Abstract Grasslands are an important component of the global carbon (C) cycle, with a strong potential for C sequestration. However, an improved capacity to quantify grassland C stocks and monitor their variation in space and time, particularly in response to management, is needed in order to conserve and enhance grassland C reservoirs. To meet this challenge we outline and test here an approach to combine C cycle modelling with observational data. We implemented an intermediate complexity model, DALEC-Grass, within a probabilistic model-data fusion (MDF) framework, CARDAMOM, at two managed grassland sites (Easter Bush and Crichton) in the UK. We used 3 years (Easter Bush, 2002–2004) of management data and observations of leaf area index (LAI) and Net Ecosystem Exchange (NEE) from eddy covariance to calibrate the distributions of model parameters. Using these refined distributions, we then assimilated the remaining 7 years (Easter Bush, 2005–2010 and Crichton, 2015) of LAI observations and evaluated the simulated NEE, above and below-ground biomass and other C fluxes against independent data from the two grasslands. Our results show that fusing model predictions with LAI observations allowed the CARDAMOM MDF system to diagnose the effects of grazing and cutting realistically. The overlap of MDF-predicted and measured NEE (both sites) and ecosystem respiration (Easter Bush) was 92% and 83% respectively while the correlation coefficient (r) was 0.79 for both variables. This study lays the foundation for using MDF with satellite data on LAI to produce the spatially and temporally-resolved estimates of C cycling needed in shaping and monitoring the implementation of relevant policies and farm-management decisions.

中文翻译：

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一种分析管理草地碳动态的模型-数据融合方法

摘要 草原是全球碳（C）循环的重要组成部分，具有很强的固碳潜力。然而，需要提高量化草地碳库并监测其空间和时间变化的能力，特别是响应管理，以保护和增强草地碳库。为了迎接这一挑战，我们在此概述并测试了一种将 C 循环建模与观测数据相结合的方法。我们在英国的两个管理草地站点（Easter Bush 和 Crichton）的概率模型数据融合 (MDF) 框架 CARDAMOM 内实施了一个中等复杂性模型 DALEC-Grass。我们使用了 3 年（Easter Bush，2002-2004）的管理数据和叶面积指数 (LAI) 和来自涡流协方差的净生态系统交换 (NEE) 的观察来校准模型参数的分布。使用这些精细的分布，我们然后同化了 LAI 观测的剩余 7 年（Easter Bush，2005-2010 和 Crichton，2015），并根据来自两个草原的独立数据评估了模拟的 NEE、地上和地下生物量和其他 C 通量. 我们的结果表明，将模型预测与 LAI 观察相结合，使 CARDAMOM MDF 系统能够真实地诊断放牧和切割的影响。MDF 预测和测量的 NEE（两个站点）和生态系统呼吸（复活节布什）的重叠分别为 92% 和 83%，而两个变量的相关系数 (r) 为 0.79。