CNDS Research Framework
One of the biggest challenges within disaster science is to integrate research from engineering and the natural and social sciences. In its inital attempt to promote the integration of research from different disciplines, CNDS developed a strategy for this in its 2011 science plan which included creating an interdiscplinary research environment consisting of PhD students, post-docs, and researchers.
More recent efforts included a proposed research framework that builds from social‐ecological systems, community resilience, climate change adaptation, and sociohydrology. This integrative framework specifies how the impacts and perceptions of natural hazards influence sociotechnical vulnerabilities, governance, and institutions, while at the same time social behavior, technical measures, and policy interventions alter the frequency, magnitude, and spatial distribution of natural hazards (see Figure below). Reciprocal effects at the local scale are also influenced by global drivers. Climate and environmental change can alter the frequency and severity of extreme weather events, while socioeconomic trends (including population growth, urbanization, and interdependent infrastructures) can increase exposure to natural hazards.
The proposed research framework integrates the current paradigms to solve empirical puzzles in DRR, and provide guidance for empirical studies to unravel the nexus between physical, human, and technological systems. These are critical steps to generate deeper knowledge about the interplay between these interconnected systems, which is essential for making wise decisions about DRR.
Read more about the research framework which was published in Nature "An Integrative Research Framework to Unravel the Interplay of Natural Hazards and Vulnerabilities by several of the CNDS researches from different disciplines. Together, they discuss the essential steps to advance systematic empirical research and evidence‐based DRR policy action.
(a) Analytical framework focusing on the dynamics produced by the (local) interplay of natural hazards and vulnerabilities under (global) environmental change and socioeconomic trends. (b) Flood risk example at the local scale: explanatory model based on Di Baldassarre et al. (2013) emphasizing hypothetical feedbacks between five key variables that are assumed to influence each other and change gradually overtime (thin arrows), while being abruptly altered by the sudden occurrence of flooding (thick arrows). Dashed arrows indicate control mechanisms: wealth influences how flood exposure can potentially change overtime and also determines whether levees can be built or not, while levees reduce the frequency of flooding. (c) Hypothetical wealth trajectories in relation to disaster occurrences: bouncing back, forward or collapsing after a major disaster. (d) Ranges of availability of systematic time series across decades in the study of flood risk dynamics. The fuzzy classification highlights the limited availability of data to carry out empirical studies about socionatural interactions.