Skip to main content

Advertisement

Log in

The danger of mapping risk from multiple natural hazards

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

In recent decades, society has been greatly affected by natural disasters (e.g. floods, droughts, earthquakes), and losses and effects caused by these disasters have been increasing. Conventionally, risk assessment focuses on individual hazards, but the importance of addressing multiple hazards is now recognised. Two approaches exist to assess risk from multiple hazards: the risk index (addressing hazards, and the exposure and vulnerability of people or property at risk) and the mathematical statistics method (which integrates observations of past losses attributed to each hazard type). These approaches have not previously been compared. Our application of both to China clearly illustrates their inconsistency. For example, from 31 Chinese provinces assessed for multi-hazard risk, Gansu and Sichuan provinces are at low risk of life loss with the risk index approach, but high risk using the mathematical statistics approach. Similarly, Tibet is identified as being at almost the highest risk of economic loss using the risk index, but lowest risk under the mathematical statistics approach. Such inconsistency should be recognised if risk is to be managed effectively, whilst the practice of multi-hazard risk assessment needs to incorporate the relative advantages of both approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Entropy measures the amount of useful information in the indicator provided. When the difference in one indicator between different assessment units is small, the entropy is great; it illustrates that this indicator provides less useful information, and the weight of this indicator should be set correspondingly small. On the other hand, if the difference is large and the entropy is small, the weight would be big.

References

  • Armonia Project–Applied Multi-Risk Mapping of Natural Hazards for Impact Assessment (2006) Applied multi-risk mapping of natural hazards for impact assessment, report on new methodology for multi-risk assessment and the harmonisation of different natural risk maps. Armonia, European Community, Genova, Italy

  • Bell R, Glade T (2004) Multi-hazard analysis in natural risk assessments. In: Brebbia CA (ed) Proceedings of the 4th international conference on computer simulation in risk analysis and hazard mitigation. WIT Press, Rhodes, pp 197–206

    Google Scholar 

  • Carpignano A, Golia E, Di Mauro C, Bouchon S, Nordvik JP (2009) A methodological approach for the definition of multi-risk maps at regional level: first application. J Risk Res 12(3–4):513–534

    Article  Google Scholar 

  • Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards*. Soc Sci Q 84(2):242–261

    Article  Google Scholar 

  • Di Mauro C, Bouchon S, Carpignana A, Golia E, Peressin S (2006) Definition of multi-risk maps at regional level as management tool: experience gained by civil protection authorities of Piemonte region. In: 5th Conference on risk assessment and management in the civil and industrial settlements. University of Pisa, Italy, 17–19 Oct 2006

  • Dilley M et al (2005) Natural disaster hotspots, a global risk analysis. World Bank, Washington

    Book  Google Scholar 

  • FEMA (2004) Using HAZUS-MH for risk assessment. http://www.fema.gov. Accessed Oct 2010

  • Frolova NI, Larionov VI, Sushchev SP, Bonnin J (2012) Seismic and integrated risk assessment and management with information technology application. In: 15th World conference on earthquake engineering. Lisbon

  • Granger K, Trevor J (2000) A multi-hazard risk assessment. In: Middelmann M, Granger K (eds) Community risk in MacKay: a multi-hazard risk assessment. Australian Geological Survey Organization, Canberra City

    Google Scholar 

  • Grünthal G, Thieken AH, Schwarz J, Radtke KS, Smolka A, Merz B (2006) Comparative risk assessment for the city of Cologne—storms, floods, earthquake. Nat Hazards 38(1–2):21–44

    Article  Google Scholar 

  • Gumbel EJ (1958) Statistics of extremes. Columbia University Press, New York

    Google Scholar 

  • Huang C (1997) Principle of information diffusion. Fuzzy Sets Syst 91(1):69–90

    Article  Google Scholar 

  • Huang C (2000) Demonstration of benefit of information distribution for probability estimation. Signal Process 80(6):1037–1048

    Article  Google Scholar 

  • ISDR (Intentional Strategy for Disaster Reduction) (2004) Living with risk. A global review of disaster reduction initiatives. United Nations Publication, New York and Geneva

    Google Scholar 

  • IUGS (1997) Quantitative risk assessment for slopes and landslides: the state of the art. In: Cruden DM, Fell R (eds) Landslide risk assessment. Balkema, Rotterdam

    Google Scholar 

  • Khatsu P, van Westen CJ (2005) Urban multi-hazard risk analysis using GIS and remote sensing: a case study from Kohima Town, Nagaland, India. In: Proceedings of the 26th Asian conference on remote sensing, Hanoi, Vietnam, 7–11 Nov 2005

  • Kunz M, Hurni L (2008) Hazard maps in Switzerland. In: Proceedings of the 6th ICA mountain cartography workshop, pp 125–130

  • Lavalle C, Barredo JI, Roo AD, Niemeyer S, Miguel-Ayanz JS, Hiederer R, Genovese E, Camia A (2005) Towards an European integrated map of risk from weather driven events. A contribution to the evaluation of territorial cohesion in Europe. Joint Research Centre, European Commission

  • Linares-Rivas A (2012) CAPRA initiative: integrating disaster risk into development policies in Latin America and the Caribbean. http://www.ecapra.org/capra-initiative-integrating-disaster-risk-development-policies-latam. Accessed Oct 2013

  • Liu B, Siu YL, Mitchell G, Xu W (2013) Exceedance probability of multiple natural hazards: risk assessment in China’s Yangtze River Delta. Nat Hazards 69(3):2039–2055

    Article  Google Scholar 

  • Marzocchi W, Mastellone ML, Di Ruocco A, Novelli P, Romeo E, Gasparini P (2009) Principles of multi-risk assessment. Interaction amongst natural and man-induced risks. European Communities, Luxembourg

    Google Scholar 

  • Marzocchi W, Garcia-Aristizabal A, Gasparini P, Mastellone M, Di Ruocco A (2012) Basic principles of multi-risk assessment: a case study in Italy. Nat Hazards 62(2):551–573

    Article  Google Scholar 

  • Miao C, Ding M (2015) Social vulnerability assessment of geological hazards based on entropy method in Lushan earthquake-stricken area. Arab J Geosci 8(12):10241–10253

    Article  Google Scholar 

  • Munich Reinsurance Company (2003) Topics: annual review: natural catastrophes 2002. Munich Re Group, Munich

    Google Scholar 

  • Norio O, Ye T, Kajitani Y, Shi P, Tatano H (2011) The 2011 eastern Japan great earthquake disaster: overview and comments. Int J Disaster Risk Sci 2(1):34–42

    Article  Google Scholar 

  • Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076

    Article  Google Scholar 

  • Rosenblatt M (1956) Remarks on some nonparametric estimates of a density function. Ann Math Stat 27(3):832–837

    Article  Google Scholar 

  • SCEMDOAG (South Carolina Emergency Management Division Office of the Adjutant General) (2009) State of South Carolina hazards assessment 2008. University of South Carolina, South Carolina Emergency Management Division Office of the Adjutant General, Hazards Research Lab, Department of Geography, South Carolina

  • Schmidt J, Matcham I, Reese S, King A, Bell R, Henderson R, Smart G, Cousins J, Smith W, Heron D (2011) Quantitative multi-risk analysis for natural hazards: a framework for multi-risk modeling. Nat Hazards 58(3):1169–1192

    Article  Google Scholar 

  • Schmidt-Thomé P (2006) The spatial effects and management of natural and technological hazards in Europe. European Spatial Planning and Observation Network (ESPON) project 1.3.1, Geological Survey of Finland, Luxembourg

  • Shi PJ (1996) Theory and practice of disaster study. J Nat Disaster 5(4):6–17

    Google Scholar 

  • Shi PJ (2011) Atlas of natural disaster risk of China. Science Press, Beijing

    Google Scholar 

  • Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72–101

    Article  Google Scholar 

  • Stedinger JR, Vogel RM, Foufoula-Georgiou E (1992) Frequency analysis of extreme events. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, New York

    Google Scholar 

  • Tarvainen T, Jarva J, Greiving S (2006) Spatial pattern of hazards and hazard interactions in Europe. In: Schmidt-Thomé P (ed) Natural and technological hazards and risks affecting the spatial development of European regions, vol 42. Geological Survey of Finland, Espoo, pp 83–92

    Google Scholar 

  • Thierry P, Stieltjes L, Kouokam E, Ngueya P, Salley P (2008) Multi-hazard risk mapping and assessment on an active volcano: the GRINP project at Mount Cameroon. Nat Hazards 45(3):429–456

    Article  Google Scholar 

  • UNDP (United Nations Development Programme) (2004) Reducing disaster risk: a challenge for development. United Nations Development Programme, Bureau for crisis prevention and recovery, New York

  • USGS (2012) Deaths from Earthquakes in 2008. http://earthquake.usgs.gov/earthquakes/eqarchives/year/2008/2008_deaths.php. Accessed Oct 2013

  • Van Westen CJ (2008) RiskCity: a training package on the use of GIS for urban multi-hazard risk assessment. In: Sassa D, Canuti P (eds) Proceedings of the first world landslide forum. United Nations University Press, Tokyo, pp 665–668

    Google Scholar 

  • Villagran de Leon JC (2006) Vulnerability: a conceptual and methodological review. The United Nations University, Institute for Environment and Human Security, Bornheim

    Google Scholar 

  • Wang J, Shi P, Yi X, Jia H, Zhu L (2008) The regionalization of urban natural disasters in China. Nat Hazards 44(2):169–179

    Article  Google Scholar 

  • Wipulanusat W, Nakrod S, Prabnarong P (2009) Multi-hazard risk assessment using GIS and RS applications: a case study of Pak Phanang basin. Walailak J Sci Technol 6(1):109–125

    Google Scholar 

  • Wisner B, Blaikie P, Cannon T, Davis I (2004) At risk: natural hazards, people’s vulnerability and disasters, 2nd edn. Routledge, London

    Google Scholar 

  • Zou ZH, Yun Y, Sun JN (2006) Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J Environ Sci 18(5):1020–1023

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yim Ling Siu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Siu, Y.L., Mitchell, G. et al. The danger of mapping risk from multiple natural hazards. Nat Hazards 82, 139–153 (2016). https://doi.org/10.1007/s11069-016-2184-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-016-2184-5

Keywords

Navigation