Skip to main content

Advertisement

SpringerLink
Log in

Spatial modelling of social and economic vulnerability to floods at the district level in Búzi, Mozambique

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

Abstract

In Mozambique, the district level is empowered through recent policies to carry out disaster risk reduction activities. To address risk and associated activities appropriately, both information on the occurrence and distribution of hazards and information on the spatial distribution of vulnerability have to be available. Within this paper, a method is presented to model the social and economic dimension of vulnerability in a spatial and integrative manner applying the geon approach. Homogenous vulnerability regions are identified through the application of integrated modelling approaches, which build on expert and upscaled local knowledge and weightings. A set of indicators is proposed, which allow the modelling of vulnerability in a data-scarce environment. Finally, the different perceptions between national experts and local community members are visualised through maps, and opportunities are discussed. Overall, the method and results presented aim to facilitate the identification of critical vulnerability areas through different views, serving the needs at decision makers’ and experts’ level.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Amarasinghe U, Samad M, Anputhas M (2005) Spatial clustering of rural poverty and food insecurity in Sri Lanka. Food Policy 30:493–509

    Article  Google Scholar 

  • Amoaka Johnson F, Hutton CW, Clark MJ (2008) Work package 4: Assessment of the human dimension (HD) Work Task 4.5: Vulnerability Mapping (Brahmaputra Basin). Report prepared within the BRAHMATWINN research project

  • Baatz M, Schäpe A (2000) Multiresolution Segmentation: an optimization approach for high quality multiscale image segmentation. In: Strobl J, Blaschke T, Griesebner G (eds) Proceedings of the Angewandte Geographische Informationsverarbeitung XII, pp 12–23

  • Benefo K, Schultz T (1996) Fertility and child mortality in Côte d’Ivoire and Ghana. World Bank Econ Rev 10:123–158

    Article  Google Scholar 

  • Benson T, Chamberlin J, Rhinehart I (2005) An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi. Food Policy 30(5–6):532–550

    Article  Google Scholar 

  • Berry J (2004) Optimal path analysis and corridor routing: infusing stakeholder perspective in calibration and weighting of model criteria. http://www.innovativegis.com/basis/present/GeoTec04/GIS04_Routing.htm. Accessed 18 August 2011

  • Birkmann J (2006) Measuring vulnerability to promote disaster-resilient societies: Conceptual frameworks and definitions. In: Birkmann J (ed) Measuring Vulnerability to Natural Hazards: towards disaster resilient societies. United Nations University Press, Tokyo, pp 9–54

    Google Scholar 

  • Blaikie P, Cannon T, Davis I, Wisner B (1994) At risk: natural hazards, people’s vulnerability and disasters. Routledge, London, New York

  • Bohle HG, Downing TE, Watts MJ (1994) Climate change and social vulnerability: the sociology and geography of food insecurity. Glob Environ Change 4:37–48

    Article  Google Scholar 

  • Boughton D et al (2007) Market participation by rural households in a Low-income country: an asset-based approach applied to Mozambique. Faith Econ 50:64–101

    Google Scholar 

  • Brooks N, Adger WN, Kelly PM (2005) The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. In: Global environmental change Part A15(2), Adaptation to climate change: perspectives across scales, pp 151–163

  • Burton I, Kates RW, White GF (1993) The Environment as Hazard. Guildford Press, New York and London

    Google Scholar 

  • Christaller W (1933) Zentrale Orte in Süddeutschland. Gustav Fischer, Jena

    Google Scholar 

  • Christie F, Hanlon J (2001) Mozambique & the Great Flood of 2000. African Issues, James Currey

    Google Scholar 

  • Clionadh R, Hegre H (2009) Population size, concentration and civil war: a geographically disaggregated analysis. Polit Geogr 28(4):224–238

    Article  Google Scholar 

  • Collischonn W, Pilar JV (2000) A direction dependent least-cost path algorithm for roads and canals. Int J Geogr Inf Sci 14(4):397–406

    Article  Google Scholar 

  • Costanza R et al (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260

    Article  Google Scholar 

  • Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate J, Webb J (2008) A place-based model for understanding community resilience to natural disasters. Glob Environ Change 18:598–606

    Article  Google Scholar 

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

    Article  Google Scholar 

  • de Koning GHJ, Veldkamp A, Fresco LO (1998) Land use in Ecuador: a statistical analysis at different aggregation levels. Agric Ecosyst Environ 70:231–247

    Article  Google Scholar 

  • Douglas DH (1994) Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica 31:37–51

    Article  Google Scholar 

  • ESRI (Environmental System Research Institute) (2010) How Path Distance works. Available online [March 2010] at http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?TopicName=How%20PathDistance%20works

  • Fekete A (2009) Validation of a social vulnerability index in context to river-floods in Germany. Nat Hazards Earth Syst Sci 9:393–403

    Article  Google Scholar 

  • Ferguson J (2005) Disaster Risk Management along the Rio Búzi—Case Study on the Background, Concept and Implementation of Disaster Risk Management in the Context of the GTZ-Programme for Rural Development (PRODER). GTZ

  • Finan F, Sadoulet E, de Janvry A (2005) Measuring the poverty reduction potential of land in rural Mexico. J Dev Econ 77:27–51

    Article  Google Scholar 

  • Gall M (2004) Where to go? strategic modelling of access to emergency shelters in Mozambique. Disasters 28(1):82–97

    Article  Google Scholar 

  • Göhl SE (2008) Local Governance and Disaster Risk Management in Mozambique. University of the Western Cape, South Africa, Mini Thesis Faculty of Economic and Management Sciences

    Google Scholar 

  • GoM (Government of Mozambique) (2006a) Action Plan for the Reduction of Absolute Poverty 2006–2009 PARPA II. Final version approved by the Council of Ministers on 2 May 2006, English version

  • GoM (Government of Mozambique) (2006b) Director Plan for Prevention and Mitigation of the Natural Calamities. Natural Calamities Prevention and Attenuation Master Plan–NCMNI. Approved by the 5th session of Council of Ministers on 14 March 2006, English version

  • Gotschi E (2006) Social Capital Development in the Smallholder Sector. Universität für Bodenkultur, Austria, Dissertation

    Google Scholar 

  • Hanlon J, Smart T (2008) Do bicycles equal development in Mozambique. Boydell & Brewer, London

    Google Scholar 

  • Helmer O (1966) The Use of the Delphi Technique in Problems of Educational Innovations. Rand Documents No. P-3499, Santa Monica: The Rand Corporation

  • INE (Instituto Nacional de Estatística de Moçambique) (2007) ESDEM—Estatisticas Sectorias de Moçambique Version 4.0. DVD

  • Kates RW (1978) Risk Assessment of Environment Hazard. John Wiley & Sons, New York

    Google Scholar 

  • Kienberger S (in press) Mapping vulnerability—integration of GIScience and participatory approaches at the local and district level. In: Birkmann J (ed) Measuring vulnerability, 2nd edition, UNU Press, Tokyo, pages pending

  • Kienberger S, Lang S, Zeil P (2009) Spatial vulnerability units—expert-based spatial modelling of socioeconomic vulnerability in the Salzach catchment, Austria. Nat Hazards Earth Syst Sci 9:767–778

    Article  Google Scholar 

  • Kristjanson P, Radeny M, Baltenweck I, Ogutu J, Notenbaert A (2005) Livelihood mapping and poverty correlates at a meso-level in Kenya. Food Policy 30(5–6):568–583

    Article  Google Scholar 

  • Krummenacher H (2008) Environmental factors as triggers for violent conflict: empirical evidence from the “FAST” Data Base. In: Monitoring Environment and Security. brief 37. Bonn International Center for Conversion, pp. 43–45

  • Lang S (2008) Object-based image analysis for remote sensing applications: modeling reality - dealing with complexity. In: Blaschke T et al (eds) Object-Based Image Analysis - Spatial concepts for knowledge-driven remote sensing applications. Springer, New York, pp 3–28

    Google Scholar 

  • Lang S, Zeil P, Kienberger S, Tiede D (2008) Geons—policy relevant geo-objects for monitoring high-level indicators. In: Car A, Griesebner G, Strobl J (eds) Geospatial Crossroads @ GI Forum’08, pp 180–185

  • Lee J, Stucky D (1998) On applying viewshed analysis for determining least-cost paths on digital elevation models. Int J Geogr Inf Sci 12(8):891–905

    Article  Google Scholar 

  • Lindelow M (2002) Health Care Demand in rural Mozambique: Evidence from the 1996/97 Household Survey. International Food Policy Research Institute, FCND Discussion Paper No 126

    Google Scholar 

  • Malczewski J (1999) GIS and Multicriteria Decision Analysis. Wiley, NY

  • Matsimbe Z (2003) The Role of local institutions in reducing vulnerability to recurrent natural disasters and in sustainable livelihoods development. University of Cape Town. http://www.fao.org/docrep/007/ae079e/ae079e00.htm#Contents Accessed 18 August 2011

  • Minot N (2000) Generating disaggregated poverty maps: an application to Vietnam. World Dev 28(2):319–331

    Article  Google Scholar 

  • Moldan B, Dahl AL (2007) Challenges to sustainability indicators. In: Hák T, Moldan B, DAHL AL (eds.) Sustainability Indicators. A Scientific Assessment. Washington DC, Island Press, pp 1–26

  • Osuntogun A (2002) Applied poverty-environment indicators: The case of Nigeria. Report submitted to the Environment Department, World Bank, Washington, DC

    Google Scholar 

  • Roque C, Tengler H (2001) Búzi No Phudji. Perspectivas de Desenvolvimento Distrital Participativo. Beira, Instituto Austríaco Norte Sul

    Google Scholar 

  • Sasin M (2008) The Flow of Information during Disaster Response: The Case of the Mozambique Floods, 2007. In: Amin S, Goldstein M (eds) Data against Natural Disasters: Establishing Effective Systems for Relief, Recovery, and Reconstruction. World Bank, Washington, pp 185–232

  • Schultz TP (1999) Health and schooling investments in Africa. J Econ Perspect 13(3):67–88

    Article  Google Scholar 

  • Shyamsundar P (2002) Poverty-Environment Indicators. Environmental Economic Series 84, World Bank, Washington, DC

  • Smith B (1995) On drawing lines on a map. In: Frank A, Kuhn W, Mark M (eds) Proceedings of COSIT 1995 - Spatial Information Theory. Springer, Berlin, pp 475–484

    Google Scholar 

  • Smith B, Varzi AC (2000) Fiat and bona fide boundaries. Philos Phenomenol Res 60:401–420

    Article  Google Scholar 

  • Sullivan CA et al (2003) The water poverty index: development and application at the community scale. Nat Resour Forum 27:189–199

    Article  Google Scholar 

  • Turner BL et al (2003) A framework for vulnerability analysis in sustainability science. PNAS 100(4):8074–8079

    Article  Google Scholar 

  • UN/ISDR (United Nations International Strategy for Disaster Reduction) (2009): UNISDR Terminology on Disaster Risk Reduction (2009). http://www.unisdr.org/eng/terminology/terminology-2009-eng.html Accessed 18 August 2011

  • UN (United Nations) (2005) Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters. World Conference on Disaster Reduction, 18-22 January 2005, Kobe, Hyogo, Japan. http://www.unisdr.org/2005/wcdr/intergover/official-doc/L-docs/Hyogo-framework-for-action-english.pdf Accessed 18 August 2011

  • Wiles P, Selvester K, Fidalg F (2005) Learning Lessons from Disaster Recovery: The Case of Mozambique. World Bank, Hazard Management Unit Working Paper Series No 12

    Google Scholar 

  • Wisner B (2009) Interactions between Conflict and Natural Hazards: Swords, Ploughshares, Earthquakes, Floods and Storms. In: Brauch HG et al (eds) Facing Global Environmental Change Environmental, Human, Energy, Food. Health and Water Security Concepts, Springer, Berlin, Heidelberg, pp 245–256

    Chapter  Google Scholar 

  • Wisner B, Blaikie P, Cannon T, Davis I (2004) At Risk: Natural hazards, people’s vulnerability and disasters. Routledge, London, New York

    Google Scholar 

  • Wu J, Harbin L (2006) Concepts of Scale and Scaling. In: Li H, Loucks OL (eds) Wu J, Jones KB. Scaling and Uncertainty Analysis in Ecology, Methods and Applications. Springer, pp 3–15

    Google Scholar 

Download references

Acknowledgments

This research was mainly funded through the Munich Re Foundation, where I would like to express my sincere gratitude to Thomas Loster. Special thanks go to the team members of CIG-UCM, INGC in Mozambique (especially Wolfgang Stiebens) and the community members and experts providing their time to participate in this research. The author would also like to thank the reviewers for providing helpful feedback and comments to the paper.

Author information

Authors and Affiliations

  1. Centre for Geoinformatics, University of Salzburg, Schillerstrasse 30, 5020, Salzburg, Austria

    Stefan Kienberger

Authors
  1. Stefan Kienberger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefan Kienberger.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kienberger, S. Spatial modelling of social and economic vulnerability to floods at the district level in Búzi, Mozambique. Nat Hazards 64, 2001–2019 (2012). https://doi.org/10.1007/s11069-012-0174-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-012-0174-9

Keywords

Search

Navigation