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Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).

The study addresses the need for flood risk anticipation and planning, through the development of a flood zone mapping approach for different return periods, in order to best prevent and protect populations. Today, traditional methods are too costly, too slow or require too many requirements to be applied over large areas. As part of a project funded by the Canadian Space Agency, Geosapiens and the Institut National de la Recherche Scientifique set themselves the goal of designing an automatic process to generate water presence maps for different return periods at a resolution of 30 m, based on the historical database of Landsat missions from 1982 to the present day. This involved the design, implementation and training of a deep learning algorithm model based on the U-Net architecture for the detection of water pixels in Landsat imagery. The resulting maps were used as the basis for applying a frequency analysis model to fit a probability of occurrence function for the presence of water at each pixel. The frequency analysis data were then used to obtain maps of water occurrence at different return preiods such as 2, 5 and 20 years. 

The normative dimensions of flood harm in flood risk management (FRM) have become salient in a milieu of extreme flood events. In this article, two types of flood harm will be discussed. They are namely, risk harm and outcome harm. Whilst risk harm suggests that risk imposition by structural FRM measures is a type of harm that can increase vulnerability and diminish well-being, outcome harm is manifested in deliberate flooding used to protect certain privileged communities at the expense of harming other less privileged ones. Risk-imposing parties are required to seek consent for imposing new risks. In contrast, outcome harm as deliberate flooding is far more pernicious and should only be exercised in extreme situations with ample provisions for restitution and recovery. The aim of this article is to foreground and examine these under-explored notions of flood harm in the FRM discourse and in tandem, to expand the normative dimensions of FRM in a milieu where difficult ethical choices abound.

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%.

Spring flooding in riparian forests can cause significant reductions in earlywood-vessel size in submerged stem parts of ring-porous tree species, leading to the presence of ‘flood rings’ that can be used as a proxy to reconstruct past flooding events, potentially over millennia. The mechanism of flood-ring formation and the relation with timing and duration of flooding are still to be elucidated. In this study, we experimentally flooded four-year-old Quercus robur trees at three spring phenophases (late bud dormancy, budswell and internode expansion) and over different flooding durations (two, four and six weeks) to a stem height of 50 cm. The effect of flooding on root and vessel development was assessed immediately after the flooding treatment and at the end of the growing season. Ring width and earlywood-vessel size and density were measured at 25- and 75-cm stem height and collapsed vessels were recorded. Stem flooding inhibited earlywood-vessel development in flooded stem parts. In addition, flooding upon budswell and internode expansion led to collapsed earlywood vessels below the water level. At the end of the growing season, mean earlywood-vessel size in the flooded stem parts (upon budswell and internode expansion) was always reduced by approximately 50% compared to non-flooded stem parts and 55% compared to control trees. This reduction was already present two weeks after flooding and occurred independent of flooding duration. Stem and root flooding were associated with significant root dieback after four and six weeks and mean radial growth was always reduced with increasing flooding duration. By comparing stem and root flooding, we conclude that flood rings only occur after stem flooding. As earlywood-vessel development was hampered during flooding, a considerable number of narrow earlywood vessels present later in the season, must have been formed after the actual flooding events. Our study indicates that root dieback, together with strongly reduced hydraulic conductivity due to anomalously narrow earlywood vessels in flooded stem parts, contribute to reduced radial growth after flooding events. Our findings support the value of flood rings to reconstruct spring flooding events that occurred prior to instrumental flood records.

A physiographical space‐based kriging method is proposed for regional flood frequency estimation. The methodology relies on the construction of a continuous physiographical space using physiographical and meteorological characteristics of gauging stations and the use of multivariate analysis techniques. Two multivariate analysis methods were tested: canonical correlation analysis (CCA) and principal components analysis. Ordinary kriging, a geostatistical technique, was then used to interpolate flow quantiles through the physiographical space. Data from 151 gauging stations across the southern part of the province of Quebec, Canada, were used to illustrate this approach. In order to evaluate the performance of the proposed method, two validation techniques, cross validation and split‐sample validation, were applied to estimate flood quantiles corresponding to the 10, 50, and 100 year return periods. Results of the proposed method were compared to those produced by a traditional regional estimation method using the canonical correlation analysis. The proposed method yielded satisfactory results. It allowed, for instance, for estimating the 10 year return period specific flow with a coefficient of determination of up to 0.78. However, this performance decreases with the increase in the quantile return period. Results also showed that the proposed method works better when the physiographical space is defined using canonical correlation analysis. It is shown that kriging in the CCA physiographical space yields results as precise as the traditional estimation method, with a fraction of the effort and the computation time.

Objectives. To assess the environmental justice implications of flooding from Hurricane Harvey in Greater Houston, Texas, we analyzed whether the areal extent of flooding was distributed inequitably with respect to race, ethnicity, and socioeconomic status, after controlling for relevant explanatory factors.Methods. Our study integrated cartographic information from Harvey’s Inundation Footprint, developed by the US Federal Emergency Management Agency, with sociodemographic data from the 2012–2016 American Community Survey. Statistical analyses were based on bivariate correlations and multivariate generalized estimating equations.Results. The areal extent of Harvey-induced flooding was significantly greater in neighborhoods with a higher proportion of non-Hispanic Black and socioeconomically deprived residents after we controlled for contextual factors and clustering.Conclusions. Results provide evidence of racial/ethnic and socioeconomic injustices in the distribution of flooding and represent an importa...