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River water quality modelling using MIKE 11 ECO Lab - Vera Jones (Atkins)


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River water quality modelling using MIKE 11 ECO Lab - Vera Jones (Atkins).

Presented at the 2014 MIKE by DHI UK Symposium on 13th to 14th May 2014.

Published in: Engineering, Technology, Business
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River water quality modelling using MIKE 11 ECO Lab - Vera Jones (Atkins)

  1. 1. River water quality modelling using Mike 11 Ecolab DHI User Group meeting 13th May 2014 Presentation by: Vera Jones
  2. 2. Introduction 2
  3. 3. 3 Impacts on water quality •Water quality is often a key concern when assessing the environmental impact of new developments, due to for example: New wastewater discharges New trade discharges Changes flow/dilution
  4. 4. 4 •EC Water Framework Directive (WFD) has put a renewed focus on water quality - target for water bodies to achieve Good Status, a number of new environmental quality standards and principle of ‘no deterioration’. Legislative considerations •Priority Substances Directive. Latest edition was published in August 2013 and will be revised every 3 years. •Urban Wastewater Treatment Directive: Urban Pollution Manual*. •Bathing Water Directive – also revised recently. Standards defining the quality of bathing waters, focusing on bacterial counts. *FWR (2012). Urban Pollution Management Manual
  5. 5. Assessing water quality impacts 5
  6. 6. 6 Range of options available: Monitoring and visual assessment of results Simple mass balance calculations Assessing water quality impacts Steady State models – SIMCAT, QUAL-2K Hydrodynamic models - Mike 11 Ecolab.
  7. 7. Issues to •Over the past years we have worked on several hydrodynamic water quality models using Mike 11 Ecolab, with a focus on dissolved oxygen and nutrient modelling. Our hydrodynamic water quality modelling capability •Hydrodynamic water quality models provide higher level of detail on temporal and spatial resolution which is often needed to assess the water quality impacts of: oNew water resources schemes oContinuous and intermittent discharges Key tool to optimise water companies’ strategic investments.
  8. 8. Effect of shading due to marginal vegetation •Results in less light in the water column and less surface water cooling Modification of standard equation to take into account localised marginal vegetation Variation in water clarity along a tidal river •Significant variation in tidal rivers, both temporally and spatially. Development of a series of equations to simulate variations in water clarity based on changing water levels or salinity along the river Taking into account the impacts of periodic algal blooms •Phytoplankton populations shrink and expand during the year Modification to the photosynthesis and respiration equations to include a time- varying chlorophyll determinand Adapting models to fit each project requirements
  9. 9. Case study 9
  10. 10. Catchment understanding at the start of the project Several wastewater treatment works Storm discharges from combined sewer overflows High nutrient load
  11. 11. Model description •Parameters modelled: Dissolved Oxygen Temperature Ammonia Nitrate Ortho-phosphate Particulate phosphorus Biochemical Oxygen Demand •Summer 2011 survey •Input from sewer model (MWH): ammonia and BOD 31 rural sub- catchments 7 wastewater treatment works 6 private discharges ~120 combined sewer overflows – DAS modelling by MWH
  12. 12. Calibration & Validation: example
  13. 13. 10-year runs – stochastic ‘baseline’ Methodology for assessment Results extracted and processed for every model node: •Water Framework Directive standards •99th percentile standards* •Fundamental Intermittent Standards (FIS)* Two years selected for further scenario testing : ‘poor’ and ‘average’ water quality Urban Wastewater Treatment Directive *FWR (2012). Urban Pollution Management Manual
  14. 14. Methodology for assessment •Programme developed for processing results at every node against the relevant standards. High Good Moderate Poor Bad For all results analyses: Good or High Status is required. 10-year model scenario runs=100 million data points per run; data processing tool developed to convert results to easy-to-read maps
  15. 15. Scenario testing: analysis against WFD standards Ortho-phosphate 2024 - baseline Ortho-phosphate 2024 – no waste water treatment works scenario Works are a key cause of failure to meet the Water Framework Directive standards High Good Moderate Poor Bad
  16. 16. Scenario testing: analysis against 99th percentile standards BOD 2024 - baseline BOD 2024 – no waste water treatment works scenario However, works also dilute intermittent untreated combined sewer overflow inputs. High Good Moderate Poor Bad Currently assisting our client to explore best strategic options for this system, including advanced treatment at wastewater treatment works.
  17. 17. Overview and Conclusion 17
  18. 18. 18 •Water quality often a key concern when new developments or schemes are planned. •Water Framework Directive has put a renewed focus on impacts on water quality. •An important tool to help us assess impacts on water quality is hydrodynamic modelling – power of quantitative assessment and scenario testing. Overview & Conclusion •Key success factors: Adapting model to suit requirements of each project Developing tools to process results efficiently – clarity of results presentation to clients/regulators.