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.

Canadian snow and sea ice: historical trends and projections - Mudryk, Lawrence; Derksen, Chris; Howell, Stephen E. L.; Laliberté, Frédéric; Thackeray, Chad W.; Sospedra-Alfonso, Reinel; Vionnet, Vincent; Kushner, Paul J.; Brown, Ross (The Cryosphere, 2018)
The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020–2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10% per decade (or 15–30% in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10% per decade (30% in total) are projected across southern Canada.

Forcing the Penman-Montheith Formulation with Humidity, Radiation, and Wind Speed Taken from Reanalyses, for Hydrologic Modeling - Ricard, Simon; Anctil, François (Water, 2019)
The Penman-Monteith reference evapotranspiration (ET0) formulation was forced with humidity, radiation, and wind speed (HRW) fields simulated by four reanalyses in order to simulate hydrologic processes over six mid-sized nivo-pluvial watersheds in southern Quebec, Canada. The resulting simulated hydrologic response is comparable to an empirical ET0 formulation based exclusively on air temperature. However, Penman-Montheith provides a sounder representation of the existing relations between evapotranspiration fluctuations and climate drivers. Correcting HRW fields significantly improves the hydrologic bias over the pluvial period (June to November). The latter did not translate into an increase of the hydrologic performance according to the Kling-Gupta Efficiency (KGE) metric. The suggested approach allows for the implementation of physically-based ET0 formulations where HRW observations are insufficient for the calibration and validation of hydrologic models and a potential reinforcement of the confidence affecting the projection of low flow regimes and water availability.

A Stochastic Data‐Driven Ensemble Forecasting Framework for Water Resources: A Case Study Using Ensemble Members Derived From a Database of Deterministic Wavelet‐Based Models - Quilty, John; Adamowski, Jan; Boucher, Marie‐Amélie (Water Resources Research, 2019)
Abstract In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand [UWD], etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature). , Key Points A stochastic data‐driven ensemble framework is introduced for probabilistic water resources forecasting Ensemble member selection and weighting uncertainties are explicitly considered alongside input data and model output uncertainties Wavelet‐based model outputs are used as input to the framework for an urban water demand forecasting study outperforming benchmark methods

Toward an Operational Water Cycle Prediction System for the Great Lakes and St. Lawrence River - Durnford, Dorothy; Fortin, Vincent; Smith, Gregory C.; Archambault, B.; Deacu, Daniel; Dupont, Frédéric; Dyck, Sarah; Martinez, Y.; Klyszejko, Erika; MacKay, M.; Liu, L.; Pellerin, Pierre; et al. (Bulletin of the American Meteorological Society, 2017)
AbstractIn this time of a changing climate, it is important to know whether lake levels will rise, potentially causing flooding, or river flows will dry up during abnormally dry weather. The Great Lakes region is the largest freshwater lake system in the world. Moreover, agriculture, industry, commerce, and shipping are active in this densely populated region. Environment and Climate Change Canada (ECCC) recently implemented the Water Cycle Prediction System (WCPS) over the Great Lakes and St. Lawrence River watershed (WCPS-GLS version 1.0) following a decade of research and development. WCPS, a network of linked models, simulates the complete water cycle, following water as it moves from the atmosphere to the surface, through the river network and into lakes, and back to the atmosphere. Information concerning the water cycle is passed between the models. WCPS is the first short-to-medium-range prediction system of the complete water cycle to be run on an operational basis anywhere. It currently produces ...

The Canadian Land Data Assimilation System (CaLDAS): Description and Synthetic Evaluation Study - Carrera, Marco L.; Bélair, Stéphane; Bilodeau, Bernard (Journal of Hydrometeorology, 2015)
AbstractThe Canadian Land Data Assimilation System (CaLDAS) has been developed at the Meteorological Research Division of Environment Canada (EC) to better represent the land surface initial states in environmental prediction and assimilation systems. CaLDAS is built around an external land surface modeling system and uses the ensemble Kalman filter (EnKF) methodology. A unique feature of CaLDAS is the use of improved precipitation forcing through the assimilation of precipitation observations. An ensemble of precipitation analyses is generated by combining numerical weather prediction (NWP) model precipitation forecasts with precipitation observations. Spatial phasing errors to the NWP first-guess precipitation forecasts are more effective than perturbations to the precipitation observations in decreasing (increasing) the exceedance ratio (uncertainty ratio) scores and generating flatter, more reliable ranked histograms. CaLDAS has been configured to assimilate L-band microwave brightness temperature TB ...

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme - Alavi, Nasim; Bélair, Stéphane; Fortin, Vincent; Zhang, Shunli; Husain, Syed Zahid; Carrera, Marco L.; Abrahamowicz, Maria (Journal of Hydrometeorology, 2016)
AbstractA new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in highe...

Evaluation of probable maximum snow accumulation: Development of a methodology for climate change studies. - Klein, Iris Maria; Rousseau, Alain N.; Frigon, Anne; Freudiger, Daphné; Gagnon, Patrick (Journal of Hydrology, 2016)
Summary Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation. This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability.
Keywords: Rousseau_Alain

Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions - Champagne, Catherine; White, Jenelle; Berg, Aaron A.; Bélair, Stéphane; Carrera, Marco L. (Remote Sensing, 2019)
Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change - Rees, Ee; Ng, V; Gachon, P; Mawudeku, A; McKenney, D; Pedlar, J; Yemshanov, D; Parmely, J; Knox, J (Canada Communicable Disease Report, 2019)
Keywords: Gachon_Philippe

Quantitative and qualitative assessment of the impact of climate change on a combined sewer overflow and its receiving water body - Gooré Bi, Eustache; Monette, Frédéric; Gachon, Philippe; Gaspéri, Johnny; Perrodin, Yves (Environmental Science and Pollution Research, 2015)
Projections from the Canadian Regional Climate Model (CRCM) for the southern part of the province of Québec, Canada, suggest an increase in extreme precipitation events for the 2050 horizon (2041–2070). The main goal of this study consisted in a quantitative and qualitative assessment of the impact of the 20 % increase in rainfall intensity that led, in the summer of 2013, to overflows in the “Rolland-Therrien” combined sewer system in the city of Longueuil, Canada. The PCSWMM 2013 model was used to assess the sensitivity of this overflow under current (2013) and future (2050) climate conditions. The simulated quantitative variables (peak flow, QCSO, and volume discharged, VD) served as the basis for deriving ecotoxicological risk indices and event fluxes (EFs) transported to the St. Lawrence (SL) River. Results highlighted 15 to 500 % increases in VD and 13 to 148 % increases in QCSO by 2050 (compared to 2013), based on eight rainfall events measured from May to October. These results show that (i) the relationships between precipitation and combined sewer overflow variables are not linear and (ii) the design criteria for current hydraulic infrastructure must be revised to account for the impact of climate change (CC) arising from changes in precipitation regimes. EFs discharged into the SL River will be 2.24 times larger in the future than they are now (2013) due to large VDs resulting from CC. This will, in turn, lead to excessive inputs of total suspended solids (TSSs) and tracers for numerous urban pollutants (organic matter and nutrients, metals) into the receiving water body. Ecotoxicological risk indices will increase by more than 100 % by 2050 compared to 2013. Given that substantial VDs are at play, and although CC scenarios have many sources of uncertainty, strategies to adapt this drainage network to the effects of CC will have to be developed.
Keywords: Gachon_Philippe

Ensemble Projections of Regional Climatic Changes over Ontario, Canada - Wang, Xiuquan; Huang, Guohe; Liu, Jinliang; Li, Zhong; Zhao, Shan (Journal of Climate, 2015)
AbstractIn this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average tempera...

Potential impact of climate change on the risk of windthrow in eastern Canada’s forests - Saad, Christian; Boulanger, Yan; Beaudet, Marilou; Gachon, Philippe; Ruel, Jean-Claude; Gauthier, Sylvie (Climatic Change, 2017)
Climate change is likely to affect windthrow risks at northern latitudes by potentially changing high wind probabilities and soil frost duration. Here, we evaluated the effect of climate change on windthrow risk in eastern Canada’s balsam fir (Abies balsamea [L.] Mill.) forests using a methodology that accounted for changes in both wind speed and soil frost duration. We used wind speed and soil temperature projections at the regional scale from the CRCM5 regional climate model (RCM) driven by the CanESM2 global climate model (GCM) under two representative concentration pathways (RCP4.5, RCP8.5), for a baseline (1976–2005) and two future periods (2041–2070, 2071–2100). A hybrid mechanistic model (ForestGALES) that considers species resistance to uprooting and wind speed distribution was used to calculate windthrow risk. An increased risk of windthrow (3 to 30%) was predicted for the future mainly due to an increased duration of unfrozen soil conditions (by up to 2 to 3 months by the end of the twenty-first century under RCP8.5). In contrast, wind speed did not vary markedly with a changing climate. Strong regional variations in wind speeds translated into regional differences in windthrow risk, with the easternmost region (Atlantic provinces) having the strongest winds and the highest windthrow risk. Because of the inherent uncertainties associated with climate change projections, especially regarding wind climate, further research is required to assess windthrow risk from the optimum combination of RCM/GCM ensemble simulations.
Keywords: Gachon_Philippe

Use of beta regression for statistical downscaling of precipitation in the Campbell River basin, British Columbia, Canada - Mandal, Sohom; Srivastav, Roshan; Simonovic, Slobodan P. (Journal of Hydrology, 2016)
Summary Impacts of global climate change on water resources systems are assessed by downscaling coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based downscaling method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to downscaling daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed downscaling model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based downscaling model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed downscaling model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system - Kushner, Paul J.; Mudryk, Lawrence; Merryfield, William J.; Ambadan, Jaison Thomas; Berg, Aaron A.; Bichet, Adeline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; et al. (The Cryosphere, 2018)
The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

Projected changes in the dynamics of flood hazard in the Grand River Basin, Canada. - Gaur, Abhishek; Simonovic, Slobodan P. (British Journal of Environment and Climate Change, 2015)
In this study future flooding frequencies have been estimated for the Grand River catchment located in south - western Ontario, Canada. Historical and future climatic projections made by fifteen Coupled Model Inter - comparison Project - 3 climate models are bias - corrected and downscaled before they are used to obtain mid - and end of 21 st century streamflow projections. By comparing the future projected and historically observed precipitation and temperature record s it is found that the mean and extreme temperature events will intensify in future across the catchment. The increase is more drastic in the case of extreme events than the mean events. The sign of change in future precipitation is uncertain. Further flow extremes are expected to increase in magnitude and frequency in future across the catchment. The confidence in the projection is more for low return period (<10 years) extreme events than higher return period (10 - 100 years) events. It can be expected that increases in temperature will play a dominant role in increasing the magnitude of low return period flooding events while precipitation seems to play an important role in shaping the high return period events.

The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada - Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René (Journal of Hydrology, 2017)
Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Quebec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.
Keywords: Rousseau_Alain

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada - Eum, Hyung-Il; Gachon, Philippe; Laprise, René (Advances in Meteorology, 2016)
This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.
Keywords: Gachon_Philippe

Spaceborne L-Band Radiometry in Environment and Climate Change Canada (ECCC)’S Numerical Analysis and Prediction Systems - Belair, Stephane; Carrera, Marco L.; Abrahamowicz, Maria; Alavi, Nasim; Badawy, Bakr; Shahabadi, Maziar Bani; Bilodeau, Bernard; Charpentier, Dorothee; Deacu, Daniel; Durnford, Dorothy; Gaborit, Etienne; Gasset, Nicolas (IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019)

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices - Joshi, Deepti; St-Hilaire, André; Ouarda, Taha B. M. J.; Daigle, Anik; Thiémonge, Nathalie (Hydrological Sciences Journal-journal Des Sciences Hydrologiques, 2016)
ABSTRACTThis work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other a...

Adaptation des outils PHYSITEL/HYDROTEL au milieu boréal québécois : modélisation des processus hydrologiques et analyses de sensibilité et d’incertitudes. - Rousseau, Alain N.; Rousseau, A.N.; Mailhot, Alain; St-Hilaire, André (2015)
Les bassins versants du Moyen‐Nord quebecois (49e au 55e parallele) se distinguent par leur climatologie et le pourcentage eleve de territoires couverts par des lacs et milieux humides (de l’ordre de 20 a 30 %) et, surtout, par leur importante contribution a la production electrique du Quebec; le complexe de la riviere La Grande generant environ 40% de l’electricite quebecoise. Dans le contexte de la gestion de la production d’electricite, Hydro‐Quebec Production fait la prevision des apports aux reservoirs de ce complexe a l’aide d’un modele hydrologique global. Par ailleurs, depuis les annees 1980, le milieu boreal quebecois a subi des hausses de temperature et de precipitation qui ont modifie le regime des apports aux reservoirs. Compte tenu de ces changements et des caracteristiques physiographiques des bassins boreaux, il a ete propose d’utiliser un modele hydrologique distribue a base physique pour examiner l’impact sur ces apports des projections climatiques produites par Ouranos. En l’occurrence le modele HYDROTEL dont la prise en mains est en train d’etre completee par Hydro‐Quebec Production. Le modele qui est maintenant convenablement cale pour un certain nombre de bassins repond aux attentes dans les bassins du sud du Quebec. Toutefois, pour les grands bassins du Nord comme ceux du Complexe La Grande, l’utilisation du modele requiert des travaux d’adaptations, entre autres, aux niveaux de la modelisation des milieux humides et de la desagregation spatiale des precipitations simulees par les modeles climatiques. Les objectifs generaux de ce projet etaient d’accroitre notre comprehension de l’hydrologie du moyen nord afin qu’elle soit bien representee dans HYDROTEL tout en tenant compte des incertitudes parametriques associees aux differentes equations gouvernant les processus physiques. Ces objectives ont ete declines en trois activites de travail : (AT1) modelisation des processus hydrologiques; (AT2) calage et analyses de sensibilite, d’identifiabilite et d’incertitudes des parametres de calage d’HYDROTEL; et (AT3) amelioration des plateformes informatiques HYDROTEL et PHYSITEL, ce dernier etant un SIG dedie a la construction des bases de donnees de modeles hydrologiques distribues. Pour Ouranos et Hydro‐Quebec les principales realisations issues de ce projet incluent : (i) le developpement d’une methode eprouvee de desagregation sous grille de la precipitation mesoechelle permettant d’evaluer a fine echelle spatiale l’impact des changements climatiques sur les precipitations; (ii) une meilleure comprehension de la dynamique des ecoulements, du stockage de l’eau et de l’evapotranspiration d’un petit bassin versant boreal incluant une grande une tourbiere minerotrophe aqualysee; (iii) l’evaluation du parametrage de la sublimation et la relocalisation de la neige dues au vent et l’identification du besoin d’inclure le rayonnement sous la canopee pour bien reproduire la crue avec un modele complexe de l'evolution du couvert nival; (iv) la detection de la quasi neutralite frequente (~76% du temps, majoritairement le jour) de l’atmosphere au‐dessus d’un milieu humide causee par une turbulence mecanique forte et une grande inertie thermique; conditions ayant permises le developpement d’un modele simple d’evapotranspiration des milieux humides base le transfert massique et la stabilite atmospherique; (v) le developpement d’un modele de rayonnement net base uniquement sur des donnees de temperatures journalieres (min, max) et une estimation des parametres permettant de valider l’utilisation de l’equation de Penman‐Monteith dans le nord quebecois; (vi) la hierarchisation des parametres de calage d’HYDROTEL selon la saison et le developpement d’une methode permettant d’evaluer l’incertitude sur les debits simules et d’identifier son importance durant la fonte et l’etiage estival; (vii) dans un contexte d’analyse frequentielle des debits simules, evaluation de l’incertitude parametrique par rapport a l’incertitude statistique, cette derniere dominant pour les periodes de retour superieures a cinq ans; (viii) a l’aide de PHYSITEL, la premiere discretisation du complexe de la riviere La Grande (136 648 km2) en six sousbassins (LG1, LG2, LG3, LG4, La Forge 1 & 2,et Caniapiscau) leur subdivision en versants permettant le calcul de crues maximales probables a l’aide d’HYDROTEL; et (ix) le developpement d’une version 64 bits d’HYDROTEL incluant de nouveaux modules de de calculs de la temperature du sol et des bilans hydriques des milieux humides et isoles. L'avancement de nos comprehensions de l'hydrologie des milieux humides et du milieu boreal en general a ete a la base du developpement des versions adaptees d'HYDROTEL et de PHYSITEL qui permettront a Hydro‐Quebec d'apprehender, avec une modelisation distribuee, l'impact des changements climatiques sur le complexe de la riviere La Grande. Ces logiciels sont transposables a l’ensemble du milieu boreal canadien. Une entente conclut, depuis 2005, entre l’INRS et Hydro‐Quebec (HQ) permet d’ailleurs une distribution commerciale des differentes versions d’HYDROTEL avec interfaces usagers de meme qu’une distribution communautaire du noyau de calcul. Cette synergie a permis de mettre en commun des ressources et des expertises qui facilitent les echanges scientifiques et techniques entre les concepteurs d’HYDROTEL, le Centre d’expertise hydrique du Quebec (CEHQ), HQ, l’IREQ (Institut de recherche en electricite du Quebec) et d’autres usagers (ex. : l’IMTA, Instituto Mexicano de Technologia del Agua). Au total, plus d’une quarantaine de licences ont ete distribuees tant pour des besoins d’enseignement (Universite de Sherbrooke) et de recherche (Universite Laval, UQTR, UQAC, IREQ, Ecole de Technologie Superieure, INRA de Montpellier, Environnement Canada, Agriculture et Agroalimentaire Canada), que des besoins de prevision hydrologique (IMTA, Ville de Quebec, Centre d’expertise hydrique du Quebec, HQ). La modularite informatique d’HYDROTEL se prete egalement bien a cette synergie car elle offre la possibilite de partager le savoir‐faire et, par l’entremise d’un site internet public (CodePlex), de mettre a la disponibilite de tous les nouvelles versions du noyau de calcul. Ces developpements ont permis a l’equipe de l’INRS‐ETE d’acquerir une reconnaissance internationale en modelisation hydrologique distribuee. En effet, HYDROTEL et PHYSITEL ont dans le passe ete identifie comme les outils a utiliser dans le cadre d’appels de proposition de projets de determination du potentiel hydroelectrique finances par la Banque Mondiale [World Bank, 2009].
Keywords: Rousseau_Alain

Future Changes in Flood Hazards across Canada under a Changing Climate - Gaur, Ayushi; Gaur, Abhishek; Simonovic, Slobodan P. (Water, 2018)
Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.