Search for: All records

The morphology of river levees and floodplains is an important control on river-floodplain connectivity within a river system under sub-bankfull conditions, and this morphology changes as a river approaches the coast due to backwater influence. Floodplain width can also vary along a river, and floodplain constrictions in the form of bluffs adjacent to the river can influence inundation extent. However, the relative controls of backwater-influenced floodplain topography and bluff topography on river-floodplain connectivity have not been studied. We measure discharge along the lower Trinity River (Texas, USA) during high flow to determine which floodplain features are associated with major river-floodplain flow exchanges. We develop a numerical model representing the transition to backwater-dominated river hydraulics, and quantify downstream changes in levee channelization, inundation, and fluxes along the river-floodplain boundary. We model passive particle transport through the floodplain, and compute residence times as a function of location where particles enter the floodplain. We find that bluff topography controls flow from the floodplain back to the river, whereas levee topography facilitates flow to the floodplain through floodplain channels. Return flow to the river is limited to locations just upstream of bluffs, even under receding flood conditions, whereas outflow locations are numerous and occur all along the river. Residence times for particles entering the floodplain far upstream of bluffs are as much as two orders of magnitude longer than those for particles entering short distances upstream of bluffs. This study can benefit floodplain ecosystem management and restoration plans by informing on the key locations of lateral exchange and variable residence time distributions in river-floodplain systems. 

Abstract Water movement in coastal wetlands is affected by spatial differences in topography and vegetation characteristics as well as by complex hydrological processes operating at different time scales. Traditionally, numerical models have been used to explore the hydrodynamics of these valuable ecosystems. However, we still do not know how well such models simulate water‐level fluctuations beneath the vegetation canopy since we lack extensive field data to test the model results against observations. This study utilizes remotely sensed images of sub‐canopy water‐level change to understand how marshes drain water during falling tides. We employ rapid repeat interferometric observations from the NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument to analyze the spatial variability in water‐level change within a complex of marshes in Terrebonne Bay, Louisiana. We also used maps of herbaceous aboveground biomass derived from the Airborne Visible/Infrared Imaging Spectrometer‐Next Generation to evaluate vegetation contribution to such variability. This study reveals that the distribution of water‐level change under salt marsh canopies is strongly influenced by the presence of small geomorphic features (<10 m) in the marsh landscape (i.e., levees, tidal channels), whereas vegetation plays a minor role in retaining water on the platform. This new type of high‐resolution remote sensing data offers the opportunity to study the feedback between hydrodynamics, topography and biology throughout wetlands at an unprecedented spatial resolution and test the capability of numerical models to reproduce such patterns. Our results are essential for predicting the vulnerability of these delicate environments to climate change. 

River deltas are dynamic systems whose channels can widen, narrow, migrate, avulse, and bifurcate to form new channel networks through time. With hundreds of millions of people living on these globally ubiquitous systems, it is critically important to understand and predict how delta channel networks will evolve over time. Although much work has been done to understand drivers of channel migration on the individual channel scale, a global-scale analysis of the current state of delta morphological change has not been attempted. In this study, we present a methodology for the automatic extraction of channel migration vectors from remotely sensed imagery by combining deep learning and principles from particle image velocimetry (PIV). This methodology is implemented on 48 river delta systems to create a global dataset of decadal-scale delta channel migration. By comparing delta channel migration distributions with a variety of known external forcings, we find that global patterns of channel migration can largely be reconciled with the level of fluvial forcing acting on the delta, sediment flux magnitude, and frequency of flood events. An understanding of modern rates and patterns of channel migration in river deltas is critical for successfully predicting future changes to delta systems and for informing decision makers striving for deltaic resilience.