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[Correction added on 06 December 2016, after first online publication: To ensure a practical file size of the final document each of the embedded videos were replaced by a screenshot of the respective videothe video on the construction of the model (doi: 10.5446/18699) and the video with the visualization of hydrological processes (doi: 10.5446/18823) are freely available (CC-BY-3.0) in the archive of the Leibniz Information Centre For Science And Technology University Library (TIB).] AbstractPhysical models are a well-established tool in education to strengthen hydrological understanding. They facilitate the straightforward visualization of hydrological processes and allow the communication of hydrological concepts, research and questions of general interest to the public. In order to visualize the water cycle in a landscape of postglacial sediments, in particular the subsurface part, a physical model was constructed. In two videos, (1) a detailed construction manual and (2) DescriptionThe importance of groundwater as a natural resource for water supply, agriculture and ecosystems is pretty obvious to most people and usually highly valued. Nonetheless, it is very striking that the picture of how natural waters are circulating becomes typically rather rough when it comes to the subsurface part (Unterbruner et al., 2016). For many laymen, 'groundwater' is something relatively vague. Questions like 'is groundwater located directly below the surface? Where does it come from? How long has it been in the ground?', etc. often quickly arise.To facilitate communication on many of these questions, we built a physical model of the water cycle in a landscape of postglacial sediments. The design of the model was inspired by Harnischmacher (2004) and the physical groundwater model of the hydrogeology group of the Institute of Geological Sciences of the Freie Universität of Berlin. The model comprises both the surface and subsurface part of the water cycle. To our knowledge, most other physical models in hydrology focus on specific parts of the water cycle. This is related to the long tradition in hydrology to use physical models to investigate specific hydrological questions such as the hydraulic properties of substrates (e.g. Darcy, 1856), the groundwater conditions at heterogeneous hillsides (Rulon et al., 1985), the processes in the hyporheic zone (Zhou and Endreny, 2013) or morphological features on Mars (Marra et al., 2014). The experimental focus implies the design of models that work directly on the 'real world' scale, e.g. a volume of soil, or models whose results are scalable to the real world (Kleinhans et al., 2010). In contrast to the models designed primarily for experimental purposes, the purpose of our model is to establish a conceptual understanding of how water is circulating through the landscape. Except from communicating hydrological research and facilitating discussions on publicly relevant hydrological questions, the model can also be used as a straightforward pedagogic tool in education. Here again, o...

Little research attention has been given to validating clusters obtained from the groundwater geochemistry of the waterworks' capture zone with a prevailing lake-groundwater exchange. To address this knowledge gap, we proposed a new scheme whereby Gaussian finite mixture modeling (GFMM) and Spike-and-Slab Bayesian (SSB) algorithms were utilized to cluster the groundwater geochemistry while quantifying the probability of the resulting cluster membership against each other. We applied GFMM and SSB to 13 geochemical parameters collected during different sampling periods at 13 observation points across the Barnim Highlands plateau located in the northeast of Berlin, Germany; this included 10 observation wells, two lakes, and a gallery of drinking production wells. The cluster analysis of GFMM yielded nine clusters, either with a probability ≥0.8, while the SSB produced three hierarchical clusters with a probability of cluster membership varying from <0.2 to >0.8. The findings demonstrated that the clustering results of GFMM were in good agreement with the classification as per the principal component analysis and Piper diagram. By superimposing the parameter clustering onto the observation clustering, we could identify discrepancies that exist among the parameters of a certain cluster. This enables the identification of different factors that may control the geochemistry of a certain cluster, although parameters of that cluster share a strong similarity. The GFMM results have shown that from 2002, there has been active groundwater inflow from the lakes towards the capture zone. This means that it is necessary to adopt appropriate measures to reverse the inflow towards the lakes.