Flood insurance is a driver of population growth in European floodplains

Domain: earth_science
Published: 2023-11-18
Editorial outcome: major_revision

Abstract
Future flood risk assessments typically focus on changing hazard conditions as a result of climate change, where flood exposure is assumed to remain static or develop according to exogenous scenarios. However, this study presents a method to project future riverine flood risk in Europe by simulating population growth in floodplains, where households’ settlement location decisions endogenously depend on environmental and institutional factors, including amenities associated with river proximity, riverine flood risk, and insurance against this risk. Our results show that population growth in European floodplains and, consequently, rising riverine flood risk are considerably higher when the dis-amenity caused by flood risk is offset by insurance. This outcome is particularly evident in countries where flood risk is covered collectively and notably less where premiums reflect the risk of individual households.

Introduction
It is imperative that climate risk assessments guide climate adaptation policies1–3. Traditionally, climate change risk has been assessed with models that mainly focus on changing hazard conditions due to biophysical processes and climate change, exogenous projections of exposed assets and people, and assuming constant vulnerability to project the potential damage of climate hazard4. Recent research recognises the dynamic nature of exposure and vulnerability with respect to climate risks, and it emphasises the need to apply these dynamics in models. For example, global flood risk modelling studies have found that socio-economic growth is the dominant driver of increasing riverine flood risk in some regions across the world5,6. These large-scale flood risk models consider exposure growth in floodplains based on storylines such as ’shared socio-economic pathways’ (SSPs), which are based on several generic scenarios of global developments such as economic growth, political stability, and technological development7. Besides exposure, the vulnerability of communities to flooding is also often assumed to remain constant over time1. Although these assumptions simplify large-scale (global) climate risk models, they disregard the fact that exposure and vulnerability are intrinsically dynamic8 and should be modelled as such9. Households may, for example, move away from or avoid settling in areas at high risk of flooding10–12 or apply flood risk reduction measures if they do reside in these areas1. Settling in floodplains may also become more attractive when this area provides aesthetic or recreational amenities13,14, when flood protection infrastructure is improved15, or when governments or insurers provide financial compensation after floods12.

Recent scientific developments address the dynamic interplay between climate hazards, exposure, and vulnerability. Instead of top-down methods, which use static exposure and vulnerability input data to project climate impacts on global or regional scales, bottom-up assessments focus on human behaviour and how individuals dynamically respond in time and space to certain (environmental) conditions9. For example, bottom-up approaches are used to project the evacuation behaviour of individuals in the face of disasters16, analyse adaptation behaviour in response to floods and insurance incentives17–20, and assess migration flows away from high-risk areas15,21. For large-scale climate risk models, it is useful to combine a top-down approach, for example to assess climate hazards, with a bottom-up approach that accounts for behavioural responses to changing hazards.

Large-scale flood risk assessments commonly use generic (top-down) scenarios of population growth, largely because population development is complex and depends on many external factors. However, projecting future flood exposure in these assessments requires a downscaling method where high-level (SSP-) scenarios can be applied to forecast exposure on a local scale. Several population projection models22,23 that are used in high-impact flood risk studies2,3,5,24,25 assess the suitability of areas to capacitate population growth by identifying geographical features that are likely to encourage or discourage populations to settle, such as elevation, steepness of the terrain, distance from urban centres, and distance from the coast. Importantly, these spatial projections represent a static view of human decision-making in relation to environmental processes, particularly regarding flood risk. A more accurate assessment of developments in flood exposure requires a coupling of human and environmental subsystems26.

This study develops a bottom-up approach to simulating dynamic processes of flood exposure and vulnerability, as well as the resulting future riverine flood risk, by considering factors that may attract or repel settlement in flood-prone areas. We analyse how population growth in floodplains may differ from existing top-down projections when considering the environmental characteristics of river-floodplains. These include flood risk and amenities associated with river proximity, but also insurance policies, which may reduce the negative impact of flood risk at a floodplain settlement location. Regarding the latter, for instance, the National Flood Insurance Programme in the US has been criticised for encouraging development in flood-prone areas because it offers subsidised coverage against flood damage27. In addition, within Europe, the subsidisation of flood insurance premiums in high-risk areas occurs in several countries, including France, Belgium, and Spain, where national flood insurance policies explicitly aim to promote solidarity among households in high- and low-risk areas. On the contrary, several countries, such as Germany and the UK, strive to implement mechanisms that stimulate household-level adaptation, including risk-based premiums28,29. This means that policyholders pay a premium that reflects the flood risk of their property. A risk-based insurance premium signals to policyholders the extent to which they are at risk of flooding, which has been found to encourage policyholders to reduce risk30.

Limiting property development in high-risk areas is perhaps the most effective method to limit population exposure to increasing flood hazard31. A variety of policy measures may be implemented to achieve this, including more controlled land-use planning in floodplains32 or exclusion from flood insurance coverage, as is applied in the UK for buildings constructed after 2009 in high risk areas. The strategy of managed retreat has also been applied more often33, such as voluntary buyout schemes that governments initiate to facilitate the relocation of households away from flood-prone areas. Beside such regulatory measures, higher risk-based costs of insurance coverage may also discourage settling in floodplains12. This study assesses the extent to which insurance affects exposure development and compares these effects for two types of insurance arrangements that are currently applied in EU countries and the UK (see Methods). The modelling approach developed in this study integrates riverine flood risk, the amenities of river proximity, insurance incentives, and household-level disaster risk-reduction (DRR) in a simulation of household settlement location decisions over time. The simulation applies data or techniques from established models, such as riverine flood risk from a spatially explicit flood risk model2, and flood insurance premiums and household-level DRR from a partial equilibrium model of European flood insurance markets19. Whereas these models assess flood risk and simulate insurance market outcomes, including premiums and incentives for household-level DRR, they do not include an integrated approach to assessing flood exposure growth through location decisions and, in particular, how this may result from insurance policies.

This study shows that population growth in European floodplains and, consequently, rising riverine flood risk are considerably higher when the dis-amenity caused by flood risk is offset by insurance. This outcome is particularly evident in countries where flood risk is covered collectively and notably less where premiums reflect the risk of individual households. With this work, we aim to inform the debate about flood insurance reforms and how insurance markets may contribute to making societies more resilient to climate risks.

Results
To clearly present the flood exposure projections developed in this study, we consistently compare our results to the data obtained from the 2UP model22. This model applies a spatial suitability map to project population growth on a local scale using generic population growth scenarios, such as SSPs. The method developed in this study differs from the 2UP model in that it considers environmental (dis)amenities (including flood risk) and insurance. The population projection using the 2UP model is henceforth referred to as the ‘baseline’ approach.

Figure 1, Panel A shows the projected population growth within floodplains for the period 2010-2050 using the baseline approach. This projection applies population growth according to SSP2, which represents a continuation of historical trends in terms of social, economic, and technological development. In this study, floodplains are defined as areas that are expected to flood due to river discharge at least once every 100 years, or with a 1% annual probability (see “Methods”). These areas are considered particularly at risk of flooding, and many flood risk management and insurance policy debates specifically concern these areas18,34. In this study, flood hazard is defined as the spatial extent and depth of inundation caused by riverine water-levels that occur with a certain probability, and flood risk is defined as the damage caused by such inundation. To enhance visibility, model output throughout this study is aggregated to the NUTS3 level, which is a level of geographic aggregation applied by the EU for specific socio-economic analyses.

Panel B of Fig. 1 shows the extent to which exposure growth projections differ from the baseline when considering environmental amenities associated with rivers and flood risk. Moreover, the analysis for Panel B takes into account the option that households have to reduce flood risk by applying wet- or dry flood-proofing measures (see “Methods”). Flood risk projections used for this assessment apply representative concentration pathway (RCP) 4.5, which represents a future where the 2 °C limit set by the Paris Climate Agreement is met35,36. Projections using alternative SSPs and RCPs are shown in the Supplementary Materials. The results in Panel C follow the same approach, while also considering the potential of insurance to limit the negative impacts of flooding for households. Each panel is discussed in a dedicated section below.

Floodplain population projections using the baseline-method
Under SSP2, following the baseline approach, Panel A of Fig. 1 shows that population growth in floodplains through 2050 occurs mainly in Western and Northern Europe, while populations are expected to decline considerably in many Eastern European regions. For example, floodplain populations are expected to grow in most regions of France, up to 44%, while only two regions show a projected decline of up to 16%. In several countries, such as Germany, Italy, Austria, and Portugal, floodplain populations are expected to remain more or less stable (over these countries, on average -4%, -0.7%, 5%, and 3%, respectively). Eastern European countries show the most considerable declines in floodplain populations, with the largest declines projected for Bulgaria and Romania (both at approximately -21%). Determinants of population growth under the baseline approach are terrain features such as steepness and roughness, but also proximity to urban centres, which is why the few regions in Eastern Europe where populations do grow are mostly around major cities there (e.g., Bucharest, Warsaw, Athens). A noteworthy observation is that changes in local floodplain populations following the baseline population projection model, as presented in Panel A of Fig. 1, show population projections with patterns similar to those of the larger regions where these floodplains are located (see Methods). This finding is sensible considering that the baseline approach does not specifically assess the suitability for population growth in floodplains. However, there are several areas where floodplain population projections (Panel A of Fig. 1) differ considerably from the overall population projections (see Methods), most notably in the Spanish region of Extremadura. In such cases, projected population growth is largely drawn towards suitable locations, such as urban areas, that are outside the floodplains. In the case of Extremadura, it is likely that attractive settlement locations in the baseline approach are on higher ground, as floodplains make up less than 5% of the landscape (see “Methods”).

Contrary to population projections following the baseline approach, recent studies that use satellite imagery show that human settlements worldwide have been growing more rapidly within than outside floodplains in many part of the world24,37–39. In Panels B and C of Fig. 1, we present floodplain population growth projections using the method developed in this study, in which we integrate specific factors that affect the suitability to settle in floodplains.

Floodplain population growth considering (dis)amenities and household-level DRR
River floodplains are associated with both positive and negative environmental amenities that impact their suitability for human settlement40,41. Panel B in Fig. 1 shows how the population projection that considers environmental (dis)amenities in floodplains and household-level DRR deviates from the baseline projection. Positive amenities associated with floodplains are approximated using values derived from studies that apply hedonic pricing techniques, which generally find that positive environmental qualities associated with river proximity decay over distance42. The predominant environmental disamenity of river proximity is perceived flood risk, which is simulated in our flood risk model (see Methods). In our simulation, household-level DRR is considered as a strategy for households to reduce flood risk, which is done through a household-level assessment of costs and perceived benefits of wet- and dry flood-proofing measures (see Methods). The option to reduce flood risk may increase the attractiveness of floodplain settlement for some households.

In Panel B of Fig. 1, blue colours indicate lower floodplain populations compared to the baseline approach, while red to yellow colours indicate a higher population projection in our simulation. Whereas a value of 1 implies that our projection is identical to the baseline approach, a value of 2 indicates that our projection is twice as high. The median value of data plotted in Panel B equals 1.1, which implies that in most regions floodplain populations are higher than the baseline projection. However, as blue is also a prominent colour in Panel B, for many regions, the baseline approach does seem to overestimate floodplain population growth when compared to our projections, which means that the flood risk outweighs the amenities of floodplain settlement in these regions.

In countries such as France, Spain, and the UK, there are stark contrasts between regions regarding the projected deviations from the baseline. A driver of these differences is flood risk. In Spain, for example, where three regions are projected to have considerably higher population growth in floodplains compared to the baseline, the annual flood risk per household for each region in 2050 is less than €30 (about €50,000 for each region as a whole), which is a fraction compared to the average per household located within floodplains for the whole of Spain (approximately €400). The same effect can be found in the UK, where, for example, higher population growth and modest annual flood risk per household intersect in several regions of Wales and Northern Scotland. Evidence of the opposite effect, meaning higher flood risk leading to lower population growth in floodplains, is also apparent, most notably for regions in Ireland and central Sweden. In central Sweden, shown in blue, where population growth is projected to be lower compared to the baseline, the annual flood risk per household in 2050 is approximately €700, which is substantially higher than the Swedish average of €270.

In our model, flood risk does not always have a considerable impact on population growth in floodplains. In southern Spain, for example, the flood risk that households face is relatively high, while population projections do not differ noticeably from the baseline projection. The main reason for this result is that general population growth projections for these regions is low, meaning that the potential change from the baseline projection is lower compared to regions where population growth projections are high.

Floodplain population growth considering insurance coverage
It is rare for households experiencing flood damage to have to completely finance the reconstruction themselves. Flood insurance is available in all EU countries and the UK, which can cover the potential damages against a premium. Panel C in Fig. 1 shows deviations from the baseline projection when households have the option to insure against flood risk.

An important element of this analysis is the flood insurance premium. Although insurers set premiums to cover the risk of flooding over time, often, premiums do not accurately reflect the risk of an individual. Instead, premiums can cover potential losses over a larger geographical space. This means that flood risk in high-risk areas may be partially subsidised by households residing in areas with lower risk. Therefore, if premiums are not risk-reflective, as they are in countries such as France, Belgium, and Spain, the benefits of residing in a river floodplain are more likely to outweigh the costs. This is why positive deviations from the baseline are generally seen in these countries in Panel C of Fig. 1. In the remaining countries, there are more regions where our population projections are lower than the baseline results. In these countries, flood insurance premiums are, to varying degrees, risk-reflective, which means that the costs related to flooding for floodplain residents with insurance coverage approaches the actual projected flood damage. The main difference is that premiums spread flood risk over time into an annual cost, while the damage associated with a flood is likely much larger, although less frequent. Risk-averse individuals will seek a mean-preserving spread of losses, meaning that such people likely prefer a lower certain loss over a larger uncertain loss with the same expected value. This implies that a household facing the choice to settle on a floodplain or on higher ground will most likely choose the floodplain when premiums are insensitive to risk, less likely when premiums are risk-reflective, and least likely without insurance availability.

The assessment done for Panel C in Fig. 1 assigns each country one of the insurance premium types described above based on their actual flood insurance market arrangements, as reviewed in 20 and presented in “Methods”.

Panel C in Fig. 1 shows that population growth in floodplains is generally higher than in Panel B. As expected, insurance availability makes the floodplain a more attractive location to settle, causing higher population growth in floodplains compared to both the baseline and the scenario without insurance availability. Overall regions included in the analysis, the mean deviation from the baseline is 2.5, which means that floodplain population growth may be more than twice as high as baseline predictions when considering flood insurance availability, in addition to elements introduced in Panel B. However, this average value is strongly impacted by certain regions where population growth through 2050 is projected to be up to 10 times higher in Panel C. Interestingly, some of these are regions where projections without insurance availability (Panel B) are lower than the baseline (e.g., Deux-Sèvres in France, Cuenca in Spain, West-Surrey in the UK), which indicates that insurance can have a considerable impact on households’ settlement location decisions.

Moreover, it is clear that insurance policy design matters. Our population growth projections in floodplains are consistently and substantially higher in France and Belgium, where premiums are relatively inexpensive in high-risk areas. Whereas average risk-based premiums in 2050 in Sweden, Ireland, and the UK are close to €400 annually per household, flat-rate premiums in Spain, France, and Belgium are approximately €13 per household. Without insurance availability, projections for France show lower floodplain population growth rates in most regions, while in the scenario with insurance availability, every region shows a higher outcome compared to the baseline (with an average of 3.5). Insurance coverage largely offsets the hazard of flood risk, and when premiums are inexpensive, the attraction of floodplain settlement more likely outweighs the costs. In countries where insurance premiums are risk-based, we generally see that population growth rates are more aligned with the baseline projection. For example, while projected population growth in the Netherlands is on average higher after introducing risk-based insurance, approximately 20% of the country’s regions still show a lower outcome compared to the baseline. The introduction of flood insurance hardly affects population projections in Austria, Slovenia, and the Czech Republic, as risk-based premiums there generally discourage settling in floodplains, similar to the scenario without insurance availability. Of the countries where premiums are risk-based, in the UK, insurance seems to drive population growth in floodplains most substantially. Although the number of UK regions where higher population growth is expected after introducing insurance availability does not change considerably (58% compared to 64% of the regions), the mean value changes from approximately 3 to 4.

A final observation in Fig. 1 is that projections for countries where premiums are risk-based in Panel C are more aligned with those in Panel B, compared to France, Belgium, and Spain (where premiums are insensitive to flood risk). This outcome is sensible considering that risk-based premiums in Panel C approach actual flood risk, as considered in Panel B. Empirical evidence that reflects this observation exists but may be mixed, as the actual implementation of accurate risk-based premiums has been limited thus far43. In general, the differences between Panels B and C are less striking for countries with risk-based compared to flat insurance premiums, which suggests that flood insurance premium policies can be considered a tool to reduce flood exposure and improve societal resilience against growing flood risk.

The following section converts our assessment of flood exposure into future flood risk.

Assessing future flood risk using new population projections
Exposure is one of three elements that comprise flood risk, in addition to hazard—that, is the inundation of land due to the over-topping of dykes or embankments—and vulnerability, the extent to which a flood of a certain magnitude damages exposed assets. Existing projections of exposure, obtained using population suitability maps, are insensitive to certain environmental and institutional factors that may attract or repel population growth. Including these dynamics in flood risk projections may increase their accuracy and allow for an assessment of how future flood risk may be impacted by changes in the environment or policy domain. To demonstrate this, we estimate expected annual flood damage (EAD) to residential and commercial properties using the population growth parameters obtained from Fig. 1. With this approach, we calculate flood risk while simultaneously accounting for households’ adaptive responses to this flood risk, as they may choose to fully avoid potential flood damage by settling on higher ground. On the other hand, we also account for insurance policies that may limit these adaptive responses by households or even trigger risk-seeking behaviour44.

Panel A of Fig. 2 presents the percentage change in EAD over the period 2010-2050 considering the impacts of climate change under RCP4.5 and exposure growth under SSP2, which is determined using the baseline approach as presented in Panel A of Fig. 1. Panels B to D of Fig. 2 each present the extent to which EAD may deviate from the baseline approach when assuming the population growth dynamics in relation to environmental (dis)amenities, household-level DRR, and insurance availability.

A notable observation in Panel A of Fig. 2 is that the increase in flood risk is higher in Eastern European regions compared to many regions in the West, despite the decline in exposed populations there under the baseline projection. In Poland, for example, in 2050, the EAD is expected to have doubled since 2010 to approximately €900 million, even though populations exposed to floods are expected to decline by 13%. This means that increasing flood hazard must be a strong driver of increasing flood risk in Poland and other Eastern European countries. Another reason for the higher projected growth in EAD in Eastern Europe is that modelled flood protection standards are generally lower there than in Western Europe, which mean that floods are expected to occur at a higher frequency in the East. In Romania, for example, overall flood protection infrastructure is designed to withstand a 1/50 year flood, while in France, it is designed to withstand a 1/100 year flood, and in the Netherlands, a 1/1000 year flood45. Finally, economic growth is a likely driver of increasing EAD in Eastern Europe. This is because economic growth (expressed as GDP) considerably influences the value of exposed assets within the flood model applied in this study2. Although GDP is generally lower in Eastern compared to Western European countries, GDP growth rates over the period 2010–2050 are higher there36.

Panels B and C of Fig. 2 present the deviation from the baseline projection in Panel A, corresponding to the new exposed population projections presented in Panels B and C of Fig. 1, respectively. As expected, deviations from the baseline flood risk projection are closely aligned with the underlying projections in exposed populations. That is, we see more or less the same patterns in Panels B and C of Fig. 2 and Panels B and C of Fig. 1. However, the extent of the deviations from the baseline in Fig. 2 is substantially higher (up to 30 times higher) in some regions compared to the deviations in exposed population projections (up to 10 times higher). This is because increasing flood hazard and exposure may reinforce each other and lead to larger increases in flood risk than each of these elements alone. Considering the results presented in Panel B of Fig. 2, flood risk projections are lower than the baseline in 80% of all regions considered. However, on average overall regions, flood risk is projected to increase slightly in this scenario, which is largely driven by several regions where exposed populations are projected to increase considerably. The availability of insurance in Panel C, on the other hand, causes higher increases in EAD compared to the baseline in many regions. The most important changes in Panel C compared to Panel B can be seen in France, Belgium, and Spain, which is largely driven by higher population growth in floodplains under a flat insurance premium structure. On average, considering insurance availability, the growth in flood risk through 2050 in each of these countries is €3.9 billion, €440 million, and €1.3 billion, respectively.

Changing to risk-based premiums may be a strategy for these countries to discourage households from moving into flood-prone areas and thus limit future flood risk. In Panel D of Fig. 2, we show results in the regular format, while we assume that current insurance systems in France, Belgium, and Spain change to become risk-based. In general, more contrasts can be seen between regions compared to Panel C, which is because local flood risk becomes a determining factor of floodplain population growth. In total, for France, Belgium, and Spain, the rise in flood risk with the availability of risk-based insurance declines by more than 50% compared to flat insurance premiums. Therefore, insurance policy design can be considered an effective tool to reduce flood risk. Although certain physical protection measures are found to be more effective at reducing flood risk, they are also highly costly. For example46, find that improving conventional flood protection standards may reduce riverine flood risk in Europe by 70% in 2100, which will cost approximately €3.1 billion annually until then. Flood adaptation through detention areas along river systems may reduce flood risk up to 83% and cost approximately €2.6 billion per year. Concerning coastal flood risk47, find that raising dykes reduces risk in Europe by approximately 97%, which will likely cost at least €2.5 billion per year until 2100. Several other adaptation measures, including the flood-proofing of buildings, relocation through a managed retreat strategy46, and nature-based solutions48, are found to be less effective than adaptation of insurance mechanisms. The exact costs of changing to a risk-based insurance system are difficult to quantify but are likely lower than the investment costs associated with the above measures. In addition to administrative costs to implement risk-based premiums, governments may need to reserve funds to assist households whose insurance coverage becomes unaffordable19.

Methods
Population development in floodplains is simulated within the existing “Dynamic Integrated Flood Insurance” (DIFI) framework19,20,58. This framework consists of three models that estimate flood risk, insurance premiums, and household preferences regarding several flood risk, insurance, and adaptation decisions. This study is concerned with household decision-making regarding settlement in- or outside flood-prone areas, where the primary innovation is introduced in the household decision model. Therefore, in this section we focus primarily on describing the procedure of the household decision model, while the description of the preceding models is briefly summarized. For a comprehensive explanation of all components of the DIFI-model we refer to 19. The applied methods and flow of modelling components are depicted in Fig. 3.

The principal component of this study, the household decision model, applies several types of input data, and serves to replace a local scale settlement suitability map as applied in 22,23. Its purpose is to apply generic population projections and simulate where populations grow on a local or regional level. Section “Population growth model” describes how ’Shared Socio-economic Pathways’ (SSPs) are applied as input for the household decision model, which comprises the choice to settle in- or outside a floodplain. In the current modelling framework, the settlement location decision that households face is based on three exogenous variables: flood risk, insurance premiums, and environmental amenities associated with river proximity.

The flood risk a household faces at a potential settlement location is based on flood hazard, which maybe impacted by climate change, but also vulnerability to flooding, which may be reduced by installing DRR measures. How this is determined is explained in Section “Flood risk model”.

A household may be more inclined to settle in a floodplain if the potential flood damage is compensated by insurance. The annual premium of this insurance coverage becomes an important input variable for this decision. Section “Insurance premiums” describes how insurance premiums and coverage is determined for two stylized insurance systems, which are based on flood insurance arrangements observed in EU-countries.

Rivers provide a quality that attracts populations to settle in floodplains. Such qualities include aesthetic, recreational, or broader economic values that have historically led population hubs to develop in close proximity to rivers. These river amenities are monetized for the decision framework using literature that applies hedonic pricing valuation techniques, as is described in detail in Section “Location amenities”.

The choice module simulates how much of the regional population growth occurs in floodplains and how much on higher ground. With more detailed insight into population growth within floodplains, and therefore flood exposure, we can estimate regional flood risk. Section “Model output” describes the procedure of this final step.

Population growth model
To assess whether population growth is likely to occur in- or outside regional floodplains, we apply exogenous population growth forecasts following the “Shared Socio-economic Pathways" (SSPs), which are extensively used in research regarding future climate risks36. In this study, three SSP-scenarios are used that encompass a range of future developments. SSP1 corresponds to high but sustainable economic growth, which leads globally to the highest decline in population towards 2100 out of all SSPs36. SSP5 represents high but unsustainable global economic growth, which leads to a slightly larger global population. SSP2 depicts a continuation of historical trends in terms of social, economic, and technological development. Global population growth, in this scenario, is highest of the three that are considered.

Global population trends may, however, differ from projected developments in Europe. In Fig. 4 population growth is shown on NUTS3-level, from 2010 to 2050, for the considered scenarios. A prominent observation across all three scenarios is that populations in Eastern European regions generally decline, while those in Western European regions are expected to increase. Population decline in Eastern Europe is most notable under SSPs1 and 2, while population growth in Western Europe is most prominent under SSP5. In several countries, such as Germany, Poland and Italy, populations are expected to decline under SSPs1 and 2, while they increase under SSP5.

Population trends shown in Fig. 4 are acquired from the 2UP-model22, which applies a suitability mapping approach to simulate where population growth is likely to occur. This approach, in other words, provides a tool to interpret more generic scenarios (e.g., the SSPs) on a regional or local scale. Although the 2UP-model works on a more detailed scale than the NUTS3-level shown here, the purpose of the current study is to improve upon this local analysis with innovations regarding the settlement location decision, making a particular distinction between flood-prone areas and areas safe from flooding. Therefore, population trends, as shown in Fig. 4, will feed into the household decision framework, where it is assessed whether population growth is likely to occur in floodplains or on higher ground. The household decision framework of this study only applies to population growth, which means that regions with declining populations are omitted in this analysis. It is, of course, possible that within regions populations may move from floodplains to higher ground or vice versa. However, such relocation models are likely to become highly complex, and may exceed the needs for flood risk assessments, which instead require tools to translate global population scenarios to local-scale population growth. Nevertheless, we do recognize the importance of considering relocation to- and from floodplains within regions, and we recommend future research to extend our model framework to include these dynamics.

The growth of population per region is simulated to choose to either settle in- or outside a floodplain. This choice is simulated for each new household in a region, which means that the regional population growth has to be corrected for the size of households, data for which has been obtained through Eurostat. The analysis on household-level is sensible, as flood risk assessments generally consider damage to houses and other buildings, and flood insurance also applies to buildings, not individuals. The procedure described in the following is, then, applied to each household new to a region. Each household that causes the population in the NUTS3-region to grow, therefore, is represented by a single iteration of the model.

Each simulated household receives a choice between a settlement location within a floodplain, and one outside the floodplain, on higher ground.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper presents an innovative method to project future flood risk under climate change scenarios that simulate population growth in floodplains, considering how amenities associated with river proximity, flood risk, adaptation options and insurance against risk influence settlement location decision. Obtained results are innovative and significant both from a scientific point of view and for decision makers involved ibn risk mitigation. I congratulate with authors for the great job. Result interpretation is mostly in agreement with model results. I do not have significant comments on the main text, only suggestions on how to improve its clarity (see specific comments in the manuscript). My main concern is about the lack of clarity on the implemented methodology that in some cases hampers the interpretation of results (for instance, on the effect of adaptation options or environmental amenities). There are parts of the methodology that are not totally clear, especially regarding how flood damage is evaluated, considering also the effect of adaptation options, or environmental amenities. I had also difficulties with the adopted symbolisms. Finally, I think more details should be supplied regarding model calibration and validation. Figures must be improved as mistakes are present and, sometimes, they are not self-explanatory. Please, refer to my specific comments in the attached annotated manuscript for further details. The manuscript is worthy for publication after unclear points will be addressed.", "reviewer_2": "The paper presents a new methodology that takes into account human’s behaviour changes in responding to flood insurance and environmental amenities in estimating population exposure to floods. It is innovative but I do have some reservation (see my comment 1 and 7). I recommend its publication after major revision. My comments are provided as follows.\n1. I find the maps shown in NUTS3-level misleading. The study focuses on river flooding but the spatial aggregation covers the coastal areas as well. The latter has very different flood exposure due to coastal flooding or combined river/coastal flooding. It is not clear if the population exposure obtained by using the 2UP model is also for river flooding and if the population data used do not include population in coastal areas. Fig 1 shows the key results within the main manuscript, but it is too small to see the spatial differences. It needs to be enlarged and ideally the coastal areas should be excluded. The discussion in the manuscript always refers to countries, and as such the country boundaries should be visible on the map.\n2. It is not clear where the 100-year floodplains are located. The authors referred to it throughout the manuscript, for example line 119-122.\n3. One can hardly see any floodplains in the maps but rather entire countries or regions without much difference in the colours.\n4. It was not explained in the manuscript why the 100-year return period was used for the analysis. Surely the more frequent floods such as 50-year would be of more interest unless the authors assume the protection standards are higher than 50-year all over Europe. The second issue is the 100-year flood extrapolated using shorter time slices is not robust.\n5. Throughout the paper, the term of ‘risk’ and ‘hazard’ are used interchangeably without making distinction. The authors need to distinguish between ‘flood risk’ and ‘flood hazard’ and define the terms of hazard, vulnerability and exposure that make up ‘flood risk’. The definitions in line 277-280 are not accurate. Hazard is not “inundation of land due to the over-topping of dykes or embankment.” Vulnerability is not just “the extent that exposed assets are damaged by a flood of a certain magnitude.“\n6. One important aspect missing in this study is government intervention in flood risk management. There are flood zones that are prohibited from development or need planning permission. For example, the Environment Agency in England provides a flood zone map and they have specific regulations on what types of building and what kind of planning permission would be required in a certain flood zone. This study seems to assume the population settlement is completely free from government regulations and planning policies. The equation A1 in Section A.1.2 is rather simplistic assuming settlement decision is completely made by individuals without government regulations. Insurance premium could put constraints on floodplain settlement, but simply increasing the premium is not a solution as it would disadvantage certain households and individuals. The message would be misleading if we simply increase insurance premiums with the aim of reducing exposure. I would like to see discussion on the impacts or effects of government intervention in addition to completely insurance policy driven analysis.\n7. Further to my comment 1, Figure A4 has major flaws with regards to the floodplains. If the study focuses on river flooding only, it is incorrect to see 100% percentage of floodplain areas along the coasts. For example, the Rhine, Po river deltas, the Broadlands in east England etc. These areas are dominated by costal flooding rather then river flooding.\n8. Fig A1: Flood risk appears in “Impact variables for choice module”, but also in “Model output”. It is very confusing. I think the Flood risk in “Impact variables for choice module” should actually be “Flood hazard” or probabilities, and the Flood risk in “Model output” should actually be “Flood damage” or EAD.\n9. Section A.2 Flood risk model\nThe heading should change to ‘Flood damage’ or ‘Flood loss’ model because that you describe here is the method obtaining the flood damage and losses.\nThe study uses 100-year return floodplain to estimate population exposure, but the method describe here uses a range of return periods from 2- to up to 1000-year. This is rather confusing. Is the annual damaged derived for 100-year return period only or all the return periods beyond 100-year?\n10. The manuscript needs English proof reading to make it easier to read. There are sentences that are formulated in strange ways and difficult to understand. Some sentences are too long and can be broken down to shorter sentences.\nTechnical corrections:\nLine 74: the most effective method to limit societal exposure\nSuggest change to “the most effective method to limit population exposure” as it is better to stick to your terminology.\nLine 167-174\nInstead of using ‘flood risk’ here, it should be ‘flood losses’ or ‘flood damage’. It is also not clear if this is annual flood damage of 100-year return flood or flood damage of any unprotected magnitude.\nLine 216, This person more likely prefers a lower\nChange to ‘Such people more likely prefer’\nLine 289: fully avoid potential flood damage by moving out of harm’s way\nWhat do you mean by “harm’s way”. Please consider rephrasing here.\nLine 334: the growth in flood risk\nShould be ‘flood damage’\nLine 452: remove ‘, then,’\nLine 571: flood risk (L)\nThis should be flood losses (L). In line 579, “flood damage (L)” is used. Please be consistent with your terminology.\nLine 618: 15 years\nPlease explain why using 15 years in your model.\nLine 839: future flood risk\nShould change to ‘future flood damage’\nLine 856: regular procedure is followed\nWhat is the regular procedure?", "reviewer_3": "The manuscript presents an analysis of future river flood exposure and risk scenarios in Europe, based on a modelling framework that can simulate population growth in floodplains considering environmental and institutional factors.\nThe methodology applied covers a relevant aspect of flood risk management which has not been explored previously by continental-scale models, at least to my knowledge. The findings are also of interest, as they shed light on some (unintended) linkages between risk insurance policies and changes in flood exposure and vulnerability. Thus, work might be an important contribution to the scientific literature in the field.\nHowever, some issues need to be addressed before reaching a conclusion about the publication of the manuscript:\n- The actual degree of novelty of the modelling framework and of the outcomes is not completely clear to me. The study builds on a number of already-existing models and studies (GLOFRIS for flood risk analysis, 2UP for general population growth, DIFI for insurance modelling) by adding new components for population growth and flood-proofing of buildings. It is not clear to me whether some results, such as EAD estimations, are taken from previous works from the same Authors or are new, and how much of the previous studies by the Authors overlap with the present work. I would invite the authors to briefly describe their past works in the topic, as well as other relevant literature works (see also the following point), and then specify what are the innovations brought by their new analyisis.\n- The modelling framework focuses on the role of individual or household-based choices in shaping flood exposure and vulnerability, which is perfectly fine. However, the overall discussion of results seem to overstate the relevance of Authors' findings while overlooking the role of flood risk control measures driven by government policies, such as structural measures, land-use planning and so on. Historically, structural flood control measures (river dikes, dams, detention areas etc) have been determinant in shaping floodplain development (Haer et al., 2016; Di Baldassarre et al., 2018), whereas government policies can strongly influence flood exposure by limiting or excluding further developments in flood-prone areas, so individual decision are only a part of this framework. In addition, a number of recent studies evaluated future risk scenarios resulting from a top-down implementation of these and other measures (Ward et al., 2017, Vousdoukas et al., 2020; Tiggeloven et al., 2020, Dottori et al., 2023, with apologies for the self-citation). My impression is that the role of these measures in shaping flood risk can be , at least, as important as the measures analysed in the present manuscript. Therefore, I would recommend including all these aspects in the introduction and in the discussion of results, in order to put the authors' findings in the right perspective.\n- In the main text there is no explanation on why only some EU countries/NUTS3 regions are represented in Panels 1B-1C and 2B-2C. My understanding from Section A1.1.1 (L500-501) is that population growth in floodplains is evaluated only in NUTS3 regions with overall increasing growing population trends, but this seems in contrast with values in Figure A2-A (where Italy seems to have positive trends, for instance) and with the projected increases in few areas in eastern countries (e.g. Bucharest,Warsaw, Athens). Could you please explain this point?\n- I understand that a formal validation of the model is probably not possible. Still, I would ask the Authors to describe how their results compare with the study by Tellman et al after the calibration (in particular, if the model was able to reproduce observed dynamics)\nMinor points\nTitle: my impression is that now the title suggests that insurance is the main driver of population growth in floodplains, which is not the case. Perhaps a more nuanced wording would be more appropriate\nYou should cleary specify in the title, abstract and introduction that the work is about river flooding and Europe. Similarly, along the text you should mention whether your statements refer to floods in general (either river, coastal, flash or pluvial) or river floods only\nL93 I would briefly mention here the SSP and RCP scenarios that have been considered in the study.\nFigure 1: Legends are not fully clear in this Figure, I suggest using the same definitions as in Figure 2. Also, for panel A, using a colour palette with different colours for negative - positive change (as in panels B-C), would make the map more readable\nL236: \"Interestingly, some of these are regions where projections without insurance availability...\" Could you list some of these regions here?\nL240-243: \"... population growth projections in floodplains are consistently and considerably higher in France and Belgium, where premiums are relatively inexpensive in high risk areas.\" Could you provide an average monetary value for risk-insensitive and risk-based premiums as estimated by the model?\n-Section 2.4: After having read section A.5, my understanding is that you considered damage to residential and commercial areas to calculate EAD, is it correct? If yes, please specify this in the main text.\nFigure 2: In panel A, using a colour palette with different colours for negative - positive change (as in panels B-C-D), would make the map more readable. Also, can you please explain how \"factor\" is calculated for panels B,C,D?\nL304-309: Did you consider also projections of economic growth (such as GDP increase/decrease) to calculate changes in EAD? If yes, differences in economic growth might be another factor in explaining the observed trends\nL347-350: this introduction is perhaps not necessary here.\nL367: do you mean 75% higher compared to status-quo insurance?\nL378-380: I would tone down this conclusion, perhaps saying \" traditional climate risk assessment methods (...), might inaccurately project changes in future flood risk\". You indeed showed the importance of endogenous factors in shaping flood exposure and vulnerability, yet several other factors influence flood risk too (structural flood control measures, centralized land-use planning etc).\nL418:can you cite here some of these studies?\nL625: could you provide a reference for the choice of this discount rate value?\nL669-670: The paper by Aerts is a literature review of previous applications of a range of risk reduction measures. Perhaps the authors should provide more details on how they used such information to model costs and effectiveness of flood-proofing measures\nFrancesco Dottori\nCIMA Research Foundation"}, "author_response": "Response to reviewer 1:\nWe greatly value the time and effort the reviewer has put into examining our manuscript. We are particularly grateful for the reviewer’s laborious inspection of the methods section, including the model description. We believe that addressing the issues put forward by the reviewer has considerably improved the article.\nBased on the reviewer’s notes, we thoroughly revised large parts of the methods section, but also addressed several issues in the main text. In the following, we summarize how we addressed the most important issues put forward by the reviewer.\n• The description of the decision model (Section A.1.2), in which households’ settlement location choices are simulated, is changed almost entirely, and now includes more comprehensive and intuitive equations. Unclear symbology in these equations is addressed, and variables are more precisely explained in text.\n• The method to estimate flood risk (Section A.2) is revised completely, as requested by the reviewer. We applied a more systematic approach to describe which variables are estimated here, for which purpose, and how these are derived using a modeling approach that is taken from previous studies.\n• How household-level adaptation is integrated in the model is described more elaborately in Section A.1.2. Also, in the main text we discuss more explicitly how household-level DRR may motivate some households to settle in floodplains, and why it is important to consider this in our study on floodplain exposure development. However, this element is not an essential focal point of this study, which is why we do not specifically address the extent to which household-level DRR motivates households to settle in floodplains. Instead, we include it as an option that households have at all times to reduce flood risk to their property.\n• The model calibration is discussed more elaborately (Appendix B). In particular, it is discussed explicitly how our model produces results that are approximately aligned with observations in Tellman et al. (2021). Limitations of the calibration process are also discussed in more detail.\n• Results presented in the sensitivity analysis (Appendix B) are more critically scrutinized. We agree that in the initial version of the manuscript the discussion of these results was insufficient. In the revised manuscript this is improved.\nBesides these general remarks, specific responses to the comments are given below, where the line number refers to the location of the comment in the file provided by the reviewer. We again thank the reviewer for their time and effort, and hope they will enjoy reading the improved manuscript.\nLine 50: “Which processes? not clear”\nWe adjusted the text to clarify this. It now states \"A more accurate assessment of developments in flood exposure requires a coupling of human and environmental subsystems \" (lines 51-52)\nLine 90: “and flood risk”\nFlood risk is considered a disamenity. We clarified this by explicitly mentioning flood risk in brackets. (line 106)\nLine 93: “I think the meaning of SSP2 in terms of population growth should be breiefly explained here as in the appendix”\nWe revised this paragraph extensively to clarify the interpretation of the results and the use of SSP-scenarios. This is what is looks like now: “To clearly present the flood exposure projections developed in this study, we consistently compare our results to the data obtained from the 2UP-model (Van Huijstee et al., 2018). The 2UP-model applies a spatial suitability map to project population growth on a local scale using generic population growth scenarios, such as SSPs. The method developed in this study differs from the 2UP-model by considering environmental (dis)amenities (including flood risk) and insurance. The population projection using the 2UP-model is henceforth referred to as the \"baseline\" approach. Figure 1, Panel A, shows the projected population growth within floodplains for the period 2010-2050 using the baseline approach. This projection applies population growth according to SSP2, which represents a continuation of historical trends in terms of social, economic, and technological development.” (lines 101-112)\nLine 99: “What do authors mean with adaptation options? They were not described before and the reader can understand its meaning only reading the appendix”\nWe agree that the description of household level DRR should be covered earlier in the manuscript. We adjusted the final paragraph of the introduction to highlight that household-level DRR is an adaptation option that households consider when settling in a floodplain. Also, this sentence is adjusted to specify the adaptation options available, which are wet- and dr- flood-proofing.\nLine 102: “This title is not self-explanatory. The same is valid for panel C”\nThe title is adjusted to be more self-explanatory and more aligned with titles in Figure 2. The title for Panel B now states: “Difference with Panel A when considering environmental (dis)amenities and household-level DRR”\nLine 102: “The meaning of the colours (i.e. the scale) in panels B and C should be described also in the caption. Likewise, the meaning of grey areas is clear only after reading the appendix. Please explain.”\nThe caption is adjusted to clarify these: “(A) Projected population development in floodplains from 2010 to 2050 in percentages using the baseline-method under SSP2. Green shades indicate a decline in floodplain populations, while orange to red shades indicate an increase. (B) the difference with the baseline-projection when considering environmental (dis)amenities of floodplains and household-level adaptation. (C) the difference with the baseline-projection when considering insurance availability in addition to determinants included in Panel B. In Panels B and C, green shades indicate a lower floodplain population compared to the baseline approach, while yellow to red shades indicate a higher population projection. For regions depicted in grey our method is not applicable due to declining populations across these regions (see Section A.1.1)”\nLines 121 & 126: “Panel B?”\nIndeed. We changed it accordingly\nLine 141: “Here we have also the influence of adaptation measures that is, however, never discussed. In any case, it is not clear to me how this is evaluated in the model (see also my comment in the appendix). Moreover, it is not clera how and if it is possible to analyse the separated effect of flood risk, environmental amenities and adaptation options.”\nWe agree that this part of the analysis is somewhat obscure. We adjusted the introduction and the first paragraph of the result to emphasize that DRR is an adaptation option for households who want to move to a floodplain. Moreover, Section A1.2 is thoroughly revised to clarify how this process is modeled.\nLine 212: “This statement sounds strange to me. Is it not the opposite? Please comment”\nThe statement is correct, but the writing may be improved to enhance clarity. The idea of risk-based insurance pricing is that premiums for those facing high risk are not subsidized by policyholders facing lower risk, which means that the premiums are more reflective of the actual flood risk. The costs of insurance, therefore, approach the actual costs of flood risk. We changed the sentence in the following way. “In these countries, flood insurance premiums are, to varying degrees, risk-reflective, which means that the costs related to flooding for floodplain residents with insurance coverage approaches the actual projected flood damage.” (lines 239-243)\nLine 291: “See previous comment on colours meaning”\nThe figure caption is adjusted to explain the meaning of colours in this figure.\nLine 382: “In fact, the effect of local amenities is not explicit in the model”\nThe reviewer is correct. We removed this statement from the sentence. See the following comment for how we changed the sentence.\nLine 383: “What about adaptation options?”\nDRR is, indeed, an option that impacts the decision to settle in a floodplain for some. I included it in this concluding statement. These sentences now state: “Based on these findings we can conclude that traditional climate risk assessment methods, that assume exogenous exposure and vulnerability developments, may inaccurately project changes in future flood risk. A reason is that households' decisions to settle in floodplains endogenously depend on flood risk, coverage of this risk by insurance, and the option to reduce risk through DRR-measures.” (lines 429-434)\nLine 434: “I think the reader is supported if the authors would use always the same terminolgy (e.g. decision model as referred to in line 429?)”\nThis is addressed. We changed the terminology to “household decision model” (line 501)\nLine 435: “Check grammar… unclear”\nThe sentence is changed to improve clarity (lines 503-506)\nLine 444: “how coverage is considered is not clear in the remaining of the text”\nCoverage is removed from this sentence because it is not considered in the model. We do include a deductible of 15% of flood damages, but we do not address the impact of different insurance coverage levels on floodplain settlement.\nLine 466: “What about SSP1?”\nSSP1 is included in this sentence in the revised manuscript\nLine 519: “Which are these variables? Not clear”\nSeveral variables are drawn randomly from distibutions or other ranges. For example, the parameters that modify flood probability and impact to integrate a subjective element in the expected utility framework are drawn randomly from normal distibutions. Moreover, the two potential settlement locations, expressed as their relative distance from a river, are randomly chosen, one within the floodplain, and one outside it. We thoroughly revised the methods section where these processes are explained. We removed the description here to limit confusion.\nLine 532: “How the time t influences the results is not clear? Please, specify”\nWe thank the reviewer for the elaborate review of the model description. We agree that some elements, including the time aspect (t), was not clearly described in the manuscript. To improve this, we thoroughly revised this section.\nLine 532: “I have difficulties with the adopted symbolism. Also becuase the subscripts used here do not correspond with those in equations A2 and A3. In fact, A, L and s are independent variables as correctly indicated in A2 and A3. Please homogeneize.”\nWe agree that the the independent variables L and A do not belong in this equation. We removed these from the formula and moved the text describing these variables below Equation A2.\nLine 540: “In fact, the variable s is not used in the following. How it influences the model?”\nAfter revising Equations A2 and A3, we completely changed the approach to describing the subscript (s) in Equation A1. Households choose to settle in a floodplain if their EU associated with one of three available strategies (s) is higher than the EU of the higher ground. These strategies include no action, apply a DRR-measure, or purchase insurance coverage. This explanation is also integrated in the text.\nLine 575: “Again. Problems with symbolism. Are authors saying that Wπ is equal to W-π?”\nThe reviewer is correct. We adjusted the notation in the manuscript.\nLine 576: “Why L is not reduced if an insurance coverage exist?”\nA good point. We adjusted the formula in the manuscript to show this.\nLine 581: “Alfa?”\nAlfa is the insurance coverage. In the revised manuscript this flood impact misperception parameter is gamma, which is also explained more thoroughly in the text.\nLine 590: “Which values can the parameter take and which is their meaning?”\nWe agree that this process is unclearly described. In the revised manuscript, we improved transparency regarding the derivation of the flood impact and probability misperception parameters, and also on which literature these are based.\nLine 610: “What about t?”\nIndeed, t also impacts expected utility on the high ground. All variables considered change over time. This element is included in Equation A3.\nLine 623: “This step is not clear to me. If amenities values are divided by 15 to consider an annual value, why probability and premiums are multiplied by 15? Please comment”\nWe agree that this is may be confusing because of the way it is written here. We adjusted this text to clarify this process. “the amenities of river proximity, reflected through housing prices, do not express a yearly value, but rather the benefits of a location over the time a household expects to reside in a property. Therefore, within the SEU-framework it is incorrect to compare a long-term benefit with annual flood or insurance costs. For this reason, the insurance premium in Equation A2 is an aggregation of the yearly premium over the expected time of residence, which is assumed to be 15 years. A residence time of 15 years also means that a flood is more likely to occur compared to a single year. For this reason, the perceived annual flood probability is multiplied by 15 in Equation A2.” (lines 701-709)\nLine 626: “This section is totally unclear. How damage is evaluated is murky. Why a Monte Carlo Analysis is required? I understand this part is not novel but the reader must be able to undersantd and replicate the methodology. I suggest a deep revision.”\nA deep revision of this section was done. The description of the Monte Carlo simulation is left out because this process is done to approach the integral value of the flood probability-impact curve, which is a practical detail in the modeling approach. The reasoning of why an integral is needed is explained in more detail in the improved manuscript.\nLine 663: “This part is totally not clear. How adaptation options influence expected utility is not described in section A.1.2. Still results always refer to the effect of adaptation options. This influences the comprehensibility of the results by readers.”\nHow adaptation (DRR-) measures are integrated in the model is explained in more detail and more intuitively using the expected utility framework in section A1.2. More specifically, applying a DRR-measure is now shown as one of three options that a household may take if settling in a floodplain. A household may be enticed to settle in a floodplain if it is able to limit potential flood damages through DRR. The expected utility of settling in a floodplain and applying a DRR-measure depends on the risk-reduction potential and the costs of the DRR-measure. This is all explained in more detail in Section A1.2\nLine 673: “If adaptation options are implemented W should reduce. Is it correct? Is this taken into account?”\nSee comment above and Section A1.2\nLine 691: “Italy does not have an insurance system”\nInsurance against climate-related events exists in Italy, but uptake by households is extremely low (<5%), which may reflect a certain undervaluation of risk, or trust in alternative financing of damages such as government compensation. In previous studies, the DIFI-model was used to project insurance uptake by households considering future flood risk (Tesselaar et al., 2020; Tesselaar et al., 2022), both of which project insurance uptake values that approach reality. Also, in the current study it can be seen that the introduction of insurance dynamics does not change population growth much, which indicates that insurance is not considered an attractive product to limit financial vulnerability to flood risk.\nLine 731: “This part is interesting but not fundamental for the understanding of the method. Considering that the method is complex, I would avoid adding additional information that can create confusion in the reader. Please, consider removing.”\nA good suggestion to remove non-essential information. This paragraph is removed in the manuscript.\nLine 737: “This concept was repeated many times. Consider removing.”\nWe agree. These sentences are removed in the revised manuscript.\nLine 754: “What do authors mean with choice? Not clear”\nThe “choice” here refers to the decision between the two potential settlement locations. However, we realize that this description skips several important steps to understand the procedure. Since floodplains take different shapes, it is difficult to randomly assign a settlement location for a household within this floodplain, and then determine the distance from the river. At least, this process would be too complex for the purpose at hand. Therefore, we chose to homogenize the shape of all floodplains, to directly surround the river throughout the entire NUTS3-region. The width of the floodplain becomes the average over all floodplains within the region. We realize that “choice” is an inaccurate word to convey this. We canged it to “the surface area of the NUTS3-region”. We also thoroughly revised this paragraph. So, we invite the reviewer to read it and check whether it is explained more clearly in the manuscript. (lines 822-844)\nLine 764: “I am not sure I totally got it. This part should be better explained.”\nWe appreciate the reviewer’s request for more clarity. To improve this, we thoroughly revised this paragraph. (lines 822-844)\nLine 805: “check grammar”\nWe adjusted the sentence: “Roughly half of all observations show positive and statistically significant effects of natural amenities on house prices.” (881-882)\nLine 832: “Which is the function at the end? in mathematical terms?”\nThis section is thoroughly revised and now provides details on the derivation of the amenity value in an equation.\nLine 849: “Why damage functions must be modified is not clear. This is becuase how damage is calculated is not clear. See my previous comment.”\nWe thoroughly revised the description of the flood risk model. We also adjusted the text here to enhance clarity (lines 935-946)\nLine 879: “which are the results of calibration? please briefly comment”\nWe added a comparison of our results with those of Tellman et al. (2021), which serves as a calibration method for our simulation. We added the following to the paragraph (lines 969-984):\n“Because Tellman et al. (2021) does not show results for some European countries, we performed the calibration on countries that did show results, including France, the UK, Germany, the Czech Republic, Poland, and Romania. Status-quo insurance arrangements were used to compare model output to results in Tellman et al. (2021), meaning flat-rate insurance premiums for France and Romania, and risk-based premiums for all remaining countries. The results of calibration show a slight overestimation from results in Tellman et al. (2021) of, on average over the considered countries, 18%. This overestimation is largely driven by our projection for the UK, which shows a 50% overestimation when considering the current insurance system. However, the flood insurance system in the UK was not yet firmly established in the period 2000-2015 (Surminski et al., 2015), which may explain at least part of the overestimation. Our projection for the UK without insurance availability shows only a slight overestimation of 10%.”\nLine 924: “They look to me different. Please, comment”\nWhat we mean here is that, although the absolute growth of floodplain populations are higher under SSP5, the overall patterns remain roughly similar. Regions where populations decline in SSP2, also decline in SSP5, and vice versa. It is mainly the extent of projected changes that are largere under SSP5 (i.e., colors become darker green in Panel H and darker red in Panel I). There are very few regions where outcomes change from declining to increasing projections (i.e., from green to red or the other way around when comparing Panels B, E, and H; or Panels C, F, and I). A reason for” stronger” findings under SSP5 is given by the following text that is added to the manuscript: “Whereas deviations in population growth when considering flood risk, adaptation options, and amenities (Panel H) shows a similar pattern to regions in other scenarios (Panels B and E), the extent of these deviations is larger under the RCP8.5-SSP5 scenario (i.e. dark green and bright red colors occur more frequently in Panel H than in panels B and E). One reason for this projection is that overall population growth in European regions is considerably higher under SSP5, as can be seen in Panel G, which means more households face the settlement location choice in our simulation, raising the potential for deviation with the baseline. The main driver of floodplain population growth remains flood risk, which is also more severe under the RCP8.5 scenario in many parts of Europe” (lines 1040-1050)\nLine 929: “I do not agree. Could authors better explain?”\nSee the response to the previous comment and the revised paragraph in the manuscript, which goes as follows: “Introducing status-quo insurance coverage (Panel I) means that the negative impact of flood risk on the expected utility of floodplain settlement is considerably reduced, particularly when insurance coverage is cheap (i.e. France and Spain), causing more households to opt for the floodplain amenities. Again, a higher number of households facing the settlement location choice under SSP5 allows for more outstanding results compared to the previous scenarios.” (1050-1056)\nLines 929, 268, & 977: Plot colours and titles are adjusted as requested.\nResponse to reviewer 2:\nFirst of all, we sincerely thank the reviewer for their time and effort spent on examining the manuscript. We are grateful for the critical perspective of the reviewer regarding the assessment of flood risk, and the presentation and discussion of results. We believe that addressing the reviewer’s critique and questions, amongst other improvements, enabled a more intuitive interpretation of the study’s results. We also thank the reviewer for suggestions made to place the study’s findings in a broader policy-context, which was followed, and clarifies the position of this study within the field of flood risk management. In the following, we address each of the reviewer’s comments point-by-point, where the reviewer’s comments are included in italics.\n“I find the maps shown in NUTS3-level misleading. The study focuses on river flooding but the spatial aggregation covers the coastal areas as well. The latter has very different flood exposure due to coastal flooding or combined river/coastal flooding. It is not clear if the population exposure obtained by using the 2UP model is also for river flooding and if the population data used do not include population in coastal areas. Fig 1 shows the key results within the main manuscript, but it is too small to see the spatial differences. It needs to be enlarged and ideally the coastal areas should be excluded. The discussion in the manuscript always refers to countries, and as such the country boundaries should be visible on the map.”\nWe appreciate your request for further clarification regarding the differences between coastal flood risk and riverine flood risk, particularly in delta regions. To address this point, it is important to emphasize that our study specifically focuses on riverine flooding and does not incorporate the complexities associated with coastal flooding or combined river/coastal flooding. Our analysis solely considers the excess surface water resulting from riverine flooding, which is modeled using a hydrological model. As a result, we did not take into account any flooding from the coastal side. Furthermore, it is important to note that while our study primarily focuses on riverine flooding, we do account for population in coastal areas that are affected by riverine flooding events. In other words, we consider the population residing in coastal areas that are at risk of flooding due to riverine sources. We added text in the title, abstract, and introduction, to emphasize that the scope of this paper concerns riverine flood risk. We also included a larger version of Figure 1 in Appendix C. Moreover, figures throughout the manuscript are adjusted to show national borders.\n“It is not clear where the 100-year floodplains are located. The authors referred to it throughout the manuscript, for example line 119-122.”\nThe 100-year floodplains directly surround river systems. Whereas in some areas floodplains may cover extensive areas, such as in delta regions, in many inland regions floodplains cover a narrow area surrounding a river. The exact location of floodplains can be observed in the Aquaduct Flood Analyzer (https://www.wri.org/data/aqueduct-global-flood-analyzer), which applies the same flood hazard model as our study. Because the 100-year floodplain is often a relatively small area, it would not be visible on a continental-scale map. For this reason, we aggregated the floodplain area to NUTS3-level. All figures, except A1-A4, show population growth or flood risk specifically for the 100-year floodplains located in a NUTS3-region. We adjusted the text in lines 116-118 to emphasize that shown results in the manuscript represent all floodplain areas within the NUTS3-region.\n“One can hardly see any floodplains in the maps but rather entire countries or regions without much difference in the colours.”\nSee our response to the comment above.\n“It was not explained in the manuscript why the 100-year return period was used for the analysis. Surely the more frequent floods such as 50-year would be of more interest unless the authors assume the protection standards are higher than 50-year all over Europe. The second issue is the 100-year flood extrapolated using shorter time slices is not robust.”\nWe agree that the choice to focus on exposure growth specifically in 100-year floodplains needs to be substantiated. In academic literature, but also for policy-design, the 100-year flood is often used to demarcate the area \"at risk\" of flooding. For example, the national flood insurance program in the US, as well as the environmental agency in England and Wales apply this standard to direct adaptation policy. We integrated this discussion in lines 114-116, including relevant references. The choice to focus our analysis on 100-year floodplains does not mean that floods of different probabilities cannot occur there. For example, a 50-year flood may occur, although causing less damage, and a 1000-year flood may occur, causing more severe damage. We thoroughly revised Section A.2 to clarify how flood risk within 100-year floodplains is estimated.\n“Throughout the paper, the term of ‘risk ’and ‘hazard ’are used interchangeably without making distinction. The authors need to distinguish between ‘flood risk ’and ‘flood hazard ’and define the terms of hazard, vulnerability and exposure that make up ‘flood risk’. The definitions in line 277-280 are not accurate. Hazard is not “inundation of land due to the over-topping of dykes or embankment.” Vulnerability is not just “the extent that exposed assets are damaged by a flood of a certain magnitude.”\nThe definitions of flood hazard, exposure, and vulnerability that we maintain throughout the article are aligned with definitions used in relevant literature (e.g., Kron, 2009; https://doi.org/10.1080/02508060508691837). We do agree with the reviewer that different definitions of these elements exist. For example, besides only focusing on assets, vulnerability and exposure sometimes also considers how floods disrupt economic activity, livelihoods, or ecosystems. However, as this study concerns an application of insurance to physical damages, we define flood risk in this study as damage to the built environment. The thoroughly revised Section A.2 now more intuitively describes the process of the assessment of flood risk in terms of hazard, exposure, and vulnerability. In particular, flood hazard is a river’s water-level associated with a certain return period, which is compared to regional protection standards to assess whether a certain water-level can cause inundation of land; exposure is the built (residential) environment at risk of inundation; and vulnerability is the extent to which the built environment is damages by a certain level of inundation, which is estimated using depth-damage curves.\n“One important aspect missing in this study is government intervention in flood risk management. There are flood zones that are prohibited from development or need planning permission. For example, the Environment Agency in England provides a flood zone map and they have specific regulations on what types of building and what kind of planning permission would be required in a certain flood zone. This study seems to assume the population settlement is completely free from government regulations and planning policies. The equation A1 in Section A.1.2 is rather simplistic assuming settlement decision is completely made by individuals without government regulations. Insurance premium could put constraints on floodplain settlement, but simply increasing the premium is not a solution as it would disadvantage certain households and individuals. The message would be misleading if we simply increase insurance premiums with the aim of reducing exposure. I would like to see discussion on the impacts or effects of government intervention in addition to completely insurance policy driven analysis.”\nWe thank the reviewer for this suggestion to enrich the discussion of the topic in a broader policy context. Indeed, the decision to settle or not in flood-prone areas is influenced by a broader range of factors than just flood risk, amenities, and flood insurance. As suggested, governments may prohibit urban development in flood-prone regions, enforce flood-prone building-standards to improve resilience against floods, or altogether reduce flood risk by improving flood protection infrastructure. We added text in lines 77-82 and 472-483 to place the proposed adaptation of insurance systems within the broader context of flood adaptation. Concerning the impact of risk-based premiums on individual households, a brief reflection and policy solution is given in lines 465-471.\n“Further to my comment 1, Figure A4 has major flaws with regards to the floodplains. If the study focuses on river flooding only, it is incorrect to see 100% percentage of floodplain areas along the coasts. For example, the Rhine, Po river deltas, the Broadlands in east England etc. These areas are dominated by costal flooding rather then river flooding.”\n1) While our study primarily focuses on river flooding, it is important to note that we acknowledge the presence of coastal flooding in certain areas. We understand that delta regions, such as the Rhine and Po river deltas, as well as the Broadlands in eastern England, are susceptible to both riverine and coastal flooding. However, due to the limitations of available data and the scope of our study, our analysis solely considers riverine flooding. This approach allows us to provide a comprehensive assessment of flood risk within the context of our research objectives.\n2) Our study employs a robust hydrological model to simulate riverine flooding based on excess surface water. This model accounts for factors such as rainfall, terrain, and land use to estimate flood risk from riverine sources. By focusing on riverine flooding, we aim to provide a detailed analysis of the specific mechanisms and impacts associated with this type of flooding. Understanding riverine flooding is crucial for effective flood management and mitigation strategies, especially in regions where it is the dominant form of flooding.\n3) We apologize for any confusion caused by the representation of floodplain areas in Figure A4. It is important to clarify that the floodplain depiction in the figure serves as a visual representation of areas potentially susceptible to riverine flooding. It does not imply that these areas are exclusively affected by riverine flooding. In the text supporting Figure A4, we added a sentence to describe more explicitly how floodplains are determined and that this specifically concerns riverine flooding (lines 825-826).\n4) As with any research, there are inherent limitations and scope considerations. While our study focuses on river flooding and its associated population exposure, we acknowledge that coastal flooding is an important aspect of overall flood risk in some regions. We believe that future research should explore the complexities of coastal flooding and its interaction with riverine flooding to provide a more comprehensive understanding of flood risk in these specific areas.\n5) The Rhine river basin, particularly in the Netherlands, is highly susceptible to riverine flood risk. The geography of the Netherlands, with large parts of the country lying below sea level, makes it particularly vulnerable to riverine flooding from rivers such as the Rhine. The Dutch have implemented extensive flood protection measures, including an intricate system of dikes, levees, and flood control structures, to mitigate the risks associated with riverine flooding. However, despite these measures, a significant portion of the Rhine river basin in the Netherlands remains at risk of riverine flooding, with estimates suggesting that over 80% of the region is susceptible to such flood events by our model.\n“Fig A1: Flood risk appears in “Impact variables for choice module”, but also in “Model output”. It is very confusing. I think the Flood risk in “Impact variables for choice module” should actually be “Flood hazard” or probabilities, and the Flood risk in “Model output” should actually be “Flood damage” or EAD.”\nWe agree that mentioning \"flood risk\" twice in the chart may be confusing. However, both occurrences are truly flood risk estimations, so we cannot follow the reviewer's suggestion to refer to \"flood hazard\". In particular, flood risk is calculated in \"impact variables for choice module\" to determine insurance premiums and influence households' decisions whether to settle in a floodplain or not. Using the new estimations of population growth in floodplains, we are interested to observe how flood risk estimations change. For this, we rescale the initial flood damage estimations to the population dynamics per region, and calculate EAD using the same method as before. To emphasize that the estimation of flood risk in \"model output\" is different from the initial estimation, we adjusted the flowchart to mention \"model output: adjusted projections\".\n“Section A.2 Flood risk model: The heading should change to ‘Flood damage ’or ‘Flood loss ’model because that you describe here is the method obtaining the flood damage and losses. The study uses 100-year return floodplain to estimate population exposure, but the method describe here uses a range of return periods from 2- to up to 1000-year. This is rather confusing. Is the annual damaged derived for 100-year return period only or all the return periods beyond 100-year?”\nWe understand the confusion about how the flood return-periods are used to estimate flood risk. To clarify this, we thoroughly revised section A.2. We do not see the added value of changing the title of the section to \"flood damage\" or \"flood loss\" model. In particular, the approach to calculate EAD considers probabilities (return-periods), which means that \"damage\" or \"loss\" would be an incomplete title for the section.\n“The manuscript needs English proof reading to make it easier to read. There are sentences that are formulated in strange ways and difficult to understand. Some sentences are too long and can be broken down to shorter sentences.”\nA professional English editing service was performed.\n“Line 74: the most effective method to limit societal exposure. Suggest change to “the most effective method to limit population exposure” as it is better to stick to your terminology.”\nThank you for the suggestion. We adjusted the text accordingly.\n“Line 167-174: Instead of using ‘flood risk ’here, it should be ‘flood losses ’or ‘flood damage’. It is also not clear if this is annual flood damage of 100-year return flood or flood damage of any unprotected magnitude.”\nThe confusion regarding terminology is hopefully clarified in responses to earlier comments. In this particular case, \"flood risk\" is the correct term because we discuss flood impacts in annual terms (EAD), which means we consider both damages and probabilities. Mentioning \"Flood damage\" or \"losses\" would, therefore, be incorrect. Regarding the second point, we refer to annual flood risk here, which considers flood events of all included probabilities. This process is described intuitively in the revised Section A.2.\n“Line 216, This person more likely prefers a lower - Change to ‘Such people more likely prefer’”\nThank you for the suggestion. We adjusted the text accordingly.\n“Line 289: fully avoid potential flood damage by moving out of harm’s way - What do you mean by “harm’s way”. Please consider rephrasing here.”\nWe changed the phrasing here to: ...\"may choose to fully avoid potential flood damage by settling on higher ground”.\n“Line 334: the growth in flood risk - Should be ‘flood damage’”\nThese numbers reflect the growth in EAD, which is better captured by \"flood risk\" than \"flood damage”.\n“Line 452: remove ‘, then,’”\nThank you for the suggestion. We adjusted the text accordingly.\n“Line 571: flood risk (L); This should be flood losses (L). In line 579, “flood damage (L)” is used. Please be consistent with your terminology.”\nThis section is revised thoroughly and now consistently uses \"flood losses\" to describe the variable L.\n“Line 618: 15 years; Please explain why using 15 years in your model.”\nThis choice is based on the average time a household resides in a home before relocating in the UK. In some countries it may be slightly more (e.g. in the Netherlands it is 20 years), or less (e.g. in the US it is 13 years). For our analysis using a single length of time for this purpose reduces complexity and improves our ability to interpret results. We added a discussion of this in the manuscript (line707), including a reference.\n“Line 839: future flood risk; Should change to ‘future flood damage’”\nWe estimate future EAD, which is better captured by \"flood risk\" than by \"flood damage”.\n“Line 856: regular procedure is followed; What is the regular procedure?”\nThis is unclear indeed. We adjusted the text, which now states \"After this, the procedure described in Section A.2 is followed to obtain EAD under the different insurance and adaptation scenarios.”\nResponse to reviewer 3:\nFirstly, we would like to take this opportunity to thank the reviewer for their effort examining the manuscript. We greatly value the reviewer's critical perspective on the interpretation of results, and suggestions made to improve the discussion of the study’s findings. By addressing the reviewer’s comments, we believe the manuscript is improved considerably. In the following, we describe how each of the reviewer’s comments is addressed in the revised manuscript. The reviewer’s comments are recited in italics, followed by our response.\n“The actual degree of novelty of the modelling framework and of the outcomes is not completely clear to me. The study builds on a number of already-existing models and studies (GLOFRIS for flood risk analysis, 2UP for general population growth, DIFI for insurance modelling) by adding new components for population growth and flood-proofing of buildings. It is not clear to me whether some results, such as EAD estimations, are taken from previous works from the same Authors or are new, and how much of the previous studies by the Authors overlap with the present work. I would invite the authors to briefly describe their past works in the topic, as well as other relevant literature works (see also the following point), and then specify what are the innovations brought by their new analyisis.”\nWe appreciate the reviewer’s request for more information regarding the novelty of the method. Text is added in the introduction to more explicitly describe the difference with previous studies and the added value of this study (lines 90-97). In particular, it is emphasized that data from GLOFRIS is used to estimate flood risk and the size of floodplains. An existing framework for estimating insurance premiums and further insurance market performance, such as insurance demand and moral hazard, is used (Hudson et al., 2019). It is explained that the main novelty of this study is the assessment of flood exposure development in the context of local flood risk, amenities, and insurance incentives. The 2UP-model is used to provide population growth estimates for the baseline scenario. The current study does not build forth on this model. It is explained in lines 103-106 that the current analysis is designed to provide a more detailed interpretation of population suitability maps as in the 2UP-model and other similar frameworks, which is done by considering local flood risk, river amenities, and the compensation of flood damages through insurance.\n“The modelling framework focuses on the role of individual or household-based choices in shaping flood exposure and vulnerability, which is perfectly fine. However, the overall discussion of results seem to overstate the relevance of Authors' findings while overlooking the role of flood risk control measures driven by government policies, such as structural measures, land-use planning and so on. Historically, structural flood control measures (river dikes, dams, detention areas etc) have been determinant in shaping floodplain development (Haer et al., 2016; Di Baldassarre et al., 2018), whereas government policies can strongly influence flood exposure by limiting or excluding further developments in flood-prone areas, so individual decision are only a part of this framework. In addition, a number of recent studies evaluated future risk scenarios resulting from a top-down implementation of these and other measures (Ward et al., 2017, Vousdoukas et al., 2020; Tiggeloven et al., 2020, Dottori et al., 2023, with apologies for the self-citation). My impression is that the role of these measures in shaping flood risk can be , at least, as important as the measures analysed in the present manuscript. Therefore, I would recommend including all these aspects in the introduction and in the discussion of results, in order to put the authors' findings in the right perspective.”\nWe thank the reviewer for this suggestion to enrich the discussion of the topic in a broader policy context. We also appreciate the information and references given to help us form the discussion. We added several sentences in the introduction (lines 77-84). In the discussion of results we added a paragraph to compare our findings to those of other adaptation measures found in literature (lines 387-404). We added a paragraph in the conclusion to place the proposed adaptation of insurance systems within the general discussion of flood adaptation (lines 472-483).\n“In the main text there is no explanation on why only some EU countries/NUTS3 regions are represented in Panels 1B-1C and 2B-2C. My understanding from Section A1.1.1 (L500-501) is that population growth in floodplains is evaluated only in NUTS3 regions with overall increasing growing population trends, but this seems in contrast with values in Figure A2-A (where Italy seems to have positive trends, for instance) and with the projected increases in few areas in eastern countries (e.g. Bucharest,Warsaw, Athens). Could you please explain this point?”\nThe interpretation of the reviewer is correct. There was a minor coding-mistake, where the overall population growth until 2080 was used instead of 2050 to determine in which regions floodplain populations may grow. This meant that some regions in Italy, amongst others, were unjustly excluded from the analysis, since populations there are projected to grow until 2050, but decline afterwards. The mistake has been corrected.\n“I understand that a formal validation of the model is probably not possible. Still, I would ask the Authors to describe how their results compare with the study by Tellman et al after the calibration (in particular, if the model was able to reproduce observed dynamics)”\nA formal validation of results is, indeed, not possible. We did, however, revise the text in Appendix B to include a more explicit comparison of our results to those of Tellman et al. (2021) in Appendix B (page 50-51). To do this comparison, an adjusted version of our modeling framework was developed and executed with relevant flood risk and population data for the period 2000-2015. As Tellman et al. (2021) is a global study, and observations for Europe are limited, the amount of data points on which to compare results is limited. We find that the extent of floodplain population growth matches reasonably well for the countries included in the analysis of Tellman et al. However, our results for the UK are considerably higher (approx. 50%), which may be caused by an inaccurate representation of insurance incentives (a reference for this is given in line 979). Apart from the UK, our result show roughly similar patterns as Tellman et al. In particular, countries that show relatively high floodplain population growth in Tellman et al. (i.e. Germany, Romania) also show higher floodplain population growth compared to other countries in our analysis. Unfortunately future exposure growth projections in Tellman et al. cannot be used as a comparison with our results for the purpose of a validation. This is because Tellman et al. apply the population projection method of the 2UP-model, which is what our method is improving upon.\n“Title: my impression is that now the title suggests that insurance is the main driver of population growth in floodplains, which is not the case. Perhaps a more nuanced wording would be more appropriate”\nWe agree with this suggestion. The title is adjusted to \"Flood insurance is a driver of population growth in European floodplains”.\n“You should clearly specify in the title, abstract and introduction that the work is about river flooding and Europe. Similarly, along the text you should mention whether your statements refer to floods in general (either river, coastal, flash or pluvial) or river floods only.”\nThe title is adjusted to specify the geographical extent of the study, which is Europe. Text is adjusted in the abstract and throughout the introduction to indicate that the study concerns riverine flood risk and the geographical extent is Europe.\n“L93 I would briefly mention here the SSP and RCP scenarios that have been considered in the study.”\nWe expanded on the explanation of SSPs and RCPs here (lines110-112 & 122-125), and referred to Appendix B for alternative scenario combinations. Specific information on scenario's shown in the appendices may be excessively detailed to include here.\n“Figure 1: Legends are not fully clear in this Figure, I suggest using the same definitions as in Figure 2. Also, for panel A, using a colour palette with different colours for negative - positive change (as in panels B-C), would make the map more readable”\nThank you for these suggestions. They are applied in the revised manuscript.\n“L236: \"Interestingly, some of these are regions where projections without insurance availability...\" Could you list some of these regions here?”\nWe added three regions in different countries as example (lines266-267). There are, however, more regions where this is the case.\n“L240-243: \"... population growth projections in floodplains are consistently and considerably higher in France and Belgium, where premiums are relatively inexpensive in high risk areas.\" Could you provide an average monetary value for risk-insensitive and risk-based premiums as estimated by the model?”\nWe thank the reviewer for the suggestion. The following text was added (lines273-275): \"Whereas average risk-based premiums in 2050 in Sweden, Ireland and the UK are close to €400 annually per household, flat-rate premiums in Spain, France and Belgium are approximately €13 per household.”\n“Section 2.4: After having read section A.5, my understanding is that you considered damage to residential and commercial areas to calculate EAD, is it correct? If yes, please specify this in the main text.”\nThe interpretation of the reviewer is correct. However, the study focuses solely on residential settlement and damages, which is now clarified better in Section A.2. Since the damages are calculated for the residential sector from the beginning, an additional adjustment to single out the residential sector, as was mentioned in Section A.5, would be incorrect. We removed this adjustment from the model and the manuscript.\n“Figure 2: In panel A, using a colour palette with different colours for negative - positive change (as in panels B-C-D), would make the map more readable. Also, can you please explain how \"factor\" is calculated for panels B,C,D?”\nThe figure is changed accordingly. \"Factor\" indicates that the shown results are multiplicative (i.e. a value of 2 indicates a doubling of the EAD compared to Panel A). This is identical to the approach in Figure 1. Therefore, to prevent confusion, \"factor\" is removed from Figure 2.\n“L304-309: Did you consider also projections of economic growth (such as GDP increase/decrease) to calculate changes in EAD? If yes, differences in economic growth might be another factor in explaining the observed trends”\nIndeed, GDP-growth affects the value of exposed assets in the food risk model. The relative change in GDP is higher in Eastern Europe under the scenarios and period considered. As suggested this is a likely cause for the observed trend. We have added this description in the manuscript (lines 349-354).\n“L347-350: this introduction is perhaps not necessary here.”\nAgreed. This sentence is removed from the manuscript.\n“L367: do you mean 75% higher compared to status-quo insurance?”\nYes, the interpretation is correct. However, this result changed when adjusting the model (when removing the double-counting of residential damages). Please find the revised text in lines 424-428, and the figure on which this conclusion is based in Appendix B.\n“L378-380: I would tone down this conclusion, perhaps saying \" traditional climate risk assessment methods (...), might inaccurately project changes in future flood risk\". You indeed showed the importance of endogenous factors in shaping flood exposure and vulnerability, yet several other factors influence flood risk too (structural flood control measures, centralized land-use planning etc).”\nWe agree to the request for a more nuanced formulation here and revised the text accordingly (lines 429-434).\n\"L418:can you cite here some of these studies?”\nWe appreciate the request for references here. Several references were added (line 485).\n\"L625: could you provide a reference for the choice of this discount rate value?”\nAfter thorough deliberation, we decided to remove the discounting of aggregate premiums over the time a household resides at a specific location. We motivated this choice in a footnote on page 31:\n“Theoretically, future premium payments need to be discounted when expressed in current terms. However, considering the small confined period of 15 years, including a discounting procedure does not change the outcome notably, as was tested using the model. Hence, we chose not to apply discounting to prevent making the model unnecessarily complex. Moreover, the short individual planning horizon of 15 years already captures time preferences to a certain degree.” Naturally, the figures and data mentioned in the text represent the output of the updated model.\n“L669-670: The paper by Aerts is a literature review of previous applications of a range of risk reduction measures. Perhaps the authors should provide more details on how they used such information to model costs and effectiveness of flood-proofing measures”\nWe agree that the application of risk reduction measures in the model was somewhat vaguely described. To clarify this better, we thoroughly revised Section A.1.2."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thanks the authors for the big efforts they made in revising the manuscript. My concerns have been addressed and, in my opionion, the paper is now ready for publication and for stimulating interesting reflections both in the scientific and policy domain. My congratulations! I really enjoed reading!", "reviewer_2": "The authors have thoroughly responded to my comments and revised the manuscript. I recommend its publication.", "reviewer_3": "I thank to Authors for addressing in detail my comments as well as the comments by the other Reviewers. The paper looks considerably improved and in my opinion it can be published."}, "author_response": "Response to reviewers’ comments\nReviewer #1 (Remarks to the Author):\nI thanks the authors for the big efforts they made in revising the manuscript. My\nconcerns have been addressed and, in my opionion, the paper is now ready for\npublication and for stimulating interesting reflections both in the scientific and\npolicy domain. My congratulations! I really enjoed reading!\nWe sincerely thank the reviewer again for the elaborate critical and constructive\nfeedback on the manuscript in the previous round of reviews, and for the kind words in\nthis round. We believe your time and effort substantially improved the work.\nReviewer #2 (Remarks to the Author):\nThe authors have thoroughly responded to my comments and revised the\nmanuscript. I recommend its publication.\nWe sincerely thank the reviewer again for the elaborate critical and constructive\nfeedback on the manuscript in the previous round of reviews, and for the kind words in\nthis round. We believe your time and effort substantially improved the work.\nReviewer #3 (Remarks to the Author):\nI thank to Authors for addressing in detail my comments as well as the\ncomments by the other Reviewers. The paper looks considerably improved\nand in my opinion it can be published.\nWe sincerely thank the reviewer again for the elaborate critical and constructive\nfeedback on the manuscript in the previous round of reviews, and for the kind words in\nthis round. We believe your time and effort substantially improved the work."}]

Decision letter