This dataset presents the raw data from two experimental series of analogue models and four numerical models performed to investigate Rift-Rift-Rift triple junction dynamics, supporting the modelling results described in the submitted paper. Numerical models were run in order to support the outcomes obtained from the analogue models. Our experimental series tested the case of a totally symmetric RRR junction (with rift branch angles trending at 120° and direction of stretching similarly trending at 120°; SY Series) or a less symmetric triple junction (with rift branches trending at 120° but with one of these experiencing orthogonal extension; OR Series), and testing the role of a single or two phases of extension coupled with effect of differential velocities between the three moving plates. An overview of the performed analogue and numerical models is provided in Table 1. Analogue models have been analysed quantitatively by means of photogrammetric reconstruction of Digital Elevation Model (DEM) used for 3D quantification of the deformation, and top-view photo analysis for qualitative descriptions. The analogue materials used in the setup of these models are described in Montanari et al. (2017), Del Ventisette et al. (2019) and Maestrelli et al. (2020). Numerical models were run with the finite element software ASPECT (e.g., Kronbichler et al., 2012; Heister et al., 2017; Rose et al., 2017).
In Finger et al. (2022), we created consistent three-dimensional models in terms of temperature, density and composition of the upper mantle of the cratonic part of the African continent by combining seismic [Celli et al., 2020] and gravity [Förste et al., 2014] data with mineral physics constraints in an iterative integrated inversion approach [Kaban et al., 2014; Tesauro et al., 2014]. Further, we calculated a new model of depth to the Moho to correct the gravity field for crustal effects and calculate the residual topography, and provide an update for the average crystalline crust density from Litho1.0 [Pasyanos et al., 2014]. To calculate depth to the Moho, data from the GSC [Global Seismic Catalog, Mooney, 2015 with updates up to 2019] were combined with those published by Globig et al. [2016]. Here, we share data used from the GSC, final models of the upper mantle and crust that are discussed in the article, as well as the test cases set up in the uncertainty assessment. The upper mantle models are given in six layers centered at 50, 100, 150, 200, 250 and 300 km. In addition, density variations determined for the crust are given in an additional layer at 15 km depth. All fields range from -40.5°N to 40.5°N and -20.5°E to 55.5°E with a 1° by 1° lateral resolution. The data is provided in binary format as three netCDF4 files, containing the final results discussed in the paper ("Results_AF"), and the two uncertainty assessment cases for an upwards/downwards shifted Moho ("Results_AF_Moho_up" / "Results_AF_Moho_down"), respectively. In addition, data extracted from "Results_AF" to create the six profiles shown in the main article, and measurements of depth to Moho from the GSC are provided as ASCII formatted .dat files.
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.
In Finger et al. (2021), we created consistent three dimensional models in terms of temperature, density and composition of the upper mantle of the cratonic South American Platform ("Upper MAntle Model") by combining seismic (Celli et al., 2020, Schaeffer & Lebedev, 2013) and gravity (Förste et al., 2014) data with mineral physics constraints in an iterative integrated inversion approach (Kaban et al., 2014; Tesauro et al., 2014). We further compiled a new crustal model ("Crustal Model"), including sediment and average crustal density and depth to the Moho to correct the gravity field for crustal effects and calculate the residual topography. To obtain these models we used data from the GSC (Global Seismic Catalog, Mooney, 2015 with updates up to 2019). To calculate depth to the Moho, the GSC data were combined with those published by Rivadeneyra-Vera et al. (2019). Here, we share the initial and final models of the upper mantle, sediment density and crust that are discussed in the article as well as the test cases set up in the uncertainty assessment. The upper mantle models are given in six layers centered at 50, 100, 150, 200, 250 and 300 km. All models range from -60.5°N to 15.5°N and -90.5°E to -29.5°E, except for the sediment density model (-59.5°N to 24.5°N and -99.5°E to -25.5°E) with a 1° by 1° lateral resolution. A detailed description of the respective files is given in each subfolder. All data are in ASCII format.