Risk analysis and modeling

The Risk, Policy and Vulnerability (RPV) Program built on its breakthrough in assessment of flood-risk distributions which accounts for spatial correlation between river basins and therefore avoids underestimation of risk. The program has now incorporated different types of copula dependency measures, such as the Archimedian- and Frank-based copula, and various dependency structures.

© slametxiryuu | Dreamstime

© slametxiryuu | Dreamstime

Using statistical dependency properties based on discharge data can reproduce specific riverine structures very well, a finding which is particularly useful when only limited geographical information is available. RPV’s advanced copula approach is now a feature of the Catastrophe Simulation (CATSIM) model, and has been used for numerous applications across Europe and Asia in 2015.

In Romania, based on maximum discharge data of river basins and stream networks, RPV researchers investigated different ways to couple loss distributions of basins while explicitly incorporating tail dependencies [1]. They distinguished between coupling methods that require river structure data for the analysis and those that do not. For the latter, they propose a “minimax” algorithm to choose coupled basin pairs so that the underestimation of risk is minimized.

Using RPV’s copula approach, a participant in the IIASA Young Scientists Summer Program, assessed current and future hydrological drought risk in the urban water supply system in London, UK, which was stress-tested across a wide range of drought conditions [2]. The researcher developed a stochastic stream flow generation technique to test the London water system’s vulnerability to drought. By using a copula-based approach, droughts with longer durations and larger deficits than the observed records could be generated. This is especially relevant for the future assessment of and adaptation to climate change.

A new revision of the CATSIM model, which included updated risk information, was applied to a case in Madagascar [3] and, as part of the UN Office for Disaster Risk Reduction working paper series, to five countries in the south-west Indian Ocean [4][5][6][7][8][9]. The team also undertook a detailed investigation of the use of the new CATSIM module on time-dependent damage estimation for stakeholder processes in Cambodia [10].

The revised CATSIM analyses also informed the Global Risk Assessment report [11] by analyzing current and future financial risk due to a broad set of sudden-onset natural hazard events on the global scale. This included an innovative assessment of the potential benefits of a disaster risk management strategy for “building back better”—a principle for improving resilience by implementing better structural design after a disaster.

References

[1] Timonina A, Hochrainer-Stigler S, Pflug G, Jongman B & Rojas R (2015). Structured coupling of probability loss distributions: assessing joint flood risk in multiple river basins. Risk Analysis, 35(11):2102-2119.  

[2] Borgomeo E, Pflug G, Hall JW & Hochrainer-Stigler S (2015). Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit. Water Resources Research, 51(11):8927-8948. 

[3] Hochrainer-Stigler S, Mechler R & Mochizuki J (2015). A risk management tool for tackling country-wide contingent disasters: A case study on Madagascar. Environmental Modelling & Software, 72:44-55. 

[4] Leste-De Perindorge P, Ishikagi K, Moriniere L, Egan C, Mochizuki J,Hochrainer-Stigler S, Mechler R & Williges K (2015). Public investment planning and financing strategy for disaster risk reduction: review of Mauritius. UNISDR Working Papers, pp.121, UNISDR, Geneva Switzerland.  

[5] Zarine WD, Ishikagi K, Moriniere L, Egan C, Mochizuki J, Hochrainer-Stigler S, Mechler R & Williges K (2015). Public investment planning and financing strategy for disaster risk reduction: review of Seychelles. UNISDR Working Papers, pp. 109, UNISDR, Geneva Switzerland.  

[6] Hendriksen G, Ishikagi K, Moriniere L, Egan C, Mochizuki J, Hochrainer-Stigler S, Mechler R & Williges K (2015). Public investment planning and financing strategy for disaster risk reduction: review of Zanzibar. UNISDR Working Papers, pp. 117, UNISDR, Geneva Switzerland.  

[7] Lazamanana AP, Ishikagi K, Moriniere L, Egan C, Mochizuki J, Hochrainer-Stigler S, Mechler R & Williges K (2015). Public investment planning and financing strategy for disaster risk reduction: review of Madagascar. UNISDR Working Papers, pp.106, UNISDR, Geneva Switzerland.  

[8] Ishikagi K, Mochizuki J, Hochrainer-Stigler S, Mechler R, Williges K, Egan C, et al. (2015). Review of South-West Indian Ocean Region. UNISDR Working Papers on Public Investment Planning and Disaster Risk Reduction, UNISDR, Geneva, Switzerland.

[9] Ishikagi K, Moriniere L, Fatouma A, Soilihi S, Kadafi S, Egan C, Mochizuki J,Hochrainer-Stigler S, Mechler R & Williges K (2015). Public investment planning and financing strategy for disaster risk reduction: review of Union des Comores. UNISDR Working Papers, pp. 93, UNISDR, Geneva Switzerland.

[10] Mochizuki J, Vitoontus S, Wickramarachchi B, Hochrainer-Stigler S,Williges K, Mechler R & Sovann R (2015). Operationalizing iterative risk management under limited information: fiscal and economic risks due to natural disasters in Cambodia. International Journal of Risk Science, 6(4): 321-334. 

[11] Williges K, Hochrainer-Stigler S, Mochizuki J & Mechler R (2015)

Modeling the indirect and fiscal risks from natural disasters for informing options for enhancing resilience and building back better. Background Paper prepared for the 2015 Global Assessment Report on Disaster Risk Reduction, UNISDR, Geneva, Switzerland. 


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Last edited: 10 May 2016

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