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Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia.

Authors :
Wang, Bin
Gray, Jonathan M.
Waters, Cathy M.
Rajin Anwar, Muhuddin
Orgill, Susan E.
Cowie, Annette L.
Feng, Puyu
Li Liu, De
Source :
Geoderma. Jan2022, Vol. 405, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

• MLR and RF were used to estimate current SOC stocks in south-eastern Australia. • Rainfall and minimum temperature were major drivers in determining SOC stocks. • The multi-GCM ensemble means suggested an overall decrease in SOC stocks. • The largest mean decrease of SOC stocks occurs in the high-rainfall alpine regions. Soil organic carbon (SOC) plays a key role in the sequestration of carbon that could otherwise be warming the atmosphere. Climate change including increased temperature and changed rainfall will greatly impact the global SOC cycle. There are still significant gaps in our knowledge of the size of the global SOC pool and how future climate will affect SOC stocks and flows in many parts of the world, including Australia. In this study, we used SOC data in a Digital Soil Mapping framework to predict current and future SOC stocks across the state of New South Wales (NSW) in south-eastern Australia. In the first phase of the study we estimated the current SOC stock using multiple linear regression (MLR) and random forest (RF) modelling, and in the second phase we projected the change of SOC stocks in the near future (2050s) and far future (2090s) under two Shared Socio-economic Pathways (SSPs) scenarios based on 25 global climate models (GCMs) from the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Our spatial modelling showed that estimated current SOC stocks in NSW decreased from east to west. Multi-GCM ensemble means suggested SOC stocks would decrease by 7.6–12.9% under SSP2-4.5 and 9.1–20.9% under SSP5-8.5 across NSW under future climate. The extent of change in SOC stocks varied spatially with the largest mean decrease of SOC stocks occurring in the North Coast and South East (alpine) regions of NSW. Our findings can support decision-making in land management and climate change mitigation strategies in NSW at the regional level. Furthermore, the modelling methods can be applied to other areas where edaphic and landscape properties, land use, and climate data are available. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00167061
Volume :
405
Database :
Academic Search Index
Journal :
Geoderma
Publication Type :
Academic Journal
Accession number :
153239317
Full Text :
https://doi.org/10.1016/j.geoderma.2021.115442