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 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.

Field kits for testing the level of a toxicant in the environment are inherently less accurate than a laboratory instrument. Using a specific example, we argue here that kit measurements still have a key role to play when the spatial distribution of a toxicant is very heterogeneous. The context is provided by the groundwater arsenic problem in Bangladesh. We combine here two data sets, a blanket survey of 6595 wells over a 25 km2 based on laboratory measurements and 900 paired kit and laboratory measurements from the same area. We explore different hypothetical mitigation scenarios based on actual data that rely on households with a high-arsenic well switching to a nearby low-arsenic well. We show that the decline in average exposure to arsenic from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. Our analysis indicates that the 50 ug/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 ug/L, is close to optimal in terms of average exposure reduction. We also show, however, that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.