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

Tu, Jiachang; Wen, Jiahong; Yang, Liang Emlyn; Reimuth, Andrea; Young, Stephen S.; Zhang, Min; Wang, Luyang; Garschagen, Matthias

doi:https://doi.org/10.5194/nhess-2021-382

Preprints

https://doi.org/10.5194/nhess-2021-382

Preprints

23 Dec 2021

| 23 Dec 2021

Status: a revised version of this preprint is currently under review for the journal NHESS.

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

Jiachang Tu, Jiahong Wen, Liang Emlyn Yang, Andrea Reimuth, Stephen S. Young, Min Zhang, Luyang Wang, and Matthias Garschagen

Abstract. 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 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, 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 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.

Received: 08 Dec 2021 – Discussion started: 23 Dec 2021

Jiachang Tu et al.

Status: final response (author comments only)

RC1: 'Comment on nhess-2021-382', Anonymous Referee #1, 28 Jan 2022

Thank you for inviting me to review the manuscript entitled 'Assessment of building damages and adaptation options under extreme flood scenarios in Shanghai'. This manuscript assesses possible exposure and damage losses of buildings in Shanghai and provides a detailed description of the technical methods and results using the case study. It is well written, and the results are clearly presented. However, my primary concern is about its theoretical or methodological contributions to the field of flood risk assessment, which are not sufficiently articulated or developed. Assessing very extreme flood scenarios (e.g., return period = 5,000 years) is not innovative enough by itself.

Other general comments

Why is it needed to assess extreme flood scenarios with return periods of 5,000 years?
Please can you provide more information about what each extreme flood scenario is like in Shanghai (e.g., their discharge or precipitation)?
What is the implication of this study to other cities or future research?
Have you considered validating your simulated results or comparing them with other Shanghai flood risk assessments?

Specific comments

Line 39. Why do you think Shanghai 'should increasingly install flood protection, with a focus on hard measures'? Please can you justify or provide evidence?
Line 53. Please explain what a two-dimensional MIKE 21 flow model is and its features as part of the introduction.
Line 68. I agree with the authors that "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." However, the challenge of obtaining accurate loss data is not the focus of this manuscript or hasn't been solved by this study. Therefore, I don't think they are directly relevant as part of the introduction. Consider moving it to the methodology section.
Line 95. What does 'construction industry value' mean?
Line 102. Three types of models were developed for the assessment, including atmospheric models, ocean models, and coastal models. Consider placing them in the methodology section instead of the data section. Again, more information about these models is expected.
Line 126, Table 1. How were the Average Construction Costs calculated? Were different building types weighted? Why is the Average Construction Cost (1157) smaller than the lower bound of the range (1228) for commercial buildings?
Line 154. The 'W' in 'Where' should be in lowercase.
Line 154. What does 'surface area of building' mean? Does it include the wall as well?
Line 164, Equation 2. f(x) means a function at an element x. However, an x is missing on the right to the equal sign. Please modify the equation and explain what x means.
Line 175, Equation 3. More information is needed to explain Equation 3.
Line 178. Explain Getis-Ord.
Line 199. Is the building asset value for the first floor of all four building types?
Line 207, Figure 4 (also Line 231, Figure 5 and Line 251 Figure 6). Since the Average Construction Cost is used for each of the four building types, is it true in Figures 4-6 that the buildings with higher 'Building Asset Values' are buildings taking a larger land area?
Line 329, Table 5. Table 5 provides a comparison of flood adaptation measures in Shanghai. However, how can these measures, especially the soft ones, be reflected in the simulations? The simulation results and the soft adaptation measures are disconnected, and more discussion is needed here.

Citation: https://doi.org/10.5194/nhess-2021-382-RC1

AC1: 'Reply on RC1', Jiachang Tu, 26 Apr 2022

Dear Reviewer,

The authors thank the reviewer for reviewing our manuscript. We appreciate the time and effort that the reviewer dedicated to providing feedback on the manuscript and we are grateful for the insightful comments and suggestions. We diligently went through your remarks and corrected our manuscript accordingly.

Please find our response and revised text blocks (in black italics) below your comments (in black bold).

General Comments

Response: First of all, thank you so much for pointing out that the theoretical or methodological aspects of the flood risk assessment are not sufficient. To introduce the methodology more clearly, we re-constructed and rewrote the paper. For example, we deleted the section “data and methods” and added the new sections “Study area” and “Materials and methods”. We have also rewritten other parts of the manuscript. For the new section “Materials and methods”, we included one graphic at the beginning of the section to clarify our methodological procedure:

In our study, the flood damages in Shanghai are estimated using three different steps (Figure 2): first, extreme flood scenarios with a return period of 1/200, 1/500, 1/1000, and 1/5000-years are simulated by a hydrodynamic model. This was done in a previous work by Wang et al., (2019). Our study builds upon these results and estimates the damages based on the obtained flood hazard maps. Finally, the overall flood risk and its spatial pattern for buildings in Shanghai, described as the estimated average annual loss (AAL), is calculated using a polynomial regression analysis. More details of the assessment are introduced in the following sections 3.1 to 3.3.

Figure 2. Risk analysis chain

Second, we provide a better explanation on what a two-dimensional MIKE is in the new section 3.1 “Flood hazard modeling”:

The study builds on the results of Wang et al. (2019), which applied a hydrodynamic modelling approach to simulate compound flooding for the region of Shanghai. Four scenarios with return periods of 200, 500, 1000, and 5000 years were simulated considering storm surge, extreme precipitation, high tide, and river flooding at a resolution of 60 m. For this purpose, several models were applied and coupled: 1) the Fujita model simulating the atmospheric conditions; 2) the TELEMAC model simulating ocean movement; 3) the TOMAWAC model simulating the propagation of waves; and 4) the MIKE 21 model simulating the hydraulic processes.

These models were calibrated using rainfall and river discharge measurement data from Typhoon Winnie. Typhoon Winnie brought the highest recorder water level of 5.72 meters since 1900, which caused the collapse of 148 meters of floodwalls and overflowed 57 km of floodwalls and 69 km of sea dikes.

In order for readers to grasp our equation, we provide two tables as samples to present the calculation process: 1) 'building asset value'; 2) 'the damage values':

Then, the asset value of one building can be approximated by the following function:

W_n= S_n× P_n (1)

Where W_n (USD) is the asset value for one building which belongs to the building type n, S_n is the surface area of building n, P_nis the average construction cost (USD/m²) for the specific type of building n. The surface area is the whole construction area (including wall area and floor area).

Table 2. Building asset value for the four selected building types.

Building id	Building type n	Surface area (m²)	Average construction cost (USD/m²)	Asset Value (USD)
1	Residential building	1662	874	1452588
2	Commercial building	1347	1407	1895229
3	Office building	776	1157	897832
4	Industrial building	2463	486	1197018

The damage values of buildings were estimated:

The damage values of one building can be expressed by the following function:

D_n=E_n×P_n ×T_n (2)

Where D_nrepresents building damage for building type n, E_n represents the exposed area of buildings for building type n, P_nis the construction cost (USD/m²) for the specific type of building n, T_n represents the damage proportion from stage-damage function for building n under different water-level depths.

Table 4. Exemplary damage values for the four building types at an inundation level of 0.5-1 m.

Building id	Building type n	Exposed area (m²)	Average construction cost (USD/m²)	Damage proportion	Building damage (USD)
1	Residential building	123	874	0.06	6450
2	Commercial building	342	1407	0.09	43180
3	Office building	539	1157	0.07	43653
4	Industrial building	29	486	0.08	1127

On the other hand, we have removed the section “Exposed building values” to simplify the manuscript and direct the readers’ attention to the building damage, risk, and its pattern in Shanghai.

Please see below, for a point-by-point response to the reviewers’ comments and concerns.

Other general comments

1. Why is it needed to assess extreme flood scenarios with return periods of 5,000 years?

Thank you for this point. We understand that Shanghai has not experienced an extreme flood with return periods of 5,000 years so far. However, it is necessary to have an assessment on such a low probability-high impact scenario that is increasingly possible to happen in the future:

• Shanghai may suffer from extreme compound flood threats in the next few decades considering risks from typhoons, sea level rise, heavy precipitation, and riverine flows due to its physical environment and location.

• Shanghai currently relies extensively on hard measures of flood protection. But the seawalls and levees can be easily destroyed because of the multiunit constructions and standards used during the long construction process.

• The seawalls and levees can’t protect Shanghai from extreme flood events especially considering the fast population growth and social economic development that aggregate flood risk. We have enhanced the description of this point in “Section 1 Introduction”.

2. Please can you provide more information about what each extreme flood scenario is like in Shanghai (e.g., their discharge or precipitation)?

We have rewritten and enhanced the description of the discharge/inundation of the extreme flood scenario from the previous study in paragraph 3 in “Section 1 Introduction”. In section 4.1, we present and compare the flood scenarios of the result of our flood hazard modelling.

Reviewing the current literature shows that various flood scenarios have been widely developed and validated for measuring flood risks in Shanghai. For example, according to the trend of relative sea level rise and the harmonic analysis of storm surges along the Shanghai coast, Yin et al. (2011) forecasts flood scenarios in 2030 and 2050, and the result shows that a possible maximum tide level of 9.82 m by 2030 and 10.04 m by 2050. All river embankment crests in Shanghai would be exceeded and destroyed by flood intrusion, according to the results. The MIKE 21 Flow Model is a modelling system for 2D free-surface flows. It has been applied for simulating future combined effects (sea level rise, land subsidence, and storm surges) of flood scenarios in 2030, 2050, and 2100 which inundate 1.5 %, 37%, and 50% of Shanghai, respectively (Wang et al., 2012). Fluvial floods from the Huangpu River were also simulated, considering land subsidence, sea level rise, and storm tide, in considering the return periods of 20, 50, 100, 200, 500, and 1000 years in Shanghai, respectively (Yin et al., 2013). The findings show the inundation area reaches 0 km²,111.30 km², 124.73 km², 143.74km², 177.96 km², and 195.77 km² in Shanghai under the present physical environment. Incorporated with three anthropogenic variables (land subsidence, urbanization, and flood defence), Yin et al. (2015) used a numerical 2D modelling approach for the return periods of 10, 100, and 1000 years. In general, the flood scenarios produced in most existing studies tended to focus on the possible future flood scenario changes rather than extreme events, e.g., the concern floods over a 1000-year return period.

3. What is the implication of this study to other cities or future research?

The methodology of this research paper can be commonly used for flood risk assessment in other areas. Especially, this study developed a method to assess the damage values of different buildings in Shanghai in four extreme flood scenarios. This method is useful for other coastal cities that have high population and are in fast urbanization, e.g., in Southeast Asia, in Africa coasts, in East coast of North America. In addition, the estimation of different building damages could inform future flood damage studies to consider various assets with more precise evaluations.

4. Have you considered validating your simulated results or comparing them with other Shanghai flood risk assessments?

Thank you for pointing this out. Other Shanghai flood risk assessments have different methodologies and assessment objectives. We have validated our results in section 5.3. Wu et al. (2019) and Shan et al. (2019) have building and residential building flood risk assessments for Shanghai, respectively.

Specific comments

1. Line 39. Why do you think Shanghai 'should increasingly install flood protection, with a focus on hard measures'? Please can you justify or provide evidence?

Thank you for this point. As the third paragraph of Section 1 describes, the seawalls and levees can easily be destroyed because of the multiunit structures’ standards used during the long temporal construction process and the historical crest height in the Huangpu River growing from 1950 to 2000. This is the reason we focus on low probability-high impact flood scenarios. We have revised this sentence to make the statement clearer.

2. Line 53. Please explain what a two-dimensional MIKE 21 flow model is and its features as part of the introduction.

Thank you for pointing this out. As responded above, we have presented MIKE 21 flow model with more details in the introduction, and also under the first paragraph of section 3 “Materials and methods”. The added description of the MIKE 21 flow model include:

MIKE 21 Flow Model is a modelling system for 2D free-surface flows. It is applicable to the simulation of hydraulic and environmental phenomena in lakes, estuaries, bays, coastal areas, and seas. It can be applied wherever stratification can be neglected. The hydrodynamic (HD) module, the basic module in the MIKE 21, simulates water level variations and flows in response to a variety of forcing functions in lakes, estuaries, and coastal region (MIKE, 2017).

Further information about the MIKE 21 is available in the reference: MIKE: MIKE 21 flow model-hydrodynamic module user guide, 2017

3. Line 68. I agree with the authors that "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." However, the challenge of obtaining accurate loss data is not the focus of this manuscript or hasn't been solved by this study. Therefore, I don't think they are directly relevant as part of the introduction. Consider moving it to the methodology section.

We agree with this comment, obtaining accurate data is not coherent in this manuscript. As the answer to the first question of the general comment, to demonstrate the methodology of our study, we have re-constructed the manuscript and have provided a detailed description.

4. Line 95. What does 'construction industry value' mean?

Thank you for pointing this out. The conception of 'construction industry value' is from the Shanghai statistical yearbook. It means the value of all the constructions in Shanghai. This contains various buildings, including residential buildings, office buildings, commercial buildings, and others.

5. Line 102. Three types of models were developed for the assessment, including atmospheric models, ocean models, and coastal models. Consider placing them in the methodology section instead of the data section. Again, more information about these models is expected.

Thank you for the suggestion. We have revised this section and provided more information of the models in section 3.1.

6. Line 126, Table 1. How were the Average Construction Costs calculated? Were different building types weighted? Why is the Average Construction Cost (1157) smaller than the lower bound of the range (1228) for commercial buildings?

Thank you for bringing this to our attention. The comment is correct. Since the inaccurate number of the average construction cost was used, the asset value of buildings, damage estimation, and risk evaluation have all been recalculated. The Average Construction Cost for commercial buildings is updated to 1407 USD/m². The revised text reads as follows on:

Table 1. Common construction costs of various buildings in Shanghai.

	Building Type	Construction Cost (USD/m² CFA)	Average Construction Cost (USD/m²)
Residential	Apartments, high rise, average standard	668-740	874
	Apartments, high rise, high end	1554-1697
	Terraced houses, average standard	446-477
	Detached houses, high end	666-740
Commercial	Retail malls, high end	1228-1585	1407
Office	Medium/high rise offices, average standard	868-1156	1157
	High rise offices, prestige quality	1158-1445
Industrial	Industrial units, shell only (Conventional single story framed units)	432-540	486

7. Line 154. The 'W' in 'Where' should be in lowercase.

Thank you for pointing this out. However, the 'W' is not the ‘W’ in ‘Where’. The ‘W’ is a representative variable.

8. Line 154. What does 'surface area of building' mean? Does it include the wall as well? Line 164, Equation 2. f(x) means a function at an element x. However, an x is missing on the right to the equal sign. Please modify the equation and explain what x means. Line 175, Equation 3. More information is needed to explain Equation 3.

Thank you so much for taking the time to write such a thorough comment. As we answered question 2 of the general comments, we have rewritten the functions and have given examples in section 3.2. The revised equation and text read as follows:

Equation 1:

W_n=S_n×P_n (1)

Where W_n (USD) is the asset value for one building which belongs to the building type n, S_n is the surface area of building n, P_nis the average construction cost (USD/m2) for the specific type of building n. The surface area is the whole construction area (including wall area and floor area).

Equation 2:

D_n=E_n×P_n ×T_n (2)

Where D_n represents building damage for building type n, E_n represents the exposed area of buildings for building type n, P_n is the construction cost (USD/m²) for the specific type of building n, T_n represents the damage proportion from stage-damage function for building n under different water-level depths.

Equation 3:

AAL=∫xf(x)dx (3)

Where x is the return period of the flood scenario, f(x) is the damages value of a single type of building.

9. Line 178. Explain Getis-Ord.

Thank you for addressing this point. We combine the Getis-Ord Gi* to the new section “materials and method”. The revised text reads as follows on:

The AAL of all sub-districts and their neighbours were compared with the AAL by Getis-Ord Gi* in ArcMap 10.6. The Getis-Ord Gi*, also known as the hot spot analysis, measures the strength of spatial autocorrelation and tests the assumption of independence between surrounded features (Manepalli et al., 2011). According to the Getis-Ord Gi*, a feature with a positive value and intense clustering of high values, the feature corresponds to hot spot clusters; a feature with a negative value and intense clustering of low values, the feature corresponds to cold spot clusters. The results contain a significant range of high values (hot spots) and low values (cold spots). In our study, hot spots mean the flood risk of a sub-district has a high AAL value and is surrounded by other sub-districts with high values, and has a higher risk for extreme flooding. Cold spots indicate an opposite situation.

Further information about the Getis-Ord is available in the reference: Manepalli, U. R., Bham, G. H., and Kandada, S.: Evaluation of hotspots identification using kernel density estimation (K) and Getis-Ord (Gi*) on I-630, in: 3rd International Conference on Road Safety and Simulation, Indianapolis Indiana, United States, 14 Sep 2011, 14-16, 2011.

10. Line 199. Is the building asset value for the first floor of all four building types?

The building asset value is calculated not only for the first floor, but for all floors. The asset value of the entire building is taken into account in this study.

11. Line 207, Figure 4 (also Line 231, Figure 5 and Line 251 Figure 6). Since the Average Construction Cost is used for each of the four building types, is it true in Figures 4-6 that the buildings with higher 'Building Asset Values' are buildings taking a larger land area?

The answer is yes if we compare 'Building Asset Values' in the same type of building. The reason for this is that the construction costs for the same type of building are the same, and the variable is only the surface area of one building. On the other hand, when we compare the 'Building Asset Values' in different types of buildings, the answer is no. Because there are two variables, one is the cost of construction, and the other is the building’s surface area.

12. Line 329, Table 5. Table 5 provides a comparison of flood adaptation measures in Shanghai. However, how can these measures, especially the soft ones, be reflected in the simulations? The simulation results and the soft adaptation measures are disconnected, and more discussion is needed here.

Thank you for pointing this out. Table 5 provides a comparison of flood adaptation measures in Shanghai, but it is not calculated in the simulations. We aim to indicate some possible and feasible measures that can be adopted in the future to prevent the simulated damages in Shanghai. We agree it is certainly valuable to connect the measures within the simulation, but that is beyond of the scope of this study. Nevertheless, we have discussed this point in the section 5.3 as a direction for further studies.

We hope that with the changes made and the answers provided will sufficiently address your concerns. Thank you for your detailed review helping us to improve our manuscript.

Kind regards,

All Co-authors

Citation: https://doi.org/10.5194/nhess-2021-382-AC1

RC2:
'Comment on nhess-2021-382', Anonymous Referee #2, 04 Jun 2022
Building damage assessment is very important in urban flood risk management. This study presents an assessment of possible exposure and damage losses of buildings in Shanghai. The topic of this study is valuable, and However, the quality and innovation of the current manuscript are not satisfactory. First of all, lots of figures are poor in quality and hard to read. Besides, the building damage assessment method used in this study lack of innovation. In any case, I have a few recommendations that I believe will help the authors to clarify their contribution and improve the readability of the text in a few passages.

Specific comments:

More information on the urban flood modelling by extreme flood scenarios caused by storm surges, precipitation, and fluvial floods, should be provided in the study. For example, what is the detailed combination of storm surges, precipitation, and fluvial floods.

Most of the figures in the manuscript are very poor in quality and hard to meet the standard for this journal, such as Figs. 5, not clear enough.

Table 5 presents comparison of flood adaptation measures in Shanghai, how does it make any sense? Anyway, the discussion in this study seems meaningless.

The building flood damage assessment method used in this study is too simple and lacks the novelty.

Should be Figure 7 and Figure 8 instead of Fig. 7, Fig. 8 in Page 14.

The methods of assessment of building damages in extreme floods used in this study are mainly derived from existing studies, thus what is the main contribution of this study.
Citation: https://doi.org/10.5194/nhess-2021-382-RC2
- AC2:
  'Reply on RC2', Jiachang Tu, 25 Jul 2022
  Dear Reviewer,
  The authors appreciate the reviewer’s time and effort in reviewing the manuscript. We are pleased with the comments and useful suggestions. We have diligently read through the remarks and adjusted the manuscript accordingly.
  Please find our response and revised text blocks (in black) below your comments (in black bold).
  General Comments
  Building damage assessment is very important in urban flood risk management. This study presents an assessment of possible exposure and damage losses of buildings in Shanghai. The topic of this study is valuable, and However, the quality and innovation of the current manuscript are not satisfactory. First of all, lots of figures are poor in quality and hard to read. Besides, the building damage assessment method used in this study lack of innovation. In any case, I have a few recommendations that I believe will help the authors to clarify their contribution and improve the readability of the text in a few passages.
  We are grateful for the reviewer’s general comment on the study. Flood risk assessment approaches are widely implemented in many regions and the approaches themself have been well developed. However, 1) there is not sufficiently flood risk assessment in Shanghai; 2) the possibility of extreme flooding has not been addressed; 3) the economic section (such as buildings) is underdeveloped and overlooked. Above all, to make the innovation and emphasis of our work clearer, we rewrote and reorganized the following three portions of the manuscript:
  Flood risk assessment based on low probability-high impact flood scenarios on buildings (residential, commercial, office, and industrial buildings) in Shanghai have not been sufficiently analyzed so far. Evidence on low probability-high impact events however become increasingly relevant due to the effects of climate change on hazard occurrences and the need for sustainable adaptation measures. We stressed it in the abstract to announce our statement at the outset (abstract. Line 1-5):
  
  “Flood risk assessment is crucial in decision making, especially in protecting enormous wealth megacities from low probability-high impact flood events. Plenty of various measures have been taken to mitigate flood risk in Shanghai, including the construction of sea dikes and floodwalls. However, the combined effects of intensified rainstorms, sea-level rise, land subsidence, and rapid urbanization are exacerbating potential flood risk in this fast-developing coastal city. In light of these changes, […]”
  On the other hand, we rewrote and rearranged the literature review presented in the first paragraph of the introduction of the article presenting based on the framework and necessities of flood risk assessment (p.1. Line 1-5):
  “Integrated flood risk assessments, which have been used in different cities, make the assumption that flood defenses may fail and acknowledge the impossibility of completely preventing floods (De Moel et al., 2015; Sairam et al., 2021). A city-scale flood risk assessment improves the understanding of water supply (Yang et al., 2013), health care (Paterson et al., 2018), infrastructure maintenance (Yang, 2020; Bubeck et al., 2019), etc., given their importance to society, the economy, emergency management and reconstruction (De Moel et al., 2015).”
  de Moel, H., Jongman, B., Kreibich, H., Merz, B., Penning-Rowsell, E., and Ward, P. J.: Flood risk assessments at different spatial scales, Mitigation and Adaptation Strategies for Global Change, 20, 865-890,doi: 10.1007/s11027-015-9654-z, 2015.
  Sairam, N., Brill, F., Sieg, T., Farrag, M., Kellermann, P., Nguyen, V. D., Lüdtke, S., Merz, B., Schröter, K., Vorogushyn, S., and Kreibich, H.: Process-Based Flood Risk Assessment for Germany, Earth's Future, 9, doi: 10.1029/2021EF002259, 2021.
  Yang, L., Zhang C, C., and Wambui Ngaruiya, G.: Water supply risks and urban responses under a changing climate: A case study of Hong Kong, Pacific Geographies, 39, 9-15, 2013.
  Bubeck, P., Dillenardt, L., Alfieri, L., Feyen, L., Thieken, A. H., and Kellermann, P.: Global warming to increase flood risk on European railways, Climatic Change, 155, 19-36, 10.1007/s10584-019-02434-5, 2019.
  
  After reviewing the current literature on what each flood scenario is like in Shanghai (general comments from #1Reviewer), we found flood scenarios and their hydrology impacts/situations have been widely developed and discussed. But, the extreme compound flood scenario, for example, over a 1000-year return period, has not been considered (p.5. Line 57-69):
  
  “Reviewing the current literature shows that various flood modelling techniques and scenarios have been created and validated to measure flood risks in Shanghai. Coastal floods (storm surges from the Shanghai coast) and fluvial floods (river floods from the Huangpu River) are the two types of floods focused on Shanghai. Coastal flood scenarios from the Shanghai coast are presented by forecasting in 2030 and 2050 (Yin et al., 2011), 2030, 2050, and 2100 (Wang et al., 2012) in Shanghai, respectively. These scenarios have examined the effects of factors like sea level rise and storm surge (Yin et al., 2011), or integer effects like sea level rise, land subsidence, and storm surge (Wang et al., 2012). In addition to examining the effects on the Shanghai coast, Yin et al. (2013) look at how sea level rise and subsidence combine with storm tide-induced river flooding in the Huangpu River floodplain in 2030 and 2050. Yin et al. (2015) used a 2D hydrodynamic model to estimate 1/10, 1/100, and 1/1000-year flood scenarios in the Huangpu River floodplain in Shanghai based on historical floodplain data. The flood scenarios produced in most existing studies tended to focus on the possible future flood scenario changes rather than extreme events, e.g., the concern floods over a 1000-year return period (Yin et al., 2011; Wang et al., 2012; Yin et al., 2013, Yin et al., 2015). Therefore, Shanghai demands a low probability compound flood scenario that combines fluvial and coastal flooding.”
  Yin, J., Yin, Z. E., Hu, X. M., Xu, S. Y., Wang, J., Li, Z. H., Zhong, H. D., and Gan, F. B.: Multiple scenario analyses forecasting the confounding impacts of sea level rise and tides from storm induced coastal flooding in the city of Shanghai, China, Environmental Earth Sciences, 63, 407-414, 10.1007/s12665-010-0787-9, 2011.
  Wang, J., Gao, W., Xu, S. Y., and Yu, L. Z.: Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China, Climatic Change, 115, 537-558, 10.1007/s10584-012-0468-7, 2012.
  Yin, J., Yu, D. P., Yin, Z. N., Wang, J., and Xu, S. Y.: Modelling the combined impacts of sea-level rise and land subsidence on storm tides induced flooding of the Huangpu River in Shanghai, China, Climatic Change, 119, 919-932, 10.1007/s10584-013-0749-9, 2013.
  Yin, J., Yu, D. P., Yin, Z. N., Wang, J., and Xu, S. Y.: Modelling the anthropogenic impacts on fluvial flood risks in a coastal mega-city: A scenario-based case study in Shanghai, China, Landscape and Urban Planning, 136, 144-155, 10.1016/j.landurbplan.2014.12.009, 2015.
  In this study, we classified building types and adopted the actual construction costs of various buildings. In this way, we are able to assess exposed groups and potential damages on small-scale on regional scale. The high level of detail characterizing the exposed objects poses an innovation compared to previous studies which usually do not divide between different building classes and storey heights.
  
  To address our goal clearly, we changed the article title from “Assessment of building damages and adaptation under extreme flood scenarios in Shanghai” to “Assessment of building damages and risk under extreme flood scenarios in Shanghai”.
  We further re-edited the figures to make them easier to read.
  Please see below, for a point-by-point response to the reviewers’ comments and concerns.
  Specific comments:
  More information on the urban flood modelling by extreme flood scenarios caused by storm surges, precipitation, and fluvial floods, should be provided in the study. For example, what is the detailed combination of storm surges, precipitation, and fluvial floods.
  
  Thank you for this point. The methodology and details of the flood scenario have been published by the co-authors in the paper Wang et al 2019. We revised our manuscript and clarified the scenarios in the methodology. The revised text reads as follows (p.11. Line 115-123):
  “The study builds on the results of Wang et al. (2019), which applied a hydrodynamic modelling approach to simulate compound flooding for the region of Shanghai. Four scenarios with return periods of 200, 500, 1000, and 5000 years were simulated considering storm surge, extreme precipitation, high tide, and river flooding at a resolution of 60 m. For this purpose, several models were applied and coupled: 1) the Fujita model simulating the atmospheric conditions; 2) the TELEMAC model simulating ocean movement; 3) the TOMAWAC model simulating the propagation of waves, and 4) the MIKE 21 model simulating the hydraulic processes.
  These models were calibrated using rainfall and river discharge measurement data from Typhoon Winnie. Typhoon Winnie brought the highest recorded water level of 5.72 meters since 1900, which caused the collapse of 148 meters of floodwalls and overflowed 57 km of floodwalls and 69 km of sea dikes.”
  Wang, L., Zhang, M., Wen, J., Chong, Z., Ye, Q., and Ke, Q.: Simulation of extreme compound coastal flooding in Shanghai, Advances in water science, 30 (04), 546-555, 2019.
  Most of the figures in the manuscript are very poor in quality and hard to meet the standard for this journal, such as Figs. 5, not clear enough.
  
  Thank you for this point, we re-export the figure and improved the quality with clearer colors.
  Table 5 presents comparison of flood adaptation measures in Shanghai, how does it make any sense? Anyway, the discussion in this study seems meaningless.
  
  Sorry that the discussion section did not well present its values. The discussions attend to address the threat of extreme flood events and their simulation results. We also narrate the potential flood adaption techniques and the discrepancy between the master plan and the academic result. The discussion would be helpful in providing information to the decision-makers and a statement for the researcher to simulate the flood scenario in Shanghai in the future. We significantly revised the section and concentrate on discussing two aspects:
  Section 5.1 analyzed the uncertainty and limitations of the study, and further analyzed the direction to enhance the model performances. The suitability of transferring the model to other study areas is also discussed (p.28. Line 265-296):
  
  “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, office, and industrial buildings under the 1/5000-year flooding scenario is more than ten times higher than the resulting damages for 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 […]
  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 of buildings and the associated properties. For instance, […]
  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 megacity 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. […] Second, the classification of different types of buildings is quite straightforward based on the land use/land cover data. […] In the end, the methodology of four extreme flood scenarios in Shanghai were taken from published models in Shanghai that are induced by the current physical environment […]”
  Section 5.2 discusses the future challenges and adaptation strategies in Shanghai (p.31. Line 299-330):
  
  “Future challenges, like extreme flood events, will become more common in Shanghai. The historical data shows how Shanghai’s extreme precipitation events have increased dramatically through time (Wang and Zhou, 2005; Liang and Ding, 2017), which increases the possibility of seawall and levee failures. One 1/1000-year return period flood occurred in Shanghai in 2013, […].
  Reviewing these findings from our risk assessment and highlight the following two points, which in our opinion, might be helpful for advancing the flood risk assessment in the future in Shanghai, or even more broadly in China.
  First, the parameters (such as flood hazard maps, damage models, exposure data, etc.) to help to do the flood risk assessment in Shanghai that is available to the public are not consistent and scarce. Flood hazard maps can be an example. […].
  Second, effective adaptation to increasing flood risks requires an integrated climate response strategy, which shall include a broad scope of intervention measures such as urban planning, structural flood management measures, early warning systems, nature-based solutions, flood awareness and risk financing instruments (Yang et al., 2015; Jongman, 2018; UN, 2020). In table 6, we list potential hard, soft, and hybrid implementation measures and their assumed efficacy in Shanghai. […]”
  Wang, Y. Q. and Zhou, L.: Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large‐scale circulation, Geophysical Research Letters, 32, 10.1029/2005GL022574, 2005.
  Liang, P. and Ding, Y. H.: The Long-term Variation of Extreme Heavy Precipitation and Its Link to Urbanization Effects in Shanghai during 1916-2014, Advances in Atmospheric Sciences, 34, 321-334, 10.1007/s00376-016-6120-0, 2017.
  Yang, L., Scheffran, J., Qin, H. P., and You, Q. L.: Climate-related flood risks and urban responses in the Pearl River Delta, China, Regional Environmental Change, 15, 379-391, 10.1007/s10113-014-0651-7, 2015.
  Jongman, B.: Effective adaptation to rising flood risk COMMENT, Nature Communications, 9, 3, 10.1038/s41467-018-04396-1, 2018.
  United Nations (UN).: United Nations World Water Development Report 2020: Water and Climate Change, 2020.
  The building flood damage assessment method used in this study is too simple and lacks the novelty.
  
  Thank you for bringing this up. As the authors clarified in their general comment, the flood risk assessment of buildings in Shanghai is thoroughly described in our paper, along with the findings. As part of the methodology, compound flood hazard modelling was done while taking storm surges, precipitation, tides, and river flooding into consideration. The precise building flood loss and risk assessment can be assessed using flood hazard modeling. In addition, based on the type of building and its construction costs, we are able to evaluate exposed, damage and risk.
  Should be Figure 7 and Figure 8 instead of Fig. 7, Fig. 8 in Page 14.
  
  We have checked all figures and tables in the manuscript and updated them on this point.
  The methods of assessment of building damages in extreme floods used in this study are mainly derived from existing studies, thus what is the main contribution of this study.
  
  Thank you for the comment. On the general response and response number 4, we responded to this comment.
  We hope that the changes made and the answers provided will sufficiently address your concerns. Thank you for your recommendations which helped us to improve our manuscript.
  Kind regards,
  All Co-authors
  
  Citation: https://doi.org/10.5194/nhess-2021-382-AC2

Jiachang Tu et al.

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Month	HTML	PDF	XML	Total
Dec 2021	120	27	3	150
Jan 2022	147	30	9	186
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Mar 2022	27	13	3	43
Apr 2022	40	26	2	68
May 2022	34	11	3	48
Jun 2022	36	23	3	62
Jul 2022	27	11	2	40
Aug 2022	23	28	0	51
Sep 2022	25	26	1	52
Oct 2022	12	25	0	37
Nov 2022	36	19	0	55
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Short summary

The study finds the outcome of building damage and its vulnerable pattern under extreme flooding events, which is higher than the present protective level of seawalls and levees in Shanghai, based on the geography-spatial tool and high-resolution information. The study shows that residential building is the most vulnerably building due to the amount of number and asset value. The Wusongkou area has the highlight concerning due to the vulnerable hotspot area for the four types of buildings.


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