Terrestrial water storage on the South American continent: Data from numerical simulations, observations, and deep learning

2026-05-27T15:38:04Zhttp://doidb.wdc-terra.org/oaip/oai

oai:doidb.wdc-terra.org:69192020-09-11T09:02:41ZDOIDBDOIDB.GFZ

false4DOIDB.GFZ 10.5880/GFZ.1.3.2020.002 Irrgang, Christopher Christopher Irrgang 0000-0001-8274-1678 GFZ German Research Centre for Geosciences, Potsdam, Germany Saynisch-Wagner, Jan Jan Saynisch-Wagner 0000-0001-9619-0336 GFZ German Research Centre for Geosciences, Potsdam, Germany Dill, Robert Robert Dill 0000-0002-9596-267X GFZ German Research Centre for Geosciences, Potsdam, Germany Boergens, Eva Eva Boergens 0000-0001-6178-9402 GFZ German Research Centre for Geosciences, Potsdam, Germany Thomas, Maik Maik Thomas GFZ German Research Centre for Geosciences, Potsdam, Germany Freie Universität Berlin, Berlin, Germany Terrestrial water storage on the South American continent: Data from numerical simulations, observations, and deep learning GFZ Data Services 2020 numerical hydrology modelling artificial intelligence deep learning terrestrial water storage Earth Observation Satellites Earth Observation Satellites > NASA Earth System Science Pathfinder > GRACE EARTH SCIENCE SERVICES > MODELS EARTH SCIENCE SERVICES > MODELS > HYDROLOGIC AND TERRESTRIAL WATER CYCLE MODELS experiment > simulation > modelling > model science > natural science > water science > hydrology Irrgang, Christopher Christopher Irrgang 0000-0001-8274-1678 GFZ German Research Centre for Geosciences, Potsdam, Germany Irrgang, Christopher Christopher Irrgang 0000-0001-8274-1678 GFZ German Research Centre for Geosciences, Potsdam, Germany Saynisch-Wagner, Jan Jan Saynisch-Wagner 0000-0001-9619-0336 GFZ German Research Centre for Geosciences, Potsdam, Germany Dill, Robert Robert Dill 0000-0002-9596-267X GFZ German Research Centre for Geosciences, Potsdam, Germany Boergens, Eva Eva Boergens 0000-0001-6178-9402 GFZ German Research Centre for Geosciences, Potsdam, Germany Thomas, Maik Maik Thomas GFZ German Research Centre for Geosciences, Potsdam, Germany Freie Universität Berlin, Berlin, Germany Irrgang, Christopher GFZ German Research Centre for Geosciences, Potsdam, Germany 2020-08-27 2003-01-01/2019-12-31 Dataset 10.1029/2020GL089258 10.2312/GFZ.b103-08095 10.1029/2006JD007847 10.5194/hess-19-4345-2015 CC BY 4.0 In Irrgang et al. (2020), we have trained a convolutional neural network to perform a so-called downscaling task. This downscaling aims to recover the fine-structure continental water storage distribution on the South American continent from coarse-resolution space-borne gravimetry observations. Here, we share data sets that were used for training the neural network, namely (1) monthly pairs of gridded terrestrial water storage anomalies (TWSA) of the South American continent and (2) surface water storage anomalies (SWSA) in the Amazonas region for the time period 2003-2019. TWSAs were used as target (output) values of the neural network and were derived from the Land Surface Discharge Model (LSDM, Dill, 2008). The corresponding input values were calculated by spatially smoothing the TWSA fields with a 600 km Gaussian filter. After training the neural network over the time period of 2003 to 2018, its performance was tested and compared to LSDM for the subsequent year 2019.
South American contient -83.25 -33.25 -56.25 13.25 Helmholtz-Gemeinschaft http://doi.org/10.13039/501100001656 Advanced Earth System Modelling Capacity (ESM)