Abstract
This paper assess sea-level rise related coastal flood impacts for Emilia-Romagna (Italy) using the Dynamic Interactive Vulnerability Assessment (DIVA) modeling framework and investigate the sensitivity of the model to four uncertainty dimensions, namely (1) elevation, (2) population (3) vertical land movement (4) scale and resolution of assessment. A one-driver-at-a-time sensitivity approach is used in order to explore and quantify the effects of uncertainties in input data and assessment scale on model outputs. Of particular interest is the sensitivity of flood risk estimates when using datasets of different resolution. The change in assessment scale is implemented through the use of a more detailed digital coastline and input data for the coastline segmentation process. This change leads to a 35-fold increase in the number of coastal segments and in a more realistic spatial representation of coastal flood impacts for the Emilia-Romagna coast. Furthermore, the coastline length increases by 43%, considerably influencing adaptation costs (construction of dikes). With respect to input data our results show that by the end of the century coastal flood impacts are more sensitive to variations in elevation and vertical land movement data than to variations in population data in the study area. The inclusion of local information on human induced subsidence rates increases the relative sea-level by 60cm in 2100, resulting in coastal flood impacts that are up to 25% higher compared to those generated with the global DIVA values, which mainly account for natural processes. The choice of one elevation model over another can result in differences of approximately 45% of the coastal floodplain extent and up to 50% in flood damages by 2100. Our results emphasize that the scale of assessment and resolution of the input data can have significant implications for the results of coastal flood impact assessments. Understanding and communicating these implications is essential for effectively supporting decision makers in developing long-term robust and flexible adaptation plans for future changes of highly uncertain scale and direction.
Highlights
Coastal flooding constitutes a major risk for coastal regions throughout the world and this risk is expected to worsen considerably during the twenty-first century with rising sealevels and as future societal development increases the number of people and value of assets in the coastal floodplain (Hinkel et al, 2014)
The high-resolution segmentation has a 28-fold increase compared to the global Dynamic Interactive Vulnerability Assessment (DIVA) assessment scale referring to the average length of segments
In the global DIVA database the entire coast of Emilia-Romagna was characterized by a sandy coastal morphology and urban settlements while in the new version a more detailed distinction (e.g., 57 segments or 86 km represents coastal settlements, 55 km are classified as sandy plus 59 km as sandy with wave breaker—see Figure 5) was made
Summary
Coastal flooding constitutes a major risk for coastal regions throughout the world and this risk is expected to worsen considerably during the twenty-first century with rising sealevels and as future societal development increases the number of people and value of assets in the coastal floodplain (Hinkel et al, 2014). Evaluating and managing coastal flood risk under climate change, as well as climate risk in general, requires to consider uncertainty about present and future risks as comprehensively as possible, because not considering uncertainty may only partially lead to maladaptation (Jones et al, 2014; Hinkel et al, 2015). Uncertainty relates to the amount or rate of sea-level rise (SLR) and socio-economic development, and to the input data used in the analysis. Hinkel et al (2014) found that coastal flood impacts are much more sensitive to elevation data uncertainty than to, e.g., sea-level rise uncertainty stemming from the choice of climate model. A significant limitation of flood impact analysis on all scales is the unavailability of free high-accuracy datasets (Gesch, 2009; Mondal and Tatem, 2012; Neumann et al, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
