Quantification of uncertainty in the assessment of future streamflow under changing climate conditions - Mandal, Sohom; Simonovic, Slobodan P. (Hydrological Processes, 2017)
Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change.

The Role of Soil Moisture–Atmosphere Interaction on Future Hot Spells over North America as Simulated by the Canadian Regional Climate Model (CRCM5) - Diro, G. T.; Sushama, Laxmi (Journal of Climate, 2017)
AbstractSoil moisture–atmosphere interactions play a key role in modulating climate variability and extremes. This study investigates how soil moisture–atmosphere coupling may affect future extreme events, particularly the role of projected soil moisture in modulating the frequency and maximum duration of hot spells over North America, using the fifth-generation Canadian Regional Climate Model (CRCM5). With this objective, CRCM5 simulations, driven by two coupled general circulation models (MPI-ESM and CanESM2), are performed with and without soil moisture–atmosphere interactions for current (1981–2010) and future (2071–2100) climates over North America, for representative concentration pathways (RCPs) 4.5 and 8.5. Analysis indicates that, in future climate, the soil moisture–temperature coupling regions, located over the Great Plains in the current climate, will expand farther north, including large parts of central Canada. Results also indicate that soil moisture–atmosphere interactions will play an imp...

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends - Gooré Bi, Eustache; Gachon, Philippe; Vrac, Mathieu; Monette, Frédéric (Theoretical and Applied Climatology, 2017)
Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern.
Keywords: Gachon_Philippe

Estimation of large wood budgets in a watershed and river corridor at interdecadal to interannual scales in a cold‐temperate fluvial system - Boivin, Maxime; Buffin‐Bélanger, Thomas; Piégay, Hervé (Earth Surface Processes and Landforms, 2017)
Abstract Large wood (LW) is a ubiquitous feature in rivers of forested watersheds worldwide, and its importance for river diversity has been recognized for several decades. Although the role of LW in fluvial dynamics has been extensively documented, there is a need to better quantify the most significant components of LW budgets at the river scale. The purpose of our study was to quantify each component (input, accumulation, and output) of a LW budget at the reach and watershed scales for different time periods (i.e. a 50‐year period, decadal cycle, and interannual cycle). The LW budget was quantified by measuring the volumes of LW inputs, accumulations, and outputs within river sections that were finally evacuated from the watershed. The study site included three unusually large but natural wood rafts in the delta of the Saint‐Jean River (SJR; Québec, Canada) that have accumulated all LW exported from the watershed for the last 50 years. We observed an increase in fluvial dynamics since 2004, which led to larger LW recruitment and a greater LW volume trapped in the river corridor, suggesting that the system is not in equilibrium in terms of the wood budget but is rather recovering from previous human pressures as well as adjusting to hydroclimatic changes. The results reveal the large variability in the LW budget dynamics during the 50‐year period and allow us to examine the eco‐hydromorphological trajectory that highlights key variables (discharge, erosion rates, bar surface area, sinuosity, wood mobility, and wood retention). Knowledge on the dynamics of these variables improves our understanding of the historical and future trajectories of LW dynamics and fluvial dynamics in gravel‐bed rivers. Extreme events (flood and ice‐melt) significantly contribute to LW dynamics in the SJR river system. Copyright © 2017 John Wiley & Sons, Ltd.
Keywords: Boivin_Maxime

Historical Spatial and Temporal Climate Trends in Southern Ontario, Canada - Wazneh, Hussein; Arain, M. Altaf; Coulibaly, Paulin (Journal of Applied Meteorology and Climatology, 2017)
Abstract Spatial and temporal trends in historical temperature and precipitation extreme events were evaluated for southern Ontario, Canada. A number of climate indices were computed using observed and regional and global climate datasets for the area of study over the 1951–2013 period. A decrease in the frequency of cold temperature extremes and an increase in the frequency of warm temperature extremes was observed in the region. Overall, the numbers of extremely cold days decreased and hot nights increased. Nighttime warming was greater than daytime warming. The annual total precipitation and the frequency of extreme precipitation also increased. Spatially, for the precipitation indices, no significant trends were observed for annual total precipitation and extremely wet days in the southwest and the central part of Ontario. For temperature indices, cool days and warm night have significant trends in more than 90% of the study area. In general, the spatial variability of precipitation indices is much higher than that of temperature indices. In terms of comparisons between observed and simulated data, results showed large differences for both temperature and precipitation indices. For this region, the regional climate model was able to reproduce historical observed trends in climate indices very well as compared with global climate models. The statistical bias-correction method generally improved the ability of the global climate models to accurately simulate observed trends in climate indices.