Data for: 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 se..., This dataset includes all underlying data included in the manuscript. This includes results from numerical model runs, river channel masks used to estimate mobility, and measurements of mobility metrics for channel mask time series. The contents are organized in a file structure following:1) AvulsionModel: This includes the avulsion model outputs as .mat files (Matlab proprietary files) for the model runs with varying avulsion trigger frequencies. We also included a Python script we use to generate equivalent binary channel masks from the .mat files and calculate mobility. 2) MeanderingModel: This includes the outputs from the meandering river model for varying cutoff thresholds (cutoff_runs) as well as varying migration constants (migration_runs). We provide numpy objects (.npy), which contain the time series of binary raster grids depicting the channel changes through time. We also include Python scripts to calculate mobility and generate figures.3) NaturalRiverData: These directories..., We recommend using an open-sourced Python-based workflow. Common packages used in the scripts here include Numpy, Scipy, Matplotlib, Rasterio, Pillow, and Pandas. For more detailed descriptions on software installation, see the related software packages. GeoTiff files can be visualized in the open-sourced QGIS software.