35 research outputs found

    Enhancing local action planning through quantitative flood risk analysis: a case study in Spain

    Full text link
    [EN] This article presents a method to incorporate and promote quantitative risk analysis to support local action planning against flooding. The proposed approach aims to provide a framework for local flood risk analysis, combining hazard mapping with vulnerability data to quantify risk in terms of expected annual affected population, potential injuries, number of fatalities, and economic damages. Flood risk is estimated combining GIS data of loads, system response, and consequences and using event tree modelling for risk calculation. The study area is the city of Oliva, located on the eastern coast of Spain. Results from risk modelling have been used to inform local action planning and to assess the benefits of structural and non-structural risk reduction measures. Results show the potential impact on risk reduction of flood defences and improved warning communication schemes through local action planning: societal flood risk (in terms of annual expected affected population) would be reduced up to 51% by combining both structural and nonstructural measures. In addition, the effect of seasonal population variability is analysed (annual expected affected population ranges from 82 to 107 %, compared with the current situation, depending on occupancy rates in hotels and campsites). Results highlight the need for robust and standardized methods for urban flood risk analysis replicability at regional and national scale.This research was conducted within the framework of the INICIA project, funded by the Spanish Ministry of Economy and Competitiveness (BIA2013-48157-C2-1-R). The article processing charges for this open-access publication will be covered by the INICIA project. We would like to thank the city of Oliva for their willingness to share data, knowledge, and experience with the authors and for initiating this risk-informed journey.Castillo-Rodríguez, J.; Escuder Bueno, I.; Perales Momparler, S.; Porta-Sancho, J. (2016). Enhancing local action planning through quantitative flood risk analysis: a case study in Spain. Natural Hazards and Earth System Sciences. 16(7):1699-1718. https://doi.org/10.5194/nhess-16-1699-2016S16991718167Barredo, J. I.: Normalised flood losses in Europe: 1970–2006, Nat. Hazards Earth Syst. Sci., 9, 97–104, https://doi.org/10.5194/nhess-9-97-2009, 2009.Castillo-Rodriguez, J. T., Escuder-Bueno, I., Altarejos-García, L., and Serrano-Lombillo, A.: The value of integrating information from multiple hazards for flood risk analysis and management, Nat. Hazards Earth Syst. Sci., 14, 379–400, https://doi.org/10.5194/nhess-14-379-2014, 2014.DEFRA: FD2321/TR1 – The Flood Risks to People Methodology, London, available at: www.defra.gov.uk/environ/fcd/research (last access: February 2016), 2006.EC: Guide to Cost Benefit Analysis of Investment Projects: European Commission, DG for Regional and Urban Policy, Brussels BELGIUM, https://doi.org/10.2776/97516, 2008.Escuder-Bueno, I., Castillo-Rodriguez, J. T., Zechner, S., Jöbstl, C., Perales-Momparler, S., and Petaccia, G.: A quantitative flood risk analysis methodology for urban areas with integration of social research data, Nat. Hazards Earth Syst. Sci., 12, 2843–2863, https://doi.org/10.5194/nhess-12-2843-2012, 2012.European Parliament: DIRECTIVE 2007/60/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 October 2007 on the assessment and management of flood risks, L 228, 27–34, 2007.Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., and Savage, W. Z.: Guidelines for landslide susceptibility, hazard and risk zoning for land use planning, Eng. Geol., 102, 85–98, https://doi.org/10.1016/j.enggeo.2008.03.022, 2008.Fuchs, S., Spachinger, K., Dorner, W., Rochman, J., and Serrhini, K.: Evaluating cartographic design in flood risk mapping, Environ. Hazards, 8, 52–70, https://doi.org/10.3763/ehaz.2009.0007, 2009.Fuchs, S., Keiler, M., and Zischg, A.: A spatiotemporal multi-hazard exposure assessment based on property data, Nat. Hazards Earth Syst. Sci., 15, 2127–2142, https://doi.org/10.5194/nhess-15-2127-2015, 2015.Gamper, C. D. and Turcanu, C.: On the governmental use of multi-criteria analysis, Ecol. Econ., 2, 298–307, https://doi.org/10.1016/j.ecolecon.2007.01.010, 2007.Generalitat Valenciana: Plan de Acción Territorial sobre prevención del Riesgo de Inundación en la Comunitat Valenciana (PATRICOVA), Valencia, 67–72, 2015.Hall, J. W., Meadowcroft, I. C., Sayers, P. B., and Bramley, M. E.: Integrated Flood Risk Management in England and Wales, 2003.Hennig, C., Dise, K., and Muller, B.: Achieiving Public Protection with Dam Safety Risk Assessment Practices, Nat. Hazards Rev., 4, 126–135, https://doi.org/10.1061/(ASCE)1527-6988(2003)4:3(126)1997, 2003.Hijós Bitrián, F., Mañueco Pfeiffer, M. G., and Segura Notario, N.: Comité nacional español de grandes presas, Congreso Nacional de Presas, Proc. of Risk-Based Decision Making in Water Resources VIII, edited by: Yacov, Y., Haimes, D., Moser, A., and Stakhiv, E. Z., 19–32, 2010.Jongman, B., Kreibich, H., Apel, H., Barredo, J. I., Bates, P. D., Feyen, L., Gericke, A., and Neal, J.: Comparative flood damage model assessment?: towards a European approach, Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, 2012.Jongman, B., Koks, E. E., Husby, T. G., and Ward, P. J.: Increasing flood exposure in the Netherlands: implications for risk financing, Nat. Hazards Earth Syst. Sci., 14, 1245–1255, https://doi.org/10.5194/nhess-14-1245-2014, 2014.Jonkman, S. N., Vrijling, J. K., and Vrouwenvelder, A. C. W. M.: Methods for the estimation of loss of life due to floods?: a literature review and a proposal for a new method, Nat. Hazards, 46, 353–389, https://doi.org/10.1007/s11069-008-9227-5, 2008.Klijn, F. and Schweckendiek, T.: Comprehensive Flood Risk Management: Research for Policy and Practice, CRC Press, Boca Raton, 297–330, 2012.Klijn, F., Kreibich, H., De Moel, H., and Penning-rowsell, E.: Adaptive flood risk management planning based on a comprehensive flood risk conceptualisation, Mitig. Adapt. Strat. Glob. Chang., 20, 845–864, https://doi.org/10.1007/s11027-015-9638-z, 2015.MAGRAMA: Propuesta de mínimos para la metodología de realización de los mapas de riesgo de inundación, Madrid, Ministerio de Agricultura, Alimentación y Medio Ambiente, Madrid, Spain, 2013.Marcotullio, P. J. and McGranahan, G.: Scaling Urban Environmental Challenges: From local to global and back, Earthscan with UNU-IAS and IIED, Earthscan, London, Sterling, VA, 2006.Mayors Adapt: The new integrated covenant of mayors for climate and energy, www.mayors-adapt.eu (last access: July 2016), 2015.Mazzorana, B., Comiti, F., Scherer, C., and Fuchs, S.: Developing consistent scenarios to assess fl ood hazards in mountain streams, J. Environ. Manage., 94, 112–124, https://doi.org/10.1016/j.jenvman.2011.06.030, 2012.Mazzorana, B., Comiti, F., and Fuchs, S.: A structured approach to enhance flood hazard assessment in mountain streams, Nat. Hazards, 67, 991–1009, https://doi.org/10.1007/s11069-011-9811-y, 2013.Merz, B. and Thieken, A. H.: Flood risk curves and uncertainty bounds, Natural Hazards, 51, 437–458, https://doi.org/10.1007/s11069-009-9452-6, 2009.Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Assessment of economic flood damage, Nat. Hazards Earth Syst. Sci., 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010.Meyer, V., Scheuer, S., and Haase, D.: A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany, Nat. Hazards, 48, 17–39, https://doi.org/10.1007/s11069-008-9244-4, 2009.Meyer, V., Kuhlicke, C., Luther, J., Fuchs, S., Priest, S., Dorner, W., Serrhini, K., Pardoe, J., and Mccarthy, S.: Recommendations for the user-specific enhancement of flood maps, Nat. Hazards Earth Syst. Sci., 12, 1701–1716, https://doi.org/10.5194/nhess-12-1701-2012, 2012.Miller, A., Jonkman, S. N., and Van Ledden, M.: Risk to life due to flooding in post-Katrina New Orleans, Nat. Hazards Earth Syst. Sci., 15, 59–73, https://doi.org/10.5194/nhess-15-59-2015, 2015.Morales-Torres, A., Serrano-Lombillo, A., Escuder-Bueno, I., and Altarejos-García, L.: The suitability of risk reduction indicators to inform dam safety management, Struct. Infrastruct. Eng., 12, 2479, https://doi.org/10.1080/15732479.2015.1136830, 2016.Nakicenovic, N., Lempert, R., and Janetos, A.: Special Issue of Climatic Change on the framework for the development of new socioeconomics scenarios for climate change research, Clim. Change, 122, 351–361, https://doi.org/10.1007/s10584-013-0982-2, 2013.Parker, D. J., Tunstall, S., and Wilson, T.: Socio-Economic Benefits of Flood Forecasting and Warning, in: International conference on innovation advances and implementation of flood forecasting technology, Session 8, 1–11, ACTIF, available at: http://www.actif-ec.net/conference2005/proceedings/index.html, (last access: July 2016), Tromso, Norway, 2005.Penning-Rowsell, E. C., Priest, S. J., Parker, D. J., Morris, J., Tunstall, S. M., Viavatenne, C., Chatterton, J., and D., O.: Flood and Coastal Erosion Risk Management, A manual for economic appraisal, London, Routledge, Chapter 4, 2013.Quan-Luna, B., Blahut, J., Westen, C. J. Van, Sterlacchini, S., Asch, T. W. J., and Van and Akbas, S. O.: The application of numerical debris flow modelling for the generation of physical vulnerability curves, Nat. Hazards Earth Syst. Sci., 11, 2047–2060, https://doi.org/10.5194/nhess-11-2047-2011, 2011.Ramis, C., Homar, V., Amengual, A., Romero, R., and Alonso, S.: Daily precipitation records over mainland Spain and the Balearic Islands, Nat. Hazards Earth Syst. Sci., 13, 2483–2491, https://doi.org/10.5194/nhess-13-2483-2013, 2013.Sayers, P. B., Horritt, M., Penning-Rowsell, E., and McKenzie, A.: Climate Change Risk Assessment 2017 Projections of future flood risk in the UK, London, 69–73, 2015.Serrano-Lombillo, A., Escuder-Bueno, I., De Membrillera-Ortuño, M. G., and Altarejos-García, L.: Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams, Risk Anal., 31, 1000–1015, https://doi.org/10.1111/j.1539-6924.2010.01547.x, 2011.Totschnig, R. and Fuchs, S.: Mountain torrents: Quantifying vulnerability and assessing uncertainties, Eng. Geol., 155, 31–44, https://doi.org/10.1016/j.enggeo.2012.12.019, 2013.UK Health and Safety Executive: Reducing Risks: Protecting People – HSE's decision making process, Norwich, Health and Safety Executive, 40–48, 2001.Université Catholique de Louvain: EM-DAT Database: The OFDA/CRED International Disaster Database, available at: http://www.emdat.be/ (last access: 1 October 2015), 2015.USACE: Economic Guidance Memorandum (EGM) 01-03: Generic Depth-Damage Relationships, 2000.Velasco, M., Cabello, À., and Russo, B.: Flood damage assessment in urban areas, Application to the Raval district of Barcelona using synthetic depth damage curves, Urban Water J., https://doi.org/10.1080/1573062X.2014.994005, 2015.Vrijling, J. K.: Probabilistic design of water defense systems in The Netherlands, 74, 337–344, 2001.Ward, P. J., De Moel, H., and Aerts, J. C. J. H.: How are flood risk estimates affected by the choice of return-periods?, Nat. Hazards Earth Syst. Sci., 11, 3181–3195, https://doi.org/10.5194/nhess-11-3181-2011, 2011.Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., Beek, R. Van, Bierkens, M. F. P., and Ligtvoet, W.: Assessing flood risk at the global scale: model setup, results, and sensitivity, Environ. Res. Lett., 044019, https://doi.org/10.1088/1748-9326/8/4/044019, 2013a.Ward, P. J., Jongman, B., Weiland, F. S., Bouwman, A., van Beek, R., Bierkens, M. F. P., Ligtvoet, W., and Winsemius, H. C.: Assessing flood risk at the global scale: model setup, results, and sensitivity, Environ. Res. Lett., 8, 044019, https://doi.org/10.1088/1748-9326/8/4/044019, 2013b.Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J., and Bouwman, A.: A framework for global river flood risk assessments, Hydrol. Earth Syst. Sci., 17, 1871–1892, https://doi.org/10.5194/hess-17-1871-2013, 2013

    Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam

    Full text link
    [EN] Dam safety is increasingly subjected to the influence of climate change. Its impacts must be assessed through the integration of the various effects acting on each aspect, considering their interdependencies, rather than just a simple accumulation of separate impacts. This serves as a dam safety management supporting tool to assess the vulnerability of the dam to climate change and to define adaptation strategies under an evolutive dam failure risk management framework. This article presents a comprehensive quantitative assessment of the impacts of climate change on the safety of a Spanish dam under hydrological scenarios, integrating the various projected effects acting on each component of the risk, from the input hydrology to the consequences of the outflow hydrograph. In particular, the results of 21 regional climate models encompassing three Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) have been used to calculate the risk evolution of the dam until the end of the 21st century. Results show a progressive deterioration of the dam failure risk, for most of the cases contemplated, especially for the RCP2.6 and RCP4.5 scenarios. Moreover, the individual analysis of each risk component shows that the alteration of the expected inflows has the greater influence on the final risk. The approach followed in this paper can serve as a useful guidebook for dam owners and dam safety practitioners in the analysis of other study cases.The authors acknowledge the Spanish Ministry for the Ecological Transition (MITECO) for its support in the preparation of this paper.Fluixá Sanmartín, J.; Morales Torres, A.; Escuder Bueno, I.; Paredes Arquiola, J. (2019). Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam. Natural Hazards and Earth System Sciences. 19(10):2117-2139. https://doi.org/10.5194/nhess-19-2117-2019S211721391910AEMET: AEMET Spain02 v5 dataset, available at: http://www.aemet.es/es/serviciosclimaticos/cambio_climat/datos_diarios/ayuda/rejilla_20km, last access: 30 September 2019. a, bAkhtar, M., Ahmad, N., and Booij, M.: The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios, J. Hydrol., 355, 148–163, https://doi.org/10.1016/j.jhydrol.2008.03.015, 2008. aArdiles, L., Sanz, D., Moreno, P., Jenaro, E., Fleitz, J., and Escuder-Bueno, I.: Risk Assessment and Management for 26 Dams Operated By the Duero River Authority (Spain), in: 6th International Conference on Dam Engineering, Lisbon, Portugal, 2011. a, b, cASCE: Hydrology handbook, no. 28 in ASCE manuals and reports on engineering practice, 2nd Edn., ASCE, New York, oCLC: 636373660, 1996. aBahls, V. and Holman, K.: Climate Change in Hydrologic Hazard Analyses: Friant Dam Pilot Study – Part I: Hydrometeorological Model Inputs, Tech. rep., US Department of the Interior, Bureau of Reclamation, Denver, Colorado, USA, 2014. aBenestad, R.: Downscaling Climate Information, in: Oxford Research Encyclopedia of Climate Science, Oxford University Press, Oxford, https://doi.org/10.1093/acrefore/9780190228620.013.27, 2016. aBoé, J., Terray, L., Habets, F., and Martin, E.: Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies, Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602, 2007. a, bBowles, D.: Advances in the practice and use of portfolio risk assessment, in: ANCOLD Conference on Dams, Cairns, Queensland, Australia, 2000. aCannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015. aCEDEX: Hydrological Yearbook – Centro de Estudios y Experimentación de Obras Públicas, available at: http://ceh-flumen64.cedex.es/anuarioaforos/default.asp, last access: 30 September 2019. a, bChernet, H. H., Alfredsen, K., and Midttømme, G. H.: Safety of Hydropower Dams in a Changing Climate, J. Hydrol. Eng., 19, 569–582, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000836, 2014. aChow, V. T., Maidment, D. R., and Mays, L. W.: Applied hydrology, McGraw-Hill series in water resources and environmental engineering, 1988 Edn., McGraw-Hill, New York, oCLC: 551823930, 2008. aConfederación Hidrográfica del Duero: Plan Hidrológico de la parte española de la demarcación hidrográfica del Duero, 2015–2021, available at: http://www.chduero.es/ (last access: 30 September 2019), 2015. a, b, cEscuder-Bueno, I. and González-Pérez, J.: Metodología para la evaluación del riesgo hidrológico de presas y priorización de medidas correctoras, Colegio de Ingeniero de Caminos, Canales y Puertos, Madrid, Spain, 2014. aFluixá-Sanmartín, J., Altarejos-García, L., Morales-Torres, A., and Escuder-Bueno, I.: Review article: Climate change impacts on dam safety, Nat. Hazards Earth Syst. Sci., 18, 2471–2488, https://doi.org/10.5194/nhess-18-2471-2018, 2018. a, b, c, d, e, fFluixá-Sanmartín, J., Altarejos-García, L., Morales-Torres, A., and Escuder-Bueno, I.: Empirical Tool for the Assessment of Annual Overtopping Probabilities of Dams, J. Water Resour. Pl. Manage., 145, 04018083, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001017, 2019. aFoehn, A., García Hernández, J., Roquier, B., Fluixá-Sanmartín, J., and Paredes Arquiola, J.: RS MINERVE – User's manual v2.12, RS MINERVE Group, Switzerland, 2019. aFrancés, F., García-Bartual, R., and Bussi, G.: High return period annual maximum reservoir water level quantiles estimation using synthetic generated flood events, in: Risk Analysis, Dam Safety, Dam Security and Critical Infrastructure Management, edited by: Escuder-Bueno, I., Matheu, E., Altarejos-García, L., and Castillo-Rodríguez, J. T., CRC Press, Leiden, 99–105, 2012. aFujihara, Y., Tanaka, K., Watanabe, T., Nagano, T., and Kojiri, T.: Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: Use of dynamically downscaled data for hydrologic simulations, J. Hydrol., 353, 33–48, https://doi.org/10.1016/j.jhydrol.2008.01.024, 2008. aGao, X., Pal, J. S., and Giorgi, F.: Projected changes in mean and extreme precipitation over the Mediterranean region from a high resolution double nested RCM simulation, Geophys. Res. Lett., 33, L03706, https://doi.org/10.1029/2005GL024954, 2006. aGarcía Hernández, J., Paredes Arquiola, J., Foehn, A., Roquier, B., and Fluixá-Sanmartín, J.: RS MINERVE – Technical manual v2.17, RS MINERVE Group, Sion, Switzerland, 2019. a, bGiorgi, F., Jones, C., and Asrar, G.: Addressing climate information needs at the regional level: the CORDEX framework, WMO Bulletin, 58, 175–183, 2009. aGu, H., Wang, G., Yu, Z., and Mei, R.: Assessing future climate changes and extreme indicators in east and south Asia using the RegCM4 regional climate model, Climatic Change, 114, 301–317, https://doi.org/10.1007/s10584-012-0411-y, 2012. aGudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a, bGupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. aGutjahr, O. and Heinemann, G.: Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM: Effects on extreme values and climate change signal, Theor. Appl. Climatol., 114, 511–529, https://doi.org/10.1007/s00704-013-0834-z, 2013. aHerrera, S., Fernández, J., and Gutiérrez, J. M.: Update of the Spain02 gridded observational dataset for EURO-CORDEX evaluation: assessing the effect of the interpolation methodology, Int. J. Climatol., 36, 900–908, https://doi.org/10.1002/joc.4391, 2016. aIPCC: Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the Intergovernmental Panel on Climate Change, 1. Edn., Cambridge Univ. Press, Cambridge, UK, and New York, NY, USA, 2012. aIPCC: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013. aIPCC: Climate Change 2014: Impacts, Adaptation, and Vulnerability, Part A: Global and Sectoral Aspects, in: Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, UK and New York, NY, USA, 2014. aiPresas: iPresas Calc., User guide, Valencia, ipresas risk analysis Edn., available at: http://www.ipresas.com, last access: 30 September 2019. aIPSL: Pierre Simon Laplace Institute (IPSL) ESGF node, available at: https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/, last access: 30 September 2019. aJacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014. aJakob Themeßl, M., Gobiet, A., and Leuprecht, A.: Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530–1544, https://doi.org/10.1002/joc.2168, 2011. aKaplan, S.: The Words of Risk Analysis, Risk Analysis, 17, 407–417, https://doi.org/10.1111/j.1539-6924.1997.tb00881.x, 1997. aKite, G. W.: Confidence limits for design events, Water Resour. Res., 11, 48–53, https://doi.org/10.1029/WR011i001p00048, 1975. a, bKite, G. W.: Frequency and risk analyses in hydrology, Water Resources Publications, Littleton, Colo., USA, 1988. aKling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. aKotlarski, S., Szabó, P., Herrera, S., Räty, O., Keuler, K., Soares, P. M., Cardoso, R. M., Bosshard, T., Pagé, C., Boberg, F., Gutiérrez, J. M., Isotta, F. A., Jaczewski, A., Kreienkamp, F., Liniger, M. A., Lussana, C., and Pianko-Kluczyńska, K.: Observational uncertainty and regional climate model evaluation: A pan-European perspective, Int. J. Climatol., 39, 3730–3749, https://doi.org/10.1002/joc.5249, 2017. aMaraun, D.: Bias Correcting Climate Change Simulations – a Critical Review, Curr. Clim. Change Rep., 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016. aMinisterio de Fomento: Norma 5.2 – IC drenaje superficial de la Instrucción de Carreteras, in: Boletín Oficial del Estado, Madrid, Spain, 18882–19023, 2016. a, bMorales-Torres, A., Serrano-Lombillo, A., Escuder-Bueno, I., and Altarejos-García, L.: The suitability of risk reduction indicators to inform dam safety management, Struct. Infrastruct. Eng., 12, 1465–1476, https://doi.org/10.1080/15732479.2015.1136830, 2016. a, bMoss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010. aNash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. aNovembre, N., Holman, K., and Bahls, V.: Climate Change in Hydrologic Hazard Analyses: Friant Dam Pilot Study – Part II: Using the SEFM with Climate-Adjusted Hydrometeorological Inputs, Technical Memorandum 8250-2015-010, US Department of the Interior, Bureau of Reclamation, Denver, Colorado, USA, 2015. aOFEV (Ed.): Adaptation aux changements climatiques en Suisse, Plan d'action 2014–2019, Deuxième volet de la stratégie du Conseil fédéral du 9 avril 2014, Bern, Switzerland, 2014. aOrlowsky, B., Gerstengarbe, F.-W., and Werner, P. C.: A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM, Theor. Appl. Climatol., 92, 209–223, https://doi.org/10.1007/s00704-007-0352-y, 2008. aOur World in Data: Future Population Growth, available at: https://ourworldindata.org/future-population-growth (last access: 30 September 2019), 2018. aPanofsky, H. and Brier, G.: Some Applications of Statistics to Meteorology, Earth and mineral sciences continuing education, The Pennsylvania State University Press, Philadelphia, 1968. aParzen, E.: On Estimation of a Probability Density Function and Mode, Ann. Math. Stat., 33, 1065–1076, https://doi.org/10.1214/aoms/1177704472, 1962. aR Development Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org (last access: 30 September 2019), 2008. aReed, D., Faulkner, D., Robson, A., Houghton-Carr, H., Bayliss, A., and Institute of Hydrology: Flood estimation handbook: procedures for flood frequency estimation, Institute of Hydrology, Wallingford, Angleterre, oCLC: 301120221, 1999. aRiahi, K., Grübler, A., and Nakicenovic, N.: Scenarios of long-term socio-economic and environmental development under climate stabilization, Technol. Forecast. Social Change, 74, 887–935, https://doi.org/10.1016/j.techfore.2006.05.026, 2007. aRiahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP 8.5 – A scenario of comparatively high greenhouse gas emissions, Climatic Change, 109, 33–57, https://doi.org/10.1007/s10584-011-0149-y, 2011. aRogger, M., Kohl, B., Pirkl, H., Viglione, A., Komma, J., Kirnbauer, R., Merz, R., and Blöschl, G.: Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story?, J. Hydrol., 456–457, 30–43, https://doi.org/10.1016/j.jhydrol.2012.05.068, 2012. aRosenblatt, M.: Remarks on Some Nonparametric Estimates of a Density Function, Ann. Math. Stat., 27, 832–837, https://doi.org/10.1214/aoms/1177728190, 1956. aSchaefli, B., Hingray, B., Niggli, M., and Musy, A.: A conceptual glacio-hydrological model for high mountainous catchments, Hydrol. Earth Syst. Sci., 9, 95–109, https://doi.org/10.5194/hess-9-95-2005, 2005. aSerrano-Lombillo, A., Escuder-Bueno, I., de Membrillera-Ortuño, M. G., and Altarejos-García, L.: Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams: Risk Calculation for Systems of Dams, Risk Analysis, 31, 1000–1015, https://doi.org/10.1111/j.1539-6924.2010.01547.x, 2011. a, b, cSerrano-Lombillo, A., Fluixá-Sanmartín, J., and Espert-Canet, V.: Flood routing studies in risk analysis, in: Risk Analysis, Dam Safety, Dam Security and Critical Infrastructure Management, edited by: Escuder-Bueno, I., Matheu, E., Altarejos-García, L., and Castillo-Rodríguez, J. T., CRC Press, Leiden, 99–105, 2012a. aSerrano-Lombillo, A., Morales-Torres, A., and García-Kabbabe, L.: Consequence estimation in risk analysis, in: Risk Analysis, Dam Safety, Dam Security and Critical Infrastructure Management, edited by: Escuder-Bueno, I., Matheu, E., Altarejos-García, L., and Castillo-Rodríguez, J. T., CRC Press, Leiden, 99–105, 2012b. aSerrano-Lombillo, A., Morales-Torres, A., Escuder-Bueno, I., and Altarejos-García, L.: Review, Analysis and Application of Existing Risk Reduction Principles and Risk Indicators for Dam Safety Management, Venice, Italy, 2013. aSPANCOLD: Risk Analysis as Applied to Dam Safety, Technical Guide on Operation of Dams and Reservoirs, Professional Association of Civil Engineers, Spanish National Committe on Large Dams, Madrid, available at: http://www.spancold.es/Archivos/Monograph_Risk_Analysis.pdf (last access: 30 September 2019), 2012. a, b, c, dSu, H.-T. and Tung, Y.-K.: Incorporating uncertainty of distribution parameters due to sampling errors in flood-damage-reduction project evaluation, Water Resour. Res., 49, 1680–1692, https://doi.org/10.1002/wrcr.20116, 2013. a, bTaylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. aTémez, J.: Extended and Improved Rational Method, Version of the Highways Administration of Spain, in: Proc. XXIV Congress IAHR, Madrid, Spain, 33–40, 1991. aThomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and Edmonds, J. A.: RCP4.5: a pathway for stabilization of radiative forcing by 2100, Climatic Change, 109, 77–94, https://doi.org/10.1007/s10584-011-0151-4, 2011. aUnited Nations: World Population Prospects: The 2017 Revision, Tech. rep., Department of Economic and Social Affairs, Population Division, available at: https://esa.un.org/unpd/wpp/Download/Standard/Population/ (last access: 30 September 2019), 2017. aUniversity of Cantabria: Santander Meteorology Group (University of Cantabria ­- CSIC), available at: http://www.meteo.unican.es/datasets/spain02, last access: 30 September 2019. aUSACE: Safety of dams – Policy and procedures, Tech. Rep. ER 1110-2-1156, US Army Corps of Engineers, Washington, D.C., 2011. aUSACE: Climate Change Adaptation Plan, Tech. rep., US Army Corps of Engineers Committee on Climate Preparedness and Resilience, Washington, D.C., USA, 2014. aUSBR: Dam Safety Public Protection Guidelines. A Risk Framework to Support Dam Safety Decision-Making, Tech. rep., US Department of the Interior, Bureau of Reclamation, Denver, Colorado, USA, 2011. aUSBR: Climate Change Adaptation Strategy, Tech. rep., US Department of the Interior, Bureau of Reclamation, Denver, Colorado, USA, 2014. a, bUSBR: Climate Change Adaptation Strategy: 2016 Progress Report, Tech. rep., US Department of the Interior, Bureau of Reclamation, Denver, Colorado, USA, 2016. a, bvan Vuuren, D. P., den Elzen, M. G. J., Lucas, P. L., Eickhout, B., Strengers, B. J., van Ruijven, B., Wonink, S., and van Houdt, R.: Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs, Climatic Change, 81, 119–159, https://doi.org/10.1007/s10584-006-9172-9, 2007. avan Vuuren, D. P., Stehfest, E., den Elzen, M. G. J., Kram, T., van Vliet, J., Deetman, S., Isaac, M., Klein Goldewijk, K., Hof, A., Mendoza Beltran, A., Oostenrijk, R., and van Ruijven, B.: RCP2.6: exploring the possibility to keep global mean temperature increase below 2 ∘C, Climatic Change, 109, 95–116, https://doi.org/10.1007/s10584-011-0152-3, 2011. aWalsh, J., Wuebbles, D., Hayhoe, K., Kossin, J., Kunkel, K., Stephens, G., Thorne, P., Vose, R., Wehner, M., Willis, J., Anderson, D., Doney, S., Feely, R., Hennon, P., Kharin, V., Knutson, T., Landerer, F., Lenton, T., Kennedy, J., and Somerville, R.: Ch. 2: Our Changing Climate, in: Climate Change Impacts in the United States: The Third National Climate Assessment, edited by: Melillo, J. M., Richmond, T (T. C.), and Yohe, G. W., u.s. global change research program Edn., 19–67, https://doi.org/10.7930/J0KW5CXT, 2014.  aYira, Y., Diekkrüger, B., Steup, G., and Bossa, A. Y.: Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations, Hydrol. Earth Syst. Sci., 21, 2143–2161, https://doi.org/10.5194/hess-21-2143-2017, 2017. 

    Accounting for climate change uncertainty in long-term dam risk management

    Full text link
    [EN] This paper presents a practical approach to adaptive management of dam risk based on robust decision-making strategies coupled with estimation of climate scenario probabilities. The proposed methodology, called multi-prior weighted scenarios ranking, consists of a series of steps from risk estimation for current and future situations through definition of the consensus sequence of risk reduction measures to be implemented. This represents a supporting tool for dam owners and safety practitioners in making decisions for managing dams or prioritizing long-term investments using a cost-benefit approach. This methodology is applied to the case study of a Spanish dam under the effects of climate change. Several risk reduction measures are proposed and their impacts are analyzed. The application of the methodology allows for identifying the optimal sequence of implementation measures that overcomes uncertainty from the diversity of available climate scenarios by prioritizing measures that reduce future accumulated risks at lower costs. This work proves that such a methodology helps address uncertainty that arises from multiple climate scenarios while adopting a cost-benefit approach that optimizes economic resources in dam risk management.Fluixá-Sanmartín, J.; Escuder Bueno, I.; Morales-Torres, A.; Castillo-Rodríguez, J. (2021). Accounting for climate change uncertainty in long-term dam risk management. Journal of Water Resources Planning and Management. 147(4):1-13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001355S1131474Amodio, S., D’Ambrosio, A., & Siciliano, R. (2016). Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, 249(2), 667-676. doi:10.1016/j.ejor.2015.08.048Ardiles L. D. Sanz P. Moreno E. Jenaro J. Fleitz and I. Escuder-Bueno. 2011. “Risk assessment and management for 26 Dams operated by the Duero River Authority (Spain).” In Proc. 6th Int. Conf. on Dam Engineering edited by C. Pina E. Portela and J. P. Gomes. Singapore: CI-premier Pte Ltd.Baecher, G. B., Paté, M. E., & De Neufville, R. (1980). Risk of dam failure in benefit-cost analysis. Water Resources Research, 16(3), 449-456. doi:10.1029/wr016i003p00449Burke, M., Dykema, J., Lobell, D., Miguel, E., & Satyanath, S. (2011). Incorporating Climate Uncertainty into Estimates of Climate Change Impacts, with Applications to U.S. and African Agriculture. doi:10.3386/w17092Chamberlain, G. (2000). Econometric applications of maxmin expected utility. Journal of Applied Econometrics, 15(6), 625-644. doi:10.1002/jae.583Chernet, H. H., Alfredsen, K., & Midttømme, G. H. (2014). Safety of Hydropower Dams in a Changing Climate. Journal of Hydrologic Engineering, 19(3), 569-582. doi:10.1061/(asce)he.1943-5584.0000836Choi, O. (2003). Climatic Change, 58(1/2), 149-170. doi:10.1023/a:1023459216609Christensen, J., Kjellström, E., Giorgi, F., Lenderink, G., & Rummukainen, M. (2010). Weight assignment in regional climate models. Climate Research, 44(2-3), 179-194. doi:10.3354/cr00916Danthine, J.-P., & Donaldson, J. B. (2015). Making Choices in Risky Situations. Intermediate Financial Theory, 55-86. doi:10.1016/b978-0-12-386549-6.00003-6Davis, J., Hands, D., & Mäki, U. (1998). The Handbook of Economic Methodology. doi:10.4337/9781781954249Dessai, S., & Hulme, M. (2004). Does climate adaptation policy need probabilities? Climate Policy, 4(2), 107-128. doi:10.1080/14693062.2004.9685515Eggleston H. S. 2006. “National Greenhouse Gas Inventories Programme and Chikyū Kankyō Senryaku Kenkyū Kikan.” In Proc. IPCC guidelines for national greenhouse gas inventories. Geneva: Intergovernmental Panel on Climate Change.Emond, E. J., & Mason, D. W. (2002). A new rank correlation coefficient with application to the consensus ranking problem. Journal of Multi-Criteria Decision Analysis, 11(1), 17-28. doi:10.1002/mcda.313Farnoud Hassanzadeh, F., & Milenkovic, O. (2014). An Axiomatic Approach to Constructing Distances for Rank Comparison and Aggregation. IEEE Transactions on Information Theory, 60(10), 6417-6439. doi:10.1109/tit.2014.2345760Ferson, S., & Ginzburg, L. R. (1996). Different methods are needed to propagate ignorance and variability. Reliability Engineering & System Safety, 54(2-3), 133-144. doi:10.1016/s0951-8320(96)00071-3Fluixá-Sanmartín, J., Altarejos-García, L., Morales-Torres, A., & Escuder-Bueno, I. (2018). Review article: Climate change impacts on dam safety. Natural Hazards and Earth System Sciences, 18(9), 2471-2488. doi:10.5194/nhess-18-2471-2018Fluixá-Sanmartín, J., Escuder-Bueno, I., Morales-Torres, A., & Castillo-Rodríguez, J. T. (2020). Comprehensive decision-making approach for managing time dependent dam risks. Reliability Engineering & System Safety, 203, 107100. doi:10.1016/j.ress.2020.107100Fluixá-Sanmartín, J., Morales-Torres, A., Escuder-Bueno, I., & Paredes-Arquiola, J. (2019). Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam. Natural Hazards and Earth System Sciences, 19(10), 2117-2139. doi:10.5194/nhess-19-2117-2019Gersonius, B., Morselt, T., van Nieuwenhuijzen, L., Ashley, R., & Zevenbergen, C. (2012). How the Failure to Account for Flexibility in the Economic Analysis of Flood Risk and Coastal Management Strategies Can Result in Maladaptive Decisions. Journal of Waterway, Port, Coastal, and Ocean Engineering, 138(5), 386-393. doi:10.1061/(asce)ww.1943-5460.0000142Giorgi, F., & Mearns, L. O. (2002). Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the «Reliability Ensemble Averaging» (REA) Method. Journal of Climate, 15(10), 1141-1158. doi:10.1175/1520-0442(2002)0152.0.co;2Haasnoot, M., Kwakkel, J. H., Walker, W. E., & ter Maat, J. (2013). Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Global Environmental Change, 23(2), 485-498. doi:10.1016/j.gloenvcha.2012.12.006Haasnoot, M., Middelkoop, H., Offermans, A., Beek, E. van, & Deursen, W. P. A. van. (2012). Exploring pathways for sustainable water management in river deltas in a changing environment. Climatic Change, 115(3-4), 795-819. doi:10.1007/s10584-012-0444-2Hallegatte, S. (2009). Strategies to adapt to an uncertain climate change. Global Environmental Change, 19(2), 240-247. doi:10.1016/j.gloenvcha.2008.12.003Hartford, D. N. D., & Baecher, G. B. (2004). Risk and uncertainty in dam safety. doi:10.1680/rauids.32705Harvey, H., Hall, J., & Peppé, R. (2011). Computational decision analysis for flood risk management in an uncertain future. Journal of Hydroinformatics, 14(3), 537-561. doi:10.2166/hydro.2011.055Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1108. doi:10.1175/2009bams2607.1Heal, G., & Millner, A. (2014). Reflections. Review of Environmental Economics and Policy, 8(1), 120-137. doi:10.1093/reep/ret023Jones, R. N. (2000). Climatic Change, 45(3/4), 403-419. doi:10.1023/a:1005551626280Kaplan, S. (1997). The Words of Risk Analysis. Risk Analysis, 17(4), 407-417. doi:10.1111/j.1539-6924.1997.tb00881.xKENDALL, M. G. (1938). A NEW MEASURE OF RANK CORRELATION. Biometrika, 30(1-2), 81-93. doi:10.1093/biomet/30.1-2.81Khatri, K., & Vairavamoorthy, K. (2011). A New Approach of Decision Making under Uncertainty for Selecting a Robust Strategy: A Case of Water Pipes Failure. Vulnerability, Uncertainty, and Risk. doi:10.1061/41170(400)116Kingston, D. G., Todd, M. C., Taylor, R. G., Thompson, J. R., & Arnell, N. W. (2009). Uncertainty in the estimation of potential evapotranspiration under climate change. Geophysical Research Letters, 36(20). doi:10.1029/2009gl040267Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010). Challenges in Combining Projections from Multiple Climate Models. Journal of Climate, 23(10), 2739-2758. doi:10.1175/2009jcli3361.1Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios. Management Science, 52(4), 514-528. doi:10.1287/mnsc.1050.0472Lempert, R., Popper, S., & Bankes, S. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. doi:10.7249/mr1626Levitan, S., & Thomson, R. (2009). The Application of Expected-Utility Theory to the Choice of Investment Channels in a Defined-Contribution Retirement Fund. ASTIN Bulletin, 39(2), 615-647. doi:10.2143/ast.39.2.2044651Leyva López, J. C., & Alvarez Carrillo, P. A. (2014). Accentuating the rank positions in an agreement index with reference to a consensus order. International Transactions in Operational Research, 22(6), 969-995. doi:10.1111/itor.12146Lind, N. (2007). Discounting risks in the far future. Reliability Engineering & System Safety, 92(10), 1328-1332. doi:10.1016/j.ress.2006.09.001Luo, K., Xu, Y., Zhang, B., & Zhang, H. (2016). Creating an acceptable consensus ranking for group decision making. Journal of Combinatorial Optimization, 36(1), 307-328. doi:10.1007/s10878-016-0086-9Meila M. K. Phadnis A. Patterson and J. A. Bilmes. 2012. “Consensus ranking under the exponential model.” Preprint submitted June 20 2012. http://arxiv.org/abs/1206.5265.Miao, D. Y., Li, Y. P., Huang, G. H., Yang, Z. F., & Li, C. H. (2014). Optimization Model for Planning Regional Water Resource Systems under Uncertainty. Journal of Water Resources Planning and Management, 140(2), 238-249. doi:10.1061/(asce)wr.1943-5452.0000303Minville, M., Brissette, F., & Leconte, R. (2010). Impacts and Uncertainty of Climate Change on Water Resource Management of the Peribonka River System (Canada). Journal of Water Resources Planning and Management, 136(3), 376-385. doi:10.1061/(asce)wr.1943-5452.0000041Morales-Torres, A., Escuder-Bueno, I., Serrano-Lombillo, A., & Castillo Rodríguez, J. T. (2019). Dealing with epistemic uncertainty in risk-informed decision making for dam safety management. Reliability Engineering & System Safety, 191, 106562. doi:10.1016/j.ress.2019.106562Morales-Torres, A., Serrano-Lombillo, A., Escuder-Bueno, I., & Altarejos-García, L. (2016). The suitability of risk reduction indicators to inform dam safety management. Structure and Infrastructure Engineering, 1-12. doi:10.1080/15732479.2015.1136830Neumayer, E., & Barthel, F. (2011). Normalizing economic loss from natural disasters: A global analysis. Global Environmental Change, 21(1), 13-24. doi:10.1016/j.gloenvcha.2010.10.004New, M., & Hulme, M. (2000). Integrated Assessment, 1(3), 203-213. doi:10.1023/a:1019144202120Palmieri, A., Shah, F., & Dinar, A. (2001). Economics of reservoir sedimentation and sustainable management of dams. Journal of Environmental Management, 61(2), 149-163. doi:10.1006/jema.2000.0392Park, T., Kim, C., & Kim, H. (2013). Valuation of Drainage Infrastructure Improvement Under Climate Change Using Real Options. Water Resources Management, 28(2), 445-457. doi:10.1007/s11269-013-0492-zPate-Cornell, E. (2002). Risk and Uncertainty Analysis in Government Safety Decisions. Risk Analysis, 22(3), 633-646. doi:10.1111/0272-4332.00043Pittock, A. B., Jones, R. N., & Mitchell, C. D. (2001). Probabilities will help us plan for climate change. Nature, 413(6853), 249-249. doi:10.1038/35095194Roach, T., Kapelan, Z., Ledbetter, R., & Ledbetter, M. (2016). Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty. Journal of Water Resources Planning and Management, 142(9), 04016028. doi:10.1061/(asce)wr.1943-5452.0000660Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., … Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3-4), 125-161. doi:10.1016/j.earscirev.2010.02.004Serrano-Lombillo A. A. Morales-Torres I. Escuder-Bueno and L. Altarejos-García. 2013. “Sharing experience for safe and sustainable water storage.” In Proc. 9th ICOLD European Club Symp. Bergamo Italy: Italian Committee on Large Dams.Spence, C. M., & Brown, C. M. (2018). Decision Analytic Approach to Resolving Divergent Climate Assumptions in Water Resources Planning. Journal of Water Resources Planning and Management, 144(9), 04018054. doi:10.1061/(asce)wr.1943-5452.0000939Street, R. B., & Nilsson, C. (2014). Introduction to the Use of Uncertainties to Inform Adaptation Decisions. Adapting to an Uncertain Climate, 1-16. doi:10.1007/978-3-319-04876-5_1Swart, R. ., Raskin, P., & Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137-146. doi:10.1016/j.gloenvcha.2003.10.002Walker, W., Haasnoot, M., & Kwakkel, J. (2013). Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability, 5(3), 955-979. doi:10.3390/su5030955Walker, W. E., Rahman, S. A., & Cave, J. (2001). Adaptive policies, policy analysis, and policy-making. European Journal of Operational Research, 128(2), 282-289. doi:10.1016/s0377-2217(00)00071-0Walsh, J., Wuebbles, D., Hayhoe, K., Kossin, J., Kunkel, K., Stephens, G., … Somerville, R. (2014). Ch. 2: Our Changing Climate. Climate Change Impacts in the United States: The Third National Climate Assessment. doi:10.7930/j0kw5cxtWeigel, A. P., Knutti, R., Liniger, M. A., & Appenzeller, C. (2010). Risks of Model Weighting in Multimodel Climate Projections. Journal of Climate, 23(15), 4175-4191. doi:10.1175/2010jcli3594.1Wilby, R. L., & Dessai, S. (2010). Robust adaptation to climate change. Weather, 65(7), 180-185. doi:10.1002/wea.543Zhang, S. X., & Babovic, V. (2011). A real options approach to the design and architecture of water supply systems using innovative water technologies under uncertainty. Journal of Hydroinformatics, 14(1), 13-29. doi:10.2166/hydro.2011.07

    Empirical Tool for the Assessment of Annual Overtopping Probabilities of Dams

    Full text link
    [EN] This paper presents a simple tool for the assessment of maximum overtopping probabilities of dams. The tool is based on empirical relations between the overtopping probability and the basic hydrological and hydraulic characteristics of the dam-reservoir system: the unit storage capacity, VF*, and the unit spillway capacity, QCap*, both weighted with the relative importance of the 1,000-year flood. The surface issued from the tool represents the limit above which no VF*-QCap* combination is statistically expected to offer a higher probability. The tool was calibrated using the detailed overtopping models of 342,233 synthetic cases generated from 30 existing dams and then validated against a set of 21 independent cases. The tool is useful when analyzing a portfolio of dams in previous screening phases of dam risk analysis. It aims at identifying overtopping as a relevant failure mode and easily classifying each dam in terms of its overtopping probability. The tool is also a support for the definition and prioritization of corrective measures since it assesses their impact in the overtopping probability reduction.Fluixá-Sanmartín, J.; Altarejos-García, L.; Morales-Torres, A.; Escuder Bueno, I. (2019). Empirical Tool for the Assessment of Annual Overtopping Probabilities of Dams. Journal of Water Resources Planning and Management. 145(1):1-12. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001017S112145

    The suitability of risk reduction indicators to inform dam safety management

    Full text link
    [EN] Risk analysis can provide very suitable and useful information to manage the safety of critical civil infrastructures. Indeed, results of quantitative risk models can be used to inform prioritisation of safety investments on infrastructures' assets and portfolios. In order to inform this prioritisation, a series of risk reduction indicators can be used. This paper reviews existing indicators for dam safety, tracks how equity and efficiency principles are captured, propose additional indicators and provides insights into how tolerability guidelines and benefit-cost analysis can also play a role in decision-making. All reviewed, analysed and/or combined indicators are later applied in a case study, a portfolio of 27 dams where 93 structural and non-structural investments are prioritised. The case study shows that prioritisation sequences based on risk model results provide suitable and useful information, acknowledging that other concerns may be conditioning decision-making processes. With the results of the case study, a full comparison between all studied risk reduction indicators is made, and three indexes are calculated for all of them to measure how close they are to a theoretical best.The Spanish Ministry of Economy and Competitiveness (MINECO) has supported the work described in this paper through the research project entitled IPRESARA (Incorporating man-made risk components into general dam risk management [BIA 2010-17852]) within the period 2011-2013 and the project INICIA (Methodology for assessing investments on water cycle infrastructures informed on risk and energy efficiency indicators [BIA 2013-48157-C2-1-R]) within the period 2014-2016.Morales Torres, A.; Serrano Lombillo, AJ.; Escuder Bueno, I.; Altarejos García, L. (2016). The suitability of risk reduction indicators to inform dam safety management. Structure and Infrastructure Engineering. 12(11):1465-1476. https://doi.org/10.1080/15732479.2015.1136830S146514761211Ayyub, B. M., McGill, W. L., & Kaminskiy, M. (2007). Critical Asset and Portfolio Risk Analysis: An All-Hazards Framework. Risk Analysis, 27(4), 789-801. doi:10.1111/j.1539-6924.2007.00911.xBaecher, G. B., Paté, M. E., & De Neufville, R. (1980). Risk of dam failure in benefit-cost analysis. Water Resources Research, 16(3), 449-456. doi:10.1029/wr016i003p00449Bohnenblust, H. (1998). Risk-Based Decision Making in the Transportation Sector. Quantified Societal Risk and Policy Making, 132-153. doi:10.1007/978-1-4757-2801-9_14Bottelberghs, P. . (2000). Risk analysis and safety policy developments in the Netherlands. Journal of Hazardous Materials, 71(1-3), 59-84. doi:10.1016/s0304-3894(99)00072-2De Blaeij, A., Florax, R. J. G. ., Rietveld, P., & Verhoef, E. (2003). The value of statistical life in road safety: a meta-analysis. Accident Analysis & Prevention, 35(6), 973-986. doi:10.1016/s0001-4575(02)00105-7Ellingwood, B. R. (2005). Risk-informed condition assessment of civil infrastructure: state of practice and research issues. Structure and Infrastructure Engineering, 1(1), 7-18. doi:10.1080/15732470412331289341Figueira, J., Greco, S., & Ehrogott, M. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science. doi:10.1007/b100605Jonkman, S. N., Jongejan, R., & Maaskant, B. (2010). The Use of Individual and Societal Risk Criteria Within the Dutch Flood Safety Policy-Nationwide Estimates of Societal Risk and Policy Applications. Risk Analysis, 31(2), 282-300. doi:10.1111/j.1539-6924.2010.01502.xJonkman, S. N., van Gelder, P. H. A. J. M., & Vrijling, J. K. (2003). An overview of quantitative risk measures for loss of life and economic damage. Journal of Hazardous Materials, 99(1), 1-30. doi:10.1016/s0304-3894(02)00283-2Joshi, N. N., & Lambert, J. H. (2007). Equity Metrics With Risk, Performance, and Cost Objectives for the Prioritization of Transportation Projects. IEEE Transactions on Engineering Management, 54(3), 539-547. doi:10.1109/tem.2007.900790Kabir, G., Sadiq, R., & Tesfamariam, S. (2013). A review of multi-criteria decision-making methods for infrastructure management. Structure and Infrastructure Engineering, 10(9), 1176-1210. doi:10.1080/15732479.2013.795978Kaplan, S. (1997). The Words of Risk Analysis. Risk Analysis, 17(4), 407-417. doi:10.1111/j.1539-6924.1997.tb00881.xKeeney, R. L., & Raiffa, H. (1993). Decisions with Multiple Objectives. doi:10.1017/cbo9781139174084Khadam, I. M., & Kaluarachchi, J. J. (2003). Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water. Environmental Impact Assessment Review, 23(6), 683-721. doi:10.1016/s0195-9255(03)00117-3Lutter, R., Morrall, J. F., & Viscusi, W. K. (1999). THE COST-PER-LIFE-SAVED CUTOFF FOR SAFETY-ENHANCING REGULATIONS. Economic Inquiry, 37(4), 599-608. doi:10.1111/j.1465-7295.1999.tb01450.xRamsberg, J. A. L., & Sjoberg, L. (1997). The Cost-Effectiveness of Lifesaving Interventions in Sweden. Risk Analysis, 17(4), 467-478. doi:10.1111/j.1539-6924.1997.tb00887.xSaaty, T. L. (1988). What is the Analytic Hierarchy Process? Mathematical Models for Decision Support, 109-121. doi:10.1007/978-3-642-83555-1_5Stewart, M. G., & Mueller, J. (2008). A risk and cost-benefit assessment of United States aviation security measures. Journal of Transportation Security, 1(3), 143-159. doi:10.1007/s12198-008-0013-0Viscusi, W. K., & Aldy, J. E. (2003). Journal of Risk and Uncertainty, 27(1), 5-76. doi:10.1023/a:1025598106257Vrijling, J. (1995). A framework for risk evaluation. Journal of Hazardous Materials, 43(3), 245-261. doi:10.1016/0304-3894(95)91197-vYamano, N., & Ohkawara, T. (2000). The Regional Allocation of Public Investment: Efficiency or Equity? Journal of Regional Science, 40(2), 205-229. doi:10.1111/0022-4146.0017

    A new risk reduction indicator for dam safety management combining efficiency and equity principles

    Full text link
    [EN] Large dams are critical infrastructures whose failure could produce high economic and social consequences. Risk analysis has been shown to be a suitable methodology to assess these risks and to inform dam safety management. In this sense, risk reduction indicators are a useful tool to manage risk results, yielding potential prioritisation sequences of investments in dams portfolios. Risk management is usually informed by two basic principles: efficiency and equity. These two principles many times conflict, requiring a tradeoff between optimising the expenditures and providing a high level of protection to all individuals. In this paper, the risk reduction indicator Equity Weighted Adjusted Cost per Statistical Life Saved (EWACSLS) is presented. This indicator allows obtaining prioritisation sequences of investments while maintaining an equilibrium between equity and efficiency principles. In order to demonstrate its usefulness, it has been applied in a real-world case study, a portfolio of 27 dams where 93 structural and non-structural investments are prioritised. The EWACSLS indicator is analysed in detail and its results are compared with other existing risk reduction indicators, showing its flexibility and how it can be a very well balanced indicator for the purpose of prioritisation of risk reduction measures.This paper was published with the support of the research project ‘INICIA’ (Methodology for Assessing Investments on Water Cycle Infrastructures informed on Risk and Energy Efficiency Indicators, BIA2013-48157-C2- 1-R, 2014-2016); co-funded by the Spanish Ministry of Economy and Competitiveness ‘Ministerio de Economía y Competitividad’ (Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad); and the European Regional Development Fund (ERDF).Serrano Lombillo, AJ.; Morales Torres, A.; Escuder Bueno, I.; Altarejos García, L. (2016). A new risk reduction indicator for dam safety management combining efficiency and equity principles. Structure and Infrastructure Engineering. 13(9):1157-1166. https://doi.org/10.1080/15732479.2016.1245762S11571166139Blackorby, C., & Donaldson, D. (1977). Utility vs equity. Journal of Public Economics, 7(3), 365-381. doi:10.1016/0047-2727(77)90055-xBleichrodt, H. (1997). Health utility indices and equity considerations. Journal of Health Economics, 16(1), 65-91. doi:10.1016/s0167-6296(96)00508-5De Blaeij, A., Florax, R. J. G. ., Rietveld, P., & Verhoef, E. (2003). The value of statistical life in road safety: a meta-analysis. Accident Analysis & Prevention, 35(6), 973-986. doi:10.1016/s0001-4575(02)00105-7(2001). The Economic Journal, 111(471). doi:10.1111/ecoj.2001.111.issue-471Dolan, P. (1998). The measurement of individual utility and social welfare. Journal of Health Economics, 17(1), 39-52. doi:10.1016/s0167-6296(97)00022-2Dundar, H. (1999). Equity, quality and efficiency effects of reform in Turkish higher education. Higher Education Policy, 12(4), 343-366. doi:10.1016/s0952-8733(99)00016-1Jonkman, S. N., van Gelder, P. H. A. J. M., & Vrijling, J. K. (2003). An overview of quantitative risk measures for loss of life and economic damage. Journal of Hazardous Materials, 99(1), 1-30. doi:10.1016/s0304-3894(02)00283-2Joshi, N. N., & Lambert, J. H. (2007). Equity Metrics With Risk, Performance, and Cost Objectives for the Prioritization of Transportation Projects. IEEE Transactions on Engineering Management, 54(3), 539-547. doi:10.1109/tem.2007.900790(1997). Risk Analysis, 17(4). doi:10.1111/risk.1997.17.issue-4Khadam, I. M., & Kaluarachchi, J. J. (2003). Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water. Environmental Impact Assessment Review, 23(6), 683-721. doi:10.1016/s0195-9255(03)00117-3Linnerooth-Bayer, J., & Amendola, A. (2000). Global Change, Natural Disasters and Loss-sharing: Issues of Efficiency and Equity. Geneva Papers on Risk and Insurance - Issues and Practice, 25(2), 203-219. doi:10.1111/1468-0440.00060(1999). Economic Inquiry, 37(4). doi:10.1111/ecin.1999.37.issue-4Morales-Torres, A., Serrano-Lombillo, A., Escuder-Bueno, I., & Altarejos-García, L. (2016). The suitability of risk reduction indicators to inform dam safety management. Structure and Infrastructure Engineering, 1-12. doi:10.1080/15732479.2015.1136830(2011). Risk Analysis, 31(6). doi:10.1111/risk.2011.31.issue-6Stewart, M. G., & Mueller, J. (2008). A risk and cost-benefit assessment of United States aviation security measures. Journal of Transportation Security, 1(3), 143-159. doi:10.1007/s12198-008-0013-0Yamano, N., & Ohkawara, T. (2000). The Regional Allocation of Public Investment: Efficiency or Equity? Journal of Regional Science, 40(2), 205-229. doi:10.1111/0022-4146.0017

    Dealing with epistemic uncertainty in risk-informed decision making for dam safety management

    Full text link
    [EN] In recent years, the application of risk analysis to inform dam safety governance has increased significantly. In this framework, considering explicitly and independently both natural and epistemic uncertainty in quantitative risk models allows to understand the sources of uncertainty in risk results and to estimate the effect of actions, tests, and surveys to reduce epistemic uncertainty. In this paper, Indexes of Coincidence are proposed to analyze the effect of epistemic uncertainty in the prioritization of investments based on risk results, which is the key issue in this paper. These indexes allow consideration of the convenience of conducting additional uncertainty reduction actions. These metrics have been applied to the prioritization of risk reduction measures for four concrete gravity dams in Spain. Results allow for a better understanding of how epistemic uncertainty of geotechnical resistance parameters influence risk-informed decision making. The proposed indexes are also useful for probabilistic risk analyses of other civil engineering structures with high epistemic uncertainty environments, since they analyze whether existing uncertainty could have an impact on decision making, outlining the need for extra studies, surveys and tests.Morales Torres, A.; Escuder Bueno, I.; Serrano Lombillo, AJ.; Castillo-Rodríguez, J. (2019). Dealing with epistemic uncertainty in risk-informed decision making for dam safety management. Reliability Engineering & System Safety. 191. https://doi.org/10.1016/j.ress.2019.106562S19

    Quantifying the Impact on Stormwater Management of an Innovative Ceramic Permeable Pavement Solution

    Full text link
    [EN] Stormwater management in cities has traditionally been based on centralized systems, evacuating runoff as quickly as possible through drainage networks that collect and convey the runoff to the final point of treatment or the receiving water body. In recent years, a different approach focused on the use of Sustainable Urban Drainage Systems (SUDS) represents a paradigm shift, promoting a decentralized management as close to the runoff source as possible. Among these techniques, permeable pavements represent an effective solution for reducing runoff and providing pollutant treatment. This contribution describes the results obtained from an innovative ceramic permeable pavement developed as part of the LIFE CERSUDS project in the city of Benicassim (Spain). This pavement, composed by modules built from ceramic tiles in stock, allows water infiltration, runoff treatment and water reuse as part of a SUDS built in 2018 and monitored from September 2018 to September 2019. The purpose of the research was to demonstrate the hydraulic performance of the proposed solution through monitoring of runoff quantity and quality variables. Monitoring data analysis have shown positive results, reducing peak runoff rates and the volume of water which is conducted downstream. From the hydrological point of view, the system capacity shown a 100% runoff management for events up to 15-25 mm of precipitation. This is a very significant threshold since these values represent, respectively, the 81% and 91% percentiles for the study area. System performance was confirmed in terms of runoff management and water infiltration. This demonstration case study represents a reference example of urban retrofitting actions which integrate social, economic and environmental aspects.This research was developed within the LIFE CERSUDS project and was financed by the LIFE Programme 2014-2020 of the European Union for the Environment and Climate Action [Reference LIFE15 CCA/ES/000091] with the collaboration of the Generalitat Valenciana through IVACE.Castillo-Rodríguez, JT.; Andrés Doménech, I.; Martín Monerris, M.; Escuder Bueno, I.; Perales-Momparler, S.; Mira-Peidro, J. (2021). Quantifying the Impact on Stormwater Management of an Innovative Ceramic Permeable Pavement Solution. Water Resources Management. 35(4):1251-1271. https://doi.org/10.1007/s11269-021-02778-7S1251127135

    Inclusión en modelos de riesgo de presas de una metodología de estimación hidrológica basada en técnicas de Monte Carlo

    Get PDF
    En este estudio se aplica una metodología de obtención de las leyes de frecuencia derivadas (de caudales máximo vertidos y niveles máximos alcanzados) en un entorno de simulaciones de Monte Carlo, para su inclusión en un modelo de análisis de riesgo de presas. Se compara su comportamiento respecto del uso de leyes de frecuencia obtenidas con las técnicas tradicionalmente utilizadas

    The role of monitoring sustainable drainage systems for promoting transition towards regenerative urban built environments: a case study in the Valencian region, Spain

    Full text link
    [EN] Sustainable drainage systems are an alternative and holistic approach to conventional urban stormwater management that use and enhance natural processes to mimic pre-development hydrology, adding a number of well-recognized, although not so often quantified benefits. However, transitions towards regenerative urban built environments that widely incorporate sustainable drainage systems are "per se" innovative journeys that encounter barriers which include the limited evidence on the performance of these systems which, in many countries, are still unknown to professionals and decision makers. A further important barrier is the frequently poor interaction among stakeholders; key items such as sustainable drainage systems provide collective benefits which also demand collective efforts. With the aim of overcoming such innovation-driven barriers, six showcase projects (including rain gardens acting as infiltration basins, swales and a green roof) to demonstrate the feasibility and suitability of sustainable drainage systems were developed and/or retrofitted in two cities of the Valencian region of Spain as a part of an European project, and their performance was monitored for a year. The data acquired, after being fully analyzed and presented to a group of key regional stakeholders, is proving to be a valuable promoter of the desired transition (for instance in influencing the support to SuDS in recent regional legislation). This paper presents detailed data on how these urban ecological drainage infrastructure elements reduce runoff (peak flows and volumes) and improve its quality, contributing to the goal of healthier and livable cities. The data show that the pilots have good hydraulic performance under a typical Mediterranean climate and also provided water quality benefits. Furthermore, it shows how engagement can contribute to smarter governance in the sense of smoothing the difficulties faced by innovation when being presented, understood, and endorsed by professionals and decision-makers in the field of stormwater management. Finally, activities undertaken in the demonstration sites monitored, show how they have been drivers of innovation and transition towards a new stormwater paradigm in Spain, serving as a reference to other urban areas in the Mediterranean. (C) 2016 Elsevier Ltd. All rights reserved.This research has been conducted as part of the Life+ program project "AQUAVAL: Sustainable Urban Water Management Plans, promoting SUDS and considering climate change, in the province of Valencia" (Life08ENV/E/000099) and the MED program project "E2STORMED: Improvement of energy efficiency in the water cycle by the use of innovative stormwater management in smart Mediterranean cities" (1C-MED12-14), both supported by European Regional Development Fund (ERDF) funding of the European Union.Momparler Perales, S.; Andrés Doménech, I.; Hernández Crespo, C.; Vallés-Morán, FJ.; Martín Monerris, M.; Escuder Bueno, I.; Andreu Álvarez, J. (2017). The role of monitoring sustainable drainage systems for promoting transition towards regenerative urban built environments: a case study in the Valencian region, Spain. Journal of Cleaner Production. 163:113-124. doi:10.1016/j.jclepro.2016.05.153S11312416
    corecore