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As a key factor in the flood damage assessment process, the damage ratio is defined as the ratio of the potential damage value to the total risk assets at a certain inundation depth. Spatial variability of flood‐bearing elements caused spatial heterogeneity, local correlation and neighborhood non‐stationarity to the damage ratio. Thus, it is challenging to scientifically and rationally measure the damage ratio. In this study, a modeling framework of stochastic flood damage assessment functions was established to measure triple uncertainties. First, based on the maximum likelihood estimation method, migration data of the flood damage ratio were constructed as the beta distribution by heterogeneous multi‐attribute analogy. Then, the Markov chain Monte Carlo method, combined with a variant sliding window method, is used for sampling to generate spatially distributed samples. In addition, three risk factors were integrated grid by grid to assess flood damage. By taking the Jinshui District of Zhengzhou City in China as an example, economic losses during the 1%, 2%, 5% and 10% annual exceedance probability (AEP) floods were assessed, respectively. The results show that the economic losses showed a significant increasing trend with AEP. The optimal domain scales for industrial, commercial, residential and public service land uses were 19, 14, 25 and 32 m, respectively, and also varied with different AEP floods. The model was effectively verified by simulating and comparing historical data of the "7·20" flood event in Zhengzhou, China. This study aims to provide feasible ideas for uncertainty research of damage assessment methods. Plain Language Summary: The damage ratio serves as the link between flood hydrological elements and damages and has an indispensable role in quantifying flood risks. However, the spatial variability of flood‐bearing elements leads to three kinds of spatial uncertainties in damage ratios: spatial heterogeneity, local correlation and neighborhood non‐stationarity. Thus, in this paper, a stochastic flood damage assessment model considering multiple uncertainties was proposed and it is found that optimal domain scales were inconsistent for different annual exceedance probability flood events and different land uses. The model was effectively verified by simulating and comparing historical data of the "7·20" flood event in Zhengzhou, China. Key Points: Accurately quantifying flood damage ratios is a challenge due to the link between flood hydrological elements and lossesSpatial variability of flood‐bearing components caused triple spatial uncertainties to damage ratiosDifferent land uses and flood events with different annual exceedance probabilities exhibited inconsistent optimal domain scales [ABSTRACT FROM AUTHOR]

The hydraulic resistance of groynes is an important factor in the determination of design flood water levels on rivers and the assessment of how much these levels are lowered by modifying the groynes. In standard one‐ or two‐dimensional numerical hydrodynamic models for flood risk management, groynes are commonly represented as subgrid features with a local energy loss according to a weir formula. We tested this representation by using a two‐dimensional horizontal mesh at various groyne submergence degrees by comparing the results with those of flume experiments. We also compared the results with simulations using different 2D and 3D approaches on finer grids that incorporate groynes in the bed topography. In one of the two tested 3D models, complete Reynolds‐averaged Navier‐Stokes equations were solved. The second tested 3D model was constructed simpler by assuming hydrostatic pressure distribution in the vertical direction. We employed Delft3D software in construction and execution of all models. One of the 3D models did predict the hydraulic resistance at low submergence better than the standard model, but it slightly underestimated the resistance at higher submergences. Despite differences in flow characteristics, weirs and groynes were found to produce similar flow resistances for the same height and boundary conditions. Simulations of groyne modifications showed that hydraulic resistance decreased nonlinearly with groyne lowering and streamlining. Plain Language Summary: Groynes are used for river training. Their positive effects include riverbank stabilization, improvement of navigability and prevention of ice jams. However, during floods they become submerged and increase the flood water depths by blocking the flow and increasing turbulence. This may lead to severe outcomes. Floods are among the most fatal disasters that affect the globe. Even an increase of flood water depths by some centimeters may cause disastrous outcomes. Engineers generally resort to approximate solutions for adding the effects of groynes into hydraulic flood models for long river reaches. In this paper, we assess the capabilities of these approximate models as well as those of more simplified and more advanced models. Insights were sought to help flood modelers for better prediction of flood water levels. Our study shows that the most widely used groyne resistance model leaves room for further development, despite demonstrated capabilities. Key Points: Characterizing the effect of groynes on river flow in two‐dimensional hydrodynamic models by using weir formulas was evaluatedAlternative methods to implement groynes into two‐ and three‐dimensional river models were proposed, and their reliability was testedThe performances of two of the available modeling options in simulating the groyne modifications were assessed [ABSTRACT FROM AUTHOR]

Failure of dams and dikes often leads to devastating consequences in protected areas. Numerical models are instrumental tools to assess flood risk and guide emergency management, but numerous uncertainties affect model outcomes. Identifying uncertain model input parameters that induce high uncertainties in model outputs is essential. This paper focuses on two model outputs: the maximum breach discharge and the time to reach this peak. Using our implementation of a simplified physically based dam breaching model developed by Wu (2016), a global sensitivity analysis was conducted based on Sobol indices of total order. Unlike in most previous studies, many different configurations (twenty‐seven), both at laboratory and field scales, were considered. For each of them, input variables were ranked according to the significance of the contribution of their uncertainty to the output variability, and the dependency between reference configurations and sensitivity analysis results was highlighted. Depending on the considered case study, input parameter uncertainties with the largest impact on output variability were different, and so was the magnitude of output uncertainties. We demonstrate that sensitivity analysis results obtained for a specific configuration cannot be transferred as it to other configurations. Finally, sensitivity and uncertainty quantification results were combined in a decision tree to determine which input parameter uncertainty is the most critical in each configuration and what standard deviation in the output variables is expected. The global sensitivity analysis procedure presented here may apply to a wide variety of models in environmental sciences. Key Points: Based on a simplified physically based dam breaching model, a global sensitivity analysis was conducted using Sobol indices of total orderThe analysis was applied on twenty‐seven configurations at both laboratory and field scalesMost influential parameters and output uncertainty magnitudes were assessed and turned out to strongly depend on the dam configuration [ABSTRACT FROM AUTHOR]

The increasing occurrence of urban flooding in recent years demands a more accurate flood hazard assessment. Therefore, a better understanding of the predominant hydraulic processes in urban flood flows is required. The present paper reports an experimental study conducted in a laboratory scale model, which represents an urban area consisting of a rectangular city block and four surrounding streets. The objective is to quantify the impact of open areas within the city block on the key features of floodwaters. Tests are carried out under unsteady flow conditions, by using three inflow hydrographs with different unsteadiness levels. For each inflow hydrograph, the space available for floodwater volume storage within the block (storage capacity, ϕ) is varied, keeping the boundary conditions unchanged. A case where the city block has no space for floodwater storage (i.e., ϕ = 0) is used as a reference case. The results indicate that the unsteadiness level of the inflow hydrograph, especially during the rising stage, has a strong influence on the floodwater volume stored within the city block. The increase in storage capacity within the city block leads to a reduction of the global peak outflow discharge, a decrease in flow depths and a local increase in velocities in some streets and within the city block. Finally, with these variations in floodwater features, the level of risk to pedestrians is also impacted, increasing locally when the storage capacity of the block increases. Key Points: Access facilities to buildings and empty spaces within city blocks can modify the flood risk for the inhabitants of an urban areaThe block storage capacity can reduce flow depths by up to 13% and increase velocities by up to 20% in the streets for the tested casesThe inflow hydrograph unsteadiness level has a strong influence on the floodwater volume stored in the block and thereby on the flood flows [ABSTRACT FROM AUTHOR]

The local‐inertial approximations of the shallow water equations (SWEs) have been used for flood forecasting at larger spatial scales owing to the improved computational efficiency and similar accuracy compared to the full 2D SWEs. With the availability of high‐resolution elevation data, the complex terrain of urban areas with various small‐scale features is represented well. Even for a local‐inertial model, utilizing such high‐resolution elevation data in flood simulations of urbanized areas increases the computational cost. A subgrid‐based local‐inertial formulation that permits large numerical grid size for computations while preserving the within‐grid topography is proposed to circumvent this. The subgrid topography can be incorporated into the coarse numerical grid computations by estimating the hydraulic properties, namely, volume and face area, based on water surface elevation variations of the associated high‐resolution terrain. The pre‐stored hydraulic properties are then used to dynamically update the hydraulic variables during the execution of the local‐inertial model. Idealized and real‐world test cases were simulated to illustrate the advantages of the proposed model. The proposed subgrid model performs better in capturing flood depth around subgrid‐scale features such as streets, highways, minor canals, etc., than the simple grid‐averaged local‐inertial models of the same grid size. The proposed model is faster than the existing local‐inertial model (e.g., LISFLOOD‐FP) (∼21–34 times) and the full 2D model (e.g., HEC‐RAS 2D) (∼361–660 times) of similar accuracy in the slow‐rising flood applications. Thus, the subgrid local‐inertial model holds promise in real‐time flood inundation forecasting, resolving smaller urban features. Plain Language Summary: Quick and accurate flood forecasting is crucial in highly populated urban areas with several small terrain features like roads, streets, highways, canals, etc. The simulation of flood depth and extent can be made faster by using simplified equations or dividing the domain into larger grids. The equations will be solved over each grid, generating flood depth and flow. However, opting for a large grid will not capture smaller within‐grid terrain elements. Therefore, this paper proposes a novel subgrid‐based local‐inertial model that uses simplified equations and operates at a large grid while still representing the within‐grid terrain. The subgrid‐inertial model simulates flooding around the smaller features of urban areas with less time than finer grid full 2D models. Meanwhile, the existing local‐inertial models operating at large grids often fail to resolve urban terrain features. Thus, using limited computing facilities, the proposed subgrid‐inertial model is highly suited for real‐time riverine flood forecasting in urbanized floodplains. Key Points: A subgrid‐based local‐inertial formulation that makes computations at coarse grids while capturing the within‐grid terrain is proposedIt is faster than a full 2D model and can resolve complex urban terrain (streets, canals, roads) where existing coarse grid models failIt improves mass flux estimation of coarse grids and holds promise in real‐time forecasting of slow‐rising urban floods [ABSTRACT FROM AUTHOR]

Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

Flood events are the most commonly occurring natural disaster, with over 5 million properties at risk in the UK alone. Changes in the global climate are expected to increase the frequency and magnitude of flood events. Flood hazard assessments, using climate projections as input, guide policy decisions and engineering projects to reduce the impact of large return period events. Probabilistic flood modeling is required to take into account uncertainties in climate model projections. However, the dichotomous relationship between probabilistic modeling, computational cost and model resolution limits the applicability of such techniques. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling (LHS) and Multi‐level Monte Carlo (MLMC) to quantify the uncertainty in flood extent resulting from input hydrograph uncertainty. The results demonstrate that MLMC is a more efficient modeling strategy than current methods (i.e., traditional Monte Carlo) with high resolution outputs produced in less time than previously possible. The novel application of MLMC technique to three Scottish case studies, demonstrating a variety of river characteristics, domain sizes and computational costs, using a high resolution 5 m grid resulted in a 99.2% reduction in computational cost compared to traditional Monte Carlo methods and up to 2.3 times speedup over Latin Hypercube Sampling. Key Points: Multi‐level Monte Carlo (MLMC) reduces the computational cost by 99.2% over traditional full Monte Carlo; tested across a range of case studiesProbabilistic flood hazard assessment accuracy can be increased by incorporating higher resolution models with minimal increase in costsMLMC allows realistic uncertainty quantification at previously unfeasible 5 m grid resolution, yielding converged estimates of flooded area [ABSTRACT FROM AUTHOR]

Global flood models integrate flood maps of constant probability in space, ignoring the correlation between sites and thus potentially misestimating the risk posed by extreme events. Stochastic flood models alleviate this issue through the simulation of flood events with a realistic spatial structure, yet their proliferation at large scales has historically been inhibited by data quality and computer availability. In this paper, we show, for the first time, the efficacy of modeled river discharge reanalyses in the characterization of flood spatial dependence in the absence of a dense stream gauge network. While global hydrological models may show poor correspondence with absolute observed river flows, we find that the rate at which they can simulate the joint occurrence of relative flow exceedances at two given locations is broadly similar to when a gauge‐based statistical model is used. Evidenced over the United States, flood events simulated using observed gauge data from the U.S. Geological Survey versus those generated using modeled streamflows have similar (i) distributions of site‐to‐site correlation strength, (ii) relationships between event size and return period, and, importantly, (iii) loss distributions when incorporated into a continental‐scale flood risk model. Extremal dependence is generally quantified less accurately on larger rivers, in arid climates, in mountainous terrain, and for the rarest high‐magnitude events. However, local‐scale errors are shown to broadly cancel each other out when combined, producing an unbiased flood spatial dependence model. These findings suggest that building accurate stochastic flood models worldwide may no longer be a distant aspiration. Plain Language Summary: Global flood risk is commonly estimated through flood inundation maps with a defined probability of occurrence. These flood simulations have a key drawback in that they fail to capture the spatial patterns exhibited during real flood events, instead modeling the same probability of flooding on every river at once. Solutions which rely on networks of gauged river flow observations will necessarily break down in the majority of the world's regions which lack such a resource. In this paper, we use historic river flows simulated by global rainfall‐runoff models (rather than observed flows) into a statistical model which captures the spatial correlation of flow extremes. If we examine the relative flow exceedance probabilities from these hydrological models rather than the volumetric flow values, flood events are generated which exhibit similar characteristics to those when gauged flow observations are used. Crucially, the simulation‐ and observation‐generated flood events produce near‐identical losses to buildings in the United States. The implications of this are that true stochastic flood risk models, which account for spatial dependence, can proliferate globally via the generation of realistic flood event sets from hydrological models. Key Points: Large‐scale flood hazard models typically neglect to represent the spatial dependence of real flood eventsRelative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event setsAt the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data [ABSTRACT FROM AUTHOR]

Probabilistic methods are widely adopted for residual flood hazard assessment in flood‐prone areas protected by levees. Such methods can consider various sources of uncertainty, including breach location and flood event characteristics, and allow for the quantification of the result confidence. However, the possible occurrence of multiple levee breaches during the same flood event is usually disregarded. This paper presents a probabilistic method based on levee fragility functions and basic probability rules to estimate the probability of selected breach scenarios, including multiple breaching events. The flood hazard classification is based on inundation variables calculated for flood events of different return periods. A combined 1D‐2D hydrodynamic model is used for flood simulations. Probabilistic inundation extent maps and probabilistic flood hazard level maps are then created. Finally, probabilistic flood hazard estimates are summarized in a map of a suitable central tendency of the hazard level, coupled with a map of the Shannon entropy as an uncertainty indicator. This pair of statistical maps provides concise and effective information on a reference flood hazard along with the associated uncertainty. The method was applied to a region located along the middle reach of the Po River (northern Italy). Comparable central flood hazard estimates are obtained for the two sets of breaching events including or not including multiple breaches, with higher uncertainty if multiple breaches are considered. This result highlights the importance of considering multiple breaching events in flood risk management. Key Points: A probabilistic method is presented for flood hazard mapping in flood‐prone areas protected by levees, considering multiple breachesBreach scenario probabilities for selected return periods are estimated using levee fragility functions and basic probability rulesA "central" hazard level map and an entropy index map are introduced to summarize probabilistic information on flood hazard [ABSTRACT FROM AUTHOR]

In the spring of 1818, ice avalanches from the Giétro Glacier created an ice dam, which in turn formed a glacial lake in the Drance Valley (Canton of Valais, Switzerland). Today, its maximum volume is estimated to have been 25×106 m3. Cantonal authorities commissioned an engineer named Ignaz Venetz to mitigate the risk of the ice dam's failure. He supervised the construction of a tunnel through which a large volume of water was drained as the lake rose (9×106 m3 according to his estimates and 11×106 m3 according to our model). After 2.5 days of slow drainage, the ice dam failed on 16 June 1818 and caused major flooding in the Drance Valley up to 40 km downstream, resulting in about 40 deaths. Venetz's lake monitoring notes, numerous testimonies gathered in the disaster's aftermath, and our field survey have made it possible to collect a wealth of information on this event, which is one of the world's major documented glacial lake outburst floods. Reconstructing major outburst floods remains challenging because not only do they involve enormous volumes of water spreading over long distances but they are also associated with additional physical processes such as massive erosion; intense transport of ice, sediment, and debris; and damage to vegetation and buildings. This paper attempts to reconstruct the 1818 Giétro flood by focusing on its water component. We develop a simple model to estimate the initial hydrograph during the slow drainage and failure phases. The flood's features are deduced by solving the shallow‐water equations numerically. The computational framework involves six free parameters, of which five are constrained by physical considerations. Using iterative manual parameter adjustments, we matched the numerical simulations to the historical data. We found that the peak discharge was close to 14,500 m3/s, the flood's front velocity was about 6 m/s, and flow depth varied considerably along the River Drance's bed (from 30 m just downstream of the ice dam to 2 m on the alluvial fan, 24 km west of the dam). To achieve a good agreement between computations and historical data, we had to select a high value for the Manning friction coefficient n (with n as large as 0.08 s/m1/3). As the Drance Valley is narrow, high flow resistance caused the flood's leading edge to behave like a plug, moving at a fairly constant velocity, with little dependence on what happened behind it. This result may explain why a simple flood routing model is able to reproduce the flood's features, because in an Alpine valley, a lateral spreading of the water volume is limited. Plain Language Summary: Every year, natural and man‐made dams fail and cause flooding. For public authorities, estimating the risk posed by dams is essential to good risk management. Efficient computational tools are required for analyzing flood risk. Testing these tools is an important step toward ensuring their reliability and performance. Knowledge of major historical floods makes it possible, in principle, to benchmark models, but because historical data are often incomplete and fraught with potential inaccuracies, validation is seldom satisfactory. Here we present one of the few major historical floods for which information on flood initiation and propagation is available and detailed: the Giétro flood. This flood occurred in June 1818 and devastated the Drance Valley in Switzerland. In the spring of that year, ice avalanches blocked the valley floor and formed a glacial lake, whose volume is today estimated at 25×106 m3. The local authorities initiated protection works: A tunnel was drilled through the ice dam, and about half of the stored water volume was drained in 2.5 days. On 16 June 1818, the dam failed suddenly because of significant erosion at its base; this caused a major flood. This paper presents a numerical model for estimating flow rates, velocities, and depths during the dam drainage and flood flow phases. The numerical results agree well with historical data. The flood reconstruction shows that relatively simple models can be used to estimate the effects of a major flood with good accuracy. Key Points: The 1818 Giétro flood was one of the world's major glacial lake outburst floods in historical timesWe apply a simple computational framework to reconstruct the initial hydrograph and flood motionFlow resistance was significant, presumably because of intense sediment and debris transport [ABSTRACT FROM AUTHOR]

Digital elevation model (DEM) as an essential input to flood risk analyzers is subject to a certain level of uncertainty, which increases as the resolution becomes coarser. To quantify this uncertainty, a probabilistic framework incorporating 2‐D hydrodynamic flood mapping by LISFLOOD‐FP along with a sequential Gaussian simulation (SGS) model is presented in this paper. Based on ordinary kriging (OK) interpolation techniques, spatial uncertainty is modeled through SGS by generating several equiprobable realizations. Furthermore, regression kriging (RK) is used in the SGS algorithm utilizing its ability to explore additional information such as terrain characteristics to estimate the elevation error. A new technique called nonstationary sequential Gaussian simulation is also proposed by introducing the spatial nonstationarity of DEM error into SGS framework based on two approaches of uniform and nonuniform moving window. Different DEM resolutions resampled from a 1‐m light detection and ranging‐derived DEM are used in the hydrodynamic model to derive the accuracy‐efficiency trade‐offs and select the most suitable spatial resolution for probabilistic analysis. A detailed comparison among OK, RK, nonstationary OK, and nonstationary RK models is made considering probabilistic floodplain characteristics. The subsequent risk curves are then derived under floods of different return periods. The methodology is implemented in the coastal areas of Lower Manhattan and Brooklyn in New York City. Results show that the proposed methodology could reduce the uncertainty in risk estimation imposed by the stationarity assumption. The methodology could help decision‐makers to more accurately utilize the existing tools and data for flood risk analysis and preparedness. Key Points: An integrated model for probabilistic flood risk analysis is proposed by coupling sequential Gaussian simulation and a hydrodynamic modelSpatial nonstationarity of DEM errors incorporated into the uncertainty analysis framework significantly affects the final risk estimationExplanatory data in the geostatistical inundation model are used that improves the result of error modeling [ABSTRACT FROM AUTHOR]

In this paper we seek to understand the nature of flood spatial dependence over the conterminous United States. We extend an existing conditional multivariate statistical model to enable its application to this large and heterogenous region and apply it to a 40‐year data set of ~2,400 U.S. Geological Survey gauge series records to simulate 1,000 years of U.S. flooding comprising more than 63,000 individual events with realistic spatial dependence. A continental‐scale hydrodynamic model at 30 m resolution is then used to calculate the economic loss arising from each of these events. From this we are able to compute the probability that different values of U.S. annual total economic loss due to flooding are exceeded (i.e., a loss‐exceedance curve). Comparing these data to an observed flood loss‐exceedance curve for the period 1988–2017 shows a reasonable match for annual losses with probability below 10% (e.g., >1 in 10‐year return period). This analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Analysis of the set of stochastic events and losses yields new insights into the nature of flooding and flood risk in the United States. In particular, we confirm the strong relationship between flood affected area and event peak magnitude, but show considerable variability in this relationship between adjacent U.S. regions. The analysis provides a significant advance over previous national flood risk analyses as it gives the full loss‐exceedance curve instead of simply the average annual loss. Plain Language Summary: Traditional flood risk analyses make the assumption that flow probability (the chance that a given river discharge is exceeded) does not vary within river catchments within an event. Real floods, however, do not look like this: In some places flooding is more severe than in others. Over a few tens of kilometers of river assuming the same event return period everywhere is perfectly fine, but over larger areas it breaks down. At national scales traditional risk analyses can only estimate the average annual loss. To estimate the total annual losses that might occur in more extreme flooding years the risk analysis needs to be based on more realistic spatial patterns of flooding. In this paper we use a sophisticated statistical model, based on U.S. Geological Survey river flow data, to simulate 1,000 years of spatially realistic U.S. flooding comprising more than 63,000 individual events. By calculating the damage for each event as a dollar value, we are able to estimate the probability of the United States experiencing particular levels of annual flood losses. We show that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year, and a 0.1% chance of them exceeding $136Bn. Key Points: 1,000 years of realistic U.S. flood patterns, comprising >63,000 individual events, are simulated using a statistical modelMonetary losses for each event are calculated using a continental hydrodynamic model at 30 m resolutionThe analysis suggests that there is a 1% chance of U.S. annual fluvial flood losses exceeding $78Bn in any given year [ABSTRACT FROM AUTHOR]

State‐of‐the‐art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of $2.15 B). We find no dependence for most months and large differences in expected damage across months ($36–166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72–83%. This is because high‐probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments. Plain Language Summary: In coastal cities, floods can result from different drivers such as intense rainfall and extreme sea levels. In order to estimate the expected annual damage (EAD) from flooding, it is important to correctly quantify the chance of these events happening and their impacts. Commonly, only the maximum value for a flood driver each year is taken, ignoring its seasonality; and these maxima are assumed to either never or always happen at the same time of the year. These assumptions become problematic when pluvial and coastal floods can occur at different times of the year and sometimes at the same time, such as in Ho Chi Minh City (HCMC). In this paper, we develop a framework to account for the seasonality of flood drivers and their mutual dependencies. Using statistical and hydrodynamic modeling, we generate the equivalent of 10,000 years of rainfall and sea level events and estimate their impact. We find the EAD for HCMC to be $1.02 B. Neglecting the seasonality of rainfall and sea level leads to an underestimation of the EAD by 72–83%. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments. Key Points: We develop a flood risk framework based on monthly time series that includes the seasonality and the dependence between flood driversNot accounting for seasonality underestimates the expected annual damage (EAD) in Ho Chi Minh City by 72–83%Rainfall and storm surge are independent for most months but, under the assumption of full correlation, EAD would increase by 15% [ABSTRACT FROM AUTHOR]

Mammal bioerosion is an emergent threat to the functionality of levees. In the present paper, the problem of assessing the failure probability of levees affected by mammal bioerosion is addressed. A fully bivariate description of peak flow discharge and flood duration is combined with a deterministic unsteady seepage flow model to obtain a suitable model of variably disturbed levee response to the observed natural variability of floods. Monte Carlo analysis is also implemented to evaluate the epistemic uncertainty connected to the description of the river system. The obtained model is tested with respect to a real‐world levee located along the Secchia River in northern Italy, which underwent a disastrous failure caused by mammal bioerosion in 2014. The convex linear combination of two Archimedean copulas is found to fit the empirical dependence structure between peak flow discharge and flood duration. The reliability of the unsteady seepage flow model is tested against detailed numerical simulations of the seepage occurring through the levee body. A limit state function is obtained by comparing the maximum extent of the seepage front to the distance between the den end and the riverside levee slope, and the corresponding levee safety and failure regions are delimited. Results obtained from the developed model reveal a significant impact of mammal dens located near the levee crest in terms of failure probability and related return period. This impact is consistent with failures observed in the study area. Plain Language Summary: Burrowing mammals often find in levees a suitable habitat. Unfortunately, mammal dens can significantly compromise the functionality of levees by creating preferential flow paths for flood water seeping through the levee bodies, and by causing ultimately levee failures due to excessive seepage and internal erosion. In fact, many levee failures have been connected to the levee weakening caused by mammal dens. Mammal bioerosion significantly increases the failure probability of levees and the related flood risk in densely populated floodplains. Estimating the failure probability of levees affected by mammal bioerosion is therefore a relevant societal need. In the present study levee, safety and failure conditions are estimated by combining a fully bivariate statistical description of peak flow discharge and flood duration with a computationally efficient unsteady seepage flow model. The resulting modeling framework incorporates the natural variability of floods and the essential hydraulic properties of disturbed/undisturbed levees. Model results reveal that the return period of levee failure due to excessive seepage reduces from 100 to 9 years, namely of −91%, when the mammal den extends for 84% of levee thickness. These results can be used to inform levee design and maintenance programs for the safety of societies living in floodplains worldwide. Key Points: Fully bivariate analysis of peak flow discharge and flood duration is used to describe the hydrologic forcing to leveesVorogushyn et al.'s unsteady seepage flow model is extended to derive the failure probability of variably disturbed leveesReturn period of levee failure due to excessive seepage reduces from 100 to 9 years when the mammal den extends for 84% of levee thickness [ABSTRACT FROM AUTHOR]

Risk analysis plays an important role in decision making for real-time flood control operation of complex flood control systems. A typical flood control system consists of reservoirs, river channels, and downstream control points. The system generally is characterized by nonlinearity and large scale. Additionally, the input variables are mostly stochastic. Because of the dimensionality problem, generally, it would not be possible to carry out risk analysis without decomposition. In this paper, we propose a decomposition-integration approach whereby the original complex flood control system is decomposed into a number of independent subsystems. We conduct risk analysis for each subsystem and then integrate the results by means of combination theory of stochastic processes. We evaluate the propagation of uncertainties through the complex flood control system and calculate the risk of reservoir overtopping, as well as the risk of flooding at selected downstream control points. We apply the proposed methodology to a flood control system in the middle reaches of the Huaihe River basin in China. The results show that the proposed method is practical and provides a way to estimate the risks in real-time flood control operation of a complex flood control system. [ABSTRACT FROM AUTHOR]

This paper proposes a method from Scan statistics for identifying flood‐rich and flood‐poor periods (i.e., anomalies) in flood discharge records. Exceedances of quantiles with 2‐, 5‐, and 10‐year return periods are used to identify periods with unusually many (or few) threshold exceedances with respect to the reference condition of independent and identically distributed random variables. For the case of flood‐rich periods, multiple window lengths are used in the identification process. The method is applied to 2,201 annual flood peak series in Europe between 1960 and 2010. Results indicate evidence for the existence of flood‐rich and flood‐poor periods, as about 2 to 3 times more anomalies are detected than what would be expected by chance. The frequency of the anomalies tends to decrease with an increasing threshold return period which is consistent with previous studies, but this may be partly related to the method and the record length of about 50 years. In the northwest of Europe, the frequency of stations with flood‐rich periods tends to increase over time and the frequency of stations with flood‐poor periods tends to decrease. In the east and south of Europe, the opposite is the case. There appears to exist a turning point around 1970 when the frequencies of anomalies start to change most clearly. This turning point occurs at the same time as a turning point of the North Atlantic Oscillation index. The method is also suitable for peak‐over‐threshold series and can be generalized to higher dimensions, such as space and space‐time. Plain Language Summary: Flood studies usually assume that the statistical characteristics of flood discharges do not change over time. Here we propose a method for identifying changes in these characteristics. Specifically, we identify periods that exhibit unusually more floods above a threshold and periods with unusually more floods below a threshold. The method goes beyond trend analysis by providing more temporal detail on flood changes. We apply the method to 2,201 observed flood series in Europe between 1960 and 2010. We find that flood‐rich and flood‐poor periods occur in the data, as the number of periods is about 2 to 3 times larger than would be expected by chance. In the northwest of Europe, the number of flood‐rich periods tends to increase over time, while in the east and south of Europe, the opposite is the case. There appears to exist a turning point around 1970 when the frequency of unusual periods starts to change most clearly. This turning point occurs at the same time as a turning point of the North Atlantic Oscillation index suggesting a role in climate fluctuations in the frequency of flood‐rich periods. Key Points: A method from Scan statistics is proposed for identifying flood‐rich and flood‐poor periods in flood discharge recordsThere is evidence of flood‐rich and flood‐poor periods based on analyzing 2,201 flood series in Europe between 1960 and 2010In the northwest of Europe, the frequency of flood‐rich periods tends to increase over time, particularly for large return periods [ABSTRACT FROM AUTHOR]

The efficacy of low‐impact development (LID) toward the attenuation of urban flood at the catchment scale is still an unsolved question. This work aims at providing insight into the effectiveness of LID at the catchment scale by a simple hydrological modeling approach that allows for an analytical solution. The paper focuses on LID design, investigating the peak discharge at the catchment outlet as a function of the return period of the rainfall event under unaltered and retrofitted conditions. The model captures the most important effects of LID on flood, that is, the temporal shift and attenuation of water discharge and the decrease of water volume conveyed by the drainage network due to increased hydrological losses. It is found that LID is likely more effective in reducing peak discharge at the local scale, while the effectiveness decreases with increasing catchment size. For some configurations, for example, when retrofit is performed in downstream areas, closer to the outlet, LID may even worsen the flood risk. The effectiveness of LID depends on the combination of several factors, including the fractional area covered by the LID infrastructures, their distribution within the catchment, and their hydraulic properties, as compared with the general hydraulic features of the catchment (network dispersion, detention, lag time, etc.). As a consequence, the LID design should be carried out considering all the relevant scales of the problem, from the local one, at which the LID infrastructure is implemented, to the catchment scale, and the discharge at the outlet in particular, in a comprehensive perspective. Key Points: We develop a simple hydrological model, based on the IUH framework, to model LID effects on peak discharge quantiles at the catchment scaleWe found that LID are effective in reducing urban flooding at the local scale, while effectiveness decreases with increasing catchment sizeWhen retrofit is performed through LID implementation in areas close to the catchment outlet, LID may even worsen the flood risk [ABSTRACT FROM AUTHOR]

The execution of hydraulic models at large spatial scales has yielded a step change in our understanding of flood risk. Yet their necessary simplification through the use of coarsened terrain data results in an artificially smooth digital elevation model with diminished representation of flood defense structures. Current approaches in dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socioeconomic data. Here, we introduce a novel solution for application at scale. The geomorphometric characteristics of defense structures are sampled, and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source digital elevation model. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed U.S. levee crest heights. When incorporated into a continental‐scale hydrodynamic model based on LISFLOOD‐FP and compared to local flood models in Iowa (USA), median correspondence was 69% for high‐frequency floods and 80% for low‐frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted, and risk‐based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering‐grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved‐accuracy simulations of flood inundation. Plain Language Summary: Traditional flood risk assessments are carried out using computer models built with local data, but their spatial coverage is impaired by how expensive and time‐consuming they are to produce. Recent advances in data availability, understanding of necessary physical process representation, and computational capacity have enabled hydraulic models of the entire globe to be built in an automated fashion at a fraction of the financial and human cost. However, their accuracy can be significantly impaired by a lack of information on flood defenses. As the model is built, elevation data are coarsened to reduce the number of calculations required to simulate flooding over such wide areas. This results in flood defense structures being smoothed out of the terrain information used in the model. Publicly available defense inventories are of insufficient coverage to ameliorate this issue. In this paper, a method is presented, which automatically detects levee‐like features in high‐resolution elevation data and accurately represents their heights during this necessary coarsening process. Simulating flood inundation over this "defended" topography results in high correspondence between local models and observations for test cases in the United States and Italy, with improvements particularly felt where a lack of defense information is the dominant source of error. Key Points: Flood defense representation is presently poor in large‐scale flood models, impairing their ability to map flood hazard accuratelyA new method is presented, which automatically identifies hydraulic structures in terrain data and accurately preserves their elevationsHydraulic simulations where a lack of defense data is the dominant error show significant improvements in skill when incorporating this method [ABSTRACT FROM AUTHOR]

Flood risk assessment and mitigation are important tasks that should take advantage of rational vulnerability models to increase their effectiveness. These models are usually identified through a relevant set of laboratory experiments. However, there is growing evidence that these tests are not fully representative of the variety of conditions that characterize real flood hazard situations. This paper suggests a citizen science-based and innovative approach to obtain information from web resources for the calibration of people's vulnerability models. A comprehensive study employing commonly used web engines allowed the collection of a wide set of documents showing real risk situations for people impacted by floods, classified according to the stability of the involved subjects. A procedure to extrapolate the flow depth and velocity from the video frames is developed and its reliability is verified by comparing the results with observation. The procedure is based on the statistical distribution of the population height employing a direct uncertainty propagation method. The results complement the experimental literature data and conceptual models. The growing availability of online information will progressively increase the sample size on which the procedure is based and will eventually lead to the identification of a probability surface describing the transition between stability and instability conditions of individuals in a flow. [ABSTRACT FROM AUTHOR]

Flood hazard maps are useful tools for land planning and flood risk management in order to increase the safety of flood‐prone areas that can be inundated in the event of levee failure. However, flood hazard assessment is affected by various uncertainties, both aleatory and epistemic. The flood hazard analysis should hence take into account the main sources of uncertainty and quantify the confidence of the results for a given design flood event. To this end, this paper presents a probabilistic method for flood hazard mapping, which considers uncertainty due to breach location and failure time. A reliability analysis of the discretized levee system, performed using the concept of fragility function, enables the preselection of a set of levee sections more susceptible to failure. The probabilities of the breach scenarios (characterized by different breach locations and times) are then calculated using the probability multiplication rule, neglecting multiple breaches. The method is applied to a 96‐km levee‐protected reach in the central portion of the Po River (Northern Italy) and to an adjacent 1,900‐km2 flood‐prone area on the right‐hand side of the river, with a focus on the piping breach mechanism. The numerical simulations are performed through a combined 1D‐2D hydrodynamic model using widespread free software. The results show that the method is effective for probabilistic inundation and flood hazard mapping. In addition, it has the advantage of requiring a smaller computational effort in comparison with the methods based on a classic Monte Carlo procedure. Key Points: A probabilistic method is presented to assess inundation hazard due to piping‐induced levee breaches in flood‐prone areasScenarios are defined in terms of breach location and time, and their probabilities are estimated using fragility functionsThe method is applied to a case study in Northern Italy to show its capabilities in probabilistic flood hazard mapping [ABSTRACT FROM AUTHOR]

Hydraulic risk maps provide the baseline for land use and emergency planning. Accordingly, they should convey clear information on the potential physical implications of the different hazards to the stakeholders. This paper presents a vulnerability criterion focused on human stability in a flow specifically devised for rapidly evolving floods where life, before than economic values, might be threatened. The human body is conceptualized as a set of cylinders and its stability to slipping and toppling is assessed by forces and moments equilibrium. Moreover, a depth threshold to consider drowning is assumed. In order to widen its scope of application, the model takes the destabilizing effect of local slope (so far disregarded in the literature) and fluid density into account. The resulting vulnerability classification could be naturally subdivided in three levels (low, medium, and high) that are limited by two stability curves for children and adults, respectively. In comparison with the most advanced literature conceptual approaches, the proposed model is weakly parameterized and the computed thresholds fit better the available experimental data sets. A code that implements the proposed algorithm is provided. [ABSTRACT FROM AUTHOR]

The accumulation of large wood debris around bridge piers obstructs the flow, producing increased upstream water levels, large horizontal structural loadings, and flow field modifications that can considerably exacerbate scour. These effects have frequently been held responsible for the failure of a large number of bridges around the world, as well as for increased risk of flooding of adjacent areas. Yet little is currently known about the time evolution and processes responsible for the formation and growth of these debris piles. This paper is aimed at deciphering the whole life of debris accumulations through an exhaustive set of 570 experiments in which debris elements were individually introduced into a flume and accumulated at a pier model downstream. Our findings show that in all experiments, the growth of accumulations is halted at a critical stage, after which the jam is removed from the pier by the flow. This condition typically coincides with the time when the dimensions of the accumulations are maxima. The values of the accumulation maximum size display a clear dependence on flow characteristics and debris length distribution. On the other hand, other variables have shown much weaker effects on the geometry of the accumulations. For a given debris length, accumulations are wide, shallow, and long at low flow velocities but become narrower, deeper, and shorter with increasing velocities. A comparison of results of accumulations formed with debris of uniform and nonuniform size distributions has revealed that the former can be up to 2.5 times wider than the latter. Key Points: Experimental results show that the growth of debris jams at piers is halted at a critical stageNew relations are proposed between the size of debris jams and flow and debris variablesAccumulations formed with uniform are considerably larger than with nonuniform debris [ABSTRACT FROM AUTHOR]

Abstract: Dam breach models are commonly used to predict outflow hydrographs of potentially failing dams and are key ingredients for evaluating flood risk. In this paper a new dam breach modeling framework is introduced that shall improve the reliability of hydrograph predictions of homogeneous earthen embankment dams. Striving for a small number of parameters, the simplified physics‐based model describes the processes of failing embankment dams by breach enlargement, driven by progressive surface erosion. Therein the erosion rate of dam material is modeled by empirical sediment transport formulations. Embedding the model into a Bayesian multilevel framework allows for quantitative analysis of different categories of uncertainties. To this end, data available in literature of observed peak discharge and final breach width of historical dam failures were used to perform model inversion by applying Markov chain Monte Carlo simulation. Prior knowledge is mainly based on noninformative distribution functions. The resulting posterior distribution shows that the main source of uncertainty is a correlated subset of parameters, consisting of the residual error term and the epistemic term quantifying the breach erosion rate. The prediction intervals of peak discharge and final breach width are congruent with values known from literature. To finally predict the outflow hydrograph for real case applications, an alternative residual model was formulated that assumes perfect data and a perfect model. The fully probabilistic fashion of hydrograph prediction has the potential to improve the adequate risk management of downstream flooding. [ABSTRACT FROM AUTHOR]

Abstract: Flood modeling is highly influenced by topography data set. Lower resolution digital elevation models (DEMs) are usually used because of their availability and less computational burden when they are used in modeling applications. However, low accuracy of these DEMs yields to even lower accuracy in flood risk analysis through spatial modeling. This study aims to explore the DEM resolution effects on coastal flood risk assessments. For this purpose, deterministic and probabilistic approaches are employed for flood inundation modeling utilizing hydrologically connected bathtub method. High‐resolution light detection and ranging (LiDAR) DEM is considered as the most accurate data from which different resolution maps are obtained using resampling techniques. This is incorporated into an error analysis framework along with U.S. Geological Survey (USGS) National Elevation Dataset (NED) DEMs. The probabilistic framework is developed by simulating the spatial variability of elevation errors compared to LiDAR DEM through a Monte Carlo‐based method called sequential Gaussian simulation. The proposed methodology is applied to the lower Manhattan in New York City. By integrating the flood model into the developed framework, flood inundation probability at each grid cells is obtained. Furthermore, using the concept of accuracy‐efficiency tradeoffs, a framework for selecting a suitable spatial resolution for probabilistic flood risk assessment has been suggested. The results show that by exercising a range of options presented in this paper, a broader insight into mapping resolution can be provided, improving flood assessment, evacuation zones, and mitigation plans depending upon the data availability in a region. [ABSTRACT FROM AUTHOR]

Abstract: Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood‐risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data‐driven artificial neural network (ANN) models of streamflow. Four well‐known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data‐driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall‐runoff models. [ABSTRACT FROM AUTHOR]

Reservoir operations may alter the characteristics of Flood Frequency Curve (FFC) and challenge the basic assumption of stationarity used in flood frequency analysis. This paper presents a combined data-modeling analysis of reservoir as a nonlinear filter of runoff routing that alters the FFCs. A dimensionless Reservoir Impact Index (RII), defined as the total upstream reservoir storage capacity normalized by the annual streamflow volume, is used to quantify reservoir regulation effects. Analyses are performed for 388 river stations in the contiguous U.S. using the first two moments of the FFC, mean annual maximum flood (MAF) and coefficient of variations (CV), calculated for the pre and post-dam periods. It is found that MAF generally decreases with increasing RII but stabilizes when RII exceeds a threshold value, and CV increases with RII until a threshold value beyond which CV decreases with RII. Hence depending on the magnitude of RII, reservoir regulation acts as a filter to increase or reduce the nonlinearity of the natural runoff routing process and alters flood characteristics. The nonlinear relationships of MAF and CV with RII can be captured by three reservoir models with different levels of complexity, suggesting that they emerge from the basic flood control function of reservoirs. However, the threshold RII values in the nonlinear relationships depend on the more detailed reservoir operations and objectives that can only be captured by the more complex reservoir models. Our conceptual model may help improve flood-risk assessment and mitigation in regulated river systems at the regional scale. [ABSTRACT FROM AUTHOR]

Accurate estimates of flood peaks, corresponding volumes, and hydrographs are required to design safe and cost-effective hydraulic structures. In this paper, we propose a statistical approach for the estimation of the design variables peak and volume by constructing synthetic design hydrographs for different flood types such as flash-floods, short-rain floods, long-rain floods, and rain-on-snow floods. Our approach relies on the fitting of probability density functions to observed flood hydrographs of a certain flood type and accounts for the dependence between peak discharge and flood volume. It makes use of the statistical information contained in the data and retains the process information of the flood type. The method was tested based on data from 39 mesoscale catchments in Switzerland and provides catchment specific and flood type specific synthetic design hydrographs for all of these catchments. We demonstrate that flood type specific synthetic design hydrographs are meaningful in flood-risk management when combined with knowledge on the seasonality and the frequency of different flood types. [ABSTRACT FROM AUTHOR]

Unexpected incidents, failures, and disasters are abundant in the history of flooding events. In this paper, we introduce the metaphors of terra incognita and terra maligna to illustrate unknown and wicked flood situations, respectively. We argue that surprise is a neglected element in flood risk assessment and management. Two sources of surprise are identified: (1) the complexity of flood risk systems, represented by nonlinearities, interdependencies, and nonstationarities and (2) cognitive biases in human perception and decision making. Flood risk assessment and management are particularly prone to cognitive biases due to the rarity and uniqueness of extremes, and the nature of human risk perception. We reflect on possible approaches to better understanding and reducing the potential for surprise and its adverse consequences which may be supported by conceptually charting maps that separate terra incognita from terra cognita, and terra maligna from terra benigna. We conclude that flood risk assessment and management should account for the potential for surprise and devastating consequences which will require a shift in thinking. [ABSTRACT FROM AUTHOR]

In flood risk assessment, there remains a lack of analytical frameworks capturing the dynamics emerging from two-way feedbacks between physical and social processes, such as adaptation and levee effect. The former, 'adaptation effect', relates to the observation that the occurrence of more frequent flooding is often associated with decreasing vulnerability. The latter, 'levee effect', relates to the observation that the non-occurrence of frequent flooding (possibly caused by flood protection structures, e.g. levees) is often associated to increasing vulnerability. As current analytical frameworks do not capture these dynamics, projections of future flood risk are not realistic. In this paper, we develop a new approach whereby the mutual interactions and continuous feedbacks between floods and societies are explicitly accounted for. Moreover, we show an application of this approach by using a socio-hydrological model to simulate the behavior of two main prototypes of societies: green societies, which cope with flooding by resettling out of flood-prone areas; and technological societies, which deal with flooding also by building levees or dikes. This application shows that the proposed approach is able to capture and explain the aforementioned dynamics (i.e. adaptation and levee effect) and therefore contribute to a better understanding of changes in flood risk, within an iterative process of theory development and empirical research. [ABSTRACT FROM AUTHOR]

This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two-stage model for dynamic control of the flood-limited water level (the maximum allowed water level during the flood season, DC-FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC-FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC-FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC-FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade-off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real-world reservoir in northeastern China. [ABSTRACT FROM AUTHOR]

Accounting for dependence between extreme rainfall and storm surge can be critical for correctly estimating coastal flood risk. Several statistical methods are available for modeling such extremal dependence, but the comparative performance of these methods for quantifying the exceedance probability of rare coastal floods is unknown. This paper compares three classes of statistical methods-threshold-excess, point process, and conditional-in terms of their ability to quantify flood risk. The threshold-excess method offers approximately unbiased estimates for dependence parameters, but its application for quantifying flood risk is limited because it is unable to handle situations where only one of the two variables is extreme. In contrast, the point process method (with the logistic and negative logistic models) and the conditional method describe the full distribution of extremes, but they overestimate and underestimate the dependence strength, respectively. We conclude that the point process method is the most suitable approach for modeling dependence between extreme rainfall and storm surge when the dependence is relatively strong, while none of the three methods produces satisfactory results for bivariate extremes with very weak dependence. It is therefore important to take the bias of each method into account when applying them to flood estimation problems. A case study is used to demonstrate the three statistical methods and illustrate the implication of dependence to flood risk. [ABSTRACT FROM AUTHOR]

The Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as 'less-than' values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust. [ABSTRACT FROM AUTHOR]

Previous studies have shown significant dependence of annual maximum floods in eastern Australia on the Interdecadal Pacific Oscillation (IPO), a low-frequency mode of natural climatic variability. However, the causative factors behind differences in flood risk observed during contrasting phases of the IPO remain poorly understood. In particular, an important question that stems from a practical flood hydrology perspective is: Does maximum precipitation exhibit a similar level of dependence on the IPO as that observed in the floods? If not, then what are the factors responsible for this disparity, and are there significant ramifications for the conventional approach of estimating design floods through a predefined design rainfall? This paper investigates the possible reasons for the disparate relationship of IPO on rainfall and streamflow design events; that is, why do flood characteristics for contrasting IPO phases differ significantly compared to less notable changes in the corresponding rainfall intensities? We hypothesize that this difference in flood characteristics as a function of the IPO is a result of catchment antecedent or wetness conditions prior to the design storm. This hypothesis is tested using data from 166 high-quality daily rainfall stations across Australia along with catchment-averaged rainfall and resulting flows across an additional 128 catchments from the same region. In addition, 35 subdaily rainfall stations with long records in east Australia were also used to support our arguments. The results of these tests suggest that catchment antecedent conditions prior to design storm events were found to vary significantly across the two IPO phases, leading to the significant differences in flood characteristics across the two phases. [ABSTRACT FROM AUTHOR]

There is growing evidence of coherent, global patterns of change in annual precipitation and runoff with high latitudes experiencing increases consistent with climate model projections. This paper describes a methodology for estimating detection times for changes in seasonal precipitation extremes. The approach is illustrated using changes in UK precipitation projected by the European Union PRUDENCE climate model ensemble. We show that because of high variability from year to year and confounding factors, detection of anthropogenic climate change at regional scales is not generally expected for decades to come. Overall, the earliest detection times were found for 10 day winter precipitation totals with 10 year return period in SW England. In this case, formal detection could be possible within a decade from now if the climate model projections are realized. The outlook for changes in summer flash flood risk is highly uncertain. Our analysis further demonstrates that existing precautionary allowances for climate change used for flood management may not be sufficiently robust in NE England and east Scotland. These findings imply that for certain types of flood mechanism, adaptation decisions might have to be taken in advance of formally detected changes in flood risk. This reinforces the case for long-term environmental monitoring and reporting of climate change indices at 'sentinel' locations. [ABSTRACT FROM AUTHOR]

This paper employs a simple choice experiment to estimate the value of management options for the Bobrek wetland in Poland. The local public's valuation of several wetland management attributes, including flood risk reduction, biodiversity conservation and improvement of recreational access, are investigated. A latent class model is estimated to account for heterogeneity in the wetland management choices of the local public. The results reveal that there is considerable heterogeneity among the local public as two distinct segments are identified. For both of the segments, flood risk reduction, in the ranges considered, is more important than the other two attributes. The results of this study are expected to assist policy makers in undertaking effective flood risk reduction measures and formulating efficient, equitable and sustainable wetland management policies in accordance with the European Union's Water Framework Directive (2000/60/EC). [ABSTRACT FROM AUTHOR]

Logjams may form at natural obstructions and are also used as nature‐based solutions for river restoration and natural flood management. Previous research has described backwater rise due to logjams that span the full channel cross‐section and logjams with a gap between the lower edge of the logjam and the bed. Logjams that fill the channel depth, but not its width, leaving a lateral gap between the logjam and the channel bank, are also common natural formations and the focus of this study. The flow distribution between the logjam and the lateral gap, backwater rise, and wake turbulence are key factors in determining the ecologic and flood risk impact of a logjam. Specifically, relative to a channel‐spanning logjam, the introduction of a lateral gap can reduce backwater rise and increase the potential for trapping particles, such as nutrients or microplastics, within the wake region, but may also promote erosion in the gap. The choice of logjam and gap widths can be used to maximize flow and habitat diversity in rivers, while reducing erosion risk. We present experimental results demonstrating that the flow distribution between the logjam and the lateral gap can be predicted by assuming equal resistance through the logjam and gap sections. Further, we show that backwater rise can be determined from the predicted discharge through the logjam using a momentum balance developed for channel‐spanning logjams. Finally, turbulence generated within the jam was observed directly downstream of the logjam, and, for the densities considered, increased with jam density. Key Points: Compared to channel‐spanning logjams, a lateral gap reduces backwater rise, but increases local shear stress and erosion potentialFlow distribution between logjam and gap can be predicted from total discharge, logjam resistance, and ratio of logjam to gap widthLog‐scale turbulence generated by the logjam persists in the wake for approximately 20 log diameters downstream of logjam [ABSTRACT FROM AUTHOR]

Prior research confirmed the substantial bias from using precipitation‐based intensity‐duration‐frequency curves (PREC‐IDF) in design flood estimates and proposed next‐generation IDF curves (NG‐IDF) that represent both rainfall and snow processes in runoff generation. This study improves the NG‐IDF technology for a snow‐dominated test basin in the Sierra Nevada. A well‐validated physics‐based hydrologic model, the Distributed Hydrology Soil Vegetation Model (DHSVM), is used to continuously simulate snowmelt and streamflow that are used as benchmark data sets to systematically assess the NG‐IDF technology. We find that, for the studied small snow‐dominated basin, the use of standard rainfall hyetographs in the NG‐IDF technology leads to substantial underestimation of design floods. Thus, we propose probabilistic hyetographs that can represent unique patterns of events with different underlying mechanisms. For the test basin where flooding events are generated entirely by snowmelt, we develop a hyetograph that characterizes snowmelt temporal patterns, which greatly improves the performance of NG‐IDF technology in design flood estimates. In contrast to the standard rainfall hyetographs characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt hyetograph displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results also show that the uncertainty of hyetography plays an important role in design flood estimation and can have important implications for future flood projections. Plain Language Summary: In recent years, flood hazards have gained increasing attention from national and international homeland security communities. Accurately assessing floods is crucial for many hydrologic applications, including infrastructure design, planning, and renewal, as well as the national flood insurance program. This research focuses on evaluating and enhancing the next‐generation flood design technology, which is an improvement over the traditional rainfall‐based method that does not account for snow processes in flood generation. Our study reveals a significant underestimation of floods when using standard rainfall temporal pattern in a small snow‐dominated basin. To address this issue, we propose probabilistic curves that consider the temporal patterns of snowmelt, resulting in a considerable reduction in flood estimation errors. In contrast to the standard rainfall temporal pattern characterized by a symmetrically peaked, bell‐shaped curve, the snowmelt temporal pattern displays a more rapid rise (i.e., greater intensity) and a distinct diurnal pattern influenced by solar energy input. The results demonstrate that the next‐generation flood design technology has the potential to complement the traditional method for hydrologic design in snow‐dominated regions, providing a consistent design approach in both rain‐dominated and snow‐dominated areas. Key Points: Standard rainfall hyetographs substantially underestimate floods in a small snow‐dominated basin in the Sierra NevadaSnowmelt hyetograph shows a more rapid rise (i.e., higher intensity) compared to the standard rainfall hyetographs used in hydrologic designA general method to develop probabilistic hyetographs that represent the underling flood‐generation mechanism is described [ABSTRACT FROM AUTHOR]

Reducing flood risk through disaster planning and risk management requires accurate estimates of exposure, damage, casualties, and environmental impacts. Models can provide such information; however, computational or data constraints often lead to the construction of such models by aggregating high‐resolution flood hazard grids to a coarser resolution, the effect of which is poorly understood. Through the application of a novel spatial classification framework, we derive closed‐form solutions for the location (e.g., flood margins) and direction of bias from flood grid aggregation independent of any study region. These solutions show bias of some key metric will always be present in regions with marginal inundation; for example, inundation area will be positively biased when water depth grids are aggregated and volume will be negatively biased when water surface elevation grids are aggregated through averaging. In a separate computational analysis, we employ the same framework to a 2018 flood and successfully reproduce the findings of our study‐region‐independent derivation. Extending the investigation to the exposure of buildings, we find regions with marginal inundation are an order of magnitude more sensitive to aggregation errors, highlighting the importance of understanding such artifacts for flood risk modelers. Of the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. This work provides insight into, and recommendations for, aggregating grids used by flood risk models. Key Points: Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study regionThis aggregation is shown to always positively bias inundation area when water depth grids are aggregatedFor example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada [ABSTRACT FROM AUTHOR]

The growing number of flood events in urban areas is causing far‐reaching economic losses and social disruptions. To implement suitable flood risk management strategies and measures for flood prevention and mitigation, it is paramount to adequately assess economy‐wide losses associated with potential flood events. The assessment of potential flood damage in the USA relies significantly on the Federal Emergency Management Agency (FEMA) Hazus Flood Model. A primary requirement to reduce uncertainty is to empirically gather user input data. Consequently, there is a need to integrate publicly accessible business data for accurate evaluation of losses in business activities. We present a novel approach for estimating both the direct losses in business activities during a flood and the total economy‐wide impact that arise from these disruptions. First, we integrate the FEMA Hazus Flood Model with Reference Solutions business‐level data to estimate the reduction in business activities (i.e., direct losses) following a 100‐year flood event in New Hanover County, North Carolina. Second, we estimate the broader economy‐wide impact these business activity losses have on total industry output, employment, value‐added and household income using a regional computational general equilibrium model. The advantage of our approach is the easy replicability of the business loss estimation process for US cities and regions of interest, which, when used in a follow‐up economic impact study, leads to estimating the total economy‐wide losses in economic activities. Policy makers relying solely on direct physical and economic damages would, by ignoring indirect losses, underestimate the benefits of investing in flood mitigation and protection. Plain Language Summary: Floods have increased globally over the last decades causing massive socioeconomic losses. Development of better resilience strategies for coastal and river cities requires methodologies that properly estimate the total, that is, the direct and the indirect, regional costs of large flood events. The first objective of this research is to estimate potential business interruptions due to flooding of firms and businesses. We demonstrate the value of using publicly available business‐level information in combination with the HAZUS flood model to estimate these direct business losses. Our second objective is to assess the economy‐wide impacts of estimated business losses within a computational general equilibrium model, a powerful regional economic impact model, since flood‐related losses of business activities within flood zones (direct effects) trigger additional losses (indirect effects) throughout the entire regional economy. Key Points: Estimate direct losses of business activities using publicly available Reference Solution data and Federal Emergency Management Agency US Hazus flood‐depth functionsAnalyze economy‐wide socioeconomic impacts of flood events using a regional computation general equilibrium modelData and methods can be applied anywhere in the USA where flood hazard information is available to extend impact assessments to include economy‐wide impacts of flooding [ABSTRACT FROM AUTHOR]

The High‐Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25‐ and 50‐km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope‐Link Model (HLM) at ∼1,000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff‐based simulations can capture the empirical distribution of flood peaks in five of the 10 models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community‐level assessment, we use nine statistical approaches to bias‐correct and downscale HighResMIP precipitation to a 4‐km resolution. The bias‐correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state. Key Points: High‐Resolution Model Intercomparison Project (HighResMIP) runoff routed through a hydrologic model generates realistic distributions of annual maximum discharge for 5 of 11 realizations assessedHighResMIP precipitation forcings generate realistic flood peaks for all realizations assessed after bias‐correction and statistical downscalingChanges in flood peaks are not detected for the future but there is an overall increase in variance at 24% of the location in Iowa [ABSTRACT FROM AUTHOR]

Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management. Plain Language Summary: Due to climate change, we can expect more frequent and severe floods in the future. This study investigates how households are affected by flooding and explores ways to reduce potential flood damage. By understanding the impact of floods on households and identifying effective measures, we can better manage flood risks and reduce damages caused by these events. We collected survey data after the 2021 Summer Floods in the Netherlands to understand the factors that contribute to flood damage. Timely warnings before flooding play a crucial role in preparing for such events. They allow households to take emergency actions like placing sandbags or moving belongings to higher floors. These actions can reduce flood damage to buildings by almost 30% of their total value and protect nearly 40% of the value of household contents. In addition to emergency measures, households can take proactive steps to prepare for future floods. This includes using waterproof materials and elevating electrical appliances, such as power sockets or kitchen appliances when constructing new homes or making renovations. Key Points: Detailed survey data allows for the update and calibration of rarely estimated empirical vulnerability curves for buildingsFlood damage mitigation (FDM) measures have the potential to reduce flood damage to both residential buildings and household contents by halfUpdated input for flood risk models in the form of multivariate damage functions that can be adjusted for FDM measures [ABSTRACT FROM AUTHOR]

The coincidence and superposition of flood processes from different rivers and regions tend to form compound flood events, determined by spatial relationship between diverse flood processes that cannot be accurately depicted and evaluated by existing dependence analysis methods. A framework, integrating multi‐dimensional vine copula model and dependence evaluation system, was developed with a testing‐oriented application to explore underlying dependence between two kinds of extreme runoff series (peak discharge and flood volume) extracted from the identified compound flood events in the upper reaches of the Yangtze River. Multi‐dimensional regular vine (R‐vine) copula models were established to depict the complex and diverse dependence, and corresponding vine structure was specified by the vine structure array that can reflect the sequence of tributaries flowing into the main stream and the spatial locations of different hydrometric stations. Dependence magnitude and association status were calculated and compared according to the optimal R‐vine copula models and information theory. Comparison with existing methods demonstrated that dependence evaluation system could reflect nonlinear and local dependence characteristics and eliminate the effect of extreme runoff series from other hydrometric station on the dependence. The association status between different extreme runoff series of the upper Yangtze River and its tributaries were diverse in view of the impact of tributary flood inflow. The proposed framework can be regarded as an effective way for dependence modeling and evaluation of compound floods, thus providing a scientific reference for the risk analysis of water resources systems. Key Points: Vine structure is specified by the sequence of tributaries flowing into main stream and spatial locations of hydrological stationsDependence evaluation system determines dependence magnitude and association status through R‐statistic and interaction gainExtreme runoff series of the same hydrometric station show distinct association status [ABSTRACT FROM AUTHOR]

We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. The model uses generalized extreme value (GEV) and Poisson distributions as marginals for these flood attributes with non‐stationary parameters. The rate parameters of the Poisson distribution and location, scale, and shape parameters of the GEV are modeled as linear functions of suitable covariates. Gaussian copulas are applied to capture the spatial dependence. The best covariates are selected using the Watanabe‐Akaike information criterion (WAIC). The modeling framework results in the posterior distribution of the flood attributes at all the gauges and various lead times. We demonstrate the utility of this modeling framework to forecast the flood risk attributes during the summer peak monsoon season (July‐August) at five gauges in the Narmada River basin (NRB) of West‐Central India for several lead times (0–3 months). As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from antecedent seasons, which have shown strong teleconnections with the Indian monsoon. We also include new indices related to the East Pacific and West Indian Ocean regions depending on the lead times. We show useful long lead skill from this modeling approach which has a strong potential to enable robust risk‐based flood mitigation and adaptation strategies 3 months before flood occurrences. Key Points: Develop a Bayesian Hierarchical Model framework for forecasting the magnitude and frequency of seasonal streamflow extremes in a river basinThe model provides ensembles forecasts of seasonal flood risk attributes at multiple lead timesApplied this for the first time in India on the Narmada River Basin using large‐scale climate indices as covariates at 0–3 months ahead [ABSTRACT FROM AUTHOR]

High computational cost is often the most limiting factor when running high‐resolution hydrodynamic models to simulate spatial‐temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low‐fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low‐resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high‐resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high‐resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent‐based LSG model rather than deriving it from water depth predictions. Plain Language Summary: Every year, lives are lost, and infrastructure is destroyed due to floods. This highlights the need for fast and accurate flood predictions to inform flood forecasting and risk assessments. However, predicting flood inundation in high resolution is often not practically feasible due to the high computational cost involved in running complex computer models. Simplified computer models can be used to provide faster flood predictions, but they lack the accuracy provided by complex models. To address this issue, this study evaluates an alternative method based on the combination of a fast simple model together with an advanced spatial feature matching method. The advanced spatial feature matching method is used to convert the predictions obtained from the simple model to accurate predictions of flood inundation depth and extent. The new approach is applied to a large floodplain in Australia and different adaptations are explored to optimize the procedure and ensure robust performance. The new approach is compared to the use of a traditional complex model and a previous approach that only predicted inundation extent. The new approach shows similar accuracy to the traditional complex model while being 12 times faster, thereby making it more practically useful for flood risk assessments. Key Points: A flood inundation model is developed, based on Low‐fidelity hydrodynamic modeling, Spatial analysis, and Gaussian Process learning (LSG)The LSG model predicts water depths with a mean Root Mean Square Error (RMSE) of 4 cm and a standard deviation of 5 cmThe LSG model is 12 times faster than a traditional high‐resolution hydrodynamic model [ABSTRACT FROM AUTHOR]

The management of stormwater runoff via distributed green infrastructures delivers a number of environmental services that go beyond the reduction of flood risk, which has been the focus of conventional stormwater systems. Not all of these services may be equally valued by the public, however. This paper estimates households' willingness to pay (WTP) for improvements in water security, stream health, recreational and amenity values, as well as reduction in flood risk and urban heat island effect. We use data from nearly 1000 personal interviews with residential homeowners in Melbourne and Sydney, Australia. Our results suggest that the WTP for the highest levels of all environmental services is A$799 per household per year. WTP is mainly driven by residents valuing improvements in local stream health, exemptions in water restrictions, the prevention of flash flooding, and decreased peak urban temperatures respectively at A$297, A$244, A$104 and A$65 per year. We further conduct a benefit transfer analysis and find that the WTP and compensating surplus are not significantly different between the study areas. Our findings provide additional support that stormwater management via green infrastructures have large nonmarket benefits and that, under certain conditions, benefit values can be transferred to different locations. [ABSTRACT FROM AUTHOR]

Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model. Key Points: Urban flood model predictions can have a significant level of uncertainty despite their reliance on comprehensive physics‐based modelsFor the city of Minneapolis, we find that there are several areas of high uncertainty in the flood predictionsThe physics‐informed Gaussian process regression forecasting method can significantly reduce uncertainty in flooding predictions [ABSTRACT FROM AUTHOR]

The annual timing of flood events is a useful indicator to study the interaction between atmospheric and catchment processes in generating floods. This paper presents an assessment of the seasonal timing of floods for 7,894 gauging locations across the globe over a common period from 1981 to 2010. The averaged ordinal date of annual maximum streamflow is then estimated for ungauged locations following a two‐stage prediction scheme. The first stage identifies regions that share a common climatic predictor of flood timing by analyzing the similarity of flood timing with seven climate variables. These variables represent precipitation timing and snowmelt dynamics and are derived from a global climate reanalysis data set. Homogeneous regions in terms of the dominant predictor are generalized in the second stage through a rule‐based classification. The classification partitions the world into 10 hydroclimate classes, where each class has flood timing predicted using the most relevant climate predictor. Using this relatively simple and interpretable model structure, flood timing could be predicted with a global mean absolute error of approximately 32 days while maintaining consistency across large regions. Potential applications of the developed map include better understanding of climatic drivers of flooding and benchmarking the performance of global hydrological models in simulating the processes relevant to flooding. Plain Language Summary: Timing of annual maximum streamflow is a useful index to relate flood occurrence to appropriate flood generation processes. This study presents an assessment of flood timing across 7,894 gauging locations globally over the period from 1981 to 2010. The averaged date of annual maximum streamflow is compared to seven climate predictors, identifying regions that are likely to share a common flood generation process. These homogeneous regions are generalized across the globe using a gridded data set of daily precipitation and temperature. To derive a global map of flood timing, the date of annual maximum streamflow is predicted for both gauged and ungauged locations, using a linear function of the most important climate predictor in each region. Key Points: Observation‐based analysis of the timing of annual maximum streamflow indicates large‐scale patterns over the common 1981–2010 periodRegional patterns of flood generation mechanisms are highlighted through a comparison between high‐flow timing and the timing of seven climate predictorsA prediction of flood timing was made for the global land mass using an atmospheric reanalysis data set [ABSTRACT FROM AUTHOR]

Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate and human‐induced factors, including sea level rise (SLR). Climate‐induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on "what‐if" scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management, via flood control structures, in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016–2020 period), CHC‐induced flooding puts an additional 73 km2 of cropland under flood risk at least half of the time (median flood event) and 65 km2 once a year (annual flood event), when compared to a past period (1993–1997). A 10‐ to 20‐fold increase relative to SLR‐induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9,900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR‐induced flooding is lower (6,000 and 3,300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities. Plain Language Summary: Current scientific literature defines sea level rise as a major factor in increasing global coastal flood risk in recent and future decades, showing that coastal flood risk, particularly over southern Louisiana, is exacerbated by natural and human‐induced subsidence. However, the impacts of climate‐induced hydrological change (CHC) on flooding and synergy with sea level rise (SLR) are often overlooked. Here, we quantify how CHC affect southern Louisiana's water dynamics and its synergy with SLR and water management that results land loss and increase in terrestrial water storage over the decades. We look to answer how much of observed changes are due to CHC and SLR, what are their individual contributions to increasing flood risk, and how local water management contributes to flood resilience. Key Points: Impacts of climate‐induced hydrological change (CHC), water management and sea level rise (SLR) on Louisiana flooding are quantifiedCHC increased flood risk over the past 28 years; flood control structures are vital in protecting Louisiana's major cities and croplandsSLR‐induced increase in median net flooded area exceeds CHC's; but annual events are more severely impacted by CHC [ABSTRACT FROM AUTHOR]

Recent catastrophic flood events, increasing flood losses, and climate change challenge current reliability estimates defined by the probability that flood levels will not exceed protection measures over a planning horizon. These estimates depict an expected reliability that mask uncertainty in streamflow and the capacity of river channels and floodplains. We described reliability as a random variable whose distribution depends on uncertainty and nonstationarity in annual maximum flood (AMF) distributions and flow capacity uncertainty. Numerical experiments quantified the impacts of nonstationarity and variance in AMFs and flow capacity uncertainty on estimates of flood protection reliability, thereby providing the first examination of their interacting effects. The distribution of reliability along a regulatory floodplain boundary was quantified through a bootstrap scheme that accounts for nonstationarity and uncertainty in AMFs and flow capacity uncertainty. Results indicated that accounting for uncertainty in flow capacity substantially reduces reliability compared to estimates based solely on flood likelihood; the divergence is greater in the presence of nonstationary AMFs. The distribution of reliability along the regulatory floodplain boundary was spatially heterogeneous due to within‐reach variation in flow capacity uncertainty. Quantifying the distribution of reliability for flood protection measures enables transparent communication and selection of a desired confidence level that is commensurate with a contextually appropriate risk tolerance. Similarly, we show that a desired level of confidence in reliability can be specified to estimate a design flood protection level. These results reveal how the combined impacts of uncertainty and nonstationarity can impact reliability estimates and confidence in those estimates. Plain Language Summary: Flood protection reliability is subject to uncertainty, and estimates can be described with a level of confidence (e.g., 90% confidence in 90% reliability over a planning horizon). In addition, changes in the magnitude and frequency of flood events can cause reliability to change through time. In other words, flood events are nonstationary; their statistical properties can change through time. However, flood protection reliability is often communicated as a precisely known value without conveying the confidence, uncertainty, or changing flood events in that estimate. We developed a method that accounts for nonstationarity and uncertainty to quantify the distribution of flood protection reliability for a given location on the floodplain. Results show that nonstationarity and uncertainty can substantially reduce flood protection reliability and require a significant increase in design flood flows depending on the desired level of confidence in a reliability estimate. These results suggest flood hazard maps that do not account for and describe uncertainty and reliability may substantially underestimate exposure to future flooding and provide a false sense of protection. Key Points: Uncertainty and nonstationarity reduce reliability of flood protection compared to estimates based only on stationary flood likelihoodFlood protection reliability distributions differ at proximate locations due to uncertainty in channel and floodplain characteristicsQuantifying the distribution of reliability enables more transparent description of confidence and specifying contextually appropriate design floods [ABSTRACT FROM AUTHOR]

This research simulates the morphodynamic evolution of an idealize estuary under four different SLR scenarios of increasing severity to investigate how SLR will influence riverine flooding in estuaries. We find that estuarine response to SLR is influenced by both morphological changes to channel capacity and the associated changes to channel hydrodynamics. Low and moderate SLR scenarios result in an increase in flood extent throughout the estuary relative to the no SLR base case. Surprisingly, more severe SLR scenarios result in decreased flood extent in upstream reaches. This shift is due to penetration of tidal energy and erosion further upstream with greater SLR, which increases channel capacity locally. A periodic pattern of local sediment transport is additionally observed due to SLR, which we attribute to the time response lag between hydrological and morphological response. The finding that increased SLR does not result in increased flood extent everywhere emphasizes that flood mitigation measures need to carefully account for non‐linear responses in the estuarine morphodynamic systems, such as the feedbacks resulting from increased tidal erosion. Key Points: Estuarine response to SLR is found non‐linear and influenced by both hydrodynamics and morphological evolutionErosion in the middle of estuary favors a concave shaped water level difference and increase water level slope at upstreamUnder severe SLR case, increased water surface slope and weakened upstream deposition together decrease riverine flood risk in the upstream [ABSTRACT FROM AUTHOR]