Table of Contents:
- <table> <tbody> <tr> <td>Data Description</td> <td> <p>Flood Hazard Map showing Pluvial modelled water depth for flood events of different return periods for the country of Tanzania.</p> <p>Pluvial flooding (local surface water flooding from extreme rainfall) has been simulated and can be displayed in a GIS system. Depths are shown in meters. Note that one would not expect all the displayed flooding to happen <em>at the same time</em>; rather, the data show the maximum water depth that would be expected if a flood event of the specified return period were occurring at that location. Another way of expressing this is to say that the data show the probability of experiencing a given water depth within a single year; i.e. depths shown by the ‘1-in-100 year’ layer have a 1-in-100 (or 1%) chance of occurrence in any given year.</p> <p>The data has been produced using the Fathom global flood hazard modelling framework (a development of Sampson et al., 2015 and Smith et al., 2015). The model uses the MERIT global DEM, and hydrography for elevation and river network data sources respectively (Yamazaki et al., 2017; Yamazaki et al., 2019). The framework automatically constructs flood models across a specified region, using the two-dimensional shallow water equations to simulate the behavior of floodwaters during the modelled flood events. The framework produces maps of flood depths at 3 arcsecond (~90m) spatial resolution for a specified range of return periods. For a detailed technical description of the methods, please see the open-access academic papers listed below.</p> <p>Given that the modelling framework used to create this data is semi-autonomous and uses data available at the regional to global scale, its accuracy is limited by the quality of this input data and the simplified range of processes it can represent. While the data is suitable for providing guidance at the regional scale, it is not recommended to use the data for detailed local scale assessments or engineering purposes.</p> </td> </tr> <tr> <td>Data Producer Attribution</td> <td> <p>SSBN Ltd (Fathom)</p> </td> </tr> <tr> <td>Project Attribution</td> <td> <p>The data was generated by SSBN Ltd (Fathom) as part of the Modelling Exposure Through Earth Observation Routines (METEOR): EO-based Exposure, Nepal and Tanzania project, a £2.8m project co-funded through the UK Space Agency’s International Partnership Programme (IPP) Call 2 as part of the Global Challenges Research Fund (GCRF) from the UK Government’s Official Development Assistance (ODA) commitment.</p> </td> </tr> <tr> <td>Data Format</td> <td>GeoTIFF</td> </tr> <tr> <td>Data Visualisation</td> <td> <p>GIS software. Suggested colour (6-digit hexadecimal format) for water depth ranges: <1m (#FA954D); <2m (#DBDBFF); <3m (#B8B8FF); <4m (#8C8CEA); <5m (#7171FF); 5m (#4D4DFF) and Permanent (#E771EB)</p> </td> </tr> <tr> <td>Spatial Resolution</td> <td>3 arcseconds (~90m)</td> </tr> <tr> <td>Geographic Coordinate Reference System</td> <td>WGS84 (EPSG:4326)</td> </tr> <tr> <td>Referenced Open Access Academic Papers</td> <td> <p>Sampson, C.C., Smith, A.M., Bates, P.D., Neal, J.C., Alfieri, L. & Freer, J.E. (2015) A high-resolution global flood hazard model, Water Resources Research, 51, 7358–7381. <a href="https://doi.org/10.1002/2015WR016954">https://doi.org/10.1002/2015WR016954</a></p> <p>Smith, A., Sampson, C. & Bates, P. (2015) Regional flood frequency analysis at the global scale, Water Resources Research, 51, 539–553. <a href="https://doi.org/10.1002/2014WR015814">https://doi.org/10.1002/2014WR015814</a></p> <p>Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J.C., Sampson, C.C., Kanae, S. & Bates, P.D. (2017) A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, <a href="https://doi.org/10.1002/2017GL072874">https://doi.org/10.1002/2017GL072874</a> </p> <p>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, 55, 5053–5073. <a href="https://doi.org/10.1029/2019WR024873">https://doi.org/10.1029/2019WR024873</a></p> </td> </tr> <tr> <td>Contact</td> <td><a href="mailto:kmcm@bgs.ac.uk">kmcm@bgs.ac.uk</a></td> </tr> </tbody> </table>