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87 results for “inundation”
Data from: Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain
Inundation patterns in Amazon River floodplains are revealed by analysis of the 37 GHz polarization difference observed by the Scanning Multichannel Microwave Radiometer on the Nimbus-7 satellite. Flooded area is estimated at monthly intervals for January 1979 through August 1987 using mixing models that account for the major landscape units with distinctive microwave emission characteristics. Results are presented separately for 12 longitudinal reaches along the Amazon River main stem in Brazil as well as for three major tributaries (the Jurua, Purus and Madeira rivers). The total area along the Amazon River main stem that was flooded (including both floodplain and open water) varied between 19000 and 91 000km2. The correlation between flooded area and river stage is used to develop a predictive relationship and reconstruct regional inundation patterns in the floodplain of the Amazon River main stem over the past 94 years of stage records (1903-1996). The mean flooded area along the Amazon River during this 94-year period was 46800km2, of which the openwater surfaces of river channels and floodplain lakes comprised about 20 700km2.
Processed dataset used for developing tsunami inundation emulators(Part-II Test Dataset)
<div> <div>This dataset is related to the main Zenodo repository: https://doi.org/10.5281/zenodo.13738078</div> <br> <div>This dataset contains some of the processed datasets covering testing datasets covering tsunami inputs and outputs (for parameters of offshore waveforms, local deformation fields and inundation depths) used in the evaluation of machine learning emulator discussed in the preprint article - "Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effect with project repo - https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</div> <br> <div>The post-processed (numpy) files for the two test locations of Catania(CT) and Siracusa(SR) are provided in compressed gzip files(.gz) typically stored in data/processed:</div> <strong>-test_dZ.tar.gz</strong> (local deformation files)</div> <div><strong>-test_d.tar.gz </strong>(inundation depth files)</div> <div><strong>-test_t.tar.gz</strong> (offshore waveform files)</div> <p>The filename follow nomenclature as below</p> <p><strong>d_CT_0.dat, dZ_CT_0.dat, dflat_CT_0.dat, dZflat_CT_0.dat, t_CT_0.dat,lat_lon_idx_CT_892.npy</strong> where the file names represent <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represent parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size. <ul> <li>0,1,2,3 - represent the approx 50000 test events divided into 4 splits</li> </ul> </li> </ol> <div>More information on the attached readme, see project structure and code is available at:</div> <div><strong>https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators</strong></div>
Processed dataset used for developing tsunami inundation emulators(Part-I Training Dataset and Model Weights)
<p>This dataset contains some of the processed training datasets covering tsunami inputs and outputs parameters (for offshore waveforms, local deformation fields and inundation depths used in developing a machine learning emulator discussed in the preprint article -<strong> Towards Using Machine Learning Emulation for Probabilistic Inundation Mapping: Multiple Earthquake Sources and Near-field Effects</strong> with project repo - <a href="https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.">https://github.com/naveenragur/ML4SicilyTsunami/tree/ptha_emulators.</a></p> <p>####Code####</p> <p>Source code from the above github project : <strong>ML4SicilyTsunami-NPJ2024.tar.gz</strong></p> <p>The post-processed numpy dat files for the two test location of <strong>Catania(CT) and Siracusa(SR)</strong> are provided for each variable retrieved from data/processed folder of the project as compressed gzip files(.gz):</p> <p>####Model Checkpoints#### <br>ML Model checkpoints from all training experiments.<br><strong>- outSR.tar.gz (Siracusa)</strong><br><strong>- outCT.tar.gz (Catania)</strong></p> <p>####Training Datasets####<br><strong>- t.tar.gz: </strong>Offshore waveforms for gauge sites in a 3D numpy file<br><strong>- dZ.tar.gz: </strong>Local deformation fields in 2D numpy files or reduced 1D flat files on flooded grids<br><strong>- d.tar.gz: </strong>Maximum inundation depth in 2D numpy files or reduced 1D flat files</p> <p>####Flood Mask####<br>- Identifies floodable and non-floodable areas in the domain.<br><strong> - zero_mask_SR_961.npy.gz</strong><br><strong> - zero_mask_CT_892.npy.gz</strong></p> <p>####Flood Indices####<br>Links between 2D and 1D numpy files:<br>- Indices connecting 2D (events x m x n) numpy files to flattened 1D (events x m) numpy files:<br><strong> - zero_indices_SR_961.npy.gz</strong><br><strong> - zero_indices_CT_892.npy.gz</strong><br>- Indices connecting latitude-longitude coordinates to 1D (events x m) numpy files:<br><strong> - lat_lon_idx_CT_892.npy.gz</strong><br><strong> - lat_lon_idx_SR_961.npy.gz</strong></p> <p>The filenames follow nomenclature representing <strong>{parameter}_{site}_{size}</strong></p> <ol> <li>The first var represents the parameter of the file. <ul> <li>d - 2 dimensional file for inundation depth (<strong>events </strong>x <strong>m </strong>x <strong>n</strong>)</li> <li>dZ - 2 dimensional file for local deformation( <strong>events </strong>x <strong>m</strong> x <strong>n</strong>)</li> <li>dflat - 1 dimensional flat file for inundation ( <strong>events </strong>x <strong>locations</strong>)</li> <li>dZflat - 1 dimensional flat file for local deformation( <strong>events </strong>x <strong>locations</strong>)</li> <li>t - 3 dimension offshore waveform for (<strong>events </strong>x <strong>gauges </strong>x <strong>timesteps</strong>)</li> <li>lat_lon_idx - index file for matching lat long coordinate with location indices(<strong>locations x lat x lon</strong>)</li> </ul> </li> <li>The second var represents site of the file. <ul> <li>CT - Catania</li> <li>SR - Siracusa</li> </ul> </li> <li>The third var represents number of events in the file or the selection size.<br> <ul> <li>892 - represents the training events for the file, of 4 different sizes for Catania and Siracusa each</li> </ul> </li> </ol> <div>More information on the attached readme.</div>
Data and scripts used to produce figures presented in: Scott et al. (2019) "Floodplain inundation spectrum across the United States"
<p>This repository contains the data and scripts used to produce figures presented in: Scott et al. (2019) <strong>"Floodplain inundation spectrum across the United States"</strong> under review in Nature Communications.</p> <p>The code is hosted in the GitHub repository:</p> <p><a href="https://github.com/gomezvelezlab/floodplainInundationCONUS">https://github.com/gomezvelezlab/floodplainInundationCONUS</a></p> <p> </p>
Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
Data for: Inundation depth stimulates plant-mediated CH4 emissions by increasing ecosystem carbon uptake and plant height in an estuarine wetland
<p>Plant-mediated CH<sub>4</sub> emission is an important part of the ecosystem CH<sub>4</sub> emission from vegetated wetlands. Inundation depth may alter the potential magnitude of CH<sub>4</sub> releases by changing CH<sub>4</sub> production and plant transport, but the relationships between plant-mediated CH<sub>4</sub> emissions and inundation depth are still uncertain, especially for estuarine wetlands with changeable hydrological processes. Besides, there are conflicting results regarding the role of inundation depth in plant-mediated CH<sub>4</sub> emissions.</p> <p>Here we conducted a novel inundation depth experiment (0, 5, 10, 20, 30 and 40 cm inundation depth) dominated by <em>Phragmites australis</em> in the Yellow River estuary, China. Soil CH<sub>4</sub> emissions, ecosystem CH<sub>4</sub> emissions, net ecosystem CO<sub>2</sub> exchange (NEE), soil organic carbon (SOC) and plant traits were measured during the growing seasons of 2018, 2019 and 2020. Plant-mediated CH<sub>4</sub> emissions were the difference between ecosystem CH<sub>4</sub> emissions and soil CH<sub>4</sub> emissions.</p> <p>The results showed that inundation depth decreased soil CH<sub>4</sub> emissions but increased ecosystem CH<sub>4</sub> emissions. Plant-mediated CH<sub>4</sub> transport from <em>Phragmites australis</em> accounted for 99% of total ecosystem CH<sub>4</sub> emissions under different inundation depths. Inundation depth strongly stimulated plant-mediated CH<sub>4</sub> emission from 0 to 20 cm during the growing seasons. The increased net ecosystem CO<sub>2</sub> exchange enhanced plant-mediated CH<sub>4</sub> emissions by altering production, suggesting that carbon components derived from photosynthetic carbon input may benefit CH<sub>4</sub> production. Additionally, the increased plant height promoted CH<sub>4</sub> emission by regulating plant transport, indicating that plant traits may play an important role in transport of CH<sub>4</sub>.</p> <p>Our findings indicated that NEE and plant height play an important role in plant-mediated CH<sub>4</sub> emissions under different inundation depths in estuarine wetland. This study also highlights that hydrological regimes and plant traits are essential for the estimation of CH<sub>4</sub> emissions in future projections of global wetland changes.</p>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
Data for: Inundation depth stimulates plant-mediated CH4 emissions by increasing ecosystem carbon uptake and plant height in an estuarine wetland
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Data from: Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation
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Data from: Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain
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Monitoring of Inundation Extent during 2010–2022 in the Inner Niger Delta using Landsat Imagery and Google Earth Engine
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CYGNSS-based inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019
<p>Inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019 at 0.5<sup>o</sup> x 0.5<sup>o </sup>resolution developed using microwave remote sensing data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation (https://cygnss.engin.umich.edu/), L1 v2.1.</p> <p>CYGNSS data used to produce these maps is available from NASA: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_V2.1</p> <p>A complete description of the method developed to obtain these maps is available in the following preprint: https://www.essoar.org/doi/10.1002/essoar.10504845.1</p> <p> </p> <p> </p>
Monthly inundated fraction over China for 2000-2015 from GIEMS-2
<p>The data set is released to support the paper: Xi, Y. et al. Trade-off between forestation and wetland conservation in China. Nat. Commun. (2021) (In revision). The NetCDF file provides the monthly inundated fraction over China for 2000-2015 at 0.5º × 0.5º spatial resolution, which is extracted from the initial GIEMS-2 data set (Prigent et al., 2020). When referring to the data set, the paper should be cited: Prigent, C., Jimenez, C. & Bousquet, P. Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2). J. Geophys. Res.-Atmos. 125, e2019JD030711, doi:10.1029/2019JD030711 (2020).</p>
Figure 3 in Marine hermit crabs as indicators of freshwater inundation on tropical shores
Figure 3. Daily shoreline salinity (dark line) and temperature (shaded line) readings between April, 1989, and January, 1992, along Keppel Bay. Arrows A indicate regular, seasonal flood events on a local scale, arrow B indicates an irregular, flood event on a large, catchment scale. From Coates, unpublished data.
Performance of Slab Geometry Constraints on Rapid Geodetic Slip Models, Tsunami Amplitude, and Inundation Estimates in Cascadia
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Linked collectors and determiners for: Flora acuática vascular y de zonas inundables del área de protección de flora y fauna Laguna de Términos, Campeche, México.
Natural history specimen data linked to collectors and determiners held within, "Flora acuática vascular y de zonas inundables del área de protección de flora y fauna Laguna de Términos, Campeche, México". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/80c60602-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/80c60602-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/80c60602-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/80c60602-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation: Model Data and Analysis Code
<p>This repository contains model data and analysis codes related to the manuscript entitled "Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation", as follows:</p> <ol> <li>Model data are maximum water surface elevations of ensemble 48-hr forecast ADCIRC model simulations for three historical US landfalling hurricanes: 2017 Irma, 2018 Florence, and 2020 Laura. These are located in the "NameYYYY_Results.tar" archive files as "maxele.63.nc" files. Also included in the tar files are the hurricane forecast track files in Automated Tropical Cyclone Forecasting (ATCF) system format (*.22) and the error variable parameters (*.json) for each forecast. </li> <li>Model data of best-track runs for the 2017 Irma, 2018 Florence, and 2020 Laura hurricanes, and astronomical tide-only runs for the corresponding time periods are located in the "NameYYYY_besttrack+tides.tar" archive files. Both the maximum water surface elevations "maxele.63.nc" and the time series of water surface elevations "fort.63.nc" are included. </li> <li>ADCIRC input mesh (*.14) and mesh property files (*.13) are included in "ADCIRC_mesh_files.zip".</li> <li>Joint Karhunen-Loeve Polynomial Chaos (KL-PC) analysis python scripts with and without considering inundation are located in "klpc_analysis_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> <li>Python scripts for analyzing and plotting the KL-PC results (Figures 6-14 and Table 1 in the manuscript) are located in "results_plotting_scripts.zip". Requires <a href="https://github.com/noaa-ocs-modeling/EnsemblePerturbation">EnsemblePerturbation</a> python toolbox. </li> </ol>
ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019
This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.
LBA-ECO LC-07 Monthly Inundated Areas, Amazon, Orinoco and Pantanal Basins: 1978-1987
This data set reports the monthly record of inundated area, in square km, for six floodplain and open water regions in South America. The following floodplains were analyzed: (1) mainstem Amazon River floodplain in Brazil; (2) Llanos de Mojos (Beni and Mamore rivers) in Bolivia; (3) Bananal Island (Araguaia River) in Brazil; (4) Roraima savannas (Branco and Rupununi rivers) in Brazil and Guyana; (5) Llanos del Orinoco (Apure and Meta rivers) in Venezuela and Colombia; and (6) Pantanal wetland (Paraguay River) in Brazil. Flooded area was estimated at monthly intervals from December 1978 through August 1987 for the Amazon mainstem region and from January 1979 through August 1987 for the other five regions. Inundated area was determined from SMMR (Scanning Multichannel Microwave Radiometer) passive microwave data. Area estimates include permanent open water as well as land subject to seasonal inundation. This data set contains five data files: two comma-delimited (.csv) ASCII data files providing the monthly inundation area values for six floodplain and open water regions in South America; a compressed (.zip) file providing seventeen ESRI Shape files for the region bounding polygons; and two .csv files providing information about the region bounding polygons and latitude/longitude verticies.
LBA-ECO LC-07 Wetland Extent, Vegetation, and Inundation: Lowland Amazon Basin
This data set provides a map of wetland extent, vegetation type, and dual-season flooding state of the entire lowland Amazon basin. As described in Hess et al. (2015), the classified image was derived from the Global Rain Forest Mapping Project (GRFM) Amazon mosaics (Rosenqvist et al 2000; Siqueira et al. 2002) acquired during Oct.-Nov. 1995 and May-June 1996, corresponding to the low-flood and high-flood seasons for much of the central Amazon. Hess et al. (2003) mapped wetland extent, vegetative cover, and flooding state for an 18 degree × 8 degree portion of the central Amazon using the dual-season GRFM mosaics. This study extends the previous wetlands mapping to report the first validated estimate of wetland extent, cover, and flooding for the lowland Amazon basin. A wetlands mask was created by segmentation of the mosaics and clustering of the resulting polygons; a rules set was then applied to classify wetland areas into five land cover classes and two flooding classes using dual-season backscattering values. The mapped wetland area of 8.4 × 105 km2 is equivalent to 14 % of the total basin area (5.83 × 106 km2) and 17% of the lowland basin (5.06 × 106 km2). The mapped flooding extent is representative of average high and low-flood conditions for latitudes north of 6 degrees S; flooding conditions were less well captured for the southern part of the basin. The wetlands map is provided in GeoTIFF format using two coordinate systems: unprojected (Geographic) with pixel size of 3 arcseconds, and Albers Conical Equal Area with pixel size of 100 m.
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Allen Brain Atlas
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OpenNeuro
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