Publications Library

This report is intended to support a local understanding of how climate across the Okanagan is projected to change, and inform regional planning on how to prepare for future climate events. This report offers climate projections for both the 2050s and the 2080s. The 2050s projections are useful for medium-term planning purposes, while the 2080s provide guidance for long-term planning and decision-making.

Quantifying bacteria fluxes and contaminants from the point and nonpoint sources in a watershed are important for the management of water quality and safeguard public health. Therefore, the appropriate characterization of bacteria from different sources is necessary for understanding of fate and transport of bacteria in watersheds. However, it is challenging to simulate the effects of pH on bacteria, such as Escherichia coli (E. coli) in the original version of Soil and Water Assessment Tool (SWAT). This study aimed to augment SWAT-bacteria module to evaluate the potential effect of pH on E. coli concentrations. We modified SWAT-bacteria module to incorporate pH factor and to check E. coli observations from four sites of Athabasca River Basin. The modified SWAT-bacteria model demonstrated a linear relationship between observed and simulated daily E. coli data with R2 values found between 0.70 and 0.80; NSE: 0.59 and 0.68; PBIAS: 7.94% and 17.85% during calibration for all monitoring sites (2010–2012). While during the validation (2013–2014) the performance statistics found to be: R2: 0.59–0.72; NSE: 0.55–0.66; PBIAS: 10–22%. The results of the sensitivity analysis confirmed that pH is one of the most significant fate factors of E. coli. The modified SWAT-bacteria module provides an improved estimate of E. coli concentration from the river basin. This study contributes new insight to E. coli modelling. Therefore, the modified SWAT-bacteria model could be a powerful tool for the future regional to global scale model of E. coli concentrations thus significantly contribute for the application of effective river basin management.

Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.

The climate of high midlatitude mountains appears to be warming faster than the global average, but evidence for such elevation-dependent warming (EDW) at higher latitudes is presently scarce. Here, we use a comprehensive network of remote meteorological stations, proximal radiosonde measurements, downscaled temperature reanalysis, ice cores, and climate indices to investigate the manifestation and possible drivers of EDW in the St. Elias Mountains in subarctic Yukon, Canada. Linear trend analysis of comprehensively validated annual downscaled North American Regional Reanalysis (NARR) gridded surface air temperatures for the years 1979–2016 indicates a warming rate of 0.028°C a−1 between 5500 and 6000 m above mean sea level (MSL), which is ~1.6 times larger than the global-average warming rate between 1970 and 2015. The warming rate between 5500 and 6000 m MSL was ~1.5 times greater than the rate at the 2000–2500 m MSL bin (0.019°C a−1), which is similar to the majority of warming rates estimated worldwide over similar elevation gradients. Accelerated warming since 1979, measured by radiosondes, indicates a maximum rate at 400 hPa (~7010 m MSL). EDW in the St. Elias region therefore appears to be driven by recent warming of the free troposphere. MODIS satellite data show no evidence for an enhanced snow albedo feedback above 2500 m MSL, and declining trends in sulfate aerosols deposited in high-elevation ice cores suggest a modest increase in radiative forcing at these elevations. In contrast, increasing trends in water vapor mixing ratio at the 500-hPa level measured by radiosonde suggest that a longwave radiation vapor feedback is contributing to EDW.

One technique to evaluate the performance of oil sands reclamation covers is through the simulation of long-term water balance using calibrated soil–vegetation–atmosphere transfer models. Conventional practice has been to derive a single set of optimized hydraulic parameters through inverse modelling (IM) based on short-term monitoring datasets. This approach is unable to characterize the impact of variability in the cover properties. This study utilizes IM to optimize the hydraulic properties for 12 soil cover designs, replicated in triplicate, at Syncrude's Aurora North mine site. The hydraulic parameters for three soil types (peat cover soil, coarse-textured subsoil, and lean oil sand substrate) were optimized at each monitoring site from 2013 to 2016. The resulting 155 optimized parameter values were used to define distributions for each parameter/soil type, while the progressive Latin hypercube sampling (PLHS) method was used to sample parameter values randomly from the optimized parameter distributions. Water balance models with the sampled parameter sets were used to evaluate variations in the maximum sustainable leaf area index (LAI) for five illustrative covers and quantify uncertainty associated with long-term water balance components and LAI values. Overall, the PLHS method was able to better capture broader variability in the water balance components than a discrete interval sampling method. The results also highlight that climate variability dominates the simulated variability in actual evapotranspiration and that climate and parameter uncertainty have a similar influence on the variability in net percolation.

Based on a newly developed observational dataset and a suite of climate model simulations, we evaluate changes in summer mean wet bulb globe temperature (WBGT) in China from 1961 through 2080. We show that summer mean WBGT has increased almost everywhere across China since 1961 due to human-induced climate change. Consequently, hot summers as measured by summer mean WBGT are becoming more frequent and more conducive to heat stress. Hot summers like the hottest on record during 1961-2015 in Western or Eastern China are now expected occur once every 3-4 years. These hot WBGT summers have become more than 140 times as likely in Eastern China in the present decade 2010s compared to a 1961-1990 baseline period, and more than 1000 times as likely in Western China. The substantially larger influence in Western China is associated with its stronger warming signal, which is likely due to the high Bowen ratio of sensible to latent heat fluxes of dry soils and increases in absorbed solar radiation from the decline in mountain snow cover extent. Observation-constrained projections of future summer mean WBGT under the RCP8.5 emissions scenario indicate that, by the 2040s, almost every summer in China will be at least as hot as the hottest summer in the historical record, and by the 2060s, common summers (that occur once every 2 years) will be even 3.0 °C hotter than the historical record, pointing to potentially large increases in the likelihood of human heat stress and to a massive adaption challenge.

Trend scaling relationships between extreme precipitation and temperature are often used to represent the influence of long-term warming on the intensity of extreme precipitation. Indeed, such scaling relationships are often regarded as providing more reliable precipitation projections than direct projection, owing to higher confidence for temperature projections in model simulations. Due to limited data availability, especially for the sub-daily rainfall, so-called binning scaling relationships, which relate extreme precipitation to temperature at the time of occurrence and are estimated empirically either through a binning technique or quantile regression, have been considered as a substitute for trend scaling to project the long-term response of local extreme precipitation to temperature change (Lenderink and Van Meijgaard, 2008; Wasko and Sharma, 2014). Estimates of binning scaling rates are generally based on seasonal subdaily precipitation observations, and thus they are influenced by factors other than temperature that change systematically within a season, synchronously with the seasonal cycle (Zhang et al., 2017). In contrast to trend scaling, binning scaling often suggests faster than Clausius-Clapeyron intensification of sub-daily precipitation extremes with temperature.

We explore this apparent contradiction between binning and trend scaling using a large ensemble of moderate resolution regional climate simulations for North America. The large amount of data that is available from this ensemble allows us to confidently estimate both trend and binning scaling rates for the climate that is simulated by that model. Specifically, we use a 35-member initial conditions ensemble of regional climate simulations produced with the Canadian CanRCM4 regional climate model for the period 1950-2100, with historical forcings for the period ending 2005 and RCP8.5 forcing subsequently. Each CanRCM4 ensemble member was driven by a corresponding member of a similar large ensemble of global simulations produced with the Canadian global Earth system model CanESM2 (Scinocca et al., 2016).

We compare binning and trend scaling of precipitation extremes across different durations (1-hour, 3-hour, and 24-hour), considering annual and seasonal values, and both local and regional spatial scales. We provide strong evidence to clarify that binning scaling cannot project the long-term change in precipitation extreme, with substantial disagreement in the spatial pattern and magnitude of scaling rates between binning and trend scaling regardless of the duration, season, and spatial scale. Using the daily dew point temperature as scaling variable rather than dry air temperature does not eliminate the differences between binning and trend scaling rates. While shorter-duration extreme precipitation does appear to intensify faster with warming in CanRCM4, we only find super-adiabatic intensification of annual precipitation extremes in isolated regions regardless of accumulation durations. Compared with annual maximum results, winter extremes intensify more strongly over the western and southeastern North America across all timescales. A decreasing tendency of summer extremes is projected over the north and central Great Plains. The seasonal timing of the occurrences of precipitation extremes are expected to shift towards the cold season, reflecting the different changing tendencies in summer and winter extremes.

Lenderink, G., and Van Meijgaard, E. 2008: Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat. Geosci., 1, https://doi.org/10.1038/ngeo262.

Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., and Flato. G. M., 2016: Coordinated Global and Regional Climate Modeling. J. Climate, 29, 17-35, https://doi.org/10.1175/Jcli-D-15-0161.1.

Wasko, C., and Sharma, A. 2014: Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour. Res., 50, 3608-3614, https://doi.org/10.1002/2013WR015194.

Zhang, X. B., Zwiers F. W., Li, G. L., Wan, H., Cannon, A. J., 2017: Complexity in estimating past and future extreme short-duration rainfall. Nat. Geosci., 10, 255-239, https://doi.org/10.1038/NGEO2911.

Analysis of extreme snow events is of great importance in many areas, including risk management and structural design. Severe natural hazards in the mountain regions, such as avalanche, are largely due to heavy snowfall events and seasonally evolving snow-pack. Moreover, guidelines for infrastructure design should include estimates of maximum snow load, not only historically but also as projected under future climate change.

In this work, we compute annual maximum time series from daily snow depth and snow water equivalent (SWE) data at over 2000 meteorological stations across Canada from 1939- 2016. The more extensive snow depth results are converted to snow loads by applying a suitable density curve that empirically relates extreme snow depth to extreme snow load. An extreme value analysis of the latter is then conducted using the generalized extreme value (GEV) distribution, providing a description of the heavy tail behaviour of the historical snow load. Finally, return values of annual snow load are computed for a range of return periods and compared to previous estimates from the 2015 edition of the National Building Code of Canada.

In the context of climate change and projected increase in global temperature, the atmosphere’s water holding capacity is expected to increase at the Clausius-Clapeyron (C-C) rate by about 7% per 1 °C warming. Such an increase may lead to more intense extreme precipitation events and thus directly affect the probable maximum precipitation (PMP), a parameter that is often used for dam safety and civil engineering purposes. We therefore use a statistically motivated approach that quantifies uncertainty and accounts for nonstationarity, which allows us to determine the rate of change of PMP per 1 °C warming. This approach, which is based on a bivariate extreme value model of precipitable water (PW) and precipitation efficiency (PE), provides interpretation of how PW and PE may evolve in a warming climate. Nonstationarity is accounted for in this approach by including temperature as a covariate in the bivariate extreme value model. The approach is demonstrated by evaluating and comparing projected changes to 6-hourly PMP from two Canadian regional climate models (RCMs), CanRCM4 and CRCM5, over North America. The main results suggest that, on the continental scale, PMP increases in these models at a rate of approximately 4% per 1 °C warming, which is somewhat lower than the C-C rate. At the continental scale, PW extremes increase on average at the rate of 5% per 1 °C near surface warming for both RCMs. Most of the PMP increase is caused by the increase in PW extremes with only a minor contribution from changes in PE extremes. Nevertheless, substantial deviations from the average rate of change in PMP rates occur in some areas, and these are mostly caused by sensitivity of PE extremes to near surface warming in these regions.