Advancing our understanding of water quality requires a multi-scale approach to groundwater-surface water connections

Both groundwater quality and quantity must be considered to meet the demand for safe drinking water for humans, irrigation water for agriculture, process water for industry, and to sustain ecosystem health. While groundwater quality processes at the catchment or aquifer scale have been studied intensively, such studies are lacking at the regional-to-continental and especially global scale. This gap may be the missing link to better assess long-term effects that may develop into creeping catastrophes, as well as the impacts of climate change and the associated intensification of hydrological extremes on groundwater quality. We hypothesize that the key to understanding groundwater quality processes lies in a multi-scale approach that represents interactions between groundwater and surface water, including coastal systems. With multi-scale, we refer to the necessity of understanding processes at their respective temporal (e.g., long-lasting legacy effects vs. extreme events) and spatial (pore scale, catchment vs. large or even global scale) scales and their connectiveness in which spatially small and temporally short impacts might emerge into future impacts that are spatially large and temporally long. To develop this multi-scale understanding, we require an inventory of dominant processes across temporal and spatial scales, informed by local-scale knowledge that already exists. Perceptual models can be effectively used not only to represent expert knowledge graphically but also to identify knowledge gaps and clearly communicate the assumptions embedded in our current understanding of dominant processes. This poster outlines the DFG-funded TRAILS (Towards a multi-scale understanding of gRoundwater quAlity InterLinkageS) network's efforts to gain this multi-scale understanding of groundwater. It presents our initial efforts towards a collective perceptual model, informed by existing literature, that identifies the dominant processes affecting groundwater quality across multiple temporal and spatial scales. Our goal is to use this knowledge to apply existing knowledge in new contexts and to gain new multi-scale understanding towards developing new approaches, such as large-scale modeling tools.