Reservoirs as hotspots of fluvial carbon cycling in peatland catchments

STOTEN-21497; No of Pages 14 Science of the Total Environment xxx (2016) xxx–xxx

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Reservoirs as hotspots of fluvial carbon cycling in peatland catchments A.G. Stimson a,⁎, T.E.H. Allott a, S Boult b, M.G. Evans a a b

Upland Environments Research Unit, School of Environment, Education and Development, The University of Manchester, Oxford Road, M13 9PL, United Kingdom School of Earth, Atmospheric and Environmental Science, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Detailed DOC, POC and CO2(aq) budgets for reservoir in degraded peatland catchment. • Reservoir is a net fluvial carbon sink and important hotspot of carbon cycling. • Flux and 14 C based evidence for inreservoir DOC production from POC. • Links between in-reservoir DOC production, rainfall and temperature. • Understanding implications for carbon cycling and composition will aid management.

a r t i c l e

i n f o

Article history: Received 31 August 2016 Received in revised form 25 November 2016 Accepted 27 November 2016 Available online xxxx Dr. J Jay Gan Keywords: Carbon cycle Peatlands Reservoirs Lakes POC DOC

a b s t r a c t Inland water bodies are recognised as dynamic sites of carbon processing, and lakes and reservoirs draining peatland soils are particularly important, due to the potential for high carbon inputs combined with long water residence times. A carbon budget is presented here for a water supply reservoir (catchment area ~ 9 km2) draining an area of heavily eroded upland peat in the South Pennines, UK. It encompasses a two year dataset and quantifies reservoir dissolved organic carbon (DOC), particulate organic carbon (POC) and aqueous carbon dioxide (CO2(aq)) inputs and outputs. The budget shows the reservoir to be a hotspot of fluvial carbon cycling, as with high levels of POC influx it acts as a net sink of fluvial carbon and has the potential for significant gaseous carbon export. The reservoir alternates between acting as a producer and consumer of DOC (a pattern linked to rainfall and temperature) which provides evidence for transformations between different carbon species. In particular, the budget data accompanied by 14C (radiocarbon) analyses provide evidence that POC-DOC transformations are a key process, occurring at rates which could represent at least ~10% of the fluvial carbon sink. To enable informed catchment management further research is needed to produce carbon cycle models more applicable to these environments, and on the implications of high POC levels for DOC composition. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Inland water bodies are a crucial link in the global carbon cycle receiving imports of terrestrial organic carbon produced in the biosphere during photosynthesis (Battin et al., 2008; Worrall et al., 2012) and inorganic carbon from lithosphere bedrock weathering (Raymond and Cole, 2003). Carbon exported to fluvial systems can remain in the hydrosphere through transport to the oceans, or be subject to further ⁎ Corresponding author. E-mail address: [email protected] (A.G. Stimson).

cycling and export to the lithosphere or atmosphere via sedimentation or gas release. Whilst it is recognised that understanding of these processes is necessary to understand the fate of anthropogenic carbon dioxide (CO2) emissions (Battin et al., 2009; Cole et al., 2007), the landocean aquatic continuum (LOAC) remains poorly constrained in recent global carbon budgets i.e. Le Quere et al. (2015) and is not included in Intergovernmental Panel on Climate Change (IPCC) or Global Carbon Project models (Regnier et al., 2013). Reservoirs are anthropogenic aquatic systems recognised as important sites of fluvial carbon cycling (Tranvik et al., 2009; Williamson et al., 2009), with processes likely to be affected by the trapping of

http://dx.doi.org/10.1016/j.scitotenv.2016.11.193 0048-9697/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

sediment behind dams and longer water residence times compared to streams or rivers (Mulholland and Elwood, 1982). Reservoirs in peatland catchments are expected to be particularly important as peat soils act as a highly concentrated store of biosphere produced carbon (Limpens et al., 2008), which may be delivered to downstream fluvial systems. Fluvial carbon export from peatlands is mainly organic; predominantly particulate organic carbon (POC) and dissolved organic carbon (DOC) although CO2 and methane (CH4) are also transported via aquatic pathways (Dinsmore et al., 2010; Worrall et al., 2009) and relative proportions may change as a result of downstream carbon transformations. Fluvial carbon entering reservoirs is subject to carbon burial in sediments (i.e. Fenner and Freeman, 2013; Ferland et al., 2014), gaseous exchange of carbon at the reservoir surface (Barros et al., 2011; St. Louis et al., 2000; Tadonléké et al., 2012), or downstream fluvial export. Within reservoirs carbon transformations between solid dissolved and gaseous phases may occur as a result of microbial, photo-mediated and physical processes (Amado et al., 2015; Benner and Kaiser, 2010; Estapa and Mayer, 2010; Koelmans and Prevo, 2003; Marín-Spiotta et al., 2014; Obernosterer and Benner, 2004). DOC may also be biologically produced in situ within the water column (autochthonous primary production), a process which utilises carbon from the atmosphere. However, this is generally small in oligotrophic humic water bodies (Algesten et al., 2004; Jansson et al., 2000). Reservoirs act as a significant trap of particulate material and globally are estimated to bury organic carbon in sediments at a rate of 160 Tg yr−1 (Dean and Gorham, 1998), with export rates of particulate organic carbon (POC) to fluvial systems primarily driven by erosion (Galy et al., 2015). However, it is difficult to gain a clear picture of the role of POC in reservoir carbon cycling as current reservoir carbon budgets i.e. (Åberg et al., 2004; Huttunen et al., 2002), in common with those from lakes in temperate peatland catchments (Carpenter et al., 1983; Jonsson et al., 2001; Kokic et al., 2014), are from low erosion systems with limited POC inputs. Additionally there is a need to provide reservoir carbon budgets with a higher temporal resolution, as current global estimates of C fluxes are often based on snapshot sampling, which does not consider variation within and between years (Knoll et al., 2013). Reservoirs built to supply drinking water to nearby urban populations are found in many peatland catchments in the UK uplands (Evans et al., 2000; Yeloff et al., 2005). Many of these are in POC rich environments where organic sediment loads may reach ~200 t km2 yr−1 (Evans et al., 2006), as a significant proportion of UK upland peatland has become degraded or is actively eroding (Stevenson et al., 1990; Tallis, 1997). The water companies involved are concerned with both reservoir sedimentation (DETR, 2001) and the high cost of DOC removal during treatment (Worrall et al., 2004) and seek to manage catchments to reduce these problems (United Utilities, 2011). However POC-DOC interactions in these reservoirs remain poorly understood, with no indepth budget studies considering both these carbon species. This paper presents data from an intensive two-year study of the Kinder reservoir located in a degraded upland peatland catchment in the South Pennines, UK. The budget study aims to quantify the major fluvial carbon inputs and outputs for the Kinder reservoir, and considers DOC, aqueous CO2 (CO2(aq)) and POC. Also included are selected 14C (radiocarbon) dated DOC and POC samples as breakdown of (old) POC from eroded peatlands may affect DOC age (i.e. Butman et al., 2015; Marwick et al., 2015). The study aims through estimation of the fluvial carbon balance, the magnitude of fluxes, and carbon age, to estimate the magnitude of carbon cycling in these environments and to infer key processes. Implications of climate change and for peatland management and water treatment are considered in the light of these findings. 2. Methodology This paper presents fluvial organic carbon budgets for Kinder reservoir in the years 2012 and 2013, detailing fluxes of DOC, CO2(aq) and POC entering and leaving the reservoir, calculated from discharge and

concentration measurements. Additionally, the fate of fluvial carbon remaining in the reservoir is modelled using estimates of burial efficiency. Carbon age data, is also presented for two occasions in 2013 and 2014 when samples were 14C dated. The study was also conducted alongside work to understand nitrogen dynamics (see Edokpa et al. 2015, 2016). 2.1. Study area and sampling sites Kinder reservoir is a water supply reservoir situated in the South Pennines within the Peak District National Park, UK. Six sampling sites were used (Fig. 1). Of these, all except R2 were used to construct the budgets and all except KROout were used for 14C dating. Reservoir inlet samples were taken from the three main feeder streams; William Clough (WCin), Kinder River (KRin), and Broad Clough (BCin). Sampling took place adjacent to monitoring equipment (water height loggers and automatic water samplers), installed at the nearest practical location to where each stream entered the reservoir. Reservoir outlet samples were taken from the bottom valve house (VHout), and at the outlet of the Kinder River (KROout). Water is abstracted from beneath the surface of the reservoir via a draw-off tower then either channelled along a pipe to a nearby treatment works, or released back into the outlet river to maintain a legal minimum flow. Sample VHout represents abstracted water destined for the treatment works, whilst sample KROout represents the released abstracted water, plus overflow water if the reservoir is full. Sample R2 represents reservoir water, and was taken at the shoreline at surface level. Further details are the reservoir and catchment area are shown (Table 1). 2.2. Discharge 2.2.1. Measurement regime At the three inlets water height (stage) was recorded using a tru-track WT-HR 1000 data logger, which measured water level at 15 minute intervals. Stage figures were adjusted for drift through the use of fortnightly manual stage board measurements and comparison of baseflow conditions across the monitoring period (Stimson, 2015). Discharge was recorded at a variety of different stage levels using the velocity area method (Herschy, 1993) (Fig. A-2). Rating relationships were established to best describe the stage/discharge relationship (Shaw et al., 2010). The full (adjusted) stage dataset was then converted into estimated discharge. Outflow discharges were provided by United Utilities at daily intervals based on stage measurements from engineered channels with known stage discharge relationships. Discharge levels at the three inlets are closely linked to rainfall events. Discharge at outlet VHout is a set amount controlled by the reservoir operator; the same applies to KROout except when the reservoir reaches overflow level and becomes sensitive to storm events. United Utilities also provided data for the reservoir top water level and corresponding capacity, which were used to calculate changes in the reservoir water volume over time. 2.2.2. Calibrating the reservoir water balance Discharge is often the main driver of overall flux, therefore accurate estimation of discharge and a balanced water budget fully accounting for all flows, is essential to produce reliable estimates (Gibson, 2009). For the Kinder reservoir, reconciliation of measured inflows with outflows was performed as described below. This method is also employed for the Kinder reservoir in Edokpa et al. (2016) for the time period covering December 2012 to November 2013. Continuous stage data for the three inlet streams were recorded from 12/01/2012 to 16/12/2013. As the reservoir was at top water level at the start and end of this period it was assumed that: Q in þ R ¼ Q out þ E

ðiÞ

In Eq. (i) Qin and Qout represent total input and output discharges, E evaporation and R direct rainfall onto the reservoir surface. Evaporation

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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Fig. 1. Study area — Kinder reservoir catchment. The 30 m dam is located at SK 055881 53.390N −1.918E (WGS 84) at an altitude of 279 m and was built in 1912. Sampling site codes are explained further in the text.

was calculated on a monthly basis using Eq. (ii), based on a variation of the Penman method outlined by (Linacre, 1977): 700T m

Table 1 Characteristics and climate of the Kinder catchment and reservoir morphometry. BCin KRin WCin Total reservoir catchment Area (km2) Reservoir Mean depth (m) morphometry/characteristics Capacity (M m3) Mean annual temperature °C Catchment climate Mean annual rainfall (mm) Land areas (km2/% blanket peat)

Catchment characteristics

1.8/20 3.9/52 2.0/11 8.5/31 0.18 12.8 2.3 8.6 1227

Bog on upland plateau, slopes mostly acid grassland with smaller areas of heather and Vegetation woodland. Small areas of grassland (some improved) near reservoir. Carboniferous interbedded sand and Geology mudstones (millstone grit series). Blanket bog of 2–4 m depth with areas of significant gullying and exposed soil. Peatland degradation/restoration Current restoration efforts focused on revegetation (see also Edokpa et al., 2015).

E0 ¼


ð100−AÞþ15 ðT−T d Þ

80−T

  −1 mm day

ðiiÞ

where Tm = T + 0.006h, h is the elevation (m), T is the mean temperature, A is the latitude (degrees) and Td is the mean dew point. In this study the outflow measurements were deemed to have the least uncertainty, as engineered weirs have been extensively studied (Ferro, 2012) and are frequently used where highly accurate discharge measurements are required (Bagheri and Heidarpour, 2010). Values generated for Eq. (i) were 30% higher for Qin + R (22.9 × 106 m3) than Qout + E (17.6 × 106 m3). Therefore it was decided to apply a calibration multiplier (C), as shown in Eq. (iii), to match the inflow to the outflow data. This was applied to the Qin data to calculate the adjusted input discharge (Qinadj) on a monthly basis: Q inadj ¼ Q in C

ðiiiÞ

Over these time periods it was also necessary to take account of emptying or filling of the reservoir. Therefore the monthly calibration factor was calculated using Eq. (iv), where D represents the monthly change in-reservoir storage: Q in C ¼ Q out þ E−D þ R

ðivÞ

The calibration factor was applied proportionally to the discharge of each input stream, so that on a monthly basis, the proportions of the total input discharge accounted for by each stream remained the same. The monthly breakdown of the corrected water balance is detailed (Table A-1).

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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2.3. Carbon concentration and age and water chemistry 2.3.1. Water sampling regime To provide the most reliable fluvial carbon flux estimates a water sampling regime was designed to capture both annual trends and high discharge events. Routine water sampling took place at approximately fortnightly intervals throughout the monitoring period. This was supplemented with intensive sampling during high discharge events at the inlet sites. There can often be an exponential relationship between concentration and discharge (especially for POC), so sampling regimes which do not specifically seek to capture storm flow events may produce flux underestimates (Pawson, 2008). High discharge sampling took place throughout the year using automatic water samplers, which were set to begin a programme of high frequency sampling, when triggered by a float switch installed at a level judged to represent storm flow. The high frequency sampling regime was designed to capture events where discharge peaks rapidly and consisted of a total of 24 samples taken at variable intervals (12 × 15 min, 6 × 30 min and 6 × 1 h). 2.3.2. DOC Water samples were filtered on site shortly after collection using 0.45 μm glass microfibre syringe filters. Samples were then analysed for DOC using a Shimadzu TOC analyser and for light absorbance using a Hach DR 5000 spectrophotometer. DOC data were unavailable for water samples taken up to 8/10/12. As a result, DOC for these samples was calculated from absorbance at 400 nm, based on the Abs400/DOC relationship of samples taken after this date. One single correction model was used for all sites, as although land use and soil type can result in differences in carbon composition (i.e. Worrall et al., 2012), plots showed the regression line provided a good fit to the data for all 5 sites separately and for inflows and outflows combined (Table A-2 and Fig. A-1). The majority of samples were analysed within three weeks for DOC, and two weeks for absorbance, with samples stored in the dark below 4 °C in the intervening time, to minimise any losses through DOC consumption. 2.3.3. CO2(aq) and pH To calculate (CO2(aq)) dissolved inorganic carbon (DIC) and pH were measured for all water samples. DIC was measured for all samples using a Shimadzu TOC analyser as per Section 2.3.2 and pH was recorded on the day of sampling using a Hanna instruments pH probe. CO2(aq) was calculated using standard equilibrium constants (Stumm and Morgan, 1996) and field measurements of pH and temperature. It was assumed that atmospheric partial pressure of CO2 was 350 ppm and, given the geology and the low Ca2+ concentration (typically b 5 mg/l) that there was no carbonate in the system. 2.3.4. POC Samples of a known volume of water were filtered through glass microfibre GF/C filter papers (approximate pore size of 1.2 μm), using vacuum filtration. The filter papers were then oven dried overnight at 40 °C. Papers were weighed prior to filtration and following drying to a precision of 0.1 mg, to establish the weight of filtered material. This was then related to the volume of filtered water to establish the suspended sediment concentration (SSC) in mg/l. Eq. (v) shows the SSC to POC conversion. POC ¼ SSC%org 0:5:

ðvÞ

In Eq. (v) %org represents the mean percentage of organic material. This was calculated separately for each site using the loss on ignition method (Heiri et al., 2001). For each site %org was calculated from a selection of sub samples representing the range of concentrations, which were placed in a furnace at 550 °C for 4 h, to calculate the loss of organic mass. In all cases carbon was assumed to represent 50% of the organic material (Pawson et al., 2012).

2.3.5. 14C dating Samples for 14C dating of DOC and POC were collected in acid washed 500 ml plastic bottles. Using data collected from monitoring of catchment hydrology and carbon concentrations, sufficient quantities of water were collected to ensure 5 mg of carbon was available for both DOC and POC dating. As funding limited the number of 14C analysis possible, 14C POC dates were not taken for the outflow sites as POC here was negligible. 14 C DOC age was only determined for outflow site VHout, but was also measured at the reservoir surface at site R2. Water was filtered using 1.2 μm GF/C filters with the filtrate and filter papers containing particulate material used for DOC and POC dating respectively. Samples were managed in a 14C tracer-free laboratory and then sent for analysis to the Scottish Universities Environmental Research Centre (SUERC) AMS Laboratory, East Kilbride (Xu et al., 2004) For further details on the sample preparation and AMS analysis performed at SUERC see Adams et al. (2015). Samples of DOC and POC were taken for 14C dating in September 2013 and May 2014, with a second sampling date required as POC dating was not possible for the 2013 samples. This work was supported by the NERC Radiocarbon Facility NRCF010001 (allocation number 1657.1012). 2.4. Flux estimation 2.4.1. Budget creation Fluxes were calculated for the two full calendar years of 2012 and 2013 and additionally DOC and CO2(aq) were calculated separately for each of the 24 months covered by the study period. Appropriate multipliers were applied to the annual, January 2012 and December 2013 monthly fluxes to take account of the hydrological monitoring period (see Section 2.2.2). Figures presented for total inflow are the sum of the three inflow streams plus a small additional amount due to rainfall for DOC and CO2(aq). Figures presented for the individual catchments were adjusted to take account of the small area of the reservoir catchment not drained by these streams (Table 1). The assumption is that that the carbon concentrations in the small unaccounted areas (totalling 8% of the catchment) are represented by the weighted average of values on the three input streams (GIS analysis indicated these areas were evenly distributed). 2.4.2. DOC and CO2(aq) With the exception of rainfall all fluxes of DOC and CO2(aq) for the reservoir inlets were calculated using the commonly applied interpolation/ratio “method 5” (Littlewood, 1995). An extrapolation approach also using the high frequency storm samples was also tried for DOC but abandoned due to poor correlations. Carbon inputs due to rainfall were calculated by multiplying rainfall water volume by concentrations based on measurements of average rainfall concentrations in the catchment (5 mg/l DOC/0.3 mg/l DIC). “Method 5” is frequently used to create flux estimates and is the method of choice for the European Harmonised Monitoring Scheme (i.e. PARCOM, 1992). The method calculates flux for the interval of discharge measurement by multiplying mean discharge by a flow weighted concentration. 2 Xn L ¼ K 4Q Xi¼1 n

CiQ i

Q i¼1 i

3 5

ðviÞ

Eq. (vi) shows “method 5” where L represents the total flux (load), Ci the concentration (mg/l) for each sample, Qi the volumetric discharge for the time unit of discharge measurement matching with a given sample, Q the average discharge (m3/s) per time unit of discharge measurement for the period, n the number of samples, and K a conversion factor to take account of the period of record and adjust for the desired units. In this study “method 5” was only applied to data collected from routine sampling as the method assumes a regular sampling regime. A study based on flow records from the River Dee, (Worrall et al., 2013), showed that “method 5” when employed to DOC data sampled at 14 day intervals produced estimates very close to that based on hourly

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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data. The flux calculations performed here are based on similar DOC concentrations to that study. Standard Error of the load (SE(L)) for fluxes calculated using “method 5” was calculated using Eq. (vii), following the method used in (Hope et al., 1997). SEðLÞ ¼ F

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  ffi var C f

ðviiÞ

where F is the total annual discharge (known with negligible error), and var Cf is the variance of the flow-weighted mean concentration. If the instantaneous flow measurements taken at the time of sampling are regarded as fixed weightings, then var(Cf) is estimated as shown in Eq. (viii).     2 Q i Q2 var C f ¼ ∑ C i −C f ∑ i2 Qn Qn

ðviiiÞ

where Ci is the instantaneous concentration associated with individual samples, Cf is the flow weighted mean concentration, Qi is the instantaneous discharge at the time of sampling and Qn =∑Qi. For calculation of errors for reservoir inputs, outputs and the overall carbon balance, errors were combined following the method shown in Eq. (ix). sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Total SEðLÞ ¼

n

∑ SEðLÞ2i i¼1

ðixÞ

2.4.3. POC Fluxes of POC for the three inflow sites were calculated following an extrapolation method. SSC and POC concentration (unlike DOC) often correlates well with discharge, and where this is the case flux estimates based on regression modelling have been found to be the most appropriate (i.e. Pawson, 2008). In this study SSC flux was estimated separately for each time unit of discharge measurement, based on least squares regression. n X

QM i ½aQMi þ b

upper (POCmax), error limits of the likely POC flux. This represents a conservative approach to error as the models predict a broad range of values. Calculations for POC outflow fluxes were performed using “method 4” (Littlewood, 1995) which is mean sample concentration multiplied by mean discharge for the period, shown in Eq. (xii).

L¼K

" # n X Ci i¼1

n

ðxiiÞ

Q

This method was selected as concentrations were low and no relationship with discharge could be established. 2.4.4. Modelling carbon burial and gaseous carbon export Carbon in the reservoir water body not exported via fluvial pathways, will be subject to burial in sediments, or gas release as CO2 or CH4. As neither of these processes is directly measured in this study, a model used by Blair and Aller (2012), which relates SSC and organic carbon burial efficiency (OCBE) is employed. An upper and lower estimate of carbon burial is estimated based on an OCBE range of 50–90%. This is the approximate range of carbon burial observed by Blair and Aller (2012), from the sediment volumes generated from small mountain rivers (SMRs). Unburied carbon is assumed to equal gaseous carbon export. 3. Results

Errors were converted into 95% confidence intervals by multiplying by 1.96.

L¼K

5

ðxÞ

3.1. Annual fluvial carbon budgets and retained carbon fate Fluvial carbon budgets are shown for the Kinder reservoir in 2012 and 2013 (Table 2). POC represents the greatest carbon input to the reservoir, and is at least 93% greater than DOC, which is in turn is at least double the CO2(aq) flux. The Kinder River catchment accounts for 50% of the area of the three input streams combined and drains the largest area of blanket peat. The corresponding input site KRin accounts for at least 42% of the input DOC, and 51% of the input POC flux. By contrast BCin which has the greatest bias towards mineral soil types has a CO2(aq) flux as least twice that of KRin

i¼1

In Eq. (x) QMi represents each 15 minute unit of measured discharge and a the slope and b the intercept of the best fit line on a Qi/ log Ci or a Q i/Ci (Q i/Ci as per Eq. (v)) plot. These two regression plots based on exponential and linear fits were constructed to calculate flux for each site using combined data from routine and high frequency samples. Additionally to account for hysteresis SSC was matched against discharge with a time lag which produced the best model fit. Key characteristics of the regression models can be seen in the appendix (Table A-2). Where fluxes were calculated based on Q/logC relationships, a multiplier was applied using the smearing estimator (Duan, 1983), shown in Eq. (xi), where ei represents the residuals. n 1X expðei Þ n i¼1

ðxiÞ

As noted by Pawson et al. (2012), the exponential equations produced the best model fit. However, at high discharges these models also produced unrealistically high concentrations so it was necessary to modify the equation to include a cut-off which set the maximum SSC concentration to a value which converted to 1200 mg/l POC (after smearing where applicable. This value was selected as it is very close to the maximum concentration recorded in Pawson et al. (2012), where an extensive study was performed in a nearby comparable fluvial system. To produce final POC fluxes from these SSC flux calculations, Eq. (v) was applied. In the final results he POC inflow fluxes generated from both linear and exponential regression, are used to represent lower (POCmin) and

Table 2 Annual DOC, DIC and POC budgets for 2012 and 2013. Flux t C yr−1 Sitea

BCin KRin WCin Rainin VHout KROout IN OUT BALANCE +/− BCin KRin Rainin WCin VHout KROout IN OUT BALANCE +/−

Year

2012

2013

DOC

DOC

Load

95% CI

22 48 10 1.3 45 27

CO2(aq)

POCMin

POCMax

Load

95% CI

Load

Load

Total fluvial carbonb

2 8 1 – 2 2

6 3 1 0.1 9 6

1 1 b1 – 1 1

8 135 13 – 10 4

32 196 14 – 10 4

48 217 24 – 64 37

89 72 −17 13 15 0.8 3 31 9

9 3 10 1 1 – b1 1 b1

11 15 4 11 4 b0.1 1 7 3

2 2 2 1 b1 – b1 1 b1

171 14 −157 11 91 – 5 9 2

265 14 −251 173 224 – 3 9 2

318 101 −217 116 176 – 9 47 13

35 40 5

2 1 2

17 9 −8

1 1 1

118 11 −107

439 11 −428

330 60 −270

a Input catchment (BCin, KRin, WCin) figures have been multiplied by 0.91 to take account of unaccounted for catchment area (Table 1). IN = rainin plus sum of BCin, KRin and WCin before adjustment, OUT = VHout + KROout, BALANCE +/− = OUT − IN. b Total fluvial carbon = DOC + CO2(aq) + POC (average of min and max).

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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Table 3 Estimates of carbon burial and gaseous carbon export based on organic carbon burial efficiency (OCBE) estimates. Units are t C yr−1 unless stated otherwise. Reservoir carbon sink OCBE (%)

90 50 a b c

Year (AD) 2012 2013 2012 2013

Modelled atmospheric C export a

CO2(aq)

DOC + POC

−4 8 −4 8

221 262 221 262

Buried OC 199 236 111 131

Totalb

(mg C m−2 d−1)c

18 34 107 139

277 520 1623 2116

CO2(aq) export 15 9 15 9

Inverse of total fluvial carbon balance (Table 2) minus CO2(aq) balance. DOC + POC minus buried OC plus CO2(aq) sink. Total modelled atmospheric CO2 export converted to mg then divided by reservoir surface area of 180,000 m2 and 365 days.

Combining all three measured fluvial carbon species shows that fluvial carbon is retained by the reservoir in both years. The retention of fluvial carbon is largely driven by POC, where flux values in both years show at least 91% of the inputs do not leave as reservoir outputs. Interestingly the data show inputs and outputs of DOC and CO2(aq) to be more closely balanced, with the reservoir acting as a DOC sink in 2012 and a DOC source in 2013, with the reverse applying to CO2(aq). The differences between these four balance calculations are greater than the standard error of the load, as the 95% confidence levels do not overlap zero, suggesting significant inter annual variability, and that the reservoir acts as both a producer and consumer of DOC and CO2(aq). Modelled rates of carbon burial and atmospheric C export for the Kinder reservoir are shown (Table 3). OC burial rates vary between 111 and 236 t C yr−1, whilst atmospheric C export and varies between 18 and 139 t C yr−1 or 277 and 2116 mg C m−2 d−1. Variation is largely driven by OCBE which is proportionally more variable than the fluvial carbon sink between years.

3.2. Monthly DOC and CO2(aq) budgets and weather patterns. To explore the variable trends in the reservoir DOC and CO2(aq) balance further this section considers DOC and CO2(aq) budgets calculated on a monthly basis using “method 5”. The reservoir DOC and CO2(aq) balance is shown in a graphical form (Fig. 2a) Additionally “method 5” flow weighted concentrations (Cf) for DOC and CO2(aq) reservoir inputs and outputs are shown (Fig. 2b). The 24 monthly reservoir DOC and CO2(aq) budgets covered by the study period show a mixture of positive and negative balances for both DOC and CO2(aq) and in keeping with the annual results the two show the opposite tendency for 17 of 24 months. For the DOC monthly budgets, 15 have a positive DOC balance, one is very close to zero, and a further two have a negative balance of less than 1 tonne of carbon. Further evidence that these monthly balances represent both production and consumption of DOC can be seen from the Cf values (Fig. 2b). As these produce the monthly balances, they also have similar average values over the 24 month period (5.7 mg/l inputs, 6.0 mg/l outputs). These similar values suggest the monthly patterns in DOC balance could just be a product of a time delay in DOC passing through the reservoir. However two arguments can be made against this. Firstly the Cf values show no consistent lag of outflows behind inflows and secondly the data suggest that the reservoir water was replaced at least every three months1 meaning any delay would only be possible over a time period shorter than this turnover interval. To consider possible errors for the DOC estimates a conservative modelling approach was performed (Fig. 2c). The two error scenarios shown were produced through a two stage process. First the approach employed in Edokpa et al. (2016) where all of the overestimate in the fluvial inlet discharge is assumed to come from one stream was applied on a monthly basis. 1 Based upon minimum 3 month outflow of 1.6 M m3, which is comparable with capacity over the same period calculated based on original capacity of 2.3 M m3, minus loss of capacity due to drawdown (23%) and to sedimentation (10%).

In the second part a further flux subtraction and addition respectively was made to the two Q error models which produced the lowest and highest input DOC flux respectively (those assuming all Q estimation error at sites KRin and WCout in turn). The DOC error was calculated based on one standard error of the Abs400/DOC regression line. This conservative approach maximised likely errors by combining uncertainty in the water balance and the Abs400/DOC model, and showed that even when errors were maximised the status of the reservoir as a DOC sink or source remained largely unchanged. Monthly variation in flow weighted mean water temperature measured at the reservoir inlets, used as a proxy for reservoir water temperature is shown (Fig. 2d). A seasonal pattern is apparent with higher temperatures seen between May and October in both years. These can be compared to the monthly balances to see that higher temperatures generally correspond to negative DOC balances (Fig. 2a). This trend is particularly apparent during the period June–September 2012. However DOC production occurs across the full range of temperatures. The monthly variation in catchment rainfall is shown alongside reservoir levels presented as reservoir drawdown on a scale between 0% (full reservoir) and 100% (empty reservoir) (Fig. 2e). Rainfall totals (recorded at a site adjacent to the reservoir dam) for 2012 and 2013 were 1678 and 1048 mm respectively, and this is reflected in monthly totals for 2013 which are below those of 2012 for all months except February and May. The low rainfall in 2013 also corresponds with an eight month period of drawdown, including five months where the drawdown is between 20% and 30%. As differences in the pattern of DOC and CO2(aq) carbon balances between 2012 and 2013 also corresponded to differences in rainfall, regression analysis was performed to test for a relationship between inflow and outflow Cf values of DOC and CO2(aq) and rainfall (Table 4). The regression was also performed with the monthly data divided by temperature using 10 °C as a cut off, as in both years temperatures above 10 °C were only seen in (all) months between June and September. Results of the analysis showed that rainfall was a good predictor of both DOC and CO2(aq) input Cf values, with the relationship strongest for CO2(aq) and improved considerably for both using the high temperature values. At the outflows there was no relationship for CO2(aq) and a weaker relationship to DOC. For DOC the relationships showed associations between higher concentrations and rainfall, whilst CO2(aq) showed the opposite pattern. 3.3. Carbon age 14 C dating results from the two sampling dates in 2013 and 2014 are shown (Table 5). Two main trends can be seen in these data. Firstly, on both sampling dates the 14C age of DOC at site VHout is greater than that at any of the three input sites (14C age in years BP at site VHout is greater by 647, 1471 and 1164; and 323, 1147, and 1301, for the 2013 and 2014 samples for sites KRin, WCin and BCin respectively). These data suggest a substantial rise in 14C DOC age through the reservoir, with the total difference between inputs and outputs likely to be in the range of 500–1000 years BP. Secondly when POC and DOC were measured on the same occasion and location, POC ages are substantially greater, by 2024, 977 and 1225 years BP for sites KRin, WCin and BCin respectively.

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

7

Fig. 2. Monthly variation in a) reservoir DOC and DIC balance, b) monthly flow weighted DOC and DIC concentration (Cf) for reservoir input and outputs calculated using “method 5” b), c) reservoir DOC balance with an upper and lower maximum error scenario, d) mean water temperature from the reservoir inlets and e) reservoir level and total monthly rainfall.

Although outflow waters were not dated for site KROout, the reservoir surface water (site R2) is older than or close equal to site VHout in 14C age. The implications of this are that the age of dissolved carbon increases through the reservoir system. 4. Discussion The results from this study show two important findings. Firstly that the reservoir is a net fluvial carbon sink, with the potential to produce significant quantities of atmospheric carbon and secondly that this POC rich reservoir shifts between a source and sink of DOC. These areas are further explored below along with the implications of the study for reservoir management and future research.

burial efficiency and rates of gas release from the trapped POC merits further discussion. SSC inputs rates to the reservoir are between 0.32 and 0.41 g cm2 yr−1 for 2012 and 2013 respectively. This is similar to sediment delivery rates observed by Blair and Aller (2012) from SMRs and so supports the use of the 50–90% OCBE estimate (see Section 2.4.4). It is also useful to make comparisons with measured CO2/CH4 values from other reservoirs. For CO2 a global study of hydroelectric reservoirs

Table 4 R2 values of correlations between reservoir monthly input and output Cf values and rainfall, with data filtered by monthly temperature where indicated. R2 value

4.1. Carbon burial and gaseous carbon release The reservoir is shown to be a significant sink of fluvial carbon, driven by its role as a trap for POC. This is largely to be expected given previous studies which report capacity loss estimates of 0.11% yr−1 as the mean rate for 95 South Pennine reservoirs (DETR, 2001) and of 15% in total for the Kinder reservoir itself (Shotbolt et al., 2001). However the

Carbon species

DOC

DIC

Data used All tN tb All tN tb

10 °C 10 °C 10 °C 10 °C

Inlets

Outlets

0.31 0.58 0.32 0.54 0.8 0.62

0.25 0.09 0.28 0.01 b0.01 b0.01

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

Table 5 14 C dating results for the study sites from 06/09/13 and 12/05/14.

Site

Type Date

Conventional 14 C enrichment 14 δ13C C age Code VPDB‰ (SUERC) % Years +/−1σ ± 0.1 +/−1σ BP modern

KRin WCin BCin R2 VHout fKRin WCin BCin R2 VHout KRin WCin BCin

DOC DOC DOC DOC DOC DOC DOC DOC DOC DOC POC POC POC

50399 50401 50405 50406 50407 54802 54803 54804 54805 54806 54373 54374 54375

06/09/13 06/09/13 06/09/13 06/09/13 06/09/13 12/05/14 12/05/14 12/05/14 12/05/14 12/05/14 12/05/14 12/05/14 12/05/14

89 99 95 67 83 88 97 99 84 84 68 86 85

0.41 0.43 0.44 0.31 0.36 0.40 0.45 0.45 0.38 0.39 0.30 0.37 0.37

895 71 378 3198 1542 1069 245 91 1400 1392 3093 1222 1316

37 35 37 37 25 37 37 37 37 37 35 35 35

−27.5 −28.4 −29.3 −29.0 −27.4 −27.7 −29.1 −28.7 −29.6 −29.0 −26.9 −27.2 −28.4

(Barros et al., 2011) gives the mean measured value for temperate reservoirs as ~ 250 mg C m−2 d− 1. For reservoirs which have flooded peatlands, CO2 values convert to 1070 and 1972 mg C m−2 d−1 from two years of measurements at the Lokka reservoir over 20 years after flooding (Huttunen et al., 2002), and are 4406, 2396 and 1333 mg C m−2 d−1 for the first, second and third years respectively following flooding for the Eastmain-1 reservoir (Teodoru et al., 2011). Values of CH4 release are given in the global study and for the Lokka reservoir and are approximately one and two orders of magnitude lower respectively than CO2 values. Overall, comparison with these measured values show a similar range to the estimated values for the Kinder reservoir, but make further refinement of the OCBE value difficult. 4.2. Reservoir carbon cycling and DOC production To the knowledge of the authors, this study represents the first time in-reservoir gains of fluvial DOC have been demonstrated in association with high POC inputs. It is therefore hypothesised that in-reservoir DOC production occurs as a result of the breakdown POC in the water body or bed sediments, a process which could also impact DOC composition. The 14 C dating evidence is also consistent with this hypothesis, as the substantial through reservoir aging of DOC could be explained by DOC production from older POC. The data in this study shows two years with very different rainfall rates which in turn is linked to reservoir input concentrations of DOC and CO2(aq) and reservoir levels. Input DOC concentrations are shown to correlate with both higher rainfall and temperature and this can be explained by flow pathways where run-off from DOC rich organic soils has a greater influence during high discharge events, combined with enhanced microbial activity in warmer soils resulting in greater DOC production. Additionally water routed through the peat matrix is likely to have a lower pH and therefore be less able to maintain inorganic carbon (CO2) in solution. This is likely to explain the inverse link between CO2(aq) concentrations and lower monthly rainfall. We suggest that the variability in the DOC balances can be largely accounted for by POC-DOC conversions dominated by physical processes. We hypothesise these are less subject to seasonal variability than microbial and photo mediated processes, which are linked to temperature (i.e. Raymond and Bauer, 2000) and levels of solar radiation (i.e. Morris and Hargreaves, 1997) respectively. This is illustrated in a conceptual model (Fig. 3), where the net effect of carbon cycling processes is a loss of DOC when warmer (Fig. 3a) and a gain when colder (Fig. 3b). The physical breakdown of POC in sediments through water turbulence is likely to occur at relatively constant rate throughout the year as is shown in the model. Laboratory mixing experiments (Koelmans and Prevo, 2003) suggest these processes are rapid (half of an initial 25 mg/l suspended sediment concentration converted to DOC in 0.5 days), and

may account for greater rates of DOC production than biological processes. Goulsbra et al. (2016) also found evidence of DOC production from POC during similar experiments. In the results presented here several positive balances are in excess of 2 t C month (Figure 2), if it is assumed that this represents a minimum rate of POC-DOC conversions then annual rates (~ 24 t C or 365 mg C m−2 d−1) could represent at least ~10% of the fluvial carbon sink (Table 2). The model does not offer an explanation for the in reservoir gains in DOC during summer 2013, however these could be explained in two ways. Firstly fluvial DOC inputs are lower. Secondly it is possible that rates of POC-DOC conversions could increase with lower reservoir levels through physical erosion of exposed shoreline sediments (i.e. Holliday et al., 2008), increased biological mineralisation of oxygenated sediments (i.e. Fenner and Freeman, 2013; Sobek et al., 2009) or photo breakdown with greater exposure of sediments to sunlight. The model makes two further assumptions 1) that POC-DOC flows are greater than in the opposite direction, with arrows representing net fluxes and 2) that autochthonous primary production of DOC does not drive the balances, as levels are estimated to be an order of magnitude below the monthly carbon balances at 0.12 t C per month and be subject to seasonal variability (i.e. Defore et al., 2016). Other data from inlet/outlet studies of water colour in similar UK reservoirs also support this model (Butcher et al., 1992; Pattinson et al., 1994). In these studies colour changes assumed to take place in reservoir, show reductions in summer/early autumn and the reverse at other times, and similar explanations of colour production from sediments, owing to disturbance or reservoir drawdown are proposed.

4.3. Implications and research needs This study suggests that reservoirs in peatland catchments that have accumulated large areas of organic sediments, act as hotspots for carbon transformations. The study demonstrates two major findings. Firstly that a reservoir in a degraded peatland catchment is a net fluvial carbon sink, with potential for significant greenhouse gas production. Secondly (and closely linked) there is evidence for in-reservoir DOC production, argued here to be driven by conversions of POC to DOC. This means that the reservoir is, in addition to storing POC acting as a site of POC turnover. DOC can be rapidly mineralised in fluvial systems so that the production of DOC from POC is a mechanism by which POC resulting from peat erosion is oxidised and eventually lost to the atmospheric carbon store. The findings from this study have implications for global carbon budgets, alongside more specific land management and water treatment concerns applicable in degraded peatland environments. That the reservoir is a net sink of fluvial carbon is in itself unsurprising given the high POC input rates. However the difficulty in constraining the CO2/CH4 release estimates also highlights the need for measurement and modelling of gas release in POC rich (and largely ice free) reservoirs, which additionally would help to validate the conceptual model proposed in Section 4.2. Catchment scale peatland restoration can help to reduce fluvial POC flux (Shuttleworth et al., 2015) and is currently underway in the Kinder reservoir catchment. However restoration will not impact legacy POC sediments and the role of these compared with fresh POC sediments in reservoir carbon cycling remains unclear. Future climate change is also important as it may increase peatland degradation (Clark et al., 2010) and increased temperatures may increase rates of carbon cycling (Barros et al., 2011; Gudasz et al., 2010). Additionally further research on water column DOC production in lentic environments would be useful to ascertain the effect on DOC composition and water treatability, and how the processes observed here could be effected by reservoir/lake size and thermal regime. 2 Estimate 1: 0.07 t C per month based on rate of 1.1 g C m2 of surface area for 84 day period (Jonsson et al., 2001), Table 4. Estimate 2: 0.07 t C per month, based on mean DOC concentration of 6 mg/l generating production rate of 4 g C L−1 d−1 based on (Jansson et al., 2000) Fig. 3, and assuming production occurs in top ¼ of lake.

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

9

Fig. 3. Conceptual model showing proposed mechanisms of temperature induced variation in reservoir DOC transformations for the Kinder reservoir. A layer of organic sediment is assumed to line the reservoir given observations from 2013 (c) and measured POC fluxes.

5. Conclusions • A reservoir draining an area of degraded peatland was shown to be an important hotspot of carbon cycling, with evidence of transformations between different carbon species. • The reservoir has high levels of POC influx is a net sink of fluvial carbon, and has potential for significant gaseous carbon export. • The budget, supported by 14C data, demonstrates seasonal DOC production in a POC rich environment, hypothesised to be as a result of production from bed/suspended sediments occurring at rates which could represent at least ~10% of the fluvial carbon sink. The mechanism is thought to be a combination of physical, biological and photo induced processes. There is evidence that the reservoir is a net producer of fluvial DOC during periods of lower precipitation and temperature. • Reservoirs are not passive stores of particulate carbon but sites where POC is mineralised and is a source of atmospheric carbon. Abbreviations DOC POC CO2 OCBE DIC CH4

dissolved organic carbon particulate organic carbon carbon dioxide organic carbon burial efficiency dissolved inorganic carbon methane

WC KR BC VH KRO 14 C LOAC IPCC SMRs

William Clough Kinder River Broad Clough bottom valve house outlet of the Kinder River radiocarbon land-ocean aquatic continuum Intergovernmental Panel on Climate Change small mountain rivers

Acknowledgements This work was part funded by United Utilities and the National Trust with in-kind support and advice from the Moors for the Future Partnership. Additionally, this work was supported by the NERC Radiocarbon Facility NRCF010001 (allocation number 1657.1012). From these partners we would particularly like to thank Dr. Mark Garnett at NRCF East Kilbride and reservoir manager Matthew Ethell for their support of the project. Thank you to all the people who helped with fieldwork including Alan Heath, Adrienne King, Andrew Harding, Roger Braithwaite and to Donald Edokpa for both field and laboratory assistance. Thank you to Jon Yarwood and John Moore at Manchester University Geography Laboratories for providing technical support. We also thank the anonymous reviewers for helpful comments which improved the manuscript.

Appendix A Table A-1 Water balance for Kinder Reservoir (2012–2013). Note: units are million m3 unless otherwise stated. Reservoir inlets Month and year Jan-12a Feb-12 Mar-12 Apr-12 May-12 Jun-12 Jul-12 Aug-12 Sep-12 Oct-12

Reservoir

Rainfall (mm) 173 73 31 190 62 214 142 147 148 140

BCin

KRin

WCin

Total

Direct rainfall

Evaporation

ΔReservoir volume

0.23 0.27 0.16 0.27 0.19 0.28 0.28 0.22 0.24 0.24

0.35 0.41 0.26 0.54 0.31 0.56 0.40 0.24 0.33 0.48

0.11 0.11 0.07 0.20 0.09 0.25 0.27 0.11 0.27 0.12

0.68 0.79 0.48 1.02 0.59 1.09 0.94 0.56 0.85 0.84

0.02 0.01 0.01 0.03 0.01 0.04 0.03 0.03 0.03 0.03

0.004 0.003 0.008 0.005 0.005 0.007 0.009 0.009 0.009 0.007

0.00 −0.01 −0.04 −0.01 0.04 −0.02 0.04 −0.10 0.01 0.08

Total IN

Total OUT

0.70 0.81 0.49 1.05 0.60 1.13 0.97 0.59 0.88 0.87

0.70 0.80 0.64 0.89 0.70 1.02 0.97 0.64 0.79 0.87

(continued on next page)

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

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A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

Table A-1 (continued) Reservoir inlets Month and year Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13a Total a

Reservoir

Rainfall (mm) 136 223 94 81 29 17 125 68 110 78 91 139 102 114 2726

BCin

KRin

WCin

Total

Direct rainfall

Evaporation

ΔReservoir volume

0.34 0.44 0.33 0.32 0.21 0.17 0.26 0.16 0.18 0.22 0.26 0.48 0.40 0.14 6.30

0.39 0.61 0.37 0.31 0.18 0.13 0.24 0.15 0.29 0.17 0.23 0.48 0.24 0.12 7.77

0.12 0.27 0.16 0.14 0.04 0.03 0.06 0.04 0.06 0.04 0.04 0.06 0.33 0.03 3.04

0.84 1.33 0.86 0.77 0.43 0.33 0.56 0.35 0.53 0.43 0.53 1.02 0.97 0.29 17.10

0.02 0.04 0.02 0.01 0.01 0.00 0.02 0.01 0.02 0.01 0.02 0.03 0.02 0.01 0.46

0.005 0.006 0.003 0.006 0.004 0.004 0.006 0.008 0.008 0.008 0.007 0.007 0.005 0.003 0.15

0.00 0.01 −0.01 0.01 −0.13 −0.21 −0.10 −0.05 −0.15 0.08 −0.13 0.14 0.54 0.01 0.00

Total IN

Total OUT

0.86 1.37 0.88 0.78 0.43 0.34 0.58 0.36 0.55 0.45 0.55 1.05 0.99 0.30 17.56

0.85 1.36 0.88 0.80 0.65 0.54 0.53 0.52 0.54 0.55 0.53 0.55 0.81 0.30 17.42

All figures for Jan 12 and Dec 13 represent the monitoring period only (12/01/2012 to 16/12/2013).

Table A-2 Key characteristics of the models used to calculate DOC from absorbance and SSC from discharge for POC load estimation. See also regression plots shown (Figs. A-2 and A-3). Site ALL BCin KRin WCin BCin KRin WCin

Method DOC POCmin POCmin POCmin POCmax POCmax POCmax

Numbera,b of samples

P value

R2 value

155 132 236 135 132 236 135

0.68 0.42 0.37 0.31 0.53 0.60 0.35

Intercept

Slope

b0.001 0.000 0.000 0.873 0.000 0.026 0.000

b0.001 0.000 0.000 0.000 0.000 0.000 0.000

Standard error

Equation (ax + b)

1.23 11.54 115.18 40.79 0.90 0.97 1.08

1.048548x + 2.384667 0.1994x − 16.9641 0.7629x − 128.1730 0.2707x − 0.8826 EXP((0.01906x) − 1.0181) EXP((0.01025x) + 0.3413) EXP((0.0079x) + 1.6524)

a POC samples comprised: [BCin] 27 routine and 105 high frequency (7 storms) [KRin] 29 routine and 207 high frequency (9 storms) [WCin] 26 routine and 109 high frequency (5 storms). b DOC was directly measured for 155 of a total of 253 routine DOC samples across the 5 inlet and outlet sites.

16 y = 1.048548x+ 2.384667 R² = 0.682441

14

DOC mg/l

12

10 8

Inflow samples

6

Outflow samples

4 2 0

DOC mg/l

0

16 14 12 10 8 6 4 2 0

2

4

6 8 abs 400 nm au m_1

10

y = 1.048548x + 2.384667 R² = 0.682441

12

BCin KRin WCin KROout VHout

0

2

4

6 8 abs 400 nm au m_1

10

12

Fig. A-1. Plots showing the absorbance at 400 nm vs DOC (mg/l) relationship used to construct the model used in this study to predict DOC for the Kinder reservoir sites when only colour data was available see Section 2.3.2. The regression model was generated from data from all 5 sites and is compared against inflow and outflow data (top) and single site data (bottom).

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

BCin

0.7 Measured discharge

Q (cumecs)

0.6

11

0.5 y = 0.003631x -1.160377 R² = 0.844556

0.4 0.3

0.2 0.1 0.0 100

200

300 400 Stage - drift corrected (mm)

KRin

0.7 Measured discharge

Q (cumecs)

0.6

500

0.5

0.4 y = 0.000499x -0.000000 R² = 1.000000

0.3

y = 0.001811x -0.241559 R² = 0.943807

0.2 0.1

0.0 0

100

200 300 400 Stage - drift corrected (mm)

600

WCin

0.7 Measured discharge

0.6 Q (cumecs)

500

0.5

y = 0.002731x -0.535718 R² = 1.000000

0.4

y = 0.004519e0.012762x R² = 0.773571

0.3

0.2 0.1 0.0 0

100

200 300 Stage - drift corrected (mm)

400

Fig. A-2. Stage discharge relationships applied to stage data from the reservoir inlet streams at sites KR, BC and WC. For site KR: a different conversion was applied to the values corresponding to a value below the lowest measured discharge to avoid negative discharge values. This assumed zero discharge = zero stage. For site WC: an exponential fit was used for stage values below 255, as it provided the best fit (R2), above this a linear fit based on a straight line to the highest measured point was used to avoid unrealistically high discharges for high stage values.

Please cite this article as: Stimson, A.G., et al., Reservoirs as hotspots of fluvial carbon cycling in peatland catchments, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.11.193

12

A.G. Stimson et al. / Science of the Total Environment xxx (2016) xxx–xxx

WCin (POCmin) 600

100

500 y = 0.199388x -16.964104 R² = 0.424091

80

SSC mg/l

SSC mg/l

BCin (POCmin) 120

60 40

y = 0.270689x -0.882613 R² = 0.307794

400 300 200 100

20 0

0

-20

-100 0

50

100

150

200

250

300

0

50

100

150

200

Qt+60

300

350

400

450

500

WCin (POCmax)

BCin (POCmax) 8

8 y = 0.019057x -1.018084 R² = 0.525820

6

Loge SSC mg/l

Loge SSC mg/l

250

Qt+75

4 2

6 4

0

0

-2

-2 0

50

100

150

200

250

300

y = 0.007993x + 1.652422 R² = 0.354039

2

0

200

300

400

500

Qt+75

Qt+60

Fig. A-3. Plots showing the discharge vs SSC concentration relationships that were used to predict POC fluxes into the Kinder reservoir from inflow stream BCin.

100

Fig. A-5. Plots showing the discharge vs SSC concentration relationships that were used to predict POC fluxes into the Kinder reservoir from inflow stream WCin.

References

Fig. A-4. Plots showing the discharge vs SSC concentration relationships that were used to predict POC fluxes into the Kinder reservoir from inflow stream KRin.

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