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Simulating the Impact of Water Quality Improvement Measures for Nutrient-Sensitive River Basins with the Aquatox Model

8 pagesPublished: September 20, 2018

Abstract

Water quality modelling studies are effective tools for the prediction of the impact of water quality improvement measures. This study aims to predict the future water quality of a nutrient-sensitive river basin assuming the implementation of water quality improvement measures by setting up and executing multiple models. The rationale behind the use of multiple models is the better suitability of each model for its relevant objective. Hydrodynamics of the river are simulated using the WASP model. Following the estimation of diffuse-source nutrient loadings in the river basin with the SWAT model, water quality of the river is simulated with a multi-segment Aquatox model. All models are calibrated to one year of observed data. Models are first executed to obtain the current water quality status and then to predict the water quality for the period of 2016-2040. For the future persion, it is assumed that measures are taken to reduce point-source and diffuse-source pollutant loadings. Model results suggest that load reductions are expected to be effective and that improvement in water quality can be predicted for all water quality indicators. TKN concentrations vary between 0.11-2.13 mg/l with the highest mean concentration occurring during the months of January. TP concentrations are expected to have a higher variability (0.032-0.65 mg/l).

Keyphrases: aquatox model, diffuse source pollution, hydrology, simulation, swat model, wasp model, water quality

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 651-658.

BibTeX entry
@inproceedings{HIC2018:Simulating_Impact_Water_Quality,
  author    = {Alper Elçi and Selma Ayaz and Sebnem Aynur},
  title     = {Simulating the Impact of Water Quality Improvement Measures for Nutrient-Sensitive River Basins with the Aquatox Model},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/fz4x},
  doi       = {10.29007/5q94},
  pages     = {651-658},
  year      = {2018}}
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