A Tracer-Based Method for Classifying Groundwater Dependence in Boreal Headwater Streams

TL;DR: In this paper, a classification method for the groundwater dependence of headwater streams was devised based on the fact that GW affects discharge, thermal regime, and water quality, which can serve as a water management tool, especially for streams of exceptional ecological importance or in places where anthropogenic activities are expected to change local hydrology and ecology.

About: This article is published in Journal of Hydrology.The article was published on 2019-10-01 and is currently open access. It has received 11 citations till now. The article focuses on the topics: STREAMS & Groundwater.

Summary

1 Introduction

• The authors combined continuous measurements of discharge, temperature (T), and electrical conductivity (EC) with use of stable water isotopes and other environmental tracers to study the GW-SW transition zones in three boreal streams known to have high proportions of GW from esker aquifers.
• The authors expected to find a transition zone within which a spring-originated stream turns into a SW-dominated system.
• By definition, SW is the water in surface storage units (e.g., lakes, streams, rivers, wetlands) and GW is the water underground.
• This work addresses the following research questions: i).

2 Materials and methods 2.1 Study sites

• The three streams selected for the study are located in Rokua and Viinivaara esker aquifer areas and are 60 km from each other.
• The study sites belong to a mid-boreal coniferous forest belt.
• In the Rokua esker area, the authors selected two streams, Siirasoja and Lohioja, where surrounding peatlands are intensively drained for forestry.
• The average channel widths and average maximum depths along the streams studied are presented in the Supporting Information Table S1 .

2.1.1 Siirasoja and Lohioja streams in Rokua esker aquifer

• The aquifer itself is unconfined, but the heavily drained peatlands in the surroundings partly confine the GW [Rossi et al., 2012] .
• The catchments are located next to each other and the land use consists mainly of peatland forestry and some agricultural land (Table 1 ).
• Some of the ditches in Siirasoja catchment have no flow and some fully penetrate the peat layer and reach the mineral soil beneath, causing increased GW discharge to the stream.
• GW discharge has been found to be either diffuse seepage or point type, and is induced by high pressure underneath the peat layer (Rossi et al. 2012) .

2.1.2 Mesioja stream in Viinivaara esker aquifer

• The authors study stream, Mesioja, discharges from a spring located at the break of slope where the sandy aquifer meets the peat formation and flows partly underground between measurement locations M2 and M3 (Table 1 ).
• The lower catchment area is mostly pristine peatland with bog-type vegetation.
• The GW dependence of the peatland area varies widely; spatial isotope studies by Isokangas et al. (2017) showed that the stable isotopic composition of the peatland pore water is not uniform near Mesioja stream, with δ 18 O values ranging between approximately -8‰ and -13‰ at 10 cm depth during the period 4-11 August 2014.

2.2 Field measurements

• Stable isotopic composition of water samples was analyzed using cavity ring-down spectroscopy with a Picarro L2120-i analyzer and the isotope ratios were expressed in δ notation relative to Vienna Standard Mean Ocean Water , with precision for δ 18 O and δ 2 H values of ±0.1‰ and ±1.0‰, respectively.
• Nutrients, alkalinity, and geochemical parameters were analyzed using Finnish national standards in an accredited (SFS-EN ISO/IEC 17025:2005) laboratory at the Finnish Environment Institute (SYKE) [National Board of Waters, 1981] .

2.2.1 Local groundwater and surface water quality

• The measurement locations are presented in the map in Supporting Information Fig. S1 .
• The nearest SW sampling locations of SYKE are situated 27 km (Nuorittajoki suu station) and 9 km (Nuorittajoki Töntönkoski station) from Mesioja catchment.
• Most of the parameters were analyzed using samples from Töntönkoski station and, although the measurements were performed before their stream sampling campaign, the data were assumed to be representative for the study period because there had not been any major changes in land use in the area.
• The SW sampling location of Nuorittajoki suu station is located rather far from their study stream but, as the land use is relatively similar to that in Mesioja catchment (Supporting Information Fig. S1 ), the data were assumed to be representative for the study area.
• In Rokua, the measurements by SYKE were also used as a reference for SW.

2.3 Data analysis

• To reduce the dimensionality of the dataset while retaining as much of its variation as possible, the authors performed principal component analysis (PCA) for the stream data (chemical and physical water quality parameters and discharge).
• It is preferred over the princomp function because of its better numerical accuracy [Anderson, 2013] .
• The authors also scaled and centered the data using the prcomp function.
• The authors performed the analysis for two datasets; the average values of all measurements for each site and the average values for the low-flow situation (July measurements).
• As each stream had almost the same number of sampling locations, they had similar weighting in the analysis and PCA was deemed suitable for use, although the autocorrelation between samples was borne in mind when analyzing the results.

2.3.1 The stream tracer index method

• 𝑛 where x variable is the classification value based on the chosen water quality variable.
• The classification values are determined by evaluating whether the tracer in question indicates a clear GW signal (x=1), a mixture of GW and SW (transition zone, x=0.5), or a clear surface water signal (x=0).
• After the appropriate values are chosen, the stream tracer index values can be calculated using equations ( 1) and ( 2).

3.1 Stream discharge and its origin

• Based on the stable water isotope dataset, the water origin in all three streams was mainly GW (Fig. 6 and Supporting Information Fig. S2 ).
• In Siirasoja and Lohioja streams at Rokua, the isotopic composition remained relatively stable except during the rain events in November 2014 (Fig. 3 ), when the isotopically more enriched SW increased the delta values of the streams.
• In Mesioja, the water isotope responses were more complex than in Lohioja and Siirasoja.
• At upstream locations, the isotopic composition resembled GW, but further downstream the delta values increased, indicating larger contributions from enriched surface runoff and soil water.
• At the furthest downstream locations, the delta values were again more negative, indicating GW discharge into the stream also at downstream locations (Fig. S2 ).

3.2 Spatial and temporal variations in stream water temperature

• During summer, water temperature generally increased from headwater to downstream in all streams (June-September, mean air T at Pudasjärvi airport 14.0 ºC and at Vaala-Pelso station 13.3 °C).
• The streams generally had different diurnal variations (Fig. 4 ).
• The coefficient of variation for temperature was smallest for headwater locations in both warm (June-September) and cold (April, May, October, November) seasons.
• This shows that GW sustains stable thermal regimes in both warm and cold seasons.
• In Lohioja and Siirasoja streams, water temperatures at different measurement points were more similar than in Mesioja.

3.3 Spatial and temporal variations in stream chemical properties

• In Lohioja, Clconcentrations indicated that the first measurement location (L1) had a clear GW signal and the other locations were in the GW-SW transition zone.
• In Siirasoja, the first three locations had Clconcentrations near to the GW reference value, the next two belonged to the GW-SW transition zone, and the last measurement location had a clear SW signal.
• In the Rokua area the reference values showed a clear distinction from each other.
• For all measurements, 75% of the variation in the data was explained by the first two principal components (PCs), while for low-flow measurements these two PCs explained 79% of the variation.
• Thus, generally low PC1 loadings indicated high GW influence.

3.4 Groundwater and surface water dominance of streams

• The authors results show that boreal headwater streams can be highly GW-dependent.
• Changes in GW discharge would particularly affect the position and length of the GW-SW transition zone in the stream continuum and could thus alter stream ecosystems.
• As GW supports stream flow, especially at upstream measurement locations, their study streams would most probably dry out without GW input at least occasionally.
• Earlier snowmelt due to climate change may also lower GW levels in the region during summer months, exacerbating the impacts of drought [Okkonen et al., 2010; Okkonen and Kløve, 2011] .

4.2 Thermal properties change radically in the stream continuum

• The authors results showed that GW plays a major role in the thermal sensitivity of the streams studied, such that GW-dependent areas were less sensitive to changes in air temperature, although still responded to it.
• The water temperature at these locations still showed diurnal variations and during exceptionally warm days the water temperature increased.
• Fortunately, these events were relatively short (some days) and the highest temperatures were only short-term mid-day events (e.g., on 1 July 2013, T in Siirasoja stream, location S3, increased to 10 °C for 45 minutes).
• Jyväsjärvi et al. (2015) showed that even a 1 °C increase in mean water temperature of springs can affect the species present in water and alter bryophyte and macroinvertebrate communities in streams discharging from springs.
• In addition, salmonid fish in particular have been found to be extremely sensitive to the current warming trend and thermal refuges are becoming even more important for preserving their populations [Isaak et al., 2015] .

4.3 The applicability of local groundwater and surface water reference values

• Moreover, GW quality reflects the local geology.
• The piezometers were sampled quarterly during 2010-2012.
• This suggests that SiO 2 and Clare more reliable tracers in this area.
• In addition, temporal variations in GW and SW quality were detected in both the Rokua and Viinivaara areas.
• The average coefficient of variation for the GW reference variables used (excluding PO 4 3--P) was 7.5% and that for the SW reference variables was 17.4%.

4.4 Surface water input causes changes in water quality in the stream continuum

• A disadvantage is that expert knowledge is needed to choose appropriate tracers for a selected area and the classification values for those tracers.
• The method resembles other management tools, such as 42 analytic hierarchy process [Subramanian and Ramanathan, 2012] , so in that regard it should be rather easy to adopt in decision making.
• Furthermore, the stream tracer index method could be especially helpful in the intense monitoring programs required in areas of exceptional ecological importance [Bertrand et al., 2014] .
• The method could also be applied in places where anthropogenic actions are expected to change the local hydrology and affect stream ecosystems.
• In addition, available historical data could be applied in some cases if there have not been any major changes in the catchment area.

5 Conclusions

• It is important to classify boreal headwater streams, owing to their ability to act as refuges, supporting stable conditions vital for specific aquatic biota in a changing climate.
• The results of this study suggest that it might be sufficient to sample stream sections only once, during summer low-flow conditions, when evaluating the groundwater dependence of streams.
• The stream tracer index method could serve as a useful management tool, especially at sites of exceptional ecological importance or at sites where anthropogenic measures are expected to change the local hydrology.