An Analysis of the Potential Impact of Climate Change on the Structural Reliability of Drinking Water Pipes in Cold Climate Regions
- Hypothesis: Pipe failures and failure rates are correlated to frost heave of the ground, and thereby also correlated to air temperature.
Reliability of Pipes
- Failure rates (number of failures per temperature per day) will be plotted against same day temperature and against the average temperature of the preceding week. Plotting failure rates instead of number of failures will adjust the data correctly since there are more days registered with warm temperatures than with cold temperatures. The potential correlation will be tested with a linear regression model, and the model will be statistically tested for a certain confidence level.
- Failures were analyzed directly against the temperature on the day the failure occurred.
- Failures were analyzed against the average temperature one week preceding (including the day of the failure) the relevant failure.
2.1. Preparing and Plotting Failure Data
- Gathering of failure data. Identification of reliable failure data for each city, which is the relevant observation window. This is based on an assessment of when the utility started to collect failure data in a structured way.
- Historical temperature data for the relevant cities was collected from a national database (at www.senorge.no).
- Failures were correlated to temperature through dates given for the failure events.
- Number of failures per temperature were counted = F/temp.
- Number of days per temperature were counted = D/temp.
- Historical failure rates (failures per day) per temperature were calculated by Equation (1).
- Failure rates (step f) were plotted against temperature.
2.2. Analyzing Correlation between Failure Rates and Temperature
- Calculation of average number of days registered per temperature according to Equation (2).
- Calculation of standard deviation for the number of days registered per temperature.
- Calculation of the minimum number of days needed for being part of the correlation calculation, based on the numbers found in points h and i.
- More specifically: minimum number of days registered for a single temperature = average number of days registered per temperature (1834 days)—standard deviation (1762 days) = 72 days.
- In order to include a temperature in the correlation analysis, we needed 72 days or more registered of the temperature.
- Plotting failure rates (failures/temperature/day) versus temperature.
- Calculating linear regression with R and R2 values.
- Statistically quantify the uncertainty of the regression line by performing a test of hypothesis of the linear correlation.
- If statistically viable, establish the regression line as a model on the impact of temperature on failure rates, with a given uncertainty.
- We assume that failures occurring during the summer, i.e., during warm temperatures, are not dependent on temperature. The failures occurring during these temperatures are therefore exclusively dependent on other parameters, like deterioration, hydraulic pressure, corrosion etc. The failure rate during these temperatures therefore constitutes the ‘baseline’ failure rate of the pipes. This approach is also considered in Le Gauffre et al. . The failure rate for the warmest temperature was at 0.21 failures/temperature/day (based on the linear regression), constituting the baseline failure rate.
2.3. Preparing Data for Expected Future Temperature Increase
- From the Norwegian Environment Agency report, we gathered reliable data on the potential future temperature increase. The estimated future temperature increase was based on the following reasoning:
- Values from three scenarios for future greenhouse gas emissions were used. They represent different expected temperature increases.
- Only median values of the climate scenario predictions were used, as they refer to the value in which 50% of the projection values are larger and 50% of the projection values are smaller. The median therefore represents a most probable outcome, as it represents a middle way between more extremes.
- T downscaling processes were used in the National Agency report. One downscaling process modelled three of the RCP scenarios, while the other downscaling process modelled two of the scenarios. Where the two downscaling processes modelled the same RCP scenario, an average of the expected temperature value was used.
- The estimated temperature increase varies by the season, so an expected temperature increase was calculated per season for the three RCP scenarios.
- The three RCP scenarios represent the following :
- RCP 2.6: Emissions are reduced drastically after 2020, and will be close to 0 within 2080.
- RCP 4.5: Emissions are reduced after 2040, and in 2080 emissions are on a level that correspond to 40% of emissions in 2012.
- RCP 8.5: ‘Business as usual’ scenario, where the increase in emissions follow the same pattern as today.
- The values given after RCP relates to the estimated extra heat supply, given in W/m2, for the emission scenario.
- A linear increase in temperature from the reference period (1971–2000) to the projection period (2071–2100) was assumed. This was done since no time series of the future temperature increase is available for the Norwegian-based temperature data (only the estimate for the projection period). Even though historic Norwegian temperature data fluctuates, it is possible to create a linear approximation to the data series for the past 50 years or so , supporting our claim that it can also be done for future temperature increase.
- The modelling in Hanssen-Bauer et al.  is based on a temperature increase from the reference period of climate in Norway, which is from 1971 to 2000. The increased temperature is projected towards the period of 2071 to 2100. From the middle of the reference period to the middle of the projection period there is 100 years (1985 to 2085). The objective of this paper is to predict the impact of climate 50 years into the future. Our year of focus is therefore roughly 2070. In 2070 we assumed that about 85% of the expected temperature increase has occurred. We based this assumption on the following principles:
- We assumed that the temperature increase from today to the projection period is linear, as argued in the previous point.
- 2070 is 85% towards 2085 (when the linear increase is assumed).
- According to Hanssen-Bauer et al. , the expected temperature increase during spring looks like the expected increase during fall, with small deviations. For this paper, we therefore looked at spring and fall as a common group representing a single season. We looked at temperature increases during three seasons; summer, winter and spring + fall.
- In order to relate temperature to season, we assumed a temperature range for each season. We divided the temperature data into three intervals to represent the three seasons:
- Summer: warmer than 12 degree Celsius.
- Winter: colder than 0 degree Celsius.
- Spring + fall: between 0 and 12 degree Celsius.
2.4. Analyzing the Future Impact of Climate on Failures
- The temperature range was divided into the three seasons as stated in segment q. The temperature range was from −23 to 25 degrees Celsius, and is illustrated in Figure 2. We now also looked at temperatures outside of the standard deviation calculated in segment j. An expected average future temperature increase was established based on the process described in segment p. These temperatures are given in Table 1.
- Expected temperatures for 2070 were calculated by increasing the historical summer, winter and spring + fall seasons with the temperatures given in Table 1.
- Calculation of the historical failure rate for each temperature by Equation (1), same as step f.
- Calculation of the share of the failure rates that are caused or driven by temperature, as given by Equation (3). The baseline failure rate was set to 0.21 failures/temperature/day (from segment o).
- The total failure rate at each temperature was given by the linear regression equation (Equation (9)).
- The share of the failure rate which is driven by temperature increases with negative temperatures in a linear fashion, in the exact same rate as the linear regression line. This is illustrated in Figure 2.
- For the historic data, an expected number of failures (for each temperature) driven by temperature was calculated according to Equation (4). This gave us a historic number for failures, which were probably temperature driven.
- For 2070 we calculated an expected number of failures caused by temperature (for the range of temperatures from −23 to25 degrees Celsius) with Equation (5).
- For this we took into consideration the ‘movement’ of the temperature curve to the right, as illustrated in Figure 2. The curve is ‘moved’ by the temperature increase calculated in segment s.
- An increase or reduction in expected number of failures in 2070 was calculated for each temperature (from −23 to 25 degrees Celsius) with Equation (6). This gave us a comparison between historical failures driven by temperature and expected number of failures in 2070 driven by temperature.
- By accumulating the numbers found for each temperature in segment x, we could calculate the total number of increased or reduced failures across the entire temperature range, in total in 2070, compared to the historical data. This was done with Equation (7).
- The accumulated number in segment y was then used to calculate the % of increase or decrease of expected failures within 2070. Equation (8) was used for this. This calculation was done for each of the three climate scenarios. The results can be used to discuss the impact of climate on the reliability of the drinking water network through the impact on failures and failure rates.
3.1. Pipe Data
3.2. The Baseline Failure Rate
3.3. Correlation of Pipe Temperature
3.3.1. Linear Regression Model
3.4. Correlation of Pipe Breaks vs. Average Temperature the Preceding Week
Linear Regression Model
3.5. Expected Impact of Climate Change on Reliability
- Failure rates increase during cold winter months.
- Frost loading and frost heave is an important factor for increased failure rates during winter months.
- Changed soil temperatures cause thermal stresses in the ground, which impact pipes.
- A large portion of failures in cold climates are transverse, which are normally caused by pipe-soil interactions and pipe bending.
- Transverse fractures can be doubled during winter months.
- Pipes in trenches which are more vulnerable to frost heave experience more failures during winter than the average of the network, thus verifying the frost heave effect on pipes during winter.
- Grey cast iron pipes are more vulnerable to failures during winter months than other materials, being the only material that has a substantially increased failure rate during winter months. These pipes are also often laid in trenches exposed to frost heave (certain construction periods), showing that there might be a connection between high failure rates of grey cast iron pipes and frost vulnerable construction periods.
Conflicts of Interest
- Ugarelli, R.; Leitão, J.P.; Almeida, M.C.; Bruaset, S. Overview of climate change effects which may impact the urban water cycle—D2.2.1. In European Union Seventh Framework Programme; European Union: Brussels, Belgium, 2010. [Google Scholar]
- Hanssen-Bauer, I.; Førland, E.J.; Haddeland, I.; Hisdal, H.; Mayer, S.; Nesje, A.; Nilsen, J.E.; Sandven, S.; Sandø, A.B.; Sorteberg, A.; et al. Klima i Norge 2100. Kunnskapsgrunnlag for Klimatilpasning Oppdatert i 2015—NCCS Report No. 2; Miljødirektoratet: Oslo, Norway, 2015. [Google Scholar]
- James, T. Cold Snap Led to Daily Water Main Breaks, City Says. Saskatoon StarPhoenix. 2017. Available online: http://thestarphoenix.com/news/local-news/cold-snap-led-to-daily-water-main-breaks-city-says (accessed on 15 January 2018).
- Wood, P.; Wells, C. Cold Snap Is Snapping Water Pipes in City, Counties. The Baltimore Sun. 2015. Available online: http://www.baltimoresun.com/news/weather/weather-blog/bs-md-cold-problems-20150217-story.html (accessed on 15 January 2018).
- Winters, C. Cold Snap Causes 3rd Everett Water Main to Break. HeraldNet. 2017. Available online: http://www.heraldnet.com/news/cold-snap-causes-3rd-everett-water-main-to-break/ (accessed on 18 January 2018).
- Sobel, A. Record Cold Does not Disprove Global Warming. 2014. Available online: http://edition.cnn.com/2014/01/07/opinion/sobel-winter-cold-global-warming/ (accessed on 17 April 2015).
- Walsh, B. Climate Change Might Just Be Driving the Historic Cold Snap. 2014. Available online: http://science.time.com/2014/01/06/climate-change-driving-cold-weather/ (accessed on 17 April 2015).
- NASA. Polar Vortex Enters Northern U.S. 2014. Available online: http://www.nasa.gov/content/goddard/polar-vortex-enters-northern-us/#.Usyt2iiyVcg (accessed on 17 April 2015).
- Brekke, K.; Husebø, G. Snart Vannkrise. 2006. Available online: http://www.nrk.no/hordaland/vannkrise-i-bergen-1.373274 (accessed on 20 April 2015).
- Herseth, S.K. Nå er det Vannkrise i Bergen. 2010. Available online: http://www.dagbladet.no/2010/02/16/nyheter/innenriks/vann/bronner/10421383/ (accessed on 20 April 2015).
- Intergovernmental Panel on Climate Change—IPCC. Climate Change 2014 Synthesis Report. 2014: Contribution of Working Groups, I.; II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R.K., Meyer, L.A., Eds.; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
- Meteorologisk Institutt. Klima Siste 150 år. 2010. Available online: http://met.no/Klima/Klimautvikling/Klima_siste_150_ar/ (accessed on 17 April 2015).
- Meteorologisk Institutt. Norge fra 1900 til i dag—Temperatur. 2014. Available online: http://met.no/Klima/Klimautvikling/Klima_siste_150_ar/Hele_landet/ (accessed on 17 April 2015).
- Meteorologisk Institutt. Temperaturavvik fra normal Norge—Vinter. 2015. Available online: http://eklima.met.no/metno/trend/TAMA_G0_24_1000_NO.jpg (accessed on 17 April 2015).
- Vreeburg, J.; Vloerbergh, I.N.; Van Thienen, P.; De Bont, R. Shared failure data for strategic asset management. Water Sci. Technol. Water Supply 2013, 13, 1154–1160. [Google Scholar] [CrossRef]
- Kutyłowska, M.; Hotloś, H. Failure analysis of water supply system in the Polish city of Głogów. Eng. Fail. Anal. 2014, 41, 23–29. [Google Scholar] [CrossRef]
- Rajani, B.; Zhan, C.; Kuraoka, S. Pipe soil interaction analysis of jointed water mains. Can. Geotech. J. 1996, 33, 393–404. [Google Scholar] [CrossRef]
- Makar, J. Failure analysis for grey cast iron water pipes. In Proceedings of the AWWA Distribution System Symposium; AWWA—American Water Works Association: Reno, NV, USA, 1999. [Google Scholar]
- Reikvam, S. Cold Induced Damages on Water Pipelines; Master Thesis at Norwegian University of Science and Technology: Trondheim, Norway, 2013. [Google Scholar]
- Le Gauffre, P.; Aubin, J.-B.; Bruaset, S.; Ugarelli, R.; Benoit, C.; Trivisonno, F.; Van den Bliek, K. Deliverable 5.5.4. Impacts of climate change on maintenance activities: A case study on water pipe breaks. European Union 7th Framework Project PREPARED. Available online: http://www.prepared-fp7.eu/viewer/file.aspx?FileInfoID=443 (accessed on 31 March 2018).
- Wu, Y.; Sheng, Y.; Wang, Y.; Jin, H.; Chen, W. Stresses and deformations in a buried oil pipeline subject to differential frost heave in permafrost regions. Cold Reg. Sci. Technol. 2010, 64, 256–261. [Google Scholar] [CrossRef]
- Xu, G.; Qi, J.; Jin, H. Model test study on influence of freezing and thawing on the crude oil pipeline in cold regions. Cold Reg. Sci. Technol. 2010, 64, 262–270. [Google Scholar] [CrossRef]
- Jin, H. Design and Construction of a Large-Diameter Crude Oil Pipeline in Northeastern China: A Special Issue on Permafrost Pipeline; Elsevier: Amsterdam, The Netherlands, 2010. [Google Scholar]
- Makar, J.; Desnoyers, R.; McDonald, S. Failure modes and mechanisms in gray cast iron pipe. In Underground Infrastructure Research; CRC Press: Boca Raton, FL, USA, 2001; pp. 1–10. [Google Scholar]
- Helbæk, M. Statistikk—Kort og Godt; Universitetsforlaget: Oslo, Norway, 2011. [Google Scholar]
- Rajani, B.; Kleiner, Y. WARP-water mains renewal planner. In Proceedings of the International Conference on Underground Infrastructure Research, Waterloo, ON, Canada, 10–13 June 2001. [Google Scholar]
- Burn, S.; Tucker, S.; Rahilly, M.; Davis, P.; Jarrett, R.; Po, M. Asset planning for water reticulation systems-the PARMS model. Water Sci. Technol. Water Supply 2003, 3, 55–62. [Google Scholar]
- Herz, R.K. Software for Strategic Network Rehabilitation and Investment Planning; Water Intelligence Online: London, UK, 2003. [Google Scholar]
- Galloway, G.E. If Stationarity Is Dead, What Do We Do Now? Wiley Online Library: Hoboken, NJ, USA, 2011. [Google Scholar]
- Waage, M. Nonstationarity Water Planning Methods. In Proceedings of the Workshop on Nonstationarity, Hydrologic Frequency Analysis, and Water Management, Boulder, CO, USA, 13–15 January 2010. [Google Scholar]
|Temperature Increase [Degree C]||RCP Scenarios|
|Season||RCP 2.6||RCP 4.5||RCP 8.5|
|Fall + spring||1.9||2.95||4.6|
|Scenario||RCP 2.6||RCP 4.5||RCP 8.5|
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Bruaset, S.; Sægrov, S. An Analysis of the Potential Impact of Climate Change on the Structural Reliability of Drinking Water Pipes in Cold Climate Regions. Water 2018, 10, 411. https://doi.org/10.3390/w10040411
Bruaset S, Sægrov S. An Analysis of the Potential Impact of Climate Change on the Structural Reliability of Drinking Water Pipes in Cold Climate Regions. Water. 2018; 10(4):411. https://doi.org/10.3390/w10040411Chicago/Turabian Style
Bruaset, Stian, and Sveinung Sægrov. 2018. "An Analysis of the Potential Impact of Climate Change on the Structural Reliability of Drinking Water Pipes in Cold Climate Regions" Water 10, no. 4: 411. https://doi.org/10.3390/w10040411