Large areas of Europe, especially in the Alps, are covered by carbonate rocks, and karst springs are an important source for drinking water supply. Because of their high variability and heterogeneity, understanding the hydrogeological functioning is of particular importance for protection of karst aquifers. In this study, hydrogeochemical investigations characterized the water of a spring draining a complex carbonate‐gypsum karst system in the Alps. The reaction of the spring to a rainfall event was examined to identify the relevant hydrological processes controlling the hydrochemistry of the spring, and to understand water‐rock interactions and conduit–matrix exchange. A fast and marked reaction of discharge and electrical conductivity was observed. The main cations are Ca2+ and Mg2+, which showed a distinct decrease after the rainfall. Bicarbonate and sulfate were identified as major anions. Although HCO3− showed only minor fluctuations, SO42− decreased by 72% after the rain event. Comparisons of ion ratios show that both carbonate and gypsum rocks influence the water chemistry of the spring. The rainfall event caused a dilution effect, but dilution alone cannot explain the observed water chemistry. A conceptual model of the spring behaviour during low‐flow and high‐flow conditions, including conduit–matrix interaction, was developed, which can explain the observations. This study aims to give new insights into the highly dynamic exchange processes between karst conduits and the surrounding matrix, and the results demonstrated that (a) during low‐flow conditions, the spring is characterized by high sulfate content, but after rainfall events, the water chemistry is dominated by bicarbonate. These findings show the dependency of water chemistry from the lithology; (b) a change in water chemistry is associated with a significant shift from low‐flow to high‐flow conditions; (c) conduit–matrix exchange is an important factor as shown by the discharge–sulfate relationship and clearly influences the behaviour of the spring.
Effective groundwater monitoring networks are important, as systematic data collected at observation wells provide a crucial understanding of the dynamics of hydrogeological systems as well as the basis for many other applications. This study investigates the influence of six groundwater level monitoring network (GLMN) sampling designs (random, grid, spatial coverage, and geostatistical) with varying densities on the accuracy of spatially interpolated groundwater surfaces. To obtain spatially continuous prediction errors (in contrast to point cross-validation errors), we used nine potentiometric groundwater surfaces from three regional MODFLOW groundwater flow models with different resolutions as a priori references. To assess the suitability of frequently-used cross-validation error statistics (MAE, RMSE, RMSSE, ASE, and NSE), we compared them with the actual prediction errors (APE). Additionally, we defined upper and lower thresholds for an appropriate spatial density of monitoring wells. Below the lower threshold, the observation density appears insufficient, and additional wells lead to a significant improvement of the results. Above the upper threshold, additional wells lead to only minor and inefficient improvements. According to the APE, systematic sampling lead to the best results but is often not suited for GLMN due to its nonprogressive characteristic. Geostatistical and spatial coverage sampling are considerable alternatives, which are in contrast progressive and allow evenly spaced and, in the case of spatial coverage sampling, yet reproducible coverage with accurate results. We found that the global cross-validation error statistics are not suitable to compare the performance of different sampling designs, although they allow rough conclusions about the quality of the GLMN.
Abstract. Groundwater monitoring and specific collection of data on the spatiotemporal dynamics of the aquifer are prerequisites for effective groundwater management and determine nearly all downstream management decisions. An optimally designed groundwater monitoring network (GMN) will provide the maximum information content at the minimum cost (Pareto optimum). In this study, PySensors, a Python package containing scalable, data-driven algorithms for sparse sensor selection and signal reconstruction with dimensionality reduction is applied to an existing GMN in 1D (hydrographs) and 2D (gridded groundwater contour maps). The algorithm first fits a basis object to the training data and then applies a computationally efficient QR algorithm that ranks existing monitoring wells (for 1D) or suitable sites for additional monitoring (for 2D) in order of importance, based on the state reconstruction of this tailored basis. This procedure enables a network to be reduced or extended along the Pareto front. Moreover, we investigate the effect of basis choice on reconstruction performance by comparing three types typically used for sparse sensor selection (i.e., identity, random projection, and SVD, respectively, PCA). We define a gridded cost function for the extension case that penalizes unsuitable locations. Our results show that the proposed approach performs better than the best randomly selected wells. The optimized reduction makes it possible to adequately reconstruct the removed hydrographs with a highly reduced subset with low loss. With a GMN reduced by 94 %, an average absolute reconstruction accuracy of 0.1 m is achieved, in addition to 0.05 m with a reduction by 69 % and 0.01 m with 18 %.
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