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

1 Introduction

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.

1.1 Coordinate system and spatial resolution

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.

1.2 Temporal resolution

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.

1.3 Units

Runoff measurements are quantified in millimeters per year (mm/year), providing hydrological data throughout the noted time frame.

1.4 Example

library(terra)
library(raster)

> dt_cc<-rast("HEMMF_ERUN_1500_1999.nc")
> dt_cc
class       : SpatRaster 
dimensions  : 70, 104, 500  (nrow, ncol, nlyr)
resolution  : 0.5, 0.5  (x, y)
extent      : -12, 40, 35, 70  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : HEMMF_ERUN_1500_1999.nc 
varname     : runoff 
names       : runoff_1, runoff_2, runoff_3, runoff_4, runoff_5, runoff_6, ... 
unit        :  mm/year,  mm/year,  mm/year,  mm/year,  mm/year,  mm/year, ... 
time (days) : 1500-01-01 to 1999-01-01 

1.5 Citation

The specific data file, named ’HEMMF ERUN 1500 1998.nc,’ 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¨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.