Large Temperatures in Water Distribution Pipes as a Water Quality Threat: Measurements and Modelling

Due to climate change, new challenges arise in drinking water infrastructure planning and in the re-assessment of well-established urban drinking water utilities. We observed temperatures exceeding 25 °C in drinking water supply pipes, which pose a health threat and water quality problem, as these temperatures are favorable for microbial growth.

We set out to predict temperatures in drinking water supply networks. The key step to achieve this goal is to monitor and model soil temperatures and soil moisture derived from meteorological forcing functions. With meteorological observations and soil material properties, we describe the heat transport and water flow from the ground surface into the subsurface and from there into the pipes and with the water in the pipes.

In order to achieve this goal, we solved the heat and water balances jointly at the atmosphere-subsurface interface, using the open-source numerical simulation framework DuMuX. We were able to do this because of the available meteorological observations (e.g., radiation balance, precipitation intensity) next to the newly installed pipes. These balances provide a novel interface condition for heat transport and water flow modelling. We coupled the heat transport through the drinking water pipe walls to the drinking water in the pipes and to the subsurface transport processes.

At a pilot site, we installed typical drinking water pipes (PE and cast iron), backfilled with known material (typical gravelly conditions below roads and naturally existing sandy clay), and applied land-cover (asphalt and natural vegetation). We were able to reproduce the joint measurements of temperatures and soil moisture under various conditions (well-draining gravel vs. less-draining clayey material; vegetation vs. asphalt).

In this presentation, we demonstrate results of the multi-year measurement campaign, the results of 1D and 2D subsurface heat transport models coupled to dynamic hydraulic conditions in the drinking water pipes, and an innovative surrogate-based Bayesian active learning-assisted model calibration methodology.

This work presents an important first step towards predicting temperatures in drinking water supply pipes and will be directly relevant for chemical and biological processes that occur in non-isothermal conditions (e.g., due to climate change), for example, in relation to contaminant remediation. Our results are of relevance for drinking water supply companies, shallow geothermal design, and urban planning.