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Uncertainty and sensitivity analysis of spatial distributed roughness to a hydrodynamic water quality model: a case study on Lake Taihu, China

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Abstract

Roughness is an important parameter in hydrodynamic and water quality modelling; it has direct effects on bottom shear stress which relied on sediment and vegetation. The varied roughness caused by spatial heterogeneity of sediment and vegetation may lead to uncertain simulation results. To investigate the effect of roughness uncertainty on the performance of hydrodynamic water quality models, a typical large shallow lake in China (Lake Taihu) was divided into eight areas for illustrating the effect of spatial variation of roughness on hydrodynamics and water quality. Total nitrogen (TN) was selected as the variable to calculate the uncertainty interval, and sensitive positions greatly affected by roughness as well as the appropriate range of roughness were explored by means of regional sensitive analysis (RSA). The results showed that roughness had the most significant effect on the bottom velocity. The uncertainty for water quality caused by roughness presented a striking spatial difference; the uncertainty interval for TN could be up to 1.3 mg/L. The posterior distribution of roughness was given to further narrowed the range of roughness, and the updated roughness range manifested that roughness value should be set higher in the area with thick sediment and abundant vegetation. It is of utmost importance to consider the comprehensive effects of sediment and vegetation in the determination of roughness. For certain lake areas with great water quality simulation error, the error could be effectively reduced by setting spatial distributed roughness. The optimization scheme was provided for the reasonable determination of roughness, so that the dynamic characteristic at the sediment-water interface could be represented synthetically. In this paper, the uncertainty and sensitivity of roughness in hydrodynamic water quality model are analyzed to provide reference for parameter setting of large shallow water lake model. For large scale lakes, parameters need to be modified according to the actual condition due to the spatial difference of friction coefficient at the bottom.

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Funding

The research was supported by National Natural Science Foundation of China (52039003, 51809102), the Fundamental Research Funds for the Central Universities, and the World‐Class Universities (Disciplines), and the Characteristic Development Guidance Funds for the Central Universities and National Natural Science Foundation of China (51779072).

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Authors and Affiliations

  1. Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, China

    Yue Cheng, Yiping Li, Yaning Wang, Chunyan Tang, Yuanyuan Shi, Linda Sarpong & Jinhua Li

  2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning, 530023, China

    Ronghui Li

  3. Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV, 89119, USA

    Kumud Acharya

Authors
  1. Yue Cheng
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  2. Yiping Li
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  3. Yaning Wang
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  4. Chunyan Tang
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  5. Yuanyuan Shi
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  6. Linda Sarpong
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  7. Ronghui Li
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  8. Kumud Acharya
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  9. Jinhua Li
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Contributions

All authors contributed to the study conception and design. Material preparation was performed by Yiping Li and Ronghui Li. Formal analysis and investigation were made by Yaning Wang and Jinhua Li. data collection and analysis were performed by Yue Cheng and Yuanyuan Shi. The first draft of the manuscript was written by Yue Cheng and Chunyan Tang and all authors commented on previous versions of the manuscript. Linda Sarpong and Kumud Acharya checked the English language. All authors read and approved the final manuscript.

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Correspondence to Yiping Li or Ronghui Li.

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Cheng, Y., Li, Y., Wang, Y. et al. Uncertainty and sensitivity analysis of spatial distributed roughness to a hydrodynamic water quality model: a case study on Lake Taihu, China. Environ Sci Pollut Res 29, 13688–13699 (2022). https://doi.org/10.1007/s11356-021-16623-2

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