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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.

Abstract. The potential impact of future climate change on runoff generation processes in two southern British Columbia catchments was explored using the Canadian Centre for Climate Modelling Analysis General Circulation Model (CGCMa1) to estimate future changes in precipitation, temperature and cloud cover while the U.B.C. Watershed Model was used to simulate discharges and quantify the separate runoff components, i.e. rainfall, snowmelt, glacier melt and groundwater. Changes, not only in precipitation and temperature but also in the spatial distribution of precipitation with elevation, cloud cover, glacier extension, altitude distribution of vegetation, vegetation biomass production and plant physiology were considered. The future climate of the catchments would be wetter and warmer than the present. In the maritime rain-fed catchment of the Upper Campbell, runoff from rainfall is the most significant source of flow for present and future climatic conditions in the autumn and winter whereas runoff from groundwater generates the flow in spring and summer, especially for the future climate scenario. The total runoff, under the future climatic conditions, would increase in the autumn and winter and decrease in spring and summer. In contrast, in the interior snow-covered Illecillewaet catchment, groundwater is the most significant runoff generation mechanism in the autumn and winter although, at present, significant flow is generated from snowmelt in spring and from glacier runoff in summer. In the future scenario, the contribution to flow from snowmelt would increase in winter and diminish in spring while the runoff from the glacier would remain unchanged; groundwater would then become the most significant source of runoff, which would peak earlier in the season. Keywords: climatic change, hydrological simulation, rainfall, snowmelt, runoff processes

The causes of peak flows in two climatically different mountainous-forested basins of British Columbia have been identified. The U.B.C. watershed model was used to identify the causes of peak flows, since this model separately calculates the runoff components, i.e. rainfall, snowmelt and glacier runoff. The results showed that the flood flows in the maritime basin of Upper Campbell are mainly generated by rainfall during the fall months and winter rain-on-snow events. Rainfall runoff constitutes the largest percentage of peak flow for all types of events. On the other hand, the flood flows in the inland basin of Illecillewaet are mainly produced by spring rain and snowmelt events, snowmelt events alone and summer events when runoff from the glacier melt contributes to peak discharge. However, snowmelt runoff is the dominant component of peak flows. Based on these findings, flood frequency analysis showed that considering the flow component frequency distributions marginally improves the probability distribution flows in the two examined watersheds.

Abstract The consensus around the need for a shift in river management approaches to include more natural processes is steadily growing amongst scientists, practitioners, and governmental agencies. The freedom space for rivers concept promotes the delineation of a single space that integrates multiple fluvial dynamics such as floods, lateral migration, channel avulsions, and riparian wetlands connectivity. The objective of this research is to assess the validity of the hydrogeomorphological approach to delineate the freedom space for an extensive sampling of river reaches, covering 167 km, in contrasting watersheds in Quebec (Canada). Comparative analysis was conducted on the relative importance of erosion and flood processes on the freedom space delineation for various fluvial types. Semiautomated tools based on light detection and ranging (LiDAR) digital elevation models were also tested on an additional 274 km of watercourses to facilitate freedom space mapping over extensive zones and for highly dynamics environments such as alluvial fans. In the studied reaches, flood and erosion processes occur respectively, on average, in a space equivalent to 2.6 and 20.6 channel widths. In unconfined landscapes, flood processes represent an area up to almost four times the area of erosion processes expected in a 50‐year period. In partly confined and confined environments, erosion processes are more likely to exceed flooding zone, and therefore need to be integrated in the mapping. This study helps better determine the conditions for which the full methodology of freedom space mapping is required or where semiautomated methods can be used. It provides useful guidelines for the implementation of the freedom space approach.

The convection-permitting climate model (CPCM), WRF-ARW at 4 km resolution, is able to capture the observed relationships between precipitation extremes and temperature (PT scaling) in western Canada. By analyzing the CPCM simulated PT scalings, we found they have robust patterns at different percentiles of precipitation intensity and even between the current and future climate. This is due to the stable annual cycle of the regional climate. The PT scaling pattern is physically governed by the amount of water vapour and the ascending velocity of air. Approximately 95% of the precipitation intensity variation can be explained by the vertical velocity and precipitable water in western Canada. The PT scaling for the current climate does not tell how precipitation extremes would response to a warmer climate. Trend scaling theory was utilized to estimate the intensification of precipitation extremes in a warmer climate. It shows that, in western Canada, the coast is particularly vulnerable to precipitation extremes under global warming. Precipitation extremes are projected to increase at a super Clausius-Clapeyron (CC) scale over the coast, approximately at a CC scale over the prairies and mountains, and a sub-CC scale over the northern region. The warming effect on precipitation extremes is even stronger when the concept of”wet-day trend scaling” is introduced.

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.

A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

Abstract Retrospective estimation of daily streamflow for all rivers within a territory is of practical interest for sustainable and optimal water management. This implies, however, the availability of methods for providing accurate estimations of flow for ungauged rivers. This study compares the potential of statistical interpolation (SI)—a simple data assimilation technique that combines observations and simulations from hydrological modelling—with four other approaches: nearest neighbour, direct use of outputs from hydrological modelling, ordinary and topological kriging. Through subsampling cross-validation analyses based on the modified Kling-Gupta efficiency indicator, we show that SI compares favourably with these other approaches. While the performance of other methods depends on the configuration of the ungauged site in regards to the neighbouring reference sites, SI is less affected by these configurations. SI outperforms the other approaches particularly where the ungauged site is relatively distant from observation sites. In these cases, SI performance depends on the performance of the background model that relies on simulations of hydrological processes forced by precipitation and temperature observations. Our findings offer the potential for heightened performance estimates through an improvement of hydrological modelling and the use of more complex assimilation techniques for exploiting the model.

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.

Climate variability influences the availability of water resources throughout Canada, and projected climate change is anticipated to affect future water availability. This is the first paper of a three-part analysis of water availability indicators in Canada (Parts II and III, this issue). The concept of water availability has been described in different ways in the literature. In Part I, the various approaches for estimating water availability are reviewed and compared, with a focus on Canadian studies. Global examples are used when necessary. The approaches to estimate water availability are organized into three categories: (1) climate-based indicators, (2) hydrology-based indicators and (3) water demand/supply-based indicators. Climate-based indicators use variables such as precipitation, and potential or actual evapotranspiration to calculate water budgets. Widely used meteorological drought indices that calculate moisture surpluses and deficits are also examined. Hydrology-based indicators focus on v...

This study quantified the contributions of overland and tile flow to total runoff (sum of overland and tile flow) and nutrient losses in a Vertisolic soil in the Red River valley (Manitoba, Canada), a region with a cold climate where tile drainage is rapidly expanding. Most annual runoff occurred as overland flow (72–89%), during spring snowmelt and large spring and summer storms. Tile drains did not flow in early spring due to frozen ground. Although tiles flowed in late spring and summer (33–100% of event flow), this represented a small volume of annual runoff (10–25%), which is in stark contrast with what has been observed in other tile‐drained landscapes. Median daily flow‐weighted mean concentrations of soluble reactive P (SRP) and total P (TP) were significantly greater in overland flow than in tile flow ( p < 0.001), but the reverse pattern was observed for NO 3 –N ( p < 0.001). Overland flow was the primary export pathway for both P and NO 3 –N, accounting for >95% of annual SRP and TP and 50 to 60% of annual NO 3 –N losses. Data suggest that tile drains do not exacerbate P export from Vertisols in the Red River valley because they are decoupled from the surface by soil‐ice during snowmelt, which is the primary time for P loss. However, NO 3 –N loading to downstream water bodies may be exacerbated by tiles, particularly during spring and summer storms after fertilizer application. Core Ideas Overland flow was the primary pathway for runoff and nutrient loss at field edge. Most runoff and nutrient loss occurred during spring snowmelt and rain events. Tile drains are unlikely to exacerbate P losses from Vertisolic soils. Tile drains may enhance N loading in this region.

Abstract. Glacier mass balance models are needed at sites with scarce long-term observations to reconstruct past glacier mass balance and assess its sensitivity to future climate change. In this study, North American Regional Reanalysis (NARR) data were used to force a physically based, distributed glacier mass balance model of Saskatchewan Glacier for the historical period 1979–2016 and assess its sensitivity to climate change. A 2-year record (2014–2016) from an on-glacier automatic weather station (AWS) and historical precipitation records from nearby permanent weather stations were used to downscale air temperature, relative humidity, wind speed, incoming solar radiation and precipitation from the NARR to the station sites. The model was run with fixed (1979, 2010) and time-varying (dynamic) geometry using a multitemporal digital elevation model dataset. The model showed a good performance against recent (2012–2016) direct glaciological mass balance observations as well as with cumulative geodetic mass balance estimates. The simulated mass balance was not very sensitive to the NARR spatial interpolation method, as long as station data were used for bias correction. The simulated mass balance was however sensitive to the biases in NARR precipitation and air temperature, as well as to the prescribed precipitation lapse rate and ice aerodynamic roughness lengths, showing the importance of constraining these two parameters with ancillary data. The glacier-wide simulated energy balance regime showed a large contribution (57 %) of turbulent (sensible and latent) heat fluxes to melting in summer, higher than typical mid-latitude glaciers in continental climates, which reflects the local humid “icefield weather” of the Columbia Icefield. The static mass balance sensitivity to climate was assessed for prescribed changes in regional mean air temperature between 0 and 7 ∘C and precipitation between −20 % and +20 %, which comprise the spread of ensemble Representative Concentration Pathway (RCP) climate scenarios for the mid (2041–2070) and late (2071–2100) 21st century. The climate sensitivity experiments showed that future changes in precipitation would have a small impact on glacier mass balance, while the temperature sensitivity increases with warming, from −0.65 to −0.93 m w.e. a−1 ∘C−1. The mass balance response to warming was driven by a positive albedo feedback (44 %), followed by direct atmospheric warming impacts (24 %), a positive air humidity feedback (22 %) and a positive precipitation phase feedback (10 %). Our study underlines the key role of albedo and air humidity in modulating the response of winter-accumulation type mountain glaciers and upland icefield-outlet glacier settings to climate.

Abstract Longwave radiation (LR) is one of the energy balance components responsible for warming and cooling water during hot summers. Both downward incoming LR, emitted by the atmosphere, and outgoing LR emitted by the land surface are not widely measured. The influence of clouds on the LR heat budget makes it even harder to establish reliable formulations for all-sky conditions. This paper uses air temperature and cloud cover from the ERA5 reanalysis database to compare 20 models for the downward longwave irradiance (DLI) at Earth’s surface and compare them with ERA5’s DLI product. Our work uses long-time continuous DLI measured data at three stations over Canada, and ERA5 reanalysis, a reliable source for data-scarce regions, such as central British Columbia (Canada). The results show the feasibility of the local calibration of different formulations using ERA5 reanalysis data for all-sky conditions with RMSE metrics ranging from 37.1 to 267.3 W m −2 , which is comparable with ERA5 reanalysis data and can easily be applied at broader scales by implementing it into hydrological models. Moreover, it is shown that ERA5 gridded data for DLI shows the best results with RMSE = 31.7 W m −2 . This higher performance suggests using ERA5 data directly as input data for hydrological and ecological models.

Given that flooding episodes are occurring at a greater rate due to climate change, individuals must adopt certain adaptation behaviors to prevent or mitigate the anticipated or negative impact of such events. However, few studies have assessed if and how households and individuals have actually taken action in this regard. Because some individual beliefs can be linked to facilitating factors and barriers to action, a better understanding of the adoption of adaptive behaviors requires a combined analysis of individual psychosocial factors. The purpose of this study was to develop a better understanding of the reasons underlying the adoption of behaviors related to structural adaptation to flooding by people living in or near flood-prone areas in the Province of Québec (Canada). Results of a series of structural equation modeling showed that behavioral, normative and control beliefs were all significant predictors of the respondents' intention to adopt structural flood protective behaviors, with normative beliefs being the strongest. By identifying the best psychosocial predictors of the adoption of such behaviors, the results of this study provide valuable insights regarding the most effective factors to be used in public health messages to promote the adoption of behaviors related to structural adaptation to flooding.

Quantification of climate change impacts on the thermal regimes of rivers in British Columbia (BC) is crucial given their importance to aquatic ecosystems. Using the Air2Stream model, we investigate the impact of both air temperature and streamflow changes on river water temperatures from 1950 to 2015 across BC’s 234,000 km2 Fraser River Basin (FRB). Model results show the FRB’s summer water temperatures rose by nearly 1.0°C during 1950–2015 with 0.47°C spread across 17 river sites. For most of these sites, such increases in average summer water temperature have doubled the number of days exceeding 20°C, the water temperature that, if exceeded, potentially increases the physiological stress of salmon during migration. Furthermore, river sites, especially those in the upper and middle FRB, show significant associations between Pacific Ocean teleconnections and regional water temperatures. A multivariate linear regression analysis reveals that air temperature primarily controls simulated water temperatures in the FRB by capturing ~80% of its explained variance with secondary impacts through river discharge. Given such increases in river water temperature, salmon returning to spawn inthe Fraser River and its tributaries are facing continued and increasing physical challenges now and potentially into the future.

This study evaluates predictive uncertainties in the snow hydrology of the Fraser River Basin(FRB) of British Columbia(BC), Canada, using the Variable Infiltration Capacity(VIC) model forced with several high-resolution gridded climate datasets. These datasets include the Canadian Precipitation Analysis and the thin-plate smoothing splines(ANUSPLIN), North American Regional Reanalysis(NARR), University of Washington(UW) and Pacific Climate Impacts Consortium(PCIC) gridded products. Uncertainties are evaluated at different stages of the VIC implementation, starting with the driving datasets, optimization of model parameters, and model calibration during cool and warm phases of the Pacific Decadal Oscillation(PDO). The inter-comparison of the forcing datasets (precipitation and air temperature) and their VIC simulations (snow water equivalent – SWE – and runoff) reveals widespread differences over the FRB, especially in mountainous regions. The ANUSPLIN precipitation shows a considerable dry bias in the Rocky Mountains, whereas the NARR winter air temperature is 2°C warmer than the other datasets over most of the FRB. In the VIC simulations, the elevation-dependent changes in the maximum SWE(maxSWE) are more prominent at higher elevations of the Rocky Mountains, where the PCIC-VIC simulation accumulates too much SWE and ANUSPLIN-VIC yields an underestimation. Additionally, at each elevation range, the day of maxSWE varies from 10to 20days between the VIC simulations. The snow melting season begins early in the NARR-VIC simulation, whereas the PCIC-VIC simulation delays the melting, indicating seasonal uncertainty in SWE simulations. When compared with the observed runoff for the Fraser River main stem at Hope, BC, the ANUSPLIN-VIC simulation shows considerable underestimation of runoff throughout the water year owing to reduced precipitation in the ANUSPLIN forcing dataset. The NARR-VIC simulation yields more winter and spring runoff and earlier decline of flows in summer due to a nearly 15-day earlier onset of the FRB springtime snowmelt. Analysis of the parametric uncertainty in the VIC calibration process shows that the choice of the initial parameter range plays a crucial role in defining the model hydrological response for the FRB. Furthermore, the VIC calibration process is biased toward cool and warm phases of the PDO and the choice of proper calibration and validation time periods is important for the experimental setup. Overall the VIC hydrological response is prominently influenced by the uncertainties involved in the forcing datasets rather than those in its parameter optimization and experimental setups.

Wastewater surveillance for SARS-CoV-2 RNA is a relatively recent adaptation of long-standing wastewater surveillance for infectious and other harmful agents. Individuals infected with COVID-19 were found to shed SARS-CoV-2 in their faeces. Researchers around the world confirmed that SARS-CoV-2 RNA fragments could be detected and quantified in community wastewater. Canadian academic researchers, largely as volunteer initiatives, reported proof-of-concept by April 2020. National collaboration was initially facilitated by the Canadian Water Network. Many public health officials were initially skeptical about actionable information being provided by wastewater surveillance even though experience has shown that public health surveillance for a pandemic has no single, perfect approach. Rather, different approaches provide different insights, each with its own strengths and limitations. Public health science must triangulate among different forms of evidence to maximize understanding of what is happening or may be expected. Well-conceived, resourced, and implemented wastewater-based platforms can provide a cost-effective approach to support other conventional lines of evidence. Sustaining wastewater monitoring platforms for future surveillance of other disease targets and health states is a challenge. Canada can benefit from taking lessons learned from the COVID-19 pandemic to develop forward-looking interpretive frameworks and capacity to implement, adapt, and expand such public health surveillance capabilities.

Une coulee de slush (bouillie de neige fondante) est un ecoulement rapide constitue d’un melange de neige fondante, d’eau, de boue et de debris de toutes sortes. Les sept sites analyses demontrent que les coulees de slush peuvent survenir dans des contextes topographiques fort differents qui presentent toutefois des similitudes au niveau du mode d’enneigement et des conditions hydro-meteorologiques. Les coulees de slush etudiees demarrent dans des ruisseaux d’ordre 1 ou 2, etroits et peu profonds, de pente tres variable (de 1° a plus de 30°), qui sont combles par des bouchons de neige dense soufflee par le vent ou transportee par les avalanches. Parce qu’ils s’opposent a la libre circulation des eaux de fusion lors des periodes de fonte acceleree, ces bouchons de neige favorisent la saturation du manteau neigeux jusqu’a la rupture sous l’effet combine de la pression hydrostatique et de la gravite. Les onze coulees analysees, qui se sont produites entre 1936 et 2013, permettent de definir deux scenarios hydro-meteorologiques propices a leur declenchement : 1) des redoux de longue duree caracterises par des temperatures qui restent positives pendant plusieurs jours consecutifs sans apport de precipitations liquides; 2) des redoux relativement courts (moins de 48 heures) couples a des precipitations liquides abondantes. Largement meconnues au Quebec, les coulees de slush pourraient etre plus frequentes a l’avenir en reponse au rechauffement climatique en cours.