John Couwenberg

Assessing greenhouse gas emissions from peatlands using vegetation as a proxy

Drained peatlands in temperate Europe are a globally important source of greenhouse gas (GHG) emissions. This article outlines a methodology to assess emissions and emission reductions from peatland rewetting projects using vegetation as a proxy. Vegetation seems well qualified for indicating GHG fluxes from peat soils as it reflects long-term water level, affects GHG emissions via assimilate supply and aerenchyma and allows fine-scaled mapping. The methodology includes mapping of vegetation types characterised by the presence and absence of species groups indicative for specific water level classes. GHG flux values are assigned to the vegetation types following a standardized protocol and using published emission values from plots with similar vegetation and water level in regions with similar climate and flora. Carbon sequestration in trees is accounted for by estimating the annual sequestration in tree biomass from forest inventory data. The method follows the criteria of the Voluntary Carbon Standard and is illustrated using the example of two Belarusian peatlands.

A matter of dispersal: REVEALSinR introduces state-of-the-art dispersal models to quantitative vegetation reconstruction

The REVEALS model is applied in quantitative vegetation reconstruction to translate pollen percentage data from large lakes and peatlands into regional vegetation composition. The model was first presented in 2007 and has gained increasing attention. It is a core element of the Landcover 6k initiative within the PAGES project. The REVEALS model has two critical components: the pollen dispersal model and pollen productivity estimates (PPEs). To study the consequences of model settings, we implemented REVEALS in R. We use a state-of-the-art Lagrangian stochastic dispersal model (LSM) and compare model outcomes with calculations based on a conventional Gaussian plume dispersal model (GPM). In the LSM turbulence causes pollen fall speed to have little effect on the dispersal pattern whereas fall speed is a major factor in the GPM. Dispersal models are also used to derive PPEs. The unrealistic GPM produces PPEs that do not describe actual pollen productivity, but rather serve as a basin specific correction factor. A test with pollen and vegetation data from NE Germany shows that REVEALS performs best when applied with the LSM. REVEALS applications with the GPM can produce realistic results, but only if unrealistic PPEs are used. We discuss the derivation of PPEs and further REVEALS applications. Our REVEALS implementation is freely available as the ‘REVEALSinR’ function within the R package DISQOVER. REVEALSinR offers an environment for experimentation and analysing model sensitivities. We encourage further experiments and welcome comments on our tool.

MARCO POLO – A new and simple tool for pollen-based stand-scale vegetation reconstruction

Hitherto, the Landscape Reconstruction Algorithm (LRA) has been the only truly quantitative approach to stand-scale palynology. However, the LRA requires information on pollen productivity and dispersal, which is not always available. The alternative approach MARCO POLO (MAnipulating pollen sums to ReCOnstruct POllen of Local Origin) presented here is solely based on pollen values and does not rely on a pollen dispersal function. In a stepwise fashion, MARCO POLO removes those pollen types from the pollen sum whose values are significantly higher than in a neighbouring large basin. The resulting regional pollen sum is free of the disturbing factor of (extra-)local pollen. Based on this sum, comparison with the pollen record from the large basin allows calculating sharp (extra-)local signals. Treating the (extra-)local pollen portion with representation factors (R-values) then produces a quantitative reconstruction of the stand-scale vegetation composition. We tested MARCO POLO and the LRA on a dataset of pollen surface samples and forest vegetation relevés from northern Central Europe. Both approaches reconstruct the presence or absence of taxa at the stand scale within a small margin of error. Where observed cover was ⩾2%, both models always reconstructed presence, where modelled cover was ⩾2% the taxon was always present. Overall, both approaches perform well in reconstructing the cover of taxa within a 100-m radius. In our tests, MARCO POLO is slightly better at reconstructing cover values for more taxa. Although some model parameters evidently need revision, the simple correlative approach of MARCO POLO appears to perform at least as well as the complex LRA model.

Carbon accumulation in a permafrost polygon peatland: steady long‐term rates in spite of shifts between dry and wet conditions

Ice-wedge polygon peatlands contain a substantial part of the carbon stored in permafrost soils. However, little is known about their long-term carbon accumulation rates (CAR) in relation to shifts in vegetation and climate. We collected four peat profiles from one single polygon in NE Yakutia and cut them into contiguous 0.5 cm slices. Pollen density interpolation between AMS (14)C dated levels provided the time span contained in each of the sample slices, which--in combination with the volumetric carbon content--allowed for the reconstruction of CAR over decadal and centennial timescales. Vegetation representing dry palaeo-ridges and wet depressions was reconstructed with detailed micro- and macrofossil analysis. We found repeated shifts between wet and dry conditions during the past millennium. Dry ridges with associated permafrost growth originated during phases of (relatively) warm summer temperature and collapsed during relatively cold phases, illustrating the important role of vegetation and peat as intermediaries between ambient air temperature and the permafrost. The average long-term CAR across the four profiles was 10.6 ± 5.5 g C m(-2) yr(-1). Time-weighted mean CAR did not differ significantly between wet depression and dry ridge/hummock phases (10.6 ± 5.2 g C m(-2) yr(-1) and 10.3 ± 5.7 g C m(-2) yr(-1), respectively). Although we observed increased CAR in relation to warm shifts, we also found changes in the opposite direction and the highest CAR actually occurred during the Little Ice Age. In fact, CAR rather seems to be governed by strong internal feedback mechanisms and has roughly remained stable on centennial time scales. The absence of significant differences in CAR between dry ridge and wet depression phases suggests that recent warming and associated expansion of shrubs will not affect long-term rates of carbon burial in ice-wedge polygon peatlands.

The extended downscaling approach: A new R-tool for pollen-based reconstruction of vegetation patterns

The extended downscaling approach (EDA) is a quantitative method in palynology that aims to detect past vegetation patterns and communities in the landscape. The EDA uses iterative forward modelling to fit vegetation composition to robust landscape patterns by comparing simulated with actually observed pollen deposition. The approach employs a set of pollen records, preferably from medium sized to large lakes or peatlands, as well as maps of robust landscape patterns, such as soils and relief. So far, the EDA has been applied in simple settings with only few taxa. To be able to apply the model also in more complex situations, we have implemented the EDA in the R environment for statistical computing. We here test the performance of the EDAinR function in five synthetic scenarios of increasing complexity. In all cases, the EDA is well able to reconstruct vegetation composition, also on rare landscape units. If uncertainty is added both to the pollen data and pollen productivity estimates, the EDA still correctly reconstructs species composition on more than 90% of the total landscape in all scenarios, underlining that the EDA performs well also in complex settings. The EDAinR function will be available within the R package DISQOVER.

The role of peatlands in climate regulation

Introduction Peatlands are the worlds most important terrestrial ecosystems with respect to carbon (C) storage, and act as a source and sink for GHGs. In this chapter we outline the importance of peatlands in climate regulation and we describe the effects of drainage and restoration. Peatlands and climate regulation Description, status and trends Peatlands are the largest terrestrial store of organic carbon Peatland ecosystems (including peat and vegetation) contain much more organic carbon than other terrestrial ecosystems. In the (sub)polar zone, peatlands contain on average 3.5 times more carbon per hectare than ecosystems on mineral soil; in the boreal zone seven times more carbon; and in the humid tropics as much as 10 times more carbon (Joosten and Couwenberg 2008). While covering only 3% of the worlds land area, peatlands contain 450 Gt of carbon in their peat (Joosten 2009c; Page, Rieley and Banks 2011a). Peatlands are the largest long-term carbon store in the terrestrial biosphere and among the Earths most important stores. The huge carbon stock of peatland ecosystems is attributable to the often thick layers of peat. Peat is a highly concentrated stockpile of carbon because it consists by definition of more than 30% (dry mass) of dead organic material that contains 48–63% of carbon. On average, the peatlands of the world hold a carbon pool in their peat of 1125 t C ha -1 (450 Gt/400 × 10 6 ha), which is the largest carbon density of any terrestrial ecosystem. The ecosystem with the second most carbon per hectare is the giant conifer forest in the Pacific West of North America, which, before human disturbance, reached only half the carbon density of the average peatland (Joosten and Couwenberg 2008). Estimates of soil C stock to 1 m depth range between 1400 and 1600 Gt C (Smith 2004). Further C is stored deeper: 491 Gt C between 1–2 m depth, and 351 Gt C at 2–3 m depth (Jobbagy and Jackson 2000). The atmosphere (in 1990) contained 750 Gt C, mainly as CO 2 and CH 4 (Houghton, Jenkins and Ephraums 1990). The global terrestrial plant biomass carbon stock is estimated to be 654 Gt (IPCC 2001) with total global forest biomass holding 335–365 Gt of carbon (Shvidenko et al. 2005).

Peatlands and Climate in a Ramsar context : A Nordic-Baltic Perspective

Peatlands in the Nordic Baltic region and elsewhere in the world store large amounts of carbon and are at the same time important for conservation of biodiversity. Thus peatlands are space-effectiv ...

International carbon policies as a new driver for peatland restoration

Introduction When – in 2006 – experts and advocacy groups for the first time raised the issue of GHG emissions from degraded peatlands at the United Nations Framework Convention on Climate Change (UNFCCC), they met with negotiators, many of whom had never heard of ‘peat’ in the first place. Six years later, the UNFCCC allowed countries to comply with their reduction commitments by peatland rewetting and included peat soils in the REDD+ mechanism to reduce emissions from tropical deforestation. After years of neglect, peatlands have gained the attention that they deserve in the face of their enormous emissions and mitigation potential (Chapter 4). This chapter discusses the potentials and complications of using climate change mitigation policies for stimulating and financing peatland restoration using the Climate Convention, in the context of voluntary carbon markets and through policies with indirect climate targets. Many land use-oriented mitigation mechanisms have been developed with a forest bias, i.e. from the perspective of biomass carbon stocks. In using existing approaches to address peatlands, concepts and criteria thus have to be modified, complemented or newly developed to accommodate the specific peculiarities of peatlands – often after wearying awareness raising. Market-based instruments are recognised as important elements of the international climate finance architecture. Since the entry into force of the Kyoto Protocol (2005), about 3 billion Kyoto units, each of these representing 1 t CO 2 e, have been traded at least once for prices ranging from less than 1 EUR per unit to 20 EUR and more. The annual market value of national and subnational cap-and-trade systems (outside Kyoto) stands at USD 30 billion (World Bank 2014). These large sums, in combination with the huge carbon stocks in peatlands, have nurtured the idea that peatland restoration is an effective way of tapping into climate finance. However, reality is more complicated – as this chapter will show. Beyond carbon trading proper, this chapter also addresses some future options for stimulating peatland restoration. Large peatland emissions occur both in industrialised and developing countries (Figure 15.1), and the ongoing negotiations within the post-2020 climate framework offer various opportunities to create peatland restoration incentives.

ROPES Reveals Past Land Cover and PPEs From Single Pollen Records

Quantitative reconstructions of past vegetation cover commonly require pollen productivity estimates (PPEs). PPEs are calibrated in extensive and rather cumbersome surface-sample studies, and are so far only available for selected regions. Moreover, it may be questioned whether present-day pollen-landcover relationships are valid for palaeo-situations. We here introduce the ROPES approach that simultaneously derives PPEs and mean plant abundances from single pollen records. ROPES requires pollen counts and pollen accumulation rates (PARs, grains cm-2 year-1). Pollen counts are used to reconstruct plant abundances following the REVEALS approach. The principle of ROPES is that changes in plant abundance are linearly represented in observed PAR values. For example, if the PAR of pine doubles, so should the REVEALS reconstructed abundance of pine. Consequently, if a REVEALS reconstruction is ‘correct’ (i.e. ‘correct’ PPEs are used) the ratio ‘PAR over REVEALS’ is constant for each taxon along all samples of a record. With incorrect PPEs, the ratio will instead vary. ROPES starts from random (likely incorrect) PPEs, but then adjusts them using an optimization algorithm with the aim to minimize variation in the ‘PAR over REVEALS’ ratio across the record. ROPES thus simultaneously calculates mean plant abundances and PPEs. We illustrate the approach with test applications on nine synthetic pollen records. The results show that good performance of ROPES requires data sets with high underlying variation, many samples and low noise in the PAR data. ROPES can deliver first landcover reconstructions in regions for which PPEs are not yet available. The PPEs provided by ROPES may then allow for further REVEALS-based reconstructions. Similarly, ROPES can provide insight in pollen productivity during distinct periods of the past such as the Lateglacial. We see a potential to study spatial and temporal variation in pollen productivity for example in relation to site parameters, climate and land use. It may even be possible to detect expansion of non-pollen producing areas in a landscape. Overall, ROPES will help produce more accurate landcover reconstructions and expand reconstructions into new study regions and non-analogue situations of the past. ROPES will be available within the R package DISQOVER.

Klimaschutz durch Schilfanbau

Die Art der Nutzung von Mooren ist in hohem Mase klimarelevant. Je nach Bewirtschaftungsweise kommt es entweder zur Emission von klimarelevanten Gasen oder zur Aufnahme und Speicherung von Kohlenstoffdioxid. Durch standortangepasste Biomasseproduktion kann ein Beitrag zum Klimaschutz geleistet werden.