Environmental drivers and remote sensing proxies of post-fire thaw depth in Eastern Siberian larch forests

• Main Authors: Diaz, Lucas Ribeiro, Delcourt, Clement J. F., Langer, Moritz, Loranty, Michael M., Rogers, Brendan M., Scholten, Rebecca C., Shestakova, Tatiana A., Talucci, Anna C., Vonk, Jorien E., Wangchuk, Sonam, Veraverbeke, Sander
• Format: Text
• Language: English
• Published: 2024
• Subjects: permafrost; Siberia
• Online Access: https://doi.org/10.5194/egusphere-2024-469; https://egusphere.copernicus.org/preprints/2024/egusphere-2024-469/

Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth ( r = 0.65, p < 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows ...