This work presents a 3D hydrostratigraphical model of the northern coast of Rio Grande do Sul state, in Brazil. Our aim with this model is to integrate all available data for the region and to identify areas that need further investigation and better understanding. This model was built using the free, robust, and open-source Python-based geomodeling library (GemPy, De La Varga et al., 2019), based on the principles of easy access and free usability (Campbell et al., 2017), so academics, non-government organizations, and other decision- and policy-makers can use it for their own needs. This model was based on the existing hydrostratigraphical conceptual model developed by (Troian et al., 2020). We encourage all users to contribute, either by improving its code or by producing and sharing data to be integrated into it, in the light of open collaboration networks (Mergel, 2015).
The work related to the model is currently under peer-review and a preprint can be accessed in https://www.researchsquare.com/article/rs-3528001/v1.
References:
Campbell, D., de Beer, J., Mielby, S., van Campenhout, I., van der Meulen, M., Erikkson, I., Ganerod, G., Lawrence, D., Bacic, M., Donald, A., Gogu, C. R., & Jelenek, J. (2017). Transforming The Relationships Between Geoscientists and Urban Decision-Makers: European Cost Sub-Urban Action (TU1206). Procedia Engineering, 209, 4–11. https://doi.org/10.1016/j.proeng.2017.11.124
De La Varga, M., Schaaf, A., & Wellmann, F. (2019). GemPy 1.0: Open-source stochastic geological modeling and inversion. Geoscientific Model Development, 12(1), 1–32. https://doi.org/10.5194/gmd-12-1-2019
Mergel, I. (2015). Open collaboration in the public sector : The case of social coding on GitHub. Government Information Quarterly, 32(4), 464–472. https://doi.org/10.1016/j.giq.2015.09.004
Troian, G. C., Reginato, P. A. R., Marquezan, R. G., & Kirchheim, R. (2020). Hydroestratigraphy conceptual model of coastal aquifer system northern portion of the state of rio grande do sul. Aguas Subterraneas, 34(3), 264–274. https://doi.org/10.14295/ras.v34i3.29883