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4 results for “fluvial landscape”
Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"
<p>This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis".<br> The parameter used to perform hydraulic simulations are also available.<br> </p>
Living landscapes: Muddy and vegetated floodplain effects on fluvial pattern in an incised river
<p>The matlab data consists of 14 model runs (lab numbers in filenames), each for two timesteps in separate files, namely one for low river flow (ecological timestep 12, which is in month 6) and one for high river flow (ecological timestep 24, which is in month 12). The hydromorphological model is Delf3D, which is coupled to a riparian vegetation model 24 times per year for 150 years (some for 300 years). Each matlab file contains all variables stored by Delft3D and by the vegetation model. The most relevant variables are three-dimensional matrices containing the yearly maps. The various model runs are permutations on only sand, including mud (at various concentrations and erosion thresholds) and/or riparian vegetation.</p> <table> <caption>Table: Model runs with lab number, number in the publication, and keyworks describing the model</caption> <thead> <tr> <th scope="col">lab run number</th> <th scope="col">run number in Kleinhans et al. 2018</th> <th scope="col">properties</th> </tr> </thead> <tbody> <tr> <td>153</td> <td>4</td> <td>only sand</td> </tr> <tr> <td>149</td> <td>3</td> <td>mud added default concentration default erosion threshold</td> </tr> <tr> <td>131</td> <td>2</td> <td>vegetation added</td> </tr> <tr> <td>132</td> <td>1</td> <td>mud and vegetation</td> </tr> <tr> <td>139</td> <td>5</td> <td>5 mg/L mud</td> </tr> <tr> <td>140</td> <td>6</td> <td>50 mg/L</td> </tr> <tr> <td>141</td> <td>7</td> <td>100 mg/L</td> </tr> <tr> <td>162</td> <td>8</td> <td>500 mg/L</td> </tr> <tr> <td>144</td> <td>9</td> <td>0.1 N/m2 critical shear stress</td> </tr> <tr> <td>145</td> <td>10</td> <td>0.5 N/m2</td> </tr> <tr> <td>161</td> <td>12</td> <td>500 mg/L 0.5 N/m2 vegetation</td> </tr> <tr> <td>160</td> <td>14</td> <td>500 mg/L 0.5 N/m2</td> </tr> <tr> <td>158</td> <td>13</td> <td>800 mg/L 0.5 N/m2 vegetation</td> </tr> <tr> <td>159</td> <td>11</td> <td>100 mg/L 0.5 N/m2 vegetation</td> </tr> </tbody> </table> <p>The open access paper describing the data and its method of production is:<br> Living landscapes: Muddy and vegetated floodplain effects on fluvial pattern in an incised river</p> <p>Maarten G. Kleinhans, Bente de Vries, Lisanne Braat, Mijke van Oorschot<br> Earth Surf. Process. Landforms 43(14), 2018<br> <a href="https://doi.org/10.1002/esp.4437">https://doi.org/10.1002/esp.4437</a></p> <p>The modelling was conducted by Bente de Vries under supervision of Maarten Kleinhans as part of her MSc thesis research, which was embedded in the ERC Consolidator project of Kleinhans.</p>
Supplements for Inferring Long-Term Tectonic Uplift Patterns from Bayesian Inversion of Fluvially-Incised Landscapes paper
<p><strong>Data and File Organization:</strong></p> <ol> <li><strong>Natural Landscapes (DEM):</strong> <ul> <li>Look for <code>.tif</code> files containing DEMs of natural landscapes. These files are in latitude-longitude coordinates; convert them to UTM if needed.</li> </ul> </li> <li><strong>Synthetic Landscapes (DEM):</strong> <ul> <li>DEM files for synthetic landscapes, ready for use in inversion schemes, are labeled with a <code>syn_</code> prefix.</li> </ul> </li> <li><strong>Climatic Data:</strong> <ul> <li>Climatic data for the Himalayas is available in <code>climate_data_him.zip</code>.</li> </ul> </li> </ol> <p><strong>Running the Code:</strong></p> <ol> <li> <p><strong>Loading DEMs:</strong></p> <ul> <li>Use the <code>loadDEM</code> package to load your DEM file.</li> <li>Specify <code>Z0</code> and <code>A0</code> values, then plot the landscape and <code>basinID</code> for reference.</li> </ul> </li> <li> <p><strong>Identifying Basins of Interest:</strong></p> <ul> <li>Determine which <code>basinID</code>s are of interest, then save them as forward objects. The functions for this process are available within the relevant packages.</li> </ul> </li> <li> <p><strong>Loading the Forward Model:</strong></p> <ul> <li>Load the forward model from the saved file using the appropriate function in the <code>frd</code> package.</li> <li>Choose the number of knots and specify if you prefer a 1D or 2D inversion.</li> </ul> </li> <li> <p><strong>Running and Plotting Inversion Results:</strong></p> <ul> <li>After running the inversion, view results in <code>inversion.step</code>.</li> <li>Plot these results using the plotting functions in the <code>frdplotting</code> package.</li> </ul> </li> </ol> <p> </p> <p> </p> <p><strong>Setup and Installation:</strong></p> <ul> <li>Install the package <code>scabbard</code> with: <div> <div> </div> <div><code>pip install pyscabbard </code></div> </div> </li> <li>All other Python dependencies are standard and can be installed via <code>pip</code> or <code>conda</code> as needed.</li> </ul>
Elevation data to accompany "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes"
<p>This repository contains elevation data, derivatives, and manual measurements used in the manuscript "A curvature-based method for measuring valley width applied to glacial and fluvial landscapes" submitted to the Journal of Geophysical Research.</p> <p>This data is derived from the 1/3 arc-second (~10 m) resolution seamless digital elevation models from the 3D Elevation Program (3DEP) in the coterminous United States [0]. The Canadian Rockies study site uses 90 m resolution data derived from the Shuttle Radar Topography Mission (SRTM) dataset [1].</p> <p>Each study site corresponds to a single directory (e.g. 'valley_width/olympics/').</p> <p>Derivatives are stored in GeoTIFF format (with no file extensions) with the following naming conventions (shown for the Olympic Mountains study area). Manual width measurements are stored as Pickle files (e.g. 'olympics_width_fluvial.p') in the measurements/ subdirectory in each case.</p> <p>- olympics_area (Catchment area)<br> - olympics_elevation (Elevation)<br> - olympics_filled (Hydrologically corrected elevation)<br> - olympics_flow_direction (Flow direction)<br> - olympics_mask_fluvial (Binary mask of fluvial catchments)<br> - olympics_mask_glacial (Binary mask of glacial catchments)<br> - olympics_unnormalized_width (Valley width estimated without using scale normalization)<br> - olympics_width (Valley width estimated using scale normalization)</p> <p>Python scripts to reproduce the major figures and analysis and their EPS output are also included.</p> <p>References</p> <p>[0] <a href="https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services">https://www.usgs.gov/core-science-systems/ngp/3dep/about-3dep-products-services</a></p> <p>[1] <a href="http://srtm.csi.cgiar.org/srtmdata/">http://srtm.csi.cgiar.org/srtmdata/</a></p>
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