Time-Lag Effect: River Algal Blooms on Multiple Driving Factors

Abstract

Compared to the eutrophication of lakes and reservoirs, the mechanism of river algal blooms in a flowing water body are more complicated, and often lead to serious consequence in catchment scale. Due to the simultaneous impact of a variety of environmental pressures, the water ecosystem integrity state often shows a response characteristic of accumulation, complexity and time lag, therefore it is difficult to use conventional hydrodynamic and water quality models to scientifically characterize and analyze. The lower Hanjiang River (HR) is an important influence area of the middle route of the South-to-North Water Diversion Project (SNWDP) in China, continuous river blooms issue has become a major national concern. In this study, a time-lag analysis approach was developed to identify the causes of algal blooms formation and the time-lag response law in the lower HR, including principal component analysis (PCA), grey relation analysis (GRA), and Almon Distributed Lag Model. Results found that, the hydrological regime (ΔH) contributes the most to the river bloom, especially due to the flow hindrance of the Yangtze River (YR) and the water project upstream. It is also found that the algal bloom outbreak in the lower HR is not an immediate response to the driving factors. It has a time lag of about 1 period (10 days) in the response with antecedent driving factors. Finally, we discussed the influence and its responding mechanism of ΔH on the growth of phytoplankton. The research can provide early warning for the prevention and control of algal blooms in the large river system.

Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters

“…Meanwhile, this study innovatively utilized the time-lag mutual information method [ 45 ] and gray time-lag correlation analysis [ 46 ] to calculate the time-lag parameter between the Ta and Tc. The time-lag parameter calculated by the time-lag mutual information method ranged from 42 to 58 min, while the gray time-lag correlation analysis-calculated time-lag parameter ranged from 76 to 98 min.…”

Influence of time lag effect between winter wheat canopy temperature and atmospheric temperature on the accuracy of CWSI inversion of photosynthetic parameters

“…Meanwhile, this study innovatively utilized the time-lag mutual information method (Albers and Hripcsak 2012) and grey time-lag correlation analysis (Liu et al 2022) to calculate the time lag parameter between Ta and Tc. The time lag parameter calculated by the time-lag mutual information method ranged from 42 to 58 min, while the grey time-lag correlation analysis calculated time lag parameter ranged from 42 to 58 min76 to 98 min.…”

Monitoring of Rivers and Streams Conditions Using Biological Indices with Emphasis on Algae: A Comprehensive Descriptive Review toward River Management

“…Algae, having increased sensitivity, often signal changes in environmental conditions by responding well before effects on higher organisms manifest themselves [29,78]. The reduction of river flow affects biofilm structure [125], for example, in terms of causing increased algae bloom [147], which prevents sunlight from penetrating the water surface [148] as well as ecosystem processes in general [149][150][151]. In the biofilm structure, diatom assemblages are shown to be highly responsive to water quality variation [152] since their assemblages are used to measure water quality [153].…”