Streams draining upland catchments carry large quantities of carbon from
terrestrial stocks to downstream freshwater and marine ecosystems. Here it
either enters long-term storage in sediments or enters the atmosphere as
gaseous carbon through a combination of biotic and abiotic processes. There
are, however, increasing concerns over the long-term stability of terrestrial
carbon stores in blanket peatland catchments as a result of anthropogenic
pressures and climate change. We analysed sub-annual and inter-annual changes
in river water colour (a reliable proxy measurement of dissolved organic
carbon; DOC) using 6 years of weekly data, from 2011 to 2016. This
time-series dataset was gathered from three contiguous river sub-catchments,
the Black, the Glenamong and the Srahrevagh, in a blanket peatland catchment
system in western Ireland, and it was used to identify the drivers that best
explained observed temporal change in river colour. The data were also used
to estimate annual DOC loads from each catchment. General additive mixed
modelling was used to identify the principle environmental drivers of water
colour in the rivers, while wavelet cross-correlation analysis was used to
identify common frequencies in correlations. At 130 mg Pt Co L
Blanket peat ecosystems occur within a relatively narrow window of climatic conditions, characterised by warmer and wetter conditions, in temperate regions where precipitation exceeds potential evaporation by a ratio of about 3 : 1 (Wieder and Vitt, 2006). Under such conditions, primary production exceeds decomposition of soil organic matter, and therefore organic carbon (C) accumulates. These ecosystems are a major terrestrial carbon store (Bain et al., 2011). Blanket peats (technically a soil with peat depth > 40 cm) are now recognised as being under threat, not only from excessive erosion due to anthropogenic pressures (for example, harvesting, burning and grazing) (Renou-Wilson et al., 2011), but also from increases in C loss related to directional climate change (Gallego-Sala and Prentice, 2013). Streams and rivers are the major pathways along which organic C is conveyed from upland peatlands to downstream lakes and oceans. In most studies which have evaluated fluvial losses of both dissolved organic carbon and particulate organic carbon, DOC has been identified as the dominant C form, representing between 60 % and 88 % of the total carbon load (Hope et al., 1997a; Tipping et al., 1997; Ryder et al., 2014). Hope et al. (1997b) concluded that, for British rivers as a whole during 1993, 0.68 Mt C of the fluvial carbon load was in dissolved form, representing 77 % of total C export. In the west of Ireland, DOC was estimated to account for 60.5 % of the total fluvial C load from the Glenamong sub-catchment (Ryder et al., 2014), a site which is also used in the current study
Stream DOC concentrations draining blanket peatland catchments in Ireland typically show a distinct seasonal pattern, with highest values from late summer to early winter and lowest values in spring (e.g. Ryder et al., 2014). Longer-term patterns in DOC concentrations or in proxies for DOC have been linked to year-to-year changes in meteorological conditions at both local and regional scales. At local scales, temperature affects peat decomposition rates and therefore the availability of DOC, while higher precipitation increases the washout of DOC from soils (Jennings et al., 2006; Ryder et al., 2014). Increases in oxygen availability within peat during droughts can also lead to higher rates of aerobic decomposition (Mitchell and McDonald, 1992; Yallop and Clutterbuck, 2009; Fenner and Freeman, 2011). At regional scales, DOC concentrations have been shown to be influenced by global weather patterns; for example, DOC concentrations in certain Canadian lakes were found to be correlated with climate indices such as the Pacific Decadal Oscillation and the Southern Oscillation Index (Zhang et al., 2010). In Europe, and Ireland in particular, such correlations would be expected to be linked to the North Atlantic Oscillation (NAO). The NAO is a weather phenomenon related to fluctuations in the difference of atmospheric pressure at sea level between the Icelandic low and the Azores high (Hurrell et al., 2003). A positive phase of the NAO reflects below-normal atmospheric pressure across Greenland and Iceland and above-normal atmospheric pressure over the central North Atlantic, the eastern United States and western Europe. A negative phase reflects an opposite pattern of atmospheric pressure anomalies over these regions. High positive values of this index over north-west Europe are associated with warmer and wetter conditions during the winter, and positive index values during the summer are linked with warm, dry and relatively cloud-free periods (Folland et al., 2008). In a 28-year study, Nõges et al. (2007) found that water colour (one of the most commonly used proxies for DOC) during spring in Estonian rivers was positively related to the previous winter's North Atlantic Oscillation (NAO) index. A similar positive relationship between the winter NAO and the total organic carbon (TOC) load was reported over 25 years in Finnish rivers (Arvola et al., 2004).
Increasing trends in fluvial DOC concentrations have been observed in many peat catchments over the last 20 years (Hongve et al., 2004; Evans et al., 2005; Monteith et al., 2007; Worrall and Burt, 2007; Erlandsson et al., 2008; Jennings et al., 2006). While these changes have been attributed in part to recovery from the effects of atmospheric acid deposition on a regional scale (Monteith et al., 2007; Erlandsson et al., 2008), the trend has also been linked to changes in the key climatic drivers of DOC export. These drivers include the effect of changes in precipitation and snowmelt patterns on flushing rates (Hongve et al., 2004; Erlandsson et al., 2008) and the impact of higher temperatures (Freeman et al., 2001; Preston et al., 2011) and of drought events (Clark et al., 2005; Jennings et al., 2006) on peat decomposition. There are, however, also studies in which DOC concentrations have been shown to have decreased (Clair et al., 2008; Worrall et al., 2018), or no increase has been observed, such as within certain catchments in the UK (Worrall and Burt, 2007). Winterdahl et al. (2014) also reported increases in TOC in only half of 130 Swedish streams but with no clear geographic pattern, highlighting the need for further examination of the complex relationship between DOC concentration and climate. Given the close relationship between peat formation and peat decomposition and climate factors such as temperature, directional climate change is likely to place additional pressures on peatland systems (Clark et al., 2010a; Coll et al., 2014). Observed and projected climate changes for Ireland include higher temperatures throughout the annual cycle, a decrease in the summer water table and higher winter streamflow (Dwyer, 2012; Nolan, 2015), a combination that has been shown to have the potential to increase fluvial DOC export (Naden et al., 2010).
In Europe, Atlantic blanket bogs are found on the western fringes of the continent and are common only in Ireland and Scotland (Sheehy Skeffington and O'Connell, 1998), reflecting the dominant influence of the Atlantic on the local climate in these countries (Coll et al., 2005; Sweeney, 2014). In Ireland, up to 75 % of soil carbon storage is in peatlands, much of which is in upland blanket peat soils (Holden and Connolly, 2011; Renou-Wilson et al., 2011). Examining riverine fluxes of carbon from these catchments provides a means to quantify export of C from long-term storage in peatland ecosystems and to explore the effects of climatic variables on these C stores. The present study expands on the work described earlier of Ryder et al. (2014), firstly by comparing colour concentrations from three contiguous peat sub-catchments that differ in their catchment characteristics and secondly by including the role of the regional climatic conditions, e.g. the NAO, as a possible driver. The principal aims of the current study, using river colour data from the Burrishoole catchment in the west of Ireland, were (1) to compare the sub-seasonal, seasonal and multi-annual trends in water colour, (2) to identify the main climatic drivers of river colour and (3) to quantify the inter-annual variability in fluvial export of DOC over the study period.
The Burrishoole catchment (
The Burrishoole catchment is located close to the north-west coast of Ireland
and experiences a temperate, oceanic climate with mild winters and
relatively cool summers. A meteorological station (Newport) has been in
operation in the catchment on the shores of Lough Feeagh since 1958.
Long-term average annual precipitation at this station (1960–2014) was 1564 mm. Average
daily rainfall for the same period was 4.3 mm (
Location of the sub-catchments in the Burrishoole catchment.
This study is focussed on three rivers and their sub-catchments in the
Burrishoole catchment. The Black and the Glenamong drain directly into Lough
Feeagh, whilst the third, the Srahrevagh, is nested within the larger Black
sub-catchment (Fig. 1). The predominant land uses in all three sub-catchments
are sheep grazing and forestry; however the Black also contains a small
proportion of more intensively managed agricultural land. Soils with a peaty,
carbon-rich top horizon are common throughout the Burrishoole catchment, and
blanket peat covers approximately 20 % of the catchment. Blanket peat has
been mapped in all three sub-catchments, with the Srahrevagh containing
approximately 5 % more peat relative to its area than the other two
sub-catchments (Kiely et al., 1974; Gardner and Radford, 1980) (Table 1). The
Glenamong has a greater percentage of stream length intersecting blanket peat
in comparison to the Black and the Srahrevagh sub-catchments (Fig. 1 and
Table 1). The CORINE land-cover data show that 32 % of the Srahrevagh
sub-catchment contains coniferous plantation compared to approximately
25 % and 17 % for the Glenamong and Black respectively (Table 1). The
Srahrevagh sub-catchment has the greatest proportion of slopes ranging
between 0 % and 20 %, while the Glenamong is the most mountainous of
the three sub-catchments, having the greatest altitude range and containing
the greatest proportion of slopes steeper than 50 % (Table 1). Glenamong
stream water is consistently more acidic than the other two sub-catchments;
however the remaining stream chemistry metrics between sub-catchments are
broadly similar (Table 1). The slope distribution (as percent) for the
Burrishoole catchment and each sub-catchment was calculated from a digital
elevation model (DEM) at a 10 m resolution (Marine Institute Data) using
the Spatial Analyst routine in ArcMap 10.3.1 (ESRI –
Sub-catchment characteristics, climate (recorded at Newport climate station) and hydrology and stream water chemistry data for the Black, Glenamong and Srahrevagh sub-catchment.
Water samples from the rivers were taken at weekly intervals over 6 years
(2011–2016) from the same sampling sites (Fig. 1). Colour
(mg PtCo L
Daily precipitation and soil temperature data were available from the Newport
weather station (Fig. 1). Water levels (cm) were recorded every 15 min at
each site using OTT Hydrometry Orpheus Mini water level loggers
(
This analysis was conducted to ascertain if there were significant
statistical differences between river colour in each catchment. A
Mann–Whitney
The time-series datasets of weekly colour concentrations in the three rivers
were examined using seasonal trend decomposition using loess (STL) (Cleveland
et al., 1990) in R (R Core Team, 2017). The STL algorithm decomposes a time
series into three separable elements: the trend, the seasonal variation and
the residual using an additive model (Eq. 1). Loess (locally weighted
smoothing) regression is a non-parametric technique that uses local weighted
regression to fit a smooth curve through points in a scatter plot. An
additive model was preferred over a multiplicative model because no obvious
non-stationarity in the time series was observed; i.e. the amplitude of the
seasonal cycle remained uniform and did not increase or decrease with the
trend in the data (Fig. 3a). Variation in the time-series data was decomposed
into a set of constituent elements: overall mean or level (
To identify the main explanatory drivers of colour in the rivers, general
additive mixed models (GAMMs) with cubic smoothing regression splines and
Gaussian distributions were developed using the mgcv package in R (Wood,
2006). Variance inflation factors (VIFs) less than 3 were used to exclude
closely related variables (Montgomery and Peck, 1992; Zuur et al., 2009). All
models were tested for violations of the assumptions of homogeneity,
independence and normality, and correlation or variance structures included
as appropriate. Models were examined for the effects of autocorrelation in
residuals by plotting the autocorrelation function (acf) (Venables and
Ripley, 2002). All analysis was carried out in R. The response variable was
the weekly colour data from the three sub-catchments. Potential explanatory
variables comprising climate and hydrological data were included as
continuous variables. The climate variables, measured at the Newport
meteorological station, were constructed as follows: the first set was the
weekly mean of each climate variable calculated from the sampling week; the
second set was the value of each variable measured on the day of sampling.
The third set was constructed by lagging each climate variable by 1-, 2- and
4-weekly time steps. The climate variables included were maximum, minimum and
mean air temperature (
A cross-wavelet transform analysis was carried out to further examine the trends and periodicities in colour concentrations with the explanatory drivers of colour in the rivers. Cross-wavelet transform analysis can be used as a method of examining pairs of time series that may be expected to be linked in some way. Continuous wavelet transforms from pairs of time series are used to construct the cross-wavelet transforms, revealing their common power and relative phase in time–frequency space. In particular, the analysis examines whether regions in time–frequency space with large common power have a consistent phase relationship, suggesting causality between the time-series pairs (Grinsted et al., 2004). A cross-wavelet power spectrum was calculated from the cross-wavelet transform results in order to estimate the covariance between each pair of time series as a function of frequency, and the statistical significance was also estimated as part of the analysis. The biwavelet package in R (R Core Team, 2017) was used for the bivariate wavelet analyses (Grinsted et al., 2004).
DOC (mg L
Weather conditions varied during the six study years, with 2013 being the
driest year, with a mean daily precipitation of 3.7 mm day
A comparison of monthly precipitation values during the 6-year study
period with precipitation from the previous 15 years (1995 to 2010) at the
Newport station showed that the first two study years, 2011 and 2012, had
near-normal precipitation totals (SPI of 1 to
Panel
The mean stream water discharge rates for the three rivers during the study
period were 1.89, 0.84 and 0.36 m
The pattern in soil moisture deficit (SMD) varied considerably over the six
years, largely reflecting the varying volumes of precipitation over the
catchment each year. The year with the greatest cumulative SMD was 2013, with
an average daily deficit of 8.3 mm. The cumulative SMD reached a
maximum of 66.2 mm in July. The least accumulated deficit
occurred in 2015 with an average of 3.9 mm, with a maximum of
35.7 mm recorded in July. The maximum daily SMD recorded
over the study period was 67.6 mm day
The colour concentration showed a strong synchronous annual pattern for all
sub-catchments, dipping to a minimum during the winter and peaking in late
summer to early autumn (Fig. 3a). The Srahrevagh River had the highest
colour concentrations, with a median colour concentration of 130 mg Pt Co L
The inter-annual trend in colour concentration (Fig. 3b) was also
synchronous across all three sub-catchments. There was a peak during the
summer of 2012 before it descended to a minimum for all three catchments in
the late summer and early autumn of 2013. The trend generally increased from
this low point to the beginning of 2016, with the exception of a minor dip
in colour concentration in January 2015. The seasonal patterns were also
almost identical for all three sites (Fig. 3c), with highest concentrations
in late summer and lowest values in January and February of each year. The
Srahrevagh again displayed the greatest range of seasonal variation, and the
seasonal variation of the Black and Glenamong was largely similar. The
decomposed random component of the colour time series (Fig. 3d) also
displayed a broad synchronicity in timing across all three sub-catchments
over the six years, indicating that the mechanisms controlling short-term
spikes and dips in colour were also synchronous across all three
sub-catchments. Similar to the pattern of the seasonal variation, the
greatest range of variation in the random component was from the Srahrevagh
River (
Results of generalised additive mixed models (GAMMs) applied to
colour in the Black River (
The optimal GAMM for the colour in the Black River included three smoothers, soil temperature at 100 cm depth, soil moisture deficit, and the weekly mean NAO (Fig. 4). This model explained 54 % of the deviance in water colour over the study period (Table 2). Explanatory variables that were measured on the day of sampling resulted in a better model fit than weekly means for the previous week. Lagging the explanatory variables by 1, 2 and 4 weeks did not improve the model. The smoother explaining the relationship between soil temperature and colour was linear in the model (estimated degrees of freedom of 1) and positive, indicating that colour increases with increasing temperature. The smoother describing the relationship between colour and soil moisture deficit was, in contrast, generally negative, indicating that colour concentrations in the river decreased with increasing SMD, while notably that describing the relationship between NAO and colour indicated that colour decreased in positive phases of the North Atlantic Oscillation. The optimal GAMM for the Srahrevagh River had the same three smoothers as the Black, soil temperature at 100 cm, SMD and the weekly NAO (Fig. 4), and the model explained 58 % of the deviance in water colour over the study period (Table 2). Again the subset of explanatory variables that were measured on the day of sampling provided the optimum model. The smoothers in the Srahrevagh River model also showed the same patterns in relationship to colour, i.e. a positive relationship between colour and soil temperature and a negative relationship between SMD and the weekly NAO.
Selected smoothers for the contribution of explanatory variables for
the optimal GAMM explaining water colour in each sub-catchment river: Black
(
The optimal GAMM for colour in the Glenamong River also had three smoothers
but differed in that it included the log of river discharge rather than SMD
(Fig. 4). The model explained 66 % of the deviance in water colour over
the study period (Table 2), with again the subset of explanatory variables
that were measured on the day of sampling providing the optimum result. The
shape of the smoother describing the relationship between colour and
discharge indicated that colour concentrations in the river increased to a
point and then stabilised at higher discharges. The smoother describing the
relationship between the NAO and colour was similar to that found in the
other two models and decreased for positive phases of the North Atlantic
Oscillation (Fig. 4g). The models described above produced the optimum
Of note also was the relative importance of the explanatory variables in each of the models. For example, in the optimum model for the Black sub-catchment, out of the total of 54 % of the variance explained by the model, soil temperature contributed 34 %, SMD contributed 17 % and the NAO contributed 3 %. For the Srahrevagh, out of the 58 % total, soil temperature contributed 40 % of the variance, SMD contributed 16 % and the NAO contributed 2 %. Out of the 66 % total of explained variance for the Glenamong, soil temperature contributed 52 %, the log of river discharge contributed 11 % and the NAO contributed 3 %. The multi-annual trend plots for the NAO, soil temperature, discharge and water colour all had similar patterns that included a distinct dip in the period from late 2012 to mid-2013 and a general upward trend after these low points (Fig. 5). These low points were sequential for the different variables, with the dip in the NAO occurring in the early winter of 2012, that in soil temperature occurring in early 2013 and that in mean colour concentrations (mean based on data for all three sites) in midsummer 2013. The trends for river discharge, here using the Glenamong as an example, had a less defined low point, which ran from early summer to midsummer 2013 (Fig. 5c). The trend in SMD displayed a distinct plateau for each year, with 2013 having the highest levels and 2015 the lowest levels (Fig. 5d).
In the cross-wavelet analysis, there was a significant common power between
river colour at the annual (52 week) timescale for all four variables, with
additional significant zones occurring intermittently at higher frequencies
(ca. 2–16 weeks) that were most notable for SMD and for the NAO (Fig. 6). For
soil temperature, the width of the orientation at the annual time step was
relatively consistent with phase arrows that all pointed right; i.e. there
was a positive correlation between soil temperature and river colour that
was consistent at the annual scale (Fig. 6a). The orientation of the phase
arrows (i.e. downward) for stream discharge indicated that river colour was
leading river discharge by 90
Cross-wavelet power spectrum of soil temperature at 1 m depth (
There was a wide range in the annual estimated loads exported from the three
sub-catchments over the six study years, both at an annual and seasonal
scale (Table 3). The higher colour concentrations in the Srahrevagh River
translate into the highest DOC exports of any of the sub-catchments, even when
the loads are area-weighted. The highest annual load was estimated for the
Srahrevagh in 2015 (38.6 t C km
Estimated DOC load (t C km
* Totals were calculated as the area-weighted average for the Black and Glenamong sub-catchments only (which are in-flows to Lough Feeagh, the main lake shown in Fig. 1).
This study highlighted the dominant influence of local and regional climate on water colour as a proxy for DOC levels. Weather-related factors explained between 54 % and 66 % of the variability in all three sub-catchment datasets, and there was strong synchronicity in these climate signals across the Burrishoole catchment. It also showed, however, that despite this synchronicity, colour concentrations in one sub-catchment (the Srahrevagh) were significantly higher than the other two monitoring sites, a difference that was consistent over seasons and the six years of the study. Colour, and therefore DOC, in these headwater rivers probably originates almost exclusively from the surrounding catchment soils, and the consistent difference in colour concentrations between each sub-catchment during the study was most likely a function of individual sub-catchment properties such as the extent of peat within catchments (Hope et al., 1997a), land use (Findlay et al., 2001), local run-off (Dillon and Molot, 2005), vegetation type (Sobek et al., 2007) and the unique morphology and geology of the sub-catchment landscape (Moore, 1998). Forestry is also known to influence DOC release from soils, and it has been observed that both afforestation and forest clearfelling result in increased DOC concentrations and that these increases may continue for several years after the initial event (Cummins and Farrell, 2003; Schelker et al., 2012). The extent of peat soils in the study catchments, the length of streams intersecting the peat, slope analysis and CORINE land cover may explain the higher levels of colour found in the Srahrevagh. However, an additional factor could be the distance between a given sampling point and the source of any coloured compounds. Dawson et al. (2002) previously observed decreases in TOC (both DOC and POC) concentrations in the Upper Hafren (a headwater stream in mid-Wales) downstream from the source that were stated to be related to a decrease in peat depth with altitude, combined with in-stream processing of DOC. A similar process may contribute to the difference in concentration between the upstream Srahrevagh and downstream Black sampling points, although there are other studies that reported no clear decrease in DOC concentration as water travelled downstream (Temnerud and Bishop, 2005; Creed et al., 2015; Winterdahl et al., 2016).
The local climate effects identified in this study included strong positive linear correlations between colour concentrations in the three rivers and soil temperature. There was also a consistent positive relationship between soil temperature and colour at the annual scale in the cross-wavelet analysis. Soil temperature was common to all three GAMMs, and so was the dominant explanatory variable, emphasising how dissolved organic carbon is released by peat soils via decomposition processes that are temperature-dependent (Christ and David, 1996; Neff and Hooper, 2002). This temperature effect is complicated, however, by an interaction between peat decomposition and the water table level. Peat soils during low-water-level conditions show increased rates of decomposition, brought about by changes in the oxygen status within the peat and subsequent changes in the microbial community structures within the soil (Mäkiranta et al., 2009). The lowered water table reduces the hydrological connection, i.e. the transport of DOC along active flow pathways in the soil. This breaks the connection between the source of DOC production and its eventual destination. Increasing temperature, therefore, will increase DOC production in the peat but only if the increase in temperature does not result in a large drawdown of the water table. The strong relationship displayed between soil temperature and water colour concentrations in the three rivers, and the significant and high common power with river colour at the yearly timescale in the cross-wavelet analysis, indicated that soil temperature was the primary driver of the seasonal pattern in water colour during the study period in this catchment. It is interesting to note that in general no relationship between seasonality and DOC concentration has been reported in some other studies (e.g. Winterdahl et al., 2014). However, these results are consistent with observations of DOC dynamics in some surface waters, whereby seasonal variation has been found to be the largest source of DOC variation in catchments with high DOC concentrations (Clark et al., 2010b; Ryder et al., 2014). Of note were also the strong similarities in the pattern in multi-annual trends of river colour and soil temperature, particularly in the first four years of the study when the pronounced sequential dip was observed in temperature and then in water colour, indicating, as might be expected, that temperature also acts at a multi-annual scale.
The relationship of colour with SMD in the Black and Srahrevagh optimal GAMM
models indicated that as soil moisture decreased, DOC concentrations also
decreased. The cross-wavelet analysis indicated a significant continuous
relationship at a yearly time step when SMD led colour by 90
River discharge was a significant explanatory variable in the optimum model
for the Glenamong, and in alternative models for the Black and the
Srahrevagh rivers, further emphasising the complex effects of hydrology on
colour, and therefore DOC concentrations, in the catchment. The reasons why
river discharge superseded SMD as an explanatory variable for the Glenamong
are likely to be its more westerly location, with higher precipitation, and
more mountainous topography. The effect of precipitation has been shown to
operate at sub-catchment spatial scales and short temporal scales in the
Burrishoole catchment. De Eyto et al. (2016) described the effects of an
intense episodic rainfall event on the ecology of Lough Feeagh, whereby anomalous high amounts of rain fell in the east side of the catchment (the
Black and Srahrevagh), while relatively moderate amounts were recorded in the
west (the Glenamong). However, that event was not associated with any
increase in water colour, while in contrast, Jennings et al. (2012)
described a large increase in DOC concentrations in the Glenamong during
increased precipitation in summer 2006. Short-duration high discharge events
were also less likely to have been picked up in the weekly sampling regime of
the current study. The GAMM smoother for colour concentration in the
Glenamong versus discharge showed that colour increases with increasing
discharge to an optimum point where it levels off and even decreases
slightly at very high discharges. The cross-wavelet time-series analysis
between river colour and river discharge in the Glenamong indicates a
significant continuous relationship at a yearly time step, with colour
concentrations leading discharge by 90
The current study indicated variable results for the effect of NAO on water colour. There was a negative relationship with the NAO indicated in both the GAMMs, and in the common power at an annual time step in the cross-wavelet analysis, but also a lagged positive effect indicated in the plots of the multi-annual trends. The effects of the NAO on the local climate and lake water temperatures have previously been described for the Burrishoole catchment (Jennings et al., 2000; Blenckner et al., 2007). Jennings et al. (2000) reported that a range of meteorological variables at two lake sites in the west of Ireland (Lough Feeagh, Co. Mayo, and Lough Leane, Co. Kerry) were influenced by the winter NAO, including positive relationships with mean winter air temperature and surface water temperature, mean winter wind speed and winter rainfall. These relationships were also apparent but to a lesser degree in the following spring and summer. Kiely (1999) also showed that positive phases of the winter NAO led to increased run-off in Irish rivers. During the construction of the GAMMs, both the weekly and monthly NAO index values were tested in the analysis. The models using the weekly data consistently explained more of the deviance in the model. This most likely reflects the proximity of the site to the Atlantic coast and the time frame over which weather systems associated with the NAO pressure difference generally reach the study location. Although the weekly NAO improved the optimum models by only between 2 % and 4 %, it was significant at the 99.9 % level in all of the models. The smoothers for the weekly NAO in the GAMMs indicated that colour decreased during positive phases of the NAO. This appears to be contrary to the literature for more northern sites, whereby DOC has been shown to increase during positive NAO phases, a relationship that has been linked to higher winter precipitation (Arvola et al., 2004; Nõges et al., 2007). However, some studies have also suggested that positive phases of the NAO during the summer are associated with warm and dry rather than warm and wet conditions over north-west Europe, in particular the UK and much of Scandinavia (Folland et al., 2008). It is possible that this negative relationship was reflected in the strong effect of SMD on colour in the current study. However, the negative relationship apparent in the cross-wavelet time-series analysis at the annual time step may also merely reflect the fact that both time series have seasonal patterns but are not linked by any causal mechanism. Examination of the multi-annual trend of the decomposed NAO values also showed a large and sustained swing in the index to negative values, beginning in late spring 2012 until spring 2013, with a subsequent return to positive values. Negative NAO values during the winter generally correspond to relatively cold and dry conditions, and dry weather was observed throughout 2013, reflected in the SPI, beginning during the winter of 2012/2013. Cold conditions were also confirmed by the sharp dip in the multi-annual trend of soil temperature observed during the same winter period. This overall trend was mirrored in the pattern in water colour but lagged by ca. 6 months, suggesting that the NAO actually has a lagged but positive effect on water colour at this timescale. Further analysis with a longer multi-annual dataset would be required to explore the effect of the NAO on water colour and therefore DOC export at this site.
The minimum annual total DOC load from the Burrishoole catchment was 11.8 t C km
The results of this study emphasised how colour concentrations, and therefore DOC levels, respond to common climatic drivers which operate at both a local and regional scale. In the Burrishoole catchment, temporal changes in stream colour levels were driven by variation in soil temperature, by hydrology (discharge and/or SMD) and by the NAO, an overarching regional climate pattern. These effects lead directly to variability in aquatic DOC concentrations in the sub-catchments that will ultimately affect the carbon budget of the downstream receiving waters of Lough Feeagh. The results presented here serve to further strengthen the well-established link between climate and aquatic carbon concentrations in peatland catchments and the vulnerability of blanket peatlands to climate change.
The underlying research data can be found in Doyle et al. (2018).
BD, MD, ED and ER collected the field data. MD maintained all of the field equipment and measuring infrastructure. BD, ER and MD carried out lab calibrations and analysis. RP manages the long-term ecological monitoring program in Burrishoole. ED, VM and EJ supervise BD's doctoral work and contributed technical advice and guidance throughout the project implementation. EJ attained the grant award. BD collated and analysed the data and wrote the first draft of the paper with contributions from EJ and ED. All other authors contributed during the paper writing stages.
The authors declare that they have no conflict of interest.
This study was supported by the Marine Institute's Cullen PhD fellowship and core research and development programmes, grant no. CF/15/05. We would like to thank the local landowners for allowing access to the rivers and providing sites for monitoring stations. Acknowledgement is also due to the staff at the Marine Institute, Furnace, Co. Mayo, and the Dundalk Institute of Technology, Dundalk, Co. Louth, without whom this work would not have been possible. Edited by: Tom J. Battin Reviewed by: two anonymous referees