Adaptive forecasting of phytoplankton communities.
Adaptive forecasting of phytoplankton communities.
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DOI：
10.1016/j.watres.2018.01.046
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发表时间：
2018-05
期刊：
影响因子：
12.8
通讯作者：
T. Page;Paul Smith;Paul Smith;K. Beven;I. D. Jones;J. Elliott;S. Maberly;E. Mackay;M. D. Ville;H. Feuchtmayr
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文献类型：
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作者：
T. Page;Paul Smith;Paul Smith;K. Beven;I. D. Jones;J. Elliott;S. Maberly;E. Mackay;M. D. Ville;H. Feuchtmayr
有害藻华的全球扩散对水资源、娱乐和生态系统构成了越来越大的威胁。因此，需要预测这些水华的发生，以协助水资源管理者做出管理决策，以减轻其影响。使用浮游植物群落模型 PROTECH 预测藻华的潜力进行了伪实时评估。这是通过使用集成卡尔曼滤波器的数据同化方案实现的，以允许将不确定性和模型非线性传播到预测输出。对英国湖区的两个中营养湖泊进行了测试，这两个湖泊的深度和营养状况各不相同。叶绿素的预测取得了一些成功，但并非所有预测都能比持久性预测表现得更好。随着预测周期的延长，预测技能普遍下降，但长达四五天的预测明显比更长周期的预测更有希望。浮游植物群落结构的相关预测与观测结果大致一致，但由于模拟功能物种的可互换性，将其转化为蓝藻预测具有挑战性。
The global proliferation of harmful algal blooms poses an increasing threat to water resources, recreation and ecosystems. Predicting the occurrence of these blooms is therefore needed to assist water managers in making management decisions to mitigate their impact. Evaluation of the potential for forecasting of algal blooms using the phytoplankton community model PROTECH was undertaken in pseudo-real-time. This was achieved within a data assimilation scheme using the Ensemble Kalman Filter to allow uncertainties and model nonlinearities to be propagated to forecast outputs. Tests were made on two mesotrophic lakes in the English Lake District, which differ in depth and nutrient regime. Some forecasting success was shown for chlorophylla, but not all forecasts were able to perform better than a persistence forecast. There was a general reduction in forecast skill with increasing forecasting period but forecasts for up to four or five days showed noticeably greater promise than those for longer periods. Associated forecasts of phytoplankton community structure were broadly consistent with observations but their translation to cyanobacteria forecasts was challenging owing to the interchangeability of simulated functional species.