Sequential calibration of a water quality model using reach-specific parameter estimates

被引:10
作者
Chaudhary, Shushobhit [1 ]
Dhanya, C. T. [1 ]
Kumar, Arun [1 ]
机构
[1] Indian Inst Technol Delhi, Dept Civil Engn, New Delhi 110016, India
来源
HYDROLOGY RESEARCH | 2018年 / 49卷 / 04期
关键词
parameter estimation; QUAL2K; water quality simulation; Yamuna River; RIVER YAMUNA; GENETIC ALGORITHMS; SIMULATION; REAERATION; STRETCH; LOAD;
D O I
10.2166/nh.2017.246
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Calibration is the most critical phase in any water quality modelling process. This study proposes a sequential calibration methodology for any water quality model using reach-specific estimates of model parameters, which would aid in the improved prediction of river water quality characteristics. The proposed methodology accounts for the heterogeneity of river reaches, i.e., diverse characteristics of different reaches on the river stretch. The water quality model, QUAL2K, is coupled with MATLAB, a computing platform, to facilitate sequential estimation of reach-wise model parameters using a grid-based weighted average optimization. The Delhi segment of the Yamuna River is selected as study river stretch. Observations of water quality variables, dissolved oxygen and biochemical oxygen demand are used to calibrate and validate QUAL2K. Desirable performance measures are obtained during the calibration and the validation period. The methodology proves superior to the existing calibration methodologies applied over the study region. The proposed technique also captures the system behaviour effectively, through a systematic, efficient and user-friendly way. The proposed approach is expected to aid decision-makers in formulating better reach-wise management decisions and treatment policies by providing a simpler and efficient way to simulate water quality parameters.
引用
收藏
页码:1042 / 1055
页数:14
相关论文
共 48 条
[21]   Reconciling observed and modelled phytoplankton dynamics in a major lowland UK river, the Thames [J].
Lazar, Attila N. ;
Wade, A. J. ;
Whitehead, P. G. ;
Neal, C. ;
Loewenthal, M. .
HYDROLOGY RESEARCH, 2012, 43 (05) :576-588
[22]   Lake water levels for calibration of the S-HYPE model [J].
Lindstrom, Goran .
HYDROLOGY RESEARCH, 2016, 47 (04) :672-682
[23]   Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales [J].
Lindstrom, Goran ;
Pers, Charlotta ;
Rosberg, Jorgen ;
Stromqvist, Johan ;
Arheimer, Berit .
HYDROLOGY RESEARCH, 2010, 41 (3-4) :295-319
[24]   Variations of carbon transport in the Yellow River, China [J].
Liu, Jianrong ;
Song, Xiangfang ;
Wang, Zhimin ;
Yang, Lihu ;
Sun, Zhenyu ;
Wang, Wenjia .
HYDROLOGY RESEARCH, 2015, 46 (05) :746-762
[25]   Using genetic algorithms to calibrate a water quality model [J].
Liu, Shuming ;
Butler, David ;
Brazier, Richard ;
Heathwaite, Louise ;
Khu, Soon-Thiam .
SCIENCE OF THE TOTAL ENVIRONMENT, 2007, 374 (2-3) :260-272
[26]   Water quality modelling for small river basins [J].
Marsili-Libelli, Stefano ;
Giusti, Elisabetta .
ENVIRONMENTAL MODELLING & SOFTWARE, 2008, 23 (04) :451-463
[27]   Reaeration equations derived from US geological survey database [J].
Melching, CS ;
Flores, HE .
JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 1999, 125 (05) :407-414
[28]   Genetic algorithms for calibrating water quality models [J].
Mulligan, AE ;
Brown, LC .
JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE, 1998, 124 (03) :202-211
[29]   Selection of genetic algorithm operators for river water quality model calibration [J].
Ng, AWM ;
Perera, BJC .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2003, 16 (5-6) :529-541
[30]  
O'Connor D.J., 1958, TRANSACTION AM SOC C, V123, P641, DOI DOI 10.1061/TACEAT.0007609