| 01 Sep 2025
Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada
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.
Review for “Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada”
I read the manuscript "Mapping and modelling a boreal forest soil organic carbon predictor in the glacial till of Newfoundland, Canada" by Quinn et al. with great interest, as the goal of the paper is to create a high-resolution geospatial model predicting the distribution of Al rich weatherable minerals in glacial till in Newfoundland. The manuscript is overall well structured and easy to follow. However, I see a few major flaws that need to be addressed before the manuscript can be considered for publication.
General comments:
Available Al. The authors describe that Al availability (%Al) is calculated via the chemical index of alteration (CIA). This is not correct. The CIA measures the proportion of Al2O3 relative to mobile cations (Ca, K, Na). A high CIA means that the sample is highly weathered, Al-rich, and mobile cations are depleted. Thus, the CIA measures relative proportions during weathering, not which Al is actually bioavailable or extractable. In equation 2, the authors calculate the fraction of mobile cations remaining (100 – CIA)/100. This is then multiplied by Al203. To me, this is conceptually backwards, because a high CIA means more Al enrichment (more weathering), but their formula gives lower %Al when CIA is high. In essence, the authors calculate Al203 x (mobile cation fraction), to me, it is unclear what this represents physically. Either the authors need to describe better what they are using as input variables and why or they have to re-consider their calculations entirely.
Motivation/Framing. I find the title and motivation laid out in the abstract and introduction very misleading. As it is described right now, the motivation is to improve SOC predictions in boreal forests using Newfoundland as a case study. However, the paper itself is about predicting %Al which is thought by the authors to be important for predicting SOC. However, the authors do not show that their %Al maps actually improve SOC predictions. Either the authors need to quantitatively show that or reframe the motivation and title of the paper.
Geospatial modeling. The authors need to better describe and explain what the difference between the two random forest model is. From the manuscript, it is not clear to me what exactly it means that one model is “drawing additionally on the spatial context of sample points”. What are the “layers” that represent the distance to each bedrock unit? Is that the distance each sample has to all bedrock units present in the region? Isn’t each sample assigned to one bedrock unit? I guess I miss something here. Additionally, it is not clear why the presented set of covariates was chosen. For example, what is the underlying process for including “Distance from Ocean”?
Specific comments:
L262f: I don’t think that based on the presented results that the authors can conclude the map addresses the need to incorporate %Al into SOC modeling. The maps do not show any relationship with SOC.
L295: I don’t see any PDP plots in the manuscript.