Discussion and Conclusion
Our results suggest that previous regional studies indicating that size
is the first order control on riverbank erosion at large scales (Hooke,
1980; Nanson and Hickin, 1986; Ielpi and Lapôtre, 2020) are largely
correct (e.g. Figure 4). However, upon closer inspection, we reveal the
limits of the global applicability of such relationships. We show large
regional differences in this first order relationship that arise from
the unique blend of secondary controls in each basin. For example,
riverbank erosion in the Congo basin, which is characterized by varied
drainage patterns due to localized geologic controls (Flügel et al.,
2015), is notably lower than the global average. The world’s largest
basin by discharge, the Amazon, has a lot of spread in this first order
relationship, a result of the varied inputs of sediment load as shown by
Constantine et al. (2014) and seen in the REAL data in Figure 4b. The
Ob’ basin is also biased low, however, likely due to low flows in the
winter and a discontinuous and sporadic permafrost setting. On the other
hand, riverbank erosion in the Ganges-Brahmputra-Meghna basin is higher
than the global average, which may be because of high relief and
sediment yield in the tectonically active headwaters and thick alluvium
downstream as well as frequent high discharge during monsoon season. As
shown in these examples, river size does provide a good first order
approximation of riverbank erosion for most basins globally, however
these specific relationships can vary greatly between regions. REAL
provides a starting point for analyzing these second order relationships
at the global scale, and further research may be able to show the
relative importance of geologic, hydrologic, and human influences on the
rate of riverbank erosion in different basins.
We have revealed and clarified some limitations in quantifying river
planform change using remotely sensed data products. First, the
resolution of Landsat (~30 meters) requires decades
between images to reliably detect the rate of change for all but the
most dynamic rivers. This long timeframe comes with an added challenge,
in that any changes in hydrology, human influence, or data quality over
the intervening years can result in biased estimates of planform change.
Higher resolution satellites like Sentinel-2 and the Planet
constellation will become increasingly viable for global riverbank
migration analysis as the length of record grows, and at the very least
would be useful for multi-source remote sensing data fusion with the
Landsat record. Second, the uncertainty in water classifications is a
well-known problem in remote sensing of planform change, however the
severity of the problem is in relation to the magnitude of actual
planform change. We found that classification uncertainties propagated
through our change calculations were greater than the observed planform
change for only about 5.7% of rivers. Improving the representation of
all sources of uncertainty and the impact on change calculations is a
necessary next step remote sensing of river morphology change detection.
We have also provided the first global accounting of highly mobile
rivers that may present a problem to remote sensing applications which
treat rivers as immobile. Our earth observation satellite record is now
several decades long, yet most river morphology data are static in time.
As the satellite record grows, dynamic methods that track the movement
and change of rivers in time will be critical. Even satellite missions
over relatively short periods, like the SWOT satellite (Biancamaria et
al., 2016), which is currently planned to launch in 2022 and will be
operational for 3 years, will require some flexibility to accommodate
the mismatch between SWORD centerline locations and highly mobile
rivers. The REAL dataset provides the historical data to predict where
and how soon these problems may arise in the future.