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Water Distribution System Failure Risks with Increasing Temperatures Emily N. Bondank, Mikhail V. Chester, and Benjamin L. Ruddell Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01591 • Publication Date (Web): 09 Aug 2018 Downloaded from http://pubs.acs.org on August 13, 2018

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Environmental Science & Technology

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WATER DISTRIBUTION SYSTEM FAILURE RISKS WITH

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INCREASING TEMPERATURES

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Emily N. Bondank1*, [email protected], 480-285-8327

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Mikhail V. Chester1

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Benjamin L. Ruddell2

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1

7

Avenue, Tempe, AZ 85281

8

2

9

Knoles Drive, PO Box 5693, Flagstaff, AZ 86011

Civil, Environmental, and Sustainable Engineering, Arizona State University, 660 S College

School of Informatics, Computing, and Cyber Systems, Northern Arizona University, 1295 S.

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* Author to whom correspondence should be addressed

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ABSTRACT

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In the coming decades, ambient temperature

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increase from climate change threatens to reduce not

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only the availability of water, but also the operational

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reliability of engineered water systems. Relatively little

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is known about how temperature stress can increasingly

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cause hardware components to fail, quality to be

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affected, and service outages to occur. Changes to the

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estimated-time- to-failure of major water system hardware and the probability of quality non-

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compliance were estimated for a modern potable water system that experiences hot summer

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temperatures, similar to Phoenix, Arizona and Las Vegas, Nevada. A fault tree model was

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developed to estimate the probability that consequential service outages in quantity and quality

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will occur. Component failures are projected to have a percent increase of 10 - 101% in scenarios

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where peak summer temperature has increased from 36 to 44o C, which create the conditions for

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service outages to have a percent increase of 7 - 91%. Increased service outages due to multiple

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pumping unit failures and water quality non-compliances are the most notable concerns for water

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utilities. The most effective strategies to prevent temperature-related component failure should 1 ACS Paragon Plus Environment


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focus on maintaining sufficient chlorine residual and cooling pumping unit motors and

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electronics.

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INTRODUCTION

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Civil infrastructure systems are vital for delivering resources, providing protection, and

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facilitating most urban activities. Typically, these systems are designed to last for long periods of

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time, often on the order of decades, and some systems persist for over a century.1 Today,

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infrastructure operational limits are designed based on historical climate conditions,1 and global

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climate models project that these conditions will likely be more frequently exceeded in the

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future.1 Consequently, the predictions of infrastructure reliability from historical climate data

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may over-predict the lifespans and reliability under future conditions. One particular climate

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change-related hazard is global temperature rise, and in regions that already have hot climates,

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further increases in temperature may pose serious risks for people and the infrastructure upon

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which they rely.2 In the US, of particular concern is the Southwest region, where limited water

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supplies coupled with further increases in temperature may pose major challenges. For example,

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the National Climate Association reports that the Southwestern US “regional annual average

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temperatures are projected to rise by 1.4-3.0oC by mid-century and by 3.0-5.3oC by end-of-

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century with continued growth in global emissions (A2 emissions scenario), and with the greatest

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increases being in the summer and fall”.3 If urban densification occurs, it has the potential to

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cause even greater temperature increases via urban heat island.4 In hot climates, it is reported that

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infrastructure components occasionally fail from high summertime temperatures because of

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overheating and increasing rates of undesirable chemical reactions.5,6 These failures can lead to

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service outages when there is not enough redundancy or emergency response.7 Thus, increased

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temperatures have the potential to increase the probability of outages if design, operation, and

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management practices remain the same. An infrastructure of particular concern is that of potable

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water distribution, where heat may result in failures that lead to disruptions of quantity and

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quality.7

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Water distribution systems are particularly critical to the economic health of cities and

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regions, especially in hot conditions. The delivery of safe and sufficient water to residents and

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commercial establishments is vital to almost all residential, commercial, industrial, and public

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operations, and the overall reliability is dependent upon both the availability of the resource, the 2 ACS Paragon Plus Environment

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reliability of the infrastructure, and the quality of the water. It is especially important that water

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systems remain reliable as temperatures rise because in addition to greater consumption by

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individuals,8 the viability of many services may also require increased consumption. The

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electricity generation and agricultural industries in particular may need increasing amounts of

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water in a hotter future.9–11 For individuals, heat exposure can also cause a variety of health

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issues that would be significantly exacerbated without access to clean water.12 Additionally,

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research has shown that from increased evaporation and decreased snowmelt, the amount of

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fresh water available to some regions will diminish with increasing global temperatures.3 When

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the availability, infrastructural reliability, and quality are all stressed by rising temperatures,

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there could be a significantly greater threat of provisional inadequacies and consequential human

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health and economic losses.

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The reliability of water systems is already challenging to maintain under current temperature

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conditions. Utilities do not always implement asset management programs to track the failure

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rates of their components and strategically plan for preventative maintenance to decrease service

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outages.13 Instead, components are typically operated to failure and utilities rely on their ability

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to quickly respond and repair, as preventative maintenance budgets are usually constrained.14,15

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There remains a question as to whether this strategy will continue to work under future

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conditions. Some utilities have identified potential threats of increased temperatures, but very

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few have formally included climate change in their design process.5 The American Society of

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Civil Engineers notes that even when scenarios of climate change are explored, there will be “a

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tradeoff between the cost of increasing the system reliability and the potential cost and

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consequences of potential failure”.1 Utilities therefore need information to help them prioritize

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decisions in planning, design, maintenance, and operations.

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Given the potential for increasing temperatures, utilities will need to know how their systems

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may be affected and where efforts should be most focused to prevent and prepare for changes in

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their system. More specifically, they will need to know how to prepare to mitigate failures ahead

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of time and how to prepare for the effects of failures.16 This study strives to answer two

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questions in the context of seasonally hot cities where temperature increases risk of failure: (1)

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What is the projected increase in risk of potable water service loss with increases in maximum

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summertime temperatures? and (2) Where should municipal water utility management strategies

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be focused to mitigate increases in risk? To address these questions, the functionality of water 3 ACS Paragon Plus Environment


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system components and water quality are analyzed under temperature conditions characteristic of

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Phoenix, Arizona and Las Vegas, Nevada, considering climate change. An exploration of the

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temperature-related sensitivities and consequential risk is valuable for understanding issues that

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may affect other cities in the future.

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METHODOLOGY

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To aid water utilities in understanding where their systems will be more vulnerable and

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how they can strategically mitigate temperature-related service interruptions, the following

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approach was used: i) potential temperature-induced sensitivities were identified for physical

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components and aspects of water quality; ii) quantitative relationships between failure and

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temperature exposure were identified; iii) ranges and distributions of major operating conditions

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impacting degradation were identified; iv) average maximum daily summertime temperature

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projection data were processed for Phoenix, Arizona and Las Vegas, Nevada; vi) Monte Carlo

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simulations were used to perform calculations of failure metrics for each temperature scenario

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given operating conditions; vi) probability distribution functions were fitted to Monte Carlo

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outputs; vii) projections of failure using failure rates for physical components and probabilities

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of water quality non- compliance were calculated from probability distribution functions; and,

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ix) fault trees were created to estimate how individual component failures and quality non-

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compliances could propagate to service outages given different operational scenarios. The

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resulting component projections of failure, percent increases in failure, and expected number of

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service outages under scenarios of the possible temperature exposure in the next 30 years

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considering best and worst case operating conditions are used to make recommendations for

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focused management strategies. A process flow diagram of the methodology is shown in SI

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Figure S1.

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Quantifying Temperature-Related Exposure and Degradation

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The physical components and aspects of water quality that have been shown to be

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sensitive to temperature were first identified through literature review to assess how operational

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failures might increase. Temperature sensitivities affect component wear and the rate of chemical

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reactions, the latter leading to the potential for increased pipe degradation and corrosion or

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quality non-compliance. Temperature-sensitivities are found in the motors17–20 and 4 ACS Paragon Plus Environment

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electronics21,22 used in pumping units, thermoplastic23 and metal pipes24, and in the chemical

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processes in the water from decay of the disinfectant residual25 and increase in disinfection

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byproduct production (DBP)26,27 (SI Table S1). While high temperature is known to affect water

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demand8, soil expansion28, material stress in pipes from temperature change29,30, the health of

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system operators31, nitrification rates32–35, and the growth of harmful bacteria like

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Mycobacterium Avium Complex36,37 and Legionella,38,39 these effects were excluded from

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analysis because of lack of basic data and quantitative relationships.

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There is ample evidence that summer temperatures contribute to degradation of water

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system components and quality. From experience of operation, the Las Vegas Valley Water

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District has found that their cooling systems for pumping units may be inadequate for higher

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summer temperatures and that thermoplastic pipes fail more frequently in the desert heat.5,40

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Observational studies of rates of corrosion and DBP production over year-long periods show that

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rates are higher during summer when the water is warmer.24,26,27,41 Additionally, an experimental

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study of chlorine residual decay in water samples also shows that reaction rates increase when

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samples are subjected to warmer water temperatures under periods of about a day.25

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Studies of environmental and public health hazards have found that the impact of a

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hazard depends on “the concentration, amount or intensity of a particular agent that reaches a

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target system in terms of its duration, frequency, and intensity”.42 It is therefore assumed that

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physical components and aspects of water quality would have varying levels of degradation as a

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function of their durations and magnitudes of exposure. While the empirical studies of water

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quality aspects and corrosion rates show that a summertime period or shorter is enough to cause

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the reported change in reaction rates, the exact duration of exposure that it takes for temperatures

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above rated thresholds to cause degradation to the physical components (i.e. PVC pipes, motors,

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and electronics) at the reported rates is unknown. We speculate that because degradation is

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cumulative for physical components, cumulative exposure duration might be important. No

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empirical data or models were identified to establish a useable relationship between cumulative

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exposure and degradation rate for these components, so we propose a theoretical relationship that

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is derived in SI Section S2. However, we are not able to employ this cumulative temperature

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model in the current paper owing to a lack of empirical data on the effects of cumulative

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exposure. Fortunately, there is some information available on the relationship between maximum

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temperature exposure and component degradation rate. One source states that reported motor 5 ACS Paragon Plus Environment


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degradation rates apply “even if the overheating was only temporary”.19 We therefore interpret

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published degradation rates for PVC pipes, motors, and electronics as being appropriate to

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characterize degradation from brief but repeated exposure of water systems to peak temperatures

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over the components’ service lives. When water systems are deployed in the field, they are

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exposed to exactly this type of pattern of “temporary”, but repeated, maximum temperatures; this

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exposure happens almost every summer afternoon. The hottest afternoons in U.S. cities tend to

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occur in June, July, and August, which is when temperatures sometimes exceed safe operating

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temperature ratings of components.

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Since both physical components and aspects of water quality are affected by high

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summertime temperatures, this study models failure due to predicted averages of maximum daily

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summertime temperatures between June and August, during the hottest three hours of each

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summer day. This model has a cumulative-peak-exposure interpretation because a component in

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our model is exposed to this peak temperature for roughly 270 hours at least once and at most

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every year (the average temperature of the hottest 3 hours each day43 for 3 months, referred to as

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“3x3” in this paper). If it is the case, however, that degradation rates of physical components

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increase every time the component experiences much smaller durations of exposure (e.g. during

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a minute, hour, or week), then the model underestimates projections of failure. If it is the case

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that the degradation occurs only given years of continuous exposure that are longer in duration

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than the 3x3 window time frame, the model overestimates failures. With empirical daily or

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monthly failure data, future studies could determine the specific duration of exposure needed to

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cause these rates of degradation to physical components.

171 172

Urban Water System Case Study

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Given their large populations, hot environments, and modern infrastructure, a potable

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water system with characteristic conditions of the cities of Phoenix, Arizona and Las Vegas,

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Nevada was modeled as a case study. These metro areas are two of the largest and fastest

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growing regions in the Southwestern US, and experience some of the highest temperatures of

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metro area across the US.44,45,47 They both have populations of around 5 million people and

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experience 35 – 40oC average daily summer temperatures.44,45 The largest water utilities in each

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metro region service around 1.5 million customers each.46,47 Since studies on temperature affects 6 ACS Paragon Plus Environment

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to operation are sparse, the failure metric equations and ranges of operational characteristics

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from literature were used from systems around the world that do not necessarily represent the

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Las Vegas and Phoenix case studies. Therefore, the temperature data and number of components

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are the aspects of the system that are characterized by the case studies. A comparison of the

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relevant water utility characteristics is shown in SI Table S2.

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Climate projections show significant increases in temperature in Phoenix and Las Vegas

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into the future. Phoenix and Las Vegas projections of the averages of maximum daily summer

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temperatures (3x3) are shown in SI Figure S3. Temperature projections were processed from

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CMIP5 12x12 km gridded data from Global Climate Model (GCM) projections of representative

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concentration pathway (RCP) scenarios 2.6, 6, 4.5, and 8.5.2 The 3x3 maximum air temperature

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in Phoenix is projected to increase to at least 40°C in 2020 and at most 44°C in 2050 (including

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the GCM’s standard deviation of temperature). In Las Vegas it is projected to increase to at least

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36°C in 2020 and at most 41°C in 2050. The temperature range used to force the failure model

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considers the total range of both cities with GCM uncertainty, i.e., 36 – 44oC at 1oC intervals.

194 195 196

Modeling Increases in Component and Water Quality Failure Failure can be measured as events where reliability requirements are not met.15 Motors

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and electronics within pumping units failing to operate, pipes breaking and not delivering water

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at required pressures, chlorine residual concentrations below regulation, and DBP concentrations

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above regulation each constitute failures. Estimated-time-to-failure (ETTF), which represents the

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lifetime of components in units of years, and chemical concentration therefore represent “failure

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metrics” that indicate the potential for failure of components and the aspects of water quality,

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respectively. With temperature sensitivities identified, a review was conducted to identify the

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quantitative relationships between temperature and component failure and chemical reaction

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rates to characterize failure metrics. The ETTF parameter was calculated when the quantitative

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relationship between exposure temperature and lifespan of component was given and was used to

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estimate effects of overheating on motors and electronics, degradation of polyvinyl chloride

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(PVC) pipes, and corrosion of iron pipes. Chemical concentration represents the failure metric of

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chlorine residual decay and the production of DBPs.

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An increase in ambient temperature threatens overheating of motors and electronics that

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are vital to the operation of the pumping units. Motors can overheat from the combined

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dissipated heat from motor windings and the ambient temperature surrounding the motor,

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causing destruction of the insulation which can lead to burnt stator windings.17–20 It is reported as

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a rule of thumb in the industry that for every 10°C increase in the operating temperature over the

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capacity of the insulation--155°C for class F motors -- the lifespan decreases by one-half (SI

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Section S4.1.1).17–20 Conversely, the lifespan increases by one-half for every 10°C decrease in

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the operating temperature below the capacity of the insulation. 18 The electronic controls that are

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used for pump operation are also sensitive to the combination of dissipated heat and the outside

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temperature within their enclosures, and for every 10°C rise in enclosure temperature above

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40oC, the lifespan of the electronics decreases by one-half (SI Section S4.1.2).21,22 It was

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assumed that electronics have the same property as motors when temperatures are below their

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capacity. With these relationships, we estimate the ETTFs that result from exposure to average

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peak summertime temperature scenarios.

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Water temperatures in the distribution system rise in response to increases in ambient

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temperatures. The relationship between water and air temperatures was modeled using the

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empirically derived linear regression equations from a study of temperatures at water treatment

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plant outlets in Japan.48 Coefficients of the regression range from 0.52 – 0.89 oC in water / oC in

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air and the constants range from 1.88 – 7.89 oC in water. Despite some novel research on the

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topic, 48,49 predicting water temperature within distribution pipes is challenging due to a lack of

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empirical data. With high water temperatures, thermoplastic pipes can experience overbearing

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pressures, with the greatest effects on PVC.23 The derating of the PVC pipe is linear with

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increasing water temperatures (SI section S4.1.3). Iron pipes are sensitive to indirect effects of

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water temperature through internal corrosion.24 The relationship between corrosion rate and

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water temperature over a year-long period has been reported in the literature for the cast iron

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pipe material, so this is the type of iron pipe modeled. Temperature may have a similar effect on

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the corrosion rate of ductile iron and steel pipes, though no relationship was found in literature.

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Corrosion rates for cast iron pipes are reported to be empirically different for water distribution

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systems (WDS) and water treatment plants (WTP) (SI Section S4.1.4). 24 Corrosion rates and

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pipe age were used to calculate pit depth and then to the calculate the ETTF from the remaining

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life of the pipe according to Randall-Smith et al.50 8 ACS Paragon Plus Environment

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Water quality is also affected by an increase in water temperature. Summertime

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temperatures increase the reaction rates between the organics and disinfectants in water, thereby

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increasing the formation of the cancerous DBP, total trihalomethane (TTHM), according to an

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empirical study on seasonal drinking water quality in Istanbul City, Turkey, where water is

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supplied through surface water and is treated through “aeration, prechlorination, coagulation,

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flocculation-sedimentation, filtration, and postchlorination” (SI Section S4.1.5).26 The

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concentration of another DBP, total haloacetic acid (THAA), is directly dependent upon the

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concentration of TTHMs and seasonal temperatures as well, according to an empirical study of

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three drinking water systems in the United Kingdom which represent a range of source water

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conditions – “upland surface water, a lowland surface water, and groundwater” with standard

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treatment mechanisms: aeration, filtration, coagulation, sedimentation, and chlorination (SI

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Section S4.1.6)27.

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The other type of temperature-related quality concern is that of chlorine residual decay as

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water travels to the consumer. The final chlorine concentration was calculated based on the

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initial concentration of chlorine from dosage at a water treatment plant, the minimum allowable

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concentration of chlorine, and the chlorine decay constant (SI Section S4.1.7). The decay

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constant’s relationship with temperature experienced over day-long periods was taken from an

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experimental study with samples from two water distribution systems in Birmingham, Alabama,

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by Hua et al.25 Table 1 shows the equations used to calculate the failure metrics for each

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component and aspect of water quality, though a more detailed discussion can be found in the SI

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section S4.

261 262 263 264 265 266 267 268 269 270

Table 1: Failure Metric Equations. More detailed equations are shown in SI section S4. T= temperature rise above component threshold , Tw = water temperature, ETTF (T/Tw) = estimated-time-to-failure after air or water exposure [years], C(Tw) = chemical concentration after water temperature exposure, MTTFT1 = historical mean-time-to-failure [years], rd = lifespan degradation fraction per 10oC above capacity [no units], t = age of pipe [years], tw = water age, Pi = internal pit depth [cm], ∂ = pipe wall thickness [cm], C0 = initial chlorine concentration [mg/L], TOC = total organic carbon [mg/L], Cl2 = chlorine dosage [mg/L], SUVA = specific UV absorbance [l/mg*m], Br = bromide [mg/L], ResT = water age [h], Season = season of year, numerically expressed as: 1 for spring, 1.46 for summer, 1.31 for autumn, and 1.01 for winter. Component/Aspect of Water Quality ETTF (T or Tw) = Motors and Electronics ‫( ∗ ܨܶܶܯ‬1 − ‫்) ݎ‬/ଵ଴ ்ଵ ௗ

Failure Metrics C(Tw) =

Source 17–22



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23

PVC Pipes ‫்ܨܶܶܯ‬ଵ ∗ (−0.0123ܶ௪ + 1.293) ‫ݐ‬ Iron Pipes ߜ 0.5‫(ݐ‬0.0774 ∗ ܶ௪ − 0.1073) + ܲ௜ Chlorine Residual Concentration TTHM Concentration THAA Concentration

24,50

‫ܥ‬଴ ݁

଴.଴଴ହ଴௘ బ.బఴరభ೅ೢ ି ௧ೢ ஼೚

25

11.967(TOC)0.398*Tw 0.158*Cl20.702

26

0.99(TTHM)0.64*(Cl2)0.15*SUVA0.09*(Br+0.005)-0.12*(ResT+5)0.07*Season

27

271 272

While temperature contributes to failure, the effects of operating characteristics can also be

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significant. SI Table S6 shows the characteristics used to calculate the failure metrics. Examples

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include the range in water temperature vs. air temperature regression coefficients, MTTFT1 and

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heat dissipation of motors and electronics, and the age of water in pipelines. Estimates of the

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possible ranges and likely distributions of these characteristics were identified through literature

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review and were modeled as uniform or lognormal distributions to account for their uncertainty.

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Uniform distributions were used when there was no information available about the relative

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likelihood of certain values over others within the reported range. Lognormal distributions were

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used when there was evidence for a skewed distribution present in real water distribution

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systems.

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Monte Carlo simulations were then performed to calculate and characterize probability

283

distributions of the failure metrics. When performing the calculation of failure metrics for each

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temperature, the distributions of operational characteristics were sampled 5,000 times. To

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estimate the variation in failure metrics under different regimes of operating characteristics, the

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ranges of operational characteristics were divided into lower and upper halves (representing best-

287

a worst- case operating conditions) and Monte Carlo simulations were run over both halves

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separately. The terms best- and worst- cases within the reported literature are not determined to

289

be optimal and sub-optimal respectively from independent sources. Distributions of “failure

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metrics” were created for each average maximum summertime temperature and operational

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scenario by performing Monte Carlo simulations for each 1oC increase in the range 36-44oC,

292

resulting in similar histogram outputs that are just shifted to higher values of failure metrics with

293

increasing temperature. The output failure metric values from the simulations were fit into

294

probability distribution functions using Anderson Darling Statistics, probability plots,

295

visualization and judgement about distributions that best fit the process underlying the data. 51 10 ACS Paragon Plus Environment

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Weibull distributions were fitted to the physical components in pumping units and pipes because

297

the form most accurately represents degradation with age and it also produced reasonable fits, as

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shown in figures S4-S11 .52 These distributions characterize the histograms of component

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failures from a population of components overtime, and time represents ages of the components

300

– starting at zero.52 Motors and PVC pipes were the only components that had outputs that were

301

statistically equal to the Weibull at the 10% significance level. The output distributions for

302

chlorine residual concentration were fitted as exponential distributions based on best fit and the

303

need to be consistent across operating scenarios. The output distributions for DBP concentration

304

were fitted as Gamma distributions based on best fit. Output data and their fit distributions and

305

probability plots are shown in figures S4-S20. Parameters of the distributions and Anderson-

306

Darling Statistics are shown in SI Tables S8 and S9.

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The projection of each component failure and type of water non-compliance was calculated

308

through either hazard functions or integration of the distributions characterized by the failure

309

metrics. The ETTF distributions of physical components were used to calculate components’

310

annual failure rates with the hazard function of the Weibull distribution (units: % failed/year),

311

which characterizes their failure behavior starting in the next instant of their lives, given that they

312

had already lived a certain number of years. 53 The time period of the rate was then converted to

313

be during the next summertime period of its life instead of a full year --as characterized by a

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period of 270 hours out of the total 8760 hours in a year (SI Section S4.3.1).53 It is necessary to

315

calculate failure rate after the components have operated up until a non-zero age because there

316

would be very little likelihood of their failure given any temperature exposure if they were brand

317

new. The DBP formation distributions were used to calculate the probability that a concentration

318

from the distribution would be above the EPA regulated threshold of 80 µg/L and 60 µg/L for

319

THMs and THAAs respectively54 for each sampling station in the network for any point in time

320

that the water temperature scenario is experienced, as shown in SI section S4.3.2. Similarly, the

321

chlorine residual distributions were used to calculate the probability that a concentration from a

322

sampling station would be undetectable and therefore non-compliant with EPA regulation for

323

any point in time that the water temperature scenario is experienced.55 A figure of the overall

324

failure metric distribution formation and probability calculation methodology is shown in Figure

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1.



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326 327 328 329 330 331

Figure 1: Methods of projection of component-level failure calculations. For physical components, the ETTF distribution is shifted to the left, which effectively shifts to the left through Monte Carlo analysis under increasing temperature scenarios. For disinfection byproducts, the failure metric, chemical concentration (C) is shifted to the right, and for chlorine residual is shifted to the left. Shaded regions represent integrated areas under the curves that are used to calculate failure rate and probability of failure.

332

Modeling Failure Cascades to Service Outages

333

Component probabilities of failure and failure rates were propagated to probabilities of

334

systemic failure using a reliability engineering fault tree analysis to assess the impact of

335

component hierarchies, points of redundancy, and presence of back-up systems on the likelihood

336

of systemic failure.53 Water distribution systemic failure is defined as the pressure, flow, or

337

quality falling below specified values at one or more nodes in the network. The three temperature

338

induced pathways to service outages are pumping station outages, pipe breaks, and water quality

339

non-compliance, and are shown as individual fault trees in Figure 2. Pipe breaks and pumping

340

station outages can both directly cause failures in the water distribution system (WDS) and

341

indirectly cause failures in the WDS due to failures of water treatment plants (WTP). The fault

342

trees highlight which component failures lead to service outages. To calculate systemic

343

probabilities of failure, physical components were assumed to have series behavior (meaning that

344

if any one component fails, the sub-system fails), and failure rates were propagated from a

345

component level to a system level according to the system reliability equations that state that

346

individual component or sub-system failure rates are summed in a series system (SI Section

347

S4).53 The use of the hazard function to calculate component failure rate allows for the

348

characterization of failure behavior of all components during the same instant in time (when they

349

are all at the certain ages in the scenario). To calculate the probability of water quality non-

350

compliance, the probability of either a non-compliance from TTHM or chlorine residual decay 12 ACS Paragon Plus Environment

Page 13 of 27


351

occurring was found through the union of their probabilities (SI Section S5).53 Expected

352

occurrences of service outages were calculated from these propagated failure rates and

353

probabilities, along with the estimates of the number of components. Scenarios of numbers of

354

available redundancies of components were explored in the calculation of pumping unit failure

355

rate leading to pumping station failure rate, water treatment or water distribution failure rate, and

356

ultimately to distribution system failure rate (SI Tables S4 & S5). These values are relative to the

357

average number of pipes, pumping stations, and quality sampling sites in Phoenix and Las Vegas

358

(SI Table S5). An important type of redundancy is stored water in tanks throughout the network

359

that can offset the pressure and flow lost during a pipe and pump station break and provide a

360

location for re-dosing the distribution system with chlorine disinfectant. Without estimating

361

water volumes at different times, water distribution tank storage was assumed to be 100% likely

362

to be inadequate for stopping a pumping station outage or pipe break from causing some

363

magnitude of water outage. A more specific representation of the availability of water storage

364

would require knowing temporal and spatial information of the system structure and operations

365

over time.

366 367 368 369 370

Figure 2: Fault Tree Diagrams showing three trees of systemic failure leading to a possible water service outage: 1) pump station outage, 2) pipe break, and 3) water quality non-compliance. The boxes represent failure events and their different colors represent hierarchies of failure (i.e. sub-component, component, intermediate systemic, ultimate systemic). It was assumed that if one out of two pumping units, pumping



371 372 373

Page 14 of 27

stations, and water treatment plants failed, demand would not adequately be met and failure would propagate as detailed in SI Table S4.

It is possible that these individual pathways to outages could also happen

374

simultaneously—leading to potentially longer and more severe supply and quality outages. For

375

example, low pressures in the system from pipe breaks or pumping station outages not only

376

independently cause quantity supply outages and seepage of contamination, but when coupled

377

with pre-existing quality issues, pose a human health threat.56 If both physical components and

378

quality fail in the same time frame, the customers could experience longer periods of low

379

pressures and durations of contamination because of the potential difficulty of rerouting and

380

flushing water.15,56 The probabilities of simultaneous occurrences of different types of water

381

outages were thus also calculated, using standard probability law of simultaneous events for the

382

expected values of service losses from physical component failure rates and water quality non-

383

compliance probabilities (SI Section S5). Simultaneous probability was estimated through

384

viewing the conditional probability portion of the physical component failure rate as stand-alone

385

under the certain time interval of 270 hours.

386

RESULTS

387

Projected Increase in Component Risk

388

Over the full range of possible temperatures in all RCPs and including GCM uncertainty,

389

the increase in component probability of failure from 2020 to 2050 ranges from 10-101% for the

390

Phoenix and Las Vegas-characteristic utility. The average probabilities of failure and failure

391

rates (between best- and worst- operating conditions) are the highest for chlorine residual and

392

pumping stations, pumping units, motors and electronics (within pumping units). This holds

393

across all temperatures, as shown in Figure 3. Detailed results are shown in SI Table S10.

394 395


Page 15 of 27

396 397 398 399 400 401 402


Figure 3: Average Component and Water Quality Projection of Failure: Component failure rates and probabilities of failure are shown as a function of temperature. Temperature ranges for Phoenix and Las Vegas are plotted on the abscissa. Chlorine residual, and pumping units are the components with the highest probability of failure. Pumping stations, inadequate chlorine residual, and motors are projected to have the greatest percent increases in failure.

The components that pose the greatest threat to reliability are those that have both the

403

highest probability or rate of failure and the greatest percent increase in these values between the

404

2020 and 2050 scenarios. Thus, the most concerning aspect is water quality non-compliances due

405

to the decay of chlorine residual and TTHM production. Chlorine residual decay will have the

406

largest probability of failure and will also have a large percent increase with increasing

407

temperatures. The relationship between inadequate chlorine residual and temperature is

408

exponential, and therefore the percent increase in failure rates between 2020 and 2050 will be

409

53% ± 36% on average. There is no validation available for the historical frequency of non-

410

compliances, though there is evidence that non-compliances have been non-zero. Annual water

411

quality reports show that out of the last 5 years in the City of Phoenix, there were 4 years where

412

concentrations fell below the minimum residual concentration at least once sometime during the

413

year.57–61 TTHM production has the second highest probability of failure but has a lower percent

414

increase of 17% ± 10% on average. It is unlikely however that the non-compliance thresholds of

415

the other form of DBP, THAA, will be exceeded under this scenario of operational practices.

416

This is because the calculated concentrations of THAA at different temperatures were only

417

around 25% of the regulated levels. Though the chance of occurrence is high, it should be noted

418

that there are no reports of DBP violations in the past in either city in recent years.46,62

419 420

The failure processes of second-most concern are those of electronics, motors, pumping units and pumping stations. Pumping station failure rates are projected to have a percent increase 15 ACS Paragon Plus Environment


Page 16 of 27

421

of 76% ± 15%, depending on operating conditions. The historical average failure rate of motors

422

across a variety of industries in the US is on average 3-12% every year under current temperature

423

conditions.17 Extending the 270-hr failure rates to yearly values provides a value to check

424

validity with historical data. The estimates of annual failure rates under all temperature scenarios

425

from 36oC (8.6% ± 5.6%) to 44oC (26% ± 16%) fall within this historical range.

426

Pipe failures show a less significant increase in threat to utility reliability in the future

427

compared to other component failures. Between the two types of degradation, corrosion causes a

428

greater increase in probability of failure than does the degradation of thermoplastic pipes, though

429

probability of failure of PVC pipes from degradation will be consistently greater than probability

430

of failure of iron pipes from corrosion. The corrosion process associated with iron pipes causes

431

the probability of failure to have a percent increase of 52% ± 12% in the average WDS, and of

432

76% ± 8% in the average WTP. The PVC pipe failure rate has a percent increase of 10% ± 0.2%

433

in the average WDS. The historical failure rates of polyethylene pipes, pipes with similar

434

degradation rates to PVC pipes, in Las Vegas in 2005 was 2.2% (679 breaks out of 25,000 PE

435

pipes) in the summer and 6.5% over the year (1623 breaks out of 25,000 PE pipes).5,40,63 The

436

PVC pipe failure rate estimate from the model given the average peak summer temperature of

437

2005 (44oC)64 in Las Vegas is 0.2% ± 0.13% in the summer and 6.5% ± 4.3% over the year, so is

438

consistent with historical data. Current annual iron pipe break rates are reported to be 6% on

439

average in the United States.47 For WTP iron pipes, the estimates of annual failure rates under all

440

temperature scenarios from 36oC (0.32% ± 0.32%) to 44oC (0.84% ± 0.84%) fall below this

441

historical range. Additionally, WDS iron pipe failure rates are negligible under all temperatures

442

(0.005% ± 0.005% per year at 44 oC). Underestimates in modeled versus observed failure rates

443

are in part due to the fact that there are multiple modes of pipe breaks in reality - longitudinal,

444

circumferential, corrosion through hole, split bell/bell shear, and joint failure28 - that are not

445

accounted for in the model. In the results, the lifetime of the pipe is only modeled from an

446

increase in corrosion and degradation. While the Weibull distribution can predict other forms of

447

aging, it cannot account for the random breaks due to factors like inadequate bedding support

448

and live loads caused by traffic that are independent of climate effects.28 Moreover, results of

449

pipe failure rates show that utilities should expect the high rate of PVC pipes to slightly increase,

450

but should also anticipate an increase in WTP iron pipe breaks with increases in temperature.

451 16 ACS Paragon Plus Environment

Page 17 of 27

452 453


Projected Increase in Service Outages The probability of service outages increases as a result of propagated component failures.

454

Individual water outages and water quality non-compliances are projected to have a percent

455

increase within the 7-91% range and simultaneous water outages and water quality non-

456

compliances are projected to have a percent increase within the 23-123% range, depending on

457

the type of event and how the system is operated. Figure 4 shows the resulting expected

458

occurrence of water outage and quality failure when all combinations of systemic probability are

459

considered.

460 461

462 463 464 465 466 467 468 469 470

Figure 4: Isolated and Simultaneous Service Outages. Dashed lines represent Las Vegas temperature ranges from 2020 – 2050 while dotted lines represent temperature ranges in Phoenix. Venn diagrams in the first column show which type of systemic failure was analyzed. The ranges of failures due to changing operating conditions are shown in colored bands to characterize opportunities for risk mitigation. All best-case operating conditions together contribute to the lower range of expected number of failures and all worst-case conditions together contribute to the upper range of expected number of failures. The large range shown from the bands suggests that the expected number of failures is sensitive to changes in operating conditions.

Results show that the components that have the highest probability of failure and failure

471

rates and that increase the most in failures - namely water quality non-compliances and pumping

472

station outages - directly contribute to the greatest increase in service outages. The type of

473

isolated water outage that is projected to increase the most is that from pump station failures. The

474

expected number of water outages from pump station failures has a percent increase of 76% ± 17 ACS Paragon Plus Environment


Page 18 of 27

475

15%. The second highest percent increase in water outages is from quality non-compliances of

476

17% ± 0.4%, and lastly, water outage percent increase from pipe breaks is estimated to be 10% ±

477

3%. Unfortunately, there was no identified available historical data for how many simultaneous

478

outages currently occur currently for direct comparison.

479

Because the individual modes of water outage that increase the most are also more likely

480

to happen simultaneously with other types of service outages, the simultaneous service outages

481

that will have the greatest percent increase are those that include pump station outages and water

482

quality non-compliances. This means that though utilities are used to responding to frequent pipe

483

breaks, the increasing simultaneous occurrence of pipe breaks with pumping station outages

484

and/or water quality non-compliances could cause outages that are increasingly difficult to

485

recover from.

486

Priority Maintenance Strategies

487

Instances of water quality non-compliance and pumping station failures are also the failure

488

modes that have the greatest potential for being prevented, and as such should be prioritized for

489

failure prevention. The large sensitivity in failures for all events that are caused by water quality

490

non-compliances and pumping station failures (as shown in the colored bands in Figure 4) show

491

that changing the operating conditions for these components would be the most effective way to

492

reduce failures. The trends in failure shift from linear to exponential between operating

493

conditions. Specifically, the results show that there is a maximum 59% absolute reduction in

494

probability of inadequate chlorine residual failure when operators inject the higher range of dose

495

of chlorine at the entrance point to the WDS, and make sure to maintain a low water age

496

throughout the system. It should be noted, however, that injecting the maximum chlorine dosage

497

and allowing for more organic carbon causes a 64% absolute increase in probability of non-

498

compliances from TTHM production. Therefore, both residual chlorine and DBP concentrations

499

should be monitored carefully under any chlorine dosing strategy. Pumping station failure rates

500

are also sensitive to operating conditions as there is a maximum 15% absolute reduction in the

501

pumping station failure rate when motors and electronics dissipate low amounts of heat, and

502

there are cooling devices implemented that reduce operating temperatures. The expected value of

503

pipe failures from corrosion and degradation is not very sensitive to changes in characteristics


Page 19 of 27


504

like pipe diameter and normal operating pressure (1% absolute reduction), and thus could not be

505

easily decreased through changing these characteristics.

506

MODEL UNCERTAINTY

507

Uncertainties associated with water temperature in underground pipes, the time it takes

508

for degradation to occur, availability of water storage, component failures from waterhammer,

509

and projections of future trends in model parameters should all be considered when assessing

510

applicability of the risk projections to a specific water utility. These characteristics were

511

necessarily inserted into the model as assumptions, but their variability would have an effect on

512

the output probabilities of failure. A 10% change to degradation rates results in a percent change

513

of 0-22% of motor and electronic failures depending on the temperature and operating conditions

514

scenario. Specific values are shown in SI Section S7. The addition of this variance would make

515

pumping stations more comparable to all other components in terms of percent increase in

516

failure. Assuming that there is a 50% chance (instead of 100% chance) of an inadequate amount

517

of water storage decreases the likelihood of outage from pipe break by 50% and an outage from

518

pumping station failure by 50%. Additionally, if quantitative relationships to describe the effect

519

of waterhammer from one component failure causing another became available, the frequency of

520

pipe and pumping unit failure might increase. Lastly, it is hard to know what the resulting risk

521

will be when normal structural and operational characteristics change over time from urban

522

expansion, transformative designs, etc.

523

DISCUSSION

524

The study is a critical first step towards helping utilities prepare for climate change and

525

extreme events by identifying and characterizing the aspects of the system and chain of failure

526

events most sensitive to heat. The results are modeled to show how discrete increases in

527

temperature increase chances of failure. As framed, the results provide a directionally reliable

528

estimate telling us that component and service failures will increase with increasing

529

temperatures. While estimates of risk contain uncertainty, comparisons between the estimated

530

increase in risk for each temperature-sensitive aspect of the system are nevertheless valuable for

531

prioritizing where strategies should be focused. 19 ACS Paragon Plus Environment


532

Page 20 of 27

The results also help identify several critical areas for future research and data collection.

533

This model can be most directly applied to warm regions around the world with modern

534

centralized water distribution infrastructure, (e.g. the US Sun Belt, Middle East, North Africa,

535

and South Asia), where a large and growing fraction of humanity lives and where a large fraction

536

of global 21st century water infrastructure investment will occur.65 Even in colder regions the

537

model may be useful, with necessary further work because an increase in annual and summer

538

temperature may benefit the system operation by helping to prevent pipes from freezing and

539

accelerating the rates of beneficial chemical reactions needed for water treatment. 66

540

Additionally, a network model of components and iterative simulations through time would be

541

beneficial to capture locations of component failures leading to different resulting magnitudes in

542

outages, and the accumulation of degradation overtime by exploring the alternative assumption

543

that shorter time periods (rather than the 3x3 duration) cause the reported degradation rates.

544

More modeling work, combined with city-specific and component-specific engineering data

545

quantifying heat-induced failure rates is necessary to more precisely quantify heat-induced

546

service failure risk in particular WDSs. An analysis of relative costs of different suites of

547

preparative actions and their consequences would also be valuable, but this requires data on

548

preventative maintenance, repair, response, lost consumer use, and capital improvement costs for

549

each type of failure. Furthermore, the methods of projecting risks of future external threats could

550

be expanded to include other threats, including flooding and wildfires. It could also be used for

551

the projection of risk in other infrastructure systems like transportation and electricity, which

552

also have temperature and other climate change event sensitivities.6,67

553

Considering the possibility of other increasingly frequent extreme weather events,

554

utilities should recognize that improved response times coupled with the capacity for agile and

555

flexible resources use (including money and equipment) will be critical. The water community

556

should also work to phase out vulnerabilities by improving system design (e.g. increasing

557

pumping unit insulation capacity, reducing water temperature in cooling towers, or adopting

558

“smart” booster chlorination and network sensors5,68), and by improving training and institutional

559

response capacity. Ultimately, in times when maintenance and response actions are severely

560

constrained by budgets, it is of the utmost importance to identify and prioritize strategies by

561

utilizing information on likely mechanisms of failure.


Page 21 of 27


562

SUPPORTING INFORMATION

563

Methodology process flow, temperature sensitivities and exposure, potable water treatment and

564

distribution system characteristics, number of water system components, redundancy scenarios,

565

number of modeled opportunities for systemic failure, modeled operating characteristics, failure

566

metric calculation method, probability distribution creation method, projection of failure

567

calculation method, system failure laws, failure percent increase results, and model uncertainty.

568

ACKNOWLEDGEMENTS

569

This material is based upon work supported by the National Science Foundation under Grant No.

570

1360509. 

571

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