Estimating carbon dioxide (CO₂) and methane (CH₄) emission rates from reservoirs is important for regional and national greenhouse gas inventories. A lack of methodologically consistent data sets for many parts of the world, including agriculturally intensive areas of the United States, poses a major challenge to the development of models for predicting emission rates. In this study, we used a systematic approach to measure CO₂ and CH₄ diffusive and ebullitive emission rates from 32 reservoirs distributed across an agricultural to forested land use gradient in the United States. We found that all reservoirs were a source of CH₄ to the atmosphere, with ebullition being the dominant emission pathway in 75% of the systems. Ebullition was a negligible emission pathway for CO₂, and 65% of sampled reservoirs were a net CO₂ sink. Boosted regression trees (BRTs), a type of machine learning algorithm, identified reservoir morphology and watershed agricultural land use as important predictors of emission rates. We used the BRT to predict CH₄ emission rates for reservoirs in the U.S. state of Ohio and estimate they are the fourth largest anthropogenic CH₄ source in the state. Our work demonstrates that CH₄ emission rates for reservoirs in our study region can be predicted from information in readily available national geodatabases. Expanded sampling campaigns could generate the data needed to train models for upscaling in other U.S. regions or nationally.

中文翻译：

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来自水库的甲烷和二氧化碳排放：控制和升级

估算来自水库的二氧化碳 (CO ₂ ) 和甲烷 (CH ₄ ) 排放率对于区域和国家温室气体清单很重要。世界许多地区（包括美国的农业密集地区）缺乏方法上一致的数据集，这对开发用于预测排放率的模型构成了重大挑战。在本研究中，我们使用系统方法来测量分布在美国农业到林地利用梯度的 32 个水库的CO ₂和 CH ₄扩散和沸腾排放率。我们发现所有储层都是 CH ₄的来源到大气中，沸腾是 75% 系统中的主要排放途径。沸腾是 CO ₂的微不足道的排放途径，65% 的采样水库是净 CO ₂汇。增强回归树 (BRT) 是一种机器学习算法，将水库形态和流域农业用地利用确定为排放率的重要预测因子。我们使用 BRT 来预测美国俄亥俄州水库的CH ₄排放率，并估计它们是该州第四大人为 CH ₄源。我们的工作表明 CH ₄可以根据现成的国家地理数据库中的信息预测我们研究区域内水库的排放率。扩大的抽样活动可以生成训练模型所需的数据，以便在美国其他地区或全国范围内进行升级。