Assessment of future change in intensity–duration–frequency (IDF) curves for Southern Quebec using the Canadian Regional Climate Model (CRCM)
Introduction
It is expected that global warming will lead to an increase in intense rainfall events (intensity and/or frequency), since a warmer atmosphere can contain more humidity (and therefore more energy) and therefore produce a more active hydrological cycle (Trenberth, 1999, Trenberth et al., 2003). Allen and Ingram (2002) have suggested, based on a globally integrated view, that the extreme precipitation is constrained by moisture availability and would therefore increase faster than the mean precipitation, constrained by the energy balance. Trend analysis suggests that it appears to be the case in several regions (e.g. South Africa, Siberia, eastern Mediterranean; see Groisman et al., 2005). Such modifications in rainfall events can result in significant impacts over many sectors (e.g. urban drainage, dam design, definition of flood plains, etc.; Kunkel et al., 1999). In urban hydrology, for example, the design of drainage infrastructures (whose life expectancy is comparable to the time scale associated to the induced climate change (CC)) has been traditionally based on statistical analyses of historical records, assuming that the intensity and frequency of past events are statistically representative of what could happen in the near future (Mailhot et al., 2006). However, in a context of CC, this hypothesis must be revisited and the design criteria for drainage infrastructures should be revised to take into consideration the expected changes in the intensity and frequency of heavy rainfall events (He et al., 2006, Grum et al., 2006, Papa et al., 2004).
It is generally difficult to detect a clear trend in extreme rainfall intensity and/or frequency since, by definition, these events rarely occur, that the inter-annual variability of heavy rainfalls is high and that records for rainfall series are usually rather short. Despite these constraints, many studies have reported an increased frequency of heavy precipitation events in the USA (Karl et al., 1995, Karl and Knight, 1998), Australia (Plummer et al., 1999), Japan (Iwashima and Yamamoto, 1993), Germany (Hundecha and Bárdossy, 2005), Denmark (Arnbjerg-Nielsen, 2006), Switzerland (Schmidli and Frei, 2005), China (Zhai et al., 2005), southern and western African countries (New et al., 2006), United Kingdom (Fowler and Kilsby, 2003) and India (Goswani et al., 2006).
Alexander et al. (2006), while analyzing daily precipitation and temperature data from many sources over the globe, have concluded that most precipitation indices show a tendency towards wetter conditions throughout the last century even though not all of them are statistically significant. These authors also observed that, when average conditions over the globe are considered, the percentage contribution from the most extreme precipitation events to the annual precipitation total has been increasing. A similar analysis, realized by Groisman et al. (2005), showed a widespread increase in the frequency of very heavy precipitations (99–99.7 percentiles) during the past 50–100 years (see also Tebaldi et al., 2006, Groisman et al., 1999 who performed trend analyses of heavy rainfall using multi-national data sets). Finally, the last report from the Working group 1 (WG1) of the International Panel on Climate Change (Summary for Policy Makers, SPM WG1-IPCC 2007) reported that heavy precipitations have increased on most of the planetary land surface during the 20th century.
More specifically for Canada, the analysis of precipitation indices (e.g. number of days with precipitation, maximum total precipitation for a five-day interval) related to daily rainfalls using historical records from all over this country, has revealed an increase in the annual total precipitation during the second half of the past century, mainly due to an increase in the number of days with precipitation, while no consistent pattern is observed for extreme wet events (Vincent and Mekis, 2005). Stone et al. (2000) also reported seasonally increasing trends in total precipitation during the 20th century for southern areas of Canada. These authors also mentioned that heavy and intermediate events are responsible for the increases observed during the past 50 years.
The estimation of future modifications in precipitation indices due to variations in greenhouse gas concentrations must rely on climate models. In previous studies, global climate model (GCM) simulations have been used to assess changes in extreme rainfall under enhanced greenhouse conditions (e.g. Zwiers and Kharin, 1998). In more recent studies, regional climate models (RCMs) have been used (e.g. Ekström et al., 2005, Fowler et al., 2005, Jones and Reid, 2001, Semmler and Jacob, 2004). RCMs present the advantage, over GCMs, that finer spatial information can be derived from these physically based models. The finer spatial resolution of RCMs is expected to be a more trustworthy representation of processes involved during heavy precipitations and therefore more adequate for water management applications which involve smaller spatial scales (e.g. urban drainage).
Recently, Frei et al. (2006) reported the results of an inter-comparison of daily (and multi-day) precipitation extremes as simulated by six different European RCMs. Evaluation of the performance of these models to simulate extreme precipitation in present climate was assessed (European Alps data were used) and led these authors to the conclusion that RCMs were capable of representing mesoscale spatial patterns in precipitation extremes that are not currently resolved by GCMs. Frei et al. (2006) also showed that a general tendency is observed between the 1961–1990 and the 2071–2100 periods towards an increase in the five-year return period value of one-day precipitation intensity over northern and eastern Europe and a decrease over southern Europe. Using the MM5 simulation model to generate daily precipitation at a 27-km horizontal resolution during the period of 1971–2100, Boo et al. (2006) showed that Korea will experience very important increases in heavy precipitation events. The change in the pattern of heavy precipitation for Korea reported by Boo et al. (2006) is consistent with previous results observed for Japan and China (Easterling et al., 2000).
Grum et al. (2006) used simulation results from the regional climate model HIRHAM to assess possible changes in amplitude and frequency of maximum 1-h events (corresponding to the maximum rainfall depth to fall during one clock-hour time; as far as we know, this is the only study considering sub-daily extreme events). An approach was proposed to estimate the expected changes at point measurement according to the simulated changes in heavy precipitation at the grid box size (25 km × 25 km) over Denmark. According to the results of Grum et al. (2006), return period for a 1-h maximum intensity in present climate (1979–1996) will approximately halve in future climate (2071–2100). Important research programs in the European Community have generated numerous simulations over Europe using many RCMs for both current and future climate. Unfortunately, such structured collaborations are still in their infancy in North-America and the number of available investigations of change in extreme precipitations is very limited.
Even if the RCMs grid box size is much smaller than the GCMs’ (e.g. for the Canadian Regional Climate Model (CRCM), the grid box size is 45 km × 45 km while, typically, it is in the order of 300 km × 300 km for GCMs), the comparison with rainfall data from stations is still challenging especially when short duration and very localized intense events are considered (Osborn and Hulme, 1997, Osborn, 1997). Therefore, in order to use RCMs output to compute statistical indices related to extreme rainfall events in a future climate, it is crucial to investigate how these estimated indices compare with those based on available rainfall data. This will be achieved in this paper using the areal reduction factor (ARF).
The objectives of this paper are twofold. First, statistical characteristics of extreme rainfall events for the period spanning 1961–1990, computed from a simulation of the Canadian Regional Climate Model (CRCM), are compared to the statistical characteristics computed from rain-gauge observations. The region analyzed covers the southern part of the Province of Quebec (Canada). Maximal rainfall depth (period from May to October) for 2-, 6-, 12- and 24-h events are considered. Secondly, similar analyses for the future climate (2041–2070) are used to estimate the expected changes in extreme rainfall events.
Section snippets
Climate models (CRCM_3.7.1 and CGCM2) and experimental set-up
Model data used in this investigation were taken from simulations generated by the Ouranos’ Climate Simulation Team as part of the Canadian Regional Climate Projections Program. The regional model used for this study is the Canadian Regional Climate Model. CRCM_3 utilizes most of the subgrid-scale physical parameterization package of the second-generation Canadian Atmospheric General Circulation Model (GCM2: McFarlane et al., 1992, Boer et al., 1992). The reader is referred to McFarlane et al.
Available rainfall data sets
The area under study covers the southern part of the Province of Quebec (Canada), as shown in Fig. 1. This area was selected because it covers the three most important urban areas of the province (Montreal, Québec City and Sherbrooke) and also because the region is relatively well covered by a reasonably dense network of rain-gauge stations.
Over Southern Quebec, 25–35% of the annual total precipitation falls as snow, mainly from December to April. Due to the cold weather conditions during
Statistical analysis of extreme rainfall
The statistical analysis of the MOAM series for a given duration (observed and simulated) allows the estimation of the MOAM rainfall intensity of a given return period at a given site (observed) or for a given grid box (simulated). Although many distributions were considered in this study, two statistical distributions were selected for use: the Generalized Extreme Value (GEV) and the Generalized Logistic (GLO) distributions (see Hosking and Wallis, 1997 for a complete description of these
Comparison of observed and simulated MOAM estimates
Validation of climate models is a difficult task. Two main reasons explain this situation. One is the lack of observational data and the other is the low spatial resolution of climate models (Emori et al., 2005). In this section, the ability of the CRCM to reproduce the statistical characteristics of annual extreme rainfall is investigated.
Two different approaches have been applied to compare heavy rainfall statistics based on observed and simulated data. The first approach compares grid box
Grid box analysis
For the grid box analysis, the MOAM magnitude for 2-, 6-, 12-, and 24-h and 2-, 5-, 10-, 25- and 50-year return periods were computed and compared on a box-to-box basis between control and future climates. Fig. 4a and b presents respectively these results for 24-h and 6-h events. As can be observed, estimates in future climate are predominantly higher than their corresponding values in control climate. It is therefore expected that the region of interest will experience increasing MOAM
Summary and conclusions
CRCM-simulated Annual May to October maximum rainfall depth (MOAM) series (2-, 6-, 12- and 24-h durations) over Southern Quebec have been analyzed and compared to available historical records. The CRCM simulations were driven by the Canadian Coupled Global Climate Model (CGCM2) following the SRES-A2 greenhouse gas emission scenario. Two periods were simulated, 1961–1990 and 2041–2070, representative of control and future climates. A total of 61 CRCM grid boxes (45 km × 45 km) and 51 rain gage
Acknowledgements
This work was funded by the Climate Change and Adaptation Program of Natural Resources Canada and by the Consortium Ouranos on Regional Climatology and Adaptation to Climate Change. The authors would like to thank the two anonymous reviewers for their comments and suggestions.
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