Summary

Disasters cost the global economy $520 billion (USD) and drive more than 26 million people into poverty each year (Hallegatte et al. 2016). Floods continue to impact the largest number of people of any type of disaster globally, having affected more than two billion people over the last decade (CRED and UNISDR 2018). The Sendai Framework for Disaster Risk Reduction has laid out ambitious goals and targets, pointing to the need to “substantially increase the availability of and access to disaster risk information and assessments to people by 2030” (UNISDR 2015 p. 12). This research seeks to contribute toward this goal by providing guidance on methodologies and standards for risk assessments – key actions under the Sendai Framework’s first priority of understanding disaster risk.

Project Team

  • Dr Aaron Opdyke
 

Funding

Faculty of Engineering, Early Career Researcher Development Grant, $50,000 AUD

Research Questions

  • How can community-based methodologies supplement and enhance engineering disaster risk assessments in resource constrained communities?

Research Methods

The United Nations Office for Disaster Risk Reduction (UNDRR) defines disaster risk as “the potential loss of life, injury, or destroyed or damaged assets which could occur to a system, society or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability and capacity” (Ki-moon 2016). Hazards are natural processes that may cause social or economic disruption, such as earthquakes and floods, while exposure is the people and infrastructure that face these threats. Vulnerability explains the differential impact across individuals and assets by offering an understanding of the physical, social, economic, and environmental factors which increase susceptibility to the impacts of hazards. These determinants can be summarised as follows: Disaster Risk=Hazard x Exposure x Vulnerability

The primary objective of this study seeks to unpack how different methods of assessing hazards influence disaster risk assessments. This study will draw from participatory action research conducted in partnership with the local government unit (LGU) of Carigara, located in the province of Leyte in the Philippines.

Population and building exposure data will be linked, drawing from drone photogrammetry stitched using PIX4Dmapper software to create high resolution imagery and population data obtained from geotagged local census records. Building footprints will be extracted from the created digital surface model (DEM). This will also enable the creation of a digital terrain model (DTM) which will be used in flood modelling to determine inundation extents. These methods will be employed as there is an absence of high-resolution imagery and elevation data at the selected location – a common issue in many developing communities.

Population and building exposure data will be linked, drawing from drone photogrammetry stitched using PIX4Dmapper software to create high resolution imagery and population data obtained from geotagged local census records. Building footprints will be extracted from the created digital surface model (DEM). This will also enable the creation of a digital terrain model (DTM) which will be used in flood modelling to determine inundation extents. These methods will be employed as there is an absence of high-resolution imagery and elevation data at the selected location – a common issue in many developing communities.

Social vulnerability data will be obtained from local census records, and linked to building footprints, including age, gender, employment, and disability of individual household members. To capture building vulnerability, inspections of households initially sampled as part of the flood hazard survey will be completed as well as additional buildings near waterways. Observations will record the building foundation type, lowest opening height (floor level above ground), number of stories, and building material (e.g. concrete, timber) as indicators of flood vulnerability. Existing signs of flood damage (e.g. water damage or scouring around foundations) will also be recorded as a validation measure.

Three sets of hazard data will form the basis for comparison in this research. A household survey of flood hazards has already been collected covering 2,162 households – an approximately 20 percent sample of the total municipal population. Survey questions asked households to assess their flood hazard exposure (low, medium, high, or no exposure). Inverse distance weighting (IDW) interpolation methods will be used in GIS to generate a flood inundation map based on these surveys. Additionally, over 400 individuals participated in community workshops to map flood hazards in their respective communities. These hazard assessments, hand drawn over base maps of communities, will be digitised, comprising the second hazard dataset. Lastly, historical weather data from a nearby station and historical flow data from local river gauges will be collected from the Philippine Atmospheric, Geophysical and Astronomical Services Administration to create flood inundation models.

Digital terrain models produced from earlier photogrammetry will be used to delineate watersheds and combined with climate data in HEC-RAS software to produce flood inundation depths based on two-dimension unsteady state flow hydraulic models. In order to compare differences in hazard exposure across data sources, flood depths from the collective-community assessments and engineering models will be overlaid on top of population and building exposure data using InaSAFE software, a GIS hazard scenario analysis tool. This will result in three flood hazard assessments for each household in the sample: (1) individual household; (2) collective community identification and; (3) engineering assessments from hydraulic modelling. These hazard evaluations will then be combined with social and physical vulnerability data to determine a risk assessment score for each household. Paired statistical tests will be used to assess differences in risk levels between the three sources. Furthermore, spatial correlation will be also used to compare the three different hazards sources.

Findings

Findings will pose new methods of collecting and leveraging data in developing countries to advance disaster risk assessments. Furthermore, results are expected to inform strategies to integrate local knowledge into local planning processes.

Global Goals and Targets

While this research contributes to many of the global targets, the following goals, targets, and priorities are expected contributions from this research.

Sustainable Development Goals​

Goal 1: End poverty in all its forms everywhere

1.5 By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters.

Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable.

11.5 By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations.
11.B By 2020, substantially increase the number of cities and human settlements adopting and implementing integrated policies and plans towards inclusion, resource efficiency, mitigation and adaptation to climate change, resilience to disasters, and develop and implement, in line with the Sendai Framework for Disaster Risk Reduction 2015-2030, holistic disaster risk management at all levels.

Sendai Framework for Disaster Risk Reduction​

This research contributes to Priority 1: Understanding disaster risk and Priority 2: Strengthening disaster risk governance to manage disaster risk.

Target E

Substantially increase the number of countries with national and local disaster risk reduction strategies by 2020.

Target G

Substantially increase the availability of and access to multi-hazard early warning systems and disaster risk information and assessments to people by 2030.