Ecosystem modelling of tropical wetlands

4.1 Background Modelling is essential for enhancing our understanding of the functioning of tropical wetland ecosystems, and for simulating future trajectories and testing for system thresholds. Anthropogenic activities such as drainage and land-use change can be integrated in models and their impacts on fluxes of greenhouse gas concentrations simulated. Models can also be used to test the response of peatlands and mangroves to climate extremes, variability and change, and to estimate reference levels and greenhouse gas emissions scenarios in the framework of climate change mitigation projects such as REDD+. In coastal settings, models are used to explore wetland resilience to sea-level rise. Finally, models can also be developed to support the decision making process by providing policyrelevant information on the consequences and trade-offs of adopting different management and climate scenarios

Soil nitrous oxide and methane fluxes from a land-use change transition of primary forest to oil palm in an Indonesian peatland

Despite the documented increase in greenhouse gas (GHG) emissions from Southeast Asian peat swamp forest degradation and conversion to oil palm over recent decades, reliable estimates of emissions of nitrous oxide (N2O) and methane (CH4) are lacking. We measured soil fluxes of N2O and CH4 and their environmental controls along a peatland transition from primary forest (PF) to degraded drained forest (DF) to oil palm plantation (OP) over 18 months in Jambi, Sumatra, Indonesia. Sampling was conducted monthly at all sites and more intensively following two fertilization events in the OP. Mean annual emissions of N2O (kg N ha−1 yr−1) were 1.7 ± 0.2 for the PF, 2.3 ± 0.2 for the DF and for the OP 8.1 ± 0.8 without drainage canals (DC) and 7.7 ± 0.7 including DC. High N2O emissions in the OP were driven by peat decomposition, not by N fertilizer addition. Mean CH4 annual fluxes (kg C ha−1 yr−1) were 8.2 ± 1.9 for the PF, 1.9 ± 0.4 for the DF, and 1.6 ± 0.3 for the OP with DC and 1.1 ± 0.2 without. Considering their 20-year global warming potentials (GWP), the combined non-CO2 GHG emission (Mg CO2-equivalent ha−1 yr−1) was 3.3 ± 0.6 for the PF and 1.6 ± 0.2 for the DF. The emission in the OP (3.8 ± 0.3 with or without DC) was similar to the PF because reductions in CH4 emissions offset N2O increases. However, considering 100-year GWP, the combined non-CO2 GHG emission was larger in the OP (3.4 ± 0.3 with DC and 3.5 ± 0.3 without) compared to both the PF and the DF (1.5 ± 0.2 and 1.2 ± 0.1, respectively). The increase in peat N2O emissions associated with the land-use change transition from primary forest to oil palm plantation at our sites provides further evidence of the urgent need to protect tropical peat swamp forests from drainage and conversion

Will CO2 Emissions from Drained Tropical Peatlands Decline Over Time? Links Between Soil Organic Matter Quality, Nutrients, and C Mineralization Rates

Peer Revie

Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon

Peru has the fourth largest area of peatlands in the Tropics. Its most representative land cover on peat is a Mauritia flexuosa dominated palm swamp (thereafter called dense PS), which has been under human pressure over decades due to the high demand for the M. flexuosa fruit often collected by cutting down the entire palm. Degradation of these carbon dense forests can substantially affect emissions of greenhouse gases and contribute to climate change. The first objective of this research was to assess the impact of dense PS degradation on forest structure and biomass carbon stocks. The second one was to explore the potential of mapping the distribution of dense PS with different degradation levels using remote sensing data and methods. Biomass stocks were measured in 0.25 ha plots established in areas of dense PS with low (n = 2 plots), medium (n = 2) and high degradation (n = 4). We combined field and remote sensing data from the satellites Landsat TM and ALOS/PALSAR to discriminate between areas typifying dense PS with low, medium and high degradation and terra firme, restinga and mixed PS (not M. flexuosa dominated) forests. For this we used a Random Forest machine learning classification algorithm. Results suggest a shift in forest composition from palm to woody tree dominated forest following degradation. We also found that human intervention in dense PS translates into significant reductions in tree carbon stocks with initial (above and below-ground) biomass stocks (135.4 ± 4.8 Mg C ha−1) decreased by 11 and 17% following medium and high degradation. The remote sensing analysis indicates a high separability between dense PS with low degradation from all other categories. Dense PS with medium and high degradation were highly separable from most categories except for restinga forests and mixed PS. Results also showed that data from both active and passive remote sensing sensors are important for the mapping of dense PS degradation. Overall land cover classification accuracy was high (91%). Results from this pilot analysis are encouraging to further explore the use of remote sensing data and methods for monitoring dense PS degradation at broader scales in the Peruvian Amazon. Providing precise estimates on the spatial extent of dense PS degradation and on biomass and peat derived emissions is required for assessing national emissions from forest degradation in Peru and is essential for supporting initiatives aiming at reducing degradation activities

Peatland core domain sets: building consensus on what should be measured in research and monitoring

It is often difficult to compile and synthesise evidence across multiple studies to inform policy and practice because different outcomes have been measured in different ways or datasets and models have not been fully or consistently reported. In the case of peatlands, a critical terrestrial carbon store, this lack of consistency hampers the evidence-based decisions in policy and practice that are needed to support effective restoration and conservation. This study adapted methods pioneered in the medical community to reach consensus over peatland outcomes that could be consistently measured and reported to improve the synthesis of data and reduce research waste. Here we report on a methodological framework for identifying, evaluating and prioritising the outcomes that should be measured. We discuss the subsequent steps to standardise methods for measuring and reporting outcomes in peatland research and monitoring. The framework was used to identify and prioritise sets of key variables (known as core domain sets) for UK blanket and raised bogs, and for tropical peat swamps. Peatland experts took part in a structured elicitation and prioritisation process, comprising two workshops and questionnaires, that focused on climate (32 and 18 unique outcomes for UK and tropical peats, respectively), hydrology (26 UK and 16 tropical outcomes), biodiversity (8 UK and 22 tropical outcomes) and fire-related outcomes (13, for tropical peatlands only). Future research is needed to tackle the challenges of standardising methods for data collection, management, analysis, reporting and re-use, and to extend the approach to other types of peatland. The process reported here is a first step towards creating datasets that can be synthesised to inform evidence-based policy and practice, and contribute towards the conservation, restoration and sustainable management of this globally significant carbon store. evidence-based policy and practice, evidence synthesis, outcomes, standardisationpublishedVersio

Risks to carbon storage from land-use change revealed by peat thickness maps of Peru

This work was funded by NERC (grant ref. NE/R000751/1) to I.T.L., A.H., K.H.R., E.T.A.M., C.M.A., T.R.B., G.D. and E.C.D.G.; Leverhulme Trust (grant ref. RPG-2018-306) to K.H.R., L.E.S.C. and C.E.W.; Gordon and Betty Moore Foundation (grant no. 5439, MonANPeru network) to T.R.B., E.N.H.C. and G.F.; Wildlife Conservation Society to E.N.H.C.; Concytec/British Council/Embajada Británica Lima/Newton Fund (grant ref. 220–2018) to E.N.H.C. and J.D.; Concytec/NERC/Embajada Británica Lima/Newton Fund (grant ref. 001–2019) to E.N.H.C. and N.D.; the governments of the United States (grant no. MTO-069018) and Norway (grant agreement no. QZA-12/0882) to K.H.; and NERC Knowledge Exchange Fellowship (grant ref no. NE/V018760/1) to E.N.H.C.Tropical peatlands are among the most carbon-dense ecosystems but land-use change has led to the loss of large peatland areas, associated with substantial greenhouse gas emissions. To design effective conservation and restoration policies, maps of the location and carbon storage of tropical peatlands are vital. This is especially so in countries such as Peru where the distribution of its large, hydrologically intact peatlands is poorly known. Here field and remote sensing data support the model development of peatland extent and thickness for lowland Peruvian Amazonia. We estimate a peatland area of 62,714 km2 (5th and 95th confidence interval percentiles of 58,325 and 67,102 km2, respectively) and carbon stock of 5.4 (2.6–10.6) PgC, a value approaching the entire above-ground carbon stock of Peru but contained within just 5% of its land area. Combining the map of peatland extent with national land-cover data we reveal small but growing areas of deforestation and associated CO2 emissions from peat decomposition due to conversion to mining, urban areas and agriculture. The emissions from peatland areas classified as forest in 2000 represent 1–4% of Peruvian CO2 forest emissions between 2000 and 2016. We suggest that bespoke monitoring, protection and sustainable management of tropical peatlands are required to avoid further degradation and CO2 emissions.PostprintPeer reviewe

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe