A Generalized Area-Based Framework to Quantify River Mobility From Remotely Sensed Imagery

Rivers are the primary conduits of water and sediment across Earth's surface. In recent decades, rivers have been increasingly impacted by climate change and human activities. The availability of global-coverage satellite imagery provides a powerful avenue to study river mobility and quantify the impacts of these perturbations on global river behavior. However, we lack remote sensing methods for quantifying river mobility that can be generally applied across the diversity of river planforms (e.g., meandering, braided) and fluvial processes (e.g., channel migration, avulsion). Here, we upscale area-based methods from laboratory flume experiments to build a generalized remote sensing framework for quantifying river mobility. The framework utilizes binary channel-mask time series to determine time- and area-integrated rates and scales of river floodplain reworking and channel-thread reorganization. We apply the framework to numerical models to demonstrate that these rates and scales are sensitive to specific river processes (channel migration, channel-bend cut-off, and avulsion). We then apply the framework to natural migrating and avulsing rivers with meandering and braided planforms. Results show that our area-based framework provides an objective and accurate means to quantify river mobility at reach- to floodplain-scales, which is largely insensitive to spatial and temporal biases that can arise in traditional mobility metrics. Our work provides a framework for investigating global controls on river mobility, testing hypotheses about river response to environmental gradients, and quantifying the timescales of terrestrial organic carbon cycling.

Automated summary

Rivers are important conduits for water and sediment, but they are being increasingly affected by climate change and human activities. The availability of global satellite imagery provides a powerful way to study river mobility and understand the impacts of these changes. However, there is a lack of remote sensing methods that can be applied to different types of rivers.