Reservoirs as hotspots of fluvial carbon cycling in peatland

catchments.

A.G. Stimsona, T.E.H. Allotta, S Boultb, M. G. Evansa

aUpland Environments Research Unit, School of Environment, Education and

Development, The University of Manchester, Oxford Road, M13 9PL, United Kingdom. bSchool of Earth, Atmospheric and Environmental Science, University of Manchester,

Oxford Road, Manchester, M13 9PL, UK.

Highlights

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 14C based evidence for in-reservoir DOC production from POC.

Links between in-reservoir DOC production, rainfall and temperature.

Understanding implications for carbon cycling and composition will aid

management.

Abstract

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 DOC, POC and 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

1

linked to rainfall and temperature) which provides evidence for transformations between

different carbon species. In particular, the budget data accompanied by 14C 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.

Keywords: Carbon cycle, Peatlands, Reservoirs, Lakes, POC, DOC

2

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 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

CO2 emissions (Battin et al., 2009; Cole et al., 2007), the land-ocean 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 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 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.

3

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 160Tg 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 km 2

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 in-depth 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, CO2(aq) and POC. Also

included are selected 14C 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.

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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 (Figure 1). Of these, all except

R2 were used to construct the budgets and all except KROout were used for 14C dating.

5

Figure 1: Study area - Kinder Reservoir Catchment. The 30m dam is located at SK 055881 53.390N -1.918E (WGS 84) at an altitude of 279m and was built in 1912 . Sampling site codes are explained further in the text.

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).

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Table 1: Characteristics and climate of the Kinder catchment and reservoir morphometry

Land areas(km2 / % blanket peat)

BCin 1.8 / 20KRin 3.9 / 52WCin 2.0 / 11

Total reservoir catchment 8.5 / 31

Reservoir morphometry/ characteristics

Area (km2) 0.18Mean depth (m) 12.8Capacity (M m3) 2.3

Catchment climate Mean annual temperature °C 8.6Mean annual rainfall (mm) 1227

Catchmentcharacteristics

Vegetation

Bog on upland plateau, slopes mostly acid grassland with smaller areas of heather and woodland. Small areas

of grassland (some improved) near reservoir.

GeologyCarboniferous interbedded

sand and mudstones (millstone grit series).

Peatland degradation / restoration

Blanket bog of 2-4m depth with areas of significant

gullying and exposed soil. Current restoration efforts

focused on revegetation (See also Edokpa et al., (2015).

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,

7

1993) (Figure 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¿+R=Qout+E (i)

In equation (i) Qin and Qout represent total input and output discharges, E evaporation and R direct rainfall onto the reservoir surface. Evaporation was calculated on a monthly basis

using equation (ii), based on a variation of the Penman method outlined by (Linacre,

1977):

8

E0=

700 T m

(100−A )+15(T−T d)80−T

(mm day−1 ) (ii)

Where Tm = T + 0.006h, h is the elevation (metres), 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

equation (i) were 30% higher for Q¿+R (22.9x106 m3) than Qout+E (17.6x106 m3).

Therefore it was decided to apply a calibration multiplier (C), as shown in equation (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:

Qinadj=Q¿C (iii)

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 equation (iv),

where D represents the monthly change in-reservoir storage:

Q¿C=Qout+E−D+R (iv)

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).

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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 x 15minutes, 6 x 30 minutes and 6 x 1 hour).

2.3.2. DOC

Water samples were filtered on site shortly after collection using 0.45µm 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 Figure A-1). The

majority of samples were analysed within three weeks for DOC, and two weeks for

10

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)) 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 350ppm and, given the geology and the low Ca2+ concentration (typically <5mg/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 1.2µm), 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. Equation (v) shows the SSC to POC conversion.

POC=SSC %org 0.5. (v)

In equation (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 four hours, to calculate the

loss of organic mass. In all cases carbon was assumed to represent 50% of the organic

material (Pawson et al., 2012).

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2.3.5. 14C dating

Samples for 14C dating of DOC and POC were collected in acid washed 500ml 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. 14C 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

12

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 (5mg/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.

L=K [Q ∑i=1

n

Ci Qi

∑i=1

n

Qi ](vi)

Equation (vi) shows “method 5” where L represents the total flux (load), C i 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.

13

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 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 equation (vii), following the method used in (Hope et al., 1997).

SE (L )=F√var (C f) (vii)

Where F is the total annual discharge (known with negligible error), and varC f 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 (C¿¿ f )¿ is estimated as shown in equation (viii).

var (C¿¿ f )=[∑ ( Ci−C f )2Qi

Qn]∑ Qi

2

Qn2 ¿

(viii)

Where C i is the instantaneous concentration associated with individual samples, C f 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 equation

(ix).

Total SE ( L )=√∑i=1

n

SE(L)i2 (ix)

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

2.4.3. POC

14

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.

L=K ∑i=1

n

QM i [aQM i+b ](x)

In equation (x) QM i 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 C i or a Qi /C i ( Qi /C i as per equation (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/log C

relationships, a multiplier was applied using the smearing estimator (Duan, 1983), shown

in equation (xi), where e i represents the residuals.

1n∑i=1

n

ex p (e i )(xi)

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 1200mg/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, equation (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 upper (POCmax), error limits of the

15

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

equation (xii).

L=K [∑i=1

n C i

n ]Q (xii)

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.

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3. Results

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

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).

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Table 2: Annual DOC, DIC and POC budgets for 2012 and 2013

Site Year Flux t C yr-1

DOC DOC CO2(aq) POCMin POCMaxTotal Fluvial Carbona

Load 95% CI Load 95% CI Load Load

BCin

2012

22 2 6 1 8 32 48KRin 48 8 3 1 135 196 217WCin 10 1 1 <1 13 14 24Rainin 1.3 - 0.1 - - - -VHout 45 2 9 1 10 10 64KROout 27 2 6 1 4 4 37IN 89 9 11 2 171 265 318OUT 72 3 15 2 14 14 101BALANCE +/- -17 10 4 2 -157 -251 -217

BCin

2013

13 1 11 1 11 173 116KRin 15 1 4 <1 91 224 176Rainin 0.8 - <0.1 - - - -WCin 3 <1 1 <1 5 3 9VHout 31 1 7 1 9 9 47KROout 9 <1 3 <1 2 2 13

IN 35 2 17 1 118 439 330OUT 40 1 9 1 11 11 60BALANCE +/- 5 2 -8 1 -107 -428 -270aInput catchment (BCin, KRin, WCin) figures have been multiplied by 0.91 to take account of unaccounted for catchment area (Table 1). IN = rain in plus sum of BCin, KRin and WCin before adjustment, OUT = VHout+KROout, BALANCE+/- = OUT-IN. bTotal fluvial carbon = DOC+CO2(aq)+POC (average of min and max).

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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 123 t C yr-1 or 102 and 684 t C km2 yr-1. Variation is

largely driven by OCBE which is proportionally more variable than the fluvial carbon sink

between years.

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.

OCBE (%)

Year (AD)

Reservoir carbon sink

Buried OC

Modelled atmospheric C export

CO2

(aq) export

CO2(aq)DOC

+ POCa

Totalb (mg C m-2 d-1)c

902012 -4 221 199 26 396 152013 8 262 236 18 279 9

502012 -4 221 111 114 1742 152013 8 262 131 123 1875 9

ainverse of total fluvial carbon balance (Table 2) minus CO2(aq) balancebDOC+POC minus buried OC minus CO2(aq) sinkctotal modelled atmospheric CO2 export converted to mg then divided by reservoir surface area of 180000 m2 and 365 days

19

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 (Figure 2a)

Additionally “method 5” flow weighted concentrations (Cf) for DOC and CO2(aq)

reservoir inputs and outputs are shown (Figure 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 (Figure 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 monthsa 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 (Figure 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.

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).aBased 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%).

20

Figure 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.

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.

21

Monthly variation in flow weighted mean water temperature measured at the reservoir

inlets, used as a proxy for reservoir water temperature is shown (Figure 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 (Figure 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) (Figure 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).

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

Carbon species Data used R2 value

Inlets Outlets

DOCAll 0.31 0.25t > 10°C 0.58 0.09t < 10°C 0.32 0.28

DICAll 0.54 0.01t > 10°C 0.8 <0.01t < 10°C 0.62 <0.01

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

22

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.

23

3.3. Carbon age

14C dating results from the two sampling dates in 2013 and 2014 are shown (Table 5).

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

Site Type DateCode(SUERC)

14C enrichmentConventional14C age

δ13CVPDB‰ ± 0.1

% Modern +/-1σ Years

BP +/-1σ

KRin DOC 06/09/13 50399 89 0.41 895 37 -27.5WCin DOC 06/09/13 50401 99 0.43 71 35 -28.4BCin DOC 06/09/13 50405 95 0.44 378 37 -29.3R2 DOC 06/09/13 50406 67 0.31 3198 37 -29.0VHout DOC 06/09/13 50407 83 0.36 1542 25 -27.4

KRin DOC 12/05/14 54802 88 0.40 1069 37 -27.7WCin DOC 12/05/14 54803 97 0.45 245 37 -29.1BCin DOC 12/05/14 54804 99 0.45 91 37 -28.7R2 DOC 12/05/14 54805 84 0.38 1400 37 -29.6VHout DOC 12/05/14 54806 84 0.39 1392 37 -29.0

KRin POC 12/05/14 54373 68 0.30 3093 35 -26.9WCin POC 12/05/14 54374 86 0.37 1222 35 -27.2BCin POC 12/05/14 54375 85 0.37 1316 35 -28.4

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 VH out 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. 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.

24

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

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

burial efficiency and rates of gas release from the trapped POC merit 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 (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.

25

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 14C 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.

Figure 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.

26

We suggest that the variability in the DOC balances can be accounted largely accounted

for by POC-DOC conversions dominated by physical processes 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 (Figure 3), where the net

effect of carbon cycling processes is a loss of DOC when warmer (Figure 3a) and a gain

when colder (Figure 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 (~24t 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 0.1 a 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.

27

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 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.

28

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 DOC production from bed

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.

29

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.

30

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Appendices.

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Table A-1: Water balance for Kinder Reservoir (2012-2013). Note: units are million m3 unless otherwise stated. aAll figures for Jan 12 and Dec 13 represent the monitoring period only (12/01/2012 to 16/12/2013).

Month and Year

Rainfall (mm) Reservoir inlets Reservoir Total IN Total OUT

BCin KRin WCin Total Direct rainfall Evaporation Δ Reservoir

volume

Jan-12a 173 0.23 0.35 0.11 0.68 0.02 0.004 0.00 0.70 0.70Feb-12 73 0.27 0.41 0.11 0.79 0.01 0.003 -0.01 0.81 0.80Mar-12 31 0.16 0.26 0.07 0.48 0.01 0.008 -0.04 0.49 0.64Apr-12 190 0.27 0.54 0.20 1.02 0.03 0.005 -0.01 1.05 0.89May-12 62 0.19 0.31 0.09 0.59 0.01 0.005 0.04 0.60 0.70Jun-12 214 0.28 0.56 0.25 1.09 0.04 0.007 -0.02 1.13 1.02Jul-12 142 0.28 0.40 0.27 0.94 0.03 0.009 0.04 0.97 0.97Aug-12 147 0.22 0.24 0.11 0.56 0.03 0.009 -0.10 0.59 0.64Sep-12 148 0.24 0.33 0.27 0.85 0.03 0.009 0.01 0.88 0.79Oct-12 140 0.24 0.48 0.12 0.84 0.03 0.007 0.08 0.87 0.87Nov-12 136 0.34 0.39 0.12 0.84 0.02 0.005 0.00 0.86 0.85Dec-12 223 0.44 0.61 0.27 1.33 0.04 0.006 0.01 1.37 1.36Jan-13 94 0.33 0.37 0.16 0.86 0.02 0.003 -0.01 0.88 0.88Feb-13 81 0.32 0.31 0.14 0.77 0.01 0.006 0.01 0.78 0.80Mar-13 29 0.21 0.18 0.04 0.43 0.01 0.004 -0.13 0.43 0.65Apr-13 17 0.17 0.13 0.03 0.33 0.00 0.004 -0.21 0.34 0.54May-13 125 0.26 0.24 0.06 0.56 0.02 0.006 -0.10 0.58 0.53Jun-13 68 0.16 0.15 0.04 0.35 0.01 0.008 -0.05 0.36 0.52Jul-13 110 0.18 0.29 0.06 0.53 0.02 0.008 -0.15 0.55 0.54Aug-13 78 0.22 0.17 0.04 0.43 0.01 0.008 0.08 0.45 0.55Sep-13 91 0.26 0.23 0.04 0.53 0.02 0.007 -0.13 0.55 0.53Oct-13 139 0.48 0.48 0.06 1.02 0.03 0.007 0.14 1.05 0.55Nov-13 102 0.40 0.24 0.33 0.97 0.02 0.005 0.54 0.99 0.81Dec-13a 114 0.14 0.12 0.03 0.29 0.01 0.003 0.01 0.30 0.30Total 2726 6.30 7.77 3.04 17.10 0.46 0.15 0.00 17.56 17.42

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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 (Figures A-2 and A-3).

Site MethodNumberab

of samples

R2

valuePvalue

Standard Error Equation (ax +b)

Intercept Slope

ALL DOC 155 0.68 <0.001 <0.001 1.23 1.048548x + 2.384667

BCin POCmin 132 0.42 0.000 0.000 11.54 0.1994x - 16.9641KRin POCmin 236 0.37 0.000 0.000 115.18 0.7629x - 128.1730WCin POCmin 135 0.31 0.873 0.000 40.79 0.2707x - 0.8826

BCin POCmax 132 0.53 0.000 0.000 0.90 EXP((0.01906x)-1.0181)

KRin POCmax 236 0.60 0.026 0.000 0.97 EXP((0.01025x)+0.3413)

WCin POCmax 135 0.35 0.000 0.000 1.08 EXP((0.0079x)+1.6524)

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

Figure A-1: Plots showing the absorbance at 400nm 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).

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Figure 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 disharge = 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 unrealisatically high discharges for high stage values.

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Figure A-4: Plots showing the discharge vs SSC concentration relationships that were used to predict POC fluxes into the Kinder reservoir from inflow streams BCin and KRin

Figure A-4: Plots showing the discharge vs SSC concentration relationships that were used to predict POC fluxes into the Kinder reservoir from inflow streams WCin.

44

List of abbreviations

DOC dissolved organic carbon

POC particulate organic carbon

CO2 carbon dioxide

OCBE organic carbon burial efficiency

O2 oxygen

DIC dissolved inorganic carbon

CH4 methane

WC William Clough

KR Kinder River

BC Broad Clough

VH bottom valve house

KRO outlet of the Kinder River

14C radiocarbon

45