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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]

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]

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]

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]

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]

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]

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 article presents a study about correcting wind-induced bias in solid precipitation measurements in case of uncertain and limited data. It discusses that automatic precipitation gauges tend to underestimate solid precipitation in the presence of wind and real-time estimation of snow depth and snow water equivalent on the ground is needed for hydrological forecasting and for meteorological forecasting. The study aims to show how the method of weighted least squares can be used to perform a regression analysis.

Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short-term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km2) located in Southern Québec, Canada. Streamflow ( Q), precipitations (rain R and snow S, or total P), temperature ( T) and snow lying ( A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow ( QSM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long-term averages, seasonal data, and QSM failed to improve the networks. [ABSTRACT FROM AUTHOR]

The reasons why most rainfall-runoff models appear relatively insensitive to potential evapotranspiration (PE) inputs, compared with rainfall inputs, are investigated. To this aim, a methodology is presented providing detailed tracking of the treatment of PE input by two rainfall-runoff models. Since uncertainties affect both the structures and the inputs of rainfall-runoff models, the analysis is based on synthetic flow data. Standard synthetic streamflow series were generated using a standard PE input. Then, the PE series were corrupted successively by random and autocorrelated errors, and the propagation of these errors through the models' state variables is followed. For comparison, the same methodology was applied to rainfall data. The analysis is focused on two lumped rainfall-runoff models (the GR4J model and a lumped version of TOPMODEL) over a large sample of 308 watersheds. The investigation shows that perturbation errors in the potential evapotranspiration are absorbed by the model's production (soil moisture accounting) reservoir, which controls the water losses from the model. Given the slow variations in the soil moisture accounting reservoir, rainfall-runoff models behave like low-pass filters, absorbing high-frequency variations of PE inputs. In contrast, the models tested here do not smooth the rainfall perturbation. [ABSTRACT FROM AUTHOR]

This paper examines the respective merits of aggregative and disaggregative approaches in hydrological modeling and aims at determining how to identify the most appropriate level of spatial distribution in rainfall-runoff modeling. The lumped approach is compared with two types of semidistributed approaches to assess the relative importance of rainfall and parameter distribution on modeling results. In order to base these comparisons on a large number of definitely heterogeneous basins, we introduce chimera watersheds. Chimera watersheds associate two actual watersheds of similar size to constitute a third, highly heterogeneous virtual basin, where the knowledge of spatialized flows makes it possible to test disaggregated approaches and to compare them to the lumped approach. We show that the greatest portion (two thirds) of improvement contributed by spatial distribution comes from accounting for rainfall variability. If spatial distribution is considered to be a useful direction leading to improving the reliability of hydrological models, we believe that efforts should be directed first and foremost toward the use of spatially distributed rainfall data and only secondary to the disaggregation of watershed (land-surface) parameters. [ABSTRACT FROM AUTHOR]

Three types of functionally different artificial neural network (ANN) models are calibrated using a relatively short length of groundwater level records and related hydrometeorological data to simulate water table fluctuations in the Gondo aquifer, Burkina Faso. Input delay neural network (IDNN) with static memory structure and globally recurrent neural network (RNN) with inherent dynamical memory are proposed for monthly water table fluctuations modeling. The simulation performance of the IDNN and the RNN models is compared with results obtained from two variants of radial basis function (RBF) networks, namely, a generalized RBF model (GRBF) and a probabilistic neural network (PNN). Overall, simulation results suggest that the RNN is the most efficient of the ANN models tested for a calibration period as short as 7 years. The results of the IDNN and the PNN are almost equivalent despite their basically different learning procedures. The GRBF performs very poorly as compared to the other models. Furthermore, the study shows that RNN may offer a robust framework for improving water supply planning in semiarid areas where aquifer information is not available. This study has significant implications for groundwater management in areas with inadequate groundwater monitoring network. [ABSTRACT FROM AUTHOR]

Artificial neural networks are alternatives to stochastic models even if the optimization of their architectures remains a tricky problem. Two different approaches in long-term forecasting of potential energy inflows using a feedforward neural network (FNN) and a recurrent neural network (RNN) are proposed. The problem of overfitting, particularly critical for limited hydrologic data records, is addressed using a new approach entitled optimal weight estimate procedure (OWEP). The efficiency of the two models using OWEP is assessed through multistep forecasts. The experiment results show that, in general, OWEP improves the models 'performance and significantly reduces the training time on the order of 60 percent. The RNN outperforms the FNN but costs about a factor of 2 longer in training time. Furthermore, the neural network-based models provide more accurate forecasts than traditional stochastic models. Overall, the RNN appears to be the best suited for potential energy inflows forecasting and therefore for hydropower systems management and planning. [ABSTRACT FROM AUTHOR]