Votre recherche

Abstract A modified hybrid terrain-following vertical coordinate has recently been implemented within the Global Environmental Multiscale atmospheric model that introduces separately controlled height-dependent progressive decaying of the small- and large-scale orography contributions on the vertical coordinate surfaces. The new vertical coordinate allows for a faster decay of the finescale orography imprints on the coordinate surfaces with increasing height while relaxing the compression of the lowest model levels over complex terrain. A number of tests carried out—including experiments involving Environment and Climate Change Canada’s operational regional and global deterministic prediction systems—demonstrate that the new vertical coordinate effectively eliminates terrain-induced spurious generation and amplification of upper-air vertical motion and kinetic energy without increasing the computational cost. Results also show potential improvements in precipitation over complex terrain.

This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041–2070 period with respect to the current 1976–2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November–March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January–May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate.

The KnnCAD Version 4 weather generator algorithm for nonparametric, multisite simulations of temperature and precipitation data is presented. The K-nearest neighbor weather generator essentially reshuffles the historical data, with replacement. In KnnCAD Version 4, a block resampling scheme is introduced to preserve the temporal correlation structure in temperature data. Perturbation of the reshuffled variable data is also added to enhance the generation of extreme values. The Upper Thames River Basin in Ontario, Canada isused as a case study and the model is shown to simulate effectively the historical characteristics at the site. The KnnCAD Version 4 approach is shown to improve on the previous versions of the model and offers a major advantage over many parametric and semiparametric weather generators in that multisite use can be easily achieved without making statistical assumptions dealing with the spatial correlations and probability distributions of each variable.

According to Department of Fisheries and Oceans Canada, culverts and other stream crossings must be designed to ensure fish passage. The effects of ice processes on these fish passage designs have never been assessed. This study is the first to document ice processes on two different types of fish passage designs (streambed simulation and baffle). The results of a 2 year field monitoring campaign showed that the culvert simulating the streambed retains a natural ice regime, i.e., both freeze-up and break-up occurred concurrently with the rest of the stream, while multiple supercooling events were recorded under a thin ice cover. As for the culvert with baffles, it was observed that the ice cover formed earlier and stayed longer in the culvert, which can create a barrier for fish transiting through them.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

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.

La modélisation numérique des estuaires hypertidaux intéresse particulièrement les ingénieurs impliqués dans la navigation maritime et le développement de projets d'énergie marémotrice. Au Québec (Canada), la majorité de ces estuaires à marée extrême sont situés dans des régions isolée de l'Arctique canadien et sont souvent des lieux de résidence des communautés autochtones du Nord canadien. La présente thèse vise à mieux comprendre les processus se manifestent dans ces environnements, avec une emphase particulière sur l'importance (1) de la forte dominance des marées, (2) de l'extrême variabilité bathymétrique et (3) de l'immense forçage climatique. La thèse tente de démontrer comment les modèles numériques peuvent être utilisés pour traiter ces particularités et peuvent être la meilleure méthode disponible pour étudier leurs effets dans des environnements éloignés peu étudies. Premièrement, dans le but d'évaluer le potentiel de courant de marée en eau libre (sans glace) de l'estuaire hypertidal de la rivière Koksoak (KRE), nous avons modélisé le débit de marée en utilisant un model numérique hydrodynamique réputé (Delft3D). Différents aspects de l'hydrodynamique côtière ont été étudiés grâce à la modélisation numérique 1D2D-3D. La variabilité spatio-temporelle de la densité de puissance hydrocinétique disponible a ensuite été quantifiée. Les résultats ont révélé l'énorme potentiel (1000 MW) d'énergie marémotrice présente à plusieurs endroits le long de l'estuaire, ce qui nécessite des études numériques plus approfondies. En mettant davantage l'accent sur la modélisation numérique du site, par exemple la publication d'un Atlas des courants de marée pour aider à la navigation maritime dans le KRE, nous avons constaté que certains problèmes de modélisation des estuaires n'étaient pas abordés. Compte tenu des conditions limites précises et des mesures in situ recueillies au cours de l'hiver 2017-2018, nous avons constaté que les meilleurs résultats pour l'étalonnage du modèle (niveau d'eau) en utilisant les paramètres/options disponibles conduisaient encore à certains ordres d'imprécision. sur les conditions aux limites de formse qualité (campagnes 2017-2018) qui ont effectivement amélioré les résultats numériques, nous avons constaté que les meilleurs résultats pour l'étalonnage du modèle (niveau d'eau) en utilisant les paramètres/options disponibles étaient encore associés à certains ordres d'imprécision. Par conséquent, l'objectif du deuxième travail était d'améliorer l'efficacité de la modélisation hydrodynamique pour les environnements de marée peu profonde. Nous avons introduit quelques hypothèses décrivant pourquoi les modèles de turbulence et de rugosité disponibles ne sont pas bien adaptés à la modélisation des estuaires avec de fortes variabilités spatiales et temporelles des profondeurs de marée. En conséquence (i) un modèle de turbulence k-ε étendu pour la paramétrisation adaptative de la viscosité turbulente en fonction de la profondeur, et une approche basée sur la direction de l'écoulement pour la paramétrisation de la rugosité du lit ont été développés, incorporés dans le modèle hydrodynamique employé (Delft3D). Le modèle modifié a montré une amélioration constante des prévisions du modèle dans les stations de champ proche et de champ lointain, par rapport aux schémas de paramétrage classiques. Enfin, un aspect manquant et mal compris des estuaires de latitude nordique est l'immense impact de l'hiver sur le flux des marées. Situé à la latitude 58°, le KRE subit l'effet intensif du climat arctique pendant la majeure partie de l'année, ce qui entraîne la formation de glace estuarienne rapide sur une grande partie de sa longueur. Plus précisément, et ce qui est le plus pertinent pour cette recherche, il est important de savoir comment le long hiver affecte les potentiels hydrocinétiques des estuaires des régions froides. Ainsi, la surfusion entraîne la formation de frasil et de glace de fond qui peuvent adhérer aux pales des turbines et provoquer leur dysfonctionnement. Dans les estuaires, la surfusion a une nature transitoire complexe car le point de congélation de l'eau salée est une fonction de la salinité et de la profondeur qui est changée par les marées au cours des cycles de marée. En raison du manque de données de terrain en hiver, nous avons collecté des paramètres hydrodynamiques en utilisant de nouvelles campagne de mesures en hiver 2018. Les observations ont montré que le risque de surfusion diminue à l'intérieur de l'estuaire, car en l'absence de débit fluvial, la salinité peut s'infiltrer beaucoup plus loin dans le fleuve. À l'intérieur, une modulation apparente de ∆T (la différence entre la température de l'eau et la température de congélation de l'eau), dépendant de la marée, a été observée avec une augmentation de la température pendant des marées montantes. Cette augmentation retarderait la surfusion, ce qui est un avantage majeur pour turbines. En réglant le module Delft3D-Ice, différents scénarios ont été définis pour l'étendue et l'épaisseur de la couvert de glace, et leurs réponses hydrodynamiques ont été analysées. Il a été démontré que la glace a des impacts complexes et non uniformes sur les caractéristiques hydrodynamiques de la KRE. Surtout, le débit des prismes de marée, qui est la principale source d'élan, peut être modifiée de manière démonstrative par la couverture de glace et la glace de marée plate. Les résultats suggèrent que les zones énergétiques sont légèrement affectées par la glace pendant la plus grande partie de l'hiver. Pendant l'hiver de pointe seulement, la glace pourrait considérablement diminuer densité moyenne de puissance des courants (par exemple, la puissance moyenne est égale ou supérieure à 7 kW m-2). Ces implications cryohydrodynamiques indiquent que l'hiver arctique n'est pas un obstacle à la production d'électricité dans le fleuve Koksoak, et l'énergie marémotrice serait un avantage annuel pour Kuujjuaq

Hydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.

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.

The contemporary definition of integrated water resources management (IWRM) is introduced to promote a holistic approach in water engineering practices. IWRM deals with planning, design and operation of complex systems in order to control the quantity, quality, temporal and spatial distribution of water with the main objective of meeting human and ecological needs and providing protection from water related disasters. This paper examines the existing decision making support in IWRM practice, analyses the advantages and limitations of existing tools, and, as a result, suggests a generic multi-method modeling framework that has the main goal to capture all structural complexities of, and interactions within, a water resources system. Since the traditional tools do not provide sufficient support, this framework uses multi-method simulation technique to examine the codependence between water resources system and socioeconomic environment. Designed framework consists of (i) a spatial database, (ii) a traditional process-based model to represent the physical environment and changing conditions, and (iii) an agent-based spatially explicit model of socio-economic environment. The multi-agent model provides for building virtual complex systems composed of autonomous entities, which operate on local knowledge, possess limited abilities, affect and are affected by local environment, and thus, enact the desired global system behavior. Agent-based model is used in the presented work to analyze spatial dynamics of complex physical-social-economic-biologic systems. Based on the architecture of the generic multi-method modeling framework, an operational model for the Upper Thames River basin, Southwestern Ontario, Canada, is developed in cooperation with the local conservation authority. Six different experiments are designed by combining three climate and two socio-economic scenarios to analyze spatial dynamics of a complex physical-social-economic system of the Upper Thames River basin. Obtained results show strong dependence between changes in hydrologic regime, in this case surface runoff and groundwater recharge rates, and regional socio-economic activities.

<p>In snow-prone regions, snowmelt is one of the main drivers of runoff. For operational flood forecasting and mitigation, the spatial distribution of snow water equivalent (SWE) in near real time is necessary. In this context, in situ observations of SWE provide a valuable information. Nonetheless, the high spatial variability of snowpack characteristics makes it necessary to implement some kind of snow modelling to get a spatially continuous estimation. Data assimilation is thus a useful approach to combine information from both observation and modeling in near real-time. </p><p>For example, at the provincial government of Quebec (eastern Canada), the HYDROTEL Snowpack Model is applied on a daily basis over a 0.1 degree resolution mesh covering the whole province. The modelled SWE is corrected in real time by in situ manual snow survey which are assimilated using a spatial particles filter (Cantet et al., 2019). This assimilation method improves the reliability of SWE estimation at ungauged sites.</p><p>The availability of manual snow surveys is however limited both in space and time. These measurements are conducted on a bi-weekly basis in a limited number of sites. In order to further improve the temporal and spatial observation coverage, alternative sources of data should be considered.</p><p>In this research, it is hypothesized that data gathered by SR50 sonic sensors can be assimilated in the spatial particle filter to improve the SWE estimation. These automatic sensors provide hourly measurements of snow depth and have been deployed in Quebec since 2005. Beforehand, probabilistic SWE estimations were derived from the SR50 snow depth measurements using an ensemble of artificial neural networks (Odry et al. 2019). Considering the nature of the data and the conversion process, the uncertainty associated with this dataset is supposed larger than for the manual snow surveys. The objective of the research is to evaluate the potential interest of adding this lower-quality information in the assimilation framework.</p><p>The addition of frequent but uncertain data in the spatial particle filter required some adjustments in term of assimilation frequency and particle resampling. A reordering of the particles was implemented to maintain the spatial coherence between the different particles. With these changes, the consideration of both manual snow surveys and SR50 data in the spatial particle filter reached performances that are comparable to the initial particle filter that combines only the model and manual snow survey for estimating SWE in ungauged sites. However, the addition of SR50 data in the particle filter allows for continuous information in time, between manual snow surveys.</p><p> </p><p><strong>References:</strong></p><p>Cantet, P., Boucher, M.-A., Lachance-Coutier, S., Turcotte, R., Fortin, V. (2019). Using a particle filter to estimate the spatial distribution of the snowpack water equivalent. J. Hydrometeorol, 20.</p><p>Odry, J., Boucher, M.-A., Cantet,P., Lachance-Cloutier, S., Turcotte, R., St-Louis, P.-Y. (2019). Using artificial neural networks to estimate snow water equivalent from snow depth. Canadian water ressources journal (under review)</p>

Generalized Additive Models (GAMs) are introduced in this study for the regional estimation of low-flow characteristics at ungauged basins and compared to other approaches commonly used for this purpose. GAMs provide more flexibility in the shape of the relationships between the response and explanatory variables in comparison to classical models such as multiple linear regression (MLR). Homogeneous regions are defined here using the methods of hierarchical cluster analysis, canonical correlation analysis and region of influence. GAMs and MLR are then used within the delineated regions and also for the whole study area. In addition, a spatial interpolation method is also tested. The different models are applied for the regional estimation of summer and winter low-flow quantiles at stations in Quebec, Canada. Results show that for a given regional delineation method, GAMs provide improved performances compared to MLR.

Abstract. Efficient adaptation strategies to climate change require the estimation of future impacts and the uncertainty surrounding this estimation. Over- or underestimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to the climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of “model democracy”, in which each climate model is considered equally fit. The newer Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, with several models showing a climate sensitivity larger than that of Phase 5 (CMIP5) and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that “hot” models be removed from impact studies to avoid skewing impact results toward unlikely futures. Indeed, the inclusion of these models in impact studies carries a significant risk of overestimating the impact of climate change. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3107 North American catchments. More precisely, the variability in future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 general circulation models (GCMs), 5 of which are deemed hot based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southeast US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability in future streamflow, indicating that global outlier climate models do not necessarily provide regional outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.

Abstract An alternate dynamical core that employs the unified equations of A. Arakawa and C.S. Konor (2009) has been developed within Environment and Climate change Canada’s GEM (Global Environmental Multiscale) atmospheric model. As in the operational GEM dynamical core, the novel core utilizes the same fully-implicit two-time-level semi-Lagrangian scheme for time discretization while the log-pressure-based terrain-following vertical coordinate has been slightly adapted. Overall, the new dynamical core implementation required only minor changes to the existing informatics code of the GEM model and from a computational performance perspective, the new core does not incur any significant additional cost. A broad range of tests – that include both two-dimensional idealized theoretical cases and three-dimensional deterministic forecasts covering both hydrostatic and non-hydrostatic scales–have been carried out to evaluate the performance of the new dynamical core. For all the tested cases, when compared to the operational GEM model, the new dynamical core based on the unified equations has been found to produce statistically equivalent results. These results imply that the unified equations can be adopted for operational numerical weather prediction that would employ a single soundproof system of equations to produce reliable forecasts for all meteorological scales of interest with negligible changes for the computational overhead.

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.

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

Abstract Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study in Quebec, Canada. FMR provides a better mean FDC estimation when obtained over sites by considering simultaneously all FDC quantiles in the assessment of each given site. However, traditional regression provides a better mean FDC estimation when obtained over given FDC quantiles by considering all sites in the assessment of each quantile separately. Mean daily streamflow estimation is similar; yet FMR provides an improved estimation for most sites. Furthermore, FMR represents a more suitable framework and provides a number of practical advantages, such as insight into descriptor influence on FDC quantiles. Hence, traditional regression may be preferred if only few FDC quantiles are of interest; whereas FMR would be more suitable if a large number of FDC quantiles is of interest, and therefore to estimate daily streamflows.

This is a review article invited by Atmosphere-Ocean to document the contributions of Recherche en Prévision Numérique (RPN) to Numerical Weather Prediction (NWP). It is structured as a historical review and documents RPN’s contributions to numerical methods, numerical modelling, data assimilation, and ensemble systems, with a look ahead to potential future systems. Through this review, we highlight the evolution of RPN’s contributions. We begin with early NWP efforts and continue through to environmental predictions with a broad range of applications. This synthesis is intended to be a helpful reference, consolidating developments and generating broader interest for future work on NWP in Canada.

The moisture maximization approach to estimate the Probable Maximum Precipitation (PMP) has a simple technique for controlling the risk of overestimating PMP: the maximization ratio is limited by an upper bound. The upper bound limit depends on storm records and watershed characteristics. However, it is not readily available in many watersheds. A robust scientific justification for limiting the maximization ratio is missing. In this paper, a novel approach is proposed to estimate the maximization ratio which does not impose an upper limit to the ratio. The new approach, which uses regional climate model data, is based on constructing annual maximum precipitable water time series with precipitable water values for which atmospheric variables are similar to the original event to be maximized. These time series are then used to estimate the 100-year return period precipitable water value required to calculate the maximization ratio. The new approach was tested in three watersheds in the province of Quebec, Canada. Results showed that maximization ratio values were lower than the proposed upper bound value for these watersheds. In comparison to the approach using an upper bound, this proposed approach reduced PMP in these watersheds by 11%. This article is protected by copyright. All rights reserved.

This study details the enhancement and calibration of the Arctic implementation of the HYdrological Predictions for the Environment (HYPE) hydrological model established for the BaySys group of projects to produce freshwater discharge scenarios for the Hudson Bay Drainage Basin (HBDB). The challenge in producing estimates of freshwater discharge for the HBDB is that it spans over a third of Canada’s continental landmass and is 40% ungauged. Scenarios for BaySys require the separation between human and climate interactions, specifically the separation of regulated river discharge from a natural, climate-driven response. We present three key improvements to the modelling system required to support the identification of natural from anthropogenic impacts: representation of prairie disconnected landscapes (i.e., non-contributing areas), a method to generalize lake storage-discharge parameters across large regions, and frozen soil modifications. Additionally, a unique approach to account for irregular hydrometric gauge density across the basins during model calibration is presented that avoids overfitting parameters to the densely gauged southern regions. We summarize our methodologies used to facilitate improved separation of human and climate driven impacts to streamflow within the basin and outline the baseline discharge simulations used for the BaySys group of projects. Challenges remain for modeling the most northern reaches of the basin, and in the lake-dominated watersheds. The techniques presented in this work, particularly the lake and flow signature clusters, may be applied to other high latitude, ungauged Arctic basins. Discharge simulations are subsequently used as input data for oceanographic, biogeochemical, and ecosystem studies across the HBDB.