Simulation Study to Evaluate Temporal Aggregation and Variability of Stochastic Water Demands on Distribution System Hydraulics and Transport
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 8
Abstract
As utilities move toward more detailed hydraulic and water quality network models, the potential for the stochastic nature of consumptive demands having an effect on transport characteristics increases. A nonhomogeneous Poisson rectangular pulse model (PRPsym) was used to generate stochastic water demands aggregated at 1-min, 10-min, and 1-h time steps for a small skeletonized network and a large all-pipe network; results were then linked with EPANET to simulate the impacts of water-demand variability on the underlying transport and water quality. In general, the impacts of temporal aggregation—as represented by flow rate and concentration variability and arrival times—increased as the evaluations moved from the main trunk lines toward dead-end nodes or pipes. More specifically, analysis of a Monte Carlo ensemble of results demonstrated that (1) a 10-min temporal aggregation captured most of the arrival time variability relative to the 1-min aggregation step; (2) hourly demand variability (e.g., from one day to another) had a greater effect on transport characteristics than the intra-hour variability from temporal aggregation; and (3) in addition to the expected dead-end nodes and loops, transport characteristics within source blending zones were consistently impacted by stochastic demands at smaller temporal aggregations. A sensitivity analysis illustrated the demand variability associated with temporal aggregation was not further affected by the distribution of demand intensity, but the distribution of demand duration, particularly the variance of demand duration, further affected demand variability through the autocorrelation of demands across time steps. Using the sensitivity results to select parameters associated with greater demand variability, another Monte Carlo ensemble was developed for the large network that only slightly increased the spatial extent of nodes within the distribution system that resulted in significant changes in transport characteristics attributable to the effects of spatial aggregation. These results provide additional insight into the magnitude of impact from demand variability (from both modeling aggregation and daily variations) and the spatial extent of such effects on transport characteristics relative to the typical modeling assumptions of utilizing deterministic, 1-h demands.
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Acknowledgments
The authors sincerely appreciate the help of Dr. Zhiwei Li, who helped in the understanding and utilization of the PRPsym code for generating EPANET input files. The authors also thank the Ohio Water Resources Center through a grant from the USGS 104(b) program and the United States Environmental Protection Agency for providing partial financial support to perform this study.
References
Aksela, K., and Aksela, M. (2011). “Demand estimation with automated meter reading in a distribution network.” J. Water Resour. Plann. Manage., 456–467.
Alvisi, S., Franchini, M., and Marinelli, A. (2003). “A stochastic model for representing drinking water demand at residential level.” Water Resour. Manage., 17(3), 197–222.
Arandia-Perez, E., Uber, J. G., Boccelli, D. L., Janke, R., Hartman, D., and Lee, Y. (2012). “Modeling automatic meter reading water demands as nonhomogeneous processes.” J. Water Resour. Plann. Manage., 55–64.
Austin, R. G., van Bloemen Waanders, B., McKenna, S., and Choi, C. Y. (2008). “Mixing at cross junctions in water distribution systems. II: Experimental study.” J. Water Resour. Plann. Manage., 295–302.
Berry, J., Hart, W. E., Phillips, C. A., Uber, J. G., and Watson, J.-P. (2006). “Sensor placement in municipal water networks with temporal integer programming models.” J. Water Resour. Plann. Manage., 218–224.
Blokker, E. J. M., Beverloo, H., Vogelaar, A. J., Vreeburg, J. H. G., and van Dijk, J. C. (2011). “A bottom-up approach of stochastic demand allocation in a hydraulic network model: A sensitivity study of model parameters.” J. Hydroinf., 13(4), 714–728.
Blokker, E. J. M., Vreeburg, J. H. G., Buchberger, S. G., and van Dijk, J. C. (2008a). “Importance of demand modelling in network water quality models: A review.” Drinking Water Eng. Sci., 1(1), 27–38.
Blokker, E. J. M., Vreeburg, J. H. G., and Vogelaar, A. J. (2008b). “Combining the probabilistic demand model SIMDEUM with a network model.” Water Distribution Systems Analysis Symp. 2006, ASCE, Reston, VA.
Blokker, E. J. M., Vreeburg, J. H. G., Beverloo, H., Arfman, M. K., and van Dijk, J. C. (2010a). “A bottom-up approach of stochastic demand allocation in water quality modelling.” Drinking Water Eng. Sci., 3(1), 43–51.
Blokker, E. J. M., Vreeburg, J. H. G., and van Dijk, J. C. (2010b). “Simulating residential water demand with a stochastic end-use model.” J. Water Resour. Plann. Manage., 19–26.
Boccelli, D. L., Tryby, M. E., Uber, J. G., Rossman, L. A., Zierolf, M. L., and Polycarpou, M. M. (1998). “Optimal scheduling of booster disinfection in water distribution systems.” J. Water Resour. Plann. Manage., 99–111.
Buchberger, S. G., Carter, J. T., Lee, Y. H., and Schade, T. G. (2003). Random demands, travel times, and water quality in deadends, American Water Works Association Research Foundation, Denver.
Buchberger, S. G., and Wu, L. (1995). “Model for instantaneous residential water demands.” J. Hydraul. Eng., 232–246.
García, V. J., García-Bartual, R., Cabrera, E., Arregui, F., and García-Serra, J. (2004). “Stochastic model to evaluate residential water demands.” J. Water Resour. Plann. Manage., 386–394.
Ho, C. K. (2008). “Solute mixing models for water-distribution pipe networks.” J. Hydraul. Eng., 1236–1244.
Khanal, N., Buchberger, S. G., and McKenna, S. A. (2006). “Distribution system contamination events: Exposure, influence, and sensitivity.” J. Water Resour. Plann. Manage., 283–292.
Li, Z., and Buchberger, S. G. (2004). “Effect of time scale on PRP random flows in pipe network.” Critical transitions in water and environmental resources management, ASCE, Reston, VA.
Li, Z., and Buchberger, S. G. (2005). PRPsym user’s manual. Univ. of Cincinnati, Cincinnati.
Li, Z., and Buchberger, S. G. (2007). “Effects of spatial-temporal aggregation on properties of PRP random water demands.” World Water and Environmental Resources Congress: Restoring our Natural Habitat, ASCE, Reston, VA.
Magini, R., Pallavicini, I., and Guercio, R. (2008). “Spatial and temporal scaling properties of water demand.” J. Water Resour. Plann. Manage., 276–284.
Moughton, L. J., Buchberger, S. G., Boccelli, D. L., Filion, Y. R., and Karney, B. W. (2006). “Effect of time step and data aggregation on cross correlation of residential demands.” Proc., Water Distribution System Analysis Symp., ASCE, Reston, VA.
Murray, R., Uber, J., and Janke, R. (2006). “Model for estimating acute health impacts from consumption of contaminated drinking water.” J. Water Resour. Plann. Manage., 293–299.
Nilsson, K. A., Buchberger, S. G., and Clark, R. M. (2005). “Simulating exposures to deliberate intrusions into water distribution systems.” J. Water Resour. Plann. Manage., 228–236.
Propato, M. (2006). “Contamination warning in water networks: General mixed-integer linear models for sensor location design.” J. Water Resour. Plann. Manage., 225–233.
Rao, H. S., and Bree, D. W., Jr. (1977). “Extended period simulation of water systems—Part A.” J. Hydraul. Div., 103(2), 97–108.
Rodriquez-Iturbe, I. (1986). “Scale of fluctuation of rainfall models.” Water Resour. Res., 22(9), 15S–37S.
Romero-Gomez, P., and Choi, C. Y. (2011). “Axial dispersion coefficients in laminar flows of water-distribution systems.” J. Hydraul. Eng., 1500–1508.
Rossman, L. A. (2000). EPANET2 user’s manual, Risk Reduction Engineering Laboratory, U.S. Environmental Protection Agency, Cincinnati.
Rossman, L. A., and Boulos, P. F. (1996). “Numerical methods for modeling water quality in distribution systems: A comparison.” J. Water Resour. Plann. Manage., 137–146.
Shamir, U., and Howard, C. D. D. (1968). “Water distribution systems analysis.” J. Hydraul. Div., 94(1), 219–234.
Shang, F., Uber, J. G., and Rossman, L. A. (2008). “Modeling reaction and transport of multiple species in water distribution systems.” Environ. Sci. Technol., 42(3), 808–814.
Song, I., Romero-Gomez, P., and Choi, C. Y. (2009). “Experimental verification of incomplete solute mixing in a pressurized pipe network with multiple cross junctions.” J. Hydraul. Eng., 1005–1011.
Tzatchkov, V. G., Aldama, A. A., and Arreguin, F. I. (2002). “Advection-dispersion-reaction modeling in water distribution networks.” J. Water Resour. Plann. Manage., 334–342.
Uber, J., Murray, R., and Janke, R. (2004). “Use of systems analysis to assess and minimize water security risks.” J. Comtemp. Water Res. Educ., 129(1), 30–40.
U.S. Environmental Protection Agency (USEPA). (2006). “Initial distribution system evaluation guidance manual.”, Office of Water, Washington, DC.
Vanmarcke, E. (1983). Random fields, MIT Press, Cambridge, MA.
Yang, X. (2010). “A full-scale simulation study of stochastic water demands on distribution system transport.” M.S. thesis, Univ. of Cincinnati, Cincinnati, OH.
Yang, X., and Boccelli, D. L. (2009). “The impacts of demand variability on distribution system hydraulics and transport.” Proc., World Water and Environmental Resources Congress, ASCE, Reston, VA.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jun 22, 2012
Accepted: Feb 6, 2013
Published online: Feb 9, 2013
Published in print: Aug 1, 2014
Discussion open until: Sep 18, 2014
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