Investigating hydrogeomorphic floodplain mapping performance with varying DTM resolution and stream order |
| |
| Authors: | A Annis F Nardi R R Morrison F Castelli |
| |
| Institution: | 1. Water Resource Research and Documentation Centre (WARREDOC), Università per Stranieri di Perugia, Perugia, Italy;2. DICEA, University of Florence, Florence, Italy;3. Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado, USA;4. DICEA, University of Florence, Florence, Italy |
| |
| Abstract: | Hydrogeomorphic models allow parsimonious, fast and effective floodplain extent mapping using topographic data as the main input. Hydrogeomorphic approaches enforce the principle that floodplains are well-distinguished and unique landscape features within river corridors. We investigated the sensitivity of a hydrogeomorphic floodplain delineation algorithm, based on a hydrological power law, relating flow depth to contributing area, digital terrain model (DTM) resolution and river network hierarchy. In addition, we compared the results to other common floodplain mapping methods using standard flood-hazard maps as a reference. Taking the Arno River Basin, Italy, as a case study, our results show a dependency between the optimal power law parameters and DTM resolution, with larger parameter values required to reach optimal consistency with flood-hazard maps as DTM resolution increased. Floodplain mapping performance was also found to depend on stream order. We further tested the model consistency at a larger scale to evaluate its performance with respect to inundation maps in Hungary, Italy, Spain and the UK. Our study suggests that pre-defined power law parameters can be assumed, considering DTM resolution and stream order, supporting the use of the presented hydrogeomorphic model for large-scale floodplain mapping in ungauged basins where reference flood-hazard maps are not available. |
| |
| Keywords: | floodplain DTM resolution stream order hydrogeomorphic model terrain analysis scaling laws |
|
|
