How will climate change affect extreme rainfall in Newfoundland and Labrador?

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Figure 1
Figure 1. Rainfall intensity-duration-frequency stations used in the study.

By Juraj Cunderlik

References listed at bottom of article

The Government of Newfoundland and Labrador recognized in its 2011 Climate Change Action Plan that climate change is a long-term challenge for the province, with significant environmental, economic and social impacts.

Projections of future climate for Newfoundland and Labrador as estimated by global circulation models (GCMs) indicate that the increase in air temperature is predicted to be most pronounced in winter and smaller in summer and autumn. By the mid 21st century, air temperatures are expected to rise by 1°C – 3°C in Newfoundland and by 2°C – 4°C in Labrador. Seasonal differences in projected changes are expected to be generally smaller in Newfoundland due to the moderating influence of the ocean (Finnis, 2013).

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Increased concentrations of greenhouse gases (GHGs) will not only increase air temperatures, but also evaporation, which in turn will enhance the atmospheric moisture content, and consequently, extreme rainfall rates (Trenberth, 1999). According to the Intergovernmental Panel on Climate Change (IPCC 2013), heavy rainfall events are expected to increase both in frequency and intensity under changing climate conditions. The return period of extreme rainfall events could be reduced by a factor of two or more by the end of this century (Kharin et al., 2007).

Increased rainfall will have a significant impact on infrastructure. Engineers, planners, and policy makers utilize rainfall intensity-duration-frequency (IDF) curves in municipal planning and infrastructure design. IDF curves characterize the relationship between the intensity of rainfall occurring over a specified period of time and its frequency of occurrence. They are based on historical observations of precipitation.

In Newfoundland and Labrador, a number of the climate stations used to produce IDF curves are no longer active. In the most recent release of IDF curves (V2.3, 2015) published by Environment and Climate Change Canada (ECCC), 13 of the 19 IDF curves in Newfoundland and Labrador were generated from inactive stations. The resulting IDF curves may not reflect recent trends in extreme rainfall. It is noted that less than 10% of the population live within a 50 km – 75 km radius of the six IDF stations still being operated by ECCC.

Future predictions of rainfall also depend on a clear understanding of current precipitation trends. In response to this concern, the Climate Change Branch of the Department of Municipal Affairs and Environment commissioned a study to update the 13 IDF curves generated from inactive stations (GHD, 2015).

Methodology

Updated IDF curves were used to generate projections of future climate IDF curves for the province. The future climate IDF curves were produced using the Intensity Duration Frequency under a Changing Climate (IDF_CC) tool developed at the University of Western Ontario (Srivastav et al., 2015), utilizing the latest representative concentration pathway (RCP) scenarios described in the IPCC Fifth Assessment Report (IPCC, 2013).

RCPs are the latest generation of scenarios that provide input to climate models. They are based on projections of GHG concentrations, as well as land use and land cover factors (Moss et al., 2010). Each RCP defines a specific emissions trajectory and subsequent radiative forcing. The RCP 4.5 scenario was selected for the development of future climate IDF curves as it represents the most likely future scenario.

The IDF_CC tool fits the Gumbel distribution to annual maximum rainfall intensities using the method of moments. The equidistant quantile matching (EQM) algorithm is applied to the IDF updating procedure (Srivastav et al., 2014). This algorithm captures the distribution of changes between the projected time period and the baseline period (temporal downscaling), in addition to spatial downscaling of the annual maximum rainfall (AMP) derived from the GCM data. The IDF_CC tool produces annual maximum intensities for all standard ECCC IDF durations (5 min, 10 min, 15 min, 30 min, 1 hr, 2 hr, 6 hr, 12 hr, and 24 hr) and return periods (2 , 5 , 10 , 25 , 50 , and 100 year).

An ensemble of 22 GCM projections was used to generate the future climate IDF curves. Using an ensemble of climate models is recommended when projecting future rainfall information (CSA, 2012). According to the Canadian Climate Change Scenarios Network (CCCSN) the use of an ensemble approach (multi model means/medians) provides the best projected climate change signal (CCCSN, 2013).

The future climate IDF curves were calculated as the median value of the 22 GCMs. In addition, knowledge of the possible range of future rainfall projections around the median projection provides important information about the uncertainty involved in projecting future climate. Determining the minimum and maximum rainfall projections from individual GCM runs addresses the uncertainty related to climate modeling (such as representation of atmospheric processes, model resolution, etc.) and captures the large variation in projected changes. The minimum and maximum future climate IDF curves were determined from individual GCMs.

Results

Minimum, median, and maximum future climate IDF curves were generated for three time horizons: 2011– 2040 (2020s), 2041 – 2070 (2050s), and 2071 – 2100 (2080s). The percent increases in rainfall amounts from the current climate IDF curves averaged for all stations are shown in Table 1.

Table 1. Average percent increase in future climate IDF curves.

Time Horizon
Percent Increase for All Stations, Durations, and Return Periods
Minimum
Median
Maximum
2011 – 2040
-5
12
35
2041 – 2070
-1
20
58
2071 – 2100
4
24
60

The median increase of rainfall amounts averaged for all return periods and rainfall durations ranged from 5% – 20% for the 2020s time horizon; 14% – 31% for the 2050s time horizon; and 13% – 33% for the 2080s time horizon (see Figure 2).

Figure 2
Figure 2. Median Increase of intensity-duration-frequency curves.

It is noted that the predicted increases of rainfall amounts/intensities correspond well with results reported in other studies conducted in Atlantic Canada, particularly Lines et al. (2009) who reported an increase of 9% – 31% for 24 hour rainfall amounts in the 2050s, and Finnis (2013) who reported an increase of 1% – 37% for 6 hour rainfall, 4% – 24% for 12 hour rainfall, and 4% – 19% for 24 hour rainfall in the 2050s.

Rainfall amounts/intensities for the 25-, 50-, and 100-year return periods were compared for the existing and future IDF curves. It was found that the current 100-year return period will become the 50-year return period for all rainfall durations at 10 of the analyzed stations in the 2020s. The situation is predicted to be more dramatic in the 2080s when current 100-year return period will become the 25-year return period for all rainfall durations at 13 of the analyzed stations.

The results also suggest that the shortest duration rainfall amounts will increase more than the longer duration rainfall amounts and the small return period events will increase more than the large return period events.

Conclusions

There are many uncertainties involved in projecting future climate. These uncertainties result in large variations in projections of future rainfall. In some regions, model projections disagree on both the sign and magnitude of the changes (ACASA, 2011). The spatial and temporal resolution of GCMs is particularly inadequate for capturing localized short-term extreme rainfall events, and may result in underestimation of rainfall extremes by GCMs (e.g., Allan and Soden, 2008; Min et al., 2011).

Results presented in this study showed that there is considerable variation in the GCM estimates of the future climate IDF curves for each station. The range for a station tended to be larger when there was a high degree of variation in the station data (large natural variability) and/or the record length was short (large statistical uncertainty).

This indicates that the global circulation model estimates reflect the current climate data at the station and that the rainfall data strongly affect the results. Therefore, it is critical to keep existing IDF curves updated at regular intervals to assure that recent trends in rainfall are reflected in the data and that the IDF curves provide up-to-date tools for climate change preparedness and adaptation.

Acknowledgement

The research presented in this study was funded by the Climate Change Branch of the Department of Municipal Affairs and Environment, Government of Newfoundland and Labrador. This support is gratefully acknowledged.

Juraj M. Cunderlik, PhD, P.Eng. is with GHD. This article appears in ES&E Magazine’s February 2018 issue.

References cited in this article

Allan, R.P., Soden, B.J., 2008. Atmospheric warming and the amplification of precipitation extremes. Science, 321/5895, 1481–1484.

Atlantic Climate Adaptation Solutions Association (ACASA), (2011). Climate Change Adaptation Measures for Greater Moncton Area, New Brunswick. Report prepared by AMEC Earth and Environmental.

Canadian Climate Change Scenarios Network (CCCSN), 2013. http://www.cccsn.ec.gc.ca. Accessed March 2015.

Canadian Standards Association (CSA). (2012). Technical Guide: Development, interpretation, and use of rainfall intensity‑duration‑frequency (IDF) information: Guideline for Canadian water resources practitioners. Report PLUS 4013‑12, Mississauga, Ontario.

GHD, 2015. Intensity‑Duration‑Frequency Curve Update for Newfoundland and Labrador, Report for the Office of Climate Change and Energy Efficiency, Newfoundland and Labrador, 2015.

Finnis, J., 2013. Projected Impacts of Climate Change for the Province of Newfoundland & Labrador. 134 p.

Intergovernmental Panel on Climate Change (IPCC). (2013). Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by: Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M., Cambridge University Press, New York, New York.

Kharin, V.V., Zwiers, F.W, Zhang, X. and G.C. Hegerl, 2007. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. Journal of Climate, 20/8: 1419–1444.

Lines, G.S., Pancura, M., Lander, C., Titus, L., 2009. Climate change scenarios for Atlantic Canada utilizing a statistical downscaling model based on two global climate models. Meteorological Service of Canada, Atlantic Region, Science Report Series 2009‑01.

Min, S‑K, Zhang, X.,Zwiers, F.W. and G.C. Hegerl, 2011. Human contribution to more intense precipitation extremes. Nature, 470/7334, 378–381, doi:10.1038/nature09763.

Moss, R.H. et al., 2010. The next generation of scenarios for climate change research and assessment. Nature, 463, 747‑756, doi:10.1038/nature08823.

Newfoundland and Labrador, 2011. Charting our Course: Climate Change Action Plan 2011.

Srivastav, R.K., Schardong, A., Simonovic, S.P., 2015. Computerized tool for the development of intensity‑duration‑frequency curves under a changing climate. Technical Manual v 1.1, Water Resources Research Report, The University of Western Ontario, 94 p.

Srivastav, R. K., Schardong. A., Simonovic, S. P., 2014. Equidistance quantile matching method for updating IDF curves under climate change, Water Resources Management, DOI 10.1007/s11269‑014‑0626‑y.

Trenberth, K.E. 1999. Conceptual framework for changes of extremes of the hydrological cycle with climate change. Climatic Change, 42, 327‑339.

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