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Geohazards associated with the dynamics of the liquid and solid water of the Earth’s hydrosphere, such as floods and glacial processes, may pose significant risks to populations, activities and properties [...]

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

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

Abstract Flood quantile estimation at sites with little or no data is important for the adequate planning and management of water resources. Regional Hydrological Frequency Analysis (RFA) deals with the estimation of hydrological variables at ungauged sites. Random Forest (RF) is an ensemble learning technique which uses multiple Classification and Regression Trees (CART) for classification, regression, and other tasks. The RF technique is gaining popularity in a number of fields because of its powerful non-linear and non-parametric nature. In the present study, we investigate the use of Random Forest Regression (RFR) in the estimation step of RFA based on a case study represented by data collected from 151 hydrometric stations from the province of Quebec, Canada. RFR is applied to the whole data set and to homogeneous regions of stations delineated by canonical correlation analysis (CCA). Using the Out-of-bag error rate feature of RF, the optimal number of trees for the dataset is calculated. The results of the application of the CCA based RFR model (CCA-RFR) are compared to results obtained with a number of other linear and non-linear RFA models. CCA-RFR leads to the best performance in terms of root mean squared error. The use of CCA to delineate neighborhoods improves considerably the performance of RFR. RFR is found to be simple to apply and more efficient than more complex models such as Artificial Neural Network-based models.

Multi-cohort forest management in northern hardwood stands may well be the best way to successfully regenerate tree species of intermediate shade tolerance, such as yellow birch (Betula alleghaniensis Britt.). The creation of large enough gaps in the canopy favors increased light availability within the opening, while soil scarification provides suitable germination seedbeds. Evidence of these methods’ success nonetheless remains mostly the purview of experimental studies rather than operational tests. In Quebec, Canada, the multi-cohort methods promoted include group selection cutting and patch cutting. The present study tested their implementation at an operational scale and over a large territory in both hardwood-dominated and mixedwood stands. We assessed their efficacy in promoting natural regeneration of commercial hardwood trees, notably yellow birch and sugar maple (Acer saccharum Marsh.). We conducted regeneration surveys at 2, 5, 10, and 15 years after harvest. Overall, group selection and patch cuttings were successful in regenerating the target species. Yellow birch, for instance, showed a mean stocking around 60% and a mean sapling density around 3400 stems ha−1 after 15 years. We compared several variables for measuring regeneration in early years, and found that the relative abundance, the stocking based on one stem per sampling unit, and the mean maximum height were good predictors of the relative presence of yellow birch and sugar maple in 15-year-old canopy openings. Using smaller sampling units (6.25 m2 rather than 25 m2) and waiting until year 5 may be more useful for making such predictions. In addition, there was an important turnover in vertical dominance in these openings. Non-commercial woody competitors were frequently dominant in early years but were often replaced by commercial hardwoods, notably yellow birch. We propose certain thresholds for assessing the success of post-harvest regeneration and for evaluating the need for a cleaning treatment.

Abstract Having a realistic estimation of snow cover by conceptual hydrological models continues to challenge hydrologists. The calibration of the free model parameters is an unavoidable step and the uncertainties resulting from the use of this optimal set remains a source of concern, especially in forecasting applications and climate changes impact assessments. This study seeks to improve the calibration of the conceptual hydrological model GR4J coupled with the Cemaneige snow model, in order to obtain a more realistic simulation of the snow water equivalent (SWE) and to reduce the uncertainty of the free parameters. The performance of the two models was tested over twelve snow-dominated basins in southern Quebec, Canada. Four calibration strategies were adopted and compared. In the first two strategies, the parameters were calibrated against observed streamflow alone using a local and a global algorithm. In the third and fourth strategies the calibration of snow and hydrological parameters was performed against observed streamflow and snow water equivalent (SWE) measured at snow course transects, first separately, and then with a multiobjective approach. An ensemble of equifinal parameters was used to compare the capacity of the global and multiobjective algorithms to improve the parameters identifiability and to assess the impact of parameter equifinality on the temperature sensitivity of spring peak streamflow. The large number of equifinal parameters found during calibration underscores the importance of structural non-identifiability of the coupled GR4J-Cemaneige model. The inclusion of snow observations within a multiobjective calibration improved the simulation of SWE, the identifiability of the parameters and their correlation with basins characteristics. Parameter equifinality caused a small but non negligible uncertainty in the simulated response of spring peak flow to warming temperatures. Parameter equifinality should be considered in climate impact studies in snow-dominated basins where poorly constrained snow parameters can affect the temperature sensitivity of streamflow.

This study examines the hydrological sensitivity of an agroforested catchment to changes in temperature and precipitation. A physically based hydrological model was created using the Cold Regions Hydrological Modelling platform to simulate the hydrological processes over 23 years in the Acadie River Catchment in southern Quebec. The observed air temperature and precipitation were perturbed linearly based on existing climate change projections, with warming of up to 8 °C and an increase in total precipitation up to 20%. The results show that warming causes a decrease in blowing snow transport and sublimation losses from blowing snow, canopy-intercepted snowfall and the snowpack. Decreasing blowing snow transport leads to reduced spatial variability in peak snow water equivalent (SWE) and a more synchronized snow cover depletion across the catchment. A 20% increase in precipitation is not sufficient to counteract the decline in annual peak SWE caused by a 1 °C warming. On the other hand, peak spring streamflow increases by 7% and occurs 20 days earlier with a 1 °C warming and a 20% increase in precipitation. However, when warming exceeds 1.5 °C, the catchment becomes more rainfall dominated and the peak flow and its timing follows the rainfall rather than snowmelt regime. Results from this study can be used for sustainable farming development and planning in regions with hydroclimatic characteristics similar to the Acadie River Catchment, where climate change may have a significant impact on the dominating hydrological processes.

Floods account for a large part of global economic losses from natural disasters. As a result, the private insurance sector is increasingly participating in the financial risk sharing, thus expanding the role of actuaries to flood risk management. In this article, we investigate pricing and spatial segmentation of flood risk in the context of private insurance, meaning that individual risk assessment should minimize adverse selection. As such, we design a hierarchical flood risk model that allows an assessment at the individual level. Our model relies on a chain of physics-based climate, hydrological, and hydraulics modules combined with civil engineering methods to map the distribution of individual flood losses at high resolution. Building on such approach, we design pricing and segmentation methods tailored for flood risk management. We then apply the methods to study flood risk in a small city in the province of Quebec. We calculate premiums, analyze the impacts of risk sharing, set pricing territories consistent with the spatial flood risk, and finally, quantify the impact of greenhouse gas emission scenarios on individual and aggregate losses, premiums, and tail risk measures.

Abstract Change point detection methods have an important role in many hydrological and hydraulic studies of river basins. These methods are very useful to characterize changes in hydrological regimes and can, therefore, lead to better understanding changes in extreme flows behavior. Flood events are generally characterized by a finite number of characteristics that may not include the entire information available in a discharge time series. The aim of the current work is to present a new approach to detect changes in flood events based on a functional data analysis framework. The use of the functional approach allows taking into account the whole information contained in the discharge time series of flood events. The presented methodology is illustrated on a flood analysis case study, from the province of Quebec, Canada. Obtained results using the proposed approach are consistent with those obtained using a traditional change point method, and demonstrate the capability of the functional framework to simultaneously consider several flood features and, therefore, presenting a comprehensive way for a better exploitation of the information contained in a discharge time series.

Sensitive clays are known for producing retrogressive landslides, also called spread or flowslides. The key characteristics associated with the occurrence of these landslides on a sensitive clay slope must be assessed, and the potential retrogressive distance must be evaluated. Common risk analysis methods include empirical methods for estimating the distance of potential retrogression, analytical limit equilibrium methods, numerical modelling methods using the strength reduction technique, and the integration of a progressive failure mechanism into numerical methods. Methods developed for zoning purposes in Norway and Quebec provide conservative results in most cases, even if they don’t cover the worst cases scenario. A flowslide can be partially analysed using analytical limit equilibrium methods and numerical methods having strength reduction factor tools. Numerical modelling of progressive failure mechanisms using numerical methods can define the critical parameters of spread-type landslides, such as critical unloading and the retrogression distance of the failure. Continuous improvements to the large-deformation numerical modeling approach allow its application to all types of sensitive clay landslides.

Abstract The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.

Abstract 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 The consensus around the need for a shift in river management approaches to include more natural processes is steadily growing amongst scientists, practitioners, and governmental agencies. The freedom space for rivers concept promotes the delineation of a single space that integrates multiple fluvial dynamics such as floods, lateral migration, channel avulsions, and riparian wetlands connectivity. The objective of this research is to assess the validity of the hydrogeomorphological approach to delineate the freedom space for an extensive sampling of river reaches, covering 167 km, in contrasting watersheds in Quebec (Canada). Comparative analysis was conducted on the relative importance of erosion and flood processes on the freedom space delineation for various fluvial types. Semiautomated tools based on light detection and ranging (LiDAR) digital elevation models were also tested on an additional 274 km of watercourses to facilitate freedom space mapping over extensive zones and for highly dynamics environments such as alluvial fans. In the studied reaches, flood and erosion processes occur respectively, on average, in a space equivalent to 2.6 and 20.6 channel widths. In unconfined landscapes, flood processes represent an area up to almost four times the area of erosion processes expected in a 50‐year period. In partly confined and confined environments, erosion processes are more likely to exceed flooding zone, and therefore need to be integrated in the mapping. This study helps better determine the conditions for which the full methodology of freedom space mapping is required or where semiautomated methods can be used. It provides useful guidelines for the implementation of the freedom space approach.

The increase in the frequency of floods, which is a projected consequence of climate change, can have wide-ranging health and economic impacts. To cope with these floods and to reduce their impacts, households can adopt some preventive behaviours. The main goal of this research was to compare the adoption of flood mitigation behaviours in three populations presenting distinctive characteristics with a valid and an invariant measure of behavioural adaptation, as well as a baseline measure (comparison group). The article also aims to test the moderated effect of having experienced a flood on the relation between the perception of risk of being flooded and the adoption of preventive behaviours. A survey was conducted in flood-prone areas and in some areas that were not at risk in Quebec, Canada, through phone interviews. Results confirmed that people who lived in an at-risk area and had experienced past flooding events are more inclined to adopt preventive behaviours than people who lived in an at-risk area but had never experienced such an event, and those who lived outside at-risk areas. In addition, our results indicate that the at-risk population who have never experienced a flood engage in few flood preventive behaviours. This is worrisome, as their rate of adopting adaptive behaviour is very similar to the one seen in populations living outside at-risk areas, despite the increased risk inherent to their situation. This could be partly explained by our data showing that around a quarter of the at-risk population did not know they were living in a flood-prone area. Our results show that communication efforts are necessary in order to better inform the population of the risk related to living in a flood-prone area and that incentives should be developed to help enhance the rate of preventive behaviours in at-risk populations having never experienced a flood.

Abstract During spring 2011, an extreme flood occurred along the Richelieu River located in southern Quebec, Canada. The Richelieu River is the last section of the complex Richelieu basin, which is composed of the large Lake Champlain located in a valley between two large mountains. Previous attempts in reproducing the Richelieu River flow relied on the use of simplified lumped models and showed mixed results. In order to prepare a tool to assess accurately the change of flood recurrences in the future, a state‐of‐the‐art distributed hydrological model was applied over the Richelieu basin. The model setup comprises several novel methods and data sets such as a very high resolution river network, a modern calibration technique considering the net basin supply of Lake Champlain, a new optimization algorithm, and the use of an up‐to‐date meteorological data set to force the model. The results show that the hydrological model is able to satisfactorily reproduce the multiyear mean annual hydrograph and the 2011 flow time series when compared with the observed river flow and an estimation of the Lake Champlain net basin supply. Many factors, such as the quality of the meteorological forcing data, that are affected by the low density of the station network, the steep terrain, and the lake storage effect challenged the simulation of the river flow. Overall, the satisfactory validation of the hydrological model allows to move to the next step, which consists in assessing the impacts of climate change on the recurrence of Richelieu River floods. , Plain Language Summary In order to study the 2011 Richelieu flood and prepare a tool capable of estimating the effects of climate change on the recurrence of floods, a hydrological model is applied over the Richelieu basin. The application of a distributed hydrological model is useful to simulate the flow of all the tributaries of the Richelieu basin. This new model setup stands out from past models due to its distribution in several hydrological units, its high‐resolution river network, the calibration technique, and the high‐resolution weather forcing data set used to drive the model. The model successfully reproduced the 2011 Richelieu River flood and the annual hydrograph. The simulation of the Richelieu flow was challenging due to the contrasted elevation of the Richelieu basin and the presence of the large Lake Champlain that acts as a reservoir and attenuates short‐term fluctuations. Overall, the application was deemed satisfactory, and the tool is ready to assess the impacts of climate change on the recurrence of Richelieu River floods. , Key Points An advanced high‐resolution distributed hydrological model is applied over a U.S.‐Canada transboundary basin The simulated net basin supply of Lake Champlain and the Richelieu River discharge are in good agreement with observations of the 2011 flood The flow simulation is challenging due to the topographic and meteorological complexities of the basin and uncertainties in the observations

Canada’s vast regions are reacting to climate change in uncertain ways. Understanding of local disaster risks and knowledge of underlying causes for negative impacts of disasters are critical factors to working toward a resilient environment across the social, economic, and the built sectors. Historically, floods have caused more economical and social damage around the world than other types of natural hazards. Since the 1900s, the most frequent hazards in Canada have been floods, wildfire, drought, and extreme cold, in terms of economic damage. The recent flood events in the Canadian provinces of Ontario, New Brunswick, Quebec, Alberta, and Manitoba have raised compelling concerns. These include should communities be educated with useful knowledge on hazard risk and resilience so they would be interested in the discussion on the vital role they can play in building resilience in their communities. Increasing awareness that perceived risk can be very different from the real threat is the motivation behind this study. The main objectives of this study include identifying and quantifying the gap between people’s perception of exposure and susceptibility to the risk and a lack of coping capacity and objective assessment of risk and resilience, as well as estimating an integrated measure of disaster resilience in a community. The proposed method has been applied to floods as an example, using actual data on the geomorphology of the study area, including terrain and low lying regions. It is hoped that the study will encourage a broader debate if a unified strategy for disaster resilience would be feasible and beneficial in Canada.