A 2D water quality model for shallow estuaries. Sensitivity analysis to model parameters and input data

2010 ◽  
pp. 525-530
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Input Data ◽  
Water Quality Model ◽  
Model Parameters ◽  
Quality Model ◽  
Shallow Estuaries

Developing a Cloud-based Toolbox for Sensitivity Analysis of a Water Quality Model

2021 ◽  
pp. 105068
Author(s):  
Soobin Kim ◽  
Yong Sung Kwon ◽  
JongChel Pyo ◽  
Mayzonee Ligaray ◽  
Joong-Hyuk Min ◽  
...  
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Water Quality Model ◽  
Quality Model

Water quality model parameter identification of an open channel in a long distance water transfer project based on finite difference, difference evolution and Monte Carlo

2013 ◽  
Vol 69 (3) ◽  
pp. 587-594 ◽  
Author(s):  
Dongguo Shao ◽  
Haidong Yang ◽  
Yi Xiao ◽  
Biyu Liu
Keyword(s):  
Water Quality ◽  
Open Channel ◽  
Differential Evolution Algorithm ◽  
Water Transfer ◽  
Water Quality Model ◽  
Model Parameters ◽  
Quality Model ◽  
Long Distance ◽  
Mcmc Simulation ◽  
Evolution Algorithm

A new method is proposed based on the finite difference method (FDM), differential evolution algorithm and Markov Chain Monte Carlo (MCMC) simulation to identify water quality model parameters of an open channel in a long distance water transfer project. Firstly, this parameter identification problem is considered as a Bayesian estimation problem and the forward numerical model is solved by FDM, and the posterior probability density function of the parameters is deduced. Then these parameters are estimated using a sampling method with differential evolution algorithm and MCMC simulation. Finally this proposed method is compared with FDM–MCMC by a twin experiment. The results show that the proposed method can be used to identify water quality model parameters of an open channel in a long distance water transfer project under different scenarios better with fewer iterations, higher reliability and anti-noise capability compared with FDM–MCMC. Therefore, it provides a new idea and method to solve the traceability problem in sudden water pollution accidents.

Improved dynamic simulation technique for hydrodynamics and water quality of river-connected lakes

2020 ◽  
Vol 20 (8) ◽  
pp. 3752-3767
Author(s):  
Bojun Liu ◽  
Jun Xia ◽  
Libin Yang ◽  
Changyong Cui ◽  
Linwei Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
Latin Hypercube Sampling ◽  
Resistance Coefficient ◽  
Water Quality Model ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
State Variables ◽  
Quality Model ◽  
Rank Regression ◽  
Hongze Lake

Abstract In this study, a two-dimensional hydrodynamic water-quality model is proposed for river-connected lakes in an effort to improve calibration accuracy and reduce computational burden. To achieve this, the sensitivity of parameters involved in the hydrodynamic model is analyzed using stepwise rank regression and Latin hypercube sampling (LHS), and the roughness coefficient, wind drag coefficient and wind resistance coefficient are identified as the most important parameters affecting the hydrodynamics of the Hongze Lake. Then, the ensemble Kalman filter (EnKF) is used to assimilate observations to the proposed hydrodynamic and water quality model. It is found that assimilation of both state variables and model parameters results in a significant improvement of the simulation of the water level, flow velocity and pollutant concentration in the Hongze Lake.

Sensitivity analysis of non-point sources in a water quality model applied to a dammed low-flow-reach river

2008 ◽  
Vol 57 (8) ◽  
pp. 1295-1300
Author(s):  
Nayana G. M. Silva ◽  
Marcos von Sperling
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Initial Conditions ◽  
Quality Parameters ◽  
Water Quality Model ◽  
Point Sources ◽  
Low Flow ◽  
Treated Wastewater ◽  
Quality Model ◽  
River Bed

Downstream of Capim Branco I hydroelectric dam (Minas Gerais state, Brazil), there is the need of keeping a minimum flow of 7 m3/s. This low flow reach (LFR) has a length of 9 km. In order to raise the water level in the low flow reach, the construction of intermediate dikes along the river bed was decided. The LFR has a tributary that receives the discharge of treated wastewater. As part of this study, water quality of the low-flow reach was modelled, in order to gain insight into its possible behaviour under different scenarios (without and with intermediate dikes). QUAL2E equations were implemented in FORTRAN code. The model takes into account point-source pollution and diffuse pollution. Uncertainty analysis was performed, presenting probabilistic results and allowing identification of the more important coefficients in the LFR water-quality model. The simulated results indicate, in general, very good conditions for most of the water quality parameters The variables of more influence found in the sensitivity analysis were the conversion coefficients (without and with dikes), the initial conditions in the reach (without dikes), the non-point incremental contributions (without dikes) and the hydraulic characteristics of the reach (with dikes).

scholarly journals Extension of the River Water Quality Model No. 1 with the fate of pesticides

2011 ◽  
Vol 14 (1) ◽  
pp. 48-64 ◽  
Author(s):  
Veerle C. J. De Schepper ◽  
Katrijn M. A. Holvoet ◽  
Lorenzo Benedetti ◽  
Piet Seuntjens ◽  
Peter A. Vanrolleghem
Keyword(s):  
Water Quality ◽  
Water Column ◽  
River Water ◽  
Bulk Water ◽  
Transport Processes ◽  
Water Quality Model ◽  
River Water Quality ◽  
Model Parameters ◽  
Quality Model ◽  
Run Off

The existing River Water Quality Model No. 1 (RWQM1) was extended with processes determining the fate of non-volatile pesticides in the water phase and sediments. The exchange of pesticides between the water column and the sediment is described by three transport processes: diffusion, sedimentation and resuspension. Burial of sediments is also included. The modified model was used to simulate the concentrations of diuron and chloridazon in the river Nil. A good agreement was found between the simulated pesticide concentrations and measured values resulting from a four-month intensive monitoring campaign. The simulation results indicate that pesticide concentrations in the bulk water are not sensitive to the selected biochemical model parameters. It seems that these concentrations are mainly determined by the imposed upstream concentrations, run-off and direct losses. The high concentrations in the bulk water were not observed in the sediment pore water due to a limited exchange between the water column and the sediment. According to a sensitivity analysis, the observed pesticide concentrations are highly sensitive to the diffusion and sorption coefficients. Therefore, model users should determine these parameters with accuracy in order to reduce the degree of uncertainty in their results.

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