Predicting spring green-up across diverse North American grasslands
Predicting spring green-up across diverse North American grasslands
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DOI：
10.1016/j.agrformet.2022.109204
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发表时间：
2022-12
影响因子：
6.2
通讯作者：
Alison K. Post;K. Hufkens;A. Richardson
中科院分区：
文献类型：
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作者：
Alison K. Post;K. Hufkens;A. Richardson
植被物候影响着许多生态系统和气候过程，如碳吸收、能量和水循环。因此，了解植被物候的驱动因素对于预测当前和未来气候变化对生态系统的影响至关重要。现有的模型可以准确地预测温带森林春季变绿的日期，但在草原系统中往往表现不佳。我们假设这是因为大多数植物没有考虑水分的可用性，这是草地植物的主要限制因素。在这项研究中，我们使用了来自北美43个不同草原的PhenoCam网络（195个站点年）的长期数字图像数据集来测试现有的春季物候模型，并开发了几个包含降水或土壤湿度的新模型（53个模型）。大多数新模型表现得更好，最好的模型需要足够的累积降水，然后是温暖的温度来触发春季的发生（预测日期和观测日期之间的均方根误差，RMSE = 16.0天）。重要的是，使用单一参数集的最佳模型在从温带到干旱草原的所有草地类型中都表现良好。由于植物对当地气候的适应性，当对四个不同的气候区域（RMSE = 10.4天）进行参数独立优化时，模型的性能进一步提高。因此，草地恢复需要足够的降水和温度，但最佳阈值因地区而异。使用预估气候数据（代表性浓度路径8.5）运行顶级模式表明，根据气候区域的不同，100年内温度受限站点的春季开始时间将提前12天，但降水受限站点的趋势不明确（延迟3.5±8.0天）。这个新的物候模型提高了我们理解和预测草地动态的能力，对当前和未来与碳和水循环相关的生态系统过程都有影响。
Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). Most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.