Nitrogen (N) pollution originating from agricultural land is among the major threats to shallow groundwater (SG). Soil N losses due to the SG table fluctuation are neglected, although a large number of studies have been conducted to evaluate N losses through leaching and runoff. Herein, the characteristics of N losses driven by SG table fluctuation were investigated using the microcosm experiment and surveyed data from the croplands around Erhai Lake. According to the results achieved, the total N (TN) loss mainly occurred during the initial 12 days when the soil was flooded, then presented N immobilized by soil and finally, basically balanced between influent and effluent after 50 days. The results demonstrated that 1.7% of the original soil TN storage (0–100 cm) was lost. The alternation of drying and flooding could greatly increase TN loss up to 1086 kg hm−2, which was 2.72 times as much as that of continuous flooding flow. The amount of soil N losses to groundwater was closely related to the soil profile biochemical characteristics (water content, soil microbial immobilization, mineralization, nitrification, and denitrification processes). Soil N loss from crop fields driven by SG table fluctuation is 26 and 6 times of the runoff and leaching losses, respectively, while the soil N loss from the vegetable fields is 33 and 4 times of the runoff and leaching losses. The total amount of N losses from the croplands around the Erhai Lake caused by flooding of shallow groundwater (SG) in 2016 was estimated at 3506 Mg. The estimations showed that N losses would decrease by 16% if vegetables are replaced with staple food crops. These results imply that the adjustment of the planting structure was the key measure to reduce soil N storage and mitigate groundwater contamination.
Bibliografisk noteFunding Information:
This work was supported by the National Natural Science Foundation of China ( 41977319 , 41661048 , 42067052 ); the Yunnan Science and Technology Talents and Platform Projects ( 2019HB033 ), and the Youth Talent Support Program of Yunnan Province , China ( YNWR-QNBJ-2018-015 ).
This work was supported by the National Natural Science Foundation of China (41977319, 41661048, 42067052); the Yunnan Science and Technology Talents and Platform Projects (2019HB033), and the Youth Talent Support Program of Yunnan Province, China (YNWR-QNBJ-2018-015).
© 2022 Elsevier B.V.