Zhao Jiahui, Jiang Peng, Shen Tongqing, Zhang Rongrong, Zhang Dawei, Zhang Nana, Ting Nie, Ding Kunqi, Yang Bin, Tan Changhai, Yu Zhongbo
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China.
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.
Sci Total Environ. 2024 Mar 1;914:169993. doi: 10.1016/j.scitotenv.2024.169993. Epub 2024 Jan 10.
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/m, 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.
土壤全氮（STN）的研究对于地球生态系统的保护和可持续发展具有重要意义。青藏高原（QTP）是世界上面积最大的高原，其生态系统极其脆弱，因此需要深入探究其STN含量。在本研究中，我们采用机器学习方法将点尺度测量的STN储量外推至整个青藏高原，并计算了0至2米深度的STN储量。我们的结果表明，尽管特定深度范围内的模拟精度存在差异，但XGB算法在模拟STN方面表现良好。整个青藏高原的STN空间分布呈现出明显的异质性，尤其是在0-50厘米的土壤层，青藏高原东南部的STN储量相对较高，而西北部的储量较低。垂直分布显示，随着深度增加，STN储量逐渐减少。0-50厘米的土壤层STN储量最高，平均约为0.78千克/平方米，几乎是50-100厘米和100-200厘米土壤层STN储量之和。同时，多年冻土区的STN储量低于非多年冻土区。我们还研究了控制STN时空分布的影响因素。结果表明，植被、降水、温度和海拔是影响STN分布的主要因素，而土壤物理性质的影响相对较小。这些发现对于理解青藏高原STN的分布和演变至关重要。
