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Abstract 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.
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Testing Predictions for Migration of Meandering Rivers: Fit for a Curvature‐Based Model Depends on Streamwise Location and Timescale
Abstract Many meandering rivers migrate, at rates that vary both along‐stream and inversely with the observation interval. Many numerical models have been developed to predict this migration; their success is usually evaluated statistically or by qualitative comparison to observations in map view. We propose a framework to test migration models that unites these statistical, spatial, and temporal perspectives. We measure model fit with a statistic that compares the magnitude and direction of migration between predictions and observations. Model fit is contextualized in space, using a dimensionless coordinate system based in the location along a half‐meander bend; and in time, using a dimensionless observation interval that accounts for channel scale and migration rate. We applied this framework to test predictions for a curvature‐driven model of channel migration, using data from seven rapidly migrating rivers in the Amazon Basin and 103 more slowly migrating rivers across the continental US, as reconstructed from a legacy data set. We find that across both datasets, channel migration rates peak slightly downstream of the bend apex. Migration rate underestimation/overestimation tends to occur when the observed rate is greater/less than its median along the channel. Predicted migration direction opposes observations for slowly migrating locations and upstream of the bend apex. Model forecasts break down if the channel migrates by more than its width. The analysis framework is portable to testing other models of channel migration, and can help improve predictions for the stability of infrastructure along rivers and for landscape change over geologic timescales.
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- Award ID(s):
- 1823530
- PAR ID:
- 10383385
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 127
- Issue:
- 12
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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