Calibrating steady-state river water quality models with field data

2004 ◽  
pp. 665-670
Author(s):  
C Liu
Keyword(s):  
Water Quality ◽  
Steady State ◽  
River Water ◽  
Field Data ◽  
River Water Quality ◽  
Quality Models ◽  
Water Quality Models

Identifiability analysis of the CSTR river water quality model

2006 ◽  
Vol 53 (1) ◽  
pp. 93-99 ◽  
Author(s):  
J. Chen ◽  
Y. Deng
Keyword(s):  
Water Quality ◽  
River Water ◽  
Sampling Frequency ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Identifiability Analysis ◽  
Quality Models ◽  
Observational Errors ◽  
Water Quality Models

Conceptual river water quality models are widely known to lack identifiability. The causes for that can be due to model structure errors, observational errors and less frequent samplings. Although significant efforts have been directed towards better identification of river water quality models, it is not clear whether a given model is structurally identifiable. Information is also limited regarding the contribution of different unidentifiability sources. Taking the widely applied CSTR river water quality model as an example, this paper presents a theoretical proof that the CSTR model is indeed structurally identifiable. Its uncertainty is thus dominantly from observational errors and less frequent samplings. Given the current monitoring accuracy and sampling frequency, the unidentifiability from sampling frequency is found to be more significant than that from observational errors. It is also noted that there is a crucial sampling frequency between 0.1 and 1 day, over which the simulated river system could be represented by different illusions and the model application could be far less reliable.

River water quality modelling: I. State of the art

1998 ◽  
Vol 38 (11) ◽  
pp. 237-244 ◽  
Author(s):  
W. Rauch ◽  
M. Henze ◽  
L. Koncsos ◽  
P. Reichert ◽  
P. Shanahan ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
State Of The Art ◽  
Water Quality Management ◽  
Simulation Software ◽  
River Water Quality ◽  
Water Quality Modelling ◽  
Quality Models ◽  
Water Quality Models ◽  
River Water Quality Modelling

River water quality models are used extensively in research as well as in the design and assessment of water quality management measures. The application of mathematical models for that purpose dates back to the initial studies of oxygen depletion due to organic waste pollution. Since then, models have been constantly refined and updated to meet new and emerging problems of surface water pollution, such as eutrophication, acute and chronic toxicity, etc. In order to handle the complex interactions caused by the increased influence of human activities in rivers it is today mandatory to couple river water quality models with models describing emissions from the drainage and sewerage system (such as the IAWQ Activated Sludge model No. 1). In this paper-which is the first of a three-part series by the IAWQ Task Group on River Water Quality Modelling-the state of the art is summarized with the above aim in mind. Special attention is given here to the modelling of conversion processes but also the methods and tools to work with the models, i.e. parameter estimation, measurement campaign design, and simulation software, are discussed.

Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

2019 ◽  
Vol 33 (6) ◽  
pp. 1927-1945 ◽  
Author(s):  
Shirin Karimi ◽  
Bahman Jabbarian Amiri ◽  
Arash Malekian
Keyword(s):  
Water Quality ◽  
Uncertainty Analysis ◽  
River Water ◽  
River Water Quality ◽  
Similarity Metrics ◽  
Quality Models ◽  
Water Quality Models

The application of the activated sludge model No. 1 to a river environment

1997 ◽  
Vol 36 (5) ◽  
pp. 201-208 ◽  
Author(s):  
F. Maryns ◽  
W. Bauwens
Keyword(s):  
Water Quality ◽  
Activated Sludge ◽  
River Water ◽  
Water Quality Management ◽  
River Water Quality ◽  
Sensitivity Analyses ◽  
Quality Models ◽  
Activated Sludge Model ◽  
Water Quality Models ◽  
Modelling Approach

The current tendency towards an integrated approach for water quality management gives rise to a demand for consistent methods for linking dynamic wastewater treatment models with river water quality models. Linking such models is difficult because of the mutual structural differences with regard to variable and parameter definitions as well as process descriptions. This paper proposes to use the same modelling approach for the simulation of activated sludge treatment and natural self-purification in rivers. Since the standard Activated Sludge Model No. 1 (ASM1) is found to be far more conceptual and consistent than traditional river water quality models, the suitability of the ASM1 modelling approach has been assessed. The traditional ASM1 matrix has been adapted and extended to a river environment and has subsequently served as the basis of an ASM1-type water quality model for the River Dender in Flanders. Sensitivity analyses on this model showed that the most sensitive parameters in the ASM1 formulation of biological decay are the ones determining hydrolysis. The model efficiently calculates BOD concentrations but the predicted DO concentrations are not very accurate, mainly because of the remaining uncertainty about the many ASM1-parameters in river conditions. This indicates the need for determining new typical value ranges for these parameters in a river environment.

Parameter identification of river water quality models using a genetic algorithm

2013 ◽  
Vol 69 (4) ◽  
pp. 687-693 ◽  
Author(s):  
Xiaodong Liu ◽  
Yuanyuan Zhou ◽  
Zulin Hua ◽  
Kejian Chu ◽  
Peng Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Water Quality ◽  
Parameter Identification ◽  
River Water ◽  
Unsteady Flows ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Quality Models ◽  
Water Quality Models

For solving the multi-parameter identification problem of a river water quality model, analytical methods for solving a river water quality model and traditional optimization algorithms are very difficult to implement. A new parameter identification model based on a genetic algorithm (GA) coupled with finite difference method (FDM) was constructed for the determination of hydraulic and water quality parameters such as the longitudinal dispersion coefficient, the pollutant degradation coefficient, velocity, etc. In this model, GA is improved to promote convergence speed by adding the elite replacement operator after the mutation operator, and FDM is applied for unsteady flows. Moreover the influence of observation noise on identified parameters was discussed for the given model. The method was validated by two numerical cases (in steady and unsteady flows respectively) and one practical application. The computational results indicated that the model could give good identification precision results and showed good anti-noise abilities for water quality models when the noise level ≤10%.

Steady State and Time-Varying Water Quality Models for the Hooghly Estuary, India

1986 ◽  
Vol 18 (4-5) ◽  
pp. 257-265 ◽  
Author(s):  
S. K. Bose ◽  
B. K. Dutta
Keyword(s):  
Water Quality ◽  
Steady State ◽  
Time Varying ◽  
Quality Model ◽  
Quality Models ◽  
System Parameters ◽  
Hooghly Estuary ◽  
Advection Diffusion ◽  
Ground Slope ◽  
Water Quality Models

Steady state and time-varying water quality models for the Hooghly estuary have been developed for the 92.5 km stretch from Tribeni to Mayapur. A hydraulic model of the estuary was previously developed in this connection, based on a simulated channel of trapezoidal cross-section, gradually increasing downstream, and with uniform ground slope. The estuary has also been assumed to be section-ally homogeneous in each of the thirty-seven sections. The advection-diffusion equations have been numerically integrated to compute the instantaneous and time-averaged distribution of BOD and DO. The monthly averages of the quantities over the year have also been determined. The different system parameters have been estimated using available equations and correlations. The calculated BOD and DO values agree reasonably well with the available field data. The study shows that a water quality model based on simulated channel geometry may work well and is useful where a more rigorous hydrodynamic model is difficult to construct and verify.

Applicability Analysis on the Improved One-Dimension Steady-State River Water Quality Model

2013 ◽  
Vol 405-408 ◽  
pp. 2254-2259 ◽  
Author(s):  
Lei Zhu ◽  
Jin Xi Song ◽  
Li Hua Liang ◽  
He Li ◽  
An Lei Wei ◽  
...  
Keyword(s):  
Water Quality ◽  
Steady State ◽  
River Water ◽  
Observational Data ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Data ◽  
Quality Model ◽  
One Dimensional ◽  
Wei River

Guanzhong Segment of Wei River was studied in this article. Based on water quality observational data (ammonia-nitrogen) and hydrology observational data (flow of the sections) of Guanzhong Segment of Wei River in the rainy, normal and wet season in 2006, improved one-dimensional steady-state river water quality model was calibrated. River water quality was predicted in the normal season which were compared with the observational data in 2005. The results show that the water quality computed by improved one-dimensional steady-state river water quality model is grossly consistent with the observational water quality data and the model may provide the technical supports for the response relationships of water quality and water quantity of Guanzhong Segment of Wei River.

scholarly journals Comparing Log-Linear and Best-Fit Models to Evaluate the Long-Term Persistence of Enteric Markers in Sewage Spiked River Water

2021 ◽  
Vol 3 ◽  
Author(s):  
Yolanda M. Brooks ◽  
Joan B. Rose
Keyword(s):  
Water Quality ◽  
River Water ◽  
Linear Models ◽  
Decay Rates ◽  
Fecal Indicator ◽  
Quality Models ◽  
Linear Pattern ◽  
Log Linear ◽  
Water Quality Models ◽  
Best Fit

Water quality models use log-linear decay to estimate the inactivation of fecal indicator bacteria (FIB). The decay of molecular measurements of FIB does not follow a log-linear pattern. This study examined the factors associated with the persistence of Escherichia coli uidA, enterococci 23S rDNA, and Bacteroides thetataiotaomicron 1,6 alpha mannanase in microcosms containing 10% (vol/vol) sewage spiked river water stored at 4°C for up to 337 days. The study estimated the markers' persistence with log-linear models (LLMs) to the best-fit models, biphasic exponential decay (BI3) and log-logistic (JM2) and compared the estimates from the models. Concentrations of B. thetataiotaomicon decreased to levels below detection after 31 days in storage and were not fit to models. BI3 and JM2 were fit to E. coli and enterococci, respectively. LLMs had larger Bayesian information criterion values than best-fit models, indicating poor fit. LLMs over-estimated the time required for 90% reduction of the indicators (T90) and did not consider dynamic rates of decay. Time in storage and indicator species were associated with the persistence of the markers (p < 0.001). Using the T90 values of the best-fit models, enterococci was the most persistent indicator. Our data supports the use of best fit models with dynamic decay rates in water quality models to evaluate the decay of enteric markers.

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