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Risk management, justice (i.e. equity, fairness), and sustainability are tightly interconnected. This literature review investigates how and why justice is considered in flood risk management. 20 scientific documents published between 2015 and 2020 are analyzed in depth. The results show a distinction between distributive and procedural justice and a complicated judgment of fairness based on different philosophies that vary depending on the country, the type of flood, and the type of strategy studied. Equity is found to be an under-discussed topic compared to its importance. Justice in flood risk management matters because (i) the impacts of floods affect different people unevenly, (ii) the interest in equity evinced by public authorities influences societal transformation, and (iii) the perception of fairness matters at both individual and collective levels. This paper analyzes the link between justice considerations and sustainability in relation to four dimensions: social, ecological, spatial, and temporal. Social and spatial issues are the most commonly studied in the literature, while ecological and temporal ones have generally been overlooked, creating a research gap. The results are discussed in terms of their diversities of justice concepts, places of investigation, and types of strategies. Various justice frameworks are used, but since none of them focus specifically on the contribution of flood risk management to sustainability through justice considerations, a flood risk justice framework is developed, which translates into theoretical and practical tools. It is based on the considerations of both humans and non-humans into different spatio-temporal scales. • Justice issues are under-discussed while they matter for flood risk management. • Diverse case studies in various places show procedural and distributive (in)justice. • There is no agreement in the literature on how to judge the fairness of a strategy. • The literature is mostly limited to social and spatial justice aspects. • Flood risk justice includes social, ecological, spatial, and temporal issues.

Abstract Impacts of floods on human society have been drawing increasing human concerns in recent years. In this study, flood observations from EM-DAT (Emergency Events Database) and DFO (Dartmouth Flood Observatory) datasets were analyzed to investigate frequency and intensity of floods, and flood-induced mortality, flood-affected population as well during 1975–2016 across the globe. Results indicated that: (1) occurrence rate of floods, flood-induced mortality and flood-affected population were generally increasing globally. However, flood-induced mortality and flood-affected people per flood event were in slight decrease, indicating that flood-induced mortality and flood-affected people due to increased floods exceeded those by individual flood event; (2) annual variation of mortality per flood event is highly related to floods with higher intensity. Specifically, the flood frequency and flood-induced mortality are the largest in Asia, specifically in China, India, Indonesia and Philippine; while significantly increased flood-affected population and mean annual mortality was detected in China, USA and Australia; (3) tropical cyclones (TC) are closely related to flood-induced mortality in parts of the countries along the western coast of the oceans. The frequency of channel floods in these regions is the largest and large proportion of flood-induced deaths and the highest flood-induced mortality can be attributed to TC-induced flash floods; (4) Population density and GDP per unit area are in significantly positive correlation with the number of flood-related victims per unit area, number of deaths and economic losses with exception of low-income countries. However, the flood-affected population and flood-induced mortality increase with decrease of per capita GDP; while the per capita economic loss increases with the increase of per capita GDP, indicating that the higher the population density and GDP per unit for a region, the higher sensitivity of this area to flood hazards.

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.

A landscape is a mosaic of natural and/or artificial communities and wa-terbodies and may contain several distinct ecosystems. Human life depends on many services delivered by the water-based aquatic and land-based terrestrial ecosystems. A wide variety of aquatic ecosystems exist and alt-hough they represent a low percentage of the Earth’s surface, their roles and functions make them crucial. Aquatic ecosystems especially inland aquatic ecosystems are rich in biodiversity and home to a diverse array of species and habitats, providing numerous economic and societal benefits to humans. Understanding diversity of aquatic ecosystems within landscape is a fundamental goal of both basic and applied ecological research. This study recognizes, defines, classifies, characterizes and compares for the first time the aquatic resources vis-à-vis aquatic ecosystems in the landscape of Adilabad District, Telangana, “Deccan Region”, India, which was selected as the study area.

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>

This study discusses the flooding related consequences of climate change on most populous Canadian cities and flow regulation infrastructure (FRI). The discussion is based on the aggregated results of historical and projected future flooding frequencies and flood timing as generated by Canada-wide hydrodynamic modelling in a previous study. Impact assessment on 100 most populous Canadian cities indicate that future flooding frequencies in some of the most populous cities such as Toronto and Montreal can be expected to increase from 100 (250) years to 15 (22) years by the end of the 21st century making these cities highest at risk to projected changes in flooding frequencies as a consequence of climate change. Overall 40–60% of the analyzed cities are found to be associated with future increases in flooding frequencies and associated increases in flood hazard and flood risk. The flooding related impacts of climate change on 1072 FRIs located across Canada are assessed both in terms of projected changes in future flooding frequencies and changes in flood timings. Results suggest that 40–50% of the FRIs especially those located in southern Ontario, western coastal regions, and northern regions of Canada can be expected to experience future increases in flooding frequencies. FRIs located in many of these regions are also projected to experience future changes in flood timing underlining that operating rules for those FRIs may need to be reassessed to make them resilient to changing climate.

Abstract This work explores the relationship between catchment size, rainfall duration, and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-h) resolution single model initial-condition large ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 h and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency, and catchment size, with the shortest durations, longest return periods, and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-yr rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration–frequency–size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return-period flows, both being conditions for which the amplification of future flow will be maximized.

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.

We analyze the impact of development on flood fatalities using a new data set of 2,171 large floods in 92 countries between 1985 and 2008. Our results challenge the conventional wisdom that development results in fewer fatalities during natural disasters. Results indicating that higher income and better governance reduce fatalities during flood events do not hold up when unobserved country heterogeneity and within-country correlation of standard errors are taken into account. We find that income does have a significant, indirect effect on flood fatalities by affecting flood frequency and flood magnitude, but this effect is nonmonotonic, with net reductions in fatalities occurring only in lower income countries. We find little evidence that improved governance affects flood fatalities either directly or indirectly.

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.

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors.