School of Geography, South China Normal University, Guangzhou, China.
Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
Glob Chang Biol. 2018 Jul;24(7):2965-2979. doi: 10.1111/gcb.14274. Epub 2018 May 8.
Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data-model comparisons.
鉴于半干旱地区对全球陆地碳循环的重要贡献，准确模拟陆地总初级生产力（GPP）的年际变化（IAV）非常重要，但仍然具有挑战性。通过将 GPP 分解为叶面积指数（LAI）和每叶光合速率（即 GPP_leaf），我们研究了中国西北温带草原 GPP 的 IAV 及其产生机制。我们进一步评估了六个生态系统模型，以评估它们在模拟中国 2004 年至 2011 年温带草原 GPP 的观测到的 IAV 方面的能力。我们观察到，LAI 和 GPP_leaf 对土壤水分的响应显著有助于草原生态系统 GPP 的 IAV。六个模型中的两个，通过规定的 LAI 模拟了 GPP 的观测到的 IAV，效果相当好，但仍然低估了 GPP_leaf 的方差，因此 GPP 的方差也被低估了。相比之下，具有预测性 LAI 的其他四个模型的模拟模式与 GPP 的观测到的 IAV 差异很大。只有一些具有预测性 LAI 的模型可以捕捉到干旱年份 GPP 的观测到的急剧下降。进一步的分析表明，准确地表示 GPP_leaf 和叶片气孔导度对土壤水分的响应对于模型模拟观测到的 GPP_leaf 的 IAV 至关重要。我们的框架还确定了 LAI 和 GPP_leaf 对观测到的 GPP 的 IAV 的贡献是相对独立的。我们得出结论，我们将 GPP 分解为 LAI 和 GPP_leaf 的框架具有显著的潜力，可促进未来的模型比较，基准测试和优化应该用于未来的数据-模型比较。
