Nonlinear time-series analysis on the causality response of phytoplankton in a tributary of the Three Gorges Reservoir

Abstract

水华的频发已成为当前三峡水库最为突出的生态环境问题之一。尽管水动力调控叠加上温度变暖和营养负荷增加会诱导水华暴发强度和频率增加,但仍缺乏有效的方法框架去利用野外观测数据评估环境因子与浮游植物间的因果关联。本研究以三峡水库澎溪河监测数据为例,采用非线性时序分析的建模框架来量化浮游植物的因果响应规律。数据来自于2007年6月至2018年9月澎溪河流域的高阳平湖和汉丰湖两个观测点,其中包含了水文、气象和水质及叶绿素a等11种变量。首先,利用奇异谱分析(SSA)分离了叶绿素a和环境因子的低维确定性动力学信号;其次,采用收敛交叉映射(CCM)方法检验了叶绿素a与环境因子间的因果关联。结果显示:① 气象因子、支流流量、水温、三峡大坝水位和上游调节坝水位是影响高阳平湖叶绿素a时序变化的重要因素;② 总氮、总磷为代表的营养盐只在汉丰湖观测点中表现出与叶绿素a的因果关系,且总氮较之于总磷对叶绿素a变化影响更为显著;③ CCM结果与传统的皮尔森相关性分析及格兰杰因果检验比较,证实非线性时序分析方法在分析浮游植物的因果响应上更具优势。本研究为水生态系统的因果建模提供了研究范例,也为推动利用长期观测数据评估三峡水库水华驱动因子提供了新的研究角度。;Harmful algae blooms in Three Gorges Reservoir (TGR) have become the most major environmental and ecological problem. Despite broad recognition that hydrological manipulation, warming climate and nutrient loading promote the intensity and frequency of bloom events, the main problem is still no science-based framework for evaluating the causal relationships between environmental factors and phytoplankton biomass by using field observation data. Thus, as a case study, a nonlinear time-series causality analysis framework was used to reveal the phytoplankton response in the TGR, China. Two sampling stations, named Lake Gaoyangping and Lake Hanfeng, were used to reconstruct the dynamics of real-world environmental systems with eleven parameters from June 2007 to September 2018. Singular spectrum analysis isolated low-dimensional, deterministic signals for chlorophyll-a(Chl.a) dynamics and other candidate drivers. Causal analysis with convergent cross-mapping (CCM) provided strong evidence that temperature, sunshine hours, precipitation, discharge in the tributary, and water level of TGR systematically influenced phytoplankton biomass dynamics in Lake Gaoyangping. In contrast, total nitrogen (TN) and total phosphorus (TP) showed a causal relationship with Chl.a only in Lake Hanfeng, and the effect of TN on Chl.a was more significant than that of TP. Finally, the research compares the nonlinear CCM analysis with the linear Pearson correlation analysis and Granger causality test. It confirms that the nonlinear causality method has more advantage in causal discovery based on long time-series monitoring data. Overall, this study provides a case for causal modelling of water ecosystems and a new research perspective for evaluating the environmental drivers of harmful algae blooms in TGR by using long-term observational data.