Data for: The pace of global river meandering influenced by fluvial sediment supply
Data files
Mar 06, 2024 version files 34.64 MB
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Dammed_Rivers.zip
13.45 MB
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README.md
9.41 KB
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Single_Rivers.zip
21 MB
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Tabular_Data.zip
180.15 KB
Abstract
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.
README: 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:
Methods
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.