Abstract：
Despite the recognized importance of flowing waters in global greenhouse gas (GHG) budgets, riverine GHG models remain oversimplified, consequently restraining the development of effective prediction for riverine GHG emissions feedbacks. Here we elucidate the state of the art of riverine GHG models by investigating 148 models from 122 papers published from 2010 to 2021. Our findings indicate that riverine GHG models have been mostly data-driven models (83%), while mechanistic and hybrid models were uncommonly applied (12% and 5%, respectively). Overall, riverine GHG models were mainly used to explain relationships between GHG emissions and biochemical factors, while the role of hydrological, geomorphic, land use and cover factors remains missing. The development of complex and advanced models has been limited by data scarcity issues; hence, efforts should focus on developing affordable automatic monitoring methods to improve data quality and quantity. For future research, we request for basin-scale studies explaining river and land-surface interactions for which hybrid models are recommended given their flexibility. Such a holistic understanding of GHG dynamics would facilitate scaling-up efforts, thereby reducing uncertainties in global GHG estimates. Lastly, we propose an application framework for model selection based on three main criteria, including model purpose, model scale and the spatiotemporal characteristics of GHG data, by which optimal models can be applied in various study conditions.
摘要：
尽管流动水域在全球温室气体（GHG）预算中具有公认的重要性，河流温室气体模型仍然过于简化，从而制约了河流温室气体排放反馈有效预测的发展。在这里，我们通过调查2010年至2021年发表的122篇论文中的148个模型，阐明了河流GHG模型的最新技术。我们的研究结果表明，河流温室气体模型大多是数据驱动模型(83%)，虽然机械模型和混合模型的应用并不常见(12%和5%，分别)。总的来说,河流温室气体模型主要用于解释温室气体排放与生化因素之间的关系，虽然水文的作用，地貌,土地利用和覆盖因素仍然缺失。复杂和高级模型的发展受到数据稀缺问题的限制；因此，努力应侧重于开发负担得起的自动监测方法，以提高数据质量和数量。为了将来的研究，我们要求进行流域尺度研究，以解释河流和陆地与地表的相互作用，鉴于混合模型的灵活性，建议使用混合模型。对温室气体动态的这种全面理解将有助于扩大规模，从而减少全球温室气体估计的不确定性。最后,我们基于三个主要标准提出了模型选择的应用框架，包括模型目的，模型尺度和温室气体数据的时空特征，通过这种优化模型可以应用于各种研究条件。
