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Tropical peatlands play an important role in global carbon (C) cycling, but little is known about factors driving carbon dioxide (CO2) and methane (CH4) emissions from these ecosystems, especially production in deeper soils. This study aimed to identify source material and processes regulating C emissions originating deep in three sites in a peatland on the Caribbean coast of Panama. We hypothesized that (1) surface-derived organic matter transported down the soil profile is the primary C source for respiration products at depth and that (2) high lignin content results in hydrogenotrophic methanogenesis as the dominant CH4 production pathway throughout the profile. We used radiocarbon isotopic values to determine whether CO2 and CH4 at depth are produced from modern substrates or ancient deep peat, and we used stable C isotopes to identify the dominant CH4 production pathway. Peat organic chemistry was characterized using 13C solid-state nuclear magnetic resonance spectroscopy (13C-NMR). We found that deep peat respiration products had radiocarbon signatures that were more similar to surface dissolved organic C (DOC) than deep solid peat. These results indicate that surface-derived organic matter was the dominant source for gas production at depth in this peatland, likely because of vertical transport of DOC from the surface to depth. Lignin, which was the most abundant compound (55 %–70 % of C), increased with depth across these sites, whereas other C compounds like carbohydrates did not vary with depth. These results suggest that there is no preferential decomposition of carbohydrates but instead preferential retention of lignin. Stable isotope signatures of respiration products indicated that hydrogenotrophic rather than acetoclastic methanogenesis was the dominant production pathway of CH4 throughout the peat profile. These results show that deep C in tropical peatlands does not contribute greatly to surface fluxes of carbon dioxide, with compounds like lignin preferentially retained. This protection of deep C helps explain how peatland C is retained over thousands of years and points to the vulnerability of this C should anaerobic conditions in these wet ecosystems change. 

These images depict drainage canals and roads in peatlands in Borneo, Sumatra, and Peninsular Malaysia at 5 meter resolution. These canals were detected from July-September 2017 Planet Basemaps satellite imagery using a convolutional neural network. Please contact Nathan Dadap (ndadap@stanford.edu) with any questions. 

Abstract 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. 

This repository contains soil moisture and vegetation optical depth (VOD) retrievals from the Multi-Temporal Dual Channel Algorithm applied to SMAP observations. The data are subset for Insular Southeast Asia (ISEA), which encompasses Sumatra, Borneo, and Peninsular Malaysia, as well as the two year period from April 1, 2015 to March 31, 2017. 

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study.Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight.Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators.Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication.We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices.Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century. 

Abstract 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. 

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model–data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions. 

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.