Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning

This directory contains files related to the scientific research project of Luc van Dijk at the Department of Earth, Energy, and Environment, University of Calgary. The project title is "High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning". This project in the field of geomorphology was a collaboration between the University of Calgary and Utrecht University in the Netherlands. The project was completed on October 27, 2023. Below is a description of the files in this directory. DisplacementVolumeDistributions_TLS.xlsxExcel file containing tabular data of the normalized sediment displacement volumes that were obtained using TLS. Each tab in the Excel file represents a period of interest in 2023. The data in this file were used to generate the 'histogram-like' figures in the report. DoD_rasters.zipFolder containing the aerial lidar DEMs of Difference (DoDs) for each period of interest. The DoDs are 'waterless', i.e. the water surface is masked. The suffix of the file name before the file extension (e.g., ..._10cm.tif) indicates the maximum REM value that was used for the automated masking of the water surface extent (see report section 3.1.2). If the file name contains "large", it refers to the upstream greater area (see report section 3.1.3).Within this folder is another folder called 'Clipped2AOIs'. This folder contains the same DoDs, but covering only the extents of the sites of interest ('AOIs' = Areas Of Interest). FilteredPointClouds_TLS.zipFolder containing the processed and filtered point clouds that were acquired throughout the summer of 2023 using TLS. These point clouds have been pre-processed and filtered to remove vegetation (see report section 3.2). They are grouped in sub-folders per acquisition date. The filenames are numbered to location, i.e. 'elbow1', 'elbow2', 'elbow3' and 'elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. PythonScripts_Discharge_Rainfall.zipFolder containing the Python scripts that were made to process the discharge and rainfall data that were sourced from Environment Canada and The City of Calgary (see report section 3.3). The scripts themselves contain descriptions of their purpose. PythonScripts_DisplacementVolumeAnalysis.zipFolder containing the Python scripts that were made to process and analyze the aerial lidar DoDs and the TLS rasterized difference point clouds (M3C2 output). The 'convert2pickle' scripts converted the sizable rasters to smaller pickle files, which were easier and faster to work with. The 'chart' scripts load the data from the pickle files, analyze them and produce the 'histogram-like' figures in the report. The scripts themselves contain descriptions of their purpose. RainfallDischargeData.xlsxExcel file containing the discharge and rainfall data from Environment Canada and The City of Calgary. The data came from different sources in different formats and were combined into this single table. RasterizedDifferencedPointClouds_M3C2.zipFolder containing the rasterized results of the differenced TLS point clouds (M3C2 output) (see report section 3.2.4). The filenames are numbered to location, i.e. 'Elbow1', 'Elbow2', 'Elbow3' and 'Elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. The numeric sequence in the file name indicates the start and end date of the change analysis in a 'mm-dd' format. The suffixes '_dist', '_unc' and '_sig' refer to the three output layers of the M3C2 algorithm: distance, uncertainty and significance of change. The main files of interest are the '.tif' files. Files sharing the same name, but with different extensions (.tfw, .tif.aux.xml, .tif.xml) are supplementary/auxiliary files for the '.tif' file, generated by ArcGIS Pro. ScarpsOfInterest_shapefile.zipFolder containing a polygon shapefile describing the extents and locations of the sites of interest. The main file of interest is the '.shp' file. The other files with the same name, but different extensions (.cpg, .dbf, .prj, .sbn, .sbx, .shp.xml, .shx) are supplementary/auxiliary files for the '.shp' file, generated by ArcGIS Pro.

opencc-by-4.0Dec 2023View details →

SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains

<h2>Description</h2> SHIFT (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data. <h2>Data Structure</h2> We provide 2 resolutions of data for different needs. <ul> <li>SHIFT_v3_90m: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li>SHIFT_v3_1km: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation. For more details, please refer to: <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li>Changes in v3 compared to v2: <ol> <li> Inclusion of Missing Level-3 Basin: We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer. </li> <li> Updated Parameters: Along with the new boundaries, updated parameters are provided in the shapefile. </li> <li> Re-estimated Global Floodplain Area: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass. </li> </ol> </li> <li>Changes in v2 compared to v1: <ol> <li>Parameter 'b' Estimation: We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li>Target Function for Parameter 'a': We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@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-4.0May 2018View details →

Rees River, New Zealand - Geomorphic Change Detection - Example Dataset

A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection on a long (31 km) dataset. Great for learning about analyses with DEMs produced from a hybrid of survey types. Used in <a href="https://gcd.riverscapes.net/Tutorials/ErrorModelling/multimethoderror.html">Multi-Method Error Estimation Tutorial</a> and <a href="https://gcd.riverscapes.net/Tutorials/GeomorphicInterpretation/morphological-approach.html">Morphological Approach Tutorial</a>. This is part of the dataset from: <ul> <li>2011. Richard Williams, James Brasington, Damia Vericat, Murray Hicks, Fred Labrosse, Mark Neal. Chapter Twenty - Monitoring Braided River Change Using Terrestrial Laser Scanning and Optical Bathymetric Mapping. Editor(s): Mike J. Smith, Paolo Paron, James S. Griffiths. Developments in Earth Surface Processes. Elsevier, Volume 15, Pages 507-532, ISSN 0928-2025. DOI: <a href="https://doi.org/10.1016/B978-0-444-53446-0.00020-3">10.1016/B978-0-444-53446-0.00020-3</a>.</li> </ul> Dataset is from: <ul> <li>2km of braided river near  <a href="https://www.google.com/maps/place/44%C2%B046'38.6%22S+168%C2%B024'17.9%22E/@-44.7767196,168.3891697,7451m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-44.777379!4d168.404972">Queenstown, New Zealand</a></li> <li>Two LiDAR surveys</li> <li>0.5m cell resolution</li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/GeoTERM_Rees.zip">Download Full</a></li> <li><a href="https://s3-us-west-2.amazonaws.com/etalweb.joewheaton.org/GCD/GCD7/Tutorials/MaskOnly_MorphologicalApproach.zip">Download Morphological Only</a></li> </ul> Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened.

opencc-by-4.0Jun 2023View details →

Simulated Geomorphically Relevant Palaeoclimate Variables for the Late Cenozoic (from Mutz and Ehlers, 2019)

Contents Description: (see more details in file header) The files contains long term means of several variables derived from an ECHAM5 palaeoclimate simulation. Details about the type of means and simulations are given in the file name (see explanation below). For information about the simulation setup and boundary conditions, we refer the user to the Mutz et al. (2018) publication. For details about the construction of contained variables, we refer the reader to the Mutz and Ehlers (2019) publication. Data package alterm contains long term annual means, whereas package mlterm contains long term monthly means. The variables included in the data packages are: - csfd: consecutive freezing days (days) - fthd: freeze thaw days (days) - cswd: consecutive wet days (days) - csdd: consecutive dry days (days) - t2am: 2m air temperature amplitude (°C) - tsam: surface temperature amplitude (°C) - pamp: precipitation amplitude (mm/day) - pmax: maximum daily precipitation (mm/day) Publications: Derived Variables (contained in these files): Mutz S.G. and Ehlers T. A. (2019). Detection and Explanation of Spatiotemporal Patterns in Late Cenozoic Palaeoclimate Change Relevant to Earth Surface Processes. Earth Surface Dynamics. doi.org/10.5194/esurf-7-663-2019 Original Simulations: Mutz S.G., Ehlers T. A., Werner M., Lohmann G., Stepanek C., Li J., (2018). Estimates of Late Cenozoic climate change relevant to Earth surface processes in tectonically active orogens. Earth Surface Dynamics. doi.org/10.5194/esurf-6-271-2018 Authors: Mutz S.G., Ehlers T. A. License: This work is distributed under the Creative Commons Attribution 4.0 International License Format: netCDF (.nc) File Names (nc): [publication]_[experiment ID]_[time period]_[horizontal resolution][vertical resolution]_[means].nc   <table> <tbody> <tr> <td>experiment ID</td> <td>usually a letter followed by 3-5 digits, e007_2 (pre-industrial), e008 (Mid-Holocene), e009 (Last Glacial Maximum), e010 (Pliocene)</td> </tr> <tr> <td>time period</td> <td>PI (pre-industrial), MH (Mid-Holocene), LGM (Last Glacial Maximum), PLIO (Pliocene)</td> </tr> <tr> <td>horizontal resolution</td> <td>spectral resolution, t followed by a number, e.g. t159</td> </tr> <tr> <td>vertical resolution</td> <td>number of vertical levels, l followed by a number, e.g. l31</td> </tr> <tr> <td>means</td> <td>alterm (annual long term means) or mlterm (monthly long term means)</td> </tr> </tbody> </table> Correspondence: Sebastian G. Mutz (sebastian@sebastianmutz.com)

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Geomorphic characteristics and algal cover for 36 tidal mudflats on the Virginia Coast

Data on sediment particle sizes, mudflat topography, and hydrologic flow-related characteristics collected for 36 mudflats. Some data were field collected while others were extracted from modeled or remotely sensed data sets. Details are included with each variable.

openCustomDec 2018View details →

opencc-by-4.0Nov 2021View details →

Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"

This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis". The parameter used to perform hydraulic simulations are also available.

opencc-by-4.0Oct 2021View details →

Supplementary files: A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau

Supplement of "A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau". The supplementary files provide the outlines and parameters of the rock glaciers inventoried by InSAR-assist kinematic-based approach in the Daxue Shan, Southeast Tibet Plateau.  Based on the Sentinel-1A ascending SAR images acquired between 2015 and 2019, we derived a five-year-long LOS mean velocity map of the study area. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images.

dryad40/100

Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor

Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two-kilometer river corridor segments of the Swan River in Montana, USA to examine relationships between wood accumulations (wood accumulation distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (density, aggregation, interspersion, and evenness of patches in the river corridor). We hypothesize that i) more dynamic river segments correlate with a greater presence, persistence, and distribution of wood accumulations; ii) years with higher peak discharge correspond with greater channel dynamism and wood accumulations; and iii) all river corridor variables analyzed play a role in explaining river corridor spatial heterogeneity. Our results suggest that decadal-scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of wood accumulations per surface area and greater persistence of these wood accumulations through time. Second, higher peak discharges correspond to greater values of wood distribution density, but not to greater channel dynamism. Third, persistent values of geomorphic heterogeneity, as reflected in the heterogeneity metrics of aggregation, interspersion, patch density, and evenness, are explained by potential predictor variables analyzed here. Our results reflect the complex interactions of water, sediment, and large wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions

opencc-zeroMay 2024View details →

Fig. 5 in Geomorphic morphometric differences between populations of Speyeria diana (Lepidoptera: Nymphalidae)

Fig. 5. Principal components analysis showing separation of male (red) and female (blue) Speyeria diana forewing shape. Male forewings (n = 166) were narrower and more angular than female forewings (n = 101), which were wider and more rounded.

Fig. 2 in Geomorphic morphometric differences between populations of Speyeria diana (Lepidoptera: Nymphalidae)

Fig. 2. Twenty-two vein intersections on the Speyeria wing were used as landmarks for this study based on Borror et al. 2005.

Fig. 1 in Geomorphic morphometric differences between populations of Speyeria diana (Lepidoptera: Nymphalidae)

Fig. 1. Female (lef) and male (right) Speyeria diana specimens were photographed for this study. All specimens were photographed with a cm ruler for proper scaling.

Fig. 6 in Geomorphic morphometric differences between populations of Speyeria diana (Lepidoptera: Nymphalidae)

Fig. 6. Principal components analysis showing separation of male Speyeria diana forewings from specimens collected from eastern (blue) and western (red) populations. Male hind wings from eastern populations (n = 131) were narrower than those from western populations (n = 102).

opencc-by-4.0Nov 2024View details →

Photographs presenting two sandy cones in the Stołowe Mountains, together with the geomorphic setting in which they are situated.

The .zip files contain selected photographs of the sandy cones BS1 and SzW1 that were subject to detailed soil study and dating (single grain OSL as well as 14C). Apart from the cones, their close vicinity is presented (the geomorphological context).

opencc-by-4.0May 2018View details →

Ngaruroro River, New Zealand - Geomorphic Change Detection - Example Dataset

A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection on a long (31 km) dataset. Great for learning about analyses with Directional Masks in GCD. Dataset is from: <ul> <li>31km river on the  <a href="https://www.google.com/maps/place/39%C2%B035'58.6%22S+176%C2%B043'23.7%22E/@-39.6060374,176.6490462,27291m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d-39.599602!4d176.723239">north island of New Zealand</a></li> <li>Two LiDAR surveys</li> <li>2m cell resolution</li> </ul> Dataset includes raw data to run exercises, as well as full *.gcd projects that can be opened.

opencc-by-4.0Apr 2018View details →

Sulphur Creek, California - Geomorphic Change Detection - Example Dataset

A simple<a href="https://gcd.riverscapes.net/Tutorials/example-data-sets.html"> Example GCD Dataset </a>illustrating topographic change detection from a single flood event. Used in Tutorials (e.g. <a href="https://gcd.riverscapes.net/Tutorials/ChangeDetection/DoD-thresholding.html">DoD Thresholding</a>). Dataset is from  <ul> <li>300m of gravel bed river near  <a href="https://www.google.com/maps/place/38%C2%B029'44.0%22N+122%C2%B028'09.0%22W/@38.4958086,-122.4803136,4904m/data=!3m1!1e3!4m5!3m4!1s0x0:0x0!8m2!3d38.49555!4d-122.469166">St. Helena California</a> that underwent a New Year's Eve flood in December 2005.</li> <li>Two surveys (Dec 2015 and Jan 2016)</li> <li>0.5m cell resolution</li> <li>Surveyed with hybrid of RTKGPS and Total Station</li> </ul>

opencc-by-4.0Mar 2023View details →

Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'

Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to Geophysical Review Letters.