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Soil organic carbon (SOC) plays an important role in global carbon cycle which is influenced by multiple factors. Elevation, as one of these factors, has a close but complex relationship with SOC concentration. The traditional 'global' statistical models cannot capture the spatial variation thus are inefficient in revealing the relationships between SOC and elevation at the local scale. In this study, a 'local' model of geographically weighted regression (GWR) was used to explore the complex relationships between SOC and elevation in the topsoil of Ireland based on the dataset from National Soil Database of Ireland. The results indicated SOC and elevation exhibited the spatially continuously varying relationships across the study area. Positive relationships in the mountainous areas suggested SOC concentration increased with the increasing elevation. Negative relationships were observed in the midlands where SOC concentration decreased with the increasing elevation. Such varying relationships between SOC and elevation in Ireland were related to the two different main types of peat: blanket peat and raised peat. These two types of peat have different formation processes, thus are distributed at different elevations. In the mountainous areas, low temperature and high humidity create cool and anoxic environment that mitigates SOC mineralization, promoting the accumulation of blanket peat at a high elevation. In the midlands, the low-lying lakes and wetlands provide anoxic environment where raised peat is developed and located at a low elevation. The findings of the spatially varying relationships between SOC and elevation in Ireland have demonstrated the importance of modelling SOC at the 'local' level. Attention is required for soil mapping using a global algorithm and it is recommended that localized algorithms are considered in modelling SOC, taking the feature of continuous variation into consideration.
Li et al. (Sun,) studied this question.