Water quality modelling for small river basins

被引:64
作者
Marsili-Libelli, Stefano [1 ]
Giusti, Elisabetta [1 ]
机构
[1] Univ Florence, Dept Syst & Comp, I-50139 Florence, Italy
关键词
river quality; model identification; parameter estimation; ecological models; sensitivity; uncertainty analysis;
D O I
10.1016/j.envsoft.2007.06.008
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Water quality modelling in small rivers is often considered unworthy from a practical and economic viewpoint. This paper shows instead that a simple model structure can be set up to describe the stationary water quality in small river basins in terms of carbon and nitrogen compounds, when the use of complex models is unfeasible. In short rivers point and nonpoint sources play a key role in shaping the model response, being as important as the self-purification dynamics. Further, the varying river characteristics, in terms of morphology, hydraulics and vegetation, require the introduction of variable parameters, thus complicating the originally simple model structure. To determine the identifiability of the resulting model an identifiability assessment was carried out, based on sensitivity analysis and optimal experiment design criteria. The identifiable subset was determined by ranking the parameters in terms of sensitivity and computing the associated Fisher Information Matrices. It was found that the inclusion of the nonpoint sources as piecewise constant parameters affected the identifiability to a considerable extent. However, the combined parameter-sources calibration was made possible by the use of a robust estimation algorithm, which also provided estimation confidence limits. The calibrated model responses are in good agreement with the data and can be used as scenario generators in a general strategy to conserve or improve the water quality. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:451 / 463
页数:13
相关论文
共 39 条
[1]   Identification of the parameters describing primary production from continuous oxygen signals [J].
Aalderink, RH ;
Jovin, J .
WATER SCIENCE AND TECHNOLOGY, 1997, 36 (05) :43-51
[2]  
[Anonymous], 1988, Nonlinear regression analysis and its applications, DOI DOI 10.1002/9780470316757
[3]  
Atkinson A.C., 1992, OPTIMUM EXPT DESIGNS
[4]   Combined use of the EPA-Qual2E simulation model and factor analysis to assess the source apportionment of point and non point loads of nutrients to surface waters [J].
Azzellino, A. ;
Salvetti, R. ;
Vismara, R. ;
Bonomo, L. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2006, 371 (1-3) :214-222
[5]   WATER-QUALITY MODELING - A REVIEW OF THE ANALYSIS OF UNCERTAINTY [J].
BECK, MB .
WATER RESOURCES RESEARCH, 1987, 23 (08) :1393-1442
[6]  
Brown L.C., 1987, Report No. EPA/600/3-87/007
[7]   Practical identifiability of ASM2d parameters -: systematic selection and tuning of parameter subsets [J].
Brun, R ;
Kühni, M ;
Siegrist, H ;
Gujer, W ;
Reichert, P .
WATER RESEARCH, 2002, 36 (16) :4113-4127
[8]  
Chapra S, 2003, MODELING FRAMEWORK S
[9]  
Chapra S.C, 1997, SURFACE WATER QUALIT
[10]   Reliability of parameter estimation in respirometric models [J].
Checchi, N ;
Marsili-Libelli, S .
WATER RESEARCH, 2005, 39 (15) :3686-3696