Patterns and driving factors of biomass carbon and soil organic carbon stock in the Indian Himalayan region

Jitendra Ahirwal; Amitabha Nath; Biplab Brahma; Sourabh Deb; Uttam Kumar Sahoo; Arun Jyoti Nath

doi:10.1016/j.scitotenv.2021.145292

Patterns and driving factors of biomass carbon and soil organic carbon stock in the Indian Himalayan region

Sci Total Environ. 2021 May 20:770:145292. doi: 10.1016/j.scitotenv.2021.145292. Epub 2021 Jan 22.

Authors

Jitendra Ahirwal¹, Amitabha Nath², Biplab Brahma³, Sourabh Deb⁴, Uttam Kumar Sahoo¹, Arun Jyoti Nath⁵

Affiliations

¹ Department of Forestry, Mizoram University, Aizawl, India.
² Department of Information Technology, North-Eastern Hill University, Shillong, India.
³ Department of Ecology and Environmental Science, Assam University, Silchar, India.
⁴ Department of Forestry and Biodiversity, Tripura University, Suryamaninagar, India.
⁵ Department of Ecology and Environmental Science, Assam University, Silchar, India. Electronic address: arunjyotinath@gmail.com.

PMID: 33736385
DOI: 10.1016/j.scitotenv.2021.145292

Abstract

Tree-based ecosystems are critical to climate change mitigation. The study analysed carbon (C) stock patterns and examined the importance of environmental variables in predicting carbon stock in biomass and soils of the Indian Himalayan Region (IHR). We conducted a synthesis of 100 studies reporting biomass carbon stock and 67 studies on soil organic carbon (SOC) stock from four land-uses: forests, plantation, agroforest, and herbaceous ecosystem from the IHR. Machine learning techniques were used to examine the importance of various environmental variables in predicting carbon stock in biomass and soils. Despite large variations in biomass C and SOC stock (mean ± SD) within the land-uses, natural forests have the highest biomass C stock (138.5 ± 87.3 Mg C ha^-1), and plantation forests exhibited the highest SOC stock (168.8 ± 74.4 Mg C ha^-1) in the top 1-m of soils. The relationship between the environmental variables (altitude, latitude, precipitation, and temperature) and carbon stock was not significantly correlated. The prediction of biomass carbon and SOC stock using different machine learning techniques (Adaboost, Bagging, Random Forest, and XGBoost) shows that the XGBoost model can predict the carbon stock for the IHR closely. Our study confirms that the carbon stock in the IHR vary on a large scale due to a diverse range of land-use and ecosystems within the region. Therefore, predicting the driver of carbon stock on a single environmental variable is impossible for the entire IHR. The IHR possesses a prominent carbon sink and biodiversity pool. Therefore, its protection is essential in fulfilling India's commitment to nationally determined contributions (NDC). Our data synthesis may also provide a baseline for the precise estimation of carbon stock, which will be vital for India's National Mission for Sustaining the Himalayan Ecosystem (NMSHE).

Keywords: Agroforest; Climate change; Forest; Himalayas; Plantation; XGBoost.

Publication types

Review