Link, Andreas; van der Ent, Ruud; Berger, Markus; Eisner, Stephanie; Finkbeiner, Matthias (2019): The fate of land evaporation - A global dataset [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.908705

This work provides a global dataset on the fate of land evaporation for a fine-meshed grid of source and receptor cells. The dataset was created through a global run of the numerical moisture tracking model WAM-2layers. The tracking was conducted on a 1.5° * 1.5° grid and was based on reanalysis data from the ERA-Interim database. Climatic input data were incorporated in 3 respectively 6-hour time steps and represent the time period from 2001 to 2018. The tracking direction was forwards in time and the geographical borders of the model were located at +/- 79.5° latitude. As a result of the model run, the annual as well as the monthly average fate of evaporation was determined for 8684 land grid cells (all land cells except those located within Greenland and Antarctica) and provided via source-receptor matrices. The gained dataset was complemented via an aggregation to country and basin scales in order to highlight possible usages for areas of interest larger than grid cells. This resulted in data for 265 countries and 8223 basins. Finally, five types of source-receptor matrices for average moisture transfers were chosen to build the core of the dataset: land grid cell to grid cell, country to grid cell, basin to grid cell, country to country, basin to basin. Besides providing results averaged over the considered period (basic dataset), inter-annual results on a grid cell basis (inter-annual dataset) were added in addition. The dataset is the first ready-to-download dataset providing the overall fate of evaporation for land-cells of a global fine-meshed grid in monthly resolution. At the same time, information on the sources of precipitation can be extracted from it. It could be used for investigations into average annual, seasonal and inter-annual sink and source regions of atmospheric moisture from land masses for most of the regions in the world and comes along with example scripts for the readout and plotting of the data.