Uncertainty in parameterizing a flux‐based model of vegetation carbon phenology using ecosystem respiration
Uncertainty in parameterizing a flux‐based model of vegetation carbon phenology using ecosystem respiration
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DOI：
10.1002/ecs2.4101
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发表时间：
2022-05
期刊：
影响因子：
2.7
通讯作者：
Yuan-Bo Gong;C. Staudhammer;S. Wiesner;Yinlong Zhang;J. Cannon;G. Starr
中科院分区：
文献类型：
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作者：
Yuan-Bo Gong;C. Staudhammer;S. Wiesner;Yinlong Zhang;J. Cannon;G. Starr
植物群落的季节动态是评估长期植被格局的重要指标，并为预测生态系统对气候变化的反应提供了有价值的信息。然而，极端天气事件频率的增加可能会迫使生态系统进入不稳定状态，这导致在确定物候指标(例如生长季长度)方面存在更大的不确定性。为了更好地理解这些不确定性，我们利用9 年的涡度协方差和遥感数据对植被相似但持水能力不同的两种亚热带长叶松林的季节生态系统呼吸(Re)模型进行了参数化。我们比较了两种常用的物候指标提取算法，生长率(GR)和三阶导数(TD)方法，这两种方法通常是在没有正当理由的情况下使用的。我们确定了算法选择对估计与植物群落碳动态相关的关键生物日期(例如，生理活动季节的开始、结束和长度，特别是Re)的影响，描述了模型对极端天气事件的响应，并将估计值与从增强植被指数(EVI)遥感绿度得出的估计值进行了比较。我们观察到，冬季变暖的时期增加了Re方面的生理活动持续时间，而夏季的水分限制导致了多峰、不对称的行为，产生了显著的不确定性。我们发现物候学指标提取算法的选择显著影响了生物事件日期；GR方法估计的物候期在两个地点都比TD更长，以及物候期的开始和结束日期更早和更晚。由于TD方法在某些天气条件下不能给出物候期转换缓冲期的估计值，GR方法可能更适合于亚热带森林的研究。从EVI绿度得到的日期很少与植物群落季节动态模型相匹配，特别是在春季和夏季。模型估计的Re长度明显长于EVI，表明EVI的使用可能导致较短的生长季估计和更大的不确定性。我们的结果为优化未来提取物候指标的方法提供了方向，并为更好地科学理解森林地表物候提供了方向，因为天气异常随着气候变化变得更加常见。
The seasonal dynamics of plant communities are important indicators for assessment of long‐term vegetation patterns and provide valuable information to predict ecosystem responses to climate change. However, increased frequency of extreme weather events can force ecosystems into unstable states, which leads to greater uncertainty in determining phenological metrics (e.g., growing season length). To better understand these uncertainties, we utilized 9 years of eddy covariance and remote sensing data to parameterize models of seasonal ecosystem respiration (Re) for two subtropical longleaf pine forests (mesic and xeric), with similar vegetation but different water holding capacity. We compared two commonly used algorithms to extract phenology metrics, the growth rate (GR) and third derivative (TD) methods, which are usually used without justification. We determined the impact of algorithm selection on estimating key biological dates related to plant community carbon dynamics (e.g., start, end, and length of physiologically active season, specifically Re), characterized the model's response to extreme weather events, and compared estimates to those derived via remotely sensed greenness from the enhanced vegetation index (EVI). We observed that periods of winter warming increased duration of physiological activity in terms of Re, and summer water limitation caused multi‐peaked, asymmetric behavior, creating significant uncertainties. We found that choice of phenology metric extraction algorithm significantly impacted biological event dates; the GR method estimated longer phenophases than the TD in both sites, as well as earlier starting and later ending dates for phenophases. Because the TD method was unable to give estimates during the buffer period of phenophase transition under certain weather conditions, the GR method may be more suitable for studies in subtropical forests. Dates derived from EVI greenness rarely matched those of plant community seasonal dynamics models, especially in spring and summer. The estimated length of Re from the model was significantly longer than that derived from EVI, indicating that the use of EVI could result in shorter growing season estimates and greater uncertainty. Our results provide direction for optimization of future approaches to extract phenological metrics and better scientific understanding of forest land surface phenology, as weather anomalies become more common with climate change.