Assessment of building damages and adaptation options under extreme flood scenarios in Shanghai

. Plenty of various measures have been taken to mitigate flood losses in Shanghai over thousands of years, including the construction of sea dikes and floodwalls. However, the combined effects of intensified rainstorms, sea-level rise, land 10 subsidence, and rapid urbanization are exacerbating extreme flood risks and potential flood losses in the fast-developing coastal city. In light of these changes, this article presents an assessment of possible exposure and damage losses of buildings in Shanghai (including residential, commercial, workplace, and industrial buildings). Based on extreme flood scenarios caused by storm surges, precipitation, and fluvial floods, current flood-defence standards will soon be overtaken. Further analyses show that the inundation area could reach 9%, 16%, 24%, and 49% of Shanghai (excluding the area of islands) under the 1/200, 15 1/500, 1/1000, and 1/5000-year flooding scenarios, respectively. This study finds, in terms of the total building damage, the 1/5000-year flood scenario damage is more than ten times the 1/200-year flood scenario. Accordingly, the average annual loss (AAL) of residential, commercial, office, and industrial buildings are 13.9, 2.3, 5.3, and 3.9 million USD. Specifically, among the 15 (non-island) districts in Shanghai, Pudong has the highest exposure and AAL at all the four flood scenarios, while the inner city (including seven districts) is also subject to extreme AAL of up to 40% of its total building values. This study further 20 addresses the possibilities of these extreme flood scenarios, and adaptation options such as: strategic urban planning, advanced building protections, and systematic flood management. Conclusions of the study provide information for scenario-based decision making and cost-benefit analysis for extreme flood risk management in Shanghai and is applicable to other similar coastal megacities.

more than 5000 households (Du et al., 2020). In 2008, floods damaged 160 streets and 13,000 residential buildings in Shanghai. 65 Therefore, local communities and governmental departments are increasingly calling for holistic analyses of possible building damage under extreme flood scenarios in order to accurately understand and assess potential flood impacts (Kelman and Spence, 2004). Accurate loss data play an integral role in assessing the damages of buildings. But obtaining accurate data is a challenge shared in many areas (Middelmann-Fernandes, 2010), especially in assessing the damage of buildings. To estimate building stocks and the values at risk under a 1/1000-year extreme flood scenario, Wu et al. (2019) integrate census-level 70 building floor-area data and geo-coded building asset value data. Shan et al. (2019) further assessed the flood losses of residential buildings and household properties in Shanghai based on a stage-damage function, building footprint, and housing prices. Mostly, deeper investigations into the uncertainties (e.g., asset values, damage rate, and flood process) with reliance on flood risks and damages of buildings (e.g., residential, commercial, office, and industrial) are also urgently needed in order to better support decision making which enhances the overall flood resiliency of the city. 75 To address these questions, we adopted the very extreme flood scenarios with return periods of 200, 500, 1000, and 5000 years.
The four extreme flood scenarios are assumed as integrative effects of multiple flood-triggering factors, such as typhooninduced storm surge, precipitation, fluvial flood in combination with a high astronomical tide, to reflect low probability-high impact flood situations in Shanghai. The objective of this paper is to assess flood losses of residential, commercial, office, and industrial buildings under extreme flood scenarios in Shanghai. To achieve the objective, we modeled building exposures at 80 four extreme flood scenarios with return periods of 1/200, 1/500, 1/1000, and 1/5000, respectively. Combining the exposure maps with the stage-damage functions, the study evaluated and identified the spatial distribution of losses for the specific types of buildings in Shanghai. Section 2 of the paper introduces the data and details of the methods, and section 3 presents the data analysis and major results of the study. Section 4 discusses future flood scenarios and proper adaptation strategies for building a flood-resilient Shanghai. Final conclusions are described in section 5. 85 2 Data and Methods

Study Area
Shanghai is the biggest coastal city in China in terms of population (24.3 million in 2019) and is the major trading and financial hub of China. The city has an area of 6340.5 km 2 that lies in the Yangtze River Delta along the northern edge of Hangzhou Bay (Figure 1). Shanghai is prone to flooding because of its flat low-lying terrain, as well as its location on the path of frequent 90 typhoons from the northwest Pacific (Balica et al., 2012). Moreover, the city experienced an average land subsidence of 1.97 meter from 1921 to 2007 and the trend is continuing  additionally driving flood risks (Quan, 2014). In addition, the total building area in Shanghai reached 1368.8 km 2 (SMBS, 2018) which includes residential buildings (686.5 km 2 ), commercial buildings (81.6 km 2 ), workplace buildings (90 km 2 ), industrial buildings (283 km 2 ), and others. The total construction industry value is 159.8 billion USD (SMBS, 2018).

Data
The study involves the data of four extreme flood scenarios, building maps, land use and land cover data, and the construction 100 costs of different buildings in Shanghai. In this study, we adopted the very extreme flood scenarios with return periods of 200, 500, 1000, and 5000 years. Based on the extreme water levels for different return periods, the hydrodynamic modelling is composed of atmospheric models (Fujita typhoon model), ocean models (TOMAWAC, TELEMAC), and coastal models (MIKE 1D/2D), developed to simulate four extreme flood scenarios, respectively combined with a fluvial flood during Typhoon Winnie in 1997 . Typhoon Winnie brought the highest recorder water level with 5.72 meters since 105 1900, which caused the collapse of 148 meters of floodwalls and overflowed 57 km of floodwalls and 69 km of sea dikes. The rainfall and river discharge data based on Typhoon Winnie in 1997 are superimposed on coastal flood simulations. These four flood scenarios are raster data with a 60-meter spatial resolution. The data for Shanghai's buildings were acquired from Baidu Map (Baidu Maps, 2017) using a python-based web crawler, and then processed with ArcGIS software. Baidu Maps provide various map services, such as satellite images, street maps, and 110 route planners in China. The shapefile data for Shanghai's buildings include the information of building type, building groundbased area, height, and the number of floors for each building. These data are used in combination with land use data to further cluster the buildings into four different types including: residential, commercial, workplace, and industrial.
The land use data of 2013 from the Shanghai Planning and Land Bureau was depicted hierarchically into 3 sectors, 15 subcategories, and 73 subclasses of land use types using ArcGIS 10.6.1. The reclassification of the land use was conducted 115 according to the National Standard of China "GB/T 2010" (CSP, 2017 and covers residential area, commercial area, workplace area, and industrial area. Specially, the land use type of residential areas covers apartment, mixed apartment, housing in the rural, and empty housing. Commercial areas are the lands that are used for commercial operations. Workplace areas include land for medical care and health, charity, education, culture, government, research, market, and insurance.
Industrial factories are classified into industrial areas. 120 The cost data of building construction used in this study are derived from the 2019 annual report provided by the consulting company Arcadis in Shanghai. The cost per square meter is based on Construction Floor Areas (CFA), which measures to the outside face of the external walls. The data set depicts four different building types (domestic, office/commercial, hotels, industrial, and other), which are further divided into 19 subsectors (Arcadis, 2020). Construction costs of various buildings are averaged in order to get the mean value for each building type (table 1). 125 The relationship between flood inundation depth and flood loss of a building or other property is depicted by a stage-damage function (Garrote et al., 2016;Mcgrath et al., 2019). Based on actual building damage data from past flood hazard events and previous empirical stage-damage functions in Shanghai Wang, 2001), Ke (2014) developed updated stage-130 damage functions to specific buildings in Shanghai ( Figure 2). These stage-damage functions represent the generalized loss of one type of buildings with similar properties, which are adopted in the present study.   (Ke, 2014)).

Methods 135
In our study, the flood damages in Shanghai are estimated within three different steps: first, we calculated the asset values for each building based on the surface area of the building and the average construction cost. Then, the exposed building areas are determined by overlaying the distribution of buildings with the inundation maps for the four extreme flood scenarios. The damage values of a building could be estimated based on the exposed building area and the stage-damage functions. Finally, the overall flood risk for buildings in Shanghai, described as the estimated average annual loss (AAL), is calculated using a 140 polynomial regression analysis. More details of the assessment are introduced in the following sections 2.3.1 to 2.3.4.

Asset value analysis of buildings
Different methods exist in the evaluation of building values. According to the Chinese National Standard (GB/T 50291-2015) and previous studies, there are four main approaches to evaluate the asset value of buildings: method of the sales comparison, method of the income capitalization, method of the construction cost, and method of the hypothetical development (CSP, 145 2015). The application of different methods depends on the specific study aims and the availability of building-specific data.
Method of sale comparison is often used when the evaluation of a similar type of building is available. Method of income capitalization is suitable for buildings that yield profits like rents. If a building is newly constructed or (to be) reconstructed after damage, method of construction cost would be suitable for the evaluation. Method of hypothetical development is applicable to the evaluation of real estate with investment development or redevelopment potentials. Since the present study 150 aims to assess flood damages on buildings, the construction cost method is used with consideration of the building surface area. Then, the asset value of one building can be approximated by the following function: Where (USD) is the asset value of buildings for building type , is the surface area of building , is the construction cost (USD/m 2 ) for the specific type of building . 155

Evaluation of building damages in flood
The water depth of the flooding scenario determines the exposed area of the buildings. If the building is flooded at depth of more than 3 meters, we assume that the exposed area covers two floors instead of one. The potential building damages are determined by the stage-damage functions. The stage-damage function is used to evaluate the damage values of residential, commercial, workplace, and industrial buildings with the probability of 1/200, 1/500, 1/1000, and 1/5000-years extreme flood 160 scenarios, respectively. The specific stage-damage functions are derived from existing studies on the relationship of various building loss rates with water-level depth in Shanghai (Penning-Rowsell et al., 2013;Ke, 2014). The damage values of one building can be expressed by the following function: Where ( ) represents building damage for building type , represents the exposed area of buildings for building 165 type , is the construction cost (USD/m 2 ) for the specific type of building , represents the damage proportion from stage-damage function for building under different water-level depths.

Integrative building damages
When expressing a city-scale flood damage for different flood scenarios, we use the already well established economic AAL (Hallegatte et al., 2013). The AAL is the sum of the probabilities of the floods for each return period, while considering the 170 approximate areas under the associated risk curve (Ward et al., 2011). In the present study, we considered only the damage value of buildings as a major part of the AAL. Particularly, the AAL represents the integrative building damage for all types of buildings in all the considered flood scenarios in Shanghai. Hence, we get the AAL values for different exceedance probabilities (extreme flood scenarios) as the sum of: Where n is the building type, is the return period of the flood scenario, ( ) is the damages value of one building.

Spatial pattern identification
In this study, the AAL of all sub-districts and their neighbours were compared with the AAL by Getis-Ord Gi* in ArcMap The flooded area extended significantly to 49% (2659 km 2 ) of Shanghai in the 5000-year flood scenario. More specifically in this exceptionally extreme scenario, 32% of the city would be flooded with a water depth of 0-0.5m, 11% with a 0.5-1.0m depth of water, and 5% with a water depth of more than 1m ( Figure 3).

Estimating the building assets
This study identified the building floor area (BFA) of each type of building in 2017, which amounts to 816 km 2 , 52 km 2 , 152 km 2 , and 300 km 2 respectively for the residential, commercial, workplace, and industrial buildings. Accordingly, the building asset values are 2494, 212, 747, and 510 billion USD considering the Average Construction Cost (see Table 1) of each building types in 2019. This is a conservative estimation as the actual present value of the buildings are certainly higher due to new 200 developments in recent years. The relative numbers show that residential buildings have the highest asset value, followed by office, industrial, and commercial building.

Exposed building values 210
The quantitative assessment and mapping generated the spatial extent of exposed buildings and the exposed building values under the four extreme flood scenarios in Shanghai ( Figure 5). The assessment shows that the total exposed building values reach 39, 107, 166, and 386 billion USD under a 1/200, 1/500, 1/1000, and 1/5000-year flood scenarios, respectively. Exposed buildings in the 1/200 scenario are located mainly along the coast and rivers, with some exposed buildings in dispersed lowlying places. With an increase of extreme flooding, the exposed areas rise rapidly and become contiguous. Under the most 215 extreme scenario of a 1/5000-year return period, the region around the Suzhou Creek Mouth in the inner city is remarkably exposed in deep water with the highest exposed values. Two contiguous inundation areas, central Shanghai and the Songjiang-Qingpu low-lying area in west Shanghai, are identified as the most seriously exposed regions in the most extreme flood scenario ( Figure 5d).
Unsurprisingly, as the residential buildings have the highest asset value, their exposed values are also the highest in the four 220 types of buildings (Appendices Table A1). Further analysis of the exposed values in different districts indicates that Pudong has the highest exposed value for all four flood scenarios (Table 3). The exposed ratio of each district in different flood scenarios is as follows: in the 1/200-year flood scenario, Fengxian has the highest percentage of exposed building values; under the 1/500-year and 1/1000-year flood scenarios, Huangpu has the highest percentage of exposed building values. Under the 1/5000 flood scenario, Hongkou has the highest percentage (Appendices Figure A1). 225 Water depth is a determinate factor of exposed building values. Though hardly visible in Figure 5, 94% of the exposed buildings are exposed to water levels of 0-0.5m in the 1/200-year flood scenario (Figure 5a). The account declines to 82%, 83% and 67% under the 1/500, 1/1000, and 1/5000-year flood scenarios, due to the increasing proportions of exposures in deeper water. In the case of the most extreme 1/5000-year flood scenario, 24% of exposed buildings are in water of 0.5-1m, and 8% of the exposed buildings are flooded in depth of 1-1.5m (Appendices Figure A2).

Damages of buildings in floods 235
The quantitative assessment provides maps of flood damages to different buildings under the four extreme scenarios ( Figure   6). The total building damages in the 1/5000-year flood scenario is 18 billion USD, which is more than 10 times the 1/200year flood scenario (1.39 billion USD). Again, residential buildings are the most damaged in all four scenarios with a damage value of up to 9.6 billion USD in the 1/5000-year scenario. Damages of industrial buildings, office buildings and commercial buildings would reach 1.6, 4.0, and 3.0 billion USD respectively under the 1/5000-year flood scenarios. 240 The damage analysis of different districts shows that Pudong has the highest overall damage in all scenarios (Table 4)

Integrative evaluation of flood damages in Shanghai
By integrating the extreme flood scenarios and associated building damages for the four types of buildings, we plotted the four average annual probability-damage curves (Fig. 7). The AALs of residential, commercial, workplace, and industrial buildings are 18.3, 3.6, 7.8, and 5.8 million dollars, respectively. It is clear that residential buildings would suffer the highest damage 260 value among the four types of buildings.
By using the Getis-Ord Gi* statistic tool (Hot Spot Analysis) in ArcMap 10.6, the results reveal the distuibution of high and low building damages for different types of buildings in the community level in Shanghai (Fig. 8)  Four hot spots of industrial buildings concentrate mainly in the north, while the city center is the main cold spot area because few industrial buildings are located there.
Overall, the city center is the hot spot area of flood damages for the residential, commercial and office buildings (Figure 8a, 270 8b and 8c). But in contrast, the city center is the cold spot area for the industrial buildings ( Figure 8d). Wusongkou is a hotspot for four different types of structures. Wusongkou floods in all four flood scenarios, and the inundation area expands as the retrun-period expands. Another reason is that the density of building in Wusongkou is higher than in other areas.

Evaluation of the flood risk in Shanghai and implications for the future 280
Our study shows that the damage to buildings in Shanghai grows exponentially with the decreasing likelihood of extreme flood scenarios. For instance, the resulting flood damages to residential, commercial, workplace, and industrial buildings under the 1/5000-year flooding scenario is more than ten times the resulting damages from a 1/200-year flooding scenario. As shown in section 3.1, the area along the Yangtze River Estuary, Hangzhou Bay and Huangpu River are broadly flooded under the 1/200, 1/500, 1/1000, and 1/5000-year flooding scenarios. The results of the study show the importance of assessing the risk to 285 extreme events on regional scale at a high spatial resolution considering the differences in the exposed assets. The hot-spot clusters are distributed over the whole study area and vary from building type to building type. In some areas, the damage is driven mainly by high inundation depths (e.g., the hotspot in the south), whereas other areas face a high risk due to the high Concerning increases in climate change, the frequency and/or severity of acute climate hazards and the intensification of chronic hazards will increase the flood risks in Shanghai in the future (Woetzel et al., 2020b). The Sixth Intergovernmental Panel on Climate Change (IPCC) found that global precipitation will intensify and become more frequent in most regions with additional global warming (IPCC, 2021). Extreme precipitation events increased dramatically by 10% to 20% every 10 years during the 1951 -2001 period in the Yangtze River basin, China (Wang and Zhou, 2005). Concerning Shanghai, after analysing 295 Shanghai's hourly precipitation records (1916 -2014), Liang and Ding (2017) found a rate increase of 1.5 and 1.8 for heavy precipitation events. Precipitation events now increase the possibility of seawall and levee failures in Shanghai. One 1/1000year return period flood occurred in Shanghai in 2013, breaking the highest crest record at Wusongkou Datum and causing levees to breakdown (Ke et al., 2018). As a result of climate change, extreme flooding events will become more common in

Shanghai. 300
Human activities can also increase the likelihood of flood risk in Shanghai. For instance, changes in land subsidence relative to the sea level rise could increase the flood risk to Shanghai. Due to the extraction of ground water, construction of high-rise buildings and underground projects , the average annual rate of land subsidence was 7 mm between 2007 and 2010 , and then the rate decreased to 5 mm/year after 2010 (Yin, 2011). However, the maximum annual subsidence rate in Shanghai could still have a chance to reach 24.12 mm/year . On the other hand, sea levels 305 will rise a maximum of 86.6 mm, 143mm, 185.6 mm, and 433.1 mm by 2030, 2040, 2050, and 2100, respectively in Shanghai Wu et al., 2012). Future flood damage in Shanghai will be exacerbated by increased precipitation, land subsidence and sea level rise, which further shows the need to adapt to the (currently) low probability-high impact events.

Adaptation strategies to extreme floods in Shanghai
Effective adaptation to increasing flood risks requires an integrated climate response strategy, which shall include a broad 310 scope of intervention measures such as urban planning, structural flood management measures, early warning systems, naturebased solutions, flood awareness and risk financing instruments (Yang et al., 2015;Jongman, 2018). Urbanization as a confirmed trend in the fast-developing coastal city may increase asset exposures to floods, but can also offer opportunities for improving flood risk management (Garschagen and Romero-Lankao, 2015). A top-down urban master plan, including land use planning, control of runoff, access to data and information, etc. should be updated, by the Shanghai Municipal Government, 315 to involve advanced risk management measures (Zhou et al., 2017). For instance, in its Master Plan 2017 -2035, Shanghai is going to further develop its five new district centers at Jiading, Songjiang, Qinpu, Fengxian and Nanhui. These five district centers are planned to be nodal areas in Shanghai and provide more public services for the growing population. However, based on the flood scenario maps, the Songjiang-Qingpu low-lying area is a hot spot of flood damages. Therefore, future flood protections in these locations, particularly the drainage system and the building structures, must be designed to a higher 320 standard.
The hard, soft and hybrid measures must be considered in implementing the planned urbanization process (Table 5). It has been widely proven that the hard strategies can effectively reduce the flood hazard probability. For instance, the direct economic flood loss significantly decreased after a series of integrated flood management followed a mega-flood across central and south China in 1998(Bryan et al., 2018. The protection level of existing seawalls and levees along the Changjiang Estuary, 325 Hangzhou Bay and Huangpu River do not provide adequate protection to meet Shanghai's current flood defense standards.
These structures are not sufficient to protect the increasing urban assets considering the combined impacts of climate change, land subsidence and typhoon events. Seawall and levees Protects areas from being flooded or eroded by extreme storms, floods, astronomical tides, and sea level rise, particularly in low lying areas.
Raise levees and construct a flood barrier in Wusongkou which could lower the flood pressure from Huangpu River (Wang et al., 2011).

Drainage system
Rapid rainfall discharge to improve transport and safeguard property.
The drainage system should protect Shanghai under 50-year flood scenario. The capacity should be enhanced to the probability period of 100 years in the vulnerability (UPLR, 2018).

Reservoir
Save part of the precipitation and reduce flood pressure in downstream or lowland areas.
Adjust stock by season or weather forecasting, relieve the pressures of the city flood management during floods. Channel Relieve the flood pressure and speed up drainage within the city.
Ensure that the dam functions could be running on the Huangpu River and Suzhou River.

Soft measures
Warning system Enable stakeholders or households to prepare for the extreme climate and react to mitigate it.
Could be used in Shanghai, especially preventing people from putting their lives in extreme flood event.
Dry proofing Being watertight with all elements substantially impermeable to the entrance of flood and with structural components having the capacity to resist flood loads (FEMA, 2013).
Help the household, especially for household that have experienced regular flooding, to stop the floodwater from their entrance door.
Wet proofing Allow water to enter the building but minimizing damage.
Minimizing the damage of property in the building in the flood-prone area.

Detention and retention areas
Alleviate flood peak by artificially made storage areas (Glavan et al., 2020) Based on Sponge City project, to capture, purify and store more water (Griffiths et al., 2020).

Emergency relief
Personnel evacuation and transfer of property from short-term extreme precipitation.
Adapted in Shanghai, especially residents who lived in inundation areas in the extreme flood scenario. Insurance Increase financial resilience to floods (Surminski and Oramas-Dorta, 2014).
Meet the needs of vulnerable individuals, households and micro, small, and mediumsized enterprises (MSMEs) in Shanghai (Hess and Fischle, 2019).

Wetlands
Provide valuable flood storage, buffer storm surge, and assist in erosion control (EPA, 2021). Absorb and slow down the floodwater from storm surge.
The wetland on the north of Hangzhou Bay, the mouth of Yangtze River should be protected to slow down the erosion from the storm surge and sea level rise. Particularly for the hot spot areas where huge damages are expected, extra coping strategies must be taken seriously into consideration. For instance, underground water storage and pumping facilities are necessary for the city center, and these shall be systematically planned together with detention and retention regions on the ground. A feasible and practical way is to make use of the old air defense facilities and some underground parking lots (Chen et al., 2018). Researchers have mentioned, and we also recommend, to enhance the bank and build a water wall in the estuary area of the Huangpu River (Chen et al., 2018), 335 because of the large population and assets which deserves a high level of protection. Developing such a flood barrier and upgrading the Huangpu-River floodwalls to a protection standard of a 1/1000-year flood event in Shanghai could significantly reduce the expected annual flood damages to 0.07 -0.5 billion/year in 2100 for the RCP (representative concentration pathway) 4.5 scenario (Du et al., 2020).
Potential flood risks coming from extremely low-probability storm surges can be further reduced by combining the soft 340 strategy. For instance, dry proofing and wet proofing, as well as the coastal wetland strategies can be combined to form a hybrid strategy in specific regions. Although soft measures on their own cannot maintain the future flood risk at a low level, they can play a critical role in reducing potential damages. Additionally, the soft measures such as maintaining coastal wetlands can enhance social welfare by providing multiple ecosystem services. Challenges may rise in that homeowners are on average not inclined to install soft strategies due to the high costs for individual households, even if they live in flood plains (De Ruig 345 et al., 2020). On the other hand, extreme flood scenarios often cause more serious flood damage to households because they occur very soon and leave less time for responding (Yang et al., 2018b). Thus, an efficient forecasting and early warning system is needed which could help individuals, businesses, communities and government leave dangerous zones, transfer house property and improve preparedness with sufficient lead time (UN, 2020). Insurance also plays a very important role in the support of various people and groups to recover from flood hazards, especially for the high-risk regions. 350

Uncertainties and limitations of the assessment
The integrative analysis of geospatial building asset maps, flood scenarios and the stage-damage functions in the study makes it possible to assess the flood damage of buildings in the mega city Shanghai with a high spatial resolution. However, the accuracy of building asset values could still be improved. First, the adopted building data of location, footprint area, height and floors didn't consider the construction materials used and years built. Also, the data from 2013 is not very new, considering 355 the fast development of Shanghai. Second, the classification of different types of buildings is quite straightforward based on the land use/land cover data. However, many buildings with multiple functions (e.g., shopping mall and offices) were identified to a single building type, which causes uncertainties of the building value. Third, existing studies' stage-damage functions for specific types of buildings are used to create the asset building loss map for the flood risk assessment. The functions must be updated and tailored to more current and specific building conditions, particularly when estimating flood damages in the future 360 (Ke, 2014).
Our assessment of the building damages is comparatively less than those in similar studies of Shanghai. The major reason is that we adopted the construction cost as the values of different buildings, while many other studies calculated the market value The four extreme flood scenarios in Shanghai were taken from published models in Shanghai that are induced by atmospheric, hydrology, and coastal models . However, the climatic impact-drives (CIDs) such as the frequency of sea level rise, heavy precipitation, and pluvial floods can be altered as a result of various greenhouse gas (GHG) emissions. For 370 example, every 0.5°C rise in global warming increases the severity and frequency of heat extremes, such as heatwaves and heavy rains, as well as agricultural and ecological droughts in some regions (IPCC 2021). Therefore, the CIDs (e.g., sea level rise, heavy precipitation) should be considered in future flood simulation models and studies.

Conclusion
This study presents an integrated impact model of flood damage to buildings based on extreme flooding scenarios in Shanghai. 375 The results show that the inundation area is significantly larger in the low probability of extreme flood scenarios. In all the four considered scenarios, the areas near the Huangpu River and along the shore are always the affected with building damages.
The central downtown areas of Shanghai have a high risk of being exposed to and affected by extreme floods, partly because of its high building density. Besides that, the Songjiang-Qingpu low-lying area in the west of Shanghai has been recognized as a noticeable area to be flooded under a 1/5000-year flood scenario. This calls for special concerns in the near future because 380 the Songjiang-Qingpu area is planned to become an important sub-center node.
Residential buildings account for the most damage of the four types of buildings, accounting for 47 percent of the total, followed by industrial, workplace, and commercial buildings, respectively. The total asset value for the four building types is 3963 billion USD while the total AAL is 22.2 million dollars. It is also noticeable that the total damage for the four types of buildings is 18 billion USD in 1/5000-year scenario, residential buildings are significantly vulnerable to extreme flooding in 385 contrast to the three other types of buildings.
The presented method offers the possibility to estimate the damage values for residential, commercial, workplace, and industrial buildings in Shanghai under extreme flooding. It increases the accuracy and details of flood damage estimates for different types of buildings by considerging the direct damage of buildings. The dynamic linkage between the extreme flooding scenarios and the distribution of asset values of the four different types of buildings allows the evaluation of the spatial 390 distribution of flood damages, which would be valuable for individual real eastate managers and also for the city government.

Building types
Flood scenarios (Return Periods)

Data availability
Data used in this study are available from the first author upon request.

Author Contributions
J.T. analyzed the data, conceived and wrote the paper; J.W. and L.Y. conceived and co-wrote the paper; A.R., M.G. and S.Y. reviewed and improved the analysis and manuscript; M.Z. and L.W. provided the simulation results of the extreme flood 410 scenarios.

Funding
This research was funded by the National Natural Science Foundation of China (Grant No. 42171080, 41771540, 41871200) and the National Key Research and Development Program of China (Grant No. 2017YFC1503001