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

The 2019 Global Assessment Report on Disaster Risk Reduction (GAR) is informed by the latest data – including Sendai Framework target reporting by countries using the Sendai Framework Monitor

Abstract The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.

ABSTRACTTrends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These in...

Quantile estimates are generally interpreted in association with the return period concept in practical engineering. To do so with the peaks‐over‐threshold (POT) approach, combined Poisson‐generalized Pareto distributions (referred to as PD‐GPD model) must be considered. In this article, we evaluate the incorporation of non‐stationarity in the generalized Pareto distribution (GPD) and the Poisson distribution (PD) using, respectively, the smoothing‐based B‐spline functions and the logarithmic link function. Two models are proposed, a stationary PD combined to a non‐stationary GPD (referred to as PD0‐GPD1) and a combined non‐stationary PD and GPD (referred to as PD1‐GPD1). The teleconnections between hydro‐climatological variables and a number of large‐scale climate patterns allow using these climate indices as covariates in the development of non‐stationary extreme value models. The case study is made with daily precipitation amount time series from southeastern Canada and two climatic covariates, the Arctic Oscillation (AO) and the Pacific North American (PNA) indices. A comparison of PD0‐GPD1 and PD1‐GPD1 models showed that the incorporation of non‐stationarity in both POT models instead of solely in the GPD has an effect on the estimated quantiles. The use of the B‐spline function as link function between the GPD parameters and the considered climatic covariates provided flexible non‐stationary PD‐GPD models. Indeed, linear and nonlinear conditional quantiles are observed at various stations in the case study, opening an interesting perspective for further research on the physical mechanism behind these simple and complex interactions.

ABSTRACT Two composite sedimentary sequences sampled in the ice‐proximal (12CS) and ice‐distal (02CS) areas of Coronation Fjord (Baffin Island, Nunavut, Canada) were investigated in order to reconstruct the effect of climate variability on 600 years of changes in sediment transfer from the eastern Penny Ice Cap (PIC). Detrital proxies, and physical and sedimentological analyses revealed that glacial meltwater discharges led to frequent rapidly deposited layers (RDLs) in ice‐proximal settings. RDLs in ice‐distal settings involved the sudden release of a large quantity of sediment‐laden water during floods probably originating from adjacent fjords with large sandur deltas. Laminated sediments with ice‐rafted debris throughout the Little Ice Age interval in the ice‐proximal environment suggest that colder conditions promoted glacier growth, leading to successive episodes of turbid hyperpycnal meltwater plumes and iceberg calving in Coronation Fjord. Since 1850 ce , the accelerated Coronation retreat in response to modern warming has led to increased sedimentation rates, abrupt mineralogical and grain size proxy variations and more frequent RDLs. Similar trends between the detrital proxies of the ice‐proximal core and Atlantic Multidecadal Oscillation record and Arctic surface air temperature suggest high connectivity between atmospheric and sea surface temperature variations and PIC dynamics over the last 600 years.

Peatlands are relatively common in the province of Quebec (Canada) where they occupy about 12% of the surface. The hydrology of peatlands remains insufficiently documented, more specifically during the spring period where data are currently lacking in many regions, including in the Quebec boreal territory. The paucity of spring data are due to snowmelt that causes flooding in peatlands and along rivers, which makes hydrometry complicated during this period of the year. In this paper, the Peatland Hydrological Impact Model (PHIM) was coupled with a snowmelt module (CemaNeige) to simulate spring flows in an ombrotrophic peatland located in the Romaine River watershed (Quebec). Discharge data from two summer seasons (2019 and 2020) were used to calibrate the hydrological model. Despite the relatively short time series, the results show a good performance. The simulated spring flows resulting from the PHIM + CemaNeige combination are of the right order of magnitude.

Over the past decades, a variety of ice control structures (ICSs) have been designed and built, but to date, there has been no systematic evaluation of the effectiveness of these structures. To achieve this objective, first an understanding of the interaction between different ice processes and the ICSs must be established. For this purpose, a total of four ICSs located in the province of Québec were monitored during the 2021–2022 winter. The results showed that the ice jam holding time could vary from 1.5 to 68.5 h. The release of the jam was mechanically driven when the ratio of release to initiation Froude number was higher than one and was thermally driven when this ratio was lower than one, and the water temperature increased between initiation and release. Also, as the ratio of the total pier spacing to upstream river width increased, the holding time decreased.

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.

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.