High frequency as well as long-term fluctuations of groundwater levels are the consequence of a large number of different processes within the aquifer system. Groundwater levels are generally influenced both by natural processes (e.g. groundwater recharge, interaction with river systems) and anthropogenic influences (e.g. water abstraction, artificial recharge and piling). Spatial and temporal superposition of these processes cause fluctuations of groundwater levels, referred to as groundwater dynamics, at the position of the well’s screen. Due to superposition, the differentiation between multiple driving forces (input signals) is difficult and requires knowledge of hydro(geo)logical properties of the system. This includes characteristics of the surface, vadose and phreatic zone on different scales. The exploration of all those characteristics is complex as well as time-consuming and, therefore, information are generally only available on a point scale. In contrast to the limited availability of system characteristics, high-resolution data records of groundwater hydrographs are more generally available. Analysis of high-resolution hydrographs is a frequently applied tool for the prediction of ungauged basins (PUB, e.g. Blöschl et al., 2013; Hrachowitz et al., 2013). The PUB community introduced a number of different quantitative indices to characterize different parts of river flow dynamics. Based on similarities of these indices, river catchments are clustered in groups of similar dynamics and can, subsequently, linked to system characteristics. Due to the various differences between surface and sub-surface systems, the transferability of streamflow indices on groundwater time series might be limited. This study is focused on the question of how transferable these river flow indices are to groundwater time series measured in alluvial aquifers in Bavaria (Southern Germany). More than 50 indices are calculated from sub-monthly groundwater level time series from different regions, with the purpose of covering various hydro(geo)logical settings. The result of hierarchical clustering (Ward Linkage algorithm) of these 50 groundwater hydrographs serves as a baseline. The ability of each index to express hydrograph similarity is quantified. Additionally, the study also analyses the ability of different indices to express similarities of groundwater hydrographs from different regions but with similar hydro(geo)logical settings.
Blöschl G, Sivapalan M, Wagener T, Viglione A, Savenije H. (2013): Runoff Prediction in Ungauged Basins:Synthesis across Processes, Places and Scales, Cambridge University Press.
Hrachowitz, M. et al. (2013): A decade of Predictions in Ungauged Basins (PUB) - a review, Hydrol. Sci. J. 58 (6), 1198–1255, doi:10.1080/02626667.2013.803183.