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François Anctil
and
Paulin Coulibaly

Abstract

The objectives of this study are to describe the local interannual variability in southern Québec, Canada, streamflow, based on wavelet analysis, and to identify plausible climatic teleconnections that could explain these local variations. Scale-averaged wavelet power spectra are used to simultaneously assess the interannual and spatial variability in 18 contiguous annual streamflow time series. The span of available observations, 1938–2000, allows depicting the variance for periods up to about 12 yr. The most striking feature, in the 2–3-yr band and in the 3–6-yr band—the 6–12-yr band is dominated by white noise and is not considered further—is a net distinction between the timing of the interannual variability in local western and eastern streamflows, which may be linked to the local climatology. This opens up the opportunity to construct two regional time series using principal component (PC) analysis. Then, for each band, linear relationships are sought between the regional streamflow and five selected climatic indices: the Pacific–North America (PNA), the North Atlantic Oscillation (NAO), the Northern Hemisphere annular mode (NAM), the Baffin Island–West Atlantic (BWA) and the sea surface temperature anomalies over the Niño-3 region (ENSO3). The correlation analysis revealed the presence of a change point in the streamflow time series, as reported by others, occurring around 1970. For example, the west and east 2–3-yr bands are positively correlated to PNA since 1970, which was not the case prior to that change point. The proposed regional east–west divide is particularly evident prior to 1970, with a negative NAM correlation for the west and a positive NAM (and negative ENSO3) for the east. The picture for the less energetic 3–6-yr band is mixed, with alternating dominance of teleconnection patterns, but the 1970 change point holds.

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Paulin Coulibaly
,
Yonas B. Dibike
, and
François Anctil

Abstract

The issues of downscaling the outputs of a global climate model (GCM) to a scale that is appropriate to hydrological impact studies are investigated using a temporal neural network approach. The time-lagged feed-forward neural network (TLFN) is proposed for downscaling daily total precipitation and daily maximum and minimum temperature series for the Serpent River watershed in northern Quebec (Canada). The downscaling models are developed and validated using large-scale predictor variables derived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. Atmospheric predictors such as specific humidity, wind velocity, and geopotential height are identified as the most relevant inputs to the downscaling models. The performance of the TLFN downscaling model is also compared to a statistical downscaling model (SDSM). The downscaling results suggest that the TLFN is an efficient method for downscaling both daily precipitation and temperature series. The best downscaling models were then applied to the outputs of the Canadian Global Climate Model (CGCM1), forced with the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario. Changes in average precipitation between the current and the future scenarios predicted by the TLFN are generally found to be smaller than those predicted by the SDSM model. Furthermore, application of the downscaled data for hydrologic impact analysis in the Serpent River resulted in an overall increasing trend in mean annual flow as well as earlier spring peak flow. The results also demonstrate the emphasis that should be given in identifying the appropriate downscaling tools for impact studies by showing how a future climate scenario downscaled with different downscaling methods could result in significantly different hydrologic impact simulation results for the same watershed.

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Mabrouk Abaza
,
François Anctil
,
Vincent Fortin
, and
Richard Turcotte
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Mabrouk Abaza
,
François Anctil
,
Vincent Fortin
, and
Richard Turcotte

Abstract

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.

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Islem Hajji
,
Daniel F. Nadeau
,
Biljana Music
,
François Anctil
, and
Jingfeng Wang

Abstract

The maximum entropy production (MEP) model based on nonequilibrium thermodynamics and the theory of Bayesian probabilities was recently developed to model land surface fluxes, including soil evaporation and vegetation transpiration. This model requires few input data and ensures the closure of the surface energy balance. This study aims to test the capability of such a model to realistically simulate evapotranspiration (ET) over a wide range of climates and vegetation covers. A weighting coefficient is introduced to calculate total ET from soil evaporation and vegetation transpiration over partially vegetated land surfaces, resulting in the MEP-ET model. Using this coefficient, the model outputs are compared with in situ observations of ET at eight FLUXNET sites across the continental United States. Results confirm the close agreement between the MEP-ET predicted daily ET and the corresponding observations at sites characterized by moderately limited water availability. Poor ET results were obtained under high water stress conditions. A regulation parameter was therefore introduced in the MEP-ET model to properly take into account the effects of soil water stress on stomata, yielding the generalized MEP-ET model. This parameter considerably reduced model biases under water stress conditions for various heterogeneous land surface sites. The generalized MEP-ET model outperforms several popular ET models, including Penman–Monteith (PM), modified Priestley–Taylor–Jet Propulsion Laboratory (PT-JPL), and air-relative-humidity-based two-source model (ARTS) at all test sites.

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Patrick Grenier
,
Annie-Claude Parent
,
David Huard
,
François Anctil
, and
Diane Chaumont

Abstract

Spatial analog techniques consist in identifying locations whose historical climate is similar to the anticipated future climate at a reference location. In the process of identifying analogs, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this study, six a priori suitable metrics are described (the standardized Euclidean distance, the Kolmogorov–Smirnov statistic, the nearest-neighbor distance, the Zech–Aslan energy statistic, the Friedman–Rafsky runs statistic, and the Kullback–Leibler divergence) and criteria are proposed and investigated in an attempt to identify the best metric for selecting spatial analogs. The case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM, version 4.2.3), from which three annual indicators (total precipitation, heating degree-days, and cooling degree-days) are calculated over 30-yr periods (1971–2000 and 2041–70). It is found that the six metrics identify comparable analog regions at a relatively large scale but that best analogs may differ substantially. For best analogs, it is shown that the uncertainty stemming from the metric choice does not generally exceed that stemming from the simulation or model choice. On the basis of the set of criteria considered in this study, the Zech–Aslan energy statistic stands out as the most recommended metric for analog studies, whereas the Friedman–Rafsky runs statistic is the least recommended.

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Gonzalo Leonardini
,
François Anctil
,
Vincent Vionnet
,
Maria Abrahamowicz
,
Daniel F. Nadeau
, and
Vincent Fortin

Abstract

The Soil, Vegetation, and Snow (SVS) land surface model was recently developed at Environment and Climate Change Canada (ECCC) for operational numerical weather prediction and hydrological forecasting. This study examined the performance of the snow scheme in the SVS model over multiple years at 10 well-instrumented sites from the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), which covers alpine, maritime, and taiga climates. The SVS snow scheme is a simple single-layer snowpack scheme that uses the force–restore method. Stand-alone, point-scale verification tests showed that the model is able to realistically reproduce the main characteristics of the snow cover at these sites, namely, snow water equivalent, density, snow depth, surface temperature, and albedo. SVS accurately simulated snow water equivalent, density, and snow depth at open sites, but exhibited lower performance for subcanopy snowpacks (forested sites). The lower performance was attributed mainly to the limitations of the compaction scheme and the absence of a snow interception scheme. At open sites, the SVS snow surface temperatures were well represented but exhibited a cold bias, which was due to poor representation at night. SVS produced a reasonably accurate representation of snow albedo, but there was a tendency to overestimate late winter albedo. Sensitivity tests suggested improvements associated with the snow melting formulation in SVS.

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Amandine Pierre
,
Sylvain Jutras
,
Craig Smith
,
John Kochendorfer
,
Vincent Fortin
, and
François Anctil

Abstract

Solid precipitation undercatch can reach 20%–70% depending on meteorological conditions, the precipitation gauge, and the wind shield used. Five catch efficiency transfer functions were selected from the literature to adjust undercatch from unshielded and single-Alter-shielded precipitation gauges for different accumulation periods. The parameters from these equations were calibrated using data from 11 sites with a WMO-approved reference measurement. This paper presents an evaluation of these transfer functions using data from the Neige site, which is located in the eastern Canadian boreal climate zone and was not used to derive any of the transfer functions available for evaluation. Solid precipitation measured at the Neige site was underestimated by 34% and 21% when compared with a manual reference precipitation measurement for unshielded and single-Alter-shielded gauges, respectively. Catch efficiency transfer functions were used to adjust these solid precipitation measurements, but all equations overestimated amounts of solid precipitation by 2%–26%. Five different statistics evaluated the accuracy of the adjustments and the variance of the results. Regardless of the adjustment applied, the catch efficiency for the unshielded gauge increased after the adjustment. However, this was not the case for the single-Alter-shielded gauges, for which the improvement of the results after applying the adjustments was not seen in all of the statistics tests. The results also showed that using calibrated parameters on datasets with similar site-specific characteristics, such as the mean wind speed during precipitation and the regional climate, could guide the choice of adjustment methods. These results highlight the complexity of solid precipitation adjustments.

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François Anctil
,
Mark A. Donelan
,
William M. Drennan
, and
Hans C. Graber

Abstract

This paper demonstrates that it is practical to measure turbulent air-sea fluxes from a discus buoy. It proposes a method to correct the measured wind flow, for velocities induced by angular and axial movements of the anemometer, allowing the estimation of the momentum flux from a floating platform. Discus buoys modified for the measurement of momentum flux were deployed during the Surface Wave Dynamics Experiment and the High Resolution Remote Sensing Programme. Successful evaluation of the wind stress was carried out in moderate sea conditions: wind speed and significant wave height, respectively, reaching 12 m s−1 and 4.25 m. Friction velocities calculated using the eddy-correlation method are shown to agree well with those determined from the less direct inertial dissipation method in conditions where the latter method is applicable.

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Habiba Kallel
,
Antoine Thiboult
,
Murray D. Mackay
,
Daniel F. Nadeau
, and
François Anctil

Abstract

Accurately modeling the interactions between inland water bodies and the atmosphere in meteorological and climate models is crucial, given the marked differences with surrounding landmasses. Modeling surface heat fluxes remains a challenge because direct observations available for validation are rare, especially at high latitudes. This study presents a detailed evaluation of the Canadian Small Lake Model (CSLM), a one-dimensional mixed-layer dynamic lake model, in reproducing the surface energy budget and the thermal stratification of a subarctic reservoir in eastern Canada. The analysis is supported by multiyear direct observations of turbulent heat fluxes collected on and around the 85-km2 Romaine-2 hydropower reservoir (50.7°N, 63.2°W) by two flux towers: one operating year-round on the shore and one on a raft during ice-free conditions. The CSLM, which simulates the thermal regime of the water body including ice formation and snow physics, is run in offline mode and forced by local weather observations from 25 June 2018 to 8 June 2021. Comparisons between observations and simulations confirm that CSLM can reasonably reproduce the turbulent heat fluxes and the temperature behavior of the reservoir, despite the one-dimensional nature of the model that cannot account for energy inputs and outputs associated with reservoir operations. The best performance is achieved during the first few months after the ice break-up (mean error = −0.3 and −2.7 W m−2 for latent and sensible heat fluxes, respectively). The model overreacts to strong wind events, leading to subsequent poor estimates of water temperature and eventually to an early freeze-up. The model overestimated the measured annual evaporation corrected for the lack of energy balance closure by 5% and 16% in 2019 and 2020.

Significance Statement

Freshwater bodies impact the regional climate through energy and water exchanges with the atmosphere. It is challenging to model surface energy fluxes over a northern lake due to the succession of stratification and mixing periods over a year. This study focuses on the interactions between the atmosphere of an irregular shaped northern hydropower reservoir. Direct measurements of turbulent fluxes using an eddy covariance system allowed the model assessment. Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement; however, the model overreacted to high wind episodes. Biases mostly occur during freeze-up and breakup, stressing the importance of a good representation of the ice cover processes.

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