气候变化领域集成服务门户

Carbon stored in soils contributes to a variety of soil functions, including biomass production, water storage and filtering, biodiversity maintenance, and many other ecosystem services. Understanding soil organic carbon (SOC) spatial distribution and projection of its future condition is essential for future CO2 emission estimates and management options for storing carbon. However, modeling SOC spatiotemporal dynamics is challenging due to the inherent spatial heterogeneity and data limitation. The present study developed a spatially explicit prediction model in which the spatial relationship between SOC observation and seventeen environmental variables was established using the Cubist regression tree algorithm. The model was used to compile a baseline SOC stock map for the top 30 cm soil depth in the State of Wisconsin (WI) at a 90 m x 90 m grid resolution. Temporal SOC trend was assessed by comparing baseline and future SOC stock maps based on the space-for-time substitution model. SOC prediction for future considers land use, precipitation and temperature for the year 2050 at medium (Al B) CO2 emissions scenario of the Intergovernmental Panel on Climate Change. Field soil observations were related to factors that are known to influence SOC distribution using the digital soil mapping framework. The model was validated on 25% test profiles (R-2: 0.38; RMSE: 0.64; ME: -0.03) that were not used during model training that used the remaining 75% of the data (R-2: 0.76; RMSE: 0.40; ME: -0.006). In addition, maps of the model error, and areal extent of Cubist prediction rules were reported. The model identified soil parent material and land use as key drivers of SOC distribution including temperature and precipitation. Among the terrain attributes, elevation, mass-balance index, mid-slope position, slope-length factor and wind effect were important. Results showed that Wisconsin soils had an average baseline SOC stock of 90 Mg ha(-1) and the distribution was highly variable (CV: 64%). It was estimated that WI soils would have an additional 20 Mg ha(-1) SOC by the year 2050 under changing land use and dimate. Histosols and Spodosols were expected to lose 19 Mg ha(-1)and 4 Mg ha(-1), respectively, while Mollisols were expected to accumulate the largest SOC stock (62 Mg ha(-1)). All land-use types would be accumulating SOC by 2050 except for wetlands (-34 Mg C ha(-1)). This study found that Wisconsin soils will continue to sequester more carbon in the coming decades and most of the Driftless Area will be sequestering the greatest SOC (+63 Mg C ha(-1)). Most of the SOC would be lost from the Northern Lakes and Forests ecological zone (-12 Mg C ha(-1)). The study highlighted areas of potential C sequestration and areas under threat of C loss. The maps generated in this study would be highly useful in farm management and environmental policy decisions at different spatial levels in Wisconsin. (C) 2019 The Authors. Published by Elsevier B.V.