Assessing Potential Algal Blooms in a Shallow Fluvial Lake by Combining Hydrodynamic Modelling and Remote-Sensed Images

Water

Monica Pinardi³  Andrea Fenocchi¹  Claudia Giardino³  Stefano Sibilla¹  Marco Bartoli²  Mariano Bresciani³ 

[1] Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, Pavia 27100, Italy; E-Mails:;Department of Life Sciences, University of Parma, Viale Usberti 11/a, Parma 43124, Italy; E-Mail:;Institute for Electromagnetic Sensing of the Environment, National Research Council, Via Bassini 15, Milan 20133, Italy; E-Mails:

关键词: fluvial lake;    chlorophyll-a distribution;    water circulation;    hydrodynamic model;    remote sensing;    hyperspectral data;    water resource management;   

DOI  :  10.3390/w7051921

来源: mdpi

【 摘 要 】

Shallow fluvial lakes are dynamic ecosystems shaped by physical and biological factors and characterized by the coexistence of phytoplankton and macrophytes. Due to multiple interplaying factors, understanding the distribution of phytoplankton in fluvial lakes is a complex but fundamental issue, in the context of increasing eutrophication, climate change, and multiple water uses. We analyze the distribution of phytoplankton by combining remotely sensed maps of chlorophyll-a with a hydrodynamic model in a dammed fluvial lake (Mantua Superior Lake, Northern Italy). The numerical simulation of different conditions shows that the main hydrodynamic effects which influence algal distribution are related to the combined effect of advection due to wind forces and local currents, as well as to the presence of large gyres which induce recirculation and stagnation regions, favoring phytoplankton accumulation. Therefore, the general characters of the phytoplankton horizontal patchiness can be inferred from the results of the hydrodynamic model. Conversely, hyperspectral remote-sensing products can be used to validate this model, as they provide chlorophyll-a distribution maps. The integration of ecological, hydraulic, and remote-sensing techniques may therefore help the monitoring and protection of inland water quality, with important improvements in management actions by policy makers.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.