气候变化领域集成服务门户(CCPortal): Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

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DOI	10.1088/1748-9326/aaed52
	Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest
	McNicol G.; Bulmer C.; D'Amore D.; Sanborn P.; Saunders S.; Giesbrecht I.; Arriola S.G.; Bidlack A.; Butman D.; Buma B.
发表日期	2019
ISSN	17489318
卷号	14 期号:1
英文摘要	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 1000 to >3000 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 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 compared very favorably to regionalized estimates from two spatially-explicit global products (Pearson's correlation: ρ = 0.73 versus 0.34). Notably, SoilGrids 250 m 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. © 2019 The Author(s). Published by IOP Publishing Ltd.
英文关键词	biogeochemistry; coastal temperate rainforest; digital soil mapping; machine learning; soil organic carbon; soil science
语种	英语
scopus关键词	Biogeochemistry; Classification (of information); Climate change; Correlation methods; Decision trees; Digital storage; Earth (planet); Ecosystems; Forecasting; Learning systems; Machine learning; Soil moisture; Soil surveys; Uncertainty analysis; Digital soil mappings; Machine learning approaches; Random forest modeling; Soil classification; Soil organic carbon; Soil science; Specific information; Temperate rainforest; Organic carbon; biogeochemistry; carbon cycle; climate change; digital mapping; machine learning; pedology; rainforest; size distribution; soil carbon; soil moisture; soil organic matter; soil science; temperate environment; uncertainty analysis; watershed; Pacific Ocean; Pacific Ocean (North)
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154728
作者单位	Alaska Coastal Rainforest Center, University of Alaska Southeast, Juneau, AK 99801, United States; B. C. Ministry of Forests, Lands and Natural Resource Operations, Forest Sciences Section, 3401 Reservoir Rd., Vernon, BC V1B 2C7, Canada; USDA Forest Service, Pacific Northwest Research Station, Juneau, AK 99801, United States; Ecosystem Science and Management Program, University of Northern British Columbia, 3333 University Way, Prince George, BC V2N 4Z9, Canada; Hakai Institute, Tula Foundation, PO Box 309, Heriot Bay, BC V0P 1H0, Canada; B. C. Ministry of Forests, Lands and Natural Resource Operations, 103-2100 Labieux Rd., Nanaimo, BC V9T 6E9, Canada; School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; School of Environmental and Forest Sciences and Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States; Department of Integrative Biology, University of Colorado, 1151 Arapahoe, Denve...
推荐引用方式 GB/T 7714	McNicol G.,Bulmer C.,D'Amore D.,et al. Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest[J],2019,14(1).
APA	McNicol G..,Bulmer C..,D'Amore D..,Sanborn P..,Saunders S..,...&Buma B..(2019).Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest.Environmental Research Letters,14(1).
MLA	McNicol G.,et al."Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest".Environmental Research Letters 14.1(2019).