Soil respiration (R_S), the soil-to-atmosphere CO₂ flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (T_soil) and water content (SWC). Regional to global-scale R_S modelling thus requires this information at local scales, but few high-quality, wall-to-wall (global) T_soil and SWC data exist. As a result, such modelling efforts commonly use air temperature (T_air) and monthly precipitation (P_m) as surrogate predictors, but their site-scale accuracy and potential bias are unknown. Here, we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of T_air as a surrogate for T_soil, and data from 507 sites to examine the suitability of P_m as a surrogate for SWC. Site-specific linear and second-order exponential non-linear models were compared using model evaluation metrics (i.e., slope, p-value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that T_soil and T_air are highly correlated and explain similar R_S variability. In contrast, P_m is not a good surrogate for SWC, even though P_m explains a similar amount of R_S variability to SWC. The wide variability in the site-specific relationships between R_S and SWC means that no single relationship can be used for large-scale modelling. The results from this study support the use of T_air in continental-to-global scale R_S models, and highlight the urgent need for continental-to-global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change.

中文翻译：

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在土壤呼吸模拟中使用气温和降水作为替代预测因子

土壤呼吸 (R _S ) 是土壤到大气的 CO ₂通量，是全球碳循环的主要组成部分，受当地土壤温度 (T _soil ) 和含水量 (SWC) 的强烈影响。因此，区域到全球尺度的 R _S建模需要在局部尺度上提供这些信息，但很少有高质量的墙到墙（全球）T_土壤和 SWC 数据存在。因此，此类建模工作通常使用气温 (T _air ) 和月降水量 (P _m) 作为替代预测因子，但它们的站点规模准确性和潜在偏差是未知的。在这里，我们使用来自各种不同环境条件（即气候、生态系统类型、海拔、植被叶片习性和排水条件）的 880 个地点的月度数据来确定 T_空气作为 T_土壤替代物的适用性，以及数据来自 507 个站点，以检查 P _m作为 SWC 替代品的适用性。使用模型评估指标（即斜率、斜率的p值、均方根误差 [RMSE]、一致性指数和模型效率）比较特定地点的线性和二阶指数非线性模型。我们发现 T_土壤和 T_空气高度相关并解释了相似的 R _S变异性。相反，尽管 P _m解释了与 SWC 相似量的 R _S可变性，但 P _{m不是 SWC 的良好替代品。R S}和 SWC之间的特定地点关系的广泛变化意味着没有单一的关系可以用于大规模建模。这项研究的结果支持在大陆到全球尺度的 R _{S模型中使用 T空气}，并强调迫切需要大陆到全球尺度的 SWC 数据集来模拟和评估全球气候变化下的未来土壤碳动态.