Modeling peat burned area and understanding its drivers with machine learning

Peatland fires pose significant environmental and societal challenges. We recently advanced the Canadian Fire Weather Index (FWI) system for northern peatlands by integrating peatland-specific hydrological data derived from assimilating Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperature observations into the NASA Catchment Land Surface model with its peatland modules, ‘PEATCLSM’. This novel FWI_peat (Mortelmans et al. 2024) was evaluated using satellite-based fire presence data over boreal peatlands from 2010 through 2018, demonstrating improved estimation of peatland fire presence.

Here, we extend the use of this renewed FWI_peat system by integrating it into a machine learning framework to gain deeper insights into when, where, and why peatlands burn. We utilize an XGBoost algorithm trained on peatland burned area data from 2012-2023, incorporating a suite of predictors, including (i) peatland distribution characteristics, (ii) peatland groundwater table, (iii) lightning occurrence, (iv) meteorological data, (v) vegetation properties, and (vi) socio-economic factors. This approach enables proactive fire risk management strategies and contributes to a comprehensive assessment of peatland fire vulnerability and resilience. Preliminary results indicate the importance of peatland groundwater table and lightning occurrence in estimating peat burned area.

Mortelmans, J., Felsberg, A., De Lannoy, G. J. M., Veraverbeke, S., Field, R. D., Andela, N., and Bechtold, M.: Improving the fire weather index system for peatlands using peat-specific hydrological input data, Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, 2024.