A parsimonious dynamic model for river water quality assessment

2010 ◽  
Vol 61 (3) ◽  
pp. 607-618 ◽  
Author(s):  
Giorgio Mannina ◽  
Gaspare Viviani

Water quality modelling is of crucial importance for the assessment of physical, chemical, and biological changes in water bodies. Mathematical approaches to water modelling have become more prevalent over recent years. Different model types ranging from detailed physical models to simplified conceptual models are available. Actually, a possible middle ground between detailed and simplified models may be parsimonious models that represent the simplest approach that fits the application. The appropriate modelling approach depends on the research goal as well as on data available for correct model application. When there is inadequate data, it is mandatory to focus on a simple river water quality model rather than detailed ones. The study presents a parsimonious river water quality model to evaluate the propagation of pollutants in natural rivers. The model is made up of two sub-models: a quantity one and a quality one. The model employs a river schematisation that considers different stretches according to the geometric characteristics and to the gradient of the river bed. Each stretch is represented with a conceptual model of a series of linear channels and reservoirs. The channels determine the delay in the pollution wave and the reservoirs cause its dispersion. To assess the river water quality, the model employs four state variables: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project in which quantity and quality data were gathered. A sensitivity analysis of the model output to the model input or parameters was done based on the Generalised Likelihood Uncertainty Estimation methodology. The results demonstrate the suitability of such a model as a tool for river water quality management.

2013 ◽  
Vol 405-408 ◽  
pp. 2254-2259 ◽  
Author(s):  
Lei Zhu ◽  
Jin Xi Song ◽  
Li Hua Liang ◽  
He Li ◽  
An Lei Wei ◽  
...  

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.


2010 ◽  
Vol 62 (2) ◽  
pp. 288-299 ◽  
Author(s):  
Giorgio Mannina ◽  
Gaspare Viviani

Numerical modelling can be a useful tool to assess a receiving water body's quality state. Indeed, the use of mathematical models in river water quality management has become a common practice to show the cause-effect relationship between emissions and water body quality and to design as well as assess the effectiveness of mitigation measures. In the present study, a hydrodynamic river water quality model is presented. The model consists of a quantity and a quality sub-model. The quantity sub-model is based on the Saint Venant equations. The solution of the Saint Venant equations is obtained by means of an explicit scheme based on space-time conservation. The method considers the unification of space and time and the enforcement of flux conservation in both space and time. On the other hand, the quality sub-model is based on the advection dispersion equation. Particularly, the principle of upstream weighting applied to finite difference methods is employed. This method enable us to reduce the numerical dispersion avoiding oscillation phenomena. The optimal weighting coefficient was calculated on the basis of the mesh Peclet number. Regarding the quality processes, the model takes into account the main physical/chemical processes; these are degradation of dissolved carbonaceous substances, ammonium oxidation, algal uptake and denitrification, dissolved oxygen balance, including depletion by degradation processes and supply by physical reaeration and photosynthetic production. To properly simulate the river water quality, four state variables were considered: DO, BOD, NH4, and NO. The model was applied to the Savena River (Italy), which is the focus of a European-financed project for which quantity and quality data were gathered. A sensitivity analysis of the model output compared to the model input or parameters was carried out.


The River has got religious importance in India. The Bhima River is beginning from Bhimashankar hill and it flows through some parts of Maharashtra and Karnataka state. The assessment of water quality for the development of the places near the bank of River is important. These is controlled by various manmade activities. The quality of river water resources is facing problems because of the continuous agricultural runoff, development and urbanization. Due to mixing of nutrients causes algal blooms, which results eutrophication. The modeling of water quality can be deliberated as useful tool for assessing river water. Bhima River is demarcated as a major and important water body located in Pandharpur, dist. Solapur, Maharashtra. As Pandharpur is having historical background and known as one of the famous Holly places in Maharashtra, this place is facing huge population fluctuation due to migrated pilgrims and rapid growth of urbanization. These two things detrimentally affect River water quality. The main objective of current study was to develop a hydrodynamic model combined with river water quality model for the Bhima River to measure and recognize the processes harmful for the River. For Bhima River a hydrodynamic model was constructed using the HEC-RAS 4.1 software combined with a river water quality model to estimate the amount, distribution and sources of algae, nitrate and temperature. The river model has standardized with the help of previous water levels near the Pandharpur region. It has standardized and calibrated for the assessed parameters by competing them with the present data. The result showed a relationship between DO and temperature range. DO level in Pandharpur and Gopalpur were observed to be fluctuating with respective temperature and during Vari season. However, wastewater discharge from Nalha in sample station 3 i.e. Goplapur shows slit changes in DO and due to this there is necessity to learn other parameters also.


2006 ◽  
Vol 53 (1) ◽  
pp. 93-99 ◽  
Author(s):  
J. Chen ◽  
Y. Deng

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.


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

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.


2009 ◽  
Vol 42 (11) ◽  
pp. 798-803 ◽  
Author(s):  
M.K. Yetik ◽  
M. Yüceer ◽  
R. Berber ◽  
E. Karadurmuş

2001 ◽  
Vol 43 (5) ◽  
pp. 31-40 ◽  
Author(s):  
P. Vanrolleghem ◽  
D. Borchardt ◽  
M. Henze ◽  
W. Rauch ◽  
P. Reichert ◽  
...  

The new River Water Quality Model no.1 introduced in the two accompanying papers by Shanahan et al. and Reichert et al. is comprehensive. Shanahan et al. introduced a six-step decision procedure to select the necessary model features for a certain application. This paper specifically addresses one of these steps, i.e. the selection of submodels of the comprehensive biochemical conversion model introduced in Reichert et al. Specific conditions for inclusion of one or the other conversion process or model component are introduced, as are some general rules that can support the selection. Examples of simplified models are presented.


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