Data-driven assessment of soil total nitrogen on the Qinghai-Tibet Plateau

Jiahui Zhao; Peng Jiang; Tongqing Shen; Rongrong Zhang; Dawei Zhang; Nana Zhang; Nie Ting; Kunqi Ding; Bin Yang; Changhai Tan; Zhongbo Yu

doi:10.1016/j.scitotenv.2024.169993

Data-driven assessment of soil total nitrogen on the Qinghai-Tibet Plateau

Sci Total Environ. 2024 Mar 1:914:169993. doi: 10.1016/j.scitotenv.2024.169993. Epub 2024 Jan 10.

Authors

Jiahui Zhao¹, Peng Jiang², Tongqing Shen¹, Rongrong Zhang³, Dawei Zhang⁴, Nana Zhang⁵, Nie Ting⁵, Kunqi Ding⁵, Bin Yang⁶, Changhai Tan⁷, Zhongbo Yu⁸

Affiliations

¹ The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
² The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; Key Laboratory of Natural Resource Coupling Process and Effects, Beijing 100055, China; The Middle Reaches of Yarlung Zangbo River, Natural Resources, Observation and Research Station of Tibet Autonomous Region, Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China; Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China. Electronic address: peng.jiang@hhu.edu.cn.
³ The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; Key Laboratory of Natural Resource Coupling Process and Effects, Beijing 100055, China; Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.
⁴ China Institute of Water Resources and Hydropower Research, Beijing 100038, China.
⁵ College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
⁶ The Middle Reaches of Yarlung Zangbo River, Natural Resources, Observation and Research Station of Tibet Autonomous Region, Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China.
⁷ Research Center of Applied Geology of China Geological Survey, Chengdu 610036, China.
⁸ The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China; Yangtze Institute for Conservation and Development, Hohai University, Jiangsu 210098, China; Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing 210098, China.

PMID: 38215840
DOI: 10.1016/j.scitotenv.2024.169993

Abstract

The investigation of soil total nitrogen (STN) holds significant importance in the preservation and sustainability of Earth's ecosystems. The Qinghai-Tibet Plateau (QTP), renowned as the world's most expansive plateau and characterized by its exceptionally delicate ecosystem, demands an in-depth exploration of its STN content. In this study, we use a machine learning approach to extrapolate point-scale measured STN stocks to the entire QTP and calculated STN storage from 0 to 2 m. Our results show that the XGB algorithm performs well in modeling STN despite variations in simulation accuracy for specific depth ranges. The spatial distribution of STN across the QTP exhibits pronounced heterogeneity, especially for the 0-50 cm soil layer, with relatively higher STN stocks in the southeast and lower stocks in the northwest of QTP. The vertical distribution reveals a gradual decrease in STN storage with increasing depth. The 0-50 cm soil layer holds the highest STN stocks, averaging around 0.78 kg/m², which is almost the sum of STN stocks in the 50-100 cm and 100-200 cm soil layers. Meanwhile, the STN stocks are smaller in permafrost zone than that in non-permafrost zone. We also investigate the impact factors that control the spatiotemporal distribution of STN. It indicates that vegetation, precipitation, temperature, and elevation are the major factors for STN distribution, while physical properties of the soil have a relatively smaller impact. These findings are crucial for understanding the distribution and evolution of STN on the QTP.

Keywords: Data-driven; Qinghai-Tibet Plateau; Soil total nitrogen; XGB.