Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk

• Main Authors: Hinkel, J., Feyen, L., Hemer, M., Le Cozannet, G., Lincke, D., Marcos, M., Mentaschi, L., Merkens, J. L., de Moel, H., Muis, S., Nicholls, R. J., Vafeidis, A. T., van de Wal, R. S.W., Vousdoukas, M. I., Wahl, T., Ward, P. J., Wolff, C.
• Other Authors: Proceskunde, Sub Algemeen Marine & Atmospheric Res
• Language: English
• Published: 2021
• Subjects: Ice Sheet
• Online Access: https://dspace.library.uu.nl/handle/1874/412724

This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data.