scholarly journals A regionalized river water quality model calibration method based on watershed physical characteristics: application to the Cau River in Vietnam

2018 ◽  
Vol 31 (3) ◽  
pp. 251-269
Author(s):  
Lise Audet ◽  
Sophie Duchesne ◽  
Nomessi Kokutse
Keyword(s):  
Water Quality ◽  
River Water ◽  
Hydrological Model ◽  
Calibration Method ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
River Stretch ◽  
Definition Of ◽  
Water Quality And Quantity

A methodology is proposed for the calibration of river water quality models on large watersheds, in the absence of intensive measurements for water quality and quantity. This methodology is based on: 1) the use of the results from a hydrological model to provide the required hydrological variables to the water quality model; 2) five assumptions for the definition of initial and boundary conditions; 3) a three-step regionalized calibration method, in which the specific characteristics of the different subwatersheds are taken into account and 4) the adjustment of some parameters in order to reproduce processes that are not explicitly represented in the model. The regionalized calibration method relies on a comprehensive study of the land use and characteristics on each subwatershed and the definition of different sets of parameters values in distinct regions. Application to the Cau River, in Vietnam, with QUAL-GIBSI, an adaptation of the QUAL2E model, showed that: i) calibration and validation results were significantly improved by applying regionalized calibration as compared to an initial calibration for which a single set of parameters values was used for the whole simulated river stretch and ii) use of a hydrological model to provide discharge at various points in the watershed allowed to overcome the lack of detailed measurements of discharge at locations other than the watershed outlet.

Detection of the most optimal measuring points for water quality variables: application to the river water quality model of the River Dender in ESWAT

2002 ◽  
Vol 46 (3) ◽  
pp. 1-7 ◽  
Author(s):  
V. Vandenberghe ◽  
A. van Griensven ◽  
W. Bauwens
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Optimal Design ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Definition Of ◽  
Synthetic Time Series ◽  
Optimal Set

This paper presents a methodology for the definition of an optimal set of sampling data for the calibration of a river water quality model. Starting with an extensive set of measurements, it is the aim to reduce those data to obtain just as much data as necessary for a calibration with an acceptable uncertainty in the parameters. The method requires a model for the river under examination and the availability of samples for a first calibration of the model. With the model, synthetic time series are generated, which can be used as virtual observations. In the next step, the method of D-optimal design is applied. The amount, frequency, period, place and kind of variables measured of the water samples that gives the most reliable estimates of the parameters of the model are considered to be the best observations that can be made for that river. Also, the percentage of improvement of the reliability can be defined, as a function of the observations. The method is applied to the river Dender.

scholarly journals Water Quality Modeling of Bhima River by using HEC-RAS Software

2020 ◽  
Vol 9 (3) ◽  
pp. 2886-2889
Keyword(s):  
Water Quality ◽  
River Water ◽  
Hydrodynamic Model ◽  
Algal Blooms ◽  
Agricultural Runoff ◽  
Water Quality Modeling ◽  
Water Quality Model ◽  
River Water Quality ◽  
Water Levels ◽  
Quality Model

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.

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.

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.

RIVER WATER QUALITY MODEL VERIFICATION THROUGH A GIS BASED SOFTWARE

2009 ◽  
Vol 42 (11) ◽  
pp. 798-803 ◽  
Author(s):  
M.K. Yetik ◽  
M. Yüceer ◽  
R. Berber ◽  
E. Karadurmuş
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Model Verification ◽  
Quality Model

River Water Quality Model no. 1 (RWQM1): III. Biochemical submodel selection

2001 ◽  
Vol 43 (5) ◽  
pp. 31-40 ◽  
Author(s):  
P. Vanrolleghem ◽  
D. Borchardt ◽  
M. Henze ◽  
W. Rauch ◽  
P. Reichert ◽  
...  
Keyword(s):  
Water Quality ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Quality Model ◽  
Conversion Model ◽  
General Rules ◽  
Model Component ◽  
Model Features ◽  
Selection Of

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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