Observation and monitoring of field parameters for springs and rivers of a Swiss alpine valley in relation to seasonality.

The study area is located in a Swiss alpine valley at the border between Switzerland and France and is situated in Valais. It is delimited by the hydrologic catchment of the river “La Vièze de Morgins”. The catchment area is situated in the Municipality of Troistorrents and of Monthey. Its population is approximately 4500 inhabitants. From the geological point of view, the valley “Val de Morgins” is mostly comprised of sedimentary rocks, amongst others breccia, schist, flysch, limestone, and quaternary sediments. The valley is affected by several natural hazards, such as landslides, rockfalls, and avalanches. Hydrogeologically, the valley contains few main springs that are outlets of porous and fissured aquifers. For this study, an inventory and monitoring of springs and rivers has been carried out since 2018 until April 2021. Particularly, more than 110 springs and rivers have been registered and observed during this time. The data includes GPS coordinates, photos, measurements of physical-chemical parameters and flowrates. Complementary to measurements, specific geological and topographical maps, and site information have been gathered. The analysis and interpretation of this huge set of hydrogeological data will be concluded with a new and innovative approach using different data science libraries that are implemented for the Python programming language. In this case study, groundwater sampling training is used to increase the understanding of the water quality. Four years of field measurements enable a better understanding of the parameter variability in relation to seasonality. Furthermore, new data analysis can aid the integrated resource management for the municipal water supply. The sampling and monitoring are key aspects to ensure water security, in terms of quality and volume. Additionally, it can also unlock prospective groundwater resources for municipal water supply. Case study data will also be compared with South African and other Swiss dataset of similar aquifer type.