Climate Change Risk Research: A Case Study on Flood Disaster Risk in China

Abstract

This paper discusses theories and methods of climate change risk studies for the research expansion in China. Climate change risks consist of three basic components including sensitivity, exposure, and possibility. Uncertainty, future events, damages, and relativity are the major features of climate change risk. Climate change risk research includes two key steps: risk assessment and risk management, the former is the process, and the latter is the ultimate goal which is the basis for actions to address climate change. We present the main framework and methods for climate change risk research. A case study on China’s floods risk is taken as an example of climate change risk study. Finally, we point out main aspects of climate change risk research, including ensemble-based probabilistic projection, quantitative risk assessment, risk zoning and mapping, and risk management.

Keywords

climate change ; risk assessment ; risk management ; uncertainty

1. Introduction

At present, global climate change research has made great progress worldwide. This includes scientific foundation [ IPCC , 2007a ], impacts and vulnerability, adaptation [ IPCC, 2007b ] and mitigation [ IPCC, 2007c ]. Scientifically estimating the possible impacts of future climate change is the prerequisite to carry out adaptation actions. At an early stage, the “increasing scenario” is often used to project the future impacts of climate change [Xu et al. , 2005] . With the Special Report on Emission Scenarios (SRES) by the IPCC, the increasing scenario is gradually substituted by special emission scenarios, projecting the possible impacts under different climate scenarios at different time intervals based on various models [Nakićenović et al. , 2000] . However, due to the uncertainty of climate scenarios, model parameters and social economic scenario, there is still much difficulty in the research on impacts and vulnerability of future climate change. Thus, it is hard for people to effectively address, manage, and adapt to climate change according to the negative impacts [ IPCC , 2001 ]. How to assess future climate change impacts quantitatively, and how to reduce the uncertainty are the key questions of the research on climate change risk.

Risk is the combination of the occurrence possibility of an adverse event and its consequences [ Liu et al ., 2005  ;  ISO, 2009 ]. Assessing climate change risk and carrying out targeted risk management actions is an effective way to address climate change. The IPCC Fourth Assessment Report assumes that climate change risk is another important research content after impacts, adaptation, vulnerability and integrated research [ IPCC , 2007b ]. As a decision-oriented not research-driven effective framework, the application of the climate change risk management has developed rapidly in the world [Willows andWillows and Connell, 2003 ; Lorenzoni et al ., 2005  ; Moench, 2007 ; Jones and Preston, 2010  ;  Ranger et al ., 2011 ].

Lots of researches on climate change risk have already carried out worldwide. For instance, the IPCC defines climate change risk as the combination of the occurrence possibility of adverse climate events and its consequences [ IPCC, 2007b ]. The research report of the World Bank suggests that climate change risk is the uncertainty associated with the consequences of climate variability or climate change in a specific area of interest [WB , 2006] . Many countries such as Australia and New Zealand have already regarded the assessment and management of climate change risk as a vital component of their national strategies for being adapted to climate change [ SASNZ, 2004 ; AGO, 2006  ;  Jones, 2010 ]. From the perspective of Chinese scholars, they are also aware of the importance and necessity of the research on climate change risk and call for research on climate change in China systematically [Zhang et al. , 2008] . This paper elaborates on the connotation of climate change risk and key elements, it further discusses the theoretical basis and methods of the research on climate change risk and presents a research framework on climate change risk assessment and management by combining actual case studies, and finally points out the key direction of the research on climate change risk.

2. Analysis of the climate change risk concept

The general definition of climate change risk includes: extreme climate events, the possibility of occurrence of future adverse events, possible losses due to climate change, and the probability of possible losses. The International Strategy for Disaster Reduction of the United Nations defines the risk of natural hazards as the possibility of the occurrence of a harmful consequence and expected losses arising from the interaction between the natural or artificial disaster and hazard bearing body’s vulnerability [ISDR, 2004] . In 2009, the International Organization for Standardization (ISO) defines risk as the combination of the possibility of the occurrence of one or many events and its consequence [ISO, 2009] . By combining the general concepts of risk and former studies, we define climate change risk in this paper as “the possible loss of social economy or resources and environment resulting from the fact that climate change exceeds a certain threshold”. Climate change risk includes two basic elements: damage degree of climate change to the system, namely the degree of negative influence; and the possibility of loss occurrence, of which the degree of negative influence is mainly decided by the sensitivity of the system to the climate change and the system exposure degree. The assessment model of climate change risk can be expressed as:

$R_{cc}=f(S,E,P),$

( 1)

where, R_CC refers to the climate change risk of the system; S refers to the sensitivity of the system’s response to climate change; E refers to the system exposure degree; and P refers to the possibility of loss occurrence.

Sensitivity is the system’s response degree and sensitivity degree towards climate change. This response might be harmful but also useful. In the climate change risk research, we mainly pay attention to the harmful responses. In general, the higher climate change sensitivity, the more easily the system is negatively impacted by climate change and the probability of risk is bigger. The system’s sensitivity to the adverse event of climate change also can be called the damage to the system by climate change. Exposure indicates the degree of regional system’s exposure to the climate. The higher the exposure degree, the bigger is the risk of negative impacts of climate change. The possibility indicates the probability of the occurrence of the adverse event. The possibility assessment is one of the key contents of the research on climate change risk. It mainly originates from the uncertainty of climate change, including different emission scenarios and climate models. Nowadays, scientists have already made attempts to estimate different warming degrees and possibility of their consequences based on multiple models and scenarios by adopting the method of Bayesian probability [ Jones and Preston, 2010  ;  Jones, 2010 ]. Based on probability estimation, the estimation of overall expectation for system’s adverse consequences brought by climate change can effectively forecast climate change risk and reduce the uncertainty of the sole research of different scenarios and models.

Climate change risk has the following characteristics: 1) Uncertainty, which is the basic characteristic of the risk and the possibility of losses due to future climate change; 2) Risk of the future event; 3) Damage, which is the negative influence of climate change. Generally, the possible negative influence of climate change is called climate change risk; and 4) Relativity (variability), which is relative range of high and low climate change risk. As for the same system, in different regions and at different time intervals, the degree of climate change risk can vary greatly.

3. Research framework of climate change risk

Climate change risk research includes two key steps: risk assessment and risk management. Risk assessment is the basis for the risk management, and assesses the degree of negative impact and its possibility which might be caused by future climate change. Risk management is the ultimate objective of the risk assessment and the process of reducing the losses resulting from climate change to the greatest extent through various pathways and technical measures.

The research framework of climate change risk includes the following procedures (Fig. 1 ):

Figure 1. Research framework of climate change risk

(1) Stakeholders’ investigation. With stakeholders’ investigation an understanding of the demands of climate change risk management by different entities and stakeholders is achieved. The objects that different stakeholders focus on are not the same. This step is easily neglected during the process of practical research. However, as long as the demands of different stakeholders are fully understood, the risk assessment and management become more relevant.

(2) Identification of exposure unit. The main purpose of this procedure is to determine the risk hazard bearing body and identify the system units exposed to the climate system, like the ecosystem, water resource, and social economic system. The exposure unit also might be a certain regional unit. After identifying the exposure units, we select an appropriate indicator system and assess the system’s exposure which might be impacted by climate change.

(3) Identification of key climate factors. The main purpose of this procedure is to identify the key factors influencing the climate within the exposure unit. There might be differences in the key climate factors of the different exposure units, such as the key climate factor of flood risk might be precipitation intensity while the key climate factor of high temperature risk might be air temperature.

(4) Establishing climate change scenario. The objective of risk analysis is to perform quantitative analysis of the relationship between the impact’s threshold value and uncertainty range. Therefore, it is necessary to establish different climate change scenarios. Generally, the social economic development scenario given by the IPCC is adopted.

(5) Sensitivity analysis. Here, the response degree of different systems to the main climate change factors, like changes possibly occurring under the scenario of 1°C warming, is identified.

(6) Key threshold values. Determining key threshold values is the key procedure of the research on climate change risk. Threshold values are estimated at which climate change reaches a dangerous degree, namely the degree of climate change where it reaches or exceeds the range that the natural or social economic system can bear. When reaching or exceeding this range, losses will appear, for which the critical point, i.e., the key threshold value, has to be determined.

(7) Possibility assessment. The probability of the adverse consequences, resulting from the system reaching or exceeding the key threshold value, can be obtained through probabilistic ensemble forecasting of the different climate change scenarios. The assessment of the possibility is a key difficulty of climate change risk assessment, mainly originating from the uncertainty of the climate scenario.

(8) Risk assessment. Based on the assessments of sensitivity, exposure degree and possibility, the climatic changes in different parameters or regions are assessed. This is followed by the risk mapping of different time intervals and regions.

(9) Risk management. Climate change risk management is the process of decision making and implementing corresponding control measures by combining various factors such as economic and social factors, and the results of the risk assessment. According to the IPCC Fourth Assessment Report, coping with climate change involves repeating risk management processes, which include mitigation and adaptation with considerations to the actual and avoidable climate change losses, co-benefits, sustainability, fairness and attitudes towards risk.

4. Flood disaster risk under future climate change

In most cases, a flood disaster is caused by excessive amounts of precipitation in a certain region during relatively short time. Therefore, its risk degree is closely associated with changes in future precipitation amounts and intensity. In this paper, we assess the flood disaster risk under future climate change in China as a case study.

4.1. Data acquisition and processing

Climate change scenario data is provided by the climate change research group of the Agricultural Environment and Sustainable Development Institute of Chinese Academy of Agricultural Sciences. This research group utilizes the PRECIS regional climate model of the Hadley Center to analyze the 21st century climate change in China under the SRES B2 scenario [Xu et al. , 2006] . The spatial resolution is 0.5°×0.5°. The data of population density and gross domestic products (GDP) under the B2 scenario is from the Greenhouse Gas Initiative scenario database of IIASA^① . The historical flood disaster data (1953–2008) comes from the National Disaster Reduction Center of the Ministry of Civil Affairs of China.

4.2. Assessment method and indices

Five indices are selected, including the torrential rain days, maximum 3-day precipitation, elevation, slope, and distance away from rivers or lakes (buffer zone of rivers or lakes). An integral weighed grade method is utilized to establish a flood disaster hazard assessment model (Eq. 2 ), and to assess the flood hazard of each river basin in the country, namely the possibility of the occurrence of flood.

$H_{F}=R_{d}W_{1}+R_{3}W_{2}+W_{3}/E+W_{4}/G+DW_{5}{\mbox{,}}$

( 2)

H_F refers to the hazard index of flood disaster, R_d , R₃ , E, G and D respectively represent the annual average torrential rain days, annual maximum 3-day precipitation, elevation, slope, and the buffer zone of rivers or lakes. W₁ , W₂ , W₃ , W4, and W₅ refer to the weighted values of the aforementioned indices, respectively. The hazard index of flood disaster was obtained by normalizing the relative factors, which were divided into five grades, 0–0.35, 0.35–0.45, 0.45–0.55, 0.55–0.65, 0.65–1.00.

We select the percentage of population density, GDP density, and agricultural acreage as the characteristic indices of the physical exposure degree of the hazard bearing body, respectively, indicating the exposure units as such as person, social wealth, and agricultural production. Based on the historical disaster database, we obtain and analyze the sensitivity of the flood disaster losses to climate change (mainly referring to the change in precipitation intensity). The combination of sensitivity and exposure degree is the flood hazard bearing body’s vulnerability. Its assessment model is listed as follows:

$V_{F}=0.3444\times D_{POP}+0.3833\times D_{GDP}+0.2722\times P_{F}{\mbox{,}}$

( 3)

where, V_F refers to the vulnerability index of the flood hazard bearing body of the assessment region; D_POP refers to the normalized population density of the assessment region; D_GDP refers to the normalized GDP density of the assessment region; P_F refers to the normalized agricultural acreage percentage of the assessment region.

4.3. Consequence analysis

Through the overlap (multiplying) of the sensitivity to flood disaster hazard (possibility), the hazard bearing body’s exposure degree, and the flood disaster losses to climate change, the flood disaster loss risk degree (standardization 0–1) can be acquired. According to the risk degree value, the flood disaster risk is divided into five grades , from grade V to I means the climate change risk is getting higher, the severest is I and the lightest is V. Figure 2 shows the spatial pattern of the flood disaster risk grades under the SRES B2 scenario in 2021–2050.

Figure 2. The flood disaster risk grade pattern under the SRES B2 scenario in 2021–2050

It can be seen that the area of grade I takes up 7.4% of the gross area of China with 599 counties; the area of grade II takes up 9.2% of the gross area with 509 counties. The regions of grade I mostly center in southern North China, the Huaihe River, the middle and lower reaches of the Yangtze River, and the Pearl River Basin. This indicates that the flood disaster risk of these regions is the highest. Therefore, it is necessary to carry out further management and prevention measures of flood disaster risk due to climate change.

5. Conclusions and prospect

5.1. Main conclusions

Based on the analysis of the concept of climate change risk, the basic framework of climate change research is proposed in this paper. The flood disaster is taken as an example and case research for flood disaster risk assessment under SRES B2 scenario. The main conclusions are as follows.

(1) Sensitivity, exposure degree and possibility are key components of climate change risk. In case of climate change risk assessment, it is of necessity to assess these elements, respectively.

(2) Risk assessment and management are two key steps in the research of climate change risk. The nine necessary procedures include: stakeholders’ investigation, identification of exposure unit, identification of key climate factors, establishing climate change scenario, sensitivity analysis, key threshold values, possibility assessment, risk assessment, and risk management of which risk management is the key issue in adapting to climate change.

(3) According to the analysis of flood disaster risk in China, it is shown that southern North China, the Huaihe River, the middle and lower reaches of the Yangtze River, and the Pearl River Basin are high flood disaster risk regions under SRES B2 scenario.

5.2. Research expectation

We should aware of the fact that research on China’s climate change risk is still weak. As for different sectors and regions, research on climate change risk still remains to be strengthened. To further promote the research on China’s climate change risk, future studies should focus on the following directions.

(1) Ensemble probability forecasting methods of climate change risk. Due to the uncertainty of the climate system, the probability forecasting methods are among the least reliability in climate change risk research. In this paper, only the SRES B2 scenario is selected. Although the B2 scenario is in line with the climate scenario of China’s long- and middle-term development planning, there still exists big uncertainty. In climate change risk assessment, it is of necessity to further explore methods for reducing such uncertainty. Based on existing scenario, more efforts should be made to further develop technical measures for ensemble probability forecasting, to establish ensemble probability forecasting scenario schemes which are based on multi-scenario and multi-model. To strengthen the climate model simulation research, we need to reduce the uncertainty of climate system simulations and improve the technical methods in climate change risk research.

(2) Combined assessment of quantitative risk losses and risk grades. As for climate change risk, not only the quantitative risk losses in different sectors due to climate change shall be estimated, but also the different grades of regional climate change risk shall be identified. In the research on climate change risk of different sectors, the quantitative risk loss assessment is needed to implement adaptation actions effectively. In an integrated risk assessment, risk grades can further manifest the spatial differences of climate change risk.

(3) Climate change risk regionalization and mapping. From the perspective of the national and regional scale, climate change risk mapping and regionalization is a major demand of national coping strategies on climate change. A climate change risk map is the spatial representation of the results of a climate change risk research. Special mapping techniques and designs are the key to an accurate visual interpretation of climate change risks which is significantly meaningful for the decision maker to guide the prevention of climate change risk. It is important to start climate change risk regionalization, based on the analysis of different regional and integrated climate change risk assessments, to formulate an index system of climate change risk regionalization in accordance with risk sources, risk types, and risk grades. Finally, this assists to form a climate change risk region division scheme on the national scale in order to manage and prevent climate change risks.

(4) Risk management-centered adaptation technical system. Adaptation is one key pathway of coping with climate change and is also the main purpose of climate change risk management. In research and action of coping with negative impacts of climate change, it is necessary to intensify the interrelation of climate change and risk management. This is done by strengthening risk research in many aspects such as comprehensive observations, information sharing, climate services, and disaster prevention and reduction. Simultaneously, as for scientific management of climate change risk, it is of necessity to support information technology and decision-making services, to construct climate change risk management information systems, to build climate change information databases and schemes for different climate change scenarios, and to couple climate change risk assessment models based on geographic spatial information technology. Thus, effective real-time assessment of climate change risk can be realized. Compiling climate change risk management plans and storing these in climate change risk management information system to realize the transfer of climate change risk management plans at any time can effectively provide decisionmaking service for governments at all levels to cope with climate change.

Acknowledgements

We would like to give many thanks to the anonymous reviewers for their constructive advice, and also to the editors for their hard work to improve this paper. This study was supported by National Science and Technology Support Program (No. 2012BAC19B10) and Knowledge Innovation Project of the Chinese Academy of Sciences (No. KZCXZ-YW-QO3-01).

References

1. AGO, 2006 AGO (Australian Greenhouse Office); Climate change impacts and risk management: A guide for business and government, Australian Greenhouse Office, Canberra (2006)
2. IPCC, 2001 IPCC; J.J. McCarthy (Ed.), et al. , Climate Change 2001: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press (2001), p. 1032
3. IPCC, 2007a IPCC; S.D. Solomon (Ed.), et al. , Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press (2007), p. 996
4. IPCC, 2007b IPCC; M.L. Parry (Ed.), et al. , Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press (2007), p. 976
5. IPCC, 2007c IPCC; M. Bert (Ed.), et al. , Climate Change 2007: Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press (2007), p. 851
6. ISDR, 2004 ISDR (International Strategy for Disaster Reduction); Living with Risk: A Global Review of Disaster-Reduction Initiatives, United Nations Publication (2004), pp. 1–126
7. ISO, 2009 ISO (International Organization for Standardization); ISO guide 73: 2009 risk management-vocabulary; International Standards Organization (2009), pp. 1–15
8. Jones, 2010 R. Jones; Climate change adaptation in New Zealand: Future scenarios and some sectoral perspectives; R.A.C. Nottage (Ed.), et al. , A risk management approach to climate change adaptation, New Zealand Climate Change Centre (2010), pp. 10–25
9. Jones and Preston, 2010 R.N. Jones, B.L. Preston; Adaptation and risk management: Climate change working paper No. 15; Centre for Strategic Economic Studies, Victoria University (2010), pp. 1–18
10. Liu et al., 2005 Y.-H. Liu, Q.-S. Ge, W.-X. Wu, et al.; Risk Management — Challenge of New Century (in Chinese), China Meteorological Press (2005), p. 430
11. Lorenzoni et al., 2005 I. Lorenzoni, N.F. Pidgeon, R.-E. O’Connor; Dangerous climate change: The role for risk research; Risk Analysis, 25 (2005), pp. 1387–1398
12. Moench, 2007 M. Moench; Adapting to climate change and the risks associated with other natural hazards: Methods for moving from concepts to actions; M. Moench (Ed.), et al. , Working with winds of change, Prevention Consortium, Institute for Social and Environmental Transition, Nepal (2007), pp. 13–48
13. Nakićenović et al., 2000 N. Nakićenović, O. Davidson, G. Davis, et al.; Special Report on Emissions Scenarios (SRES): A Special Report of Working Group III of the IPCC, WMO, UNEP, Cambridge University Press (2000), p. 599
14. Ranger et al., 2011 N. Ranger, S. Hallegatte, S. Bhattacharya, et al.; An assessment of the potential impact of climate change on flood risk in Mumbai; Climatic Change, 104 (2011), pp. 139–167
15. SASNZ, 2004 SASNZ(Standards Australia and Standards New Zealand); AS/NZS Australian/New Zealand standard for risk management: AS/NZS 4360 2004, Standards Australia and Standards New Zealand (2004), pp. 1–38
16. WB, 2006 WB(World Bank); Managing climate risk: Integrating adaptation into World Bank group operations (2006), pp. 1–32
17. Willows and Connell, 2003 R. Willows, R. Connell; Climate adaptation: Risk, uncertainty, and decision making; UK Climate Impacts Programme, Oxford (2003)
18. Xu et al., 2005 Y.-L. Xu, X.-Y. Huang, Y. Zhang, et al.; Statistical analyses of climate change scenarios over China in the 21st century; Advances in Climate Change Research (in Chinese), 1 (2) (2005), pp. 80–83
19. Xu et al., 2006 Y.-L. Xu, Y. Zhang, E.-D. Lin, et al.; Analyses on the climate change responses over China under SRES B2 scenario using PRECIS; Chinese Science Bulletin, 51 (18) (2006), pp. 2260–2267
20. Zhang et al., 2008 Y.-H. Zhang, S.-H. Wu, E.-F. Dai, et al.; New classification method of climate change risks; Geographical Research (in Chinese), 27 (4) (2008), pp. 763–774

Notes

①. Data source: http://www.iiasa.ac.at/Research/GGI/DB/