Quantitative risk assessment of storm surge using GIS techniques and open data: A case study of Daya Bay Zone, China

Highlights

• The risk assessment was made by integrating three components with GIS techniques.

• It is the first to make a risk assessment based on depth-damage functions in China.

• The quantitative risk can be utilized to make a cost-benefit analysis.

• The risk zonation maps are helpful to make strategies based on different risk levels.

• The analysis and maps have been used in practice by the local decision-makers.

Abstract

Storm surge is a natural disaster, often causing economic damage and loss of human life in the coastal communities. In recent decades, with more people attracted to coastal areas, the potential economic losses resulted from storm surges are increasing. Therefore, it is important to make risk assessments to identify areas at risk and design risk reduction strategies. However, the quantitative risk assessment of storm surge for coastal cities in China is often difficult due to the lack of adequate data regarding the building footprint and vulnerability curves. This paper aims to provide a methodology for conducting the quantitative risk assessment of storm surge, estimating direct tangible damage, by using Geographical Information System (GIS) techniques and open data. The proposed methodology was applied to a coastal area with a high concentration of petroleum industries in the Daya Bay zone. At first, five individual typhoon scenarios with different return periods (1000, 100, 50, 20, and 10 years) were defined. Then, the Advanced Circulation model and the Simulating Waves Nearshore model were utilized to simulate storm surge. The model outputs were imported into GIS software, transformed into inundation area and inundation depth. Subsequently, the building footprint data were extracted by the use of GIS techniques, including spatial analysis and image analysis. The layer containing building footprints was superimposed on the inundation area layer to identify and quantify the exposed elements to storm surge hazard. Combining the exposed elements with their related depth–damage functions, the quantitative risk assessment translates the spatial extent and depth of storm surge into the estimation of economic losses. The quantitative risk assessment and zonation maps for sub-zones in the study area can help local decision-makers to prioritize the sub-zones that are more likely to be affected by storm surge, make risk mitigation strategies, and develop long-term urban plans.

Introduction

Storm surge associated with tropical cyclones is a destructive natural disaster in terms of economic losses to many coastal regions of the world (UNISDR, 2015). During the period from 1949 to 2016, the direct economic loss resulted from tropical cyclone-induced storm surges reached approximately 4952 billion dollars (USD) worldwide (Jin, 2018). In China, storm surges have caused about 44 yearly deaths on average and annual average direct economic losses approximately amounted to RMB 10.17 billion between 1998 and 2019, according to statistical data from the Chinese Department of Natural Resources. The casualties and related economic losses resulted from storm surges are likely to increase in the future because of some factors, including sea-level rise, growing population residing in the coastal strip, and industrial development in coastal areas (Oppenheimer et al., 2019; Neumann et al., 2015; Merkens et al., 2016; Snaiki et al., 2020). With increasing economic losses, reducing storm surge risks for coastal communities is becoming more important for decision-makers at the local government level. To that end, storm surge risk reduction policies-based risk assessment and mapping can effectively mitigate risks and substantially alleviate the impacts of storm surge. The risk assessment and maps provide detailed information regarding the possible inundation areas induced by the storm surge and the spatial distribution of storm surge risk over the geographical area, which decision-makers can take into account in planning the evacuation route and in designing land use plans to minimize risks. Generally, the two most common methods of assessment and mapping are the quantitative risk assessment approach and the risk matrix approach. (Aerts et al., 2018; Shreve et al., 2014; Murnane et al., 2016).

The quantitative risk assessment approach allows damage and risk to be quantified in monetary value as the combination of the temporal probability of a hazard and its negative consequence, where the consequence is the interaction between the geographic coverage of the hazard, the exposure of elements to the hazard, and the vulnerability of exposed elements (Granger, 2003; Kron, 2005; EC, 2007; Armenakis and Nirupama, 2013; Yang et al., 2020; Adnan et al., 2020). When the quantitative risk assessment approach is used to assess storm surge risk, the geographic coverage of the hazard refers to the inundation area and inundation depth of storm surge, the exposure of elements to the hazard corresponds to the quantification of elements being inundated, and the vulnerability of exposed elements describes the degree of exposed elements being direct tangible damage (the direct damage quantified in monetary value). Thus, the consequence in terms of total economic losses caused by storm surge can be calculated based on the inundation area of storm surge, the quantification of the inundated elements, and the degree of exposed elements being direct tangible damage.

In the procedure of performing quantitative risk assessment, the quantification of elements at risk and the estimation of the direct tangible damage are the essential and challenging steps. In respect of the quantification of elements at risk, building footprint data, which provide basic information including location, boundary, and size of each building in a given element, is necessary for identifying and quantifying elements at risk. With regard to the estimation of the direct tangible damage, intensity–damage functions, which represent the correlation between the intensity of storm surge and the degree of damage, are the essential data. For intensity–damage functions, the intensity can be the flow velocity, direction, or duration (Thieken et al., 2005). Generally, the water depth is regarded as the most common intensity to calculate the direct tangible damage for a set of exposed elements (Merz et al., 2004, 2010; Thieken et al., 2007). These depth–damage functions, also called vulnerability curves, are not site-specific, and there is a wide variety of regional-based depth–damage functions to assess vulnerability worldwide. In the US, the popular models containing depth–damage functions are the HAZUS (Federal Emergency Management Agency, 2015) and HEC-FDA (U.S. Army Corps of Engineers, 2016). In Europe, many models such as Multi-Coloured Manual, FLEMO, and Damage Scanner have been applied to perform the quantitative risk assessment (Moel et al., 2012; Kreibich et al., 2017; Penning-Rowsell et al., 2014).

However, to our knowledge, few works develop depth–damage functions for estimating the direct tangible damage consequent to the impact of storm surge in China. Even fewer data regarding building footprints for Chinese cities used to identify and quantify the elements at risk are open and accessible. Due to the lack of national depth–damage functions and the limited building footprint data, Chinese experts in disaster prevention and mitigation domain proposed a methodology in a standard guideline used for conducting a qualitative risk assessment of storm surge (Ministry of Natural Resources of the People's Republic of China, 2012). The guideline recommends that land use types rather than exposed elements are utilized to make exposure assessment, and vulnerability of land use types, which are assigned the values from 0 to 1 based on experts' judgment, instead of depth–damage functions are employed to evaluate vulnerability levels. The qualitative risk assessment and mapping of storm surge is implemented by the spatial overlay of inundation depth and vulnerability levels using the risk matrix approach. These storm surge risk maps can provide a basis for identifying the areas needing attention and for discussing risk reduction strategies. The methodology for carrying out a qualitative risk assessment and mapping of storm surge has been extensively adopted in many Chinese coastal cities (Fang et al., 2016; Xianwu et al., 2020). Nonetheless, the risk matrix approach is subjective and not very reliable because vulnerability values can be defined in different ways by experts. Moreover, the qualitative risk assessment of storm surge cannot provide information on damages and risks in monetary terms used in cost-benefit analyses.

In the light of the above, the purpose of the present work is to propose a methodology for conducting a quantitative risk assessment of storm surge, estimating direct tangible damage, by the use of the Geographical Information System (GIS) software and open data, based on depth–damage functions. The building footprint data used for identifying elements at risk are extracted from the open data and the potential monetary damages are calculated by using depth–damage functions proposed by Huizinga (T Huizinga et al., 2017) for different types of buildings in Chinses cities. The area under study is the Daya Bay Economic and Technological Development Zone (hereinafter referred to as the Daya Bay Zone), China. The quantitative risk zonation map and analysis of the study area during a specific typhoon intensity can help decision-makers to identify the high-risk sub-zones, support appropriate evacuation strategies, and plan sustainable economic development. The rest of the paper is organized as follows: Section 2 presents the study area and data; Section 3 depicts the methodology for making a quantitative risk assessment, followed by results and discussion in Section 4; the conclusion of the study is described in Section 5.