We use the high-resolution Fathom-Global 2.0 model (Sampson et al., 2015), which accounts for both fluvial and pluvial inundation. This global model uses the Multi-Error-Removed Improved-Terrain (MERIT) digital elevation model (Yamazaki et al., 2017) and MERIT Hydro (Yamazaki et al., 2019) as the topography and hydrography datasets, respectively. These data provide the most accurate representation of ground surface elevation and location of the rivers at the global scale, which is critical to building robust flood models (Fathom, 2022). Fluvial inundation is simulated in all river basins with upstream catchment areas larger than 50 km2. At the same time, pluvial flooding is captured for all catchment sizes by simulating rainfall directly onto the modeled topography. The model considers a 2D shallow-water formulation to explicitly simulate flood wave propagation and a regionalized flood frequency analysis (Smith et al., 2015) to derive river discharge. Fathom-Global provides 90 m-resolution maps of flood extents and flood depths for multiple mean return periods (from 5-year to 1000-year). Note that hazard data of finer resolutions (e.g., 10 m or lower) are generally recommended for capturing the highly localized nature of flood hazard (e.g., for representing small streams accurately) and associated risks at the urban scale (e.g., Afifi et al., 2019; Nofal and van de Lindt, 2021) (although the exact benefits of using high-resolution hazard data can vary between contexts and must also account for the resolution of exposure data used; see Sect. 5 for a more extensive discussion on this issue). In addition, urbanization effects on flood hazard (i.e., the replacement of natural ground with impermeable surfaces, changes to drainage or irrigation systems, and deforestation can increase runoff during precipitation events) are not explicitly accounted for by the Fathom-Global model and are therefore neglected in our analyses. However, the primary purpose of this study is to test different exposure and vulnerability scenarios using a common flood hazard input that is open and easily accessible; developing bespoke fine-resolution flood hazard models for the study area is not within the scope of this work.

We consider two cases of flooding occurrence in Kathmandu Valley. The first case is based on the Fathom-Global undefended flood map with a 100-year mean return period (i.e., 1 % annual exceedance probability). Decision makers frequently use this type of map (e.g., to identify flood risk zones in the United States) (Ludy and Kondolf, 2012; Federal Emergency Management Agency, FEMA, 2010). The second flood occurrence case reflects a situation in which flooding is more severe and is based on the Fathom-Global undefended flood map with a 1000-year mean return period. The flood maps are resampled to 30 m using the nearest neighbor method to match the spatial resolution of the exposure maps (Díaz-Pacheco et al., 2018). We combine individual flood maps into aggregated hazard maps that represent fluvial–pluvial flooding for each mean return period by taking their maximum depths in line with the method of Tate et al. (2021), who mosaicked fluvial and pluvial flood grids to generate an aggregated flood hazard map for the United States. The fluvial–pluvial hazard maps for each considered mean return period are presented in Fig. 4; the individual flood maps are available online through the METEOR project (https://maps.meteor-project.org/map/flood-npl/, last access: 1 December 2022). Hereafter, we describe the flooding occurrence cases using only the terms “100-year” and “1000-year”, omitting the description “mean return period” for brevity. Overall, the aggregated flood maps are largely dominated by the effects of pluvial flooding: in both the 100-year and 1000-year aggregated flood maps, around 15 % of the flooded areas are exposed to both types of flooding, 84 % are only exposed to pluvial flooding, and less than 1 % are only exposed to fluvial flooding. It should be noted that fluvial flooding generally results in low-velocity flows dominated by hydrostatic pressure, while pluvial flooding often features higher flow velocities (Gentile et al., 2022); these differences in velocity characteristics could be important for estimating flood damage in areas with steep terrain (Nofal and van de Lindt, 2022). However, we use only flood depth as the intensity measure in this study, since it is widely used for flood loss estimation (e.g., FEMA, 2022; Nofal and van de Lindt, 2022), and flood velocities are more difficult to record than flood depths, requiring hydraulic simulations (e.g., Kreibich et al., 2009).

We use the four exposure scenarios A–D for Kathmandu Valley developed in Mesta et al. (2022a), which are based on the National Population and Housing Census 2011 (Central Bureau of Statistics, CBS, 2012) and various assumptions on the estimated population and number of households for 2021 and 2031. Note that the government of Nepal postponed the census planned for 2021 due to the COVID-19 pandemic, so the characterization of 2021 urban development relies on 10-year-old data.

The proposed scenarios portray different conditions for Kathmandu Valley in terms of urban growth, the prevalence of varying building typologies, and the implementation of DRR measures (see Table 1). Seven building typologies are included in the considered exposure scenarios: adobe (A), brick and stone masonry with mud mortar (BSM), brick and stone masonry with cement mortar (BSC), wood-frame (W), current-construction-practice reinforced concrete (RC-CCP), well-designed reinforced concrete (RC-WDS), and reinforced masonry (RM). These typologies have been previously used by Chaulagain et al. (2016, 2015) as well as Mesta et al. (2022a) to estimate seismic losses in Nepal and Kathmandu Valley. A, BSM, and BSC buildings constitute unreinforced masonry (URM) structures. RC-CCP refers to RC frame structures constructed without technical supervision. In contrast, RC-WDS are RC structures with ductile detailing designed according to seismic provisions. RM corresponds to the RM1 (reinforced masonry bearing walls with wood or metal deck diaphragms) building typology from the HAZUS earthquake model (FEMA, 2020).

The specifications of each scenario are primarily provided in Mesta et al. (2022a); any deviations in these details for this specific study are documented in Sect. 2.3.1 to 2.3.3. Note that we do not consider the presence of basements for any building typology since they do not seem to be a common feature of buildings in the valley (except for a minor proportion of RC buildings). For instance, an extensive post-earthquake damage assessment conducted by the National Society for Earthquake Technology-Nepal (NSET, 2016) described the presence of basements in modern high-rise RC buildings; however, buildings with six or more stories only represent 1 % of all buildings in Kathmandu Valley. Suwal et al. (2017) identified the presence of basements in 31 % of RC buildings in the valley, but their study was limited to only 64 buildings.

Since no specific flood vulnerability functions are developed for the study area, we adopt the depth–damage functions of the global flood depth–loss model developed by the Joint Research Center (JRC) of the European Commission (Huizinga et al., 2017). More sophisticated analytical flood fragility and vulnerability functions, which propagate uncertainties in the hazard-dependent failure of building components and associated repair and replacement costs (e.g., Nofal et al., 2020; Nofal and van de Lindt, 2021), would require detailed component-level vulnerability information that is not available for this study.

The JRC vulnerability functions were developed for distinct continents and building occupancies (e.g., residential, commercial, industrial). They express flood depth in meters and losses as mean loss ratios (i.e., financial losses as a percentage of the building replacement cost). We select the JRC vulnerability function for residential buildings in the Asian continent as our baseline function. We modify it to consider specific features of Kathmandu Valley's building stock. Firstly, we set the maximum damage to be 100 % for A and W and 60 % for all brick (BSM/BSC, RM) and concrete (i.e., RC-CCP, RC- and WDS) typologies, following JRC recommendations. The 60 % maximum damage threshold used for some typologies reflects the assumption that a flood cannot damage major water-resistant structural components, which represent a substantial portion of building construction costs (Huizinga et al., 2017). This assumption is in line with other studies such as the Central American Probabilistic Risk Assessment (CAPRA) initiative (CAPRA, 2012), which assigns 60 % maximum flood losses to masonry and concrete buildings, and FEMA (2022), which indicates that major structural components are expected to withstand flood events. The damage thresholds are used to scale the loss values of the baseline function and derive two material-specific (i.e., non-resilient and resilient) vulnerability functions. Secondly, we adjust the two modified material-specific versions of the baseline function to account for different numbers of stories. Since JRC does not provide relevant information to infer the contribution of different building heights to losses, we assume that the baseline function is generally representative of two-story buildings and calculate appropriate height-adjustment factors in line with the procedure introduced in Gentile et al. (2022). These factors are used to multiply the loss values of the two material-specific functions and obtain the six vulnerability functions illustrated in Fig. 5. The A building typology is highly vulnerable to material deterioration from prolonged contact with flood water (e.g., De Risi et al., 2013; Medero et al., 2011; Tiepolo and Galligari, 2021). The W building typology is also considerably vulnerable to floods (especially high-velocity flows; e.g., Becker et al., 2011). Both brick (BSM/BSC, RM) and concrete (i.e., RC-CCP, RC-WDS) typologies have higher durability compared with A and W, low permeability, and represent the most flood-resistant buildings (e.g., Balasbaneh et al., 2019; Li et al., 2016). Although the flood vulnerability may vary slightly between brick and concrete typologies (e.g., URM buildings are less able to resist the pressure of flood water exerted on walls than RM and RC buildings; Englhardt et al., 2019), these differences are not accounted for in the JRC vulnerability functions and thus are not included in this study.

The height-adjustment procedure of Gentile et al. (2022) assumes that the building replacement value is directly proportional to the number of stories, which may not be strictly valid (e.g., electrical and mechanical equipment are usually installed on the ground floor and mixed occupancy buildings can have commercial areas on the ground floor, increasing the relative replacement values of this story). Furthermore, the construction costs considered in this study exclude content costs, such that the resulting financial losses may be underestimated. However, it should be emphasized that the exact losses for a given exposure scenario (absolute or relative) are not strictly of interest in this study. Instead, we focus on producing comparable loss outputs for all exposure scenarios that are based on consistent assumptions. In this way, we aim to investigate how risk changes across the exposure scenarios in a relative sense. It is also worth noting that the uncertainty in the vulnerability model may strongly affect the loss estimation, particularly in terms of loss variability for a given mean return period. However, such uncertainty may be neglected if mean loss quantities are considered for comparison across different scenarios, as in this study.

Moreover, to avoid overestimating losses, we account for the difference between the ground level (above which flood depth is reported in the hazard maps) and the ground-floor level (above which flood depth is measured in the vulnerability functions). We set this difference at 0.2 m, as suggested in previous studies on flooding vulnerability for residential buildings (e.g., Dabbeek et al., 2020; Maqsood et al., 2014; Olsen et al., 2015) and after consulting construction blueprints of buildings in the study area. Furthermore, we use the procedure detailed in Mesta et al. (2022b) to classify populations per municipality as low, middle, or high income for facilitating socioeconomic disaggregation of financial losses. The classification is based on three variables (i.e., access to mobile and/or telephone services, mass media communication, and means of transportation) recorded in the 2011 census, which are treated as proxies for economic wealth. The census data are aggregated at the municipality level; therefore, any variability in the population's income level within each municipality is not (and cannot be) assessed. The classification is quantile, such that the three income categories contain an equal number of municipalities. We assume that the population's income level did not vary between 2011 and 2021 and would remain unchanged in 2031, given the lack of available data to make confident projections. This assumption is partially supported by previous work from Cutter and Finch (2008), who suggested that the social vulnerability of a community, which is influenced by its underlying socio-economic and demographic characteristics (e.g., income level, gender, age), is not expected to vary significantly over timeframes similar to that considered in this study.

Figure 6a and Table 2 present a breakdown of the expected number of buildings and the proportions of the building stock within various depth ranges of the 100-year floodplain across the four exposure scenarios. In Scenario A, 108 922 buildings (14 % of the 2021 building stock) are located in this floodplain, of which 80 % and 20 %, respectively, experience flood depths below and above 2.0 m. To provide some context, a 2.0 m flood depth produces mean loss ratios between 43 % and 72 % for non-elevated buildings with one or two stories and between 17 % and 29 % for non-elevated buildings with three, four, and five stories. In Scenario B, the larger building stock results in 130 106 buildings positioned within the floodplain (19 % more than Scenario A), although the overall proportion of buildings in the floodplain (14 % of the 2031 building stock) and their distribution by flood-depth range remains practically the same as in Scenario A. This means that, considering a 100-year flooding occurrence, future urban expansion (driven by the constraints of past planning decisions) is projected to continue occurring in both inundated and non-inundated areas of the valley. Scenarios A, C, and D yield identical results in Fig. 6, since the flood-hazard-informed land-use planning imposed as part of Scenarios C and D means that the expected number of buildings within the floodplain in 2031 (Scenarios C, D) remains limited to 2021 levels (Scenario A). This measure decreases the proportion of flood-exposed buildings in both Scenarios C and D (grouped in Table 2) by 2.2 %.

Figure 6b and Table 2 present a breakdown of the expected number of buildings and the proportions of the building stock within various depth ranges of the 1000-year floodplain across the four exposure scenarios. In Scenario A, 117 179 buildings (15 % of the 2021 building stock) are located in this floodplain, of which 66 % and 34 %, respectively, experience flood depths below and above 2.0 m. Scenario B results in 180 478 buildings positioned within the floodplain (54 % more than Scenario A). In contrast with the 100-year flood occurrence case, Scenario B shows a notable increase in the total proportion of buildings within the floodplain (19 % of the 2031 building stock) relative to Scenario A. This means that, considering a severe flooding occurrence, future expansion (conditioned on past planning decisions) is projected to disproportionately occur in inundated areas. In Scenarios C and D, restricting future urban growth within the floodplain reduces the proportion of flood-exposed buildings by 6.7 %.

Figure 7 presents additional municipality-level insights on the spatial distribution of buildings within the 100-year floodplain. We identify a large variability in the percentage of buildings in inundated areas across the valley. For Scenario A, proportions of buildings within the floodplain are equal to or less than 10 % for 56 municipalities, between 10 % and 20 % for 40 municipalities, and between 20 % and 24 % for 8 municipalities. Most municipalities with the largest number and proportions of buildings in inundated areas are located around the central and northern parts of the valley. Scenario B reflects the effects of not controlling future urbanization in flood-prone areas; proportions of buildings within the floodplain are equal to or less than 10 % for only 44 municipalities, between 10 % and 20 % for 50 municipalities, and between 20 % and 30 % for 10 municipalities. In contrast, the proportions of buildings within the floodplain for Scenarios C and D are equal to or less than 10 % for 79 municipalities, between 10 % and 20 % for 22 municipalities, and between 20 % and 21 % for 3 municipalities, reflecting the benefits of constraining future urbanization to non-inundated areas.

Figure 8 presents the municipality-level spatial distribution of buildings within the 1000-year floodplain. For Scenario A, proportions of buildings within the floodplain are equal to or less than 10 % for 49 municipalities, between 10 % and 20 % for 43 municipalities, and between 20 % and 28 % for 12 municipalities. Corresponding Scenario B proportions are equal to or less than 10 % for 31 municipalities, between 10 % and 20 % for 50 municipalities, and between 20 % and 40 % for 23 municipalities. Corresponding proportions for Scenarios C and D are equal to or less than 10 % for 75 municipalities, between 10 % and 20 % for 24 municipalities, and between 20 % and 25 % for 5 municipalities.

Figure 9a, b presents the mean loss ratios associated with the 100-year flooding occurrence, disaggregated by district and income level. From panel a, we observe some variability in the mean loss ratios by district. The Bhaktapur district exhibits the largest mean loss ratios for all the scenarios, which is explained by its disproportionate share of exposure in inundated areas (there are only minor differences in the prevalence of different building typologies between districts, and the three districts are dominated by brick and concrete building typologies that are assumed to have the same level of flood vulnerability; see Sect. 2.3) For instance, in Scenario A, the percentage of buildings in the floodplain is 14.9 % in Bhaktapur, 14.5 % in Kathmandu, and only 9.8 % in Lalitpur. Proportions of buildings that experience flood depths below and above 2.0 m, respectively, are 72 %–28 % in Bhaktapur, 84 %–16 % in Kathmandu, and 66 %–34 % in Lalitpur. Similar results are observed for Scenario B, because the overall proportion of buildings within different flood-depth ranges of the floodplain remains largely identical (see Table 2), and the building stock's vulnerability is not changed. While DRR measures implemented in Scenarios C and D reduce the mean loss ratios compared with those of Scenario B, the relative differences in mean loss ratios between districts are not particularly affected.

From Fig. 9b, we identify some variability in the mean loss ratios by income level. All scenarios produce the highest mean loss ratios for the high-income population, which reflects their disproportionate share of buildings in inundated areas (there are also minor differences in the prevalence of building typologies between income groups, but the three income groups are dominated by brick and concrete typologies). For instance, in Scenario A, the proportion of buildings in the floodplain is 15 % in high-income municipalities, 11 % in middle-income municipalities, and 12 % in low-income municipalities. Proportions of buildings that experience flood depths below and above 2.0 m, respectively, are 81 %–19 % for high-income municipalities, 75 %–25 % for middle-income municipalities, and 84 %–16 % for low-income municipalities. Scenario B shows similar results to Scenario A due to its similar proportions of buildings within different flood-depth ranges of the floodplain (see Table 2) and its identical quality of building stock. In addition, the benefits that result from the building elevation strategy and flood-hazard-informed land-use planning proposed in Scenario C are larger for the low-income population than for the other income groups; the mean loss ratio decreases from Scenario B to C by 44 % for the low-income municipalities, by 28 % for the middle-income municipalities, and by 22 % for the high-income municipalities. There are two main reasons for this trend. On the one hand, low-income municipalities contain the largest proportion of flood-exposed buildings in areas with flood depths below 1.0 m, where the effects of the elevation strategy are more noticeable (as implied by the steep initial slopes of the vulnerability curves presented in Fig. 5). In Scenario B, the proportion of buildings that experience flood depths below 1.0 m is 63 % for low-income municipalities, 50 % for middle-income municipalities, and 51 % for high-income municipalities. On the other hand, the proportions of new Scenario B buildings located in the floodplain are higher across low-income municipalities (34 % in total) than across middle-income (24 %) and high-income (11 %) municipalities. Furthermore, the benefits from the multi-hazard (i.e., flooding and seismic) risk-mitigation measures integrated within Scenario D are slightly better than those from the single-hazard-focused Scenario C: between Scenarios B and D, the mean loss ratio drops by 45 % for the low-income municipalities, by 29 % for the middle-income municipalities, and by 23 % for the high-income municipalities.

Figure 9c, d presents the mean loss ratios associated with the 1000-year flooding occurrence, disaggregated by district and income level. Similar to the 100-year flood case, there is an implicit relationship between the mean loss ratios and the extent of exposure in inundated regions; the same general trends for mean loss ratio across districts and income levels are observed for the more severe flooding occurrence. Bhaktapur district exhibits the highest mean loss ratios for all exposure scenarios, followed by Kathmandu and Lalitpur. All exposure scenarios result in the highest mean loss ratios for the high-income population, followed by the middle-income and low-income populations. The largest benefits from the risk-mitigation strategies are also associated with the low-income population.

Table 3 summarizes the absolute changes in mean loss ratios and mean absolute financial losses (i.e., repair and reconstruction costs) for both flooding occurrences, considering Scenario A as a baseline. These results reveal how “no action” and implementing DRR measures could affect flood risk in Kathmandu Valley. In the 2021 exposure scenario, 3 151 741 people live in 789 898 buildings, with a total replacement value of EUR 17.1 billion. The 100-year mean absolute financial losses estimated for Scenario A are almost EUR 473 million (corresponding to a mean loss ratio of 2.8 %), while the mean absolute financial losses for the more severe 1000-year flooding occurrence are nearly EUR 775 million (corresponding to a mean loss ratio of 4.5 %).

In the 2031 exposure scenarios, 3 792 232 people are allocated across 524 943 606 buildings, which have a total replacement value of EUR 20.3 billion in Scenario B, EUR 20.4 billion in Scenario C, and EUR 26.4 billion in Scenario D. Changes to mean absolute financial losses associated with the 100-year flooding occurrence and the 2031 exposure scenarios are as follows: they increase by more than EUR 74 million (+16 %) in Scenario B, decrease by more than EUR 63 million (-13 %) in Scenario C, and rise by more than EUR 52 million in Scenario D (+11 %), relative to Scenario A. For the 1000-year flooding occurrence, mean absolute losses increase by nearly EUR 108 million (+14 %) in Scenario B, decrease by more than EUR 66 million (-9 %) in Scenario C, and rise by more than EUR 130 million in Scenario D (+17 %), relative to Scenario A. The relative increase in mean absolute financial losses for Scenario B is due to the presence of more assets in the floodplain. In other words, Scenario B demonstrates that a larger population can easily lead to greater flood losses when risk mitigation is neglected. In contrast, the relative decrease in mean absolute financial losses for Scenario C shows that, despite a growing population, elevating existing buildings and implementing flood-hazard-informed land-use planning could significantly reduce flood losses in the future. However, it should be noted that risk-mitigation actions implemented in Scenario C would still leave the building stock highly vulnerable to earthquakes, and thus do not completely address multi-hazard risk in the valley, which is left to Scenario D. Note that a previous study by the authors (Mesta et al., 2022a) revealed that not implementing seismic risk-mitigation actions for the valley (i.e., analogous to Scenario C in this study) could increase mean absolute financial seismic losses in the future (2031) by more than EUR 1.7 billion (+20 %) relative to equivalent current levels. In contrast, improving the seismic strength of buildings (i.e., similar to Scenario D in this study) could reduce mean absolute financial seismic losses in the future by more than EUR 1.1 billion (-14 %) relative to equivalent current levels. The relative increase in mean absolute financial losses in Scenario D is associated with the larger replacement value of its building stock (due to the structural retrofitting and building code enforcement measures implemented). This highlights a tension between short-term up-front costs (incurred before the occurrence of hazard events) and long-term benefits (after the occurrence of hazard events) associated with holistic DRR measures. In summary, Scenario D demonstrates that, despite a growing population, adequate DRR measures that aim to improve the building stock's quality (for better sustaining both flood and earthquake damage) as well as incentivize urbanization away from flood-sensitive areas can limit (but not reduce) mean absolute financial flood losses in the future.

Absolute changes to the mean loss ratios provide additional interesting findings. In Scenario A, the mean loss ratios associated with the 100-year and 1000-year flooding occurrences are 2.8 % and 4.5 %, respectively. In Scenario B, as future urbanization continues occurring in both inundated and non-inundated areas and there are no changes in the building stock's quality, the mean loss ratios only show minimum variations compared to Scenario A. In Scenario C, elevating buildings and the promotion of flood-hazard-informed land use produce a significant decrease in the mean loss ratios, which drop to 2.01 % and 3.5 % (27 % and 23 % smaller than in Scenario A), respectively. Due to additional improvements in the building stock's quality in Scenario D, the mean loss ratios drop further to 1.99 % and 3.4 % (28 % and 24 % smaller than in Scenario A, respectively). By comparing the mean loss ratios from both Scenarios C and D relative to Scenario A, we notice that seismic risk-mitigation interventions by themselves do not contribute much to reducing flood risk in the valley due to the low replacement rate (< 5 %) of non-flood-resilient buildings (i.e., A, W) with flood-resilient buildings (i.e., RM) as a result of the seismic upgrading process. However, it is important to remember that Scenario D represents a much more robust approach to multi-hazard risk mitigation than Scenario C.

Figures 10 and 11 present additional insights on the municipality-level spatial distribution of the mean loss ratio for the 100-year and 1000-year flooding occurrences, respectively. The alignment between the proportion of buildings located in inundated areas and the mean loss ratios is clear when the maps from Figs. 10 and 11 are compared with those from Figs. 7 and 8; many municipalities with the largest mean loss ratios are situated in or around the central part of the valley. However, municipalities with notable mean loss ratios are not always associated with the largest proportions of buildings in inundated areas; some are simply subjected to relatively high flood depths (see Figs. S1 and S2 in the Supplement).

Figure 12 illustrates the absolute changes to the municipality-level mean loss ratios for the 100-year flooding occurrence, considering Scenario A as a baseline. In Scenario B, the mean loss ratios show small absolute variations (between -1.0 % and +1.2 %) compared to Scenario A, since future urbanization continues occurring in both flooded and non-flooded areas. Some municipalities experience a decrease in mean loss ratio (see Fig. 7), where future urbanization outside the floodplain is larger than that within it. The relative effects of the building elevation strategy and the flood-hazard-informed land-use planning proposed in Scenario C are noticeable: absolute reductions in mean loss ratios for Scenario C relative to Scenario A ranges between 2.0 %–2.9 % in five municipalities, between 1.0 %–2.0 % in 18 municipalities, and are less than 1.0 % in the remaining 81 municipalities. The benefits of implementing additional multi-hazard DRR measures in Scenario D are almost equivalent to those in Scenario C because the seismic upgrading of the building stock does not contribute much to reducing flood risk. Figure 13 presents the absolute changes to the municipality-level mean loss ratios for the 1000-year flooding occurrence considering Scenario A as a baseline. In Scenario B, the mean loss ratios exhibit some absolute variations (between -1.0 % and +2.3 %) relative to Scenario A, which are larger than in the 100-year flood case; in other words, the consequences of not controlling future urbanization in flood-prone areas can increase with the severity of the considered flooding occurrence. The effects of the flood-specific DRR measures implemented in Scenario C are as follows: absolute reductions in mean loss ratios for Scenario C relative to Scenario A ranges between 2.0 %–5.6 % in 9 municipalities, between 1.0 %–2.0 % in 32 municipalities, and are less than 1.0 % in the remaining 63 municipalities. The benefits of the combined DRR measures in Scenario D are comparable to those in Scenario C.