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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.

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.