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Disaster Risk, Political Decentralization and the Use of Indices for Evidence-Based Decision Making

Applications of a Nexus Observatory

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Resources, Services and Risks

Part of the book series: SpringerBriefs in Environmental Science ((BRIEFSENVIRONMENTAL))

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Abstract

Disaster risk governance is a function of institutions at multiple levels of government to predict and effectively respond to threats posed by global changes such as urbanization, climate and demographic change. An important determinant of government’s effectiveness in responding to environmental threats could be levels of administrative and political decentralization. In Chap. 3, we discussed two case studies and examined how results-based financing (RBF) models could support a strategy for the delivery of water and sanitation services. In this chapter, we outline tentative research propositions to discuss the role of decentralization in predicting and responding to disaster risk. We also outline the applications of a Nexus Observatory by discussing the role of the following tools: nexus index, data visualization, scenario analysis, and benchmarking. We argue that the above tools could support the use of RBF approaches that strengthen accountability in revenue and expenditure decisions of local authorities.

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Notes

  1. 1.

    Since 2007, national governments have been self-assessing their progress against the five priorities outlined by the HFA: (1) governance: organizational, legal and policy frameworks; (2) risk identification, assessment, monitoring and early warning; (3) knowledge management and education; (4) reducing underlying risk factors; and (5) preparedness for effective response and recovery. In 2009, a regional self-assessment process was established and, in 2011, a similar process began for local governments. The HFA review process is voluntary and intends to stimulate an inter-disciplinary planning process of disaster risk. The HFA Monitor is an online tool, facilitated by UNISDR, which allows for comparisons of data over time and across countries on progress regarding indicators of HFA.

  2. 2.

    Center for Research on Epidemiology for Disaster (CRED): http://www.emdat.be/glossary/9.

  3. 3.

    According to Wilhite et al. (2014), the inexistence of a universally accepted definition of drought leads to confusion about its real existence and degree of severity.

  4. 4.

    According to the 2014 report of the Intergovernmental Panel on Climate Change, vulnerability is the propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.

  5. 5.

    Improvements in transport and health facilities, namely in East Asia and the Pacific, facilitated evacuation plans and medical assistance, reducing vulnerability, particularly in the case of flood and tropical cyclones, although exposed population increased. However, in areas where increased exposure was not accompanied by measures to reduce vulnerability, i.e. sub-Saharan Africa, flood mortality has been growing since 1980 (UNISDR 2011).

  6. 6.

    For more information, please refer to their website at http://unescoihefvi.free.fr/vulnerability.php.

  7. 7.

    Zargar et al. (2011) reviews 74 operational meteorological, agricultural, and hydrological drought indices used for forecasting, monitoring and planning for drought.

  8. 8.

    As we have argued in Chap. 3, data from various sources, including from earth observations could make synergies and trade-offs more explicit, thereby, drawing a more holistic and comprehensive picture.

  9. 9.

    For more information on the PREVIEW Global Risk Data Platform, please refer to http://preview.grid.unep.ch/.

  10. 10.

    The physical exposure measure is the expected average annual population (2007 as the year of reference) exposed (inhabitants) to floods/droughts. The estimate of the annual physical exposition to flood is based on four sources: (1) A GIS modelling using a statistical estimation of peak-flow magnitude and a hydrological model using HydroSHEDS dataset and the Manning equation to estimate river stage for the calculated discharge value; (2) Observed flood from 1999 to 2007, obtained from the Dartmouth Flood Observatory (DFO); (3) The frequency was set using the frequency from UNEP/GRID-Europe PREVIEW flood dataset. In areas where no information was available, it was set to 50 years returning period; (4) A population grid for the year 2010, provided by LandScanTM Global Population Database (Oak Ridge, TN: Oak Ridge National Laboratory). For drought, the dataset includes an estimate of global drought annual repartition based on the Standardized Precipitation Index, and is based on three sources: (1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia); (2) A GIS modelling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology; (3) A population grid for the year 2007, provided by LandScanTM Global Population Database (Oak Ridge, TN: Oak Ridge National Laboratory). In the case of economic exposure, population is replaced by GDP (US$, year 2000 equivalent), obtained from the World Bank.

  11. 11.

    National disaster loss data for 72 countries is available from the Desinventar (http://www.desinventar.net) Disaster Information Management System. In case studies focusing on just one, or on a very small number of countries, it may be possible to collect more detailed data and to use more comprehensive drought indices than the SPI, which would then allow the construction of more precise measures of exposure to floods and droughts.

  12. 12.

    A wide portfolio of revenue sources improves governments’ risk sharing and helps to handle the impact of unexpected events in revenues. Furthermore, when sub-national governments are highly dependent on transfers, they tend to be more fiscally irresponsible. For a discussion on decentralization, see Veiga et al. (2015).

  13. 13.

    That was the procedure adopted in UNDP (2004), where risk, assessed by the number of deaths caused by a natural hazard (using data from EM-DAT), was then regressed on exposure and on several vulnerability indicators. A similar methodology was also applied by UNISDR (2009b, 2011, 2013), Yohe and Tol (2002) and Okazawa et al. (2011).

  14. 14.

    The determinants of economic losses caused by floods or droughts could be investigated by replacing “deaths” with “economic losses” (from EM-DAT or Desinventar) and “physical exposure” with “economic exposure.” It is worth noting that “vulnerability” does not represent one single variable, but a vector of indicators of susceptibility (Tables 2 and 3) and coping and adaptation capacities (Table 4).

  15. 15.

    An estimate of the global risk induced by flood hazard is available from the PREVIEW Global Risk Data Platform. This index, used in GAR13 (UNISDR 2013), varies from 1 (not null) to 10 (extreme). A new, probabilistic risk assessment approach is currently under way and the new flood risk index should be made available in the GAR15. Its key output is the likelihood of having certain losses from floods expressed in terms of their occurrence rate, expressed per year.

  16. 16.

    For applications of indices to regions of specific countries see, among others, Bündnis Entwicklung Hilft (2011) for Indonesia, UNISDR (2013) for several individual countries, DARA (2013) for six West African Countries, and DARA (2011) for seven Central American countries.

  17. 17.

    See Kurian (2010) for an analysis of soil conservation interventions in Laos and the importance of socio-ecological data for planning and management interventions.

  18. 18.

    For a detailed description of the Nexus Observatory, see Kurian and Meyer (2014).

  19. 19.

    Proposed SDG Goal 17 also highlights multi-stakeholder partnerships.

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Authors and Affiliations

  1. UNU-FLORES, Dresden, Germany

    Mathew Kurian, Reza Ardakanian & Kristin Meyer

  2. School of Economics and Management, University of Minho, Braga, Portugal

    Linda Gonçalves Veiga

Authors
  1. Mathew Kurian
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  2. Reza Ardakanian
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  3. Linda Gonçalves Veiga
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  4. Kristin Meyer
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Correspondence to Mathew Kurian .

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Kurian, M., Ardakanian, R., Gonçalves Veiga, L., Meyer, K. (2016). Disaster Risk, Political Decentralization and the Use of Indices for Evidence-Based Decision Making. In: Resources, Services and Risks. SpringerBriefs in Environmental Science. Springer, Cham. https://doi.org/10.1007/978-3-319-28706-5_4

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