Improving urban flood estimation and flood risk assessment using landscape metrics 

Abstract

Urbanization creates impervious coverage and increases peak flows during storm events. Flood estimation methods in the United Kingdom characterize these impacts using a weighted measure of catchment urban land cover, but research suggests that consideration of spatial effects could improve lumped catchment modelling of storm runoff. This paper employs spatially explicit landscape metrics alongside lumped catchment descriptors to assess the potential for improving flood estimates in urbanised catchments over existing methods used to assess flood risk. Such metrics are found to provide a useful means of improving flood estimates and flood risk evaluation in ungauged urban catchments, particularly smaller urban catchments. Results have pointed to the potential value in refining catchment datasets to exclude less-suitable data in order to reduce associated uncertainty in flood estimates. They further demonstrate the value of considering spatial arrangements of urban land cover. We conclude that it is not always beneficial to have more data, but to have the most suitable data for the application, and that this could be dependent on the scale or level of urbanization. The study of landscape metrics is an emerging area of research for bridging the gap between lumped and distributed hydrological modelling, and improving urban flood risk estimates.