Short-term forecasting of cyanobacteria blooms in Yuqiao reservoir, China. Issue 2 (1st April 2017)

• Record Type: Journal Article
• Authors: Vinçon-Leite, Brigitte; Fadel, Ali; Lemaire, Bruno J.; Bonhomme, Céline; Li, Yinghao; Le Divechen, Gwendal; Zhang, Jun; Luo, Yang
• Is Part Of: La Houille Blanche. Volume 103:Issue 2(2017)
• Page Start: 35
• Page End: 44
• Publication Date: 2017-04-01
• Subjects: deterministic model -- temperature -- phytoplankton -- vertical profiles -- meteorological forecast.; modélisation déterministe -- température -- phytoplancton -- profils verticaux -- prévisions météorologiques.
• DOI: 10.1051/lhb/2017015
• Languages: English
• ISSNs: 0018-6368

Abstract

Abstract : Agriculture fertilizers and untreated sewage in the catchment of freshwater bodies increase their nutrient concentrations. The resulting eutrophication promotes phytoplankton blooms, in many cases of toxic cyanobacteria. Ecological models can simulate the main processes driving algal blooms. They help to understand how phytoplankton responds to different forcing. Another major use of models is as predictive tool supporting the management of water resources. Yuqiao Reservoir is the main drinking water source for Tianjin, the fifth most populated city in China. During the last decade, drinking water production has been impaired by recurrent cyanobacteria blooms. The water quality management of the reservoir, conducted by the Hai River Water Conservancy Commission, requires an early warning system in order to anticipate the cyanobacteria blooms and to adapt the treatment process. Therefore, within the framework of a French-Chinese project on Integrated Water Resources Management (IWRM) of the catchment of the Hai river, a modelling approach of cyanobacteria growth, which could be later embedded in an early warning system, was proposed. In this study, we calibrated a one-dimensional vertical, physical-ecological model aimed at forecasting cyanobacteria blooms in the Yuqiao Reservoir. We first introduce the study site and its ecological status. Then we describe the model and the input data set. The calibration and validation results are presented for water temperature and chlorophyll-a concentration at the measurement dates. The short-term forecast modelling is performed for temperature and cyanobacteria over a 5-day ahead period. Two weather scenarios are considered: one favourable for cyanobacteria and one disfavourable.