•Restoration potential is understated when assessments ignore avoided emissions.•Avoided emissions from degraded wetlands are critical for near-term climate action.•Restoration targets should not be limited by assumptions about social resistance.

Climate change and biodiversity losses have necessitated innovative approaches to peatland management. This study examines pivotal historical landmarks and the recent forces of change that have affected peatlands in Finland, Ireland and Scotland, highlighting how national contexts, such as land ownership, forestry, agriculture and the need for domestic energy sources, have shaped the peatland use in those countries. We further introduce national and EU policies, which include, for example, national peatland strategies, and identify barriers to sustainable management of these important ecosystems. We propose six key solutions that could improve peatland persistence more broadly in northern Europe: (1) adoption of an integrated, landscape-scale strategy for rewetting and restoration with multi-stakeholder collaboration, (2) enhancement of monitoring to improve outcomes and refine best practices, (3) alignment of both national and EU policies across relevant sectors (energy, climate change, biodiversity, land use) to promote sustainable peatland management, (4) minimisation of trade-offs between green energy transition and sustainable peatland management, (5) engagement with local communities in restoration efforts for better acceptability and outcomes, and (6) wider leverage of market-based mechanisms, such as carbon, biodiversity and water credits, to finance peatland restoration. Together, these measures provide a pathway for the sustainable management of northern peatlands by balancing environmental integrity with socio-economic needs.

Peatlands have the capacity to sequester large quantities of carbon and can therefore play an important role in climate change mitigation. However, anthropogenic activities alter their hydrological regimes, converting them from net CO2 sinks into net sources. In England and elsewhere, lowland peatlands have been heavily drained and modified for agricultural land use, resulting in some of the most productive farmland in the UK. Estimates of CO2 emissions and water use from the area are scarce, but these data are required to understand the consequences of maintaining agricultural output whilst simultaneously reducing GHG emissions. In this paper, we compiled a uniquely comprehensive dataset of CO2 and H2O flux measurements from flux towers on cropped lowland peat, and coupled this with crop calorific values to estimate carbon and water use intensities of food production on peat. Our results showed that croplands on peat emitted 23.1 ± 10.4 ton CO2 ha-1 y-1 (mean ± SD). Sites with peat depth > 40 cm emitted 25.1 ± 9.2 ton CO2 ha-1, while wasted peat sites emitted 11.8 ± 4.8 ton CO2 ha-1. Effective water table depth and organic carbon content were the main drivers of variation in annual net ecosystem production and ecosystem respiration across sites; crop type partly followed these gradients, so may not be a direct driver of variations in emissions. ET was less variable across site-years and depended on the phenology of crop production. When considering CO2 emissions and water use per calorie produced, lettuce and celery rotations were the most C and water use intensive crops, with values an order of magnitude larger than cereal crops. Overall, this paper highlights the scale of CO2 emissions from managed peatlands and the importance of balancing GHG emissions and water use with ongoing food production from these economically important areas.

Restoring degraded peatlands is a key strategy for climate change mitigation. This has driven increased restoration efforts, especially in northern regions with widespread degradation. Continuous spatial monitoring is critical, and remote sensing enables it by providing large-scale data. In our study, we analyzed restoration-induced changes in essential climate variables across degraded northern peatlands in Finland, Estonia, Latvia, Lithuania, the UK, Canada, and the USA. We hypothesized that, prior to restoration, degraded peatlands with different initial land cover types display more pronounced differences in essential climate variables compared to intact peatlands, but these differences diminish as restoration progresses. Using over 20 years of satellite data, we observed changes driven by restoration in vegetation cover, surface temperature, and albedo, with the latter two showing the strongest indications of peatlands gradually recovering their natural state over time.

Peatland erosion and resulting particulate organic carbon (POC) flux is an international problem that is causing loss of peat carbon to the atmosphere and contributing to climate change. Peatlands from around the world are eroding and losing carbon for a range of reasons, from overgrazing to climate change, and the POC is subsequently exposed to a diverse range of conditions, depending on the geographical context. The context, drivers of erosion and downstream environment will directly influence the rate at which POC is mineralised to CO2 by microbial communities. Despite the potential large carbon losses from POC and subsequent CO2 emissions, the mechanisms for emissions reporting at international and national level are undeveloped. Here we highlight the key limitations for understanding and quantifying emissions that result from peat erosion and discuss the research that is required to address these limitations. We particularly consider quantification of direct CO2 emissions from bare peat and resedimentation and further turnover at different scales. By integrating biological and geomorphological process understanding we can work towards better quantification of peatland emissions and the emissions that can be avoided through peatland ecosystem restoration.

High water table depths (WTD) and water-saturated soil are important elements for peatlands to remain healthy and are crucial targets in peatland restoration projects. Recent studies have suggested that Earth Observation data might be applicable for this task, but proposed models often lack either the spatial extension or the temporal dimension. This study has been developing a spatio-temporal model of peatland water table depth by combining time series of satellite data, namely Sentinel-1, Sentinel-2; aerial data, namely Getmapping Digital Surface Model (DSM); and field collected water table depth measurements to provide the ability to evaluate WTD changes both spatially and over time. Water table depth measurements were collected from 59 loggers between February and September of 2018, with loggers covering peatlands in various condition clustered around four research sites in the North of Scotland. NDVI, NDWI and OPTRAM indices were derived from reconstructed cloud-free imagery of Sentinel-2 at 5-day intervals. Similarly, VV, VH, and incidence angle values were obtained from Sentinel-1 imagery at the same time interval. Finally, a Topographic Wetness Index (TWI) was calculated using GetMapping DSM data. A Generalised Additive Model (GAM) was then fitted using all above mentioned inputs with 70% training and 30% testing split method. The model showed a good overall fit (R2=0.59 for training data; R2=0.49 for testing data), with optical covariates outperforming the radar covariates. The model was then applied spatially using the R terra package, providing raster imagery of predicted WTD for 24 unique dates with clear distinction in wetness both over different seasons, and spatially in the landscape.Following this successful application, work is in progress to test the model on additional sites across Scotland and on European level to further test the applicability of the model to a wider range of northern peatlands.

Peatlands are water-logged ecosystems that limit microbial decomposition making them effective carbon sinks. However, drainage or erosion removes these constraints on decomposition, switching them to carbon sources. Restoration aims to reverse these trends. Microbial ecophysiology influences carbon fluxes but how it responds to peatland degradation and restoration is poorly understood. Here we used metagenomics to study microbial functions and quantified growth rates using isotope labelling across seven sites in Britain, each with restored, degraded, and near-natural peatlands. We found that growth rates in restored treatments were comparable to the near-natural, but were significantly higher in degraded. This growth rate reduction in restored peatlands was dependent on the scale of degradation and the length of restoration, and was underpinned by a shift towards energetically less favourable metabolic pathways such as anaerobic respiration, fermentation, and carbon fixation. A peatland ecosystem health index estimated based on measurements of peat moisture, oxygen, pH, organic matter chemistry, and moss cover, explained a significant amount of variation in microbial ecophysiology across the gradient. We demonstrate that microbial ecophysiology changes with peatland ecosystem health in a predictable manner. This knowledge can inform restoration targets and monitoring of recovery to maximise the return of carbon accrual functions of peatlands. ### Competing Interest Statement The authors have declared no competing interest. UK Research and Innovation (UKRI), Natural Environment Research Council (NERC), Scottish Universities Partnership for Environmental Research (SUPER) Doctoral Training Partnership (DTP), NERC Environmental Omics Facility (NEOF) International Human Frontier Science Program Organization, https://ror.org/02ebx7v45, RGP018/2024 FWF Austrian Science Fund, 10.55776/COE7

Peatlands store significant amounts of carbon, which is released as greenhouse gases when peatlands are degraded. Restoration and rewetting can help prevent these emissions, while continuous monitoring is critical for evaluating their success. Using satellite-derived observations of essential climate variables, we conducted the first large-scale assessment of how peatland restoration influences land surface temperature (LST), albedo, and vegetation across 72 sites in North America and Europe. Our findings indicated that before restoration, degraded peatlands had a commonly lower daytime LST and albedo but higher nighttime LST, leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (FPAR) compared to intact sites. The largest restoration-induced absolute values of monthly changes reached +3.18 °C (daytime LST), −1.22 °C (nighttime LST), −2.54 (LAI), −0.29 (FPAR), and −0.16 (albedo). While restored peatlands tended to align more closely with intact sites a decade after restoration began, the probability of this alignment varied depending on the climate variables. Restored peatlands became more similar than different to intact sites in nighttime LST and albedo after a post-restoration decade, with high similarity projected within five decades. Peatland restoration modifies local and regional climate and should be included in future climate projections.

Abstract. Peatland erosion and resulting particulate organic carbon (POC) flux is an international problem that is causing loss of peat carbon to the atmosphere and contributing to climate change. Peatlands from around the world are eroding and losing carbon for a range of reasons, from overgrazing to climate change, and the POC is subsequently exposed to a diverse range of conditions, depending on the geographical context. The context, drivers of erosion and downstream environment will directly influence the rate at which POC is mineralised to CO2 by microbial communities. Despite the potential large carbon losses from POC and subsequent CO2 emissions the mechanisms for emissions reporting at international and national level are undeveloped. Here we highlight the key limitations for understanding and quantifying emissions that result from peat erosion and discuss the research that is required to address these limitations. We particularly consider quantification of direct CO2 emissions from bare peat and resedimentation and further turnover at different scales. By integrating biological and geomorphological process understanding we can work towards better quantification of peatland emissions and the emissions that can be avoided through peatland ecosystem restoration.

Peatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE-PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland-specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO2 fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy-covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single-site calibration) or all sites simultaneously (multi-site calibration). Single-site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi-site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait-climate correlations). Site-optimized parameters, such as Q10, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, Q10 decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single-site optimization, we incorporated two key trait-climate relationships for Q10 and Vcmax (maximum carboxylation rate) into a new version of the ORCHIDEE-PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands.

Accurately quantifying carbon dynamics in peatlands is critical to assess their role in regulating global climate. Within hotspots of peatland degradation, such as in Europe and South-east Asia, skilful assessment of the spatial and temporal impacts of climate change and different land management options is required to meet emissions reductions targets and improve regional management planning.To address this challenge, a random forest-based metamodel was evaluated to assess its utility in simulating various greenhouse gas (CO2) emission components, including Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), and Ecosystem Respiration (ER) across two Scottish peatlands. The metamodel mimicked the complex Wetland-DNDC model at a higher level of abstraction with increased efficiency and lower computational time.While Wetland-DNDC also simulates NEE, GPP and ER, it typically involves a considerable number of parameters related to soil properties, climate data, vegetation characteristics, biogeochemical processes, hydrology, nutrient cycling, and microbial activity. Many of these parameters (more than 100) are challenging to measure in the field, and literature values are often adopted, which may not necessarily reflect local site conditions. In essence, this multidimensional parameter space introduces high uncertainties in modelling carbon fluxes.In contrast, random forest-based metamodel preserved the key relationships between NEE and input variables (air and soil temperature, water table, precipitation, vegetation, and soil properties) as described in the Wetland-DNDC model with lower parameter requirements (less than 20) and increased accuracy. Similar unique relationships were established for GPP and ER. The random forest-based metamodel represented the Wetland-DNDC model within the spectrum of input values and parameters across which it was simulated.The simulation was conducted in two locations across Scotland with contrasting contemporary carbon dynamics: a near natural blanket bog in Cross Lochs, Forsinard, currently functioning as a resilient net carbon dioxide sink (UK-CLS; Lat. = 58.37, Long. = -3.96; altitude = 207 m) and an eroding oceanic blanket bog located in the Cairngorms, currently net emitting carbon dioxide (UK-BAM; Lat. = 56.92, Long. = -3.15, altitude = 642 m). The simulation was validated against eddy covariance flux measurements under varying climate conditions.In contrast to Wetland-DNDC (R2 = 0.43), the metamodel provided a much-improved fit to the 1:1 line for NEE (R2 = 0.83). Model accuracy was slightly lower for the former (RMSE = 0.72) compared to its metamodel version (RMSE = 0.699). Similar trends were observed for GPP and ER simulations. At a monthly resolution, Wetland-DNDC-derived NEE, GPP, and ER consistently deviated by more than 20% from the eddy covariance-derived estimates, whereas its metamodel version showed deviations of less than 10%. Currently, work is in progress to incorporate management and drought simulation within a metamodel framework, as well as to upscale carbon fluxes from tower to landscape resolution.The simulation of carbon fluxes using the metamodel-based approach holds the promise of enhancing emission reporting to Tier 3 standards and offers a hopeful avenue for modelling carbon dynamics in peatlands.

Peat makes up approximately a quarter of Scotland's soil by area. Healthy, undisturbed, peatland habitats are critical to providing resilient biodiversity and habitat support, water management, and carbon sequestration. A high and stable water table is a prerequisite to maintain carbon sink function; any drainage turns this major terrestrial carbon store into a source that feeds back further to global climate change. Drainage and erosion features are crucial indicators of peatland condition and are key for estimating national greenhouse gas emissions. Previous work on mapping peat depth and condition in Scotland has provided maps with reasonable accuracy at 100-m resolution, allowing land managers and policymakers to both plan and manage these soils and to work towards identifying priority peat sites for restoration. However, the spatial variability of the surface condition is much finer than this scale, limiting the ability to inventory greenhouse gas emissions or develop site-specific restoration and management plans. This work involves an updated set of mapping using high-resolution (25 cm) aerial imagery, which provides the ability to identify and segment individual drainage channels and erosion features. Combining this imagery with a classical deep learning-based segmentation model enables high spatial resolution, national scale mapping to be carried out allowing for a deeper understanding of Scotland's peatland resource and which will enable various future analyses using these data.

Peatlands are carbon-rich wetland ecosystems and represent the largest terrestrial carbon store. Although they are natural carbon sinks, damage, drainage and extraction over recent decades has turned peatlands into a global carbon source, contributing ~5% of the total anthropogenic CO 2 emissions [1] , [2] . To tackle this nearly irreversible loss, peatland conservation and restoration projects on global and national levels have been increasing in numbers. Restoration projects aim to stabilise eroding peat, enhance carbon sequestration through ecosystem recovery, and prevent further degradation [3] . These projects directly target human-caused damage, such as drainage ditches, afforested peatlands, peat burning and overgrazing - as well as more indirect or natural degradation, such as peat gullies, bare peat, peat haggs and pipes. Peatland restoration efficacy can be assessed from multiple criteria, e.g., return of high water table depth (WTD), revegetation, including the return of native species, and higher carbon uptake [4] – [6] . A regular physical collection of such data in the field would be impractical and difficult to accomplish. Remotely sensed data analysis, therefore, offers an attractive alternative for landscape-scale peatland restoration progress monitoring.

Globally, major efforts are being made to restore peatlands to maximise their resilience to anthropogenic climate change, which puts continuous pressure on peatland ecosystems and modifies the geography of the environmental envelope that underpins peatland functioning. A probable effect of climate change is reduction in the waterlogged conditions that are key to peatland formation and continued accumulation of carbon (C) in peat. C sequestration in peatlands arises from a delicate imbalance between primary production and decomposition, and microbial processes are potentially pivotal in regulating feedbacks between environmental change and the peatland C cycle. Increased soil temperature, caused by climate warming or disturbance of the natural vegetation cover and drainage, may result in reductions of long-term C storage via changes in microbial community composition and metabolic rates. Moreover, changes in water table depth alter the redox state and hence have broad consequences for microbial functions, including effects on fungal and bacterial communities especially methanogens and methanotrophs. This article is a perspective review of the effects of climate change and ecosystem restoration on peatland microbial communities and the implications for C sequestration and climate regulation. It is authored by peatland scientists, microbial ecologists, land managers and non-governmental organisations who were attendees at a series of three workshops held at The University of Manchester (UK) in 2019–2020. Our review suggests that the increase in methane flux sometimes observed when water tables are restored is predicated on the availability of labile carbon from vegetation and the absence of alternative terminal electron acceptors. Peatland microbial communities respond relatively rapidly to shifts in vegetation induced by climate change and subsequent changes in the quantity and quality of below-ground C substrate inputs. Other consequences of climate change that affect peatland microbial communities and C cycling include alterations in snow cover and permafrost thaw. In the face of rapid climate change, restoration of a resilient microbiome is essential to sustaining the climate regulation functions of peatland systems. Technological developments enabling faster characterisation of microbial communities and functions support progress towards this goal, which will require a strongly interdisciplinary approach.

Peatland is a globally important store of carbon. Peatland restoration efforts are being increasingly undertaken yet effective monitoring of landscape-scale restoration projects has been limited. A particular gap in our understanding is the length of time required before a site reaches the target state. To address this, a classification model based on remote sensing data was developed for a peatland restoration area on blanket bog in northern Scotland, UK, to evaluate whether post-restoration trajectories followed predictable trends over time. The model was trained against a chronosequence of sites within a 20 x 10 km study area that are being restored following drainage and intensive non-native afforestation. Two versions of the model were created to compare the accuracy obtainable from the suite of Sentinel-2 satellite data versus sub-metre resolution aerial imagery from GetMapping (RGB and IR). The Sentinel-2 based model greatly outperformed the aerial imagery-based model. Adding surface slope to the classification did not significantly improve the accuracy of prediction. Prediction of starting and target land covers was very robust, and both the most recent and oldest restoration sites were well predicted spatially. The main uncertainties in the model were within sites of intermediate restoration age, and sites which underwent additional treatments after the initial restoration. Using standard vegetation and wetness indices as indicators, it was possible to track the progression of areas that had been felled and rewetted towards the spectral signal of the control blanket bog locations. A further study examined the use of multiple years of satellite data (2015-2021) and including Sentinel-1 SAR imagery, and confirmed the findings obtained with only a single climatically average year, and furthermore examined the efficacy of different restoration methods. We observed consistent trends of restoration sites beginning to resemble the target hydrologically and ecologically functional blanket bog state after 10-20 years post intervention.

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.

<p>Peatlands occupy 12% of the UK territory and can store large amounts of carbon (C). However, drainage, peat extraction, and other management activities have turned these ecosystems into greenhouse gas (GHG) emitters. Currently, peatlands account for ~ 4% of the UK’s total annual GHG emissions. Eddy covariance is considered the best method to measure landscape scale GHG exchange (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O), between the Earth’s surface and the atmosphere. Recently many flux towers have been installed on UK peatlands under different land-use and in different condition, with some undergoing restoration. In total there are currently 30 operating, with 9 in Scotland (SCO2FLUX managed by The James Hutton Institute, JHI) and 21 across England, Wales and Northern Ireland (managed by UKCEH), including the Auchencorth Moss ICOS site. As part of the projects, NERC-MOTHERSHIP and SRC-CENTREPEAT, these peatland sites are being harmonised into a network. The data is being analysed using standard protocols in order to generate a powerful dataset to examine the exchange of CO<sub>2</sub> and CH<sub>4 </sub>over UK peatlands. Some of the topics being investigated are: the spatial and temporal variability of emissions for all peatland classifications; the main drivers and controlling mechanisms of GHG exchange, such as the effect of water table depth on gas exchange and restoration impacts (e.g. raising water levels in agricultural peatlands); the value and effectiveness of restoration techniques (e.g. the timeline of recovery in the transition from forest to bog); improving the modelling of peatlands in JULES and other land-surface models; ground-proofing data for Earth observation techniques; assessing the contribution of peatlands to achieving net zero; examining the impact of wildfire on restoration from forest to bog.</p> <p>An overview of the network of sites and some highlights of the analysis to date will be presented.</p> <p> </p>

Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of the key monitoring elements, where it is used as a proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data in situ can be difficult to collect, and therefore, remotely sensed data offer an attractive alternative for landscape-scale monitoring. In this study, we illustrate the application of Sentinel-1 SAR backscatter for water table depth monitoring in near-natural and restored blanket bogs in the Flow Country of northern Scotland. Among the study sites, the near-natural peatlands presented the smallest fluctuations in the WTD (with depths typically between 0 and 15 cm) and had the most stable radar signal throughout the year (~3 to 4 dB amplitude). Previously drained and afforested peatlands undergoing restoration management were found to have higher WTD fluctuations (depths up to 35 cm), which were also reflected in higher shifts in the radar backscatter (up to a ~6 dB difference within a year). Sites where more advanced restoration methods have been applied, however, were associated with shallower water table depths and smoother surfaces. Three models—simple linear regression, multiple linear regression, and the random forest model—were evaluated for their potential to predict water table dynamics in peatlands using Sentinel-1 SAR backscatter. The random forest model was found to be the most suited, with the highest correlation scores, lowest RMSE values, and overall good temporal fit (R2 = 0.66, RMSE = 2.1 cm), and multiple linear regression came in a close second (R2 = 0.59, RMSE = 4.5 cm). The impact of standing water, terrain ruggedness, and the ridge and furrow aspect on the model correlation scores was tested but found not to have a statistically significant influence. We propose that this approach, using Sentinel-1 and random forest models to predict the WTD, has strong potential and should be tested in a wider range of peatland sites.