Search for: All records

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

 

Peatlands account for 15 to 30% of the world’s soil carbon (C) stock and are important controls over global nitrogen (N) cycles. However, C and N concentrations are known to vary among peatlands contributing to the uncertainty of global C inventories, but there are few global studies that relate peatland classification to peat chemistry. We analyzed 436 peat cores sampled in 24 countries across six continents and measured C, N, and organic matter (OM) content at three depths down to 70 cm. Sites were distinguished between northern (387) and tropical (49) peatlands and assigned to one of six distinct broadly recognized peatland categories that vary primarily along a pH gradient. Peat C and N concentrations, OM content, and C:N ratios differed significantly among peatland categories, but few differences in chemistry with depth were found within each category. Across all peatlands C and N concentrations in the 10–20 cm layer, were 440 ± 85.1 g kg -1 and 13.9 ± 7.4 g kg -1 , with an average C:N ratio of 30.1 ± 20.8. Among peatland categories, median C concentrations were highest in bogs, poor fens and tropical swamps (446–532 g kg -1 ) and lowest in intermediate and extremely rich fens (375–414 g kg -1 ). The C:OM ratio in peat was similar across most peatland categories, except in deeper samples from ombrotrophic tropical peat swamps that were higher than other peatlands categories. Peat N concentrations and C:N ratios varied approximately two-fold among peatland categories and N concentrations tended to be higher (and C:N lower) in intermediate fens compared with other peatland types. This study reports on a unique data set and demonstrates that differences in peat C and OM concentrations among broadly classified peatland categories are predictable, which can aid future studies that use land cover assessments to refine global peatland C and N stocks. 

Fires that emit massive amounts of CO₂and particulate matter now burn with regularity in Southeast Asian tropical peatlands. Natural peatlands in Southeast Asia are waterlogged for most of the year and experience little or no fire, but networks of canals constructed for agriculture have drained vast areas of these peatlands, making the soil vulnerable to fire during periods of low rainfall. While soil moisture is the most direct measure of peat flammability, it has not been incorporated into fire studies due to an absence of regional observations. Here, we create the first remotely sensed soil moisture dataset for tropical peatlands in Sumatra, Borneo and Peninsular Malaysia by applying a new retrieval algorithm to satellite data from the Soil Moisture Active Passive (SMAP) mission with data spanning the 2015 El Niño burning event. Drier soil up to 30 days prior to fire correlates with larger burned area. The predictive information provided by soil moisture complements that of precipitation. Our remote sensing-derived results mirror those from a laboratory-based peat ignition study, suggesting that the dependence of fire on soil moisture exhibits scale independence within peatlands. Soil moisture measured from SMAP, a dataset spanning 2015-present, is a valuable resource for peat fire studies and warning systems.

 

Peatlands cover many low‐lying areas in the tropics. Tropical peatlands are intriguing systems because of their tight coupling between hydrology and carbon storage: They accumulate carbon over thousands of years because of waterlogging, and they remain waterlogged after growing into domed shapes because peat restricts drainage. This feedback between waterlogging and landscape morphology generates landforms with special hydrologic properties that enable simplifications of standard watershed models. In natural tropical peatlands, the water table is always near the surface and infiltration is almost immediate. In addition, water table fluctuations relative to the peat surface are remarkably uniform across tropical peatlands because these peatlands acquire shapes with a uniform topographic wetness index. In this paper, we show that because of these distinctive properties, simple hydrologic models that represent the hydraulic state of a catchment by a scalar quantity that describes total water storage are useful and physically meaningful in tropical peatlands. We demonstrate how to efficiently derive hillslope‐scale parameterizations of transmissivity and specific yield as functions of water table height for a tropical peatland from water table, rainfall, and topographic data. Our findings suggest that natural tropical peatland subcatchments could be usefully modeled as single hydrologic response units for river flow and flood forecasting.