Abstract. To improve the understanding of how aquifers in different alluvial settings respond to extreme events in a changing environment, we analyze standardized time series of groundwater levels (Standardized Groundwater level Index -SGI), precipitation (Standardized Precipitation Index -SPI), and river stages of three subregions within the catchment of the river Mur (Austria). Using correlation matrices, differences and similarities between the subregions, ranging from the Alpine upstream part of the catchment to its shallow foreland basin, are identified and visualized.Generally, river stages exhibit the highest correlations with groundwater levels, frequently affecting not only the wells closest to the river, but also more distant parts of the alluvial aquifer. As a result, human impacts on the river are transferred to the aquifer, thus affecting the behavior of groundwater levels. Hence, to avoid misinterpretation of groundwater levels in this type of setting, it is important to account for the river and human impacts on it.While the river is a controlling factor in all of the subregions, an influence of precipitation is evident too. Except for deep wells found in an upstream Alpine basin, groundwater levels show the highest correlation with a precipitation accumulation period of 6 months (SPI6). The correlation in the foreland is generally higher than that in the Alpine subregions, thus corresponding to a trend from deeper wells in the Alpine parts of the catchment towards more shallow wells in the foreland.Extreme events are found to affect the aquifer in different ways. As shown with the well-known European 2003 drought and the local 2009 floods, correlations are reduced under flood conditions, but increased under drought. Thus, precipitation, groundwater levels and river stages tend to exhibit uniform behavior under drought conditions, whereas they may show irregular behavior during floods. Similarly, correlations are found to be weaker in years with little snow as compared with those with much snow. This is in agreement with typical aquifer response times over 1 month, suggesting that short events such as floods will not affect much of the aquifer, whereas a long-term event such as a drought or snow-rich winter will.Splitting the time series into periods of 12 years reveals a tendency towards higher correlations in the most recent time period from 1999 to 2010. This time period also shows the highest number of events with SPI values below −2. The SGI values behave in a similar way only in the foreland aquifer, whereas the investigated Alpine aquifers exhibit a contrasting behavior with the highest number of low SGI events in the time before 1986. This is a result of overlying trends and suggests that the groundwater levels within these subregions are more strongly influenced by direct human impacts, e.g., on the river, than by changes in precipitation. Thus, direct human impacts must not be ignored when assessing climate change impacts on alluvial aquifers situated in populated valleys.

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Changing political frameworks in addition to novel and more cost-effective means to investigate the subsurface have led to an increase in the availability of hydrological data. This wealth of data, however, poses new challenges in effectively making use of it. Traditional tools such as spreadsheets or proprietary datalogger software often do not scale easily with a larger amount of available datasets, requiring considerable user interaction. Also, comparing different locations and types of data can be difficult and tedious. Thus, a python script is presented that enables the user to quickly visualize and compare different types of data such as for example groundwater levels or precipitation amounts. This is done by first standardizing the data using different drought indices and, subsequently, visualization of correlation matrices or plots of data on maps. This approach can be used for data quality control (identifying erroneous data, classifying data into different types), data comparison (comparing different types of data, such as groundwater and precipitation; comparing different locations) and to visualize and analyze the development of hydrological data and their correlation patterns over time. Prospects and limitations of the approach are illustrated and discussed using various example applications.

The assessment and monitoring of the ecological quality and status of groundwater is a timely issue. At present, various assessment tools have been developed that now await application and validation. One of these, the D‑A‑C index, evaluates the microbiological-ecological quality of groundwater based on of prokaryotic cell counts, microbial activity measurements, and the qualitative characterization of dissolved organic carbon (DOM). The purpose of this paper is to illustrate the different ways of application of the D‑A-(C) index making use of a recently collected data set (n = 61) from the river Mur valley, Austria. First, we present an extension of the D‑A-(C) index by including measurements of dissolved organic matter quality (DOM) derived from fluorescence spectroscopy as additional variables to supplement the analysis of microbial cell density and activity levels. Second, we illustrate how the definition of a reference status for a ‘good’ microbiological-ecological state can improve the analysis and allow for a more sensitive and accurate detection of impacts on groundwater ecosystems. Based on our results, we advocate that the analysis be performed by making use of expert knowledge for the definition of reference sites to which target sites are to be compared.