Big Data Approaches for coastal flood risk assessment and emergency response

Abstract

Big Data Approaches refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organisational, storage and analytical capabilities. Despite widespread recognition of the commercial benefits of Big Data Approaches, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterise Big Data Approaches (volume, variety, velocity and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low-lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. Firstly, coastal flood risk assessment, focusing on better characterisation of hazard sources, facilitative pathways and vulnerable receptors. Secondly, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings and response monitoring. Critical commentary regarding technical, contextual, institutional and behavioural barriers to the implementation of Big Data Approaches is offered throughout including a discussion of two fundamental difficulties associated with applying Big Data Approaches to coastal flood risk management: the role of Big Data Approaches in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches.