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In northeastern boreal Canada, the long-term perspective on spring flooding is hampered by the absence of long gage records. Changes in the tree-ring anatomy of periodically flooded trees have allowed the reconstruction of historical floods in unregulated hydrological systems. In regulated rivers, the study of flood rings could recover past flood history, assuming that the effects of hydrological regulation on their production can be understood. This study analyzes the effect of regulation on the flood-ring occurrence (visual intensity and relative frequency) and on ring widths in Fraxinus nigra trees growing at five sites distributed along the Driftwood River floodplain. Driftwood River was regulated by a dam in 1917 that was replaced at the same location in 1953. Ring width revealed little, to no evidence, of the impact of river regulation, in contrast to the flood rings. Prior to 1917, high relative frequencies of well-defined flood rings were recorded during known flood years, as indicated by significant correlations with reconstructed spring discharge of the nearby Harricana River. After the construction and the replacement of the dam, relative frequencies of flood rings and their intensities gradually decreased. Flood-ring relative frequencies after 1917, and particularly after 1953, were mostly composed of weakly defined (less distinct) flood rings with some corresponding to known flood years and others likely reflecting dam management. The strength of the correlations with the instrumental Harricana River discharge also gradually decrease starting after 1917. Compared with upper floodplain trees, shoreline trees at each site recorded flood rings less frequently following the construction of the first but especially of the second dam, indicating that water level regulation limited flooding in the floodplains. Compared with the downstream site to the dam, the upstream ones recorded significantly more flood rings in the postdam period, reemphasizing the importance of considering the position of the site along with the river continuum and site conditions in relation to flood exposure. The results demonstrated that sampling trees in multiple riparian stands and along with various hydrological contexts at a far distance of the dams could help disentangle the flooding signal from the dam management signal.

Data include tree-ring widths and wood anatomical chronologies of Pinus banksiana and Fraxinus nigra trees growing in eastern boreal Canada, as well as the reconstructed spring mean temperature, reported in "A 247-years tree-ring reconstruction of spring temperature and relation to spring flooding in eastern boreal Canada" published in "International journal of Climatology" by Nolin et al., 2021. PIBA_FRNI_Chronos.csv, the tree-ring widths and wood anatomical chronologies (1706-2017) used in this study (species and sites are coded as in Table 1); PIBA_FRNI_SampDepth.csv, the annual replication of samples used to produce each chronologies (1706-2017); PIBA_FRNI_RecSpringTemp.csv, the reconstructed mean spring temperature (1770 to 2016) LAT_LON_SpringTemp.kml, the coordinate data for each sampling site: metadatas.txt, a set of self-explanatory instructions and descriptions for data files. All other data are available upon request to the corresponding author at alexandreflorent.nolin@uqat.ca (institutional email), alexandreflorent.nolin@gmail.com (permanent email).

Data include flood ring (F1, F2) and earlywood vessel chronologies (MVA, N) derived from black ash (Fraxinus nigra Marsh.) trees growing in eastern boreal Canada near Lake Duparquet (Quebec) reported in "Spatial coherence of the spring flood signal among major river basins of eastern boreal Canada inferred from flood rings" published in "Journal of Hydrology" by Nolin et al. in 2021. F1_F2_chrono.csv, as in Figure 3, the F1 and F2 flood-ring chronologies per sites (sites are coded as in Table 1) with sample replication (n); LAT_LON.kml, the coordinate data for each site and sampled tree; MVA_N_chrono.csv, as in Figure 5, the MVA and N chronologies per river basins (river basins are coded as in Table 1); REC1.csv, the reconstruction of the Harricana River spring discharge from 1771 to 2016 reported in "Multi-century tree-ring anatomical evidence reveals increasing frequency and magnitude of spring discharge and floods in eastern boreal Canada" published in "Global and Planetary Change" by Nolin et al. 2021. metadatas.txt, a set of self-explanatory instructions and descriptions for data files. All other data are available upon request to the corresponding author at alexandreflorent.nolin@uqat.ca (institutional email), alexandreflorent.nolin@gmail.com (permanent email).

Climate variability is recognized as an important influence on the availability of water throughout Canada, and projected climate change is anticipated to alter the amount, timing and distribution of water. This is Part II of a three-part (Parts I and III, this issue) analysis of water availability in Canada. Part II surveys current research, primarily Canadian in origin, on historical trends in climate and hydrologic indicators relevant to assessing water availability. Information on hydro-climate trends is not evenly distributed across Canada. Hydrologic trend research focuses on the North, British Columbia and the Prairies (Saskatchewan) with some research in Quebec, very little in Ontario and minimal analysis for Atlantic Canada. Overall, there is less research on trends in climatological indicators (drought, evapotranspiration, soil moisture); generally, the focus is on the Prairies. Hydrologic trends from basin-scale case studies are reported but inter-comparison is constrained by different periods ...

Abstract With the recent Coupled Model Intercomparison Project Phase 6 (CMIP6), water experts and flood modellers are curious to explore the efficacy of the new and upgraded climate models in representing flood inundation dynamics and how they will be impacted in the future by climate change. In this study, for the first time, we consider the latest group of General Circulation Models (GCMs) from CMIP6 to examine the probable changes in floodplain regimes over Canada. A set of 17 GCMs from Shared Socioeconomic Pathways (SSPs) 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1980 to 2019), near-future (2021 to 2060), and far-future (2061 to 2100) time-periods are selected. A comprehensive framework consisting of hydrodynamic flood modelling, and statistical experiments are put forward to derive high-resolution Canada-wide floodplain maps for 100 and 200-yr return periods. The changes in floodplain regimes for the future periods are analyzed over drainage basin scale in terms of (i) changes in flood inundation extents, (ii) changes in flood hazards (high and very-high classes), and (iii) changes in flood frequency. Our results show a significant rise (>30%) in flood inundation extents in the future periods; particularly intense over western and eastern regions. The flood hazards are expected to cover ~16% more geographical area of Canada. We also find that large areas in northern and western Canada and a few spots in the eastern parts of Canada will be getting flooded more frequently compared to the historical period. The observations derived from this study are vital for enhancing flood preparedness, optimal land-use planning, and refurbishing both structural and non-structural flood control options for improved resilience. The study instills new knowledge on revamping the existing flood management approaches and adaptation strategies for future protection.

Flood events and their associated damages have escalated significantly in the last few decades. To add to the gruesome situation, many reports and studies warn that flood risk would aggravate significantly in future periods due to significant alterations in the climate patterns and socio-economic dynamics. Floodplain mapping is looked upon as a viable option to tackle this global issue as it provides both quantitative and qualitative information on flood dynamics. Moreover, with the increasing availability of global data and enhancement in computational simulations, it has become easier to simlate flooding patterns at large scales. This study deter-mines the usability of publicly available datasets in capturing flood hazards over Canada. Run-off data set from the North American Regional Reanalysis (NARR), along with a few other rele-vant inputs are fed to CaMa-Flood, a robust global hydrodynamic model to generate flooding patterns for 1 in 100 and 1 in 200-yr return period events over Canada . The simulated maps are compared and validated with the existing maps of a few flood-prone regions in Canada, thereby establishing their performance over both regional and country-scale. Later, the simulated flood-plain maps are used in conjunction with property related information at 34 cities (within the top 100 populous cities in Canada) to determine the degree of exposure due to flooding in 1991, 2001, and 2011. The results indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-yr event, which is roughly 4 percent more than simulated for 100-yr event. NARR derived floodplain maps perform very well while compared over the six flood-prone regions. While analyzing the exposure of properties to flooding, we notice an in-crease in the number during the last three decades, with the maximum rise observed in Toronto, followed by Montreal, and Edmonton. To disseminate the extensive flood-related information, a web-based public tool, Flood Map Viewer (http://www.floodmapviewer.com/) is developed. The development of the tool was motivated by the commitment of the Canadian government to provide $63 M over the next three years for the completion of flood maps for higher-risk areas. The study reaches out to demonstrate how publicly available datasets can be utilized with a lesser degree of uncertainty in representing flooding patterns over large regions. The flood re-lated information derived from the study can be used along with vulnerability for quantifying flood risk, which will help in developing appropriate pathways for resilience building for long-term sustainable benefits.

Abstract In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past.

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.

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.

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...