DOI: 10.5061/dryad.jq2bvq8g3
title: Data for: The pace of global river meandering influenced by fluvial sediment supply

description: Meandering rivers move gradually across the floodplains, and this river
 movement presents socioeconomic risks along river corridors and regulates
 terrestrial biogeochemical cycles. Experimental and field studies suggest
 that fluvial sediment supply can exert a primary control on lateral
 migration rates of rivers. However, we lack an understanding of the
 relative importance of environmental boundary conditions, such as
 floodplain vegetation and sediment supply, in setting the pace of river
 meandering across different environmental settings. Here, we combine the
 analysis of satellite imagery and global-in-scale sediment and water
 discharge models to evaluate the controls on lateral migration rates of
 139 meandering rivers that span a wide range in size, climate, and bank
 vegetation. We show that migration rates normalized by the channel width
 monotonically increase with the volumetric sediment flux normalized by the
 characteristic size of the river. This relation is consistent across
 rivers in vegetated and unvegetated catchments, indicating that enhanced
 lateral migration rates in unvegetated basins is likely not only
 facilitated by lower bank mechanical strength, but also by higher
 normalized sediment supply in ephemeral rivers. Using three case examples,
 we also demonstrate that width-normalized meander migration rates respond
 to spatial gradients in sediment supply caused by river impoundments,
 highlighting the prominent role of sediment supply in setting the pace of
 meander migration. Our results suggest that sediment-supply variations
 caused by climate, land-cover and land-use changes can lead to predictable
 changes in meandering river evolution and ultimately drive architectural
 changes in sedimentary stratigraphy.
description: This dataset includes all underlying data used for the associated
 manuscript. We include all surface water mask files (.tif), derived
 channel centerline files (.csv, .pkl), bar-averaged migration files, and
 aggregated tabular data. To work with any of the derived data, we
 recommend using a Python-based workflow. The dataset includes three
 sections: 1) Dammed_Rivers This
 includes underlying data for the upstream-to-downstream comparisons of
 river mobility across river dams. For each included river dam, we provide
 files that show the study area, binary geotiffs of channel water,
 generated centerlines, all migration data, and samples WBMsed model
 sediment flux and water discharge information. There are data for 3 rivers
 (Flint, Iowa, and Red) included in this section. 2)
 Single_Rivers This includes all underlying data for the
 individual river analyses included in our data compilation. We include the
 study location, binary Geotiffs of channel water, centerlines, migration
 data, and WBMsed model data. There are data for 55 rivers included in this
 section. 3) Tabular_Data This
 includes the aggregated tabular data for the data compilations. We
 aggregate the 55 rivers (from the Single_Rivers section) into a tabular
 .csv database. We also include data from 84 additional rivers that have
 published migration rates.  More detail on the file
 structure and data contents can be found in the README.md file. For more
 detail on the Python-based workflow to generate channel water masks,
 centerline vector products, and migration rate measurements, please see
 the associated software.
description: # Data for "The Pace of Global River Meandering Set by Fluvial
 Sediment Supply" --- This dataset includes all underlying data used
 in the manuscript. This includes surface water mask files (.tif), derived
 channel centerline files (.csv, .pkl), bar-averaged migration, and
 aggregated tabular data. To work with any of the derived data, we
 recommend using a Python-based workflow. ## Description of the data and
 file structure The contents of the dataset is organized into 3
 directories: ### 1) Dammed\_Rivers: This includes the underlying data for
 the upstream-to-downstream comparisons of river mobility across dams. It
 includes files for the Flint, Iowa, and Red Rivers. Files are organized
 by: ├── Dammed_Rivers/   ├── FlintRiver/    ├── compare.py    ├──
 FlintRiver_WBMdata.csv    ├── gpkg_shapes/    ├── masks/     ├──
 FlintDownstream/      ├── bar_migration/      ├── centerline/      ├──
 centerline_csv/      ├── mask/     ├── FlintUpstream/    ├──
 WBM_columns.txt   ├── IowaRiver/    ├── compare.py    ├──
 IowaRiver_WBMdata.csv    ├── gpkg_shapes/    ├── masks/     ├──
 IowaDownstream/      ├── bar_migration/      ├── centerline/      ├──
 centerline_csv/      ├── mask/     ├── IowaUpstream/    ├──
 WBM_columns.txt   ├── RedRiver/    ├── codes/    ├── data/     ├── 1995/
      ├── mask/      ├── width/     ├── 2015/     ├── migration/      ├──
 bar/      ├── combine.py      ├── DownstreamMigration.csv      ├── points/
      ├── UpstreamMigration.csv The structures of the Iowa and Flint River
 directories are roughly the same: * **compare.py**: A Python script that
 provides the analysis for the two rivers comparing the upstream and
 downstream (of the dam) portions of the reaches. * The derived **WBMsed
 data** along the river path. e.g. FlintRiver_WBMdata.csv. I've
 included a description of columns as an additional text file
 (WBM_columns.txt). * **gpkg_shapes/**: A directory that holds .gpkg files
 of Polygon shape files that cover the analyzed reaches. * **masks/**:
 Holds all of the geospatial and derived migration data. *
 **bar_migration/**: Holds the aggregated bar-scale migration data. The
 naming convention includes the year1 and year2 over which the migration is
 measured. e.g. 1990_2021 indicates the migration comparing the 1990 and
 2021 year centerlines. The column descriptions are given in the directory
 (bar_migration_csv_column_desc.txt). * **centerline/**: Holds .pkl objects
 of the centerlines derived from the channel masks. The method to open and
 work with these pickle files is provided in the github repository:
 10.5281/zenodo.8341894. * **centerline_csv/**: Holds .csv files for the
 channel masks. The file naming convention includes the channel mask year
 the centerline is derived from. e.g. FlintDownstream_1990_centerline.csv
 is the centerline from 1990. The column descriptions are given as a
 separate file in the directory (centerline_csv_column_desc.txt). *
 **mask/**: Contains the raster data for the channel masks used to generate
 the centerlines. These are provided as single band binary .tif files. The
 structure for the Red River directory is slightly different because this
 analysis was completed earlier than the other two rivers. Descriptions
 follow: * **codes/:** Holds a number of codes used to merge all derived
 centerline files, calculate the migration rates, and compare upstream and
 downstream portions of the reach. * **combine_widths.py**: Combines all
 the width csv files into a single dataframe. * **compare.py**:
 Statistically compares the upstream and downstream portions of the reach.
 * **get_migration.py**: Calculates the migration rates from the width
 dataframes. * **get_sinuosity.py**: Calculates the sinuosity from the
 width dataframes. * **Data/**: Holds all the used data for this analysis.
 * 1995 and 2015 are the two years compared to get the migration rate. *
 **mask/**: Holds all of the .tif raster files for channel water. The
 entire measured reach is broken down into 65 segments. * **width/**: Holds
 the centerline .csv files. The column descriptions are given in a separate
 file (red_river_width_column_desc.csv). * **migration/**: contains the
 migration data comparing the two timesteps. * **bar/**: Bar aggregated
 migration distances for each of the 65 segments. Column descriptions are
 given in separate file (bar_column_desc.txt). * **points/**: Point
 comparisons pinned to the 1995 centerline showing the migration distances.
 Column descriptions are given in separate file (point_column_desc.txt). *
 **combine.py**: Python script combining the 65 segment data into single
 data tables. * **DownstreamMigration.csv**: Bar-scale migration data
 downstream of Lake Texoma. Column descriptions are found in a separate
 file (migration_csv_column_desc.txt). * **UpstreamMigration.csv**:
 Bar-scale migration data upstream of Lake Texoma. Column descriptions are
 found in a separate file (migration_csv_column_desc.txt). *
 **RedRiver_WBMdata.csv**: Contains the WBMsed data for the Red River
 portion. Column descriptions are given as a separate file
 (red_river_wbm_column_desc.txt). ### 2) Single\_Rivers: This includes the
 underlying data for the individual rivers for which I estimated my own
 migration rates. The file structure is the same for each river. I give one
 example below, which follows: ├── Single_Rivers/   ├── Algeria/    ├──
 Algeria/     ├── bar_migration/     ├── centerline/     ├──
 centerline_csv/     ├── mask/    ├── Algeria.gpkg   ├── ...   ├──
 Column_Desc/    ├── bar_migration_column_desc.txt    ├──
 centerline_csv_column_desc.txt   ├── ... Descriptions of what each of the
 subfolders contains: * **bar_migration/**: .csv file containing bar-scale
 migration rates for the compared timesteps. The file naming convention
 contains the compared years. e.g. Algeria_1991_2021_bar_migration.csv is
 the migration data between 1991 and 2021. The column descriptions are
 provided in a separate file (Column_Desc/bar_migration_column_desc.txt). *
 **centerline/**: .pkl objects containing the centerline data. This data
 format is used by the software I use to generate the centerline data. You
 can find more information on this in the github repository: **TODO** *
 **centerline_csv/**: .csv files for the centerlines generated from the
 channel water masks. Column descriptions are given in a separate file
 (Column_Desc/centerline_csv_column_desc.txt). * **mask/**: Binary raster
 .tif files containing channel water. I used these to generate centerlines.
 ### 3) Tabular\_Data This contains all of the aggregated tabular data used
 in the analysis. I have here the collected primary data, collated
 published data, and averaged WBMsed data. Note that N/A values populate
 empty cells. These are missing values that are not available in the
 published sources or not present in the WBMsed model. ├── Single_Rivers/
   ├── Column_Desc/   ├── combine_data.py   ├──
 FullCombinedAvgData_050423.csv   ├── FullCombinedData_050423.csv   ├──
 FullWBM_data.csv   ├── Primary_Data_050423.csv   ├── Published_Data/
    ├── bend_data/     ├── "river".csv    ├── citations.txt
    ├── Column_Desc    ├── Published_Data_050423.csv    ├──
 PublishedBendData_050423.csv For .csv files, column descriptions are given
 as separate files in the Column_Desc/ directory following the pattern of
 "*_file_name_column_desc.txt*." There is overlap between column
 names. I've included enough to understand all columns in the files
 provided. * **combine_data.py**: A Python script used to aggregate the
 individual bend-scale river migrationi data. *
 **FullCombinedAvgData_050423.csv**: All reach-averaged data for the rivers
 within the dataset. * **FullCombinedData_050423.csv**: All bend-scale data
 for the rivers within the dataset. * **FullWBM_data.csv**: All WBMsed data
 for the measured rivers. * **Primary_Data_050423.csv**: Just the primary
 data. * **bend_data/**: Contains the published bend-scale data for each
 river it exists for. Note, the meander wavelength field was measured by me
 for this study. * **citations.txt**: Sources used for published migration
 rates. * **Published_Data_050423.csv**: Aggregated reach-average published
 data. * **PublishedBendData_050423.csv**: Aggregated bend-scale published
 data. ## Sharing/Access information We leverage Google Earth Engine (GEE)
 Landast data for the natural data. Links to the relevant datasets are:
 [Landsat catalog on
 GEE](https://developers.google.com/earth-engine/datasets/catalog/landsat)