Abstract
Abstract. Soil organic carbon (SOC) stored in northern peatlands and permafrost-affected soils are key components in the global carbon cycle. This article quantifies SOC stocks in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The random forest model performed best and was used to predict SOC for several depth increments at a spatial resolution of 1 m (1×1 m). A high-resolution (1 m) land cover classification generated for this study is the most relevant predictive variable. The landscape mean SOC storage (0–150 cm) is estimated to be 8.3 ± 8.0 kg C m−2 and the SOC stored in the top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predictive modeling highlights the relative importance of wetland areas and in particular peat plateaus for the landscape's SOC storage. The total SOC was also predicted at reduced spatial resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a significant drop in land cover class detail and a tendency to underestimate the SOC at resolutions > 30 m. This is associated with the occurrence of many small-scale wetlands forming local hot-spots of SOC storage that are omitted at coarse resolutions. Sharp transitions in SOC storage associated with land cover and permafrost distribution are the most challenging methodological aspect. However, in this study, at local, regional and circum-Arctic scales, the main factor limiting robust SOC mapping efforts is the scarcity of soil pedon data from across the entire environmental space. For the Abisko region, past SOC and permafrost dynamics indicate that most of the SOC is barely 2000 years old and very dynamic. Future research needs to investigate the geomorphic response of permafrost degradation and the fate of SOC across all landscape compartments in post-permafrost landscapes.
Highlights
Northern high latitudes are among the regions most affected by increasing temperatures and climate change (IPCC, 2013)
The land cover classification (LCC) showed good agreement with the classes that have been observed in the field and areas that have been visually identified in the orthophoto (Fig. 4a and b)
The accuracy assessment results in a kappa value of 0.71 and an overall accuracy of 74 % (Table S2). These values are comparable to other high-latitude LCC accuracy assessments (Schneider et al, 2009; Siewert et al, 2015; Virtanen et al, 2004; Virtanen and Ek, 2014)
Summary
Northern high latitudes are among the regions most affected by increasing temperatures and climate change (IPCC, 2013). Cold temperatures and waterlogging are characteristics of wetlands, peatlands and permafrost-affected soils that reduce decomposition rates of SOC (Davidson and Janssens, 2006; Ping et al, 2015). This has led to the accumulation of large stocks of SOC in high-latitude ecosystems (Tarnocai et al, 2009). SOC stocks in the circumpolar permafrost region are estimated to be ∼ 1300 Pg, including soils to a depth of 3 m and other unconsolidated deposits (Hugelius et al, 2014) This corresponds to around half of the global SOC stocks (Köchy et al, 2015). Warming temperatures and environmental changes caused by warming of soils and consequent permafrost degradation are projected to lead to a gradual and prolonged release of greenhouse gases in the future (Schuur et al, 2015)
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