Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years

<p>1 Introduction</p> <p>The data archive provides the reconstructed dataset capturing the annual runoff across Europe, partitioned into a grid format and preserved in NetCDFv4 (.nc) format for enhanced geospatial information.</p> <p>1.1 Coordinate system and spatial resolution</p> <p>Each grid cell in the dataset corresponds to a 0.5-degree spatial resolution, using the World Geodetic System 1984 (WGS84) as the standard coordinate frame.</p> <p>1.2 Temporal resolution</p> <p>The data encapsulates a yearly temporal resolution, offering a comprehensive outlook from 1500 to 1999. For example data for 1500 are represented by the layer 01/01/1500.</p> <p>1.3 Units</p> <p>Runoff measurements are quantified in millimeters per year (mm/year), providing hydrological data throughout the noted time frame.</p> <p>1.4 Example</p> <p>library(terra)<br> library(raster)</p> <p>&gt; dt_cc&lt;-rast(&quot;HEMMF_ERUN_1500_1999.nc&quot;)<br> &gt; dt_cc<br> class &nbsp; &nbsp; &nbsp; : SpatRaster&nbsp;<br> dimensions &nbsp;: 70, 104, 500 &nbsp;(nrow, ncol, nlyr)<br> resolution &nbsp;: 0.5, 0.5 &nbsp;(x, y)<br> extent &nbsp; &nbsp; &nbsp;: -12, 40, 35, 70 &nbsp;(xmin, xmax, ymin, ymax)<br> coord. ref. : lon/lat WGS 84 (EPSG:4326)&nbsp;<br> source &nbsp; &nbsp; &nbsp;: HEMMF_ERUN_1500_1999.nc&nbsp;<br> varname &nbsp; &nbsp; : runoff&nbsp;<br> names &nbsp; &nbsp; &nbsp; : runoff_1, runoff_2, runoff_3, runoff_4, runoff_5, runoff_6, ...&nbsp;<br> unit &nbsp; &nbsp; &nbsp; &nbsp;: &nbsp;mm/year, &nbsp;mm/year, &nbsp;mm/year, &nbsp;mm/year, &nbsp;mm/year, &nbsp;mm/year, ...&nbsp;<br> time (days) : 1500-01-01 to 1999-01-01&nbsp;</p> <p>&nbsp;</p> <p>1.5&nbsp;Citation</p> <p>The specific data file, named &rsquo;HEMMF ERUN 1500 1998.nc,&rsquo; is conveniently structured to facilitate easy handling and interpretation of the information. Please ensure to attribute the correct citation when utilizing this dataset, adhering to the subsequent reference: [Singh et al., 2023] References Ujjwal Singh, Petr Maca, Martin Hanel, Yannis Markonis, Rama Rao Nidamanuri, Sadaf Nasreen, Johanna Ruth Bl&uml;ocher, Filip Strnad, Jiri Vorel, Lubomir Riha, and Akhilesh Singh Raghubanshi. Hybrid multi-model ensemble learning for reconstructing gridded runoff of europe for 500 years. Information Fusion, 97:101807, 2023. ISSN 1566-2535. doi: https://doi.org/10.1016/j.inffus. 2023.101807. URL https://www.sciencedirect.com/science/article/pii/S1566253523001161#d1e5346.</p>