Nguyen Ngoc B, Migliavacca Mirco, Bassiouni Maoya, Baldocchi Dennis D, Gherardi Laureano A, Green Julia K, Papale Dario, Reichstein Markus, Cohrs Kai-Hendrik, Cescatti Alessandro, Nguyen Tuan Dung, Nguyen Hoang H, Nguyen Quang Minh, Keenan Trevor F
Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA USA.
European Commission, Joint Research Centre, Ispra, Italy.
Nat Geosci. 2025;18(9):869-876. doi: 10.1038/s41561-025-01754-9. Epub 2025 Jul 31.
Dryland carbon fluxes, particularly those driven by ecosystem respiration, are highly sensitive to water availability and rain pulses. However, the magnitude of rain-induced carbon emissions remains unclear globally. Here we quantify the impact of rain-pulse events on the carbon balance of global drylands and characterize their spatiotemporal controls. Using eddy-covariance observations of carbon, water and energy fluxes from 34 dryland sites worldwide, we produce an inventory of over 1,800 manually identified rain-induced CO pulse events. Based on this inventory, a machine learning algorithm is developed to automatically detect rain-induced CO pulse events. Our findings show that existing partitioning methods underestimate ecosystem respiration and photosynthesis by up to 30% during rain-pulse events, which annually contribute 16.9 ± 2.8% of ecosystem respiration and 9.6 ± 2.2% of net ecosystem productivity. We show that the carbon loss intensity correlates most strongly with annual productivity, aridity and soil pH. Finally, we identify a universal decay rate of rain-induced CO pulses and use it to bias-correct respiration estimates. Our research highlights the importance of rain-induced carbon emissions for the carbon balance of global drylands and suggests that ecosystem models may largely underrepresent the influence of rain pulses on the carbon cycle of drylands.
旱地碳通量，尤其是那些由生态系统呼吸作用驱动的碳通量，对水分供应和降雨脉冲高度敏感。然而，全球范围内降雨引起的碳排放规模仍不明确。在此，我们量化降雨脉冲事件对全球旱地碳平衡的影响，并描述其时空控制特征。利用全球34个旱地站点的碳、水和能量通量的涡度协方差观测数据，我们编制了一份包含1800多个手动识别的降雨诱发二氧化碳脉冲事件的清单。基于这份清单，开发了一种机器学习算法来自动检测降雨诱发的二氧化碳脉冲事件。我们的研究结果表明，现有的分配方法在降雨脉冲事件期间会将生态系统呼吸作用和光合作用低估多达30%，降雨脉冲事件每年对生态系统呼吸作用的贡献为16.9±2.8%，对净生态系统生产力的贡献为9.6±2.2%。我们发现碳损失强度与年生产力、干旱程度和土壤pH值的相关性最强。最后，我们确定了降雨诱发二氧化碳脉冲的普遍衰减率，并将其用于对呼吸作用估计值进行偏差校正。我们的研究强调了降雨诱发碳排放对全球旱地碳平衡的重要性，并表明生态系统模型可能在很大程度上低估了降雨脉冲对旱地碳循环的影响。
