Continental Scale Assessment of Variation in Floodplain Roughness With Vegetation and Flow Characteristics

Quantifying floodplain flows is critical to multiple river management objectives, yet how vegetation within floodplains dissipates flow energy lacks comprehensive characterization. Utilizing over 3.4 million discharge measurements, in conjunction with aboveground biomass and canopy height measurements from NASA's Global Ecosystem Dynamics Investigation (GEDI), this study characterizes the floodplain roughness coefficient Manning's n and its determinates across the continental United States. Estimated values of n show that flow resistance in floodplains decreases as flow velocity increases but increases with the fraction of vegetation inundated. A new function (RMSE = 0.024, r2 = 0.74) is proposed for predicting n based on GEDI vegetation characteristics and flow velocity, with GEDI derived n values improving predictions of discharge relative to those based only on land cover. This analysis provides evidence of key hydraulic patterns of energy dissipation in floodplains, and integration of the proposed function into flood and habitat models may reduce uncertainty. Quantifying the capacity of floodplains to dissipate energy from flowing water is important in managing rivers, restoring habitats, and reducing flood risks. By integrating overbank flood characteristics measured at USGS gauging stations with vegetation properties of floodplains measured by NASA, this study analyzed how energy dissipation in the floodplain, via a hydraulic roughness coefficient, varies with vegetation biomass and flood depths. Results indicate that floodplain roughness increases with the density of vegetation and decreases with flow velocity. A new mathematical function is presented to estimate floodplain roughness based on remotely sensed vegetation properties for various velocities. 4,927 estimates of floodplain roughness were calculated using flow observations and compared to LiDAR vegetation dataFloodplain roughness increases with increasing biomass and inundation depths and decreases with increasing flow velocityOur model's Manning's n estimates yield lower errors in reach-scale floodplain flow predictions than n based solely on land cover