Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data

Abstract

Abstract. The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (local models) that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model (global model) that covers the whole area. The goal of this study was therefore to (1) assess how global and local models differ in predicting the humus layer, mineral soil, and total SOC stock in Swedish forests and (2) identify the key factors for SOC stock prediction and their scale of influence. We used the Swedish National Forest Soil Inventory (NFSI) database and a digital soil mapping approach to evaluate the prediction performance using random forest models calibrated locally for the northern, central, and southern Sweden (local models) and for the whole of Sweden (global model). Models were built by considering (1) only site characteristics which are recorded on the plot during the NFSI, (2) the group of covariates (remote sensing, historical land use data, etc.) and (3) both site characteristics and group of covariates consisting mostly of remote sensing data. Local models were generally more effective for predicting SOC stock after testing on independent validation data. Using the group of covariates together with NFSI data indicated that such covariates have limited predictive strength but that site-specific covariates from the NFSI showed better explanatory strength for SOC stocks. The most important covariates that influence the humus layer, mineral soil (0–50 cm), and total SOC stock were related to the site-characteristic covariates and include the soil moisture class, vegetation type, soil type, and soil texture. This study showed that local calibration has the potential to improve prediction accuracy, which will vary depending on the type of available covariates.

Soil moisture controls the partitioning of carbon stocks across a managed boreal forest landscape

“…The bulk density of the mineral soil horizons was calculated using the SFSI procedure, which is based on a pedotransfer function that depends on the C concentration and depth (cm) 10 , 33 : …”

“…It is well established that soil forming factors are sensitive to climate, time, organisms, parent material and topography 8 , all of which by extension influence the development of the SOC pool. Several studies have identified climate as a key driver of SOC accumulation on global and regional scales, mainly because of its impact on temperature and precipitation 9 , 10 . However, on smaller landscape scales (up to several tens of km 2 ), site-specific soil-forming factors such as local topography may be more important because some of the factors mentioned above can be considered constant and are thus controlled for in small scale observational studies 11 .…”

Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions

“…The soil moisture regime, which is also a strong regulator of soil organic matter (SOM) dynamics, is a critical factor for ecosystem functioning and management in the boreal regions (Ivanov, 1981;Sewell et al, 2020). Soil moisture and SOM feedback has been clearly documented, for example, in central and northern Sweden which comprises a key boreal forest region (Hounkpatin et al, 2021). In Swedish boreal podzols, dry sites have an average soil organic carbon (SOC) stock of 6.7 kg C m −2 while mesic-moist sites had 9.7 kg C m −2 in the mineral horizons and 2.0 to 4.4 kg C m −2 alone in the organic horizon (Olsson et al, 2009).…”