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Remote Sens. 2022, 14, 1383 15 of 22 the flexibility and repeatability of the workflow enable the calculation to be revisited once more data and understanding become available. 6. Conclusions In this study, we have developed a complete workflow to characterise the movement of lithium from the watershed into the salt lake. Figure 14 shows the overall conceptual framework, where the groundwater flow and particle tracking models integrate the data workflows. The detailed processing frameworks are shown in Figures 7, 9 and 10, which run sequentially and where the interconnection between frameworks is illustrated by em- ploying the “intermediate output” block which represents an output from a previously run framework. At their hearts is the use of remote sensing data to provide the underpinning datasets. Firstly, remote sensing data feed into the geological modelling (Section 3), which ensures that the geology is as accurate as possible. The extents of geological units are encap- sulated in a shapefile which can be ingested in the groundwater flow model. This shapefile is parameterised with hydrogeological properties (Section 4) based on data previously col- lected on related studies in the region and for similar geological settings. The groundwater flow model is relatively data-hungry (Section 5) and amongst other data requires input from a recharge model. The recharge model, which is itself data-intense, relies on rainfall and potential evaporation data, along with land use, DEM, and the drainage or river network. The extent of the models is defined by the basin boundary. The outflow from the recharge model is fed into the groundwater flow model which along with the parametrised geology and the river network can be used to set up the groundwater flow model. The output from the groundwater flow model is used to drive the particle tracking model in conjunction with the porosity distribution, which is again derived from the geology. Appendix B details the data requirements to build the framework. The models are then used to determine both surface and groundwater flow paths in the basin and how water–rock interaction can allow the waters to pick up lithium and then bring it into the salar. Whilst lithium has accumulated in the salar over the long timescales, this is difficult to simulate as both the rock mass and the climate vary over this time period. For example, the region is subject to wetter and drier periods on the cyclicity of 10,000 years [39]. Further, the groundwater inflows to the salar may not be recharged contemporaneously; rather, they may have been built up during wetter periods and are only now finding their way to the salar [52]. This is borne out by the timescales predicted by the modelling, albeit with uncertainties associated with simulating processes over such long timescales. The workflow is flexible and can be used with different model codes as well as simpler approaches, such as distributed rainfall rather than a fully featured recharge model. Whilst the workflow presented here is an example based on open data sources, measurements can and should be used to validate the approach. However, given the extent of open data sources available, the workflow demonstrates how far it is possible to go with freely available data. Given the open approach, uncertainty can be addressed in a number of different ways. Firstly, choosing and swapping out different components of the workflow can be used to assess how differing data sources affect the outcome. Secondly, the ease by which the process can be reproduced can enable sensitivity analysis to be undertaken. Finally, the availability of the workflow enables independent assessment and scrutiny of the process and its outcome. The outputs from this study have been benchmarked against previous work undertaken in the Uyuni watershed as well as other studies in salars in the Lithium Triangle countries, e.g., Atacama.PDF Image | Lithium Brine Deposit Formation
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