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At high latitudes, lake-atmosphere interactions are disrupted for several months of the year by the presence of an ice cover. By isolating the water column from the atmosphere, ice, typically topped by snow, drastically alters albedo, surface roughness, and heat exchanges relative to the open water period, with major climatic, ecological, and hydrological implications. Lake models used to simulate the appearance and disappearance of the ice cover have rarely been validated with detailed in situ observations of snow and ice. In this study, we investigate the ability of the physically-based 1D Canadian Small Lake Model (CSLM) to simulate the freeze-up, ice-cover growth, and breakup of a small boreal lake. The model, driven offline by local weather observations, is run on Lake Piché, 0.15 km2 and 4 m deep (47.32°N; 71.15°W) from 25 October 2019 to 20 July 2021, and compared to observations of the temperature profile and ice and snow cover properties. Our results show that the CSLM is able to reproduce the total ice thickness (average error of 15 cm) but not the ice type-specific thickness, underestimating clear ice and overestimating snow ice. CSLM manages to reproduce snow depth (errors less than 10 cm). However, it has an average cold bias of 2°C and an underestimation of average snow density of 34 kg m−3 . Observed and model freeze-up and break-up dates are very similar, as the model is able to predict the longevity of the ice cover to within 2 weeks. CSLM successfully reproduces seasonal stratification, the mixed layer depth, and surface water temperatures, while it shows discrepancies in simulating bottom waters especially during the open water period. [ABSTRACT FROM AUTHOR]

In the boreal forest of eastern Canada, winter temperatures are projected to increase substantially by 2100. This region is also expected to receive less solid precipitation, resulting in a reduction in snow cover thickness and duration. These changes are likely to affect hydrological processes such as snowmelt, the soil thermal regime, and snow metamorphism. The exact impact of future changes is difficult to pinpoint in the boreal forest, due to its complex structure and the fact that snow dynamics under the canopy are very different from those in the gaps. In this study, we assess the influence of a low-snow and warm winter on snowmelt dynamics, soil freezing, snowpack properties, and spring streamflow in a humid and discontinuous boreal catchment of eastern Canada (47.29° N, 71.17° W; ≈ 850 m a.m.s.l.) based on observations and SNOWPACK simulations. We monitored the soil and snow thermal regimes and sampled physical properties of the snowpack under the canopy and in two forest gaps during an exceptionally low-snow and warm winter, projected to occur more frequently in the future, and during a winter with conditions close to normal. We observe that snowmelt was earlier but slower, top soil layers were cooler, and gradient metamorphism was enhanced during the low-snow and warm winter. However, we observe that snowmelt duration increased in forest gaps, that soil freezing was enhanced only under the canopy, and that snow permeability increased more strongly under the canopy than in either gap. Our results highlight that snow accumulation and melt dynamics are controlled by meteorological conditions, soil freezing is controlled by forest structure, and snow properties are controlled by both weather forcing and canopy discontinuity. Overall, observations and simulations suggest that the exceptionally low spring streamflow in the winter of 2020–2120 was mainly driven by low snow accumulation, slow snowmelt, and low precipitation in April and May rather than enhanced percolation through the snowpack and soil freezing. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020–2023. Precipitation phase observations showed a 2-m air temperature interval between 0–4 °C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5 °C, a linear transition model with dual temperature thresholds of –0.38 and 5 °C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance (F1 score of 0.74) due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. PGP_hydromet and PGP_full displayed the best classification performance (F1 score of 0.84). In terms of partitioning error, PGP_full had the lowest RMSE (0.27 mm) and the least variability in performance. The RMSE of the single temperature threshold model was the highest (0.40 mm) and showed the greatest performance variability. An input variable importance analysis revealed that the additional data used in the more complex PGP models mainly improved mixed-phase precipitation prediction. The improvement of mixed-phase prediction remains a challenge. Relative humidity was deemed the least important input variable used, due to consistent near water vapor saturation conditions. Additionally, the reanalysis atmospheric data proved to be an important factor to increase the robustness of the partitioning process. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models. [ABSTRACT FROM AUTHOR]

The hydrological processes of cascading hydroelectric reservoirs differ from those of lakes, due to the importance of the inflows and outflows that vary with energy demand. These heat and water advection terms are rarely considered in water body energy balance analyses even though reservoirs are common man-made structures, especially in North America, and thus may affect the regional climate. This study provides a comprehensive assessment of the water and energy balance of the 85-km2 Romaine-2 northern reservoir (50.69°N, 63.24°W), mean depth of 44 m, highlighting the significant contribution of the advection heat fluxes. The water balance input was primarily controlled by upstream (turbine) inflows (77.6%), while lateral (natural) inflows and direct precipitation represented 21.2% and 1.2%, respectively. As for the reservoir's heat budget, the net advection of heat accounted on average for 25.0% of the input, of which net radiation was the largest component (73.3%). After accounting for the absence of energy balance closure, latent heat and sensible heat fluxes represented 73.2% and 25.1% of total energy output from the reservoir, respectively. The thermal regime was influenced by the hydrological flow conditions, which were regulated by reservoir management. This played a major role in the evolution of the thermocline and the temperature of the epilimnion, and ultimately, in the dynamics of the turbulent heat fluxes. This study suggests that the heat advection term represents a large fraction of the heat budget of northern reservoirs and should be properly considered. [ABSTRACT FROM AUTHOR]

Given that flooding episodes are occurring at a greater rate due to climate change, individuals must adopt certain adaptation behaviors to prevent or mitigate the anticipated or negative impact of such events. However, few studies have assessed if and how households and individuals have actually taken action in this regard. Because some individual beliefs can be linked to facilitating factors and barriers to action, a better understanding of the adoption of adaptive behaviors requires a combined analysis of individual psychosocial factors. The purpose of this study was to develop a better understanding of the reasons underlying the adoption of behaviors related to structural adaptation to flooding by people living in or near flood‐prone areas in the Province of Québec (Canada). Results of a series of structural equation modeling showed that behavioral, normative and control beliefs were all significant predictors of the respondents' intention to adopt structural flood protective behaviors, with normative beliefs being the strongest. By identifying the best psychosocial predictors of the adoption of such behaviors, the results of this study provide valuable insights regarding the most effective factors to be used in public health messages to promote the adoption of behaviors related to structural adaptation to flooding. [ABSTRACT FROM AUTHOR]

In the boreal forest, winter temperatures are projected to increase substantially by 2100, resulting in a reduction in snow cover thickness and duration. These changes are likely to affect hydrological processes such as snowmelt, the soil thermal regime, and snow metamorphism. The exact impact of future changes is difficult to pinpoint in the boreal forest, due to its complex structure, and the fact that snow dynamics under the canopy are very different from those in the gaps. Although the effects of warmer winters on snow-related processes are well documented, their interactions to influence the spring runoff in evergreen forest remain poorly understood. In this observational study, we assess the influence of a low-snow and warm winter on snowmelt dynamics, soil freezing, snowpack properties, and spring streamflow in a humid and discontinuous boreal catchment of eastern Canada (47.29° N, 71.17° W, » 850 m ASL). We monitored the soil and snow thermal regimes and sampled physical properties of the snowpack under the canopy and in two forest gaps during an exceptionally low-snow and warm winter, plausibly representative of future winters, and during a winter with conditions close to normal. We observe that snowmelt was earlier but slower, top soil layers were cooler, and gradient metamorphism was enhanced during the low-snow and warm winter. However, we observe that snowmelt duration increased in forest gaps, that soil freezing was enhanced only under the canopy, and that snow permeability increased more strongly under the canopy than in either gap. Overall, we observe that the spring streamflow discharge was significantly reduced in the warmest year due to a slower melt and low precipitation in April and May. Our results, based solely on field observations, highlight the complex effects of warmer winters on snow hydrology in discontinuous boreal forests. [ABSTRACT FROM AUTHOR]

A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. This alternative approach is designed by combining asynchronous hydroclimatic modelling and quantile perturbation applied to streamflow observations. Calibration is run by forcing hydrologic models with raw climate model outputs using an objective function that excludes the day-to-day temporal correlation between simulated and observed hydrographs. The resulting hydrologic scenarios provide useful and reliable information considering that they (1) preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascade despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four sub-catchments of the Chaudière River, Canada, using nine North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) simulations and a pool of lumped conceptual hydrologic models. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. They also highlight the sensibility of the proposed workflow to strong biases affecting raw climate model outputs, frequently causing outlying projections of the hydrologic regime. Inappropriate forcing climate simulations were however successfully identified (and excluded) using the performance of the simulated hydrologic response as a ranking criterion. Results finally suggest that further works should be conducted to confirm the reliability of the proposed workflow to assess the impact of climate change on high- and low-flow events. [ABSTRACT FROM AUTHOR]

Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast. [ABSTRACT FROM AUTHOR]

Water bodies such as lakes and reservoirs affect the regional climate by acting as heat sinks and sources through the evaporation of substantial quantities of water over several months of the year. Unfortunately, energy exchange observations between deep reservoirs and the atmosphere remain rare in northeastern North America, which has one of the highest densities of water bodies in the world. This study characterizes the dynamics of turbulent heat fluxes by analysing in‐situ observations of a compact and dimictic reservoir (50.69° N, 63.24° W) located in a subarctic environment. The reservoir is characterized by a mean depth of 44 m and a surface area of 85 km2. Two eddy covariance (EC) systems, one on a raft and one onshore, were deployed from 27 June 2018 to 12 June 2022. The thermal regime of the reservoir was monitored using two vertical chains of thermistors. Results indicate a mean annual evaporation rate of 590 ± 66 mm, which is equivalent to ≈51% of the annual precipitation, with 84% of the evaporation occurring at a high rate from August to freeze‐up in late December through episodic pulses. It was difficult to close the energy balance because of the magnitude and the large time lag of the heat storage term. To circumvent this problem, we opted to perform calculations for a year that started from the first of March, as the heat storage in the water column was at its lowest at that point and could thus be ignored. From June to December, monthly Bowen ratios increased from near‐zero negative values to about 1.5. After September, due to smaller vapour pressure deficits, latent heat fluxes steadily decreased until the reservoir had a complete ice cover. Opposite diurnal cycles of sensible and latent heat fluxes were revealed during the open water period, with sensible heat fluxes peaking at night and latent heat fluxes peaking in the afternoon. [ABSTRACT FROM AUTHOR]

There is currently much discussion as to whether probabilistic (top–down) or possibilistic (bottom–up) approaches are the most appropriate to estimate potential future climate impacts. In a context of deep uncertainty, such as future climate, bottom-up approaches aimed at assessing the sensitivity and vulnerability of systems to changes in climate variables have been gaining ground. A refined framework is proposed here (in terms of coherence, structure, uncertainty, and results analysis) that adopts the scenario–neutral method of the bottom–up approach, but also draws on some elements of the top–down approach. What better guides the task of assessing the potential hydroclimatological impacts of changing climatic conditions in terms of the sensitivity of the systems, differential analysis of climatic stressors, paths of change, and categorized response of the scenarios: past, changing, compensatory, and critical condition. The results revealed a regional behavior (of hydroclimatology, annual water balances, and snow) and a differential behavior (of low flows). We find, among others, the plausible scenario in which increases in temperature and precipitation would generate the same current mean annual flows, with a reduction of half of the snow, a decrease in low flows (significant, but differentiated between basins), and a generalized increase in dry events. [ABSTRACT FROM AUTHOR]

Observations of turbulent heat fluxes over inland water bodies are scarce despite being critical to adequate lake parametrization for numerical weather forecast and climate models. Scintillometry has allowed for the regional (~ km2) estimation of turbulent heat fluxes, but few studies have assessed its performance over water. We compare scintillometry-derived turbulent heat fluxes over an 85-km2 dimictic boreal hydropower reservoir in eastern Canada (50.69° N, 63.24° W) with data from a raft-based eddy-covariance system. To the best of our knowledge, this is one of the first studies to quantify evaporation over an inland water body using a set of optical and microwave scintillometers. The scintillometer beam path extended 1.7 km over a section of the reservoir with depths of up to 100 m, from 14 August to 9 October 2019. Forty-nine days of data were retained. This study quantifies the impact of atmospheric stability on the derived fluxes and explores the use of temperature differences at the water–air interface from a point close to the centre of the scintillometer beam to properly estimate the direction of the sensible heat flux. The scintillometry approaches were well correlated with the eddy-covariance estimations for sensible heat fluxes (R2 up to 0.86, 32% bias), while the agreement decreased for latent heat fluxes (R2 up to 0.59, 69% bias). The scintillometer measured much larger latent heat fluxes than the eddy-covariance set-up. These results may be due to the larger footprint of the scintillometers capturing greater heterogeneity in the fluxes. [ABSTRACT FROM AUTHOR]

A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. This alternative approach is designed by combining asynchronous hydroclimatic modelling and quantile perturbation applied to streamflow observations. Calibration is run by forcing hydrologic models with raw climate model outputs using an objective function that exclude the day-to-day temporal correlation between simulated and observed hydrographs. The resulting hydrologic scenarios provide useful and reliable information considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascade despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. They also highlight the sensibility of the proposed workflow to strong biases affecting raw climate model outputs, frequently causing outlying projections of the hydrologic regime. Inappropriate forcing climate simulations were however successfully identified (and excluded) using the performance of the simulated hydrologic response as a ranking criterion. Results finally suggest further works should be conducted to confirm the reliability of the proposed workflow to assess the impact of climate change on extreme hydrologic events. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the effectiveness of three distinct methodological frameworks of frequency analysis for flood risk assessment in Québec. The frameworks involved the use of instantaneous peak flow data, daily peak flow data, and a regional frequency analysis. Also cited is the use of daily and instantaneous data to determine the empirical peak effect factors.

Forecast uncertainties are unfortunately inevitable when conducting a deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical system and to capture the uncertainties associated with different sources. In practice, the proper interpretation of the predictive uncertainties and model outputs will also have a crucial impact on risk-based decisions. In this study, the performance of evolutionary multi-objective optimization (i.e., non-dominated sorting genetic algorithm II – NSGA-II) as a hydrological ensemble post-processor was tested and compared with a conventional state-of-the-art post-processor, the affine kernel dressing (AKD). Those two methods are theoretically/technically distinct, yet share the same feature in that both of them relax the parametric assumption of the underlying distribution of the data (the streamflow ensemble forecast). Both NSGA-II and AKD post-processors showed efficiency and effectiveness in eliminating forecast biases and maintaining a proper dispersion with increasing forecasting horizons. In addition, the NSGA-II method demonstrated superiority in communicating trade-offs with end-users on which performance aspects to improve. [ABSTRACT FROM AUTHOR]

Arctic landscapes are covered in snow for at least 6 months of the year. The energy balance of the snow cover plays a key role in these environments, influencing the surface albedo, the thermal regime of the permafrost, and other factors. Our goal is to quantify all major heat fluxes above, within, and below a low-Arctic snowpack at a shrub tundra site on the east coast of Hudson Bay in eastern Canada. The study is based on observations from a flux tower that uses the eddy covariance approach and from profiles of temperature and thermal conductivity in the snow and soil. Additionally, we compared the observations with simulations produced using the Crocus snow model. We found that radiative losses due to negative longwave radiation are mostly counterbalanced by the sensible heat flux, whereas the latent heat flux is minimal. At the snow surface, the heat flux into the snow is similar in magnitude to the sensible heat flux. Because the snow cover stores very little heat, the majority of the upward heat flux in the snow is used to cool the soil. Overall, the model was able to reproduce the observed energy balance, but due to the effects of atmospheric stratification, it showed some deficiencies when simulating turbulent heat fluxes at an hourly timescale. [ABSTRACT FROM AUTHOR]

This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: system A, forcing, system B, forcing and initial conditions, system C, forcing and model structure, and system D, forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to 7 d of forecast horizon. The evaluation is carried out across 30 catchments in the province of Quebec (Canada) and over the 2011–2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast attribute, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable, and in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation of how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems. [ABSTRACT FROM AUTHOR]

Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 ∘ C). Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Measuring cold content is a labour-intensive task as it requires extracting in situ snow temperature and density. Hence, few studies have focused on characterizing this snowpack variable. This study describes the multilayer cold content of a snowpack and its variability across four sites with contrasting canopy structures within a coniferous boreal forest in southern Québec, Canada, throughout winter 2017–2018. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60 % of the snow cover period were examined. During the second step, a reconstructed time series of modelled CC was produced and analyzed to highlight the high-resolution temporal variability of CC for the full snow cover period. To accomplish this, the Canadian Land Surface Scheme (CLASS; featuring a single-layer snow model) was first implemented to obtain simulations of the average snow density at each of the four sites. Next, an empirical procedure was used to produce realistic density profiles, which, when combined with in situ continuous snow temperature measurements from an automatic profiling station, provides a time series of CC estimates at half-hour intervals for the entire winter. At the four sites, snow persisted on the ground for 218 d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (- 2.62 MJ m -2) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was - 0.47 MJ m -2 , representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R2=0.64 and percent bias (Pbias) =-17.1 %), snow density (R2=0.71 and Pbias =1.6 %), and cold content (R2=0.93 and Pbias =-3.3 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R2=0.42 and Pbias =5.17 %). Thanks to these encouraging results, the reconstructed and continuous CC series could be analyzed at the four sites, revealing the impact of rain-on-snow and cold air pooling episodes on the variation of CC. The continuous multilayer cold content time series also provided us with information about the effect of stand structure, local topography, and meteorological conditions on cold content variability. Additionally, a weak relationship between canopy structure and CC was identified. [ABSTRACT FROM AUTHOR]

Rising temperatures in the southern Arctic region are leading to shrub expansion and permafrost degradation. The objective of this study is to analyze the surface energy budget (SEB) of a subarctic shrub tundra site that is subject to these changes, on the east coast of Hudson Bay in eastern Canada. We focus on the turbulent heat fluxes, as they have been poorly quantified in this region. This study is based on data collected by a flux tower using the eddy covariance approach and focused on snow-free periods. Furthermore, we compare our results with those from six Fluxnet sites in the Arctic region and analyze the performance of two land surface models, SVS and ISBA, in simulating soil moisture and turbulent heat fluxes. We found that 23% of the net radiation was converted into latent heat flux at our site, 35% was used for sensible heat flux, and about 15% for ground heat flux. These results were surprising considering our site was by far the wettest site among those studied, and most of the net radiation at the other Arctic sites was consumed by the latent heat flux. We attribute this behavior to the high hydraulic conductivity of the soil (littoral and intertidal sediments), typical of what is found in the coastal regions of the eastern Canadian Arctic. Land surface models overestimated the surface water content of those soils but were able to accurately simulate the turbulent heat flux, particularly the sensible heat flux and, to a lesser extent, the latent heat flux. Significance Statement: Isostatic uplift after the last deglaciation led to the emergence of beaches, which represent a large area in the Canadian Arctic. We studied the surface energy budget of such a beach that emerged 6000 years ago in northeastern Canada. Results suggest that evaporation is up to 20% less than at previously studied sites, which is explained by sandy soils retaining little moisture despite abundant precipitation. Deployed numerical models showed difficulties in simulating the soil conditions but proved successful in simulating the surface energy budget after manual adjustments of the soil conditions. These simulation difficulties probably apply to other parts of the Canadian Arctic, possibly leading to errors in meteorological and climate forecasting. [ABSTRACT FROM AUTHOR]

Statistical post-processing of climate model outputs is a common hydroclimatic modelling practice aiming to produce climate scenarios that better fit in-situ observations and to produce reliable stream flows forcing calibrated hydrologic models. Such practice is however criticized for disrupting the physical consistency between simulated climate variables and affecting the trends in climate change signals imbedded within raw climate simulations. It also requires abundant good-quality meteorological observations, which are not available for many regions in the world. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations, nor for statistical post-processing of climate model outputs, nor for calibrating hydrologic models. By combining asynchronous hydroclimatic modelling, an alternative framework designed to construct hydrologic scenarios without resorting to meteorological observations, and quantile perturbation applied to streamflow observations, the proposed workflow produces sound and plausible hydrologic scenarios considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascades despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Forced with raw climate model outputs, hydrologic models are calibrated over the reference period according to a calibration metric designed to function with temporally uncorrelated observed and simulated streamflow values. Perturbation factors are defined by relating each simulated streamflow quantiles over both reference and future periods. Hydrologic scenarios are finally produced by applying perturbation factors to available streamflow observations. [ABSTRACT FROM AUTHOR]

This study aims to decipher the interactions of a precipitation post-processor and several other tools for uncertainty quantification implemented in a hydrometeorological forecasting chain. We make use of four hydrometeorological forecasting systems that differ by how uncertainties are estimated and propagated. They consider the following sources of uncertainty: A) forcing, B) forcing and initial conditions, C) forcing and model structure, and D) forcing, initial conditions, and model structure. For each system's configuration, we investigate the reliability and accuracy of post-processed precipitation forecasts in order to evaluate their ability to improve streamflow forecasts for up to seven days of forecast horizon. The evaluation is carried out across 30 catchments in the Province of Quebec (Canada) and over the 2011-2016 period. Results are compared using a multicriteria approach, and the analysis is performed as a function of lead time and catchment size. The results indicate that the precipitation post-processor resulted in large improvements in the quality of forecasts with regard to the raw precipitation forecasts. This was especially the case when evaluating relative bias and reliability. However, its effectiveness in terms of improving the quality of hydrological forecasts varied according to the configuration of the forecasting system, the forecast lead time, and the catchment size. The combination of the precipitation post-processor and the quantification of uncertainty from initial conditions showed the best results. When all sources of uncertainty were quantified, the contribution of the precipitation post-processor to provide better streamflow forecasts was not remarkable and, in some cases, it even deteriorated the overall performance of the hydrometeorological forecasting system. Our study provides an in-depth investigation on how improvements brought by a precipitation post-processor to the quality of the inputs to a hydrological forecasting model can be cancelled along the forecasting chain, depending on how the hydrometeorological forecasting system is configured and on how the other sources of hydrological forecasting uncertainty (initial conditions and model structure) are considered and accounted for. This has implications for the choices users might make when designing new or enhancing existing hydrometeorological ensemble forecasting systems. [ABSTRACT FROM AUTHOR]

Hydrological models used for reservoir management typically lack an accurate representation of open‐water evaporation and must be run in a scarce data context. This study aims to identify an accurate means to estimate reservoir evaporation with simple meteorological inputs during the open‐water season, using long‐term eddy covariance observations from two boreal hydropower reservoirs with contrasting morphometry as reference. Unlike the temperate water bodies on which the majority of other studies have focused, northern reservoirs are governed by three distinct periods: ice cover in the cold season, warming in the summer and energy release in the fall. The reservoirs of interest are Eastmain‐1 (52°N, mean depth of 11 m) and Romaine‐2 (51°N, mean depth of 42 m), both located in eastern Canada. Four approaches are analysed herein: a combination approach, a radiation‐based approach, a mass‐transfer approach, and empirical methods. Of all the approaches, the bulk transfer equation with a constant Dalton number of 1.2 x 10−3 gave the most accurate estimation of evaporation at hourly time steps, compared with the eddy covariance observations (RMSE of 0.06 mm h−1 at Eastmain‐1 and RMSE of 0.04 mm h−1 at Romaine‐2). The daily series also showed good accuracy (RMSE of 1.38 mm day−1 at Eastmain‐1 and RMSE of 0.62 mm day−1 at Romaine‐2) both in the warming and energy release phases of the open‐water season. The bulk transfer equation, on the other hand, was incapable of reproducing condensation episodes that occurred soon after ice breakup. Basic and variance‐based sensitivity analyses were conducted, in particular to measure the variation in performance when the bulk transfer equation was applied with meteorological observations collected at a certain distance (~10–30 km) from the reservoir. This exercise illustrated that accurate estimates of open water evaporation require representative measurements of wind speed and water surface temperature. [ABSTRACT FROM AUTHOR]

The impacts of climate and land-use changes make the stationary assumption in hydrology obsolete. Moreover, there is still considerable uncertainty regarding the future evolution of the Earth's climate and the extent of the alteration of flow regimes. Climate change impact assessment in the water sector typically involves a modelling chain in which a hydrological model is needed to generate hydrologic projections from climate forcings. Considering the inherent uncertainty of the future climate, it is crucial to assess the performance of the hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. The differential split-sample test based on a HMM classification is implemented on the time series of monthly river discharges in the upper Senegal River basin in West Africa, a region characterized by the presence of low-frequency climate signals. A comparison with the results obtained using classical rupture tests shows that the diversity of hydrologic sequences identified using the HMM can help with assessing the robustness of the hydrologic model. [ABSTRACT FROM AUTHOR]

This study evaluates the simulation of water balance components at half-hourly time steps from the Canadian Land Surface Scheme (CLASS) when driven by a 500-year stochastic meteorological data set produced by the Advanced WEather GENerator (AWE-GEN) at two boreal sites with contrasting water availability. The CLASS was driven by ERA5 reanalysis data (CLASS-CTL) over 39 years and its output was used as a surrogate for land surface observations. At both sites, the mean monthly and annual values of all meteorological variables used to drive CLASS, including precipitation, are well captured by AWE-GEN, but their variability is, sometimes, biased. In general, CLASS driven by stochastic data (CLASS-WG) tends to produce higher evapotranspiration compared to values simulated by CLASS-CTL, especially during spring and summer at the wet site. The interannual evapotranspiration-precipitation and runoff-precipitation relationships derived from CLASS-WG and those derived from CLASS-CTL were very similar to each other at the dry site; they both indicate that evapotranspiration and runoff are limited by water availability. At the wet site, however, CLASS-WG only captured well the interannual runoff-precipitation relationship. The sensitivity analysis shows that CLASS water fluxes are particularly affected by the replacement of physically consistent input time series of incoming short-wave radiation, precipitation, temperature, and specific humidity. In conclusion, the results show that even though a weather generator can produce coherent climate time series, the use of this synthetic data as meteorological forcing in a physically based land surface model does not necessarily reproduce the complex surface processes, such as the surface water fluxes. More studies are encouraged to further analyze the constraints of this framework. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Snow cover plays a key role in the water and energy budgets over cold regions. Understanding and parameterizing water and heat exchange over snow surfaces in hydrologic models remains a major challenge. An innovative approach based on the theory of maximum entropy production (MEP) was developed for modeling energy budgets for snow‐covered surfaces. This study generalizes the MEP model to simulate surface water vapor (latent heat) fluxes over an entire snowpack lifecycle, including snow accumulation and melting during the early growing season. The expanded MEP model combines soil evaporation, canopy transpiration, and snow sublimation to evaluate snow water loss during the lifecycle of the snowpack. Two hypotheses are tested: (1) sublimation becomes negligible during snowmelt when snowpack is isothermal (0°C) and (2) transpiration is progressively activated as a function of the air temperature during vegetation awakening. The proposed approach is shown to be effective for modeling the total surface water vapor fluxes over the snowpack's lifecycle. Both the hypotheses are supported by field observations. Key Points: The MEP model is generalized for simulating surface water vapor fluxes over the lifecycle of the snowpack using Fluxnet dataTwo hypotheses are tested during the transition periods (snow melting and early growing seasons): (1) sublimation becomes negligible during snowmelt when snowpack is isothermal (0°C) and (2) transpiration is progressively activated as a function of air temperature during vegetation awakeningThe effectiveness of the MEP‐ET model was confirmed through testing the hypotheses [ABSTRACT FROM AUTHOR]

The impacts of climate and land-use changes make the stationary assumption in hydrology obsolete. Moreover, there is still considerable uncertainty regarding the future evolution of the Earth's climate and the extent of the alteration of flow regimes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. We propose a calibration/validation protocol based on the differential split-sample test and numerous, contrasted, climate sequences identified through a Hidden Markov Model (HMM) classification. The proposed protocol is tested on the Senegal River in West Africa. Results show that when the time series of river discharges does not exhibit a clear climate trend, or when it has multiple change points, classical rupture tests are useless and HMM classification is a viable alternative as long as the climate sub-sequences are long enough. [ABSTRACT FROM AUTHOR]

Climate change is predicted to increase the frequency and intensity of floods in the province of Quebec, Canada. Therefore, in 2015, to better monitor the level of adaptation to flooding of Quebec residents living in or near a flood-prone area, the Quebec Observatory of Adaptation to Climate Change developed five indices of adaptation to flooding, according to the chronology of events. The present study was conducted 4 years later and is a follow-up to the 2015 one. Two independent samples of 1951 (2015) and 974 (2019) individuals completed a questionnaire on their adoption (or non-adoption) of flood adaptation behaviors, their perception of the mental and physical impacts of flooding, and their knowledge of the fact that they lived in a flood-prone area.The results of the study demonstrated the measurement invariance of the five indices across two different samples of people over time, ensuring that the differences (or absence of differences) observed in flood-related adaptive behaviors between 2015 and 2019 were real and not due to measurement errors. They also showed that, overall, Quebeckers' flood-related adaptive behaviors have not changed considerably since 2015, with adaptation scores being similar in 2019 for four of the five flood indices. Moreover, the results indicated an increase in self-reported physical and mental health issues related to past flooding events, as well as a larger proportion of people having consulted a health professional because of these problems. Thus, this study provides a better understanding of flood adaptation in Quebec over the past 4 years and confirms that the five adaptive behavior indices developed in 2015 are appropriate tools for monitoring changes in flood adaptation in the province. Finally, our results showed that little has changed in Quebeckers' adoption of adaptive behaviors, highlighting the need for awareness raising in order to limit the impacts that climate change will have on the population. [ABSTRACT FROM AUTHOR]

Forecast uncertainties are unfortunately inevitable when conducting the deterministic analysis of a dynamical system. The cascade of uncertainty originates from different components of the forecasting chain, such as the chaotic nature of the atmosphere, various initial conditions and boundaries, inappropriate conceptual hydrologic modeling, and the inconsistent stationarity assumption in a changing environment. Ensemble forecasting proves to be a powerful tool to represent error growth in the dynamical 5 system and to capture the uncertainties associated with different sources. However, space still exists for improving their predictive skill and credibility through proper hydrologic post-processing. We tested the post-processing skills of Affine kernel dressing (AKD) and Non- dominated sorting genetic algorithm II (NSGA-II). Those two methods are theoretically/technically distinct, yet however, share the same feature that both of them relax the parametric assumption of the underlying distribution of the data (i.e., streamflow ensemble forecast). AKD transformed ensemble and the Pareto fronts 10 generated with NSGA-II demonstrated the superiority of post-processed ensemble in efficiently eliminating forecast biases and maintaining a proper dispersion with the increasing forecasting horizon. [ABSTRACT FROM AUTHOR]

In snow‐fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km2 forested catchment in southern Québec, Canada (47°N, 71°W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high‐resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index [LAI], tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small‐scale variability across the catchment and 1810 manual snow samples were collected throughout the consecutive winters of 2016–17 and 2017–18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the LAI. We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression, binary regression tree and neural networks (NN). We were unable to establish any relationship between the CV of SWE and the LAI. However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (Nash‐Sutcliffe efficiency [NSE] = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE. Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66). [ABSTRACT FROM AUTHOR]

Two-wavelength scintillometer systems can provide much needed measurements of area-averaged sensible and latent heat fluxes. However, these devices rarely have been deployed on canopy-covered complex terrain, and never in the circumpolar boreal biome, where large-scale fluxes are essential to hydroclimate modellers. We present a comparison of fluxes measured above a boreal-forested valley with a two-wavelength scintillometer and an eddy covariance system. Instruments were deployed in late summer 2017, and 19 days of data were retained for the analysis. The scintillometer path was 1347-m long and projected across the valley between 5 and 100 m above the ground, with an effective height of ≈ 88 m. The limitations of deriving surface fluxes using scintillometry in complex terrain are discussed, and the effects of atmospheric conditions on the flux comparison are quantified. Fluxes are calculated with the scintillometer only, and using a number of atmospheric variables from the eddy-covariance system; impacts of these calculation methods on the correlation between instrumental systems are assessed. Despite a weak agreement of structure parameters between instruments, the comparison of scintillometer and eddy-covariance fluxes yields good correlation ( R 2 up to 0.82). Scintillometry correlates best with eddy-covariance data when the atmospheric surface-layer top is above the scintillometer effective height, but R 2 only drops slightly otherwise (average decrease of 0.11). The validity of scintillometer fluxes appears dubious during night-time and stable periods. We show that area-averaged flux measurements using two-wavelength scintillometers are possible in hilly forests, but more studies are needed to pinpoint the best methodological framework. [ABSTRACT FROM AUTHOR]

This paper introduces the HydrOlOgical Prediction LAboratory (HOOPLA) developed at Université Laval for ensemble lumped hydrological modelling. HOOPLA includes functionalities to perform calibration, simulation, and forecast for multiple hydrological models and various time steps. It includes a range of hydrometeorological tools such as calibration algorithms, data assimilation techniques, potential evapotranspiration formulas and a snow accounting routine. HOOPLA is a flexible framework coded in MATLAB that allows easy integration of user-defined hydrometeorological tools. This paper also illustrates HOOPLA's functionalities using a set of 31 Canadian catchments. [ABSTRACT FROM AUTHOR]

Total terrestrial evaporation, also referred to as evapotranspiration, is a key process for understanding the hydrological impacts of climate change given that warmer surface temperatures translate into an increase in the atmospheric evaporative demand. To simulate this flux, many hydrological models rely on the concept of potential evaporation (PET), although large differences have been observed in the response of PET models to climate change. The maximum entropy production (MEP) model of land surface fluxes offers an alternative approach for simulating terrestrial evaporation in a simple way while fulfilling the physical constraint of energy budget closure and providing a distinct estimation of evaporation and transpiration. The objective of this work is to use the MEP model to integrate energy budget modelling within a hydrological model. We coupled the MEP model with HydroGeoSphere (HGS), an integrated surface and subsurface hydrologic model. As a proof of concept, we performed one-dimensional soil column simulations at three sites of the AmeriFlux network. The coupled model (HGS-MEP) produced realistic simulations of soil water content (root-mean-square error – RMSE – between 0.03 and 0.05 m 3 m -3 ; NSE – Nash–Sutcliffe efficiency – between 0.30 and 0.92) and terrestrial evaporation (RMSE between 0.31 and 0.71 mm d -1 ; NSE between 0.65 and 0.88) under semi-arid, Mediterranean and temperate climates. At the daily timescale, HGS-MEP outperformed the stand-alone HGS model where total terrestrial evaporation is derived from potential evaporation, which we computed using the Penman–Monteith equation, although both models had comparable performance at the half-hourly timescale. This research demonstrated the potential of the MEP model to improve the simulation of total terrestrial evaporation in hydrological models, including for hydrological projections under climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Total terrestrial evaporation is a key process to understand the hydrological impacts of climate change given that warmer surface temperatures translate into an increase in the atmospheric evaporative demand. To simulate this flux, many hydrological models rely on the concept of potential evaporation (PET) although large differences have been observed in the response of PET models to climate change. The Maximum Entropy Production (MEP) model of land surface fluxes offers an alternative approach to simulate terrestrial evaporation in a simple and parsimonious way while fulfilling the physical constraint of energy budget closure and providing a distinct estimation of evaporation and transpiration. The objective of this work is to use the MEP model to integrate energy budget modeling within a hydrological model. We coupled the MEP model with HydroGeoSphere, an integrated surface and subsurface hydrologic model. As a proof-of-concept, we performed one-dimensional soil column simulations at three sites of the AmeriFlux network. The coupled HGS-MEP model produced realistic simulations of soil water content (RMSE between 0.03 and 0.05 m³ m-3, NSE between 0.30 and 0.92) and terrestrial evaporation (RMSE between 0.31 and 0.71 mm day-1, NSE between 0.65 and 0.88) under semiarid, Mediterranean and temperate climates. HGS-MEP outperformed the standalone HGS model where total terrestrial evaporation is derived from potential evaporation which we computed using the Penman-Monteith equation. This research demonstrated the potential of the MEP model to improve the simulation of total terrestrial evaporation in hydrological models, including for hydrological projections under climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Earth Sciences is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie), the 0.5° CFSR product (Climate Forecasting System Reanalysis) and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16- and 32-km SAFRAN grids. The ~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16- and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations' dataset confirmed the advantage of using the SAFRAN product in SWAT modelling. [ABSTRACT FROM AUTHOR]

This work describes the implementation of the distributed GEM-Hydro runoff modeling platform, developed at Environment and Climate Change Canada (ECCC) over the last decade. The latest version of GEM-Hydro combines the SVS (Soil, Vegetation and Snow) land-surface scheme and the WATROUTE routing scheme in order to provide streamflow predictions on a gridded river network. SVS is designed to be two-way coupled to the GEM (Global Environmental Multi-scale) atmospheric model exploited by ECCC for operational weather and environmental forecasting. Although SVS has been shown to accurately track soil moisture during the warm season, it has never been evaluated before for hydrological prediction. This paper presents a first evaluation of its ability to simulate streamflow for all major rivers flowing into Lake Ontario. The skill level of GEM-Hydro is assessed by comparing the quality of simulated flows to that of two established hydrological models, MESH and WATFLOOD, which share the same routing scheme (WATROUTE) but rely on different land-surface schemes. All models are calibrated using the same meteorological forcings, objective function, calibration algorithm, and watershed delineation. Results show that GEM-Hydro performs well and is competitive with MESH and WATFLOOD. A computationally efficient strategy is proposed to calibrate the land-surface model of GEM-Hydro: a simple unit hydrograph is used for routing instead of its standard distributed routing component. The distributed routing part of the model can then be run in a second step to estimate streamflow everywhere inside the domain. Global and local calibration strategies are compared in order to estimate runoff for ungauged portions of the Lake Ontario watershed. Overall, streamflow predictions obtained using a global calibration strategy, in which a single parameter set is identified for the whole watershed of Lake Ontario, show skills comparable to the predictions based on local calibration. Hence, global calibration provides spatially consistent parameter values, robust performance at gauged locations, and reduces the complexity and computational burden of the calibration procedure. This work contributes to the Great Lakes Runoff Inter-comparison Project for Lake Ontario (GRIP-O) which aims at improving Lake Ontario basin runoff simulations by comparing different models using the same input forcings. [ABSTRACT FROM AUTHOR]

Seeking more accuracy and reliability, the hydrometeorological community has developed several tools to decipher the different sources of uncertainty in relevant modeling processes. Among them, the ensemble Kalman filter (EnKF), multimodel approaches and meteorological ensemble forecasting proved to have the capability to improve upon deterministic hydrological forecast. This study aims to untangle the sources of uncertainty by studying the combination of these tools and assessing their respective contribution to the overall forecast quality. Each of these components is able to capture a certain aspect of the total uncertainty and improve the forecast at different stages in the forecasting process by using different means. Their combination outperforms any of the tools used solely. The EnKF is shown to contribute largely to the ensemble accuracy and dispersion, indicating that the initial conditions uncertainty is dominant. However, it fails to maintain the required dispersion throughout the entire forecast horizon and needs to be supported by a multimodel approach to take into account structural uncertainty. Moreover, the multimodel approach contributes to improving the general forecasting performance and prevents this performance from falling into the model selection pitfall since models differ strongly in their ability. Finally, the use of probabilistic meteorological forcing was found to contribute mostly to long lead time reliability. Particular attention needs to be paid to the combination of the tools, especially in the EnKF tuning to avoid overlapping in error deciphering. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic forcing factors and their changes significantly impact water resources in many river basins around the world. Information on such changes can be derived from fine scale in situ and satellite observations, used in combination with hydrological models. The latter need to account for hydrological changes caused by human activities to correctly estimate the actual water resource. In this study, we consider the catchment area of the Garonne river (in France) to investigate the capabilities of space-based observations and up-to-date hydrological modeling in estimating water resources of a river basin modified by human activities and a changing climate. Using the ISBA-MODCOU and SWAT hydrological models, we find that the water resources of the Garonne basin display a negative climate trend since 1960. The snow component of the two models is validated using the moderate-resolution imaging spectroradiometer snow cover extent climatology. Crop sowing dates based on remote sensing studies are also considered in the validation procedure. Use of this dataset improves the simulated evapotranspiration and river discharge amounts when compared to conventional data. Finally, we investigate the benefit of using the MAELIA multi-agent model that accounts for a realistic agricultural and management scenario. Among other results, we find that changes in crop systems have significant impacts on water uptake for agriculture. This work constitutes a basis for the construction of a future modeling framework of the sociological and hydrological system of the Garonne river region. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Earth Sciences is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite many recent improvements, ensemble forecast systems for streamflow often produce under-dispersed predictive distributions. This situation is problematic for their operational use in water resources management. Many options exist for post-processing of raw forecasts. However, most of these have been developed using meteorological variables such as temperature, which displays characteristics very different from streamflow. In addition, streamflow data series are often very short or contain numerous gaps, thus compromising the estimation of post-processing statistical parameters. For operational use, a post-processing method has to be effective while remaining as simple as possible. We compared existing post-processing methods using normally distributed and gamma-distributed synthetic datasets. To reflect situations encountered with ensemble forecasts of daily streamflow, four normal distribution parameterizations and six gamma distribution parameterizations were used. Three kernel-based approaches were tested, namely, the 'best member' method and two improvements thereof, and one regression-based approach. Additional tests were performed to assess the ability of post-processing methods to cope with short calibration series, missing values or small numbers of ensemble members. We thus found that over-dispersion is best corrected by the regression method, while under-dispersion is best corrected by kernel-based methods. This work also shows key limitations associated with short data series, missing values, asymmetry and bias. One of the improved best member methods required longer series for the estimation of post-processing parameters, but if provided with adequate information, yielded the best improvement of the continuous ranked probability score. These results suggest guidelines for future studies involving real operational datasets. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Ice processes taking place in steep channels are sensitive to the thermal and hydrological regimes of upstream reaches and tributaries as well as to the local channel morphology. This work presents freezeup, mid-winter, and breakup data from four channels of increasing order located in a cold temperate watershed during the winter 2010-2011. From headwater channels to the main drainage system, water temperature, ice coverage, and ice processes are reported and related to weather conditions and to channel characteristics. Headwater channels only formed ephemeral ice features, and their water temperature reached as much as 4 °C in mid-winter. On the other hand, larger channels formed impressively large ice dams, some of them reaching 2 m in height. The development of a suspended ice cover partially insulated the channels; as a result, water temperatures remained above 0 °C even for air temperatures well below freezing. This work presents steep channels ice processes that have not been described in previous publications. The concept of a watershed cryologic continuum (WCC) is developed from the data collected at each channel order. This concept emphasizes the feedback loops that exist between morphology, hydrology, heat, and ice processes in a given watershed and can lead to a better understanding of ice processes taking place at any channel location within that watershed. The WCC can also contribute in improving our understanding of the impacts of climate change on the cryologic and thermal regimes of steep channels. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast. However, for hydrological forecasting, their low resolution currently limits their use to large watersheds. To bridge this gap, various implementations of a spatial statistical downscaling method were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 × 70-km resolution down to 6 × 4-km while increasing each pixel's rainfall variance and preserving its original mean. This was applied for nine consecutive days of summer 2009 with strong rain events over Quebec City, Canada. For comparison purposes, simpler methods were also implemented such as the bilinear interpolation, which disaggregates global forecasts without modifying their variance. The meteorological products were evaluated, using different scores and diagrams, against observed values taken from Quebec City rain gauge network. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using the variance-enhancing method were of similar quality than bilinear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared with the bilinear interpolation, which is a decisive advantage. Therefore, there is an interest in implementing variance-enhancing methods to disaggregate global ensemble rainfall forecasts. Copyright © 2012 Her Majesty the Queen in right of Canada. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper examines the controlling influence of snow and rain on river ice processes in creeks and streams. Winter precipitation (in the form of rain and snow) has been observed to affect river ice processes and channel parameters of low and high gradient channels in unsuspected ways that can have significant impacts on channel hydraulics, hydrology and habitat. On a low gradient stream, a snowfall event initiated the development of an ice cover by creating unconsolidated snow slush bridges that eventually froze in place. Afterward, both snowfalls and rainfalls in alternation with cold spells dramatically increased the thickening rate of the ice cover well beyond that predicted by classic equations. In a smaller low-gradient agricultural creek, wind-blown snow impeded the formation of an ice cover by insulating the flow from cold atmospheric conditions. On steep channels (of different sizes and morphologies), anchor snow slush has been seen to accumulate on the bed substrate. As opposed to anchor ice, anchor snow slush is not believed to require supercooling water conditions to form nor to stay in place. Finally, in a steep headwater creek, a rain-on-snow event generated a snow slush flow and multiple snow slush jams. This phenomenon was seen to divert most of the water out of the channel into another watershed and concomitantly signalled a mid-winter breakup in the greater watershed downstream. These observations suggest that the role of precipitation on small channel winter ice morphology and water flows, levels and currents has been severely underestimated and that any ecological winter studies, hydraulic structure designs and river modelling efforts need to include processes that are sometimes dominated by rain, slush and snow. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper traces two decades of neural network rainfall-runoff and streamflow modelling, collectively termed ‘river forecasting’. The field is now firmly established and the research community involved has much to offer hydrological science. First, however, it will be necessary to converge on more objective and consistent protocols for: selecting and treating inputs prior to model development; extracting physically meaningful insights from each proposed solution; and improving transparency in the benchmarking and reporting of experimental case studies. It is also clear that neural network river forecasting solutions will have limited appeal for operational purposes until confidence intervals can be attached to forecasts. Modular design, ensemble experiments, and hybridization with conventional hydrological models are yielding new tools for decision-making. The full potential for modelling complex hydrological systems, and for characterizing uncertainty, has yet to be realized. Further gains could also emerge from the provision of an agreed set of benchmark data sets and associated development of superior diagnostics for more rigorous intermodel evaluation. To achieve these goals will require a paradigm shift, such that the mass of individual isolated activities, focused on incremental technical refinement, is replaced by a more coordinated, problem-solving international research body. [ABSTRACT FROM PUBLISHER]