Investigation of the climatological impacts of agricultural management and abandonment on a boreal bog in western Newfoundland, Canada

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Please cite this article as: M. Wang, J. Wu, P.M. Lafleur, J. Luan, Investigation of the climatological impacts of agricultural management and abandonment on a boreal bog in western Newfoundland, Canada, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134632

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Investigation of the climatological impacts of agricultural management and abandonment on a boreal bog in western Newfoundland, Canada

Running head: comparison of greenhouse gases fluxes and albedo between an abandoned peatland pasture and an adjacent undisturbed boreal bog

Mei Wang1,2, Jianghua Wu2*, Peter M. Lafleur3,Junwei Luan4 1School

of Geography, South China Normal University, Guangzhou 510631, China

2Environment

and Sustainability, School of Science and the Environment, Grenfell Campus,

Memorial University of Newfoundland, Corner Brook, A2H 5G4, Canada 3School

of the Environment, Trent University, Peterborough, ON, K9L 0G2, Canada

4International

Center for Bamboo and Rattan, Beijing, 100102, China

*Correspondence author: Jianghua Wu, Ph.D. Email: [email protected] Environment and Sustainability, School of Science and the Environment, Memorial University of Newfoundland, Corner Brook, NL, A2H 5G4, Canada Tel: 001-709-639-2735 Fax: 001-709-639-8125

Investigation of the climatological impacts of agricultural management and abandonment on a boreal bog in western Newfoundland, Canada Running head: comparison of greenhouse gases fluxes and albedo between an abandoned

peatland pasture and an adjacent undisturbed boreal bog Mei Wang1,2, Jianghua Wu2*, Peter M. Lafleur3,Junwei Luan4 1School

of Geography, South China Normal University, Guangzhou 510631, China

2Environment

and Sustainability, School of Science and the Environment, Grenfell Campus,

Memorial University of Newfoundland, Corner Brook, A2H 5G4, Canada 3School

of the Environment, Trent University, Peterborough, ON, K9L 0G2, Canada

4International

Center for Bamboo and Rattan, Beijing, 100102, China

*Correspondence author: Jianghua Wu, Ph.D. Email: [email protected] Tel: 001-709-639-2735 Fax: 001-709-639-8125

Abstract We compared greenhouse gas (GHG) fluxes and albedo of a pristine boreal bog and an adjacent abandoned peatland pasture in western Newfoundland, Canada to estimate the magnitude of radiative forcing (RF) created by agricultural drainage and abandonment. Our results indicated that these anthropogenic activities induced a climate cooling effect (negative RF), with the magnitude of the RF caused by the albedo change comparable to that induced by altered GHGs. Although the albedo-induced RF was positive in winter and negative in summer, the summer effect dominated because of greater solar radiation received. The climate cooling effect of GHGs change was due to an increase in the carbon dioxide sink capacity and a reduction in methane emissions under lower water table levels following agricultural drainage and abandonment. Calculation of

sustained-flux global warming/cooling potentials also supported this finding. Our results show that the overall increase in albedo resulting from agricultural drainage and abandonment contributes significantly to the negative RF, strengthening the cooling effect due to the changing GHG fluxes. Therefore, changes in albedo due to altered vegetation coverage and hydrology and GHG fluxes should be considered when assessing the climatic impacts from land-use change in northern peatland. Keywords: Agricultural management, boreal bog, climate effect, global warming potential, biophysical process, radiative forcing

Introduction Peatlands play an important role in determining the greenhouse gas burden in the atmosphere by fixing carbon dioxide (CO2) from the atmosphere via plant photosynthesis and emitting methane (CH4) and nitrogen dioxide (N2O) to the atmosphere. Considering that N2O flux is quite small in pristine northern peatlands, the ratio of CO2uptaketo CH4emission largely determines their impact on the global climate system (Frolking et al., 2006). It has been suggested that the effect of CH4 emission dominates the radiative forcing (RF) impact of peatlands on climate only in the first few decades to centuries of their initial development, after which the impact of CO2fixation gains dominance (Frolking et al., 2006; Frolking and Roulet, 2007). Thus, the RF impact of undisturbed northern peatlands has tended to cool the climate over the past 5 to 7 thousand years.

In recent decades, agricultural management involving drainage, cultivation and fertilization has widely affected northern peatlands (Oleszczuk et al., 2008), with approximately 14-20% (nearly 3×105 km2) of these ecosystems being converted to meadows and pastures (Lappalainen, 1996,

Worrall et al., 2010). Drainage of peatlands for agriculture has resulted in significant peat soil subsidence caused by changes in physical structure and enhanced oxidation rates (Couwenberg, 2011, Couwenberg & Hooijer, 2013, Dawson et al., 2010, Leifeld et al., 2011, Pronger et al., 2014). As a result of these activities agriculturally managed peatlands can be ‘hotspots’ for greenhouse gases (GHGs) emissions because of their enhanced emissions of CO2 and N2O (Maljanen et al., 2010).Yet, GHG emissions following agriculture management can be highly variable depending on the peatland initial conditions, management intensity, amount of fertilizer applied, cultivation species and time for plant regeneration (Beetz et al., 2013, Maljanen et al., 2010, Schrier-Uijl et al., 2014).

Agricultural management of peatlands can have varied effects on the different species of GHGs. In general, agricultural drainage exposes more peat to aerobic conditions, which promotes peat mineralization and increased carbon (C) loss as CO2 (Ballantyne et al., 2014, Frolking et al., 2011, Joosten, 2009, Salm et al., 2012). On the other hand, plant cultivation on drained peatlands or plant regeneration on abandoned agriculturally managed peatlands can lead to greater C sequestration through enhanced photosynthesis, which can partially compensate for C losses (Hendriks et al., 2007, Shurpali et al., 2009). Drainage lowers the water table, which decreases CH4 production and increases CH4 oxidation potential, usually resulting in reduced CH4 emissions (Lai, 2009). Hence, most studies suggest CH4 emissions are reduced by agricultural practices, causing peatlands to be a small CH4 sources or even small sinks of CH4 (Knox et al., 2015, Maljanen et al., 2010,SchrierUijl et al., 2014, Smith and Conen, 2004, Wang et al., 2017). N2O emissions are very small in pristine peatlands because they have low background soil nitrogen (N) content and, in some cases they can even be net sinks for N2O (Martikainen et al., 1993, Regina et al., 1996). However, large

N2O emissions occur at nutrient-rich agriculturally managed peatlands due to enhanced nutrient substrate availability following N application and drainage (Alm et al., 2007, Van Beek et al., 2011, Salm et al., 2012, Teh et al., 2011).

Beside biogeochemical processes, agricultural management can also influence biogeophysical processes (Betts, 2007, Bonan, 2013) and have a significant impact on their radiation budget, thus affect climate (Betts, 2001, Gibbard et al., 2005, Govindasamy et al., 2001). One important way that land management influences surface energy fluxes is by altering the albedo of the land surface (Hannesp & Davidneil, 2010, Jones et al., 2015). Landscapes with denser vegetation and darker colored species tend to have a lower albedo than those with less vegetation cover and lighter colored species (Lohila et al., 2010, Miller et al., 2016).For example, Lohila et al. (2010) showed that converting peatlands to forests decreases albedo, which can counterbalance the cooling effects of increased C fixation. Peatland drainage and cultivation for agriculture alters vegetation composition and structure, by decreasing the abundance and biomass of species adapted to wet conditions, such as sedges and herbs, and increasing the cover of shrubs and forage grasses (Laiho et al., 2003, Laine et al., 1995).Since vegetation type and stature can affect albedo these changes can cause differences in both snow-free and snow-covered albedos between agriculturally managed peatlands and pristine peatlands. However, currently there are no studies available on these effects and the importance of albedo change by converting peatlands to pasture and the associated RF change are largely unknown.

In the context of climate warming, the Fifth Intergovernmental Panel on Climate Change assessment report discusses restoration of agriculturally managed peatlands to mitigate their

climate effect (Hiraishi et al., 2014).However, this report only focuses primarily on the biochemical impacts and ignores the biophysical impacts, where a limited studies have investigated the impact of agricultural management on the GHGs fluxes. Hence, the biogeophysical (e.g., change in the albedo) impacts resulting from agricultural management on northern peatlands and their effect on RF has not been quantified.

The aim of this study was to fill the knowledge gap on the impacts of agricultural management and abandonment on peatland climate impacts by quantifying the RF caused by changes in GHG fluxes and albedo derived by comparing a pristine boreal bog and an adjacent abandoned peatland pasture. Both sites are part of the same peatland complex. Our assessment is based on two years of data from April 2014 to April 2016 and consists of landscape-scale CO2 and CH4 fluxes via eddy covariance (EC) measurements, N2O flux during the growing seasons of 2014-2016 from static chamber measurements and radiation components from April 2014 to April 2016. Based on the literature discussed above, we hypothesized that the RF caused by previous agricultural management and then abandonment would result in a net cooling effect, and that differences in albedo would enhance this effect. Methods Study site Our sites are located in the Robinsons pasture, western Newfoundland, Canada (48.264 N, 58.665 W). The detailed site description can be found in our previously published papers (Wang et al., 2017, 2018a, b). The research sites are in a peatland complex and comprise an undisturbed bog and an abandoned peatland pasture. The pasture (~0.2 km2) was originally an ombrotrophic bog similar to the bog site. It was drained in the 1970s by a network of ditches (~0.5 m in depth and

~30 cm in width) running along an east-west transect with a distance of 20-30 m between ditches. At the time of drainage, pasture forage grasses were introduced and the site was used as pasture for about 10 years and then abandoned. After abandonment, the site was left to regenerate naturally without active management for ~25 years. The ditches, which were not blocked, are now partially filled by the regenerated vegetation yet provide sufficient drainage as to maintain deeper water table levels than at the pristine site. The pristine bog (0.36 km2) is located adjacent to the east of the abandoned peatland pasture and has remained undisturbed throughout this time period. We did not have detailed information on the groundwater flow pattern of this peatland complex. However, in the field we observed that the surface water of the bog flowed into a pond, a water accumulation body for the entire peatland complex. The growing season water table averaged -42 cm at the pasture, significantly lower than that of -21 cm at the bog in 2014 (Wang et al. 2018a). Therefore, we assume that the agricultural drainage has not significantly affected hydrological flow paths at the natural bog. The undisturbed bog belongs to the typical peatland type in Newfoundland, with component landforms of hummocks, hollows and pools and a substrate dominated by brown bog moss (Sphagnum capillifolium) with a sparse covering of reindeer lichens (Cladina spp.) on elevated microtopography. Hollows are dominated by sedges and hummocks by shrubs. The dominant herbs and sedges species are deergrass (Muhlenbergiarigens), cloudberry (Rubuschamaemorus) and tufted bulrush (Trichophorumcespitosum), and the dominant shrubs include black crowberry (Empetrum nigrum), sheep laurel (Kalmia angustifolia), Labrador tea (Rhododendron groenlandicum), Canadian dwarf cornel (Cornus Canadensis) and bog rosemary (Andromeda polifolia). The abandoned peatland pasture is a mosaic of vegetation patches dominated by reed canary grass (RCG: Phalaris arundinacea), with a mix of low herbaceous and graminoid species (Carex spp., Ranunculus acris, Ranunculus repens, Hieracium sp.) and several

shrubs [sweet gale (Myrica gale), Labrador tea (Rhododendron groenlandicum), mountain fly honeysuckle

(Loniceravillosa),

rhodora

(Rhododendron

canadense), and

chokeberry

(Photinia sp.)]. CO2 and CH4 flux and meteorological measurements Identical EC systems were operated at the bog and pasture for the period from April 2014 to April 2016. Detailed descriptions of these systems and data processing can be found in Wang et al. ( 2017, 2018a,b).Here we give only a brief summary. A three-dimensional (3-D) sonic anemometer (Gill WindMaster, Gill Instruments Ltd, Lymington, Hampshire, UK), a fast response infrared enclosed gas analyzer (IRGA:LI-7200 Enclosed CO2/H2O Analyzer, Li-Cor Inc., Nebraska, USA) and an open path infrared gas analyzer (LI-7700, Li-Cor Inc., Nebraska, USA) were used to measure wind vectors, variations in CO2 and H2O molar densities and CH4 concentration, respectively(LI-7700, Li-Cor Inc., Nebraska, USA).Instantaneous measurements of CO2 and H2O concentrations were measured inside the sampling cell, along with instantaneous air temperature and air pressure.

At both sites, a series of environmental variables including photosynthetically active photon flux density (PPFD) (LI-190SL-50, LI-COR Inc., Nebraska, USA), air temperature (Ta)and relative humidity(HMP155,Vaisala,Vantaa, Finland), soil temperature (Ts) (LI7900-180, LI-COR Inc., Nebraska, USA), solar radiation(CNR4, Kipp and Zonen, Delft, the Netherlands), rainfall(TR525USW, Texas Electronics, Texas, USA), soil moisture (Delta-TML2x, Delta-T Devices, U.K.) and water table depth (WTD) (CS451, Campbell Scientific, Utah, USA) were measured. These instruments were mounted on the EC system tower or inserted in the soil next to the tower. The

soil temperature was measured at the depth of 1 cm, 5 cm, 10 cm, 30 cm, 50 cm and 100 cm for the abandoned peatland pasture and at the same depths to 50 cm at the bog. EC flux data Processing EC data were processed with EddyPro 5.2.1 software (Li-Cor Inc., Nebraska, USA), where 10 Hz raw data were filtered and subject to various instrumental and theoretical corrections to produce output of the final corrected CO2, H2O and CH4 flux over 30-min intervals. We used the default settings for statistical tests for raw high-frequency data (despiking) (Vickers & Mahrt, 1997), block averaging detrending, correction for frequency response (Moncrieff et al., 1997, 2004), WebbPearman-Leuning density fluctuations for CH4 flux (Webb et al., 1980), sonic anemometer tilt correction with double rotation (Wilczak et al., 2001), angle-of-attack correction for wind components (Nakai and Shimoyama, 2012), lag minimization using maximum covariance with default lag of 0, and calculation of friction velocity (u*) using both along and cross wind shear. Quality flags for the flux calculation were determined following Mauder and Foken (2011). Poor quality data, i.e., with a quality flag of 2, a mean value of received signal strength indicator for LI7200 and LI-7700 less than 20%, u* less than the threshold values, and rainfall event were discarded. The flux threshold of u* was determined as 0.12 m s-1 for the bog and 0.15 m s-1 for the abandoned peatland pasture according to the Moving Point Test (Papale et al., 2006). In addition, footprint lengths were calculated following Kljun et al. (2004) and we discarded flux data when the 70% cumulative footprint was beyond the sites boundary. Further details can be found in Wang et al. (2017, 2018a,b).

For the CO2 flux, an online tool was used to do the flux gap filling and partitioning (http://www.bgc-jena.mpg.de/~MDIwork/eddyproc/) (Wang et al., 2018a). CH4 flux was gap-

filled with an artificial neural network (ANN) routine following Dengel et al. (2013). The ANN was trained with a Levenberg-Marquard back-propagation algorithm (trainlm) (Dengel et al., 2013, Riedmiller, 1994). Complete details can be found in Wang et al. (2017, 2018b). Chamber measurements of N2O The plot set-up for N2O flux measurements using the static chamber approach can be found in (Luan & Wu, 2014, 2015) and briefly described here. Three plots, representing three replicates, were established in both the bog and the abandoned peatland pasture in May 2013. At the bog, three subplots in each plot were established in order to sample one each of hummock, hollow and pool. At the abandoned peatland pasture, three subplots were established in each main plot in order to sample one drainage ditch and two different plant dominated patches (i.e., lower herbaceous, graminoid dominated and shrub dominated). PVC collars (inner diameter of 26 cm) were inserted into the peat to a depth of 10 cm, and measurement did not start until 2 weeks after the collars were established to minimize disturbance effects. For each bog pool, 4 circular collars made of ABS plastic (inner diameter of 26 cm) were inserted into the pool substrate and these served as the base for floating chambers. A small groove was cut into the top edge of each collar. The chambers, with a dimension of 25 cm in diameter and 50 cm in height, were constructed of opaque PVC. During measurements they were placed on the groove of the collars and water was added to create an airtight water seal. Air samples were taken into 30 ml plastic syringes equipped with a three-way stopcock inserted through a rubber septum at the top of the chamber. 4 samples were taken over a 30 minute incubation period at an interval of 10 minutes.

The gas samples were analyzed within one week of sampling using a Bruker gas chromatograph equipped with an electron capture detector, which was calibrated by two concentrations of standard

gases (Livingston and Hutchinson, 1995). The gas flux rate was calculated from the linear change in N2O concentration over time as F = (dc/dt) *(M/Vo)*(P/Po)*(To/T)*H , where dc/dt is the rate of concentration change with time and computed with linear regression, M is molar mass of N2O(g mol-1), Vo is the molar volume of gas at standard condition (m3), Po is standard atmospheric pressure (Pa), To is standard atmospheric temperature (℃), P and T are atmospheric pressure (Pa) and atmospheric temperature (℃) at sampling site, H is the height of the chamber (m).Sampling was conducted during the growing seasons from 2014 to 2015 at an interval of two to four weeks.

Cumulative growing season fluxes of N2O were obtained through linear interpolation of biweekly/monthly static chamber measurements (Luan & Wu, 2015,Teh et al., 2011). Spatial weightings were applied to each of the three communities based on their respective areal fractions as follows, weighted mean = (fiXi/fi),where Xi is the flux for the given land form and fi is the percentage of coverage corresponding to the given land form. Hummocks, hollows and pools occupy approximately 45%, 45% and 10% of the bog, respectively; grass-dominated communities, shrub-dominated communities and ditches account for nearly 45%, 45% and 10% of the pasture, respectively. Albedo measurement A four-component net radiometer (CNR4, Kipp & Zonen, Delft, The Netherlands) was used to measure incoming and reflected shortwave radiation, down-welling and up-welling longwave radiation. The instrument was amounted at a height of ∼3.0 m at the bog and 3.5 m at the Sout

pasture. Albedo was calculated as a ratio of Sout to Sin: albedo = Sin , where Sout is the reflected and Sin is the incoming shortwave radiation. Daytime means were used for this calculation. RF due to changes in albedo and GHGs

To calculate RF we followed the methodology outlined in Lohila et al. (2010). For this exercise changes were computed as pasture minus bog, reflecting the alteration of the natural landscape. The total RF due to changes in albedo (alb) and changes in greenhouse gas fluxes (GHG), RFtotal, was computed as follows: RFtotal=RFΔalbs+ RFΔGHGs = RFΔalb×As/Aglob +RFΔGHGs, where RFΔalb is the global RF due to albedo change, As is site area, Aglob is the surface area of the Earth (5.1×1015m2), RFΔalbs is the RF caused by a change in the albedo for our site and RFΔGHGs is the RF due to changes of GHGs fluxes. RFΔalb was calculated by multiplying average between-site (pasture minus bog) albedo difference with average incoming solar radiation. After Lohila et al. (2010), all the calculations were made for one square meter of peatland (As = 1 m2). A modified version of the REFUGE model was used to calculate RFΔGHGs (Monni et al., 2003). In this model, RFΔGHGs is estimated by time-integrating the response function related to an instantaneous concentration pulse, considering background concentration of the long-lived GHGs as well as variation in the surface flux exchange. Then t

concentration change ∆X due to GHGs fluxφcan be expressed as: ∆X(t)= k∫0φ(t)𝑓𝑎(t ― τ)dτ, where k denotes the emission-concentration conversion factor resulting from the instantaneous and complete atmospheric mixing assumed in the model, and fa is an atmospheric lifetime function that indicates the airborne fraction of the pulse. τ is the mean life-time of GHGs.

The removal of CO2 from the atmosphere is modeled by a superposition of three relaxation timescales, while that of CH4 and N2O is approximated by a single exponential. For the present work, the lifetime functions were updated according to Forster et al. (2007). The RF function for differences in GHG fluxes (i.e., CO2, CH4 and N2O) are based on the “simplified

expressions” of the Intergovernmental Panel on Climate Change and include the indirect RF effects of CH4 and the spectral interactions between CH4 and N2O and the detailed information can be found in the sourced references (Forster et al., 2007, Ramaswamy et al., 2001).Therefore, the radiative efficacy for each GHG was given in Table S1(Myhre et al. 2013). Sustained-flux global warming/cooling potentials (SGWP, SGCP) calculations We used SGWP and SGCP to describe the warming/cooling functions of the GHGs. On a 100year time horizon, the SGWP of CH4 and N2O flux is 45 and 270 times that of CO2; the SGCP of CH4 and N2O flux is 203 and 349 times that of CO2 (Neubauer and Megonigal, 2015). The net flux data of CH4 and NO2 were converted to equivalent CO2 units in order to compare the total global warming/cooling potential (GWP, GCP) of all three greenhouse gases. Note, negative (-) and positive (+) signs of GHGs fluxes mean uptake from and emission to the atmosphere, respectively. Uncertainty estimation For CO2 and CH4 fluxes, we considered two main error sources of the random uncertainty (σ1) and uncertainty due to gap-filling (σ2) for EC measurement data, which are computed in the EddyPro software. σ1 is estimated based on the method in Finkelstein and Sims (2001). This method requires the preliminary estimation of the Integral Turbulence time-Scales, which can be defined as the integral of the cross-correlation function between vertical wind component and any scalar of interest (e.g. temperature, gas concentration, etc.).The detailed calculation equations can be found in Finkelstein and Sims (2001). σ2 is based on the methods in Falge et al. (2001) and Reichstein et al. (2015). In brief, the method involves simulating gaps in the measured data and applying the gap-filling procedure described above. The error is computed from the difference between the measured data and their gap-filled counterpart as: σ2 =1/N ∑(Pi −

Oi) where N is the number of available measure (Oi) and predicted (Pi) flux pairs. The total uncertainty was calculated following the equation: σ=[σ12 +σ22]1/2. The standard error of N2O flux and albedo was estimated based on the following equation: σ =

1 N ∑ N i = 1(xi

― ｕ)2,where

N is the total number of measured values, xi is the measured individual value, ｕis the average valueof measured data. The uncertainty in GWP was calculated based on the following equation: σsgwp2 = σco22 + 452σch42 +2702σn2o2, where σsgwp, σco2, σch4, σn2o are the uncertainty of SGWP, CO2, CH4 and N2O fluxes, respectively. Results Comparison of albedo The bog and pasture showed similar seasonal variation in the albedo (Fig.1). During the snow-free period, albedo at the bog showed no seasonal pattern, with an average of 0.16, whereas the pasture showed a distinct seasonal trend in albedo peaking in mid-summer near 0.24, with an average of 0.20. Albedo increased rapidly due to snowfall in middle November (~DOY 310) in both years and rapidly decreased when snowmelt occurred at the end of April in 2015 (~DOY 120) and early in April in 2016 (~DOY 100). While the two sites were snow covered, albedo averaged 0.63 at the bog and 0.56 at the pasture.

Between-site albedo difference (pasture minus bog) ranged from -0.05 to 0.13 and more than 95% values were within the range of 0 to 0.06 during the snow free period (Figs.2 & 3).During the snow-covered period, the between-site albedo difference varied between -0.54 and 0.14, with approximately 95% values distributed in the range of -0.5 to 0 (Figs.2 & 3). Comparison of GHGs CO2 flux exhibited a distinct seasonal pattern at both sites, showing uptake by the ecosystem during

the growing season and small emissions during the snow-covered periods (Fig. 4). However, uptake in the snow free season was smaller at the bog, a total net uptake of 368 ± 211 g CO2 m-2 yr-1 in 2014 and 242 ± 139 g CO2 m-2 yr-1in 2015 (Table 1). In contrast, the pasture accumulated uptake was 825 ± 359 g CO2 m-2 yr-1 in 2014 and 744g ± 290 CO2 m-2 yr-1in 2015 during the snowfree period. During the snow-covered period emissions were similar for the two sites and reduced the accumulated uptake, such that annual total CO2 flux was only -168 ± 132 g CO2 m-2 yr-1 in 2014-15 and 4 ± 4 g CO2 m-2 yr-1in 2015-16 at the bog and -469 ± 220 g CO2 m-2 yr-1 in 2014-15 and -454 ± 205 g CO2 m-2 yr-1at the pasture (Table 1).

CH4 flux showed a strong seasonal pattern at the bog, with values near zero in the snow free period and peaking in mid- to late-summer (Fig. 4). Net annual emissions were 3.52 ± 0.68g CH4 m-2 yr1 in

2014-15 and 3.08 ± 0.67 g CH4 m-2 yr-1 in 2015-16. CH4 fluxes at the pasture had no seasonal

trend and exhibited small emissions throughout the year. The annual flux at the pasture was 0.36 ± 0.68g CH4 m-2 yr-1 in 2014-2015 and 0.13 ± 0.39 g CH4 m-2 yr-1 in 2015-2016 (Table 1). N2O fluxes were very small at both sites and tended toward uptake from the atmosphere. The annual accumulated snow-free period N2O flux was -0.16 ± 0.11g N2O m-2 in 2014-15 and -0.21 ± 0.71 g N2O m-2 in 2015-16 at the bog and -0.01 ± 0.17g N2O m-2 in 2014-15 and -0.25 ± 0.63g N2O m-2 in 2015-16 at the pasture (Table 1).

Considering the net fluxes of GHGs, the bog acted as near climate neural, with a SGWP/SGCP of -65 ± 70 g CO2 equivalents m-2 yr-1 in 2014-15 and 69 ± 247 g CO2 equivalents m-2 yr-1 in 201516 (Table 2). Conversely, the abandoned peatland pasture had a climate cooling effect with a SGCP of -456 ± 222 g CO2 equivalents m-2 yr-1 in 2014-15 and -535 ± 284 g CO2 equivalents m-2 yr-1 in

2015-16 (Table 1). As a result, the effect of the change in GHG fluxes after agricultural management and abandonment is a climate cooling effect primarily resulting from the change in CO2 and CH4 flux. Radiative forcing induced by change in albedo and GHGs fluxes RFΔalbs followed the seasonal pattern of between-site albedo differences, with the largest daily RF occurring near the end of April (~DOY 100-120) when the albedo difference and received solar radiation were both high (Fig. 2). RFΔalbs ranged from -2.78x10-15 W m-2 to 9.97x10-15W m-2, with 90% of the distribution within the range 0 to 6 x10-15 W m-2 during the snow-free period. When snow was present RFΔalbs ranged between -2.76 x10-14 and 6.26 x10-15 W m-2, with 90% values within the range from -1 x10-14 to 0 (Fig.2b & d).

RFCO2was negative and had a strong seasonal pattern peaking in mid-summer during the snowfree period, but was small and dominantly positive in the snow covered period. On an annual basis, RFCO2 was negative, with values of -5.30 ± 3.10 (10-16 W m-2）in 2014-15 and -8.06 ± 3.61(1016

W m-2）in 2015-16 (Fig.5, Table 2). Annual RFCH4 was -6.70 ± 1.18 (10-16 W m-2）in 2014-

15 and -6.23 ± 1.15 (10-16 W m-2）in 2015-16 (Table 2). RFN2O flux change was positive in 2014 and negative in 2015, but one order of magnitude smaller than that caused by changes in CO2 and CH4 flux (Table 2). The total RFΔGHGs was -11.47± 3.32 (10-16 W m-2）in 2014-2015 and -14.43 ± 3.81 (10-16 W m-2 ） in 2015-2016, comparable to the climate-cooling effect due to the albedo change of -13.6 ± 1.7 (10-16 W m-2）in 2014-2015, but larger than the RFalbs of -1.27 ± 2.6 (10-16 W m-2）in 2015-2016 (Table 2).

Total RF induced by differences in albedo and GHGs following agricultural management and

abandonment was -25.07 ± 3.73 (10-16 W m-2 ）in 2014-15 and -15.70 ± 4.61 (10-16 W m-2 ）in 2015-16 (Table 2). Discussion RF due to albedo change The RF induced by an albedo change can have a significant impact on climate (Betts, 2011), though limited studies have considered such effects for boreal peatlands. Our results indicated that albedo change following agricultural drainage and abandonment on a boreal peatland has induced a negative RF during the snow-free period and a positive RF during the snow-covered period. The negative RF resulting from higher albedo at the pasture during the snow-free period was predominantly due to differences in vegetation amount and species composition. Compared to the bog, the pasture has higher aboveground biomass, and presumably greater leaf area, therefore resulting in more reflective leaves and therefore higher albedo. Previous studies suggested that peatland albedo is closely linked to increases in leaf area with plant growth and maximum albedo coincides with the period of full leaf (Kellner et al., 2001, Lafleur et al., 1997). In addition, the drier soil surface at the pasture was another possible contributor to its higher albedo because drier surfaces are generally more reflective than wetter ones (Shimoyama et al., 2004). During the snowcovered period, albedo of the bog was higher than that of the pasture by about 7%, leading to positive RF. The lower albedo at the pasture occurred because the pasture vegetation consisted of taller grasses and shrubs than at the bog, hence more vegetation stems were visible through the snow in winter at the pasture, which resulted in greater solar absorption and lower albedo. Overall, the negative RF during the snow-free period outweighs the warming impact in the snow-covered period because received solar radiation in the snow-free period is more intense. Hence, our results indicated that annual RF due to albedo change resulting from agricultural management and then

abandonment on a boreal peatland resulted in a climate cooling effect. In contrast, forestation on peatlands had the opposite effect because the tree cover significantly decreases albedo in winter (by about half) and summer and albedo differences between the peatlands and forests were very small in summer, the net effect was a positive annual RF (Lohila et al., 2010). SGWP/SGCP and RF due to changing GHGs fluxes The potential climate effects resulting from changes in GHG exchange due to peatland conversion to agriculture and then abandonment were examined through SGWP/SGCP and RF. Although they are different quantities, we found that the net effects on climate from these two measures were consistent. SGWP/SGCP has been widely used as a measure of the impact of ecosystem GHGs exchange on climate. For the bog, the GHG balance was mainly co-determined by the CO2 sequestration and CH4 emissions, contributing to a small climate effect with SGWP/SGCP of -65 ± 70 g CO2 equivalents m-2 yr-1 in 2014-15 and 69 ± 247 g CO2 equivalents m-2 yr-1 in 2015-16, which was within the previous reported range of values for natural peatlands (Blais et al., 2005, Levy and Gray, 2015, Nilsson et al., 2008,Nykanen et al., 1995,Pullens et al., 2016, Roulet et al., 2007). At the abandoned peatland pasture, CO2 flux was found to be the main determinant of GHG balances, resulting in a negative climate effect with SGCP of -456 ± 222 g CO2 equivalents m-2 yr-1 in 2014-15 and -535 ± 284 g CO2 equivalents m-2 yr-1 in 2015-16. These results contrast those from previous studies where agricultural management was associated with large emissions of CO2 and N2O on peatlands, making drained and agriculturally managed peatlands large GHG sources, with the GWP ranging from 100 to 3140 g CO2-equivalents m-2 yr-1 (Frolking & Roulet, 2007, Hendriks et al., 2007, Maljanen et al., 2010,Strackand Waddington, 2007,Teh et al., 2011). Many of the previous studies have mainly focused on actively drained peatlands, but our pasture was previously cultivated and abandoned, then left to regenerate naturally for decades, as a result the

dense standing biomass has increased CO2 fixation beyond CO2 loss due to ecosystem respiration. In addition, cultivation of highly productive perennial bioenergy crops, such as reed canary grass (RCG, Phalarisarundinaceae, L.) on drained or rewetted peatlands is an acknowledged land use option, with the potential to reduce GHG emissions (Don et al., 2012,Schröder et al., 2015). At our pasture site, RCG is one of the dominant species. Hence, large CO2 uptake and small CH4 and N2O emissions made our abandoned peatland pasture a GHG sink.

Although the climate effect of peatlands under land use change is of significant concern (Pachauri et al. 2014), few studies have directly quantified RFΔGHGs following agricultural drainage and abandonment and our results help fill such knowledge gap. We found that RFΔGHGs due to conversion from the bog to abandoned peatland pasture was negative, similar to previous findings which suggested that forestry drainage in Finland has caused a negative RF due to decreasing CH4 emissions and increasing CO2 fixation (Laine et al. 1996, Lohila et al., 2010,Minkkinen et al., 2002). In contrast, Dommain et al. (2018) found that peatland conversion to agricultural plantations of oil palm and Acacia leads to an immediate positive trend in RF (climate warming), with peak RF ranging from 3.3 to 8.7×10-13 W m-2 if drainage continues, due to large emissions of both CO2 and CH4. RF due to changing albedo and GHGs We found that changes in both albedo and GHGs following agricultural drainage and abandonment caused a negative RF, with the RF due to albedo change comparable to that caused by altered GHG fluxes. Moreover, we found the inter-annual variation in RF caused by changing CO2 and CH4 was very small, while the RF induced by altered N2O flux during snow-free period during the snowcovered period varied significantly. The variable N2O-induced RF change may be due to the lack

of continuous measurements. Regardless, the RF induced by N2O change was rather small, with RFN2O flux one order of magnitude smaller than RF for the other GHGs and albedo, thus having little impact on the total RF. Although RF due to albedo differences in the snow-covered period was small, it can exert an impact on the magnitude or even sign of the RF under certain conditions. For example, the length of the snow-covered period could be important. If snow-cover extended into the late spring, when receipts of solar radiation are large this could significantly impact the magnitude and potentially even the sign of the annual total RF. This highlights the significance of including RF induced by albedo change in RF calculations. Therefore, calculating the RF due to changes in both GHG fluxes and albedo would offer a better indicator than the differences in GWP due to changes in GHGs since it does reflect the full picture of the impact of land-use change in northern peatlands on climate. Overall, our results imply that abandonment on agriculturally managed peatlands potentially increases the “climate cooling” function beyond the level of their original pristine state, which is an important for GHG inventories. However, as a climate change mitigation strategy, we recognize that a full analysis of the expected large C loss after initial drainage (Maljanen et al., 2010) would need to be considered. Acknowledgments This study was made possible by the support of the following funding to J. Wu: Natural Sciences and Engineering Research Council of Canada (NSERC)-Discovery Grant, Canada Foundation for Innovation-John R. Evans Leaders Fund, Research & Development Corporation (RDC, NL)Leverage R&D, RDC-Ignite R&D, Agricultural Research Initiative (NL), Humber River Basin Research Initiative of NL, Grenfell Campus’ Start-up Research Fund and Vice-President Research Fund, and the Graduate Student Stipend funding from the Institute for Biodiversity, Ecosystem Science, and Sustainability (IBES, NL) and to M. Wang: scientific research start-up Fund of

Guangdong Provincial Natural Science Foundation (Grant No. 2018A030310518), general project of Guangzhou Scientific Research Program (Grant No. 201904010160) and the National Science Foundation for Young Scholars of China (Grant No. 31901163). Special thanks are given to Prof. Henry Mann for his help in vegetation identification, Ms. Denise Bouzane for her constructive guidance on the selection of our research site, the Department of Facility Management at Grenfell Campus, Memorial University for logistic support, and Mr. Keon Noseworthy for his help in the installation of our EC towers.

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Fig. 1 Seasonal dynamic pattern of albedo for the bog and pasture during the study period. Fig. 2 Seasonal dynamic pattern of (a) daytime average incoming solar radiation, (b) between-site albedo difference (pasture minus bog) and (c) radiative forcing due to albedo change (RFΔalbs). Fig. 3 Frequency distributions of between-site difference in albedo and radiative forcing induced by albedo change during the snow-free period (a, b respetively) and snow-covered period (c, d respectively). Fig. 4 Comparison of net exchange of ecosystem CO2 (NEE), methane (CH4) and nitrogen dioxide (N2O) between the bog and pasture. Note that BP, BO and BU represent pool, hollow and hummock in the bog, respectively and PD, PG and PS are communities dominated by drainage ditches, grasses and shrubs. Fig. 5 Seasonal pattern of the difference in daily radiative forcing (RF) caused by difference in net exchange of ecosystem CO2 (RF Δ CO2) and methane (CH4, RF Δ CH4) flux between the bog and pasture (pasture minus bog).

Fig.1

Fig.2

Fig.3

Fig.4

Fig. 5

Table1 The total accumulated fluxes of CO2, CH4 and N2O, the sustained-flux global warming potential (SGWP) for gas emissions and sustained-flux global cooling potential (SGCP) for gas

uptake for the snow-free and snow-covered period and the annual budget in two years from April2014 to April 2016 for both bog and pasture. Note, CO2 flux values between September 2015 and April 2016 were derived based on the relationship between the monthly average flux and air temperature, and N2O flux was not available in the snow-covered season at both sites. The positive and negative signs represent emission and uptake of greenhouse gases to/from the atmosphere, respectively. Site

Bog

Pasture

Period

CO2 flux (g CO2 m-2 )

CH4 flux (g CH4 m-2 )

N2O flux (g N2O m-2 )

SGWP/SGCP (g CO2-eq m-2)

2014-15

2015-16

2014-15

2015-16

2014-15

2015-16

2014-15

2015-16

Snow-free

-368 ± 211

-242 ± 139

2.03 ± 0.71

2.22 ± 0.61

-0.16 ± 0.11

-0.21 ± 0.71

-329 ± 193

-215 ± 219

Snow-covered

200 ± 233

246 ± 282

1.49 ± 0.17

0.86 ± 0.31

n/a

n/a

267 ± 263

285 ± 248

Annual

-168 ± 132

4±4

3.52 ± 0.68

3.08 ± 0.67

-0.16 ± 0.11

-0.21 ± 0.71

-65 ± 70

69 ± 247

Snow-free

-825 ± 359

-744 ± 290

0.17 ± 0.25

0.27 ± 0.32

-0.01 ± 0.17

-0.25 ± 0.63

-821 ± 370

-819 ± 349

Snow-covered

356 ± 319

290 ± 326

0.18 ± 0.16

-0.15 ± 0.19

n/a

n/a

364 ± 322

260 ± 296

Annual

-469 ± 220

-454 ± 205

0.36 ± 0.31

0.13 ± 0.39

-0.01 ± 0.17

-0.25 ± 0.63

-456 ± 222

-535 ± 284

Table 2 Annual radiative forcing (RF, 10-16 W m-2 per square meter) caused by difference in GHGs and albedo between bog and pasture sites during the two study years. Period

RF ΔCO2

RFΔ CH4

RF ΔGHG

RF ΔN2O

RFΔ albs

2014-15

2015-16

2014-15

2015-16

2014-15

2015-16

2014-15

2015-16

2014-15

2015-16

Snow-free

-8.04 ± 5.11

-8.84 ± 4.48

-3.93 ± 1.40

-4.12 ± 1.10

0.53 ± 0.05

-0.14 ± 0.38

-11.44 ± 5.30

-13.10 ± 4.63

-30.2 ± 1.5

-17.7 ± 1.5

Snowcovered

2.75 ± 3.83

0.77 ± 2.88

-2.77 ± 0.17

-2.13 ± 0.52

n/a

n/a

-0.02 ± 3.83

-1.36 ± 2.83

8.66 ± 2.3

20.8 ± 4.1

Annual

-5.30 ± 3.10

-8.06 ± 3.61

-6.70 ± 1.18

-6.23 ± 1.15

0.53 ± 0.05

-0.14 ± 0.38

-11.47 ± 3.32

-14.43 ± 3.81

-13.6 ± 1.7

-1.27 ± 2.6

Highlights  Agricultural drainage and abandonment induced a cooling climate effect  The radiative forcing caused by albedo change was comparable to that induced by altered GHGs  Climate cooling effect of GHGs change was due to increase in CO2 update and decrease in CH4 emission  Changes in vegetation and hydrology significantly increased the summer albedo