Articles | Volume 21, issue 10
https://doi.org/10.5194/nhess-21-3057-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-3057-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Are interactions important in estimating flood damage to economic entities? The case of wine-making in France
David Nortes Martínez
G-EAU, Univ. Montpellier, AgroParisTech, CIRAD, IRD, INRAE, Montpellier SupAgro, Montpellier, France
G-EAU, Univ. Montpellier, AgroParisTech, CIRAD, IRD, INRAE, Montpellier SupAgro, Montpellier, France
Pauline Brémond
G-EAU, Univ. Montpellier, AgroParisTech, CIRAD, IRD, INRAE, Montpellier SupAgro, Montpellier, France
Stefano Farolfi
CIRAD, UMR G-EAU, 34398 Montpellier, France
CEE-M, Univ. Montpellier, 34090 Montpellier, France
Juliette Rouchier
LAMSADE, CNRS, PSL (Université Paris-Dauphine), Paris, France
Related authors
David Nortes Martinez, Frédéric Grelot, Cécile Choley, and Pascal Finaud-Guyot
Proc. IAHS, 385, 247–252, https://doi.org/10.5194/piahs-385-247-2024, https://doi.org/10.5194/piahs-385-247-2024, 2024
Short summary
Short summary
Classical hydraulic approaches of urban floods do not consider flow exchanges between streets and buildings, which might be introducing a bias in the estimation of property damage. Using coupled hydraulic-economic models we analyze the effect of considering porous buildings in the assessment of material damage at a district level. Our results show potentially significant differences in flood damage when using porous buildings in comparison with more classic approaches.
David Nortes Martinez, Frédéric Grelot, Cécile Choley, and Pascal Finaud-Guyot
Proc. IAHS, 385, 247–252, https://doi.org/10.5194/piahs-385-247-2024, https://doi.org/10.5194/piahs-385-247-2024, 2024
Short summary
Short summary
Classical hydraulic approaches of urban floods do not consider flow exchanges between streets and buildings, which might be introducing a bias in the estimation of property damage. Using coupled hydraulic-economic models we analyze the effect of considering porous buildings in the assessment of material damage at a district level. Our results show potentially significant differences in flood damage when using porous buildings in comparison with more classic approaches.
Pauline Brémond, Anne-Laurence Agenais, Frédéric Grelot, and Claire Richert
Nat. Hazards Earth Syst. Sci., 22, 3385–3412, https://doi.org/10.5194/nhess-22-3385-2022, https://doi.org/10.5194/nhess-22-3385-2022, 2022
Short summary
Short summary
It is impossible to protect all issues against flood risk. To prioritise protection, economic analyses are conducted. The French Ministry of the Environment wanted to make available damage functions that we have developed for several sectors. For this, we propose a methodological framework and apply it to the model we have developed to assess damage to agriculture. This improves the description, validation, transferability and updatability of models based on expert knowledge.
Cited articles
Barendrecht, M. H., Viglione, A., and Blöschl, G.: A dynamic framework for flood risk, Water Secur., 1, 3–11, https://doi.org/10.1016/j.wasec.2017.02.001, 2017. a
Bauduceau, N.: Éléments d'analyse des répercussions des inondations de novembre 1999 sur les activités agricoles des départements de l'Aude, des Pyrénées-Orientales et du Tarn, Tech. rep., Équipe pluridisciplinaire Plan Loire Grandeur Nature, Orleans, France, 2001. a
Bosello, F. and Standardi, G.: A Sub-national CGE Model for the European
Mediterranean Countries, in: The New Generation of Computable General
Equilibrium Models: Modeling the Economy, edited by: Perali, F. and Scandizzo, P. L., Springer International Publishing, 279–308, https://doi.org/10.1007/978-3-319-58533-8_11, 2018. a
Brémond, P. and Grelot, F.: Taking into account recovery to assess
vulnerability: application to farms exposed to flooding, in: Managing Resources of a Limited Planet, 2012 International Congress on Environmental
Modelling and Software, edited by: Seppelt, R., Voinov, A. A., Lange, S., and
Bankamp, D., International Environmental Modelling and Software Society,
Leipzig, Germany, 2012. a
Brémond, P., Grelot, F., and Agenais, A.-L.: Review Article: “Flood
damage assessment on agricultural areas: review and analysis of existing
methods”, Nat. Hazards Earth Syst. Sci., 13, 2493–2512,
https://doi.org/10.5194/nhess-13-2493-2013, 2013. a, b, c
Brouwer, R. and van Elk, R.: Integrated ecological, economic and social impact assessment of alternative flood control policies in the Netherlands,
Ecol. Econ., 50, 1–21, https://doi.org/10.1016/j.ecolecon.2004.01.020, 2004. a
Brouwers, L. and Boman, M.: A computational agent model of flood management
strategies, in: Computational Methods for Agricultural Research: Advances and
Applications, chap. 14, edited by: do Prado, H. A., Luiz, A. J. B., and Filho, H. C., IGI Global, Hershey, PA, 2010. a
Carrera, L., Standardi, G., Bosello, F., and Mysiak, J.: Assessing direct and
indirect economic impacts of a flood event through the integration of spatial
and computable general equilibrium modelling, Environ. Model. Softw., 63, 109–122, 2015. a
CCMSA: Conférence de presse de rentrée de la CCMSA, Mutualité
Sociale Agricole, Direction de la communication – Service Presse, available at: https://www.msa.fr/lfy/documents/98830/41910604/Dossier de presse de la conference de presse de rentree 2017/77fafd3b-bc63-44a0-9808-8168b3bac248 (last access: January 2018), 2017. a, b
Chambre d'agriculture Var: Action 25 du PAPI d'intention de l'Argens,
Réalisation de diagnostics de vulnérabilité d'installations
agricoles en zone inondable, France, 2014. a
Chevet, J. M.: Le rôle des caves coopératives dans le regroupement de
l'offre en France au XXème siècle, Tech. Rep. 2004-12, INRA-CORELA
working papers, available at:
https://www6.versailles-grignon.inra.fr/aliss/content/download/3348/35635/file/WP04-12.pdf (last access: 11 October 2021) 2004. a, b, c
Chongvilaivan, A.: Thailand's 2011 flooding: Its impact on direct exports and
global supply chains, ARTNeT Working Paper Series 113, ARTNeT – Asia-Pacific Research and Training Network on Trade, ESCAP, Bangkok, available at: https://www.unescap.org/sites/default/files/AWP No. 113.pdf (last access: 11 October 2021), 2012. a
Cochrane, H. C.: Indirect Losses from Natural Disasters: Measurement and Myth, in: Modeling Spatial and Economic Impacts of Disasters, Advances in Spatial Science, chap. 3, edited by: Okuyama, Y. and Chang, S. E., Springer,
Berlin, Heidelberg, 37–52, https://doi.org/10.1007/978-3-540-24787-6_3, 2004. a
Collombat, P.-Y.: Rapport d'information fait au nom de la mission commune
d'information sur les inondations qui se sont produites dans le Var, et plus
largement, dans le sud-est de la France au mois de novembre 2011, available at: https://www.senat.fr/rap/r11-775/r11-7751.pdf (last access: 11 October 2021), 2012. a
Crawford-Brown, D., Syddall, M., Guan, D., Hall, J., Li, J., Jenkins, K., and
Beaven, R.: Vulnerability of London's Economy to Climate Change: Sensitivity to Production Loss, J. Environ. Protect., 4, 548–563, 2013. a
Crespi, V., Galstyan, A., and Lerman, K.: Top-down vs bottom-up methodologies
in multi-agent system design, Autonom. Robots, 24, 303–313,
https://doi.org/10.1007/s10514-007-9080-5, 2008. a
Dawson, R. J., Peppe, R., and Wang, M.: An agent-based model for risk-based
flood incident management, Nat. Hazards, 59, 167–189,
https://doi.org/10.1007/s11069-011-9745-4, 2011. a
Donaghy, K. P., Balta-Ozkan, N., and Hewings, G. J.: Modeling Unexpected Events in Temporally Disaggregated Econometric Input-Output Models of Regional Economies, Econ. Syst. Res., 19, 125–145, https://doi.org/10.1080/09535310701328484, 2007. a
Dubbelboer, J., Nikolic, I., Jenkins, K., and Hall, J.: An Agent-Based Model of Flood Risk and Insurance, J. Artif. Soc. Social Simul., 20, 6, https://doi.org/10.18564/jasss.3135, 2017. a, b
EEA: Corine Land Cover, European Environment Agency, available at:
https://land.copernicus.eu/pan-european/corine-land-cover (last access: 11 October 2021), 2012. a
Erdlenbruch, K. and Bonté, B.: Simulating the dynamics of individual
adaptation to floods, Environ. Sci. Policy, 84, 134–148, https://doi.org/10.1016/j.envsci.2018.03.005, 2018. a, b
Erdlenbruch, K., Thoyer, S., Grelot, F., Kast, R., and Enjolras, G.:
Risk-sharing policies in the context of the French Flood Prevention Action
Programmes, J. Environ. Manage., 91, 363–369, https://doi.org/10.1016/j.jenvman.2009.09.002, 2009. a, b
Ferrarese, C. and Mazzoli, E.: Analysis of Local Economic Impacts Using a
Village Social Accounting Matrix: The Case of Oaxaca, in: The New Generation of Computable General Equilibrium Models: Modeling the Economy, edited by: Perali, F. and Scandizzo, P. L., Springer International Publishing, 85–116, https://doi.org/10.1007/978-3-319-58533-8_5, 2018. a
Field, C. B., Barros, V., Stocker, T. F., Dahe, Q., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M., and Midgley, P. M.: Managing the Risks of Extreme Events and Disasters to
Advance Climate Change Adaptation, in: A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University
Press, Cambridge, 2012. a, b
Filatova, T.: Empirical agent-based land market: Integrating adaptive economic behavior in urban land-use models, Computers, Environ. Urban Syst.,
54, 397–413, https://doi.org/10.1016/j.compenvurbsys.2014.06.007, 2015. a, b
Filatova, T., Veen, A. V. D., and Parker, D. C.: Land Market Interactions
between Heterogeneous Agents in a Heterogeneous Landscape – Tracing the
Macro-Scale Effects of Individual Trade-Offs between Environmental Amenities
and Disamenities, Can. J. Agricult. Econ., 57, 431–457, https://doi.org/10.1111/j.1744-7976.2009.01164.x, 2009. a, b
Filatova, T., Parker, D., and van der Veen, A.: The Implications of Skewed Risk Perception for a Dutch Coastal Land Market: Insights from an Agent-Based
Computational Economics Model, Agricult. Resour. Econ. Rev., 40, 3, https://doi.org/10.1017/S1068280500002860, 2011. a, b
Grames, J., Prskawetz, A., Grass, D., Viglione, A., and Blöschl, G.:
Modeling the interaction between flooding events and economic growth, Ecol. Econ., 129, 193–209, https://doi.org/10.1016/j.ecolecon.2016.06.014, 2016. a
Grames, J., Grass, D., Kort, P. M., and Fürnkranz-Prskawetz, A.: Optimal
investment and location decisions of a firm in a flood risk area using
Impulse Control Theory, ECON WPS – Vienna University of Technology Working
Papers inEconomic Theory and Policy No. 01/2017, Vienna University of
Technology, Vienna, 2017. a
Grelot, F., Bertrand, C., Besson, P., Bonté, B., Brémond, P., Cherel, J.-P., Collard, A.-L., Défossez, S., Erdlenbruch, K., Heaumé, C., Moatty, A., Nortes Martinez, D., Payan, C., Richert, C., Sanseverino-Godfrin, V., Vinet, F., and Zerluth, N.: Résilience des territoires face à l'inondation. Pour une approche préventive par l'adaptation post-événement, Rapport final, Ministère de l'Écologie, du Développement Durable et de l'Énergie, 2017. a
Haer, T., Botzen, W. W., and Aerts, J. C.: The effectiveness of flood risk
communication strategies and the influence of social networks – Insights from an agent-based model, Environ. Sci. Policy, 60, 44–52,
https://doi.org/10.1016/j.envsci.2016.03.006, 2016a. a, b
Haer, T., Wouter Botzen, W. J., de Moel, H., and Aerts, J. C. J. H.:
Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An
Agent-Based Model Approach, Risk Anal., 37, 1977–1992, https://doi.org/10.1111/risa.12740, 2016b. a, b
Hallegatte, S.: An Adaptive Regional Input-Output Model and its Application to the Assessment of the Economic Cost of Katrina, Risk Anal., 28, 779–799, 2008. a
Hallegatte, S.: Modeling the Role of Inventories and Heterogeneity in the
Assessment of the Economic Costs of Natural Disasters, Risk Anal., 34, 152–167, 2014. a
Hallegatte, S. and Ghil, M.: Natural disasters impacting a macroeconomic model with endogenous dynamics, Ecol. Econ., 68, 582–592, 2008. a
Hallegatte, S. and Przyluski, V.: The Economics of Natural Disasters. Concepts and Methods, Policy Research Working Paper 5507, The World Bank, 2010. a
Hallegatte, S., Hourcade, J.-C., and Dumas, P.: Why economic dynamics matter in assessing climate change damages: Illustration on extreme events, Ecological Economics, 62, 330–340, https://doi.org/10.1016/j.ecolecon.2006.06.006, 2007. a
Hess, T. M. and Morris, J.: Estimating the value of flood alleviation on
agricultural grassland, Agr. Water Manage., 15, 141–153, 1988. a
Jansen, J., van Ittersum, M., Janssen, S., and Reidsma, P.: Integrated
Assessment of Agricultural Systems at the European Level, Policy Brief LIAISE
– SEAMLESS, available at:
https://www.wur.nl/upload_mm/c/f/3/6f3b0caa-bf89-40a5-a0d9-0f9c5ad87cf5_Policy_brief_final_A.pdf
(last access: 11 October 2021), 2016. a
Jenkins, K., Surminski, S., Hall, J., and Crick, F.: Assessing surface water
flood risk and management strategies under future climate change: Insights
from an Agent-Based Model, Sci. Total Environ., 595, 159–168,
https://doi.org/10.1016/j.scitotenv.2017.03.242, 2017. a, b
Kajitani, Y. and Tatano, H.: Estimation of production capacity loss rate after the great east Japan earthquake and tsunami in 2011, Econ. Syst. Res., 26, 13–38, https://doi.org/10.1080/09535314.2013.872081, 2014. a
Kelly, S.: Estimating economic loss from cascading infrastructure failure: a
perspective on modelling interdependency, in: Infrastructure Complexity, Vol. 2, Springer, https://doi.org/10.1186/s40551-015-0010-y, 2015. a
Koks, E. E., Bockarjova, M., de Moel, H., and Aerts, J. C. J. H.: Integrated
Direct and Indirect Flood Risk Modeling: Development and Sensitivity
Analysis, Risk Anal., 35, 882–900, https://doi.org/10.1111/risa.12300, 2014. a
Koks, E. E., Carrera, L., Jonkeren, O., Aerts, J. C. J. H., Husby, T. G., Thissen, M., Standardi, G., and Mysiak, J.: Regional disaster impact analysis: comparing input–output and computable general equilibrium models, Nat. Hazards Earth Syst. Sci., 16, 1911–1924, https://doi.org/10.5194/nhess-16-1911-2016, 2016. a
Kreibich, H. and Bubeck, P.: Natural hazards: Direct Costs and Losses Due to
the Disruption of Production Processes, Tech. rep., UNISDR GAR, available at: https://www.preventionweb.net/publication/natural-hazards-direct-costs-and-losses-due-disruption-
production-processes
(last access: 11 October 2021), 2013. a, b, c
Linghe, Y. and Masato, A.: The impacts of natural disasters on global supply
chains, ARTNeT Working Paper Series 115, ARTNeT – Asia-Pacific Research and Training Network on Trade, ESCAP, Bangkok, 2012. a
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article “Assessment of economic flood damage”, Nat. Hazards Earth Syst. Sci., 10, 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010. a, b
Meyer, V., Becker, N., Markantonis, V., Schwarze, R., Aerts, J. C. J. H.,
van den Bergh, J. C. J. M., Bouwer, L. M., Bubeck, P., Ciavola, P., Daniel,
V., Genovese, E., Green, C., Hallegatte, S., Kreibich, H., Lequeux, Q.,
Lochner, B., Logar, I., Papyrakis, E., Pfurtscheller, C., Poussin, J.,
Przyluski, V., Thieken, A. H., Thompson, P., and Viavattene, C.: Costs of
Natural Hazards – A Synthesis, Tech. rep., European research project CONHAZ,
CONHAZ consortium, 2012. a
Meyer, V., Becker, N., Markantonis, V., Schwarze, R., van den Bergh, J. C. J. M., Bouwer, L. M., Bubeck, P., Ciavola, P., Genovese, E., Green, C., Hallegatte, S., Kreibich, H., Lequeux, Q., Logar, I., Papyrakis, E., Pfurtscheller, C., Poussin, J., Przyluski, V., Thieken, A. H., and Viavattene, C.: Review article: Assessing the costs of natural hazards – state of the art and knowledge gaps, Nat. Hazards Earth Syst. Sci., 13, 1351–1373, https://doi.org/10.5194/nhess-13-1351-2013, 2013. a, b, c, d, e, f, g
Morris, J. and Brewin, P.: The impact of seasonal flooding on agriculture: the spring 2012 floods in Somerset, England, J. Flood Risk Manage., 7, 128–140, https://doi.org/10.1111/jfr3.12041, 2014. a
Morris, J. and Hess, T. M.: Agricultural flood alleviation benefit assessment: a case study, J. Agricult. Econ., 39, 402–412, 1988. a
National Research Council: The Impacts of Natural Disasters: A Framework for
Loss Estimation, The National Academies Press, Washington, DC, 1999. a
OCDE: Étude de l'OCDE sur la gestion des risques d'inondation: la Seine en Île-de-France, Tech. rep., OECD Publishing, Paris, https://doi.org/10.1787/9789264207929-fr, 2014. a
Okuyama, Y. and Santos, J. R.: Disaster impact and input-output analysis,
Econ. Syst. Res., 26, 1–12, https://doi.org/10.1080/09535314.2013.871505, 2014. a
Oosterhaven, J. and Többen, J.: Wider economic impacts of heavy flooding in Germany: a non-linear programming approach, Spat. Econ. Anal., 12, 404–428, https://doi.org/10.1080/17421772.2017.1300680, 2017. a, b
Otto, C., Willner, S., Wenz, L., Frieler, K., and Levermann, A.: Modeling
loss-propagation in the global supply network: The dynamic agent-based model
acclimate, J. Econ. Dynam. Control, 83, 232–269,
https://doi.org/10.1016/j.jedc.2017.08.001, 2017. a
Penning-Rowsell, E. C., Priest, S. J., Parker, D. J., Morris, J., Tunstall, S. M., Viavattene, C., Chatterton, J., and Owen, D.: Flood and Coastal Erosion Risk Management: A Manual for Economic Appraisal, Routledge,
Isbn 9780203066393, https://doi.org/10.4324/9780203066393, 2013. a, b
Penning-Rowsell, E. C. and Green, C. H.: New Insights into the Appraisal of
Flood-Alleviation Benefits: (1) Flood Damage and Flood Loss Information, Water Environ. J., 14, 347–353, https://doi.org/10.1111/j.1747-6593.2000.tb00272.x, 2000. a
Posthumus, H., Morris, J., Hess, T. M., Neville, D., Philips, E., and Baylis,
A.: Impacts of the summer 2007 floods on agriculture in England, J. Flood Risk Manage., 2, 182–189, https://doi.org/10.1111/j.1753-318X.2009.01031.x, 2009. a
Przyluski, V. and Hallegatte, S.: Indirect Costs of Natural Hazards, CONHAZ
WP2 Final Report, SMASH–CIRED, CONHAZ consortium, 2011. a
Putra, H. C., Zhang, H., and Andrews, C.: Modeling Real Estate Market Responses to Climate Change in the Coastal Zone, J. Artif. Soc. Social Simul., 18, 18, https://doi.org/10.18564/jasss.2577, 2015. a, b
Rollins, N. D., Barton, C. M., Bergin, S., Janssen, M. A., and Lee, A.: A
Computational Model Library for publishing model documentation and code,
Environ. Model. Softw., 61, 59–64, https://doi.org/10.1016/j.envsoft.2014.06.022, 2014. a
Rose, A. and Liao, S. Y.: Modeling Regional Economic Resilience to Disasters: A Computable General Equilibrium Analysis of Water Service Disruptions, J. Reg. Sci., 45, 75–112, 2005. a
Rouchon, D., Peinturier, C., Christin, N., and Nicklaus, D.: Analyse
multicritère des projets de prévention des inondations – Guide
méthodologique 2018, Tech. rep., MTES, Paris, France, 2018. a
Safarzyńska, K., Brouwer, R., and Hofkes, M.: Evolutionary modelling of the macro-economic impacts of catastrophic flood events, Ecol. Econom., 88, 108–118, 2013. a
Santos, J. R., Yu, K. D. S., Pagsuyoin, S. A. T., and Tan, R. R.: Time-varying disaster recovery model for interdependent economic systems using hybrid input-output and event tree analysis, Econ. Syst. Res., 26, 60–80, https://doi.org/10.1080/09535314.2013.872602, 2014. a
Scawthorn, C., Flores, P., Blais, N., Seligson, H., Tate, E., Chang, S.,
Mifflin, E., Thomas, W., Murphy, J., Jones, C., and Lawrence, M.: HAZUS-MH
Flood Loss Estimation Methodology. II. Damage and Loss Assessment, Nat. Hazards Rev., 7, 72–81, 2006. a
Smajgl, A. and Barreteau, O.: Empiricism and Agent-Based Modelling, in:
Empirical Agent-Based Modelling – Challenges and Solutions, vol. 1, The Characterisation and Parameterisation of Empirical Agent-Based Models, chap. 1, edited by: Smajgl, A. and Barreteau, O., Springer-Verlag, New York,
https://doi.org/10.1007/978-1-4614-6134-0, 2014.
a
Smajgl, A. and Barreteau, O.: Framing options for characterising and
parameterising human agents in empirical ABM, Environ. Model. Softw., 93, 29–41, https://doi.org/10.1016/j.envsoft.2017.02.011, 2017. a
SwissRE: Natural catastrophes and man-made disasters in 2016: a year of
widespread damages, Sigma No. 2/2017, Swiss Reinsurance Company, Zurich, 2017. a
Tesfatsion, L.: Agent-Based Computational Economics: Growing Economies From the Bottom Up, Artif. Life, 8, 55–82, https://doi.org/10.1162/106454602753694765, 2002. a
Tonn, G. L. and Guikema, S. D.: An Agent-Based Model of Evolving Community
Flood Risk, Risk Anal., 38, 1258–1278, https://doi.org/10.1111/risa.12939, 2017. a, b
Van der Veen, A., Steenge, A. E., Bockarjova, M., and Logtmeijer, C. J. J.:
Structural economic effects of large scale inundation: a simulation of the
Krimpen dike breakage, Tech. rep., University of Twente, Twente, 2003. a
Viglione, A., Di Baldassarre, G., Brandimarte, L., Kuil, L., Carr, G., Salinas, J. L., Scolobig, A., and Blöschl, G.: Insights from socio-hydrology modelling on dealing with flood risk – Roles of collective memory, risk-taking attitude and trust, J. Hydrol., 518, 71–82,
https://doi.org/10.1016/j.jhydrol.2014.01.018, 2014. a
Vinet, F.: Crues et inondations dans la France méditerranéenne. Les
crues torrentielles des 12 et 13 novembre 1999 (Aude, Tarn,
Pyrénées-Orientales et Hérault), Questions de Géographie,
Éditions du Temps, Nantes, France, 2003. a
Xie, W., Li, N., Wu, J.-D., and Liu, X.-Q.: Evaluation of indirect loss from hypothetical catastrophes in two regions with different economic development levels in China, Nat. Hazards Earth Syst. Sci., 12, 3325–3335, https://doi.org/10.5194/nhess-12-3325-2012, 2012. a
Xie, W., Li, N., Wu, J.-D., and Hao, X.-L.: Modeling the economic costs of disasters and recovery: analysis using a dynamic computable general equilibrium model, Nat. Hazards Earth Syst. Sci., 14, 757–772, https://doi.org/10.5194/nhess-14-757-2014, 2014. a
Yang, L., Kajitani, Y., Tatano, H., and Jiang, X.: A methodology for estimating business interruption loss caused by flood disasters: insights from business surveys after Tokai Heavy Rain in Japan, Nat. Hazards, 84, 411–430, https://doi.org/10.1007/s11069-016-2534-3, 2016. a
Short summary
Estimating flood damage, although crucial for assessing flood risk and for designing mitigation policies, continues to face numerous challenges, notably the assessment of indirect damage. We focus on flood damage induced by the interactions between economic activities. By modeling the production processes of a cooperative wine-making system, we show that these interactions are important depending on their spatial and temporal characteristics.
Estimating flood damage, although crucial for assessing flood risk and for designing mitigation...
Altmetrics
Final-revised paper
Preprint