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Hazard, impact and risk management

Short documentation related to: A. Hazard B. Impact ( vulnerability, exposure, adaptation) C. Risk D. Risk optimization Schematic the four elements are found in the figure. Reference: Components of risk. Source: Schmidt-Thome (2006). In connection with the definition of various categories in A, B and C's one can mention monographic Salvadori et al. (2007) p. 53-59 which give information on the functions that describe the risk factors in both the discrete and continuous spatial representation as: R- risk function; H- hazard function; I- impact function, where : R= R(H, I). H belongs to [0, 1]: H = 0 means a null hazard, H=1, maxim hazard. The hazard is expressed in the probability terms and it is defined as: where f(x) is probability density and F(x) the probability distribution function of the event x. H is an objective variable and independent of I impact and cause different events more or less dangerous and it is expressed in terms of probability. I belongs to [0, 1] interval with the obvious interpretation, I ≈ 0 negligible impact, I ≈ 1 represents a catastrophic impact. Impact of I is a deterministic function depending on the intensity and resistance phenomenon produced object that supports it. In fact the impact transforms hazard in danger and it is associated to this. Obviously the existing domain of R is [0, 1] with R ≈ 0, at low risk and R≈ 1 an extremely risk. The I function impact is difficult to be quantified and it has two main aspects: the exposure and vulnerability V, namely: I = I (e, V). Exposure e represents the potentially dangerous elements; vulnerability V quantifies any damages and losses. Another definition of risk given by UNESCO (Salvadori et al. 2007) is: R = H * e * V where: H -hazard; e-exposure; V-vulnerability; "*" - convolution operator. In my opinion it is more appropriate to associate the column vector H (h1, h2, ... ,hn), quantifying the impact in a line vector I (i1, i2, .... , im) of the product which results in a matrix of size R with (n , m) dimension describing quantified risk. Of course extreme events with the highest values (maximum intensity) are most dangerous when it comes to geophysical phenomena such as earthquakes. But in the geophysical events as climate change in particularly the precipitation example, not only maximum events (most intense) but the lowest ones produce huge losses, such as droughts. Steps to follow, of course, in determining the risk are: - Processing of experimental data in determining the probability of the occurrence of X event; - Determination of the extreme value distributions of X event, e.g. GEV or GP (Katz, et al. 2005) to assess the return period T; - H Hazard assessment (Varnes, (1984), Schmidt –Thomé (2005,2006)); - Impact assessment (Cascini, et al., 2005, Fowler et al. (2007), Fell et al. (2008), Joigneaux et al., (2011)); - Risk assessment: In Kaplan and Garrick (1981) the risk definition is extended to include uncertainty and completness and the use of Bayes’s theorem is described in this connection; Jones (2010) showed that since uncertainty permeates climate change assessments, projects have relied on specialised methods, such as the development and use of climate scenarios. But the uncertainty in predicting future climate is one reason why it is recommendable that adaptation assessment be anchored with an understanding of current climate risk. This helps to provide a road map from known territory into uncertain futures. - Reduce risk by: diminution dangerous effects of the hazard, i.e., by reducing exposure and by the measures to decrease vulnerability (Arnell and Osborn (2006), Beguería and Vicente-Serrano, (2006)). Part D is a special segment of risk assessment based on risk state parameter. In general, R = R (θ, d), namely risk depends on the state parameter θ and the decision function d. From this point of view, the risk R is the expected cost, a minimum value of the experiment, or cost function for one of the state θ and leading to decision d (Murphy et al., 1985). Minimizing risk is sometimes made depending on the economic possibilities of the decision maker. References Arnell, N. W., and T. Osborn, (2006): Interfacing climate and impacts models in integrated assessment modelling, Tech. Rep. 52, Tyndall Cent. for Clim. Change Res., Univ. of East Anglia, Norwich, U. K. Beguería S., S.M. Vicente-Serrano, (2006): Mapping the hazard of extreme rainfall by peaks-over-threshold extreme value analysis and spatial regression techniques. Journal of Applied Meteorology and Climatology, 45(1): 108—124 Cascini, L., Bonnard, Ch., Corominas, J., Jibson, R., Montero-Olarte, J., (2005): Landslide hazard and risk zoning for urban planning and development. In: Hungr, O., Fell, R., Couture, R., Eberthardt, E. (Eds.), Landslide Risk Management. Taylor and Francis, London, 199–235. Fell, R., Corominas, J., Bonnard, Ch., Cascini, L., Leroi E. and Savage, W. Z. (2008): on behalf of the JTC-1 Joint Technical Committee on Landslides and Engineered Slopes, “Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning”, Engineering Geology, 102, 99-111. Fowler H.J., et al. (2007): Review Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int. J. Climatol. 27: 1547–1578 Ghosh, S., and P. P. Mujumdar (2007): Nonparametric methods for modeling GCM and scenario uncertainty in drought assessment, Water Resour. Res., 43, W07405, doi:10.1029/2006WR005351. Joigneaux, E., P. Alberic, H. Pauwels, C. Pagé, L. Terray, and A. Bruand, (2011): Impact of climate change on groundwater point discharge: backflooding of karstic springs (Loiret, France), Hydrol. Earth Syst. Sci. Discuss., 8, 2235–2262, 2011,www.hydrol-earth-syst-sci-discuss.net/8/2235/2011/doi:10.5194/hessd-8-2235-2011 Jones, R.N. and Preston, B.L. (2006): ‘Climate Change Impacts, Risk and the Benefits of Mitigation: A report for the Energy Futures Forum’, CSIRO, Victoria. Jones, R. (2010): A risk management approach to climate change adaptation, in Nottage, R.A.C., Wratt, D.S., Bornman, J.F., Jones, K. (eds.), Climate change adaptation in New Zealand: Future scenarios and some sectoral perspectives. New Zealand Climate Change Centre, Wellington, 10–25. Kaplan, S. and Garrick, B.J. (1981): On the quantitative definition of risk, Risk Analysis, 1 (1), 11–27. Katz RW, Grace SB and Parlange M.B. (2005): Statistics of extremes: modeling ecological disturbances. Ecology 86(5):1124–1134 Murphy, A., R. W. Katz, R. L. Winkler and Wu-Ron Hsu (1985): Repetitive Decision Making and the Value of Forecasts in the Cost-Loss Ratio Situation: A Dynamic Model. Mon.Wea.Rev. 113, 801-813. Salvadori G., C. De Michele,T. N. Kottegoda and R. Rosso (2007): Extremes in Nature- An Approach Using Copulas. Water Science and Technology Library, 56. Ed. Springer.292 p. Schmidt -Thomé, P. (editor) (2005): The Spatial Effects and Management of Natural and Technological Hazards in Europe – final report of the European Spatial Planning and Observation Network (ESPON) project 1.3.1. Geological Survey of Finland. 197 p. Schmidt -Thomé, P. (editor) (2006): Natural and technological hazards and risks affecting the spatial development of European regions. Geological Survey of Finland, Special Paper 42. 167 pages, 35 figures, 56 tables, 22 maps. Varnes, J. D., (1984): Landslide hazard zonation : a review of principles and practice. Published in 1984 by United Nations Educational, Scientific and Cultural Organisation 7 place de Fontenoy.75700 Paris. Printed by Darantiere, Quetingny. ISBN 92-3-101895-7.UNESCO 1984.
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