Center dexpertise hydrique du Quebec

Québec, Canada

Center dexpertise hydrique du Quebec

Québec, Canada
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Mailhot A.,INRS Eau | Lachance-Cloutier S.,Center dExpertise Hydrique du Quebec | Talbot G.,INRS Eau | Favre A.-C.,Polytechnic School of Algiers
Journal of Hydrology | Year: 2013

The Peak-Over-Threshold (POT) approach is an interesting alternative to the one based on Annual Maxima (AM) series since it gives the opportunity to take into consideration extreme events that would not be considered otherwise. It has also been recognized that the regional approach improves statistical inference when compared to the local approach, assuming that the region is statistically homogeneous. A regional POT approach was developed and applied to the network stations located in southern Québec. POT series for 5-, 10-, 15-, 30-min and 1-, 2-, 6- and 12-h durations were constructed assuming a fixed exceedance rate. An analysis of local POT series showed that the intra-annual variability of the Generalized Pareto Distribution (GPD) parameters needs to be taken into consideration. Models of various complexities were defined combining local and regional representations as well as the intra-annual variability of GPD parameters. Regional likelihood was estimated and models were compared based on the Akaike Information Criterion (AIC). Models with regional shape and scale parameters and accounting for intra-annual variability were selected for all durations. Spatial covariates were also introduced through a simple model linking GPD parameters to latitude, longitude and altitude. The sensitivity of results to threshold values and selected models was also investigated. Interpolated maps of intense rainfall over the studied area are finally proposed. © 2012 Elsevier B.V.

Velazquez J.A.,Consortium Ouranos | Velazquez J.A.,Laval University | Schmid J.,Ludwig Maximilians University of Munich | Ricard S.,Center dExpertise Hydrique du Quebec | And 8 more authors.
Hydrology and Earth System Sciences | Year: 2013

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971-2000) and a future (2041-2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model. © Author(s) 2013.

Muerth M.J.,Ludwig Maximilians University of Munich | Gauvin St-Denis B.,Consortium Ouranos | Ricard S.,Center dExpertise Hydrique du Quebec | Velazquez J.A.,Consortium Ouranos | And 6 more authors.
Hydrology and Earth System Sciences | Year: 2013

In climate change impact research, the assessment of future river runoff as well as the catchment-scale water balance is impeded by different sources of modeling uncertainty. Some research has already been done in order to quantify the uncertainty of climate projections originating from the climate models and the downscaling techniques, as well as from the internal variability evaluated from climate model member ensembles. Yet, the use of hydrological models adds another layer of uncertainty. Within the QBic3 project (Québec-Bavarian International Collaboration on Climate Change), the relative contributions to the overall uncertainty from the whole model chain (from global climate models to water management models) are investigated using an ensemble of multiple climate and hydrological models. Although there are many options to downscale global climate projections to the regional scale, recent impact studies tend to use regional climate models (RCMs). One reason for that is that the physical coherence between atmospheric and land-surface variables is preserved. The coherence between temperature and precipitation is of particular interest in hydrology. However, the regional climate model outputs often are biased compared to the observed climatology of a given region. Therefore, biases in those outputs are often corrected to facilitate the reproduction of historic runoff conditions when used in hydrological models, even if those corrections alter the relationship between temperature and precipitation. So, as bias correction may affect the consistency between RCM output variables, the use of correction techniques and even the use of (biased) climate model data itself is sometimes disputed among scientists. For these reasons, the effect of bias correction on simulated runoff regimes and the relative change in selected runoff indicators is explored. If it affects the conclusion of climate change analysis in hydrology, we should consider it as a source of uncertainty. If not, the application of bias correction methods is either unnecessary to obtain the change signal in hydro-climatic projections, or safe to use for the production of present and future river runoff scenarios as it does not alter the change signal.

The results of the present paper highlight the analysis of daily runoff simulated with four different hydrological models in two natural-flow catchments, driven by different regional climate models for a reference and a future period. As expected, bias correction of climate model outputs is important for the reproduction of the runoff regime of the past, regardless of the hydrological model used. Then again, its impact on the relative change of flow indicators between reference and future periods is weak for most indicators, with the exception of the timing of the spring flood peak. Still, our results indicate that the impact of bias correction on runoff indicators increases with bias in the climate simulations. © 2013 Author(s).

Abaza M.,Laval University | Anctil F.,Laval University | Fortin V.,Recherche en prevision numerique environnementale | Turcotte R.,Center dExpertise Hydrique du Quebec
Advances in Water Resources | Year: 2015

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability. © 2015 Elsevier Ltd.

Abaza M.,Laval University | Anctil F.,Laval University | Fortin V.,Recherche en prevision numerique environnementale | Turcotte R.,Center dExpertise Hydrique du Quebec
Journal of Hydrology | Year: 2014

This paper evaluates the application of the Ensemble Kalman Filter (EnKF) for streamflow assimilation within an ensemble prediction system designed for short-term hydrological forecasting at the outlet of the au Saumon watershed. The EnKF updates three state variables of a distributed hydrological model (soil moisture in the intermediate layer, soil moisture in the deep layer, and land routing) to improve the initial conditions of the forecasts. A systematic method for the identification of the perturbation factors (ensemble generation) and for the selection of the ensemble size is discussed. EnKF results show a substantial improvement in performance and reliability over the open-loop estimates. Manual assimilation was also assessed and led to a performance similar to the EnKF; however, the EnKF forecasts are substantially more reliable. While an ensemble size of 1000 members was required to fully sample the hydrological and meteorological uncertainty, similar results are obtained in terms of skill when limiting the ensemble size to 50. © 2014 Elsevier B.V.

Abaza M.,Laval University | Anctil F.,Laval University | Fortin V.,Recherche en Prevision Numerique Environnementale | Turcotte R.,Center dExpertise Hydrique du Quebec
Monthly Weather Review | Year: 2013

Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty. © 2013 American Meteorological Society.

Bergeron J.,Université de Sherbrooke | Royer A.,Université de Sherbrooke | Turcotte R.,Center dExpertise Hydrique du Quebec | Roy A.,Université de Sherbrooke
Hydrological Processes | Year: 2014

Estimation of the amount of water stored in snow is a principal source of error for spring streamflow simulations in snow-dominant regions. Measuring this variable throughout large and often remote areas using snow surveys is an expensive task since they are practically point measurements. Remote sensing is an alternative method, which can cover much larger areas in little time, but further research is required to reduce uncertainties on snow water equivalent (SWE) estimations, especially during the melting period. However, optical-near infrared (NIR) and passive microwave remote sensing can detect snow cover area (SCA) with greater certainty, which can be used as a proxy for SWE. The two datasets work in complementary ways considering their spatial resolutions and cloud cover limitations. This study developed an SCA product from blended passive microwave (Advanced Microwave Scanning Radiometer - Earth Observing System: AMSR-E) and optical-NIR (Moderate Resolution Imaging Spectroradiometer: MODIS) remote sensing data to improve estimates of streamflow caused by snowmelt during the spring period. The blended product was assimilated in a snowmelt model (SPH-AV) coupled with the MOHYSE hydrological model through a modified direct insertion method. SCA estimated from AMSR-E data was first compared with in situ snow-depth measurements and SCA estimated with MODIS. Results showed an agreement of over 95% between AMSR-E-derived and cloud-free MODIS-derived SCA products in the spring. Comparison with ground stations confirmed the underestimation of snow cover by AMSR-E. Assimilation of the blended snow product in SPH-AV coupled with MOHYSE yielded an overall improvement of the Nash-Sutcliffe coefficient comparable with simulations with no updates, which is comparable to results driven by biweekly snow surveys. Assimilation of remotely sensed passive microwave data was also found to have little positive impact on streamflow simulation due to the difficulty of differentiating melting snow from snow-free surfaces. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.

Roy A.,Université de Sherbrooke | Royer A.,Université de Sherbrooke | Turcotte R.,Center dExpertise Hydrique du Quebec
Journal of Hydrology | Year: 2010

Snow estimation is the major source of errors for spring streamflow simulations in Quebec, Canada. The objective of the study is to improve melting discharge estimation computed with the operational MOHYSE hydrological model by integrating remote sensing snow-cover area in its snow module (SPH-AV). The satellite-derived snow-cover area (SCA) (MODIS & IMS) is first compared with in situ snow depth data measurements and simulated snow-cover area. Results show that the remote sensing products underestimate the snow-cover area on the mainly forested study region. A direct-insertion method of daily satellite SCA images is developed based on an empirical snow water equivalent threshold compensating, on a pixel-by-pixel basis, for the small amount of snow that satellite sensors can not identify during the melting period. This approach improves the streamflow simulation for spring periods (25th March to 25th of May) over 4. years (2004-2007) with a Nash-Sutcliffe coefficient enhancement of 0.11 and a root mean square error (RMSE) improvement of 21% on the Du Nord watershed, for which the threshold was optimized. The threshold found on the Du Nord basin was then directly applied on another watershed (Aux Écorces basin) for validation. The simulated streamflow is significantly improved as compared to the observed streamflow for these 4. years (mean Nash increase from 0.72 to 0.85 and RMSE decrease by 22%).The method improves streamflow peaks identification as much as 36% on the Du Nord watershed and 19% on the Aux Écorces watershed. © 2010 Elsevier B.V.

Ricard S.,Center dexpertise hydrique du Quebec | Bourdillon R.,Center dexpertise hydrique du Quebec | Roussel D.,Center dexpertise hydrique du Quebec | Turcotte R.,Center dexpertise hydrique du Quebec
Journal of Hydrologic Engineering | Year: 2013

Large-scale distributed hydrological modeling is nowadays more widely applied for water resource management. To ensure the spatial consistency of distributed parameters, an alternative calibration strategy is suggested: the global calibration. This approach aims to identify a single parameter set suitable for every stations located within the modeled domain. A global calibration procedure is applied and tested over a large portion of the St. Lawrence River basin (387,000 km2) using Hydrotel, a physically based semidistributed hydrological model. Despite limitations in estimating high flows on small catchments, global calibration is regarded as a fair trade-off between local performance and regional consistency of parametric information. In the near future, the global calibration strategy will act as a key methodological framework for large-scale hydrological assessments. © 2013 American Society of Civil Engineers.

Guay C.,Hydro - Quebec | Choquette Y.,Hydro - Quebec | Durand G.,Center dExpertise Hydrique du Quebec
Canadian Water Resources Journal | Year: 2012

Significant progress has been made in the past years to improve the quality of streamflow data and to assess the potential of hydroacoustics. However, little effort has been made in monitoring techniques for under-ice streamflow. This paper presents a case study in which the under-ice index velocity method is used for monitoring ice-affected streamflow with an acoustic Doppler velocity meter (ADVM). The method is particularly interesting since it is reliable during periods of backwater effects, and it is fully automated and objective. In this particular case study, a reliable rating was obtained in five years, with only two discharge measurements per year. © 2012 Canadian Water Resources Association.

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