Introduction
Humans have long relied on rivers as a source fresh water, as a means of
transportation, and because of their role in shaping the landscape. Our
attraction to rivers consequently means people, buildings, and
agriculture are at risk from riverbank erosion. For example, in
Bangladesh, an estimated average of 60,000 people per year are displaced
by riverbank erosion of the Ganges-Brahmaputra-Jamuna system (Mutton and
Haque, 2004). In this context, it is easy to understand why the United
States Army Corps of Engineers spends more than $200 million per year
managing the Mississippi River (James, 2019). However, sediment
deposition and riverbank accretion create natural riparian habitats and
fertile floodplains (Florsheim et al., 2008). Humans are dramatically
modifying river sedimentation, both directly through river management,
infrastructure, and land use development (Shields et al., 2000; Kondolf
et al., 2002), as well as indirectly through climate change (East and
Sankey, 2020). Land development has greatly increased the total sediment
transport of rivers, but trapping in reservoirs has resulted in a net
reduction in sediment export from land (Syvitski et al., 2005). These
changes in water and sediment supply alter riverbank migration rates as
the morphology adjusts to the new conditions (Brandt, 2000). The
deposition and erosion of material on the banks acts as a temporary
store of sediment, attenuating the impact of changing sediment supply,
and as such is an important input to sediment flux models (Kronvang et
al., 2013). As we think about the consequences of human impacts on
climate and the landscape, assessing large-scale patterns in
geomorphology is becoming increasingly valuable (Grill et al., 2019).
The many applications and broad importance of riverbank erosion has
resulted in diverse measurement methods. On annual timescales and local
spatial scales, bank migration and channel sedimentation can be measured
volumetrically with bank and bathymetry surveying techniques (e.g., De
Rose and Basher, 2011). Tectonic geomorphologists collect sediment cores
and use radiometric dating to relate sediment accumulation rates (which
are largely transported and deposited by river processes) to
mountain-building uplift processes over geologic time scales (e.g.,
Walling, 1999). Over annual to decadal time scales and large spatial
scales, fluvial geomorphologists rely on remote sensing methods like
repeat lidar and optical remote sensing (e.g., De Rose and Basher,
2011). Several methods have been developed to allow efficient analysis
of large domains from satellite or aerial imagery (Peixoto et al., 2009;
Monegaglia et al., 2018). Many of these methods find and track the
movement of the river centerline, which works well for well-behaved
single-threaded channels, but have difficulty representing the
complexity of anabranching or braided rivers (Parker et al., 2011;
Schwenk et al., 2017). Other approaches quantify bank migration by the
area that changes between river and non-river pixels (Rowland et al.,
2016; Nagel et al., 2022). Because these methods calculate change on a
pixel-by-pixel basis, they can more easily capture complex morphologies
and irregular change that are not well represented by a river
centerline. Among these many methods, there are also many ways to define
riverbank migration; some consider only the erosion of stable, vegetated
banks and bars (Boruah et al., 2008), while others include the shifting
of channel bars (Lane et al., 2010). None of these methods have been
used at global scales to produce a consistent dataset of riverbank
migration across all climates and geographies. As a result, our existing
geomorphological understanding of riverbank erosion and accretion are
limited to, and biased by, the patchwork of studies and locations where
we have observations.
Predicting and modeling bank erosion as a means of understanding
sediment processes has a long history in geomorphology. Over that
history, variables that describe the overall size of a river, such as
catchment area, discharge, and width have all been found to be highly
correlated with bank erosion (Hooke, 1980; Nanson and Hickin, 1986).
River size is considered a “first order” control on bank migration
(i.e. big rivers move faster than small rivers), and many researchers
now investigate “second order” influences on bank erosion by first
normalizing bank migration by the river width (Jarriel et al., 2021).
Regardless of scale, we know from physical modelling that the rate of
bank erosion is a balance between shear stress and bank resistivity
(Ikeda et al., 1981). This balance points to second order controls that
affect the interaction between flow and the banks. However, unlike the
first order controls described above, the results of these studies are
varied. For example, Constantine et al. (2014) found that suspended
sediment flux is the primary control of width-normalized bank migration
rate for 20 river reaches in the Amazon Basin. Ielpi and Lapôtre (2020)
showed that the presence of vegetation slowed down width-normalized bank
migration by an order of magnitude using a global sample of 983
meanders. Both of these variables can be heavily modified by human
interventions, such as damming (Shields et al., 2000) and bank
stabilization with concrete or vegetation (Grizzetti et al., 2017).
These examples show the range of second order controls in the literature
which arise from the different subsets of rivers studied. The
interactions and hierarchies of these controls at the global scale have
not yet fully been evaluated.
Here, we present the first global observations of decadal-scale average
riverbank erosion and accretion for all rivers wider than 150 meters. We
use a surface water occurrence dataset derived from Landsat satellite
imagery, a river centerline dataset, and cloud computing to efficiently
estimate decadal-scale average bank erosion and accretion rates. We
analyze these data in the context of previous literature to show that
existing methods of estimating riverbank erosion and accretion
accurately capture large-scale patterns. However, the specific form of
these relationships between geomorphic predictors and riverbank erosion
and accretion varies from basin to basin. These data importantly confirm
geomorphic scaling theories and present a new, uniform dataset that can
help advance geomorphic modeling efforts.