The Elusive Nature of Roughness: Linking Hydraulics and Graph Theory for Water Distribution Networks Model Calibration

TL;DR

This paper explores how network partitioning using hydraulic and graph attributes can improve pipe roughness calibration in water distribution networks, offering a systematic and efficient alternative to manual methods.

Contribution

It introduces a topology-informed grouping approach that enhances calibration robustness and reduces computational effort in large-scale water network modeling.

Findings

Attribute-based grouping yields stable calibration results.

Graph attributes improve robustness and stability.

Density clustering reduces computational effort.

Abstract

Accurate pipe roughness estimation in large-scale water distribution networks is often hindered by the high cost of traditional field methods. This study investigates whether network partitioning, by utilizing hydraulic and graph-derived attributes, can enhance the calibration of these parameters. Using a high-fidelity model of a real network as a benchmark, we evaluate density-based clustering, and topology-driven grouping strategies. Optimization experiments demonstrate that attribute-based grouping yields stable, repeatable results comparable to manual calibration for hydraulically significant pipes. While hydraulic attributes generate more distinct cluster structures, the inclusion of graph-based data improves calibration robustness by stabilizing the optimization process. Notably, density-based clustering achieves similar accuracy to k-means while reducing computational effort in specific configurations. Although the method does not eliminate all sources of uncertainty, results suggest that topology-informed grouping provides a systematic, reproducible, and computationally efficient alternative to manual heuristics, highlighting the critical role of network structure in reliable parameter estimation.