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Nitrogen loss from karst area in China in recent 50years: Anin-situ simulated rainfall experiment's assessment TEXT SIZE: A A A
Karst topography covers more than 1/3 of the People's Republic of China in area. The porous, fissured, and soluble nature of the underlying karst bedrock (primarily dolomite and limestone) leads to the formation of underground drainage systems. Karst conduit networks dominate this system, and rainfall takes a crucial role on water cycle at China karst area. Nitrogen loss from the karst system is of particular concern, with regard to nutrient use efficiency as well as water quality, as much of the karst system, including steeply sloping terrain, is used for intensive agriculture. We use simulated rainfall experiments to determine the relationship between rainfall and nitrogen loss at typical karst slope land and then estimate nitrogen loss from the karst soil. The results show that both surface runoff and subsurface runoff have a significant linear correlation with rainfall at all studied sites. Subsurface runoff is larger than surface runoff at two karst sites, while the opposite is true at the non-karst site. Exponential function satisfactorily described the correlation between rainfall and nitrogen concentrations in runoff. Nitrates accounted for 60%-95% of the dissolved nitrogen loss (DN, an index of N-loss in this research). The estimated annual N-loss load varies between 1.05 and 1.67 Tg N/year in the whole karst regions of China from 1961 to 2014. Approximately, 90% of the N-loss load occurred during the wet season, and 90% of that passed through the subsurface. Understanding the processes and estimating N-loss is highly valuable in determining long-term soil security and sustainability in karst regions.

Publication name

 ECOLOGY AND EVOLUTION, 7 (23):10131-10142; 10.1002/ece3.3502 DEC 2017


 Song, Xianwei; Gao, Yang; Green, Sophie M.; Dungait, Jennifer A. J.; Peng, Tao; Quine, Timothy A.; Xiong, Bailian; Wen, Xuefa; He, Nianpeng

Corresponding author(s) 

 GAO Yang 
 -Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
 -Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China

Author(s) from IGCAS   PENG Tao

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