scholarly journals Variability effect of hydrological regime on river quality pattern and its uncertainties: case study of Zarjoob River in Iran

2021 ◽  
Author(s):  
Saman Ebrahimi ◽  
Mahdis Khorram
Keyword(s):  
Water Quality ◽  
River Water ◽  
Hydrological Regime ◽  
River Water Quality ◽  
Pollution Load ◽  
Quality Model ◽  
Discharge Data ◽  
Load Uncertainty ◽  
Climate Conditions

Abstract River water quality assessment, affected by pollution load, and river regime changes in various climate conditions, is an implementation that simplifies water resources management, and justifies terms for increases or decreases in human activities. The current paper aims to offer a water quality model of a river considering parametric, hydrologic, and pollution load uncertainty by using uncertainty indexes like Plevel, ARIL, and NUE. These indexes were used to analyze the influences of the model's parameters and the river's regime alternations on the results. A Qual2K model, calibrated with PSO algorithm, is presented and connected to GLUE algorithm to assess the model's uncertainties like effective input parameters on the modeled variations, headwater flow, and input pollutions. Zarjoob River, in the north of Iran, was chosen as the case study. The results illustrate that the interaction among parameters, hydrologic and pollutant discharge data should be considered in river water quality simulation. The presented methodology can analyze the influences of parametric uncertainty, parametric and hydrologic uncertainty, and pollution input load uncertainty according to any quantity of observations and the modeled results of any river.

Ranking stormwater control strategies under uncertainty: the River Cam case study

2001 ◽  
Vol 43 (7) ◽  
pp. 311-320 ◽  
Author(s):  
S. Duchesne ◽  
M. B. Beck ◽  
A. L. Reda
Keyword(s):  
Water Quality ◽  
River Water ◽  
Control Strategies ◽  
Treatment Plant ◽  
River Water Quality ◽  
Ammonium Concentration ◽  
Model Parameters ◽  
Quality Model ◽  
Parameter Values

Monte Carlo simulations taking uncertainty in model parameters into account were performed on a river water quality model. The simulation results were used to rank wastewater treatment plant control strategies according to their impacts on river water quality. This impact is estimated by the maximum ammonium concentration and by the duration of dissolved oxygen concentration below 4 g/m3 at the downstream boundary of the system. The strategies were classified according to the previous criteria using 4 ranking methods, one of them being based on the concept of stochastic dominance. Results are presented for a case study based on a 10 km stretch of the River Cam as it passes through the city of Cambridge in Eastern England. It was found that ranking was robust in face of uncertainty in the parameter values for the control strategies considered as being superior in terms of river water quality impacts.

River Water Quality Model no. 1 (RWQM1): Case study I. Compartmentalisation approach applied to oxygen balances in the River Lahn (Germany)

2001 ◽  
Vol 43 (5) ◽  
pp. 41-49 ◽  
Author(s):  
D. Borchardt ◽  
P. Reichert
Keyword(s):  
Water Quality ◽  
Water Column ◽  
River Water ◽  
Water Quality Model ◽  
River Water Quality ◽  
Combined Sewer Overflows ◽  
Quality Model ◽  
Combined Sewer ◽  
Sewer Overflows

A case study on the application of the River Water Quality Model No. 1 (RWQM1) is presented in order to illustrate the importance of modelling a sediment compartment for an ecologically meaningful assessment of the impact of wastewater effluents and combined sewer overflows. The focus of this case study is on the compartmentalisation approach of the RWQM1 that makes such a description possible. In contrast to this, a strongly simplified biochemical submodel is used that considers only oxygen and dissolved substrate. The object of the case study is the River Lahn, a moderately polluted 5th order stream in Germany, for which the connectivity of surface/subsurface flows and mass fluxes within river sediments have been intensively investigated. The hyporheic flow between a downwelling and upwelling zone of a riffle-pool sequence has been studied with the aid of tracer experiments and continuous records of water constituents. High diurnal fluctuations of oxygen travelled to considerable depth of the sediment and oxygen in the interstitial water decreased considerably while travelling through the riffle. Starting with the implementation of a strongly simplified version of the biochemical part of the RWQM1, but with the consideration of a sediment pore water compartment in addition to the water column compartment, a calibration procedure is performed using tracer data from the water column and the sediment. The calibrated model is then used to study the system response to wastewater treatment plant effluent and combined sewer overflow emissions. The modelling approach makes it possible to quantify the sediment oxygen demand and the spatial and temporal extent of sediment zones with oxygen depletion. However, the spatially averaged approach does not account for inhomogeneities in the sediment. It is shown that for this river with its alluvial coarse sediments even moderate emissions from sewerage systems may be high enough to drop sediment oxygen concentrations to low levels while those in the surface flow remain close to saturation. Similarly, it is demonstrated that combined sewer overflows may cause anoxic sediment oxygen conditions for extended time periods. The implications for ecologically sound river water quality modelling and for specific quality objectives are discussed.

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.

Monitoring Urban River Water Quality Using Macroinvertebrate and Physico-Chemical Parameters: Case study of Penchala River, Malaysia

2013 ◽  
Vol 13 (6) ◽  
pp. 474-482 ◽  
Author(s):  
Akmal Mahazar ◽  
Mohammad Shuhaimi-O ◽  
Ahmad Abas Kutty ◽  
Mohamed Nor Mohamed De
Keyword(s):  
Water Quality ◽  
River Water ◽  
River Water Quality ◽  
Urban River ◽  
Chemical Parameters ◽  
Physico Chemical

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 River water quality assessment using environmentric techniques: case study of Jakara River Basin

2013 ◽  
Vol 20 (8) ◽  
pp. 5630-5644 ◽  
Author(s):  
Adamu Mustapha ◽  
Ahmad Zaharin Aris ◽  
Hafizan Juahir ◽  
Mohammad Firuz Ramli ◽  
Nura Umar Kura
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
River Water ◽  
River Basin ◽  
Water Quality Assessment ◽  
River Water Quality
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