Hydrological drivers of hydrogen cyanide wildfire emissions from Indonesian peat fires during the 2015, 2019, and 2023 El Niño events

Indonesian peatlands store vast amounts of carbon that are highly vulnerable to fire during El Niño-driven droughts. When ignited, peat releases large quantities of greenhouse gases and other species with significant environmental impacts, including hydrogen cyanide (HCN), a sensitive tracer of smouldering combustion. In this work, we use new satellite retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) spectra using the University of Leicester IASI retrieval scheme (ULIRS), that we validate against ground-based Fourier transform infrared (FTIR) measurements at Reunion Maido, TOMCAT atmospheric model simulations, hydrological information, and fire activity observations to evaluate the factors driving trace gas emissions during the 2015, 2019, and 2023 El Niño events.

The 2015 El Niño produced large burdens of HCN and carbon monoxide (CO), unprecedented in the satellite observational era and driven by exceptionally low soil moisture, depressed groundwater levels, and deep burn depths. In contrast, the 2019 and 2023 events exhibited markedly weaker emissions despite similar Oceanic Niño Index (ONI) anomalies, reflecting more favourable hydrological conditions. Comparisons of the satellite trace gas observations with simulations of the TOMCAT model show that burned-area-based inventories such as the Global Fire Emissions Database (GFED) version 4 substantially overestimate emissions from peat fires in 2015, while a new peat-specific database, FINNpeatSM (Fire Inventory from NCAR (FINNv1.5) New peat with Soil Moisture), better represents fire season timing and burn depth by incorporating soil moisture constraints. From satellite-derived HCN : CO enhancement ratios, we provide new emission factors for HCN that offer benchmarks for new emission inventories.

Our results show that peat fire intensity and emissions are driven not only by El Niño strength but also by local hydrological conditions such as soil water content and precipitation. Integrating hydrological indicators with satellite observations of atmospheric composition is therefore critical for improving fire emission inventories.