Search for: All records

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.

Mobile river channels endanger human life and property and over centuries shape ecosystems, landscapes, and stratigraphy. Quantifying channel movements from remote sensing is difficult, in part due to the diversity of river mobility processes (e.g., channel migration, cutoffs, avulsion) and planform morphologies (e.g., meandering, braided). Here, we present a framework for quantifying riverbank migration from remote sensing that upscales recent methodological advances from laboratory flume studies utilizing particle image velocimetry (PIV). We apply PIV to image time series of 21 rivers worldwide, showing PIV ignores cutoff and avulsion processes by design and is well suited for tracking riverbank migration regardless of planform morphology. We show that PIV‐derived results for riverbank migration are consistent with published results from centerline‐ and bank‐based Lagrangian methods. Unlike existing methods, PIV offers a grid‐based Eulerian framework where defining channel centerlines is unnecessary and quantified uncertainty in riverbank positions is propagated into uncertainty in migration rates. PIV offers means to efficiently extract global patterns in riverbank migration from decades of satellite data, as well as investigate river response to climate change and human activities in our rapidly changing world.