globalchange  > 气候减缓与适应
DOI: 10.1088/1748-9326/aaed52
WOS ID: WOS:000455056100004
Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest
Author: McNicol, Gavin1; Bulmer, Chuck2; D'; Amore, David3; Sanborn, Paul4,5; Saunders, Sari6; Giesbrecht, Ian5,7; Arriola, Santiago Gonzalez5; Bidlack, Allison1; Butman, David8; Buma, Brian9
Corresponding Author: McNicol, Gavin
ISSN: 1748-9326
Publishing Year: 2019
Volume: 14, Issue:1
Language: 英语
Keyword: machine learning ; digital soil mapping ; coastal temperate rainforest ; soil organic carbon ; biogeochemistry ; soil science
WOS Category: Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS Research Area: Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
English Abstract:

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 1m. 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 water sheds 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 PgC(to 1m) 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 fromtwo spatially explicit global products (Pearson's correlation: rho = .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 amoisture-dependent hotspot for SOCstorage at mid-latitudes.

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Document Type: 期刊论文
Appears in Collections:气候减缓与适应

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Affiliation: 1.Univ Alaska Southeast, Alaska Coastal Rainforest Ctr, Juneau, AK 99801 USA
2.BC Minist Forests Lands & Nat Resource Operat, Forest Sci Sect, 3401 Reservoir Rd, Vernon, BC V1B 2C7, Canada
3.US Forest Serv, USDA, Pacific Northwest Res Stn, Juneau, AK 99801 USA
4.Univ Northern British Columbia, Ecosyst Sci & Management Program, 3333 Univ Way, Prince George, BC V2N 4Z9, Canada
5.Tula Fdn, Hakai Inst, POB 309, Heriot Bay, BC V0P 1H0, Canada
6.BC Minist Forests Lands & Nat Resource Operat, 103-2100 Labieux Rd, Nanaimo, BC V9T 6E9, Canada
7.Simon Fraser Univ, Sch Resource & Environm Management, 8888 Univ Dr, Burnaby, BC V5A 1S6, Canada
8.Univ Washington, Sch Environm & Forest Sci & Civil & Environm Engn, Seattle, WA 98195 USA
9.Univ Colorado, Dept Integrat Biol, 1151 Arapahoe,SI 4101, Denver, CO 80301 USA

Recommended Citation:
McNicol, Gavin,Bulmer, Chuck,D',et al. Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest[J]. ENVIRONMENTAL RESEARCH LETTERS,2019-01-01,14(1)
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