Yuchuan Lai

This is part of a series of studies I conducted during my PhD to investigate location-specific climate change and provide future information for engineering use.

See other pages about forecasting historical data and using climate models to inform future conditions.

In our study published in Water, we investigated the impacts of climate change—specifically rising air temperatures—on the temperature and quality of water within drinking water distribution systems (DWDS). While drinking water sector is generally known to be vulnerable to changing climate conditions (also because of the aging infrastructure in the US), the specific investigation of DWDS water temperature changes resulting from non-stationary climate conditions has been limited.

1. Objectives

The primary objective of our study was to investigate the effects of ambient air temperature changes on water temperature and varying water quality parameters in DWDS. We recognized that drinking water temperature is a key parameter influencing physical, chemical, and biological processes in distribution systems and is strongly affected by ambient air temperature. While previous studies have examined premise plumbing in individual buildings, we focused exclusively on the effect of interannual ambient air temperature changes on the water temperature and quality of the distribution systems themselves.

We aimed to provide both a preliminary analysis of a specific location and an overview of spatial variations across the United States. To achieve this, we assessed Washington D.C. as a case study for water quality parameters and expanded our scope to 91 U.S. cities to estimate general historical and future changes in water temperature and quality.

2. Methodology

Our approach involved three main components: acquiring regional air temperature data, estimating DWDS water temperature based on that data, a nd finally estimating temperature-related water quality parameters.

Modeling Water Temperature

We utilized the National Renewable Energy Lab (NREL) model to estimate water temperature. This model assumes that water temperature in a DWDS is strongly affected by soil temperature, which in turn is determined by ambient air temperature. The model produces a daily water temperature estimate that exhibits a sinusoidal shape with a lag relative to ambient temperature changes.

• We calibrated this model using water temperature measurements from seven specific locations in the U.S., including Washington D.C., to minimize errors and improve accuracy.
• For future projections, we used the G-ARIMA model to generate city-level temperature projections under the Representative Concentration Pathway (RCP) 8.5 scenario.

Modeling Water Quality

We selected separate methods to link water temperature to three specific water quality parameters:

• Chlorine Bulk Decay: We used a first-order bulk decay model dependent on temperature and total organic carbon (TOC).
• Total Trihalomethane (TTHM) Formation: We applied an empirical predictive model considering chlorine dosage, temperature, TOC, pH, and bromide concentrations.
• Bacterial Activity: We utilized a mechanistic model based on the Monod equation to describe the effect of temperature on bacterial regrowth.

Scope of Analysis

We analyzed historical data from two periods (1951–1970 and 2001–2020) and future projections (2051–2070) to assess changes over time. While the Washington D.C. case study utilized available local measurements for calibration, the estimates for the 91 cities were not calibrated with local measurements and were intended to provide a general overview of expected changes.

Interactive plots for historical and future projected DWDS water temperature

The interactive graphs below present the empirical results on the estimated DWDS water temperature. The results presented are intended for providing general information only. It may take up to a minute for the graphs to be loaded. View the webpage with the desktop version is recommended.

3. Results

Water Temperature Trends

Our analysis confirmed a strong positive correlation between ambient air temperature and DWDS water temperature.

• Model Accuracy: By calibrating the NREL model parameters for our seven study locations, we were able to reduce root mean square errors (RMSEs) by approximately 50%, providing estimates that better aligned with seasonal variations.
• Temperature correlation: We found that, on an annual average basis, estimated water temperature changes in DWDS are generally equivalent to air temperature changes.
• Quantified Increases: For the 91 U.S. cities assessed, we observed an average water temperature increase of approximately 0.8 °C from the 1951–1970 level to the 2001–2020 level. Looking forward under the RCP8.5 scenario, we project a further average increase of 2.3 °C (averaged across the 91 cities) by the 2051–2070 period.

Water Quality Impacts

Using Washington D.C. as our primary case study, we observed that water quality parameters exhibit seasonal sinusoidal patterns consistent with water temperature.

• Modest Historical vs. Future Changes: While changes in water quality parameters between the two historical periods were modest, our results suggest these changes will intensify in the future.
• Amplification via Water Age: We found that higher water age can amplify the temperature effect. In a hypothetical extreme case with a 400-hour water age, the combined effect of temperature on chlorine decay and bacterial activity resulted in a projected 20–23% increase in bacterial activity levels for the future period compared to the recent historical period.
• Aggregate Effects: We highlighted that the effects of temperature on water quality are often inter-related, yielding an aggregated effect; for instance, higher temperatures accelerate chlorine decay, which in turn can further promote bacterial activity.