Time series of groundwater head measurements serve as a primary source of information on groundwater systems. In different groundwater systems, and across several scales, we observe a multitude of patterns in groundwater time series, resulting from complex hydrogeological setups. Unlike in surface hydrology, there is no generalized classification to categorize and quantify the dynamics in groundwater time series. This leads to a lack of tools that could help us disentangle the information contained in groundwater time series in a systematic way. To approach such a classification, we present a principle for organization to qualitatively describe and to quantify groundwater dynamics in a nonredundant and data efficient way. We devise a descriptive typology of groundwater dynamics and assign quantitative measures, mathematically expressing these dynamics. Based on an extensive data set of daily groundwater hydrographs from central Europe, we analyze the relationship between indices and typology based on principal component analysis. The principal component analysis is also used to investigate and discuss redundancy, that is, indices expressing similar information content of hydrographs. Further, the indices' sensitivity to measurement interval and length of the overall observed period is investigated. Finally, a case study demonstrates the potential of the typology and index approach to link groundwater dynamics to the underlying hydrogeological process controls. The tools provided for characterization and quantification of groundwater dynamics should improve future efforts of groundwater classification and prediction in ungauged aquifers and other applications.
Groundwater level fluctuations are caused by spatial and temporal superposition of processes within and outside the aquifer system. Most of the subsurface processes are usually observed on a small scale. Upscaling to the regional scale, as required for future climate change scenarios, is difficult due to data scarcity and increasing complexity. In contrast to the limited availability of system characteristics, highresolution data records of groundwater hydrographs are more generally available. Exploiting the information contained in these records should thus be a priority for analysis of the chronical lack of data describing groundwater system characteristics. This study analyses the applicability of 63 indices derived from daily hydrographs to quantify different dynamics of groundwater levels in unconfined gravel aquifers from three groundwater regions (Bavaria, Germany). Based on the results of two different skill tests, the study aids index selection for different dynamic components of groundwater hydrographs.
Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large-scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics-based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from lowrisk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk-reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.
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