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18 results for “river width”

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edi48/100

Kuskokwim River Floodplain: White Spruce (Picea glauca) annual tree-ring width measurements (mm) at breast height from tree-core samples taken above Red Devil on the Kuskokwim River in July, 2007

This dataset contains annual raw ring width measurements in the Tucsan (decadal format) (.rwl file extension) of 14 large white spruce trees growing within 50m of the Kuskokwim River. Ring-widths were measured to 0.001mm on a velmex laser micrometer and accuracy was checked by crossdating using COFECHA. Annual values were measured from 1779-2006.

openOpenJul 2008View details →

zenodo44/100

MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"

Corresponding peer-reviewed publication This dataset corresponds to all input and output files that were used in the study reported in: <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Summary The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes. We aim to answer two questions: 1.     What is the intrinsic spatial resolution of global river dynamics? 2.     How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes? Data sources The following sources were used to produce files in this dataset: <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> Software The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling. Data Products The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii). Riv_coast.zip contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean. ·       riv_coast.zip     o      cor: riv_coast_pfaf_ii_COR.shp     o      uncor: riv_coast_pfaf_ii_UNCOR.shp   Qout_rivwidth.zip contains csv files of the aggregate river discharge to the ocean (km3/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       Qout_rivwidth.zip: Qout_pfaf_ii_rivwidth.csv   V_rivwidth_low.zip contains csv files of the aggregate river storage (km3) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_low.zip: V_pfaf_ii_rivwidth_low.csv   V_rivwidth_nrm.zip contains csv files of the aggregate river storage (km3) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_nrm.zip: V_pfaf_ii_rivwidth_nrm.csv   V_rivwidth_hig.zip contains csv files of the aggregate river storage (km3) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions. ·       V_rivwidth_hig.zip: V_pfaf_ii_rivwidth_hig.csv   Largest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins. ·       largest_rivs.zip     o      cat: cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins     o      csv: Q_df_top10.csv – total discharge contributed by each basin     o      riv: riv_top10_nxx.shp – river reaches that drain the 10 largest basins   Smallest_rivs.zip contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m. ·       smallest_rivs.zip     o      cat: cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii     o      csv: Q_df_top10.csv – total discharge to the ocean from each narrow river reach     o      riv: riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii   Global_summary.zip contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage. ·       global_summary.zip     o      Qout_rivwidth: global summary files for discharge to the ocean (km3/yr) under river width sampling     o      V_rivwidth_low: global summary files for total river storage (km3) for the low residence time scenario under river width sampling     o      V_rivwidth_nrm: global summary files for total river storage (km3) for the normal (medium) residence time scenario under river width sampling     o      V_rivwidth_hig: global summary files for total river storage (km3) for the hig residence time scenario under river width sampling     o      cat_small_gl: cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean   Rivwidth_sens.zip contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations. ·       Rivwidth_sens.zip     o      riv_coast     o      Qout_rivwidth_VIC     o      Qout_rivwidth_CLSM     o      Qout_rivwidth_NOAH     o      V_rivwidth_low_VIC     o      V_rivwidth_nrm_VIC     o      V_rivwidth_hig_VIC     o      V_rivwidth_low_CLSM     o      V_rivwidth_nrm_CLSM     o      V_rivwidth_hig_CLSM     o      V_rivwidth_low_NOAH     o      V_rivwidth_nrm_NOAH     o      V_rivwidth_hig_NOAH     o      global_summary_VIC     o      global_summary_CLSM     o      global_summary_NOAH   Cor_sens.zip contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis. ·       Cor_sens.zip     o      Qout_rivwidth_ENS     o      V_rivwidth_low_ENS     o      V_rivwidth_nrm_ENS     o      V_rivwidth_hig_ENS     o      global_summary_ENS   Width_val.zip contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018). ·       Width_val.zip: width_validation_pfaf_ii.csv   Known bugs in this dataset or the associated manuscript No bugs have been identified at this time.   References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588. https://doi.org/10.1126/science.aat0636 Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. Nature Geoscience, 1–7. https://doi.org/10.1038/s41561-024-01421-5 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 55(8), 6499–6516. https://doi.org/10.1029/2019WR025287 Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). Bulletin of the American Meteorological Society, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

opencc-by-sa-4.0Sep 2024View details →

zenodo44/100

Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms

This repository provides three data files in CSV format: 1. Gauge name, lat/lon information 2. Gauge name, date, and multi-temporal river width extracted from Landsat 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.   Reference: Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. RSE.   Updated: 2022/6/17, 2023/1/19

opencc-by-4.0Jun 2022View details →

edi44/100

Tree-ring width measurements and isotope data for riparian Populus species, Santa Clara River, 2019

This data set comprises tree-ring data collected from 114 cottonwood trees (Populus trichocarpa and Populus fremontii) within the floodplain of the Santa Clara River, CA. Tree-ring data include annual ring width measurements for all rings of each individual as well as semi-annual (earlywood and latewood) measurements of stable carbon and oxygen isotopes for pure alpha cellulose extracted from annual growth rings corresponding to calendar years 2010-2019 for a subset of 48 individuals. This data set is completed. Carbon and oxygen isotope ratios are reported using “delta” notation (i.e. δ13C and δ18O) calculated by the equation: δ13C (or δ18O) = (Rsample/Rstandard - 1) x1000 where R is the molar ratio of 13C/12C (or 18O/16O), with Rsample being that of tree ring cellulose and Rstandard that of Vienna Pee Dee Belemite (VPDB) for δ13C and Vienna Standard Mean Ocean Water (VSMOW) for δ18O. These data were used for the following publication: Williams, J., J.C. Stella, S.L. Voelker, A.M. Lambert, L. Pelletier, J.E. Drake, J.M. Friedman, D.A. Roberts, M.B. Singer. (2022). Local groundwater decline exacerbates response of dryland riparian woodlands to climatic drought. Global Change Biology.

openCC (other)Jun 2022View details →

edi44/100

Standardized ring-width chronologies of thinleaf alder growth along the Tanana River floodplains spanning 1968-2006.

This dataset contains landscape level and site level standardized ring-width chronologies (1968-2006) of thinleaf alder growth along the Tanana River floodplains. The raw tree ring-width series from each alder disk was individually detrended to remove age-related growth trends. The chronologies represent the high-frequency variation in ring-width to be analyzed at the inter-annual time scale.

openOpenMar 2009View details →

edi44/100

Coweeta Synoptic Data from 49 sampling sites in the Upper Little Tennessee River Basin from 2009 to 2010 (active channel width, bankfull width, and channel depth data)

This data was generated as part of synoptic sampling conducted at the Coweeta LTER between June 2009 and May 2010. 49 wadeable streams with low levels of development were sampled throughout the Upper Little Tennessee River Basin in the Southern Appalachians. Active channel width, bankfull width, and channel depth were measured every 5 meters for 150 meters at synoptic stream sites. Effects of riparian vegetative conditions on a suite of channel morphological variables were investigated: active channel width, variability of width within a reach, large wood frequency, mesoscale habitat distributions, median particle size, and percent fines. At each site, a uniform 150 meter section of stream was surveyed. Within each reach active channel width, bankfull channel width, and channel depth were measured every 5 meters. Active channel width was defined as the vegetationless channel bed from left vegetation break to right vegetation break. A whitepaper on the Synoptic field sampling activites can be found at: http://coweeta.uga.edu/publications/white%20paper%20summary%20of%20synoptic%20sampling.pdf

openCustomJan 2020View details →

zenodo40/100

A Scene-Level Method for Estimating Small River Widths in Complex Terrain Using Remote Sensing

Files and Descriptions: 1. TP_Lake.csv: This CSV file contains identified lakes in the Tibetan Plateau. 2. TP_River_Monthly_Statistics.csv: Monthly statistics for river data on the Tibetan Plateau, including estimations like active channel percentage and width for different river orders. 3. S2RiverWidth.py: The Python script that contains the main code for river width estimation model. This script includes the functions for preprocessing Sentinel-2 images and predicting river widths. 4. best_model_vCloud10.pth: The pre-trained deep learning model weights used for river width estimation. This model is a ResNeXt model fine-tuned on our dataset. It accepts Sentinel-2 TOA image with cloud percentage <10% (SCL). 5. Example.tif: A Sentinel-2 TOA image in .tif format, used for testing the river width estimation model. Model Input Requirements: The model requires a Sentinel-2 TOA image in .tif format as input. The image should be scaled by a factor of 10,000 (with reflectance values range from 0 to 1). The `get_model_input` function automatically crops the image to 224x224 pixels around the center to fit the model's input requirements.

opencc-by-4.0Nov 2024View details →

zenodo40/100

Global Long-term River Width (GLOW)

This repository archives the Global LOng term river Width (GLOW) dataset measured from Landsat for 1984-2020. There are three datasets in this repository: Width, Cross_section, and Environmental_parameter_global. Width contains the GLOW dataset, which has three attributes: ID, date (mm/dd/yyyy), and width (unit: meters). Each cross-section is identified by a unique ID in the format of R########XS#######. The dataset is grouped into eight regions based on the geospatial location. Cross-section contains the shapefiles indicating the GLOW cross-section locations, which have three attributes: ID, lat (latitude), and lon (longitude). Environmental_parameter_global contains the environmental parameters generated in this study for the Random Forest analysis, which has eighteen parameters for all global rivers at the river reach level, including the temporal trend slope, interquartile range, 25th percentile, and 75th percentile of air temperature, snowmelt, precipitation, evapotranspiration, and river discharge.    Please refer to the following paper for more details: Feng, D., Gleason C.J., Yang X., Allen G.H., and Pavelsky T.M., How have global river widths changed over time? Water Resources Research

opencc-by-4.0Apr 2022View details →

zenodo40/100

Global River Widths from Landsat (GRWL) Database

If you use the GRWL Database in your work, please cite: Allen and Pavelsky (2018) Global Extent of Rivers and Streams. Science. <a href="https://doi.org/10.1126/science.aat0636">https://doi.org/10.1126/science.aat0636</a>   This long-term repository contains three files: 1) Simplified GRWL Vector Product: GRWL_summaryStats_V01.01.zip 2) GRWL Mask (raster): GRWL_mask_V01.01.zip  3) GRWL Vector Product: GRWL_vector_V01.01.zip   Other data: - Location map of the individual GRWL tiles: <a href="https://drive.google.com/file/d/1K6x1E0mmLc0k7er4NCIeaZsTfHi2wxxI/view?usp=sharing">Shapefile download</a>   - River and stream surface area totals by drainage basin (Fig. 4 in Allen & Pavelsky, 2018): <a title="RSSA Basins" href="https://drive.google.com/file/d/1PmoSbDFHUcQ9KXo23noQbIExAY5vJ0qb/view?usp=sharing" target="_blank" rel="noopener">Shapefile download</a>     1) Documentation for the Global River Width from Landsat (GRWL) Simplified Vector Product V01.01 This zip file contains a single ESRI shapefile polyline of river centerlines.  Projection: Geographic WGS84  This file is a simplified version of the raw GRWL vector product (see #3 below). This product is a smaller and more wieldy compared to the raw GRWL vector dataset and most users of GRWL will prefer to use this simplified version. This simplified vector product reduces the number of feature vertices and attributes by simplifying the polyline geometry and by calculating summary statistics along each polyline segment. Polyline segments are roughly the line segments between each tributary junction.   For each polyline segment, the shapefile contains the following attributes: 1.    width_min: the minimum of river width measurements along the segment at mean discharge (meters) 2.    width_med: the median of river width measurements along the segment at mean discharge (meters) 3.    width_mean: the mean of river width measurements along the segment at mean discharge (meters) 4.    width_max: the maximum of river width measurements along the segment at mean discharge (meters) 5.    width_sd: the standard deviation of river width measurements along the segment at mean discharge (meters) 6.    lakeflag: integer specifying if segment is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). This information is of much higher quality in the Global River Width from Landsat (GRWL) Vector Product V01.01 (product #3 below).  8.    nSegPx: number of pixels within the segment (N pixels) 9.    Shape_Leng: length of the segment (kilometers)   2) Documentation for the Global River Width from Landsat (GRWL) Mask V01.01 This zip file contains 830 GeoTIFF tiles of water masks at mean discharge. The assembly of this database is described in Allen and Pavelsky (2018) “Global Extent of Rivers and Streams” published in Science. The GRWL mask is an intermediate product in the production the GRWL vector product and thus is not explicitly validated.  Tile coverage: 4 degrees latitude by 6 degrees longitude File format: GeoTIFF (unsigned byte) Projection: Geographic WGS84  Resolution: 30 m Pixel classifications:  DN = 256 : No Data DN = 255 : River DN = 180 : Lake/reservoir  DN = 126 : Tidal rivers/delta  DN = 86 : Canal DN = 0 : Land/water not connected to the GRWL river network   3) Documentation for the Global River Width from Landsat (GRWL) Vector Product V01.01 This zip file contains 829 ESRI shapefile polylines of river centerlines.  Tile coverage: 4 degrees latitude by 6 degrees longitude.  Projection: Geographic WGS84  Resolution: 30 m At each GRWL measurement location, the shapefile contains the following attributes: 1.    utm_east: UTM Easting (UTM Zone is given in tile file name; meters) 2.    utm_north: UTM Northing (UTM Zone is given in tile file name; meters) 3.    width_m: wetted width of river (meters) note: width_m == 1 indicates NA (no width data along the centerline)  4.    nchannels: braiding index (-) 5.    segmentID: unique ID of river segment in each tile 6.    segmentInd: Index of each observation in each segment. Not sorted by upstream or downstream 7.    lakeflag: integer specifying if observation is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3).  8.    lon: Longitude (decimal degrees) 9.    lat: Latitude (decimal degrees) 10.    elev: Elevation (meters) – sampled from the Hydro1k DEM

opencc-by-4.0Jun 2018View details →

zenodo40/100

Tree-ring width chronologies of subfossil oak from Seda River, Latvia

The dataset includes six mean curves/chronologies of subfossil oak from Seda River, northern Latvia. Series No. 1-5 were radiocarbon dated and the chronology No. 6 was absolute dated against chronologies from the Baltic region or Baltic timber imported to the Western Europe.

opencc-by-4.0Dec 2022View details →

zenodo36/100

A simple global river bankfull width and depth database

A simple global database of river widths and depths was derived using the HydroSHEDS river topology data set and simple geomorphic relationships among area, discharge, width, and depth. This database can be useful to provide initial estimates for hydraulic or hydrologic modeling where other suitable measurements are unavailable. The purpose of this database is not to replace more detailed estimates of river width and depth, but it is a first attempt at mapping these river characteristics with near-global coverage.

opencc-zeroSep 2016View details →

zenodo36/100

Data for channel width of the Cannon River, Minnesota, 1938-2017

Datasets analyzed and generated to measure channel width change of the Cannon and Straight Rivers, Minnesota. Includes aerial imagery and derived files to describe river geometry. Aerial imagery was originally accessed from USGS Earth Explorer and the University of Minnesota Minnesota Historical Aerial Photographs Online portal. 1939_ASCS.zip, 1951_USDA.zip, 1948_USGS.zip, 1964_ASCS.zip, 1974_USGS_mosaic.zip, 1974_single_images.zip, 1980_USGS.zip, 2002_NAIP.zip, 2010_NAIP.zip, 2017_NAIP.zip These zip files contain aerial images from from 1938, 1951, 1958, 1964, 1974, 1980, 1991, 2002, 2010, and 2017, sorted into folders by year. Aerial images are included as raw downloaded images and (where applicable) georectified images. Ground control points used for rectification are included for rectified photos. For a number of years, a mosaicked image was created and is included. shapefiles.zip This zip file contains derived shapefiles to describe river geometry. The active channel of the river was digitized for each year of available imagery. This included the upper Cannon River (upstream of its confluence with the Straight River), the Straight River, and the lower Cannon (downstream of the confluence with the Straight River).  From the active channel boundaries, the centerline and width was extracted using the Planform Statistics tool in Arcmap (Lauer, W. NCED Stream Restoration Toolbox-Channel Planform Statistics And ArcMap Project, National Center for Earth-Surface Dynamics). Width was extracted at 10 m intervals, and the data is stored in points at each 10 m interval. Meander migration between years was also calculated using the same toolbox.  Sinuosity was calculated for channel segments of various lengths.

opencc-by-4.0Mar 2024View details →

zenodo36/100

Global database of river width, slope, catchment area, meander wavelength, sinuosity, and discharge

1.Summary This document describes the database that accompanies the article written by the authors of this dataset and accepted by Geophysical Research Letters (doi: 10.1029/2019GL082027).The database is distributed as a set of shapefiles, containing polylines that define the geometry of river centerlines located between 60°N and 56°S, with attributes described below.  The shapefiles are organized according to continent and further broken into major basins to allow for manageable file sizes.  A more complete dataset is available in the netCDF format upon request (please email Renato Frasson at renato.prata.de.moraes.frasson@jpl.nasa.gov). This database was partially funded by the Algorithm Definition Team contract to the Ohio State University, University of North Carolina at Chapel Hill, and Remote Sensing Solutions, Inc. 2.Polyline geometry The centerline geometry is defined by sets of points located approximately every 30 m based on the Global River Widths from Landsat (GRLW) database (Allen & Pavelsky, 2015; 2018).  Each line describes a meander and features the following attributes. 3.Attribute description <ul> <li>SegmentID: identification number of the river segment (segments are parts of a river delimited by confluences).</li> <li>lakeFlag: 0 – river, 1 – lake, 2 – river under the influence of tide, 3 – canal, 4 – unable to connect GRWL with HydroSHEDs, 5 – dam, -9999 – no data.</li> <li>Width: average width in the meander, disregarding small river widths assigned to locations undetected by Landsat but known to be inundated. Locations where no width could be produced are marked as -9999.</li> <li>Elevation: mean elevation from SRTM (90m) per river meander in meters.  SRTM pixels are assigned to equally spaced points (every ~30m) over the river centerlines using the nearest neighbor approach.  The average elevation of all valid points per meander is reported here.  Locations where no elevation could be produced are marked as -9999.</li> <li>Slope: water surface slope in centimeter per kilometer.  Slope is initially computed over 10 km reaches, then used to compute optimum reach lengths using a modified version of the equation proposed by LeFavour and Alsdorf (2005) in the form of RL=2σ /S, where RL is the optimum reach length, σ is the height uncertainty (5.51 m from LeFavour and Alsdorf, 2005) and S the initial slope estimate.  Final slopes are computed over the optimum reach lengths using elevations assigned to the 30 m river points using either classic linear regression or the Theil-Sen estimator depending on which method produces the best coefficient of determination.  Locations where no slope could be produced are marked as -9999.</li> <li>Meandwave: Meander wavelength in meters.  This is computed by first smoothing the 30 m resolution river centerlines using a 5-point moving average and then identifying inflection points on the smoothed river centerlines.  Finally, the meander wavelength takes the value of twice the distance between consecutive inflection points according to the definition given by Leopold and Wolman (1960).</li> <li>Sinuosity: Dimensionless sinuosity of each river meander computed the ratio of the length between meander endpoints measured along the river centerline to half the meander wavelength as defined by Leopold and Wolman (1960).</li> <li>catch_area: Catchment area was derived from flow direction and corresponding flow accumulation grids based on HydroSHEDS (Lehner et al., 2008). The flow accumulation grid describes, for any location (i.e. pixel), the number of upstream raster pixels that drain to that particular location.  We translated flow accumulation given in number of pixels into catchment area (in m2) by multiplying the number of pixels flowing to a location by the average area of SRTM pixels according to the latitude of the centroid of the river segment.</li> <li>QWBM: mean annual flow estimated with the water balance model WBMsed (Cohen et al., 2014).</li> <li>Strpwr_len: stream power normalized by width (W/m).</li> <li>Strpwr_are: stream power normalized by area (W/m2).</li> </ul> Acknowledgements Use of this database should be acknowledged appropriately. The WBM data used in this database were provided by Dr. Albert Kettner at INSTAAR, University of Colorado at Boulder. References Allen, G. H., and T. M. Pavelsky (2015), Patterns of river width and surface area revealed by the satellite-derived north american river width data set, Geophysical Research Letters, 42(2), 395-402, doi: 10.1002/2014gl062764. Allen, G. H., and T. M. Pavelsky (2018), Global extent of rivers and streams, Science, doi: 10.1126/science.aat0636. Cohen, S., A. J. Kettner, and J. P. M. Syvitski (2014), Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, Glob. Planet. Change, 115, 44-58, doi: https://doi.org/10.1016/j.gloplacha.2014.01.011. LeFavour, G., and D. Alsdorf (2005), Water slope and discharge in the amazon river estimated using the shuttle radar topography mission digital elevation model, Geophysical Research Letters, 32(17), doi: 10.1029/2005gl023836. Lehner, B., K. Verdin, and A. Jarvis (2008), New global hydrography derived from spaceborne elevation data, EOS, TRANSACTIONS, AMERICAN GEOPHYSICAL UNION, 89(10), 93-94, doi: doi:10.1029/2008EO100001. Leopold, L. B., and M. G. Wolman (1960), River meanders, Geological Society of America Bulletin, 71(6), 769-793, doi: 10.1130/0016-7606(1960)71[769:RM]2.0.CO;2.

opencc-by-4.0Feb 2019View details →

zenodo36/100

Long Profiles and Valley Width Data for Arroyo Hondo, Alameda Creek, and the Eel River

These files contain 4 columns of data. Column 1 is distance downstream, column 2 is river elevation, column 3 is floodplain width, column 4 is an identifier indicating whether there is a landslide intersecting the channel at that location, in which case it is 1.  For the Eel River site, we use lidar data as described by Mackey and Roering (2011). For the Alameda Creek/Arroyo Hondo site, we use one-ninth second USGS NED data.

opencc-by-4.0Sep 2019View details →

zenodo36/100

At-a-station hydraulic geometry exponent b derived from Landsat river width and discharge observation

<h2>Description</h2> Global at-a-station hydraulic geometry exponent b dataset derived from Landsat river width (Feng et al., 2022) and discharge observation (Lin et al., 2019).   For more details, please refer to: <div> </div> Zimin Yuan, Peirong Lin, Xiwei Guo, Kai Zhang, Hylke E. Beck. Revisiting At-a-Station Hydraulic Geometry Using Discharge Observations and Satellite-Derived River Widths. J Remote Sens. 2024;4:0271. DOI:<a href="https://doi.org/10.34133/remotesensing.0271">10.34133/remotesensing.0271</a>   <h2>Contacts</h2> <ul> <li>Zimin Yuan, <a href="mailto:ziminyuan@pku.edu.cn">ziminyuan@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul>

opencc-by-4.0Aug 2024View details →

zenodo36/100

Ring Width Index (RWI) and Standard Precipitation Index (SPI) data for Atmospheric River reconstruction

These are the two datasets used for reconstructing atmospheric river activity along the U.S. west coast (under review at the Journal of Geophysical Research: Atmosphere). The first file rwi.mat contains the ring-width-index for the chronologies used to develop the second dataset (SPI_recon.nc). This file is used as the covariate to develop the AR reconstructions (with Poisson regression) and goes back to 1400 CE.

opencc-by-4.0May 2023View details →

dryad36/100

Response of atmospheric river width and intensity to aquaplanet warming: A detection algorithm- and background moisture-independent approach

Open the record for dataset details and reuse information.

publicMay 2025View details →

zenodo24/100

Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis

1. Summary Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", Geophysical Research Letters (accepted).   2. File Description Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km2. Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki et al., 2019).   3. Attribute Description <ul> <li>COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019);</li> <li>Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km2;</li> <li>Area: Upstream drainage basin area in km2;</li> <li>Sin: Sinuosity of the river segment (unitless);</li> <li>Slp: mean slope of the river segment (unitless);</li> <li>Elev: mean elevation of the river segment;</li> <li>K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);</li> <li>P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);</li> <li>AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);</li> <li>LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013);</li> <li>SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);</li> <li>CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);</li> <li>SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);</li> <li>Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018);</li> <li>WTD: mean water table depth (m below surface) of the unit catchment;  data extracted from Fan et al. (2013);</li> <li>HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016)</li> <li>DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019)</li> <li>QMEAN: mean annual discharge (m3/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019);</li> <li>Q2: 2-year return period flood discharge (m3/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019);</li> <li>Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;</li> <li>Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study</li> </ul>   4. References Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636 Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881 Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9 Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748 Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860 Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287 Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055 Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3 Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618 Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874 Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873 Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs5020927

opencc-by-4.0Nov 2019View details →

ScienceDex guides

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

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Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record