Near‐term forecasts of NEON lakes reveal gradients of environmental predictability across the US
Near‐term forecasts of NEON lakes reveal gradients of environmental predictability across the US
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NEON 湖泊的近期预测揭示了美国各地环境可预测性的梯度
DOI：
10.1002/fee.2623
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发表时间：
2023
影响因子：
10.3
通讯作者：
Carey, Cayelan C
中科院分区：
文献类型：
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作者：
Thomas, R Quinn;McClure, Ryan P;Moore, Tadhg N;Woelmer, Whitney M;Boettiger, Carl;Figueiredo, Renato J;Hensley, Robert T;Carey, Cayelan C
美国国家生态观测网络(NEON)的标准化监测计划为比较生态系统的可预测性提供了前所未有的机会。为了利用霓虹灯数据的力量来检验环境可预测性，我们将一个短期、迭代的水温预报系统扩展到了美国毗邻的所有六个霓虹湖。我们使用基于过程的流体动力学模型生成提前1天到35天的预测，该模型在观测数据可用时进行了更新。在湖泊中，提前35天的预报比零模型更准确，提前1天的累积均方根误差(RMSE)为0.61°C，提前35天的RMSE为2.17°C。水温预报精度与湖泊深度和水体透明度呈正相关，与取水和集水区大小呈负相关。我们的分析结果表明，湖泊特征与天气相互作用，控制着热结构的可预测性。我们的工作提供了一些对霓虹灯站点的第一次概率预测，并提供了一个检验大陆范围可预测性的框架。
The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near‐term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1‐day‐ahead to 35‐days‐ahead forecasts using a process‐based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35‐days‐ahead, with an aggregated 1‐day‐ahead root‐mean square error (RMSE) of 0.61°C and a 35‐days‐ahead RMSE of 2.17°C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental‐scale predictability.