Data from: Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Accurate soil organic carbon (SOC) maps are needed to predict the
terrestrial SOC feedback to climate change, one of the largest remaining
uncertainties in Earth system modeling. Over the last decade, global scale
models have produced varied predictions of the size and distribution of
SOC stocks, ranging from 1,000 to > 3,000 Pg of C within the top 1
m. Regional assessments may help validate or improve global maps because
they can examine landscape controls on SOC stocks and offer a tractable
means to retain regionally-specific information, such as soil taxonomy,
during database creation and modeling. We compile a new transboundary SOC
stock database for coastal watersheds of the North Pacific coastal
temperate rainforest, using soil classification data to guide gap-filling
and machine learning approaches used to explore spatial controls on SOC
and predict regional stocks. Precipitation and topographic attributes
controlling soil wetness were found to be the dominant controls of SOC,
underscoring the dependence of C accumulation on high soil moisture. The
random forest model predicted stocks of 4.5 Pg C (to 1 m) for the study
region, 22% of which was stored in organic soil layers. Calculated stocks
of 228 ± 111 Mg C ha-1 fell within ranges of several past regional studies
and indicate 11-33 Pg C may be stored across temperate rainforest soils
globally. Predictions were compared very favorably to regionalized
estimates from two spatially explicit global products (Pearson's
correlation: ρ = 0.73 vs. 0.34). Notably, SoilGrids250m was an outlier for
estimates of total SOC, predicting 4-fold higher stocks (18 Pg C) and
indicating bias in this global product for the soils of the temperate
rainforest. In sum, our study demonstrates that CTR ecosystems represent a
moisture-dependent hotspot for SOC storage at mid-latitudes.