Exploring the sensitivity of coastal inundation modelling to DEM vertical error

West, Harry; Horswell, Michael; Quinn, Nevil

Abstract

© 2018 Informa UK Limited, trading as Taylor & Francis Group. As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.

• Journal Article Type: Article
• Acceptance Date: Feb 19, 2018
• Online Publication Date: Mar 14, 2018
• Publication Date: Jun 1, 2018
• Deposit Date: Feb 21, 2018
• Publicly Available Date: Mar 14, 2019
• Journal: International Journal of Geographical Information Science
• Print ISSN: 1365-8816
• Electronic ISSN: 1365-8824
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 6
• Pages: 1172-1193
• DOI: https://doi.org/10.1080/13658816.2018.1444165
• Keywords: Sea level rise; DEM error; uncertainty; digital elevation or terrain models; coastal applications
• Public URL: https://uwe-repository.worktribe.com/output/870675
• Publisher URL: http://dx.doi.org/10.1080/13658816.2018.1444165
• Additional Information: Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science on 14th March 2018, available online: http://dx.doi.org/10.1080/13658816.2018.1444165.
• Contract Date: Feb 21, 2018