Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake

• Published in: Ecological modelling Vol. 327; pp. 74 - 84
• Main Authors: Yi, Xuan, Zou, Rui, Guo, Huaicheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 10.05.2016
• Subjects: algae; Chla; chlorophyll; dissolved oxygen; dynamic models; Dynamic tests; Dynamics; EFDC; EFDC model; fluid mechanics; Heterogeneity; hydrologic models; Lakes; Mathematical models; Modelling; Morris screening; nitrogen content; nutrients; OAT; phosphorus; Sensitivity analysis; SOD; spatial variation; Spatiotemporal sensitivity indices; temporal variation; uncertainty analysis; Water quality; Water quality model; WQMs; China; China, People's Rep., Yunnan Prov., Dianchi L; China, People's Rep; EFDC; OAT; Sensitivity analysis; Spatiotemporal sensitivity indices; WQMs; SOD; DO; EFDC model; Morris screening; Water quality model; Chla; TN; TP
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2016.01.005

•We applied Morris sensitivity analysis in a complex 3-dimensional water quality model EFDC.•We compared different sample sizes, perturbation ranges, and output metrics.•We analyzed and compared the influential factors of four different water quality constituents.•We analyzed the spatiotemporal variability in sensitivities. Sensitivity analysis is a primary approach used in mathematical modeling to identify important factors that control the response dynamics in a model. In this paper, we applied the Morris sensitivity analysis method to identify the important factors governing the dynamics in a complex 3-dimensional water quality model. The water quality model was developed using the Environmental fluid dynamics code (EFDC) to simulate the fate and transport of nutrients and algal dynamics in Lake Dianchi, one of the most polluted large lakes in China. The analysis focused on the response of four water quality constituents, including chlorophyll-a, dissolved oxygen, total nitrogen, and total phosphorus, to 47 parameters and 7 external driving forces. We used Morris sensitivity analysis with different sample sizes and factor perturbation ranges to study the sensitivity with regard to different output metrics of the water quality model, and we analyzed the consistency between different sensitivity scenarios. In addition to the analysis with aggregate outputs, a spatiotemporal variability analysis was performed to understand the spatial heterogeneity and temporal distribution of sensitivities. Our results indicated that it is important to consider multiple characteristics in a sensitivity analysis, and we have identified a robust set of sensitive factors in the water quality model that will be useful for systematic model parameter identification and uncertainty analysis.