ML4WX

Applications & impacts

Climate change-induced peatland drying in Southeast Asia Authors

Nathan C. Dadap, Ale×ander R. Cobb, Alison May Hoyt, Charles F. Harvey, Andrew F. Feldman, Eun‐Soon Im, Alexandra G. Konings

Environmental Research Letters · · Massachusetts Institute of Technology (MIT), NASA, Stanford University

Paper

In one sentence. Neural networks trained on SMAP soil moisture project Southeast Asian peat drying under RCP8.5, finding decreases about half as strong as recent El Niño droughts.

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 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.

Labels

Topic
Applications & impacts, Climate analysis & attribution
Type
Application study
Method
Classical ML (trees, GP, linear, kernels)
Time scale
Climate projections / centennial
Domain
Regional
Data
CMIP / climate model output, Satellite
Status
Research
Identifiers
DOI:10.1002/essoar.10511429.1 OpenAlex:W4281260359
Added
2026-10-22

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