Drinking Water Engineering and Science www.drink-water-eng-sci.net 1996-9457 1996-9465 1 1 2008 10.5194/dwes-1-27-2008 http://www.drink-water-eng-sci.net/1/27/2008/ http://www.drink-water-eng-sci.net/1/27/2008/dwes-1-27-2008.html http://www.drink-water-eng-sci.net/1/27/2008/dwes-1-27-2008.pdf 27 38 2008-09-25 Importance of demand modelling in network water quality models: a review E. J. M. Blokker mirjam.blokker@kiwa.nl J. H. G. Vreeburg S. G. Buchberger J. C. van Dijk Kiwa Water Research Groningenhaven 7, 3430 BB Nieuwegein, The Netherlands Delft University of Technology, Department of Civil Engineering and Geosciences, P.O. Box 5048, 2600 GA Delft, The Netherlands University of Cincinnati, Department of Civil and Environmental Engineering, P.O. Box 210071 Cincinnati, OH 45221-0071, USA Today, there is a growing interest in network water quality modelling. The water quality issues of interest relate to both dissolved and particulate substances. For dissolved substances the main interest is in residual chlorine and (microbiological) contaminant propagation; for particulate substances it is in sediment leading to discolouration. There is a strong influence of flows and velocities on transport, mixing, production and decay of these substances in the network. This imposes a different approach to demand modelling which is reviewed in this article. <br><br> For the large diameter lines that comprise the transport portion of a typical municipal pipe system, a skeletonised network model with a top-down approach of demand pattern allocation, a hydraulic time step of 1 h, and a pure advection-reaction water quality model will usually suffice. For the smaller diameter lines that comprise the distribution portion of a municipal pipe system, an all-pipes network model with a bottom-up approach of demand pattern allocation, a hydraulic time step of 1 min or less, and a water quality model that considers dispersion and transients may be needed. <br><br> Demand models that provide stochastic residential demands per individual home and on a one-second time scale are available. A stochastic demands based network water quality model needs to be developed and validated with field measurements. Such a model will be probabilistic in nature and will offer a new perspective for assessing water quality in the drinking water distribution system. Alcocer-Yamanaka, V. H., Tzatchkov, V. G., and Buchberger, S. G.: Instantaneous water demand parameter estimation from coarse meter readings, Water Distribution System Analysis #8., Cincinnati, Ohio, USA, 2006, 2006. Alvisi, S., Franchini, M., and Marinelli, A.: A Stochastic Model for Representing Drinking Water Demand at Residential Level, Water Resour. Manag., 17, 197–222, 2003. Austin, R. G., Waanders, B. v. B., McKenna, S., and Choi, C. Y.: Mixing at cross junctions in water distribution systems. II: experimental study, J. Water Res. Pl.-Asce, 134, 295–302, 2008. Babayan, A. V., Savic, D. A., and Walters, G. A.: Multiobjective optimization for the least-cost design of water distribution system under correlated uncertain parameters, Impacts of Global Climate Change; 2005 World water and environmental resources congress, Anchorage, Alaska, 2005. Blokker, E. J. M.: Determining the capacity of drinking water distribution systems at street level, Kiwa N. V., Nieuwegein, 2005 (in Dutch). Blokker, E. J. M. and Vreeburg, J. H. G.: Monte Carlo Simulation of Residential Water Demand: A Stochastic End-Use Model, Impacts of Global Climate Change; 2005 World water and environmental resources congress, Anchorage, Alaska, 15–19 May 2005. Blokker, E. J. M., Vreeburg, J. H. G., and Vogelaar, A. J.: Combining the probabilistic demand model SIMDEUM with a network model, Water Distribution System Analysis #8, Cincinnati, Ohio, USA, 27–30 August 2006. Blokker, E. J. M., Vreeburg, J. H. G., Schaap, P. G., and Horst, P.: Self-cleaning networks put to the test, World environmental and water resources congress 2007 - Restoring our natural habitat, Tampa, Fl, USA, 15–18 May 2007. Blokker, E. J. M., Buchberger, S. G., Vreeburg, J. H. G., and van Dijk, J. C.: Comparison of water demand models: PRP and SIMDEUM applied to Milford, Ohio, data., WDSA 2008, Kruger Park, South Africa, August 2008. Bowden, G. J., Nixon, J. B., Dandy, G. C., Maier, H. R., and Holmes, M.: Forecasting chlorine residuals in a water distribution system using a general regression neural network, Math. Comput. Model., 44, 469–484, 2006. Boxall, J. B. and Saul, A. J.: Modeling Discoloration in Potable Water Distribution Systems, J. Environ. Eng. ASCE, 131, 716–725, 2005. Buchberger, S. G. and Wu, L.: Model for Instantaneous Residential Water Demands, J. Hydraul. Eng.-ASCE, 121, 232–246, 1995. Buchberger, S. G. and Wells, G. J.: Intensity, duration and frequency of residential water demands, J. Water Res. Pl-ASCE, 122, 11–19, 1996. Buchberger, S. G., Carter, J. T., Lee, Y. H., and Schade, T. G.: Random demands, travel times, and water quality in dead ends, American Water Works Association Research Foundation, Denver, Colorado, USA, 2003. Buchberger, S. G., Blokker, E. J. M., and Vreeburg, J. H. G.: Sizes for Self-Cleaning Pipes in Municipal Water Supply Systems, WDSA 2008, Kruger Park, South Africa, August 2008. Filion, Y. R., Karney, B. W., and Adams, B. J.: Stochasticity of demand and probabilistic performance of water networks, Impacts of Global Climate Change; 2005 World water and environmental resources congress, Anchorage, Alaska, 2005, 49, 2005. Filion, Y. R., Karney, B. W., Moughton, L. J., Buchberger, S. G., and Adams, B. J.: Cross correlation analysis of residential demand in the city of Milford, Ohio, Water Distribution System Analysis #8, Cincinnati, Ohio, USA, 2006. García, V., García-Bartual, R., Cabrera, E., Arregui, F., and García-Serra, J.: Stochastic model to evaluate residential water demands, J. Water Res. Pl.-ASCE, 130, 386–394, 2004. Gill, W. N. and Sankarasubramanian, R.: Exact analysis of unsteady convective diffusion, P. Roy. Soc. Lond. A. Mat., 316, 341–350, 1970. Grainger, C., Wu, J., Nguyen, B. V., Ryan, G., Jayaratne, A., and Mathes, P.: Particles in water distribution systems – 5th progress report; part I: settling, re-suspension and transport, CSIRO-BCE, Australia, 71 p., 2003. Grayman, W. M., Speight, V. L., and Uber, J. G.: Using Monte-Carlo simulation to evaluate alternative water quality sampling plans, Water Distribution System Analysis #8, Cincinnati, Ohio, USA, 2006. Guercio, R., Magini, R., and Pallavicini, I.: Instantaneous residential water demand as stochastic point process, in: Water resources management, Progress in water resources, 4, WIT Press, Southampton, UK, 129–138, 2001. Jonkergouw, P. M. R., Khu, S.-T., Kapelan, Z. S., and Savic, D. A.: Water quality model calibration under unknown demands, J. Water Res. Pl-ASCE, 134, 326–336, 2008. Jung, B., Boulos, P. F., and Wood, D. J.: Impacts of skeletonization on distribution system hydraulic transient models, World environmental and water resources congress 2007 – Restoring our natural habitat, Tampa, Fl, USA, 2007. Kapelan, Z.: Calibration of water distribution system hydraulic models, University of Exeter, PhD thesis, 334 p., 2002. Karney, B. W., Jung, B., and Alkozai, A.: Assessing the degree of unsteadiness in flow modeling; from physics to numerical solution, Water Distribution System Analysis #8, Cincinnati, Ohio, USA, 2006. Lee, Y.: Mass dispersion in intermittent laminar flow, University of Cincinnati, Cincinnati, Ohio, 167 pp., 2004. Li, Z. and Buchberger, S. G.: Effect of time scale on PRP random flows in pipe network, Critical Transitions In Water And Environmental Resources Management, Salt Lake City, Utah, 2004, conference paper available on CD, doi:10.1061/40737(2004)461, 2004. Li, Z.: Network Water Quality Modeling with Stochastic Water Demands and Mass Dispersion, University of Cincinnati, PhD thesis, 166 p., 2006. McInnis, D. and Karney, B. W.: Transients in distribution networks: Field tests and demand models, J. Hydraul. Eng., 121, 218–231, 1995. McKenna, S. A., Buchberger, S. G., and Tidwell, V. C.: Examining the effect of variability in short time scale demands on solute transport, World Water and Environmental Resources Congress and Related Symposia, Philadelphia, Pennsylvania, USA, 2003. McKenna, S. A., van Bloemen Waanders, B., Laird, C. D., Buchberger, S. G., Li, Z., and Janke, R.: Source location inversion and the effect of stochastically varying demand, Impacts of Global Climate Change; 2005 World water and environmental resources congress, Anchorage, Alaska, 2005, 47, 2005. Menaia, J., Coelho, S. T., Lopes, A., Fonte, E., and Palma, J.: Dependency of bulk chlorine decay rates on flow velocity in water distribution networks, Water Science and Technology: Water Supply, 3, 209–214, 2003. Nilsson, K. A., Buchberger, S. G., and Clark, R. M.: Simulating exposures to deliberate intrusions into water distribution systems, J. Water Res. Pl.-ASCE, 131, 228–236, 2005. Pasha, M. F. K. and Lansey, K.: Analysis of uncertainty on water distribution hydraulics and water quality, Impacts of Global Climate Change; 2005 World water and environmental resources congress, Anchorage, Alaska, 2005. Powell, J., Clement, J., Brandt, M., R, C., Holt, D., Grayman, W., and LeChevallier, M.: Predictive Models for Water Quality in Distribution Systems, AwwaRF, Denver, Colorado, USA, 132 p., 2004. Propato, M. and Uber, J.: Vulnerability of Water Distribution Systems to Pathogen Intrusion: How Effective Is a Disinfectant Residual?, Environ. Sci. Technol., 38, 3713–3722, 2004. Romero-Gomez, P., Ho, C. K., and Choi, C. Y.: Mixing at cross junctions in water distribution systems. I: numerical study, J. Water Res. Pl.-ASCE, 134, 285–294, 2008a. Romero-Gomez, P., Li, Z., Choi, C. Y., Buchberger, S. G., Lansey, K. E., and Tzatchkov, V. T.: Axual dispersion in a pressurized pipe under various flow conditions, WDSA 2008, Kruger Park, South Africa, August 2008b. Rossman, L. A., Clark, R. M., and Grayman, W. M.: Modeling chlorine residuals in drinking-water distribution systems, J. Environ. Eng., 120, 803–820, 1994. Rossman, L. A.: EPANET 2 user manual, United States Environmental Protection Agency, Cincinnati, 200 p., 2000. Ryan, G., Mathes, P., Haylock, G., Jayaratne, A., Wu, J., Noui-Mehidi, N., Grainger, C., and Nguyen, B. V.: Particles in water distribution systems, Cooperative Research Centre for Water Quality and Treatment, Salisbury, Australia, 106 pp., 2008. Slaats, P. G. G., Rosenthal, L. P. M., Siegers, W. G., van den Boomen, M., Beuken, R. H. S., and Vreeburg, J. H. G.: Processes involved in the generation of discolored water, Awwa Research Foundation, Denver, Co, USA, 2003. Taylor, G.: Dispersion of Soluble Matter in Solvent Flowing Slowly through a Tube, P. Roy. Soc. Lond. A Mat., 219, 186–203, 1953. Tzatchkov, V. G., Aldama, A. A., and Arreguin, F. I.: Advection-dispersion-reaction modeling in water distribution networks, J. Water Res. Pl.-ASCE, 131, 334–342, 2002. Tzatchkov, V. G., and Buchberger, S. G.: Stochastic demand generated unsready flow in water distribution networks, Water Distribution System Analysis #8, Cincinnati, Ohio, USA, 2006. USEPA: Initial Distribution System Evaluation Guidance Manual for Final Stage 2 Disinfection Byproducts, Office of Water, 2006. Vreeburg, J. H. G., Schaap, P. G., and van Dijk, J. C.: Measuring discoloration risk: resuspention potential method, Leading edge Technology conference, Prague, 1–3 June 2004. Vreeburg, J. H. G.: Discolouration in drinking water systems: a particular approach, PhD thesis, 183 p., 2007. Vreeburg, J. H. G. and Boxall, J. B.: Discolouration in potable water distribution systems: A review, Water Res., 41, 519–529, 2007.