Abstract

The occurrence and related risks from cyanobacterial blooms have increased world-wide over the past 40 years. Information on the abundance and distribution of cyanobacteria is fundamental to support risk assessment and management activities. In the present study, an approach based on Empirical Orthogonal Function (EOF) analysis was used to estimate the concentrations of chlorophyll a (Chla) and the cyanobacterial biomarker pigment phycocyanin (PC) using data from the MODerate resolution Imaging Spectroradiometer (MODIS) in Lake Chaohu (China's fifth largest freshwater lake). The approach was developed and tested using fourteen years (2000–2014) of MODIS images, which showed significant spatial and temporal variability of the PC:Chla ratio, an indicator of cyanobacterial dominance. The results had unbiased RMS uncertainties of <60% for Chla ranging between 10 and 300 μg/L, and unbiased RMS uncertainties of <65% for PC between 10 and 500 μg/L. Further analysis showed the importance of nutrient and climate conditions for this dominance. Low TN:TP ratios (<29:1) and elevated temperatures were found to influence the seasonal shift of phytoplankton community. The resultant MODIS Chla and PC products were then used for cyanobacterial risk mapping with a decision tree classification model. The resulting Water Quality Decision Matrix (WQDM) was designed to assist authorities in the identification of possible intake areas, as well as specific months when higher frequency monitoring and more intense water treatment would be required if the location of the present intake area remained the same. Remote sensing cyanobacterial risk mapping provides a new tool for reservoir and lake management programs.

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