Spring data analyses for revealing basin-scale groundwater flow characteristics

Abstract

Springs sensu stricto indicate the location at the land surface where groundwater leaves the lithosphere ending the underground course and discharges to the surface. Springs sensu lato are ecosystems fed by groundwater reaching the (near-)surface, therefore, these are considered groundwater-dependent ecosystems (GDEs). Springs provide naturally accessible high-quality drinking water serving as a critical, and sometimes the only water resource in many places. Also, springs can offer food (plants and fishes) and raw materials (water and wood) for humans through their provisioning services. Besides these direct values, the GDEs often exhibit rich biodiversity with rare, unique and endangered species, serve as refugia during droughts and changing climate; bear historical, religious, ethnological, cultural and touristic importance; and play an essential role in the regulation of climate change, water flows and extreme events, waste treatment and soil fertility. Furthermore, springs can also deliver valuable insight into the hydrogeologic processes of a mountainous region, a natural conservation area or a remote study site with no wells. In order to assess the appearance, peculiarities, quality, stability, longevity and resilience of springs and related ecosystems, they need to be regarded in the context of basin-scale groundwater flow systems. When springs are interpreted on the basin-scale, 1) horizontal and vertical components of groundwater flow can be determined and refined, 2) surface water-groundwater interaction can be quantified, and the type of interaction can be deduced, and 3) the feeding basin-scale groundwater flow systems can be revealed. The application of spring data evaluation on a basin-scale is demonstrated via the carbonate system of Transdanubian Mts., Hungary. The readily measurable physical parameters of springs, the elevation of spring orifice, temperature and volumetric discharge rate provided reasonable classification and characterisation of springs and the related groundwater flow systems. Applying these parameters seemed prospective in a basin-scale understanding of flow systems in data-scarce regions, as monitoring discharge rate and water temperature are cost-effective, requiring no specific tools and analysing procedures. The combined cluster and discriminant analysis (CCDA) can handle uneven data distribution, unequal length and spacing of time series, data gaps, and consider the time-dependent variability of parameters. The optimal number of groups can be determined based on frequently sampled springs. The less monitored springs can be classified using a similarity-based approach and linear discriminant analysis (LDA). Systematic analysis of springs can lead to a comprehensive conceptualisation of groundwater flow systems revealing the basin-scale groundwater flow pattern and the hydraulic connection between parts aiding a local-scale groundwater model construction considering the system's behaviour. Furthermore, diagnosing the relation of springs to groundwater flow systems can advance sustainable water resources management considering the ecological water needs maintaining various ecosystem services, and enhancing the resilience of springs and groundwater-dependent ecosystems.

Full text

Spring data analyses for revealing basin-scale groundwater flow characteristics
Ádám Tóth1; Solt Kovács2; József Kovács3; Judit Mádl-Szőnyi1
1 József & Erzsébet Tóth Endowed Hydrogeology Chair, Department of Geology, Institute of Geography and Earth
Sciences, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary
2 Seminar for Statistics, Department of Mathematics, ETH Zurich, Rämistrasse 101, 8092 Zurich, Switzerland
3 Department of Geology, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Pázmány Péter
sétány 1/C, 1117 Budapest, Hungary
Corresponding Author(s): toth.adam@ttk.elte.hu
Springs sensu stricto indicate the location at the land surface where groundwater leaves the lithosphere ending the
underground course and discharges to the surface. Springs sensu lato are ecosystems fed by groundwater reaching
the (near–)surface, therefore, these are considered groundwater-dependent ecosystems (GDEs). Springs provide
naturally accessible high-quality drinking water serving as a critical, and sometimes the only water resource in many
places. Also, springs can offer food (plants and fishes) and raw materials (water and wood) for humans through their
provisioning services. Besides these direct values, the GDEs often exhibit rich biodiversity with rare, unique and
endangered species, serve as refugia during droughts and changing climate; bear historical, religious, ethnological,
cultural and touristic importance; and play an essential role in the regulation of climate change, water flows and
extreme events, waste treatment and soil fertility. Furthermore, springs can also deliver valuable insight into the
hydrogeologic processes of a mountainous region, a natural conservation area or a remote study site with no wells.
In order to assess the appearance, peculiarities, quality, stability, longevity and resilience of springs and related
ecosystems, they need to be regarded in the context of basin-scale groundwater flow systems.
When springs are interpreted on the basin-scale, 1) horizontal and vertical components of groundwater flow can be
determined and refined, 2) surface water–groundwater interaction can be quantified, and the type of interaction can
be deduced, and 3) the feeding basin-scale groundwater flow systems can be revealed.
The application of spring data evaluation on a basin-scale is demonstrated via the carbonate system of
Transdanubian Mts., Hungary. The readily measurable physical parameters of springs, the elevation of spring
orifice, temperature and volumetric discharge rate provided reasonable classification and characterisation of springs
and the related groundwater flow systems. Applying these parameters seemed prospective in a basin-scale
understanding of flow systems in data-scarce regions, as monitoring discharge rate and water temperature are cost-
effective, requiring no specific tools and analysing procedures. The combined cluster and discriminant analysis
(CCDA) can handle uneven data distribution, unequal length and spacing of time series, data gaps, and consider the
time-dependent variability of parameters. The optimal number of groups can be determined based on frequently
sampled springs. The less monitored springs can be classified using a similarity-based approach and linear
discriminant analysis (LDA).
Systematic analysis of springs can lead to a comprehensive conceptualisation of groundwater flow systems revealing
the basin-scale groundwater flow pattern and the hydraulic connection between parts aiding a local-scale
groundwater model construction considering the system’s behaviour. Furthermore, diagnosing the relation of springs
to groundwater flow systems can advance sustainable water resources management considering the ecological water
needs maintaining various ecosystem services, and enhancing the resilience of springs and groundwater-dependent
ecosystems.