GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 243-6
Presentation Time: 3:05 PM


CLUBB, Fiona J.1, MUDD, Simon Marius2, MILODOWSKI, David T.2, VALTERS, Declan A.3, SLATER, Louise J.4, HURST, Martin D.5 and LIMAYE, Ajay1, (1)Dept. of Earth Sciences, University of Minnesota, 2 SE Third Ave., Minneapolis, MN 55414, (2)School of GeoSciences, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, United Kingdom, (3)Met Office, FitzRoy Road, Exeter, EX1 3PB, United Kingdom, (4)Department of Geography, Loughborough University, Geography Building, Central Park, Loughborough, LE11 3TU, United Kingdom, (5)Department of Geographical and Earth Sciences, University of Glasgow, East Quadrangle, Glasgow, G12 8QQ, United Kingdom,

Identifying floodplains and terrace features is an important problem within geomorphology, providing opportunities for understanding current and past fluvial processes, such as the influence of climate and tectonics on channel profiles; sediment storage and dynamics; and fluvial response to changing discharge and sediment flux. Traditional methods of mapping floodplain and terrace features generally require either intensive modelling studies or field mapping campaigns, which are time-consuming and expensive. Therefore, many studies have attempted to identify these features remotely from digital elevation models (DEMs). However, these previous methods tend to be semi-automated requiring either manual editing by the user after feature extraction or calibration with independent datasets.

Here we present a new method of identifying floodplains and fluvial terraces from DEMs based on two thresholds: elevation compared to the nearest channel, and local gradient. We employ statistical techniques to calculate these thresholds from the DEM using quantile-quantile plots, meaning that they do not need to be set manually by the user for each landscape. We test our method against field-mapped data from eight field sites with varying topographic characteristics, using a combination of floodplain initiation points, published flood hazard maps, and digitised terrace surfaces. For each site we use high-resolution lidar-derived DEMs as well as coarser resolution national datasets to test the sensitivity of our method to grid resolution. We find that our method is successful in extracting floodplain and terrace features compared to the field data from the range of landscapes and grid resolutions tested. Our method is most accurate where the floodplains or terraces are in confined valleys, or where there is a marked contrast in slope and elevation between the extracted features and the surrounding landscape. This new method provides new opportunities for objectively mapping floodplain and terrace features on a landscape scale, with applications including landscape evolution modelling, quantification of sediment storage, and flood risk mapping.