Development and validation of a sub-national multi-hazard risk index for the Philippines

Abstract

Disasters in relation to natural hazards continue to have a heavy toll on humans, ecosystems and economies. They therefore undermine efforts for sustainable development, particularly in transitional countries. The Philippines is amongst the most disaster-prone countries on the globe, due to its high exposure to natural hazards and considerable societal vulnerabilities. While a number of global risk assessments have helped to identify risk hotspots at the level of individual countries, sub-national and local risk assessments for informing disaster risk management on the ground are often lacking. To address this gap, we provide a down-scaled risk assessment, at the municipality level, for the Philippines. In the interests of coherency and scale hierarchy, we draw on the modular approach used in the World Risk Index, considering hazard exposure, susceptibility, lack of short-term coping capacity, and lack of long-term adaptive capacity. The paper aims not only to present the results but also to debate key methodological questions behind the development of sub-national multi-hazard risk indices. The outlook looks at the applicability, from the end-user's perspective, of this level of risk assessment for decisionmaking at local and national levels.

Total Carbon Column Observing Network Philippines: Toward Quantifying Atmospheric Carbon in Southeast Asia

“…The United Nations' World Risk Report 2016 ranked the Philippines third among countries most vulnerable to natural disasters (e.g., effects of climate change), after Vanuatu and Tonga (Garschagen et al, 2016). This is because the Philippines is highly exposed to natural hazards coupled by significant social vulnerabilities (Wannewitz, Hagenlocher, & Garschagen, 2016). Natural disasters common to the Philippines include earthquakes, volcanic eruptions, tsunamis, and other events that could be exacerbated by climate change such as flooding, typhoons, storm surges, landslides, and erosion.…”

Vulnerability and risk of deltaic social-ecological systems exposed to multiple hazards

“…Our results confirm the need emphasized by De Lange et al 2010to systematically consider indicators of environmental vulnerability when assessing disaster risk and identifying risk reduction and adaptation strategies, while reemphasizing the need for enhanced efforts to sustainably manage, conserve, and restore ecosystems and their services. Through its modular design, the GDRI also overcomes the limitations of existing multi-hazard risk assessment approaches that do not differentiate between hazard-dependent and hazard-independent indicators (Greiving, 2006;Liu et al, 2013;BEH and UNU-EHS, 2016;Wannewitz et al, 2016) -a need that has been underscored in recent reviews of multi-hazard risk methodologies for natural hazards (Kappes et al, 2012;Gallina et al, 2016). Going beyond case studies at the local level (de Andrade et al, 2010;Birkmann et al, 2012;Dinh et al, 2012;Islam et al, 2013) and global assessments that do not capture differences in vulnerability and risk within deltas (Tessler et al, 2015), the sub-delta scale applied here enables the identification of hotspots and variability of risks within deltas.…”

“…were combined in a (multi-hazard) risk index (RISKSES) through multiplicative aggregation, whereby both risk components are weighted equally. Thereby, the hazard component of the framework was indirectly considered in the exposure term of the equation following existing risk assessment approaches(Hagenlocher and Castro, 2015;BEH and UNU-EHS, 2016;Wannewitz et al, 2016). Aggregation of indicators and domains was carried out in Excel and results visualized in a GIS.…”

Assessment of Future Heat Stress Risk in European Regions: Towards a better Integration of Socio-economic Scenarios

“…Diverse methods of statistical downscaling could also be applied to obtain socio-economic projections at the desired unit of analysis, as already shown with previous IPCC SRES scenarios (Gaffin et al, 2004). Finally, although understanding fully the complexity of vulnerability requires the use of numerous socio-economic variables (Wannewitz et al, 2016), such complexity may be reduced through a condensed vulnerability index made of proxy variables that can be projected through their correlation with existing projections of common variables such as GDP and population growth (Kienberger et al, 2015).…”