Harnessing new data technologies for nature-based solutions in assessing and managing risk in coastal zones

Responsiveness and resilience to coastal disasters is an increasingly critical need for governments, businesses, and communities as the intensity of hurricanes, coastal storms and sea level rise exacerbates flood and erosion damages. The number and severity of disasters have increased significantly over the past two decades while intense coastal development has steadily degraded the protective and other ecosystem benefits from natural habitats such as coral reefs, mangroves and seagrasses. Such ecosystems can provide cost-effective, long-term risk reduction and other benefits to coastal communities. Yet easy, low-cost methodologies for incorporating such natural capital values into metrics that can inform policy and investment decisions have been lacking until recently. Whereas new approaches and studies have developed ways to quantify and assess such risk reduction benefits, finding the data to assess such services in data poor environments, both for regional and local scales, remains challenging. This paper outlines how breakthroughs in data technologies can help with incorporating ecosystem benefits into real-world policy and investment applications. Specific new data and analytical technologies are increasingly being used to quantify coastal risk reduction and other ecosystem benefits. We review several case studies where ecosystem benefits were quantified based on new data technologies and used for policy and investment strategies to improve a broader suite of ecosystem benefits to coastal communities. Equipping public and private sector actors with cost-effective and accessible approaches and tools to incorporate nature-based solutions into disaster risk management and coastal zone management planning more generally can transform climate resilience strategies around the world.

Author:	Ruckelshaus, M.;et al.
Language:	English
Pubished By:	International Journal of Disaster Risk Reduction
Pubished date:	November 2020