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Gravel-bed rivers are disproportionately important to regional biodiversity, species interactions, connectivity, and conservation. , Gravel-bed river floodplains in mountain landscapes disproportionately concentrate diverse habitats, nutrient cycling, productivity of biota, and species interactions. Although stream ecologists know that river channel and floodplain habitats used by aquatic organisms are maintained by hydrologic regimes that mobilize gravel-bed sediments, terrestrial ecologists have largely been unaware of the importance of floodplain structures and processes to the life requirements of a wide variety of species. We provide insight into gravel-bed rivers as the ecological nexus of glaciated mountain landscapes. We show why gravel-bed river floodplains are the primary arena where interactions take place among aquatic, avian, and terrestrial species from microbes to grizzly bears and provide essential connectivity as corridors for movement for both aquatic and terrestrial species. Paradoxically, gravel-bed river floodplains are also disproportionately unprotected where human developments are concentrated. Structural modifications to floodplains such as roads, railways, and housing and hydrologic-altering hydroelectric or water storage dams have severe impacts to floodplain habitat diversity and productivity, restrict local and regional connectivity, and reduce the resilience of both aquatic and terrestrial species, including adaptation to climate change. To be effective, conservation efforts in glaciated mountain landscapes intended to benefit the widest variety of organisms need a paradigm shift that has gravel-bed rivers and their floodplains as the central focus and that prioritizes the maintenance or restoration of the intact structure and processes of these critically important systems throughout their length and breadth.

Abstract Topo‐bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real‐world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real‐world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping.

<p>Devastating floods in southeast Queensland in 2011 were the combination of flash flooding in the Lockyer Valley with riverine flooding in the Brisbane metropolitan area. While there is considerable information about the immediate impact on those affected, there is less understanding of the long-term health effects that follow such events. This study explored the perceptions of health effects and support received by people affected by the 2011 southeast Queensland flood six years after the event. A cross-sectional survey of 327 people was conducted in areas affected by the floods. The questionnaire sought information about the ongoing social, economic, demographic and self-declared physical and mental health effects. The data were analysed through comparison of those unaffected with those directly affected by the floods. Residents whose households were flooded were more likely to score their health negatively than non-affected residents and had higher reported rates of trauma, injury and mental illness. Twenty-six per cent of this group reported that they still experience some adverse health effects from the floods. Managing the long-term health implications of a flood-affected population is an important public policy task. Dissatisfaction with recovery operations and perceived injustices associated with insurance and compensation arrangements may aggravate health consequences. Early recognition and intervention may assist with reducing secondary effects.</p>

Devastating floods occur regularly around the world. Recently, machine learning models have been used for flood susceptibility mapping. However, even when these algorithms are provided with adequate ground truth training samples, they can fail to predict flood extends reliably. On the other hand, the height above nearest drainage (HAND) model can produce flood prediction maps with limited accuracy. The objective of this research is to produce an accurate and dynamic flood modeling technique to produce flood maps as a function of water level by combining the HAND model and machine learning. In this paper, the HAND model was utilized to generate a preliminary flood map; then, the predictions of the HAND model were used to produce pseudo training samples for a R.F. model. To improve the R.F. training stage, five of the most effective flood mapping conditioning factors are used, namely, Altitude, Slope, Aspect, Distance from River and Land use/cover map. In this approach, the R.F. model is trained to dynamically estimate the flood extent with the pseudo training points acquired from the HAND model. However, due to the limited accuracy of the HAND model, a random sample consensus (RANSAC) method was used to detect outliers. The accuracy of the proposed model for flood extent prediction, was tested on different flood events in the city of Fredericton, NB, Canada in 2014, 2016, 2018, 2019. Furthermore, to ensure that the proposed model can produce accurate flood maps in other areas as well, it was also tested on the 2019 flood in Gatineau, QC, Canada. Accuracy assessment metrics, such as overall accuracy, Cohen’s kappa coefficient, Matthews correlation coefficient, true positive rate (TPR), true negative rate (TNR), false positive rate (FPR) and false negative rate (FNR), were used to compare the predicted flood extent of the study areas, to the extent estimated by the HAND model and the extent imaged by Sentinel-2 and Landsat satellites. The results confirm that the proposed model can improve the flood extent prediction of the HAND model without using any ground truth training data.

Floodplains, one of the most biologically diverse and productive ecosystems, are under threat from intensive crop production. Implementing perennial strips alongside agricultural ditches and streams could reduce negative impacts of intensive agriculture and restore wildlife habitats in cultivated floodplains. To successfully set up perennial strips, it is important to understand the parameters that drive their establishment. Here we assessed the establishment success of reed canarygrass (RCG; Phalaris arundinacea ) strips in the lake Saint Pierre (LSP) floodplain, Québec, Canada by monitoring RCG biomass and vegetation height over 4 years and identify the factors driving its establishment. A total of 26 RCG strips across six municipalities of LSP were monitored. Biomass and vegetation height of RCG increased over time to reach an average of 5048 kg/ha in year 4 and 104 cm in year 3 in established strips. The RCG established successfully in 62% of surveyed plots and three environmental parameters explained 61% of this success. Establishment of RCG was most successful when a first rain came right after seeding (<3 days). High clay content and low elevation were associated with establishment failures. Overall, our results highlight the ability of RCG strips to restore dense perennial vegetation cover in cultivated floodplain, thereby providing suitable habitat for fish spawning during spring floods. This study provides significant insight into the drivers of establishment of perennial grass strips in highly constrained cultivated areas such as floodplains.

Abstract Large‐scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high‐resolution (1 m) LiDAR DEMs are employed to present a novel large‐scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large‐scale 1D/2D flood model LISFLOOD‐FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS‐automated procedure allows to obtain the average width required to run LISFLOOD‐FP. The GIS‐automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM. Results show significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.

Abstract Flood risk management decisions in many countries are based on decision‐support frameworks which rely on cost‐benefit analyses. Such frameworks are seldom informative about the geographical distribution of risk, raising questions on the fairness of the proposed policies. In the present work, we propose a new decision criterion that accounts for the distribution of risk reduction and apply it to support flood risk management decisions on a transboundary stretch of the Rhine River. Three types of interventions are considered: embankment heightening, making Room for the River, and changing the discharge distribution of the river branches. The analysis involves solving a flood risk management problem according to four alternative formulations, based on different ethical principles. Formulations based on cost optimization lead to very poor performances in some areas for the sake of reducing the overall aggregated costs. Formulations that also include equity criteria have different results depending on how these are defined. When risk reduction is distributed equally, very poor economic performance is achieved. When risk is distributed equally, results are in line with formulations based on cost optimization, while a fairer risk distribution is achieved. Risk reduction measures also differ, with the cost optimization approach strongly favoring the leverage of changing the discharge distribution and the alternative formulations spending more on embankment heightening and Room for the River, to rebalance inequalities in risk levels. The proposed method advances risk‐based decision‐making by allowing to consider risk distribution aspects and their impacts on the choice of risk reduction measures.

Abstract Study Region: In Canada, dams which represent a high risk to human loss of life, along with important environmental and financial losses in case of failure, have to accommodate the Probable Maximum Flood (PMF). Five Canadian basins with different physiographic characteristics and geographic locations, and where the PMF is a relevant metric have been selected: Nelson, Mattagami, Kenogami, Saguenay and Manic-5. Study Focus: One of the main drivers of the PMF is the Probable Maximum Precipitation (PMP). Traditionally, the computation of the PMP relies on moisture maximization of high efficiency observed storms without consideration for climate change. The current study attempts to develop a novel approach based on traditional methods to take into account the non-stationarity of the climate using an ensemble of 14 regional climate model (RCM) simulations. PMPs, the 100-year snowpack and resulting PMF changes were computed between the 1971-2000 and 2041-2070 periods. New Hydrological Insights for the Region: The study reveals an overall increase in future spring PMP with the exception of the most northern basin Nelson. It showed a projected increase of the 100-year snowpack for the two northernmost basins, Nelson (8%) and Manic-5 (3%), and a decrease for the three more southern basins, Mattagami (-1%), Saguenay (-5%) and Kenogami (-9%). The future spring PMF is projected to increase with median values between -1.5% and 20%.

Variability and nonstationarity in flood regimes of cold regions are examined using data from hydrometric reference streamflow gauging stations from 27 natural watersheds in Canada and adjacent areas of the United States. Choosing stations from reference networks with nearly 100 years of data allows for the investigation of changes that span several phases of some of the atmospheric drivers that may influence flood behavior. The reference hydrologic networks include only stations considered to have good quality data and were screened to avoid the influences of regulation, diversions, or land use change. Changes and variations in flood regimes are complex and require a multifaceted approach to properly characterize the types of changes that have occurred and are likely to occur in the future. Peaks over threshold (POT) data are extracted from daily flow data for each watershed, and changes to the magnitude, timing, frequency, volume, and duration of threshold exceedences are investigated. Seasonal statistics are used to explore changes in the nature of the flood regime based on changes in the timing of flood threshold exceedences. A variety of measures are developed to infer flood regime shifts including from a nival regime to a mixed regime and a mixed regime to a more pluvial-dominated regime. The flood regime at many of the watersheds demonstrates increased prominence of rainfall floods and decreased prevalence of snowmelt contributions to flood responses. While some individual stations show a relationship between flood variables and climate indices, these relationships are generally weak.

In gravelly floodplains, streamflood events induce groundwater floodwaves that propagate through the alluvial aquifer. Understanding groundwater floodwave dynamics can contribute to groundwater flood risk management. This study documents groundwater floodwaves on a flood event basis to fully assess environmental factors that control their propagation velocity, their amplitude and their extension in the floodplain, and examines the expression of groundwater flooding in the Matane River floodplain (Quebec, Canada). An array of 15 piezometers equipped with automated level sensors and a river stage gauge monitoring at 15-minute intervals from September 2011 to September 2014 were installed within a 0.04-km2 area of the floodplain. Cross-correlation analyses were performed between piezometric and river-level time series for 54 flood events. The results reveal that groundwater floodwave propagation occurs at all flood magnitudes. The smaller floods produced a clear groundwater floodwave through the floodplain, while the largest floods affected local groundwater flow orientation by generating an inversion of the hydraulic gradient. Propagation velocities ranging from 8 to 13 m/h, which are two to three orders of magnitude higher than groundwater velocity, were documented while the induced pulse propagated across the floodplain to more than 230 m from the channel. Propagation velocity and amplitude attenuation of the groundwater floodwaves depend both on flood event characteristics and the aquifer characteristics. Groundwater flooding events are documented at discharge below bankfull (< 0.5 Qbf). This study highlights the role of flood event hydrographs and environmental variables on groundwater floodwave properties and the complex relationship between flood event discharge and groundwater flooding. The role that groundwater floodwaves play in flood mapping and the ability of analytical solutions to reproduce them are also discussed.

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone.

Floods are among natural disasters that increasingly threaten society, especially with current and future climate change trends. Several tools have been developed to help planners manage the risks associated to flooding, including the mapping of flood-prone areas, but one of the major challenges is still the availability of detailed data, particularly bathymetry. This manuscript compares two modeling approaches to produce flood maps. An innovative large-scale approach that, without bathymetric data, estimates by inverse modeling the bed section for a given flow and a given roughness coefficient through 1 D/2D hydraulic modeling (LISFLOOD-FP). And a local approach, with a detailed coupled 1 D/2D hydraulic model (HEC-RAS) that uses all available information at the bed and floodplain (LiDAR and bathymetry). Both implementations revealed good performance values for flood peak levels as well as excellent fit indices in describing the areal extent of flooding. As expected, the local approach is more accurate, but the results of the large-scale approach are very promising especially for areas lacking bathymetric data and for large-scale governmental programs.

The summer 1993 flooding of the upper Mississippi River valley reminds us that floods are the most globally pervasive, environmentally diverse and continually destructive of all natural hazards. The fact that flood damages continue to rise raises commonsense questions about conventional flood science. Like much modern environmental science, conventional flood science has followed the model of theoretical physics. It advanced from early emphasis on streamflow measurement to the use of simple formulae, and finally to the abstract theoretical sophistication of modern modeling studies. Two approaches are now used to “predict” flood phenomena: (1) beginning with the conventional database of measured properties of small common floods, a conceptual generalization is made to the idealized properties of the large, rare floods from which society is assumed to be at risk, and (2) explanation of detailed, specific flood phenomena is achieved through theoretical generalization (models) based on “first principles”, which are assumed to apply to the entire class of phenomena. Unfortunately, both approaches devote almost all their attention to methodology, increasingly mathematical, without questioning basic underlying assumptions. Increasingly it is the assumptions, often unstated, that serve to embody the understanding of floods as real-world particular phenomena, rather than as conceptual generalities. Such trends lead to an unease that it is not floods that are being researched by much of conventional flood science. Rather, such flood “science” is increasingly becoming the mathematical manipulation of idealized parameters that are assumed to have flood-like properties. These idealizations of flood attributes are generalized, and the resulting predicted consequences are imposed upon society through engineering designs, flood-hazard zonations, and the like. Geomorphological understanding of floods derives a from along geological tradition of studying indices of real processes operating in the past. In contrast to the conceptual, theoretical treatment of floods as classes or generalizations, geomorphologists study particular floods revealed as a natural experience that is recorded in the sediments, landforms, and erosional scars of past floods. The strength of this approach is in its affinity to the commonsense perceptional basis that underpins human action. Geomorphological flood studies, including recent advances in paleoflood hydrology, are needed as a complement to conventional hydrological approaches. The resulting complementarity will allow the predictions of the conventional approach to be grounded in the concrete particulars of experience. Without such grounding, flood science risks continuing as an empty quest for universal ideals while humanity, paralyzed by inaction, continues to suffer from the reality of particular floods.

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.

ABSTRACTTwo modelling approaches are presented in this article for spatial and temporal analysis of water resources risk. Major sources of uncertainty in water resources management are spatial and temporal variability. Spatial variability occurs when values fluctuate with the location of an area and temporal variability occurs when values fluctuate with time. System dynamics (SD) simulation and hydrodynamic modelling are presented in this article as tools for modelling the dynamic characteristics of flood risk and its spatial variability. The first modelling framework presents SD simulation coupled with 3D fuzzy set theory. Whereas the second modelling framework presents hydrodynamic modelling coupled with 3D fuzzy set theory. The two integrated modelling frameworks are illustrated and compared using the Red River flood of 1997 (Manitoba, Canada) as a case study. For the 1997 Red River case study, SD simulation proved to be efficient modelling approach for capturing the feedback-based dynamic processes oc...

Au printemps 2017 et 2019, plus 300 municipalités du Québec ont été confrontées à de graves inondations qui ont provoqué d’importants dommages aux propriétés, aux biens personnels de milliers de citoyens et à plusieurs infrastructures municipales. Dans le contexte des inondations de 2019, il faut toutefois souligner l’importante différence entre celles vécues par la municipalité de Sainte-Marthe-sur-le-Lac et celles survenues dans les autres municipalités du Québec. À Sainte-Marthe-sur-le-Lac, les inondations ont été soudaines, et rapides, car elles ont été provoquées par la rupture d’une digue. Ce sinistre, de nature anthropique, a occasionné la relocalisation d’urgence de plusieurs centaines de familles. Quant aux autres municipalités, c’est la crue printanière qui a généré des inondations fluviales, un sinistre de cause naturelle, dont l’ampleur et la durée ont dépassé les précédents évènements historiques, y compris ceux de 2017. Lors de ces inondations, les municipalités et divers partenaires gouvernementaux (CIUSSS/CISSS, MSP, SQ…) et certains organismes bénévoles en sécurité civile (Croix-Rouge Canadienne, Armée du Salut, Ambulance St-Jean, etc.), ont déployé leurs intervenants afin d’apporter leur aide et leur soutien aux municipalités et aux personnes sinistrées. Des centaines de policiers, pompiers, employés municipaux, gestionnaires, chefs d’équipe, militaires, intervenants psychosociaux, bénévoles spécialisés en recherche et sauvetage ou en soutien émotionnel ont alors travaillé sans relâche pour assurer la sécurité des personnes et des biens, mais pour aussi amortir, autant que possible, les impacts psychosociaux inévitablement causés par ce type de sinistre. Ce rapport synthèse présente le point de vue d’une centaine d’intervenants, provenant de différentes régions du Québec qui ont contribué à la gestion et la coordination des efforts pour orchestrer la réponse nécessaire lors des inondations de 2019. Ils ont été invités à documenter les stratégies mises en place à court et à moyen terme qui, selon leurs observations, ont contribué à : •Augmenter le sentiment de sécurité des sinistrés ; •Diminuer leur niveau d’anxiété et d’isolement ; et •Prévenir la détérioration de leur état de santé physique et psychologique.