Response Surfaces for Water Distribution System Pipe Roughness Calibration

Sanghoon Jun, Georg Arbesser-Rastburg, Daniela Fuchs-Hanusch, Kevin Lansey

Research output: Contribution to journalArticlepeer-review


Water distribution system (WDS) model calibration research has focused on estimating model input/output parameters and analyzing several uncertainties (e.g., model uncertainty) to improve models with best-fit parameters. Numerous studies have shown that optimization algorithms generally quickly converge to very good parameter solutions. However, the generality and reasoning behind this have not been identified. This paper examines the shape and convexity of WDS response surfaces (i.e., objective function surfaces) and whether the surfaces have single global or multiple local optima. To that end, three networks with different network topologies are evaluated: (1) the Modena network as presented, (2) a modified form of the Modena network, and (3) a real Austrian network. Various conditions were evaluated to consider field measurement error, parameter uncertainty through pipe grouping, and model uncertainty. Results demonstrate that the response surfaces remained smooth and convex even when uncertainties are introduced, but the best parameter solutions are shifted from the true solution. The impact and sensitivities of the uncertainties are evaluated by examining the change in best-fit parameter estimates.
Original languageEnglish
Article number04021105
JournalJournal of Water Resources Planning and Management
Issue number3
Early online date20 Dec 2021
Publication statusPublished - 1 Mar 2022


  • Fitness landscape
  • Hydraulic modeling
  • Sensitivity analysis
  • Uncertainties

ASJC Scopus subject areas

  • Water Science and Technology
  • Geography, Planning and Development
  • Management, Monitoring, Policy and Law
  • Civil and Structural Engineering

Fields of Expertise

  • Sustainable Systems


Dive into the research topics of 'Response Surfaces for Water Distribution System Pipe Roughness Calibration'. Together they form a unique fingerprint.

Cite this