Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long-term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional-scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread-occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates, and corresponding carbon losses from decomposition, are 3.2 times larger in intensively drained areas than in non-drained areas, highlighting the central role of drainage in mediating peat carbon fluxes. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our dataset can be used to improve carbon emissions predictions in peatlands and to target areas for hydrologic restoration.

Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSMTrop,Nat) and drained (PEATCLSMTrop,Drain) tropical peatlands. The operational CLSM version (which includes peat as a soil class) and PEATCLSMTrop,Nat were forced with global meteorological input data and evaluated over the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. Evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation over all three study regions. Over Southeast Asia, an additional simulation with PEATCLSMTrop,Drain was run to address the large fraction of drained tropical peatlands in this region. PEATCLSMTrop,Drain outperformed both CLSM and PEATCLSMTrop,Nat over drained sites. Despite the overall improvements of both tropical PEATCLSM modules, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model.

Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly-decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The curved shape of peat domes accounts for much of the carbon storage in these landscapes, but their subtle topographic signal is difficult to measure. As many of the world’s tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. However, for better and for worse, tropical peatlands may also support forests with high above-ground biomass—averages of over 200 Mg C / ha have been reported—which increases their carbon stocks but further complicates determination of their surface topography using laser altimetry. In this work, we evaluate retrieval of ground elevations and canopy metrics derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne laser scanning dataset covering an area of over 100 km^2 in the Belait District of Brunei Darussalam. We find that despite infrequent ground retrievals, with regularization these spaceborne platforms can provide useful data for tropical peatland surface altimetry.

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 SMAP 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. Such reductions may be expected to accelerate peat 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.