Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China

Long Jiang, Yiping Li, Xu Zhao, Martin R. Tillotson, Wencai Wang, Shuangshuang Zhang, Linda Sarpong, Qhtan Asmaa and Baozhu Pan

Ecological Modelling, 2018, vol. 375, issue C, 1-12

Abstract: Lake Taihu was chosen as a case for parameter uncertainty and sensitivity analysis of water quality simulation in large shallow lakes. Forty parameters in Environmental Fluid Dynamic Code model (EFDC) were filtered and analyzed. The results showed that parameters had a considerable influence on simulation and three groups of parameters related to algal kinetics (i.e. PMc, BMRc and PRRc), light (KeChl) and temperature (KTG1c) were very sensitive. For shallow lakes with frequent algal blooms, light extinction due to Chlorophyll-a is also a sensitive parameter. While the temperature effect coefficient for algal growth is sensitive for lakes with seasonal temperature variation. Sensitive parameters and their relevant uncertainty varied spatially. For high nutrients and algae concentration subareas, temperature was more likely to be a limiting factor, whereas sensitive factors could be light in lower concentration subareas. Since most sensitive parameters were related to algae, uncertainty in simulation increased with increasing algal kinetic processes over time and varied in different subareas. Lower nutrients and algae concentration subareas were more easily influenced by model parameters while nearshore areas were highly influenced by boundary conditions. For better simulation of water quality, variable stoichiometry phytoplankton models should be considered and zooplankton need to be integrated into the model explicitly rather than a fixed predation rate.

Keywords: Key words; Lake Taihu; Sensitivity analysis; Uncertainty analysis; Water quality models (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:375:y:2018:i:c:p:1-12

DOI: 10.1016/j.ecolmodel.2018.02.014

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