Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes

环境科学 空间变异性 温带气候 时间尺度 二氧化碳 温室气体 甲烷 大气科学 大气(单位) 生物地球化学循环 水文学(农业) 生态学 地质学 地理 气象学 生物 统计 岩土工程 数学
作者
Nicholas E. Ray,Meredith A. Holgerson,Mikkel René Andersen,Jānis Bikše,Lauren E. Bortolotti,Martyn N. Futter,Ilga Kokorīte,Alan Law,Cory P. McDonald,Jorrit P. Mesman,Mike Peacock,David C. Richardson,Julien Arsenault,Sheel Bansal,Kaelin M. Cawley,McKenzie A. Kuhn,Amir Reza Shahabinia,Facundo Smufer
出处
期刊:Limnology and Oceanography [Wiley]
卷期号:68 (7): 1530-1545 被引量:12
标识
DOI:10.1002/lno.12362
摘要

Abstract Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO 2 ) and methane (CH 4 ) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO 2 and CH 4 . Here, we measured partial pressures of CO 2 ( p CO 2 ) and CH 4 ( p CH 4 ) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of p CO 2 and p CH 4 in space and time. Summer means of p CO 2 and p CH 4 were inversely related to waterbody size and positively related to floating vegetative cover; p CO 2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal p CO 2 by up to 26%, mean seasonal p CH 4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in p CH 4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.
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