Rebecca M. Page

Faecal Indicator Bacteria: Groundwater Dynamics and Transport Following Precipitation and River Water Infiltration

Faecal contamination of drinking water extracted from alluvial aquifers can lead to severe problems. River water infiltration can be a hazard for extraction wells located nearby, especially during high discharge events. The high dimensionality of river–groundwater interaction and the many factors affecting bacterial survival and transport in groundwater make a simple assessment of actual water quality difficult. The identification of proxy indicators for river water infiltration and bacterial contamination is an important step in managing groundwater resources and hazard assessment. The time resolution of microbial monitoring studies is often too low to establish this relationship. A proxy-based approach in such highly dynamic systems requires in-depth knowledge of the relationship between the variable of interest, e.g. river water infiltration, and its proxy indicator. In this study, continuously recorded physico-chemical parameters (temperature, electrical conductivity, turbidity, spectral absorption coefficient, particle density) were compared to the counts for faecal indicator bacteria, Escherichia coli and Enterococcus sp. obtained from intermittent sampling. Sampling for faecal indicator bacteria was conducted on two temporal scales: (a) routine bi-weekly monitoring over a month and (b) intense (bi-hourly) event-based sampling over 3 days triggered by a high discharge event. Both sampling set-ups showed that the highest bacterial concentrations occurred in the river. E. coli and Enterococcus sp. concentrations decreased with time and length of flow path in the aquifer. The event-based sampling was able to demonstrate differences in bacterial removal between clusters of observation wells linked to aquifer composition. Although no individual proxy indicator for bacterial contamination could be established, it was shown that a combined approach based on time-series of physico-chemical parameters could be used to assess river water infiltration as a hazard for drinking water quality management.

Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment.

Detailed measurements of physical, chemical and biological dynamics in groundwater are key to understanding the important processes in place and their influence on water quality – particularly when used for drinking water. Measuring temporal bacterial dynamics at high frequency is challenging due to the limitations in automation of sampling and detection of the conventional, cultivation-based microbial methods. In this study, fully automated online flow cytometry was applied in a groundwater system for the first time in order to monitor microbial dynamics in a groundwater extraction well. Measurements of bacterial concentrations every 15 minutes during 14 days revealed both aperiodic and periodic dynamics that could not be detected previously, resulting in total cell concentration (TCC) fluctuations between 120 and 280 cells μL−1. The aperiodic dynamic was linked to river water contamination following precipitation events, while the (diurnal) periodic dynamic was attributed to changes in hydrological conditions as a consequence of intermittent groundwater extraction. Based on the high number of measurements, the two patterns could be disentangled and quantified separately. This study i) increases the understanding of system performance, ii) helps to optimize monitoring strategies, and iii) opens the possibility for more sophisticated (quantitative) microbial risk assessment of drinking water treatment systems.

Online analysis: Deeper insights into water quality dynamics in spring water

We have studied the dynamics of water quality in three karst springs taking advantage of new technological developments that enable high-resolution measurements of bacterial load (total cell concentration: TCC) as well as online measurements of abiotic parameters. We developed a novel data analysis approach, using self-organizing maps and non-linear projection methods, to approximate the TCC dynamics using the multivariate data sets of abiotic parameter time-series, thus providing a method that could be implemented in an online water quality management system for water suppliers. The (TCC) data, obtained over several months, provided a good basis to study the microbiological dynamics in detail. Alongside the TCC measurements, online abiotic parameter time-series, including spring discharge, turbidity, spectral absorption coefficient at 254nm (SAC254) and electrical conductivity, were obtained. High-density sampling over an extended period of time, i.e. every 45min for 3months, allowed a detailed analysis of the dynamics in karst spring water quality. Substantial increases in both the TCC and the abiotic parameters followed precipitation events in the catchment area. Differences between the parameter fluctuations were only apparent when analyzed at a high temporal scale. Spring discharge was always the first to react to precipitation events in the catchment area. Lag times between the onset of precipitation and a change in discharge varied between 0.2 and 6.7h, depending on the spring and event. TCC mostly reacted second or approximately concurrent with turbidity and SAC254, whereby the fastest observed reaction in the TCC time series occurred after 2.3h. The methodological approach described here enables a better understanding of bacterial dynamics in karst springs, which can be used to estimate risks and management options to avoid contamination of the drinking water.

Process-based monitoring and modeling of Karst springs – Linking intrinsic to specific vulnerability

The presented work illustrates to what extent field investigations as well as monitoring and modeling approaches are necessary to understand the high discharge dynamics and vulnerability of Karst springs. In complex settings the application of 3D geological models is essential for evaluating the vulnerability of Karst systems. They allow deriving information on catchment characteristics, as the geometry of aquifers and aquitards as well as their displacements along faults. A series of Karst springs in northwestern Switzerland were compared and Karst system dynamics with respect to qualitative and quantitative issues were evaluated. The main objective of the studies was to combine information of catchment characteristics and data from novel monitoring systems (physicochemical and microbiological parameters) to assess the intrinsic vulnerability of Karst springs to microbiological contamination with simulated spring discharges derived from numerical modeling (linear storage models). The numerically derived relation of fast and slow groundwater flow components enabled us to relate different sources of groundwater recharge and to characterize the dynamics of the Karst springs. Our study illustrates that comparably simple model-setups were able to reproduce the overall dynamic intrinsic vulnerability of several Karst systems and that one of the most important processes involved was the temporal variation of groundwater recharge (precipitation, evapotranspiration and snow melt). Furthermore, we make a first attempt on how to link intrinsic to specific vulnerability of Karst springs, which involves activities within the catchment area as human impacts from agriculture and settlements. Likewise, by a more detailed representation of system dynamics the influence of surface water, which is impacted by release events from storm sewers, infiltrating into the Karst system, could be considered. Overall, we demonstrate that our approach can be the basis for a more flexible and differentiated management and monitoring of raw-water quality of Karst springs.

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon’s Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.

Examples and Case Studies

The presented examples and case studies illustrate specific applications of adaptive management of water resources in the region of Basel, Northwestern Switzerland. Such concepts together with the setup of tools and process-oriented experiments allow testing hypotheses. The applied methods facilitated us to fill several gaps of knowledge of subsurface processes. The examples focus on questions with practical as well as research. Most topics are relevant for urban areas and the sustainable use of subsurface resources in general.