Big Data Approaches for coastal flood risk assessment and emergency response

Abstract

Big Data Approaches (BDAs) 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 organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (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. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches. This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice Climate, History, Society, Culture > Technological Aspects and Ideas

Understanding spatio-temporal barrier dynamics through the use of multiple shoreline proxies

“…The diversity of coastal processes captured in this study suggest that using a single shoreline proxy out of ease of extraction, or from the demands of legacy/continuity, runs the risk of narrowing understanding of coastal zones and the internal and external forcing to which they are exposed. This assertion is particularly pertinent given that technological advances (see Viles, 2016) are generating large quantities of data available with potential (though not inevitable) value to the study of coastal systems (Pollard et al, 2018;Rumson et al, 2017).…”

“…Recent technological advances, particularly through various forms of remote sensing, has increased the variety of data sources from which shorelines can be mapped (Dolan et al, 1980;Pollard et al, 2018;Stockdon et al, 2002). Routine shoreline extraction from vertical aerial photography has been ongoing since the post-WWII era, though earlier comments on Journal Pre-proof J o u r n a l P r e -p r o o f 5 the potential of aerial photography for understanding coastal systems pre-date this time (e.g.…”

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

“…During flooding events, RS provides a cost-effective way for delineating and tracking surface water dynamics, including the extents and water stage. Pollard et al [61] discussed Big Data approaches for handling coastal flooding on issues related to synthesis of coastal datasets, data handling and validation, and integration with processbased models in real time. Huang et al [62] reviewed sources and techniques for detecting, extracting, and monitoring surface water extents using optical remote sensing.…”

Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis

“…Furthermore, coastal environments are associated with a high concentration of critical infrastructure 2 and valuable ecosystems 3,4 . Technological advance is revolutionising our ability to collect datasets relevant to the study of coastal systems 5 and their management 6,7 . This includes improvements in the temporal frequency and resolution of datasets such as aerial imagery which have been collected for decades.…”