games in flood risk management, the reader is referred to Forrest et al. (2022).

Comment 2: It could be helpful to give more meaningful abbreviations to E1, E2, E3 that indicate the type of information given, to help the reader more easily interpret the figures. 

Response: Largely, E1, E2 and E3 represent the experiments undertaken to test the suitability of different information sets for making decisions. In the revised manuscript, we will make it more explicit by using the word ‘Experiment’ to represent the acronyms E1, E2 and E3.

Comment 3: I suggest that you discuss the tradeoffs of what information can be made available (e.g. like relative time to prepare, ease or cost of availability, etc) in terms of why not suggest that participants are given all/best of the available information (i.e. high res rainfall, exposure, vulnerability). 

Response: Thank you for the suggestion. There are various other information that can be used to achieve better decisions. However, in India, having access to data, especially socio-economic, is difficult because of two main reasons: (1) these data are generally generated once in a decade, and (2) even if the data is available with some agencies, it is not publically available. The socio-economic and flood related data used in this study to calculate exposure and vulnerability have been obtained by the Bombay Municipal Corporation (BMC) upon request. The data was in the form of hardcopies which are not fully available online. The BMC uses this data, along with other classified data, to make their decisions.

            The raw data obtained from the BMC was processed and statistically used to calculate the different kinds of information which was given to the participants in the game to make decisions. For instance, in our study vulnerability is calculated using 15 indicators which leads to a color-code or a value for the area. If such an index is not available, understanding and visualizing all 15 indicators to make a decision will be highly difficult for a decision-maker. Such statistical processing of raw data will save time of the decision-makers.

Comment 4: It would be helpful to expand on discussion about the 10% of participants that took actions in Gamma and were perceived to have misunderstandings. Could you learn anything from those participants that could improve the game?

Response: Thank you for the comment. It was a deliberate effort to select Gamma as one of our towns in the game with a clear motive to identify participants who could not understand the flood situation in the game or the rules to make the decisions (see lines 129-131 of the original manuscript). Gamma was the least affected town in the game, at least in the first round. We were cautious that there may be participants who may not understand all the aspects of the game fully and hence play the game based on chance rather than their scientific conscious. The scores of these players would have eventually influenced the overall performance of all the players, which would not have been fair to them or to the overall results. Therefore we chose to remove those participants’ response from the analysis.

Comment 5: In discussing implications of results, the statement is made around line 315 that information on vulnerability helps make better decisions than just exposure. But yet just before this, it was stated that only in 1 round (R2) did vulnerability info yield the highest score. Please be clearer and not mis-leading/overly generalizing in your conclusions. 

Response: We thank the Reviewer for pointing this out. Looking back, we do not see the relevance of the sentence, ‘This suggests that the information of vulnerability helps to make better decisions compared to the information of exposure’ in the present context at lines 314-315. We would remove the sentence from the revised manuscript. 

Detailed comments:

Comment 6: pg2- line 25- in this paragraph, missing an obvious one of increasing population, thus more potential impacts.

Response: We are not fully sure what the Reviewer is suggesting in the comment. We suppose that the Reviewer is suggesting to add one more reason in the paragraph based on population. We would like to kindly inform the Reviewer that, in the paragraph, we are more focused on highlighting the increase of impacts because the information of rainfall forecast are not used properly. We are trying to emphasize that the there is a need for not just ‘forecasts’, but ‘Impact-based Forecasts (line 32). Hence, including a point of increasing population in the paragraph may not be ideal. Also, we believe that impacts and population are not directly proportional all the time. For instance, extreme precipitation situation can have larger impacts over an airport, but a lesser population may be impacted. Hence, we would like to keep three reasons only. 

Comment 7: pg5- mention somewhere in this section what type of players this is targeted to? e.g. educated audience that would play role of flood manager vs general public game scoring- it would be good to mention here that detailed information on decisions is in the appendix. Also, please clarify whether the managers on whom correct scores are based have 'hindsight' on what the best decision was based on what actually happened in the real event. 

Response: The game is targeted to be played by anybody who can understand the flood context and the game's rules. However, we indeed targeted an educated audience who may understand the role of flood managers, the information they may have to process and the decisions they may have to make. Since, we belong to research and educational institutions, we had access to students enrolled in higher studies. Hence, the majority of the participants in the game are master’s students, PhDs and Researchers from recognized universities/institutes. We will mention this on page 5 when we get the opportunity to revise the manuscript.

Regarding “detailed information on decisions is in the appendix”. We would mention the optimal decisions presented in Appendix C, Figure C1 on page 5 to make it easy for the reader.

Regarding “managers on whom correct scores are based have hindsight……” – Yes, the manager made the optimal decisions based on the hindsight of what actually happened during the flood event in 2005. The manager is affiliated with the very institution responsible for making the real-time decisions in the 2005 event. We will clarify the same in the revised manuscript.  

Comment 8: pg 5, line 144- it is mentioned that a modified precipitation forecast is used as the original severely underpredicted the actual event. This is worth more discussion later, given that flood managers cannot make good decisions when the information that they have ends up not at all aligning with the actualized event. 

Response: We completely agree with the Reviewer, and that’s why we chose to present the participants with a modified forecast. The forecast models were unable to predict the magnitude of rainfall which occurred during the 27-28 July 2005 event. There was a heavy rainfall forecast; however, it was far less than the actual rainfall that was received. In the game, we wanted to ensure that the participants did not make decisions based on an underestimated forecast. Such a forecast would have severely limited their ability to anticipate the impacts, and they would have overly estimated the role of exposure and vulnerability, leading to flawed decisions. This would have compromised the main aim of the experiments, which is to test the different combinations of the hazard and vulnerability information and identify the best-suited combination for emergency decision-making.

The modified forecast used in the game is close to the observed rainfall. The observed rainfall was not used exactly to ensure that the rainfall in the three selected towns was contrasted, which allowed for playing out diverse decision-making contexts in the game.

Comment 9: pg 6, line 158- this line noting '55% of its actual value' is confusing. Perhaps re-word to something like ''each indicator value of Alpha was scaled by 55%.' 

Response: Thank you for the suggestion. We will rephrase the following sentences in the revised manuscript: “Each indicator is then scaled based on the actual area of the ward which was under flood during the 2005 event. For instance, close to 55% of Alpha’s total area was flooded in the 2005 event, which implies that each indicator value of Alpha is scaled by 55% of its actual value in this study.”

Comment 10: pg7, line 185- it would be helpful to briefly explain how the indices (e.g. vulnerability) are calculated or their primary data inputs, rather than simply referring to a reference for all information. 

Response: Taking into account the suggestion of the Reviewer, we will add the following paragraph in the revised manuscript – “The normalized values of the indicators (Subsection 3.4.2) for each town are multiplied by their corresponding weights (Subsection 3.4.3) to calculate the weighted sum. The weighted sum of the indicators is divided by the sum of the weights. The values of the calculated sub-indices are used to calculate the flood vulnerability index.”

Comment 11: pg9- line 251- it would be good to expand upon 'not overly straightforward' to connect to the fact that this demonstrates how flood managers can have trouble identifying the optimal outcome in the midst of the event. 

Response: Thank you for the suggestion. We believe it will be a good idea to expand on the same in the Discussion section of the revised manuscript. Here we present an excerpt. The low scores of participants in the game indicate that decision-making is not simple, especially in the case of emergency events such as flash floods. Considering that the game was designed in such a way that it closely represents the actual flood event of 2005, low scores of participants demonstrate how flood managers can have trouble identifying the optimal outcome in the midst of the event. This also suggests that it is important that the decision-makers are experts in their fields and are well trained to cope with difficult situations. Prior experience of decision-making can also help in analyzing the best possible options corresponding to the available resources and accuracy of information.  

Comment 12: pg 10, line 274- when discussing how medians in R2 have shifted higher but tails are negative, should have 'but' instead of 'and' in 'located close together, and the tails of their distributions...'

Response: Thank you for pointing it out. In the revised manuscript, we will replace ‘but’ with ‘and’ in the designated text.   

Comment 13: Chart of E1,2,3 info could be helpful, like a more detailed version of Figure 2 left side. 

Response: We would add more detailed information about E1, E2, and E3 in other figures to make it easier for the reader to interpret.

Comment 14: Figure 1- should be a bit larger so that sub-figure panels and associated text are more visible. 

Response: We agree with the Reviewer. There was some issue in the latex template, which prevented the Figure from coming out larger. We will definitely solve the problem in the revised manuscript.

References:

Arnal L, Ramos M-H, Coughlan De Perez E, Cloke HL, Stephens E, Wetterhall F, Jan Van Andel S, Pappenberger F. 2016. Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game. Hydrol. Earth Syst. Sci, 20: 3109–3128. https://doi.org/10.5194/hess-20-3109-2016.

Crochemore L, Ramos MH, Pappppenberger F, Van Andel SJ, Wood AW. 2016. An Experiment on Risk-Based Decision-Making in Water Management Using Monthly Probabilistic Forecasts. Bulletin of the American Meteorological Society. American Meteorological Society, 97(4): 541–551. https://doi.org/10.1175/BAMS-D-14-00270.1.

Forrest SA, Kubíková M, Macháč J. 2022. Serious gaming in flood risk management. Wiley Interdisciplinary Reviews: Water. John Wiley & Sons, Ltd, 9(4): e1589. https://doi.org/10.1002/WAT2.1589.

Sermet Y, Demir I, Muste M. 2020. A serious gaming framework for decision support on hydrological hazards. Science of The Total Environment. Elsevier, 728: 138895. https://doi.org/10.1016/j.scitotenv.2020.138895.

Terti G, Ruin I, Kalas M, Láng I, Cangròs I Alonso A, Sabbatini T, Lorini V. 2019. ANYCaRE: A role-playing game to investigate crisis decision-making and communication challenges in weather-related hazards. Natural Hazards and Earth System Sciences, 19(3): 507–533. https://doi.org/10.5194/nhess-19-507-2019.

Response to the comments of Reviewer #2

The paper by Singhal et al. describes a serious game mimicking the decision process during an extreme flood event. The game is based on the record floods that affected Mumbai in 2005. Overall, the paper is well written and relatively clear except for certain method points discussed below. The topic of using games to help understand and improve emergency management is highly relevant for the HESS journal in the global context of increased population in flood prone areas and changing climate. By establishing a certain distance between the players and reality, a game constitutes an efficient tool to extreme and often dramatic events. However, the game presented by the authors suffers from several fundamental flaws that make it unsuitable for publication in its present form. Two major flaws are discussed in the following section with more detailed comments provided in a subsequent part of the review report. All comments are numbered to facilitate later reference.

>>> Major comments

Comment #1 - No considerations of ethic: serious games are qualified as “serious” because they are closely related to real situations and, hence, can have a powerful impact on their players. More generally, a serious game is a social experiment on human beings which requires a detailed assessment on the ethic of the process to ensure that players are protected from harm. The authors never mention this aspect which is surprising considering the policy of their respective institutions on this aspect (IISER, 2021; Université Grenoble Alpes, 2024). Following Fisher & Anushko (2008), ethical considerations (1) must address potential conflict of interest between the researchers and the participants, (2) must ensure informed consent of participants, (3) must ensure equitable treatment of participants regardless of their cultural or socio-economic background. We noticed several elements in the authors’ game design that would require careful review in the light of these three principles:

 (1.1) The 2005 Mumbai floods was an extremely traumatic experience. There is a high risk of participants being negatively affected by the game if they were associated with the event. There is no information in the paper on how the participants were identified, if they are voluntary, or if the purpose of the game was clearly explained to them.

Response: We appreciate the comment of the Reviewer. However, all these aspects have been well covered in the original manuscript which the Reviewer probably missed. We respond to each part of the comment sequentially.

Regarding the comment “high risk of participants being negatively affected by the game if they were associated with the event” – We have explicitly mentioned in the manuscript in line 135 (subsection 3.2.5) that the actual backdrop of the game, the Mumbai flood, is revealed to the participants in the debriefing session. The debriefing session occurs after the end of the game when the players have made all the decisions. We respectfully disagree with the Reviewer that the players, or their decisions, would have been affected by the game. All discussions regarding the game being inspired by the Mumbai flood happened after the game.

Regarding the comment “There is no information in the paper on how the participants were identified, if they are voluntary” – We have mentioned in the manuscript that all game sessions were conducted in academic and research institutions (lines 231-232; 452-453). Extensive details about the institutions and the identified participants have also been presented in Table B1. We also mention, at several instances in the manuscript, that the majority of participants are senior-level students, PhDs (48%), Masters (24%), and researchers (22%) at lines 235-236, 289-290 etc., and in Figure 3. Further, all the participants voluntarily participated in the game. We had taken due permission from the respective institutions to conduct the game session (please see the acknowledgement section in the manuscript), and the participation in the game was entirely voluntary. To make it clearer, we will add a sentence in the revised manuscript to highlight the voluntary involvement of participants in the game.

Regarding the comment “if the purpose of the game was clearly explained to them” – We would like to assure the Reviewer that the purpose was clearly explained to the participants. Utmost care was taken to ensure that the participants understood the context of the game, the scenarios, the information provided to them, the purpose of the game, the rules they had to play and the decisions they had to make (please see lines 106-107, 112-116, and 137-141 of the manuscript). In short, the participants were provided with all the information which are laid out in sections 3.2 and 3.3 of the manuscript. Also, we were cautious in ensuring that the game was made simple, yet most of the complications involved in decision-making were retained.

(1.2) India is a country with a large cultural, linguistic and socio-economic diversity. It is not clear how this diversity was represented in the group of participants beyond their academic level. For example, the game seems to be based on questions asked in English with answers provided in the same language through a spreadsheet. This favours disproportionally participants with an academic background such as PhD or researchers who, unsurprisingly, scored best in the game.

Response: We assure the Reviewer that we have already considered including diverse social groups in the game. One of the reasons why we chose national academic institutions to convene the game sessions was to ensure a diverse inclusion of social, cultural and linguistic participation in the game. We want to inform the Reviewer that the three institutes where the game session was convened - Central University of South Bihar, Indian Institute of Science Education and Research, Bhopal and Indian Institute of Tropical Meteorology, are well-reputed national institutes of India where students from across the country come for their higher education. These students belong to diverse cultural and socio-economic diversity; some may be poor or rich.

We were especially concerned about knowing the ‘native city’ of the participants as it is a better indicator of whether they have lived in a region prone to frequent floods. To this end, we explicitly asked the participants to share their ‘native city and country’ as can be referred to on page- 34, Section A of Figure A1 of the manuscript. 

Regarding the comment, “the game seems to be based on questions asked in English…….. scored best in the game”, – The Reviewer has perhaps assumed that English is not the language in which students go about their education in India, which is false. English is the primary medium of education in India, especially in central and national institutes. All exams, oral or written, theoretical research or practical, are conducted in the English medium. The three institutes where we conducted the game function in the English language. The students of the three institutes write their exams in English; hence, we are confident that language was not a problem during the game. Hence, we assure the Reviewer language had nothing to do with PhD or researchers scoring the best in the game.

(1.3) The participants are ignorant that the game is about Mumbai until this fact is revealed at the end of the game. This practice is a deceptive method which is highly debated in social sciences (Fisher & Fyrberg, 1994). Although not firmly prohibited, we are personally sceptical about its benefits due to the lack of trust it generates between participants and game organisers. This aspect may not be significant here due to the absence of on-going relationships between the authors and participants (the game seems to be a “one-off”). However, it could trigger problematic situations in relation to our point 1.1 above if participants suddenly realise that they have been playing with data from a flood and a city they are familiar with.

Response: The choice of not revealing that the game is based on an actual event in Mumbai is not deceptive in our case, as the Reviewer pointed out that the game is a “one-off”. ‘Deceptive’ is a harsh word to use which we would have surely avoided. Anyway, we firmly believe that revealing the actual backdrop of the game before the decision-making exercise would have actually created biases since many of the participants would have made the decisions based on their knowledge of the event rather than the information provided to them during the game. The game would have become more about how much the participants knew about the 2005 Mumbai event and less about what decision they made and why.

            Based on their declaration (page 34, Section A of Figure A1 of the manuscript), 12 of the 123 participants who participated in the game belonged to Mumbai. We want to assure the Reviewer that during the debriefing session or even after, there were no problematic situations, none of the participants shared with us that the game negatively affected them by any means or they developed a lack of trust in us. The one feedback we received from most of them was that the game presented a simplified nature of the actual events. We have this feedback in cognizance and acknowledged the same in the manuscript (please see lines 434-435 and 474-475 of the original manuscript).

Comment #2 - Bias in research analysis: The method presented by the author suffers from several biases that could potentially affect their conclusions and limit their applicability to real-life emergency decision making. More specifically:

Comment (2.1): The authors excluded responses from participants that made more decisions for the “Gamma” district (Line 129 of the manuscript). This is not acceptable as it modifies the outcome of the game arbitrarily. There could be many reasons why participants decided to take such decisions. For example, they could have favoured economic interests in the “Gamma” district against population safety in other districts. Such decisions are morally questionable but they remain part of the game nonetheless.

Response: Excluding the responses from participants who made more decisions for Gamma was a deliberate effort to eliminate biases from overall results. Gamma was selected as one of the towns in the game with a clear motive to identify participants who could not understand the flood situation in the game or the rules to make the decisions (see lines 129-131 of the original manuscript). These participants would have played the game based on chance rather than their scientific conscience and would have eventually made some decisions correctly. The scores of these players would have negatively influenced the overall scores of all the players, which would not have been fair, nor would it have led to an accurate assessment of the decision-making abilities of other players.

Comment (2.2): The game duration is extremely short with 25 minutes for the two rounds and an additional 15 minutes of questions and discussion. In addition, the game is played individually without any interaction between the participants except during the last 15 minutes. Consequently, the game does not explore human interactions and coordination at all, which are fundamental in analysing emergency response (Drabek, 1985).

Response: The participants are given 25 minutes to make the decisions. The time spent on providing the background information regarding the study area, the structure of the two rounds, and the rules to play the game are not included in these 25 minutes. We wanted to keep the game time short and the game was structured in such a way that it should not take more than 60 minutes to complete one game session (including game background, rules, game-play and debrief). We also pilot-tested the game with several volunteers and the actual decision-making did not go beyond 25 minutes. Also, in none of the sessions, participants demanded or wished for any extra time. Generally, situations of flash floods in a metropolitan area require quick response and action. Sharifzadeh et al. (2020) reviewed 101 serious games in the health sector and reported the most common gameplay duration was 30-45 min.

            Regarding the comment – “The game is played individually without any interaction between the participants” – In our study, we did not intend to explore the interaction of participants during decision-making. There are several serious games in the literature which have already accounted for human interactions in the game (Rusca et al., 2012; Terti et al., 2019; Bakhanova et al., 2020; Neset et al., 2020). We did not aim to understand how humans interact in their roles to make decisions. Rather, our aim is much broader. We aim to test different combinations of the hazard (extreme precipitation) and vulnerability information and identify the best-suited combination for emergency decision-making and show that vulnerability and its underlying components need to be included in IBFs and decision-making protocols. We could have included the component of role-playing to make the decision-making interactive, however, we felt it was not necessary considering the objectives of the study.

Comment (2.3) All decision variables are colour coded, which removes the ability for the participants to weight quantitatively the information provided. We appreciate the author’s intent to simplify the information and allow the participants to compare disparate data. However, this is not the reality of an emergency decision process where flood managers must deal with sometimes confusing data.

Response: We would like to inform the Reviewer that all decision variables in the game are not color-coded. The ‘rainfall forecast’ and ‘flood-prone population density’ provided to the participants are quantitative (please refer to lines 139-140, 218, 223, Table 2 and Figure 2 of the manuscript).     

            Regarding the color-coded form of information, the Reviewer commented that it ‘removes the ability for the participants to weight quantitatively the information provided’. This is little surprising considering that it’s the quantitative form of information which generally makes it difficult to weigh the information, and not qualitative. Usually, qualitative information is preferred because one can easily distinguish between the variables based on the colors. The colors (say green, blue, and red) are classified based on a particular scale (say low, medium and high) which makes it easier to interpret. On the other hand, to weigh the quantitative form of information one has to understand what the numbers mean. For instance, a decision-maker who does not have a strong understanding of precipitation amounts will find it difficult to understand what 10 mm, 25 mm, 50 mm and 100 mm mean in terms of their severity and potential impacts. However, the same precipitation amounts assigned colors as green, blue, orange and red make it easier for the decision-maker to make better decisions.

Regarding the comment that ‘flood managers must deal with sometimes confusing data’ – We agree with the Reviewer and that is why we are trying to investigate through this game whether complex information about hazard, exposure and vulnerability can be simplified to make emergency decisions. Results show that participants made better decisions with the qualitative form of information compared to the quantitative form.

Comment (2.4) Vulnerability data are presented to participants at the same time or even after rainfall forecast data. The game setup seems to reproduce the case of an untrained manager going through her or his very first flood and who discovers vulnerability hot spots at the same time than rainfall forecasts arrive. This is not realistic for a seasoned manager who knows the city well. We suggest reconsidering this point and present the vulnerability data well in advance to the players so that they understand the layout of the city before the game starts. The lack of context understanding seems to be confirmed by the game results where participants obtained better score in the second round compared to the first (see Line 254).

Response: We think the Reviewer has misunderstood the design of the game. We would like to clarify each misunderstanding sequentially.

            Regarding the sub-comment “Vulnerability data are presented to participants at the same time or even after rainfall forecast data” – We would like to clarify to the Reviewer that the vulnerability data is presented to the participants at the same time with the rainfall forecast information, and not later.

            Regarding the sub-comment “The game setup seems to reproduce the case of an untrained manager going through her or his very first flood and who discovers vulnerability hot spots at the same time than rainfall forecasts arrive. This is not realistic for a seasoned manager who knows the city well” –We take this opportunity to clarify our point here. In the game, we have clearly explained that an imaginary crisis unit provides participants with different kinds of information based on which the flood risk manager (participant) makes the decisions. It should be common for any flood emergency meeting to bring all the available information to the discussion, including vulnerability. It would be unreasonable for any decision-maker to remember all the vulnerability information of the whole city to expect him to make decisions just based on rainfall forecast. Further, we have nowhere mentioned in the manuscript that the decision-maker ‘discovered’ vulnerability for the first time. It is realistic for a manager to consider all the available information while making decisions, especially in a city where close to 20 million people live. Moreover, there may be a strong case that the vulnerability of the city is constantly updated. The manager must have the latest information, including that of vulnerability, on his or her table while making decisions. According to us, a flood manager who makes decisions based on its historical understanding of the city’s vulnerability is not untrained, but rather careless.

Regarding the comment “We suggest reconsidering this point and present the vulnerability data well in advance to the players so that they understand the layout of the city before the game starts”- We do inform the participants the area they will have to manage, with relevant geographical and socio-economic characteristics of the prior to the decision-making (see lines 106-107). We also provide them certain field information water level of the lake and river, sea tide height, prevailing ground situation and possible future developments before the rounds of the game starts (see lines 113-114). We believe all of these information did help the participants to understand the layout of the city before the game started.

Comment (2.4) The game assumes that there is a “correct” answer for every round defined by local experts. This aspect is quite disturbing as it is difficult to know what the best decision in a city as complex as Mumbai is when facing a flood as extreme as 2005. In addition, there is little information about who the experts are and if participants accept them as experts whereas the definition of a correct decision in this case is likely to be highly contested. We suggest considering more diverse form of rewards such as achieving consensus (if debate was allowed between participants) or showing consistency throughout the game (an important quality of emergency decisions).

Response: We would like to clarify the Reviewer that the optimal decisions are not based on the 2005 extreme event. They are based on the game that we have designed in this manuscript. The expert who provided the optimal decisions actually played the game to reach to those decisions as the participants have. The expert is a real decision-maker who works in the Municipal Corporation of Greater Mumbai which is the premier government department to make emergency decisions during a flash flood. The expert had no idea about the three towns in the game and their locations. We used the 2005 event only to provide a background of the INSPIRE city in the game, its geographical information, socio-economic conditions and to assess the hazard, exposure and vulnerability information. We will add more information regarding the background of the expert and the basis of the decisions in the revised manuscript.

            Regarding the comment “if participants accept them as experts whereas the definition of a correct decision in this case is likely to be highly contested” – We have obtained optimal decisions for the game from a real decision-maker in Mumbai who goes through the decision-making process regularly. We don’t believe it would be right to understand from the participants whether they accept the decisions or not. In reality, the general public do not generally contest the decisions of the decision-makers. We have explained the basis of the optimal decisions. To make it clearer, we will add more information regarding it in the revised manuscript.

>>> Minor comments

Comment 3: Line 25, “Three main reasons may be …”:  a fourth more fundamental reason is simply that  extreme rainfall do not necessarily translates into high hazard. There are hydrogical (e.g. antecedent conditions, non-linear runoff generation, ...) and hydrodynamic (e.g. topography, levee systems, backwater effects, ...) factors that complicate flooding processes and reduce the value of rainfall information.

Response: We thank the Reviewer for the suggestion. However, we feel adding a reason about hydrological and hydrodynamic factors is beyond the scope of this study. Studies based on hydrological factors have been done in India, but for a different city called Chennai by Ghosh et al. (2019). We derive inspiration from the mentioned study but in the context of socio-economic impacts, hazards and vulnerability. Adding contexts regarding antecedent conditions, non-linear runoff generation, levee systems and backwater effects lays emphasis on aspects which are different from our study. Our emphasis is more on precipitation forecasts and the impacts on general population due to imperfections in proper use of their information.

Comment 4: Line 85, “Gupta and Nair”: the reference is not about the Mumbai flood but about floods in Chennai and Bangalore. Please remove this reference and replace it by a more appropriate one.

Response: Thank you for the suggestion. We will remove the reference “Gupta and Nair” from the revised manuscript and replace it with (Gupta, 2007).

Comment 5: Line 105, “Flood Manager”: this role needs to be defined in greater details. There is a great diversity of flood managers ranging from liaison officers to operators of major infrastructures. Please clarify this point and explain how it was presented to the participants.

Response: The main role of the “Flood Risk Manager” was to make the best possible emergency decisions to minimize the impact of the extreme precipitation and flood. The manager led a fictitious team of representatives from the Meteorological Department, Department of Town Planning, Department of River Management, Department of Coast Management and the media cell (please see lines 114-115). As suggested by the Reviewer, we will define the role more clearly and in greater detail in the revised manuscript. 

Comment 6: Line 115, “Meteorological Department, Department of Town Planning, Department of River Management, Department of Coast Management and the media cell”: why are there so many organisations providing information and only one role for the participants (Flood Manager)? Please clarify why it is important to distinguish the information provider and its effect on the responses during the game.

Response: These organizations have been included in the game to make it more interesting for the participants and keep the game closer to reality. In an event of flash floods, generally a meeting is called where representatives from different departments discuss a variety of information before making any decision. In our game, the Flood manager is the head of the crisis management unit. The names of different departments which provide the information has no effect on the responses of the participants. Even if the names were not included in the manuscript, the participants would have received those information. However, the names of these departments make it easier for the participants to understand and remember the provided information.

Comment 7: Line 142, “The accumulated rainfall forecast, used in the game, is a slight modification”: Please clarify if there was an attempt to reproduce the skill level of recent rainfall forecast. This information is important to assess if the forecasts are realistic for current decision making in Mumbai.

Response: We would like to clarify the Reviewer that there was no attempt to reproduce the skill level of the rainfall forecast of the 2005 event. Instead, we used the observed rainfall for the particular event. The rainfall forecast was highly underestimated in comparison to the actual rainfall that was received (lines 144-145). The best model that predicted the extreme rainfall was UKMO which predicted 120-160 mm (lead time day 3), 280-320 (lead time day 2) and 200-240 (lead time day 1) as reported by Bohra et al. (2006). We believe there was no point to use the underestimated forecast since it would have led to incorrect decisions by the participants. The difference between the forecast and observed rainfall was such that it would have been incredibly difficult for any post-processing technique to match the observation.  

Comment 8: Line 147, “The information of exposure and vulnerability is statistically calculated”: this sentence is not clear. Remove this statement and refer to the following sections which explain the process.

Response: As suggested by the Reviewer, we will remove the statement from the revised manuscript and instead refer to section 3.4.

Comment 9: Line 152, “Vulnerability and Exposure analysis”: the concept of exposure is confusing. As indicated by the authors and following Gallopín  (2006), vulnerability can be decomposed into exposure, sensitivity and adaptive capacity. Consequently, exposure is a part of vulnerability, not an independent concept. However, the section title at line 152 suggests that it is distinct. Please clarify.

Response: The Reviewer is correct that vulnerability can be divided into exposure, sensitivity and adaptive capacity. We have mentioned the same in our manuscript at line 152. We do not agree with the Reviewer that exposure is not an independent component. It is the individual sub-indices of exposure, sensitivity and adaptive capacity that forms the vulnerability index. Vulnerability cannot be determined without its components of exposure, sensitivity and adaptive capacity, however, the individual components are not dependent on vulnerability. In this study, the components are calculated individually and are then aggregated to form the vulnerability index. Exposure is an independent concept while vulnerability comprises each of exposure, sensitivity and adaptive capacity. Several studies have considered the concept of exposure separately to the overall vulnerability (Carrão et al., 2016; Byers et al., 2018; Singhal and Jha, 2021; Tanim et al., 2022)

Comment 10: Line 156, “standardized”: please remove this word. The authors are simply calculating the value of each indicator based on the proportion of area flooded in the ward assuming an homogeneous distribution of the indicator across the ward. Standardized has a different meaning which often relates to subtracting the mean and dividing by the standard deviation.

Response: We will use the word ‘scaled’ instead of ‘standardized’ in the revised manuscript. The following sentences will be rephrased in the revised manuscript: “Each indicator is then scaled based on the actual area of the ward which was under flood during the 2005 event. For instance, close to 55% of Alpha’s total area was flooded in the 2005 event, which implies that each indicator value of Alpha is scaled by 55% of its actual value in this study.”

Comment 11: Line 160, “normalized”: Please define this normalisation.

Response: We will mention the use of maxima-minima method for normalization at line 160. We will also define the method at line 170 of the revised manuscript. The definition would contain “The maxima – minima method scales the value of an indicator between 0 and 1. The minimum value of the indicator is subtracted from the value of a selected indicator which is then divided by the range of the indicator”.

Comment 12:Line 193, “based on the beta distribution”: this approach seems overcomplicated for the definition of simple indicators. The use of the beta distribution adds the uncertainty associated with the choice of the distribution and its parameter values. We suggest replacing this by the quantiles of the indicators across the 24 wards.

Response: Based on the suggestions of the Reviewer, we will replace the beta distribution method with the quantile method in the revised manuscript.

Comment 13:Line 220, “qualitative rainfall forecasts”: please clarify how are rainfall forecasts color coded.

Response: Since the extreme rainfall event witnessed over Mumbai in few hours was unprecedented, there is no existing criteria that can be used to classify that amount of rainfall. Hence, we developed a criteria for classifying the rainfall forecast as color codes in the manuscript. First, historical observed rainfall amounts were explored to find the highest ever 1- hourly and 3- hourly rainfall over the Mumbai city. These rainfall amounts were 113 and 253 mm respectively. The rainfall amounts were then classified into four categories based on equal proportion.

If Rainfall (mm) <=113, the category is defined as Level I (green),

If Rainfall (mm) >113 to 183, the category is Level II (yellow)

If Rainfall (mm) >183 to 253, the category is Level III (orange)

If Rainfall (mm) >253, the category is Level IV (red)

Comment 14: Line 302, “level of education does play a role in decision-making”: it is not obvious that researchers are best placed to take high risk decisions under intense time pressure. We believe that this statement is in fact the result of the multiple biases introduced by the game described in the previous section.

Response: We beg to disagree with the Reviewer. We have already responded to each ‘bias’ the Reviewer has referred to in the previous comments. According to our understanding, the responses to those comments will satisfy the lack of clarity of the Reviewer.

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