Preprints
https://doi.org/10.5194/egusphere-2025-3475
https://doi.org/10.5194/egusphere-2025-3475
01 Sep 2025
 | 01 Sep 2025
Status: this preprint is open for discussion and under review for Biogeosciences (BG).

Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada

Scout M. Quinn, Benjamin Misiuk, Mackenzie E. Patrick, and Susan E. Ziegler

Abstract. Boreal forest soils store 30 % of global forest soil carbon, making them a crucial component of the carbon cycle. However, climate change at high latitudes is resulting in heightened temperatures and increasingly unpredictable precipitation patterns. Soil organic carbon (SOC) formation and stabilization is tied to precipitation patterns, thus climate change will inevitably influence the stability and longevity of boreal forest SOC. The current size and distribution of this reservoir is poorly understood, creating uncertainty under current and future climate scenarios. Previous research demonstrates mineral soil properties may be used to model boreal forest SOC accurately. The surface slope, depth of carbon enriched horizon, and climate characteristics are important parameters for modelling SOC and can generally be obtained or estimated via remote sensing. However, information about aluminum availability – the weatherable aluminum capable of interacting with organic matter to form stable carbon rich organometal complexes in mineral soils – is not widely available but is controlled by soil parent material. To bridge this gap, the Newfoundland and Labrador till geochemistry dataset was used here to map and model aluminum availability in glacial till across the island of Newfoundland as a function of geology and climate. The Random Forest Algorithm was employed to develop two models: one relying solely on the strength of geological and climatic variables, and the other drawing additionally on the spatial context of sample points. The first model performed well (R2=0.60), however, adding a spatial component increased the performance of the second model (R2=0.71). Bedrock type and proximity of the samples to certain units were indicated to be the strongest controls on aluminum availability, while environmental factors were less influential. Additionally, model uncertainty was calibrated empirically and mapped spatially, providing reliable and actionable information about the confidence of predictions over the study area. This project demonstrates the value of predictive geospatial modelling for till geochemistry mapping and delivers key aluminum availability predictions for deriving SOC reservoir estimates across Newfoundland.

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Scout M. Quinn, Benjamin Misiuk, Mackenzie E. Patrick, and Susan E. Ziegler

Status: open (until 13 Oct 2025)

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Scout M. Quinn, Benjamin Misiuk, Mackenzie E. Patrick, and Susan E. Ziegler

Data sets

Replication Data for: Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada Version 1.1 Scout M. Quinn et al. https://doi.org/10.5683/SP3/6AONRU

Model code and software

smquinn99/SMQuinn_Mapping_and_modelling_a_boreal_forest_soil_organic_carbon_predictor_in_glacial_tills_of_NL Scout M. Quinn https://doi.org/10.5281/zenodo.16109126

Scout M. Quinn, Benjamin Misiuk, Mackenzie E. Patrick, and Susan E. Ziegler

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Short summary
Boreal forest soils store approximately one third of forest soil carbon globally. Stabilization of organic carbon in these soils is largely controlled by reactive aluminum content derived from parent material, which in boreal regions is typically glacial till. We used a Random Forest approach to predictively map and model reactive aluminum and its uncertainty in glacial till across Newfoundland. This supports future modelling efforts and estimates of the soil carbon reservoir in boreal forests.
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