| Autor: |
Barnard PL; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA. pbarnard@usgs.gov., Erikson LH; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA., Foxgrover AC; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA., Hart JAF; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA., Limber P; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA.; Coastal Carolina University, Department of Marine Science, Conway, SC, 29528, USA., O'Neill AC; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA., van Ormondt M; Deltares, Delft, The Netherlands., Vitousek S; United States Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA, 95060, USA.; University of Illinois at Chicago, Department of Civil and Materials Engineering, Chicago, IL, 60607, USA., Wood N; United States Geological Survey, Western Geographic Science Center, Portland, OR, 97201, USA., Hayden MK; Point Blue Conservation Science, Petaluma, CA, 94954, USA., Jones JM; United States Geological Survey, Western Geographic Science Center, Menlo Park, CA, 94025, USA. |
| Abstrakt: |
Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world's 5 th largest economy, over $150 billion of property equating to more than 6% of the state's GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning. |
