BG3.10

Globally, peatlands are important carbon and freshwater storage areas that have been extensively degraded by anthropogenic and natural disturbances. Considering the interconnected processes and high degree of heterogeneity in peatlands, simplified modelling approaches for peatland dynamics are prone to uncertainty, which has contributed to a proliferation of conceptualizations and parameterizations. While it is essential to improve modelling frameworks, creating new models with the same approaches and structures can make it difficult to identify uniqueness within the plethora of models, resulting in duplication of effort and resource waste. Here, we present a systematic review based on peer-reviewed journal publications to identify the most commonly used process-based models for simulating peatland dynamics. We selected 44 models that appeared at least twice and reviewed their corresponding publications (n = 211) to determine principal use-cases, types, and locations of peatlands. As a result, the models were grouped into four main types: terrestrial ecosystem models (biogeochemical and global dynamic vegetation models, n = 21), hydrological models (n = 13), land surface models (n = 7), and eco-hydrological models (n = 3). Out of all the models, 11 were peatland-specific, while the rest were generic. The scale of the studies ranged spatially from the catchment area to global and temporally from months to decades. In total, 19 studies were conducted at the global scale, 6 in the northern hemisphere, 5 in lab-based or synthetic setups, and 181 involved regional scale catchments; Among the latter, the majority were northern (temperate to arctic) peatlands. When the types of peatlands were mentioned, bogs were the most common (n = 52), followed by fens (n = 41), permafrost (n = 31, 20 of which were combined with bogs, fens, or peat swamps), mixed fen-bogs (n = 27), and blanket peats (n = 11), most of which were drained. Only three models were explicitly used to simulate tropical peatlands, and no models were found for Patagonia (Latin America). The simulations primarily focused on hydrology (39%), followed by carbon dynamics over large spatial scales (33%), energy fluxes and soil temperature (12%), peat accumulation (7%), and nitrogen fluxes (3%). Following a FAIR (Findable, Accessible, Interoperable, Reusable) assessment, the number of models was reduced to 12. Then, we compared the spatio-temporal resolution flexibility, input data file format, and modular structure of the shortlisted models. We found that several models of the same type have been developed/modified for the same application, climate zone, and with similar approaches. This points to opportunities for reducing duplication of effort and reusing models, while also suggesting that models developed for a similar use-case required similar parameterization. In this respect, our review highlights the need for a 'peatland community modelling' strategy that allows researchers to collaborate more efficiently and consolidate knowledge gleaned from various models. 

How to cite: Mozafari, B., O'Loughlin, F., Bruen, M., and Donohue, S.: Modelling peatland dynamics: Many models, same questions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-728, https://doi.org/10.5194/egusphere-egu22-728, 2022.

Peatlands play a major role in the global carbon cycle as they constitute around 20% of the soil carbon (C) stock and act simultaneously as C sinks (for CO₂ absorption) and sources (of CH₄ emission). Additionally, they support biodiversity preservation and provide important ecosystem services like climate regulation. Draining the peat for agriculture purposes results in its consolidation, enhanced decomposition, and subsequent subsidence. This accompanied by global warming promotes the emission of greenhouse gases making peatlands a C source ecosystem. Globally, as pent-up demand, different initiatives are put forward to protect, properly manage, and restore peatlands mainly to reduce these emissions and slow down climate change. For example, from 2021 onwards, under the EU 2030 climate and energy framework, all the member states are supposed to report on the emissions and removals of greenhouse gases from wetland areas. Denmark has its own national goal of reducing CO₂ emissions by 70% by 2030. However, the extent and status of peatlands are still poorly determined. Comprehensive mapping is required to enforce measures to prevent their further degradation, estimate the C stock and forecast the future emissions from peatlands. The conventional mapping approach using peat probes is time-consuming, tedious, and provides only localized and discrete measurements. Though these measurements are somewhat reliable, it is still challenging because occasionally the probes are obstructed by stones or human artefacts. On contrary to the latter, sometimes they might also easily penetrate the soil underlying the actual peat. While remote sensing based on satellite and aerial imagery makes delineation of the spatial extent possible, electromagnetic methods that have a deeper penetration into the soil are required to provide knowledge on peat volume estimates and groundwater depth. As a part of the ReDoCO2 (viz. Reducing and Documenting CO2 emissions from Peatlands) project, we employ state-of-the-art geophysical sensors, precisely, working on electromagnetic induction, ground-penetrating radar, and gamma-ray radiometric principles to accurately characterize three peatland areas in Denmark. The sensors are being tested in both proximal and remote configurations and efforts are underway to develop a novel drone-based transient electromagnetic induction sensor. Later, we plan to fuse the multisource datasets using machine learning to improve the prediction accuracy and advanced modelling techniques to study the effects of different management scenarios on greenhouse gas emissions. We envision developing a framework for detailed three-dimensional mapping of peatlands and a tool to estimate the reduction in greenhouse gas emissions to support decision-makers in choosing an appropriate management strategy. The project outcomes will have a significant economic, societal, and environmental impact strengthening Denmark’s position as a green frontrunner.

How to cite: Koganti, T., Vigah Adetsu, D., Andreasen, F., Skovgaard Mohr, K., Beucher, A., and H. Greve, M.: Mapping Peatlands in Denmark Using Electromagnetic Methods, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-791, https://doi.org/10.5194/egusphere-egu22-791, 2022.

Pristine peatlands are unique ecosystems for biodiversity conservation and climate regulation. They have the capacity to regulate local hydrology and balance carbon (C) fluxes between land and the atmosphere. Despite their importance, most peatlands can no longer be considered pristine mainly due to anthropogenic alterations. Although existing peatlands still support ecosystem services, they do so at a reduced capacity. Peatlands are the largest natural terrestrial C reserve with a global C stock estimated at about 20%.  Naturally, peatlands act as C sinks. However, excessive drainage for agricultural use and rising global temperatures may tip them into C sources and risk an increase in the emission of greenhouse gases (GHGs). Therefore, it is important to assess the magnitude of the coupled impacts of climate and anthropogenic changes on peatland status and coverage. A major limitation in achieving this lies in the lack of coherent detailed records documenting the spatiotemporal changes in the peat properties such as its thickness and spatial extent. Increasingly, there is a global interest in sustainable, and restorative peatland research as both a mitigation and adaptation strategy to climate change. The challenge still holds where without sufficient understanding of the status, extent, and controls on the changes in peat, there could be a mismatch between targeted management strategies for conservation. This study will focus on characterizing a peatland area in Store Vildmose, Denmark. This is a  the largest raised bog in Denmark and selected due to its age, various land uses over time and historical significance. There is compelling evidence for peat subsidence in this area due to anthropogenic influence. This can be jointly attributed to both the State and individual activities over the years. For example, the conversion of part of the bog to grazing lands by the State in 1920 (which required drainage by digging ditches and laying an extensive pipe network) and construction of cattle farms considerably degraded the peat. Additionally, the consumption of peat as an energy source favoured its extraction over conservation historically. In spite of the physical evidence, there is no accurate estimate of the changes in peat volume through time. This information is crucial to estimate the depletion and the current status of C stocks. Therefore, we propose to assess the changes in the peatland extent and volume by the use of historical cadastral maps (starting from 1880 onwards and yet to be digitized) and recent digital maps generated by the digital soil mapping approach. We will further perform scenario analysis and predictive modelling of the peat coverage with machine learning algorithms using additional covariates to more accurately quantify the C stocks and GHG emissions. The findings from the study will support stakeholder decision making for reducing the peatlands’ CO₂ emissions.

How to cite: Adetsu, D. V., Greve, M. H., Beucher, A. M., and Koganti, T.: Mapping Spatiotemporal Changes in Peatland Coverage: A Case Study on Store Vildmose, Denmark, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2227, https://doi.org/10.5194/egusphere-egu22-2227, 2022.

A better understanding of the short term and seasonal peat surface vertical displacements (bog breathing) (Roulet 1991) initiated by changes in the water table is needed to improve spatial models of greenhouse gas emissions (Dise 2009). Synthetic Aperture Radar Interferometry (InSAR) is a promising tool for the task but accounting for relatively large peat surface displacements (Fritz 2006, Howie & Hebda 2018) may cause propagation of ambiguity errors and unreliability (Alshammari et al. 2018, Heuff & Hanssen 2021). This is usually overlooked and the absence of ground levelling data for validation is characteristic of InSAR research in peatlands (Cigna & Sowter 2017, Alshammari et al. 2018).

We calculated distributed scatterer (DS) time series over 2014–2020 for Sentinel-1 relative orbits number (RON) 80 (descending) and 160 (ascending). The high frequency continuous in situ ground levelling measurements cover the snow and ice-free period of 2016 (April–October). Limited by the availability of Sentinel-1 data, 13 images from both stacks were evaluated against the levelling of a hummock plot. DS points used in the comparison were located around the plot at 125–315 m. The bog points were referenced to the stable DS points from a nearby village (4 km away) to account for atmospheric effects. InSAR line of sight deformation results were projected to vertical dimension (u_LOS).

Concerning only the dates when we had SAR acquisitions, the largest change relative to the maximum surface level of the period is -6.6 cm and median change -2.4 cm for RON 80, and -7.5 cm and -2.4 cm for RON 160. The maximum deviation between the u_LOS and the levelling is 5.6 cm and median 2.11 cm for RON 80. For RON 160, the maximum deviation is 5.85 cm and median 2.81 cm. The Spearman correlation coefficient (r_s) between the u_LOS and the levelling is 0.84 for RON 80 and r_s = 0.81 for RON 160 (p-value < 0.001 in both cases).

To reduce the need for ambiguity resolution in the DS time series, we used relative changes between two consecutive acquisitions (baseline of 12 or 6 days) instead of accounting for the absolute change. The in situ relative surface changes between the consecutive acquisition dates of RON 80 are -2.55...2.1 cm (median -0.08 cm) and the deviation of the DS from the levelling is -1.17...1.28 cm (median 0.38 cm). For RON 160, levelling values are -0.9...3.3 cm (median -0.3 cm) and the deviation -3.06…0.81 cm (the former is -0.45 if the 12-day image pair corresponding to the change larger than the u_LOS height of ambiguity is removed), median 0.23 cm. Between the levelling and DS data r_s = 0.67 (p-value 0.035) and 0.77 (p-value 0.005), respectively for RON 80 and 160. Based on the in situ levelling, we demonstrated that 1) Sentinel-1 DS time series severely underestimate real surface changes over the bog and 2) despite a serious ambiguity problem, DS time series contain the useful signal because 6-day surface changes are relatively small and usually do not need ambiguity resolution.

How to cite: Tampuu, T., De Zan, F., Shau, R., Praks, J., Kohv, M., and Kull, A.: Reliability of Sentinel-1 InSAR distributed scatterer (DS) time series to estimate the temporal vertical movement of ombrotrophic bog surface, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2387, https://doi.org/10.5194/egusphere-egu22-2387, 2022.

Peatlands are recognized as important carbon sequestration centres. Through restoration projects of peatlands in which the water table is raised, they may become carbon neutral or possibly carbon negative. National restoration plans require a knowledge of peatland extent and spatial distribution across large geographic areas.

Recently the availability of large geo-spatial datasets has increased. These range from soil, quaternary, and geology maps to airborne geophysical and satellite remote sensing data. Combining such datasets may provide a means to spatially map peatland extents and boundaries traditionally mapped via in-situ measurements. However, such datasets, and the relationship between them, are often complex. Modern Machine Learning methods can play a role in combining and analysing such multi-variate data within the discipline of Digital Soil Mapping.

Current peatland maps are created using combination of optical satellite remote sensing and legacy soil/quaternary maps. Optical remote sensing cannot detect peatlands under landcover such as forest or grassland. Legacy maps are often created from sparse in-situ augur, borehole, or trial pit data. These types of measurements do not allow for accurate measurement of boundaries or intra-peat variation.

Modern airborne geophysical datasets offer a potential means to update national and local scale peatlands maps. Radiometrics, a geophysical method that measures radiation emitted from geological materials, is particularly suited to peatland studies. Peat is a mostly organic material and so is, generally, not a source of radiation. Peat is also very saturated and water acts to scatter the emitted gamma rays. These effects combined means that peatlands act as a blanket to any source of radiation from below and show as “low” radiometric signal in the landscape.

This study aims to use Airborne Radiometric data combined with modern machine learning classification techniques to examine the current spatial distribution a peatland database in the west of Ireland. The Quaternary Geology database currently maps peatland extent where peat thickness is greater than 1m at the surface and was created using traditional mapping techniques. The methodology shows that a direct measurement, such as radiometric data, analysed in a supervised machine learning framework, provides more accurate and justifiable estimates of peatland extent in this region.

How to cite: O Leary, D., Daly, E., and Brown, C.: Regional Mapping of Peatland Boundaries using Airborne Radiometric Data and Supervised Machine Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5148, https://doi.org/10.5194/egusphere-egu22-5148, 2022.

Peatlands are unique ecosystems and despite covering only 3% of our planet, they store twice as much carbon as global forest cover. Healthy, fully functioning peatlands are the most effective natural carbon store and therefore it is important to keep them wet. When disturbed, peatlands release greenhouse gasesinto the atmosphere and lose carbon via surface runoff. Since peatlands cover around 20% of the land area in the Republic of Ireland, their drainage status and condition are of particular significance to reduce national emissions from the Land Use, Land Use Change and Forestry (LULUCF) sector.

Ireland is obligated to report anthropogenic emissions from organic soils in annual National Inventory Reports (NIR) under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol requirements. Ireland’s National GHG inventories comply with the methodology described in the Intergovernmental Panel on Climate Change (IPCC) Guidelines and the Wetlands Supplement 2013. The latest provided globally applicable ‘default’ emission factors (EFs) for calculating emissions and removals from drained and rewetted organic soils. However, the default EFs were based on field data often collected from geographical areas climatically and ecologically dissimilar to Ireland. Moreover, these EFs were limited by data availability and the level to which they could be disaggregated. In our work, we developed further stratification of peatlands land use categories based on peatlands characteristics and management in Ireland.

Here we review GHG emissions studies within Ireland and for the first time derived country-specific emission factors (EFs). We combined emissions of carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O) and fluvial losses (DOC, POC, DIC) into a new national database encompassing main land-use categories and types of organic soils in Ireland. We estimated total emissions from Irish peatlands at the national level (excluding horticulture and combustion) and identified the large uncertainties are associated with the estimated value. This new peatland emissions database will assist future NIR reporting and help calibrate widely used indirect land-use emission factor proxies that are currently based on data from continental European sites to more regionally appropriate estimates.

How to cite: Aitova, E., Morley, T., Wilson, D., and Renou-Wilson, F.: Updated GHG emission factors for Irish peatlands: a review, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6190, https://doi.org/10.5194/egusphere-egu22-6190, 2022.

Rewetted peatlands can be a significant source of methane, but in coastal systems, input of sulfate-rich seawater could potentially reduce these emissions. The presence of sulfate is known to suppress methanogenesis, by encouraging the growth of sulfate-reducers, which outcompete methanogens for substrate. After a drought in 2018 and a storm surge in the following winter, we investigated the effects of the drought and the brackish water inflow on the microbial communities relative to methane exchange in a rewetted fen at the southern Baltic Sea coast.

We took peat cores at four previously sampled locations along a salinity gradient to compare the soil and pore water geochemistry as well as the microbial methane and sulfate cycling communities to the common freshwater rewetting state and the drought 2018. We used high-throughput sequencing and quantitative polymerase chain reaction (qPCR) to characterize pools of DNA and cDNA targeting total and putatively active bacteria and archaea. While sequencing was done for the 16S rRNA gene, qPCR was performed on key functional genes of methane production (mcrA), methane oxidation (pmoA) and sulfate reduction (dsrB) in addition to 16S rRNA. Furthermore, we measured local methane (CH₄) fluxes with closed chambers and retrieved soil plugs to determine the concentrations and isotopic signatures of dissolved gases in the pore water.

The sequence of the drought and the inflow of brackish water increased the absolute abundance of sulfate reducing bacteria (SRB) by two orders of magnitude. We did not observe a decrease of absolute methanogenic archaea abundance after the inflow as we expected parallel to the increase of SBRs, but saw that changes in the methanogenic communities’ compositions already took place in the drought year 2018. After the inflow, absolute abundance of aerobic methanotrophic bacteria decreased back to their pre-drought level, following an increase during 2018 drought conditions. The expected establishment of methanotrophic archaea (ANME), which are capable of sulfate-mediated anaerobic methane oxidation, was not recorded though. While CH₄ fluxes showed a strong decline of almost 90 % to a new minimum since rewetting in 2009, dissolved CH₄ pore water concentrations and a strong depletion of ¹³C-values of CH₄ and CO₂ (DIC) indicated ongoing methanogenesis and lack of methane oxidation after the brackish water inflow. The observed reduction of CH₄ emissions might be a result of methane oxidation and sulfate reduction in the brackish water column above the peat soil. The legacy effect of the preceding drought likely influenced the microbial communities and pore water geochemistry simultaneously suggesting a mixed effect of drought and inflow. Overall, our study revealed that the sequence of drought conditions followed by the inflow of brackish water enlarged the sulfate reducing microbial communities and substantially reduced the CH₄ emissions in a rewetted fen. However, unlike drought, which is associated with a rapid and irreversible peat degradation through aerobic decomposition processes, brackish water inflow encourages peat preservation by maintaining anaerobic conditions. Still, further research is needed to directly study the complex effects of brackish water rewetting on peatlands.

How to cite: Gutekunst, C., Liebner, S., Jenner, A.-K., Knorr, K.-H., Unger, V., Koebsch, F., Racasa, E. D., Yang, S., Böttcher, M. E., Janssen, M., Kallmeyer, J., Otto, D., Schmiedinger, I., Winski, L., and Jurasinski, G.: Inflow of brackish water and a preceding drought changes methanecycling microbial communities in a freshwater rewetted coastal fen, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8384, https://doi.org/10.5194/egusphere-egu22-8384, 2022.

Many degraded UK blanket peatland sites have been subjected to restoration using dams in eroded gully systems to trap sediment, slow the flow of water and promote revegetation of bare peat surfaces. There are few studies on how gully blocks evolve with time, what this means for changing ecosystem functions, and the natural flood management benefits of restoration. This study focuses on gully block evolution on the Kinder Scout Plateau, where the blanket peatland was restored in 2011/12 and >2500 gully blocks installed.

We took a random sample of 500 small stone and timber dams 8-9 years following restoration, representing c.20% of the total number of blocks. We measured sediment accumulation behind the dams, vegetation cover and abundance and their propensity to continue to store water with respect to gully morphology. Principal component analysis suggested dam dimensions, channel, and wall slope are associated with sedimentation and change in water storage behind the dams, while other gully attributes were more associated with the change in vegetation cover. Dams installed in gullies with steeper walls and channel slopes typically accumulated more sediment. There was more variability in the evolution of stone dams, typically installed in wider, deeper gullies with shallower peat substrates and larger contributing areas than timber dams. 72% of surveyed dams were actively pooling water, and only two had visibly collapsed. On average, gully floors had 93% vegetation cover, whereas gully walls and dam tops had 90% and 45% vegetation cover, respectively. Sediment accumulation was not significantly different between the stone and timber dams at the 95% confidence interval. A random sub-sample of 26 gullies found no significant difference in sediment depths between subsequent dams in the same gullies (p = 0.255). Comparisons with an earlier survey suggest most sediment accumulation happens in the first year, rapidly reaching an equilibrium. As such, dams may exhibit similar properties regardless of the materials used and gully attributes. Dam top vegetation cover was positively correlated with gully dimensions, and 21% of dams were completely covered by vegetation. However, on average, 58% of the storage available after installation remained behind dams. Therefore, remaining storage combined with additional channel surface roughness may provide more favourable conditions for attenuating runoff 8-9 years after installation than the first year after restoration. We conclude that despite the differences between stone and timber dams, the gully blocking outcomes are very similar 8-9 years after restoration. Perhaps the most striking outcome was the high vegetation cover in channel floors and gully walls which will likely benefit peatland ecosystem functioning and natural flood management.

How to cite: Howson, T., Evans, M., Allot, T., Shuttleworth, E., Johnson, A., Rees, J., Milledge, D., Edokpa, D., Kay, M., Spencer, T., Brown, D., Goudarzi, S., and Pilkington, M.: The evolution of stone and timber dams, as part of peatland restoration, in eroded gully systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10365, https://doi.org/10.5194/egusphere-egu22-10365, 2022.

Links proposed recently between tropical peatland Greenhouse Gas (GHG) emissions and peat-surface displacements as estimated remotely by Interferometry of Synthetic Aperture Radar (InSAR) could provide a basis for low-cost estimation of peatlands GHG emissions on a global scale. However, links between InSAR estimates and peat ecohydrological parameters remain uncertain. We compared InSAR products (interferograms, coherence maps and temporal evolutions of displacements) from Sentinel-1 data for two well-studied Irish raised bogs with in-situ ecohydrological measurements: Clara bog (Co. Offaly) and Ballynafagh bog (Co. Kildare). On the individual raised bog of Clara, we demonstrate heterogeneity of peat-surface displacements in both space and time: the western part of Clara is in subsidence (up to 15 mm.yr^-1) while the eastern part shows uplift of some millimetres per year. In addition, these long-term evolutions are affected by annual oscillations of displacements due to the variations of water-table levels and to the meteorological conditions (rainfall and temperature). All of this, therefore, makes it difficult to use an InSAR-GHG proxy on temperate peatlands. Ballynafagh bog shows similar displacement behaviour to Clara bog. Furthermore, we show that the InSAR coherence is not affected by changes to vegetation wrought by a wildfire. This can be interpreted as evidence that the satellite-derived C-band radar waves penetrate through the 10-20 cm thick mossy vegetation layer and into the upper few 10’s cm of the underlying peat. Moreover, in-situ data show that the coherence is directly related to the soil moisture within the peat. Implications of this observation are (1) that InSAR displacements could be modified by soil moisture, resulting in biased InSAR-derived displacements during the annual oscillations and (2) that coherence mapping may provide a new method to estimate soil moisture on peatlands. Finally, future work should focus on directly validating InSAR displacements from in-situ data.

How to cite: Hrysiewicz, A., Holohan, E. P., and Dohonue, S.: New insights on links between InSAR data products and ecohydrological parameters of raised peatlands., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10535, https://doi.org/10.5194/egusphere-egu22-10535, 2022.

Tropical cyclones and winter storms are pervasive dangers to coastal communities in eastern Canada and, under future climate change regimes, these extreme weather events are projected to increase. However, it remains challenging to establish the relationship between external forcing mechanisms, such as the North-Atlantic Oscillation (NAO) and the Atlantic Multidecadal Oscillation (AMO), and temporal variability of past storms on short timescales in this region. This is due to the scarce availability of and difficulty of producing high-resolution paleo-storm records. Here, we contribute to Northeast Atlantic paleo-storm research by showing how to leverage the advantages of using ombrotrophic peatlands in building a high-resolution paleo-storm reconstruction. Ombrotrophic peatlands receive water and minerals exclusively from atmospheric deposition and often accumulate sediments rapidly, making them excellent repositories of past storms.

Our reconstruction is based on a multi-proxy analysis of two peat sequences of 3.25 m (TAC – Tourbière-de-l’Anse-à-la-Cabane) and 7.00 m (TLM – Tourbière-du-lac-Maucôque) that were recovered from two ombrotrophic peat bogs on Île du Havre-Aubert, the southernmost island of the Magdalen Islands archipelago, in eastern Canada. The samples cover the entire peat sequence, with the base of the cores ending in sediments of glacial or marine origin. The cores were dated by ¹⁴C and ²¹⁰Pb.The bottommost peat sediments date to ~8500 BP for TLM and ~4200 BP for TAC, with mean accumulation rates of 10 years/cm and 20 years/cm, respectively. We used a combination of X-ray microfluorescence (μ-XRF) measurements, computerized-tomography density, and Aeolian Sand Influx (ASI) measurements to identify allochthonous material from the ocean and surrounding beaches and sandstone cliffs in the peat cores. Analyses show high frequency variability in bromine and chlorine, which we hypothesize to be associated to sea-spray, and in terrigenous elements (potassium, titanium, manganese, iron), which we hypothesize to be associated to surrounding beaches and cliff sediments. ASI variations in the core closely match variations in terrigenous elements. It is hypothesized that mineral particles were deposited in the peat bogs during extreme weather events; this is suported by short-term peaks in chemical elements and aeolian sand from the topmost portion of the core that are correlated to known extreme events from modern instrumental data. High frequency (decadal) variability seen in the elemental and aeolian sand data throughout the core could possibly result from variation in storminess.

By applying a multi-proxy approach that combines μ-XRF geochemistry, density measurements, aeolian sand influx, and modern instrumental data on the full peat sequence, we were able to identify storm-derived materials with confidence while building a high-resolution paleo-storm reconstruction. With this data, we can establish the relationship between external forcing mechanisms and past storms.

How to cite: Lachance, A., Peros, M., and St-Jacques, J.-M.: Peatbogs to the rescue! Opportunities and challenges in using ombrotrophic peat cores for a reconstruction of paleo-storms during the Holocene in eastern Canada, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10953, https://doi.org/10.5194/egusphere-egu22-10953, 2022.

Microcatchments (<10 ha) are often used to monitor the effect of disturbances or restoration on the hydrological functioning of peatlands. Catchment areas serve as the spatial limits within which fluvial processes are studied and topographic parameters are derived. Digital Elevation Models (DEMs) and Digital Surface Models (DSMs) are used in standard practice to delineate the watershed boundary of microcatchments. These digital representations of topography contain errors, meaning the derived catchment areas have inherent uncertainty. The low-slope and hummock/hollow nature of peatland surfaces mean catchment delineation is particularly sensitive to vertical errors, so understanding the potential effect of uncertainty on the accuracy of catchment delineation is essential to providing a reliable account of peatland microcatchments hydrology.

This paper investigates the sensitivity of catchment delineation to DEM/DSM error for 30 peatland microcatchments across the Peak District National Park, UK. To evaluate the suitability of DSMs for hydrological applications in peatlands, a 0.25m photogrammetric DSM is used and evaluated against a 1m LiDAR DEM. Monte Carlo simulation is applied to produce a range of realisations of the DSM and DEM within their vertical error margins, from which a range of catchment areas are calculated. The variability of the watershed boundaries of each catchment is evaluated in the context of local gradient, difference from mean elevation and extent of gullying, to determine the relationship between terrain characteristics and variability in catchment delineation. Findings will have implications for the generation of catchment areas in peatland hydrology.  

How to cite: Johnston, A., Shuttleworth, E., Evans, M., Allott, T., and Pilkington, M.: Uncertainty in delineation of peatland microcatchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11998, https://doi.org/10.5194/egusphere-egu22-11998, 2022.