Water quality modelling of the river Sava, Slovenia

Drolc, Andreja; Končan, J. Zagorc

doi:10.1016/s0043-1354(96)00154-6

Cited by 45 publications

(20 citation statements)

References 5 publications

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“…River pollution is one of the most serious water resources problems of the present day. These problems for various river systems have been reported frequently (Drolc & Koncan, 1996;Liu et al, 2005;Gonçalves et al, 2011).…”

Section: Introductionmentioning

confidence: 81%

Mathematical Model for the Simulation of Water Quality in Rivers Using the Vensim Ple® Software

Gonçalves¹,

Giorgetti²

2013

J. Urb. and Environ. Engng

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Abstract:Mathematical modeling of water quality in rivers is an important tool for the planning and management of water resources. Nevertheless, the available models frequently show structural and functional limitations. With the objective of reducing these drawbacks, a new model has been developed to simulate water quality in rivers under unsteady conditions; this model runs on the Vensim PLE ® software and can also be operated for steady-state conditions. The following eighteen water quality variables can be simulated: DO, BOD c , organic nitrogen (N o ), ammonia nitrogen (N a ), nitrite (N i ), nitrate (N n ), organic and inorganic phosphorus (F o and F i , respectively), inorganic solids (Si), phytoplankton (F), zooplankton (Z), bottom algae (A), detritus (D), total coliforms (TC), alkalinity (Al.), total inorganic carbon (TIC), pH, and temperature (T). Methane as well as nitrogen and phosphorus compounds that are present in the aerobic and anaerobic layers of the sediment can also be simulated. Several scenarios were generated for computational simulations produced using the new model by using the QUAL2K program, and, when possible, analytical solutions. The results obtained using the new model strongly supported the results from the QUAL family and analytical solutions.

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Section: Introductionmentioning

confidence: 81%

Mathematical Model for the Simulation of Water Quality in Rivers Using the Vensim Ple® Software

Gonçalves¹,

Giorgetti²

2013

J. Urb. and Environ. Engng

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“…Some water quality models, such as QUAL2E as applied in the River Sava, Slovenia, implicitly assume inclusion of biomass production and respiration through SOD rates @rolc and Koncan, 1996). The Segment Travel River Ecosystem Autograph Mode1 (STREAM) uses fixed photosynthetic and respiration rates and does not relate the nutrients, such as soluble phosphorus, to the potential for aquatic plant growth (Park and Lee, 1996).…”

Section: Other Aquatic Plant Modelsmentioning

confidence: 99%

Incorporation and Testing of Revised Algorithms for the Aquatic Plant Growth Model, ECOL

Humphries

James

2000

JWMM

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Aquatic plant growth in river systems provides a link between phosphorus enrichment and fluctuations in dissolved oxygen. Most aquatic plant models simulate the growîh of a single generalized species and therefore cannot account for the wide variability in growth patterns shown by differing species. ECOL, an aquatic plant growth model developed in the late 1970s, was incorporated into the Grand River Simulation Model (GRSM) to model the growth of the three main plant species found in the Grand River watershed: CladophDra glornerata, Potamogeton peclinam and Myriophylfzim spicatum. ECO L calculates plant uptake of phosphorus, biomass production and Ioss, and the resulting production and consumption of dissolved oxygen (DO), using a Zhour time step, New algorithms to improve the sub-models in ECOL for light, temperature and phosphorus are developed and tested in this study. The present work evaluates and corrects sources of error in GRSM96, recalibrates the improved model and identifies and quantifies remaining weaknesses.The resulting modef, GRSM98AH, has an average error between computed and observed DO of 19.6%, which is regarded as satisfactory.

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“…The deterioration of aquatic ecosystems results from decreases in the dissolved oxygen concentration (DOC) due to pollutant degradation by microorganisms, chemical oxidation, and plant, algal, and phytoplankton respiration (Drolc and Konkan, 1996). The impacts of low DOC or even of anaerobic conditions causes mortality of fish and other aquatic organisms, unpleasant odors, and other aesthetic damage (Cox, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…A better alternative would be to estimate the impact of changes in anthropogenic discharges on a river's water quality using mathematical models, since that allows immediate remediation before problems become prohibitively difficult to solve. This practice has grown in popularity in recent years, and is becoming a common tool for the management of aquatic resources (Drolc and Konkan, 1996).…”

Section: Introductionmentioning

confidence: 99%

Water quality prediction using the QUAL2Kw model in a small karstic watershed in Brazil

Camargo¹,

Calijuri²,

Santiago³

et al. 2010

Acta

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Aim: The Tancredo Neves International Airport (TNIA) complex is situated in Brazil's Fidalgo watershed. Since its construction, the TNIA complex has attracted urban development, leading to expansion of the complex and occupation of the surrounding area. However, this area lacks basic infrastructure such as wastewater treatment facilities. This paper had the objectives of calibrating and validating a water quality prediction model and of assessing the capacity of small karstic watersheds to assimilate non-point source pollutant loads; Methods: We used the QUAL2Kw model for modeling the water quality. We performed model calibration for the rainy period then validated the results for the dry period; Results: The model adequately represented the physical, chemical, and hydraulic aspects of the Fidalgo watershed. The pH, EC, TDS, TP, alkalinity and E. coli presented the closest simulation values for the rainy period. For the dry period, the best simulations were obtained for pH, EC, TDS, TP, and alkalinity. We concluded that the calibration and validation periods had similar RMSE values for flow rate, TDS and DOC. The differences were greater for pH, EC, NO 3 , TP, and E. coli. The lowest dissolved oxygen contents obtained during the calibration and validation periods were 5.4 and 4.7 mg.L The conservation of the water resources in this region should therefore consider the adoption of preventive measures such as protecting exposed soils and decreasing the nutrient influx from agricultural and livestock activities.Keywords: water quality, karstic region, modeling, QUAL2Kw, urban pollution.Resumo: Objetivo: O Aeroporto Internacional Tancredo Neves (AITN) está localizado na microbacia do córrego do Fidalgo, Brasil. Desde sua construção, o AITN tem atraído a instalação de indústrias e loteamentos residenciais sem que a região oferecesse condições básicas de infraestrutura. Este trabalho teve por objetivo calibrar e validar modelo de predição da qualidade da água e avaliar a capacidade de assimilação de cargas difusas em microbacia cárstica; Métodos: Utilizou-se o QUAL2Kw para a modelagem da qualidade da água. A calibração do modelo foi realizada no período chuvoso e a validação para o período seco; Resultados: O modelo demonstrou ser capaz de representar com habilidade e flexibilidade os aspectos físicos, químicos e hidráulicos observados na microbacia. Os parâmetros pH, CE, STD, PT, alcalinidade e E. coli apresentaram os melhores ajustes entre as previsões do modelo e os dados observados para o período chuvoso. Para o período seco, os melhores ajustes foram obtidos para pH, EC, TDS, TP e alcalinidade. Os valores de REMQ foram similares entre os períodos para vazão, SDT e OD. As maiores diferenças foram observadas para os parâmetros pH, CE, NO 3

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Water quality modelling of the river Sava, Slovenia

Cited by 45 publications

References 5 publications

Mathematical Model for the Simulation of Water Quality in Rivers Using the Vensim Ple® Software

Mathematical Model for the Simulation of Water Quality in Rivers Using the Vensim Ple® Software

Incorporation and Testing of Revised Algorithms for the Aquatic Plant Growth Model, ECOL

Water quality prediction using the QUAL2Kw model in a small karstic watershed in Brazil

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