Uncertainty in parameterising floodplain forest friction for Natural Flood Management, using remote sensing

One potential Natural Flood Management option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterised for hydraulic models. Remote sensing offers high-resolution datasets capable of characterising vegetation structure from a variety of platforms, but contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood and leaf area indices (20, 10, 30, 20% respectively), resulted in a combined Manning's n uncertainty from 11-13% to 11-17% at 2m to 8m flow depths. This equates to 7-8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, and especially for flow though Pine plantations. For deeper flows, leaf and woody area become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4-6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterisation and flood modelling for Natural Flood Management