Abstract. Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux, P_r, accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive a high-resolution P_r map over the contiguous U.S. (CONUS) in five major steps: 1) selecting 2595 headwater catchments where observed riverine DOC data are available with reasonable quality; 2) estimating catchment-average SOC for the 2595 catchments based on high-resolution SOC data; 3) estimating the P_r values for these catchments based on the generic formula and catchment-average SOC; 4) developing a predictive model of P_r with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and 5) deriving a national map of P_r, based on the ML model. For evaluation, we compare the DOC concentration derived using the P_r map and the observed DOC concentration values at another 3210 headwater gauges. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate the sensitivity of P_r to different factors are discussed. To illustrate the use of such a map, we derive a riverine DOC concentration reanalysis dataset for more than two million small catchments over CONUS. The map, robustly derived and empirically validated, lays a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales but also hold promises for informing policy decisions related to decarbonization and climate change mitigation.

中文翻译：

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得出美国本土溶解有机碳的转化率图

摘要。河流溶解有机碳（DOC）在区域和全球碳循环中发挥着至关重要的作用。然而，人们对土壤有机碳 (SOC) 转化为 DOC 并渗入河流的过程了解不够，表征不一致，参数化也很差，特别是在陆地表面和地球系统模型中。作为填补这一空白的首次尝试，我们提出了一个通用公式，将 SOC 浓度与源头水流中的 DOC 浓度直接联系起来，其中单个参数，即土壤中 SOC 到 DOC 淋滤通量的转化率P _r解释了控制 SOC 转化为 DOC 以及从土壤（连同径流）浸入水源流的总体过程。然后，我们通过五个主要步骤得出美国本土 (CONUS) 的高分辨率P _r地图：1）选择 2595 个源头集水区，其中观测到的河流 DOC 数据质量合理； 2) 根据高分辨率 SOC 数据估算 2595 个流域的流域平均 SOC； 3)根据通用公式和流域平均 SOC 估算这些流域的P _r值； 4)利用机器学习 (ML) 技术和流域规模气候、水文、地质和其他属性开发P _r预测模型； 5)基于 ML 模型导出P _r的国家地图。为了进行评估，我们将使用P _r图得出的 DOC 浓度与另一个 3210 个水源表观测到的 DOC 浓度值进行比较。得出的平均绝对比例误差和确定系数分别为 0.73 和 0.47，表明整体方法的有效性。讨论了限制不确定性和评估P _r对不同因素的敏感性的努力。为了说明此类地图的用途，我们得出了美国本土超过 200 万个小型流域的河流 DOC 浓度再分析数据集。该地图经过严格推导和实证验证，为更好地模拟地表和地球系统模型中的陆地碳循环奠定了关键的基石。我们的研究结果不仅为提高我们对区域和全球范围内陆地碳循环的预测性理解奠定了基础，而且有望为脱碳和减缓气候变化相关的政策决策提供信息。