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

  • Bin Wang
  • , Jonathan M. Gray
  • , Cathy M. Waters
  • , Muhuddin Rajin Anwar
  • , Susan E. Orgill
  • , Annette L. Cowie
  • , Puyu Feng
  • , De Li Liu
  • NSW Department of Primary Industries
  • Industry and Environment
  • China Agricultural University
  • University of New South Wales

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number115442
JournalGeoderma
Volume405
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Climate change
  • Global climate models
  • Multiple linear regression
  • Random forest
  • Soil organic carbon
  • South-eastern Australia

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