This paper describes benchmark testing of a diffusive and an inertial formulation of the de St. Venant equations implemented within the LISFLOOD-FP hydraulic model using high resolution terrestrial ...LiDAR data. The models are applied to a hypothetical flooding scenario in a section of Alcester, UK which experienced significant surface water flooding in the June and July floods of 2007 in the UK. The sensitivity of water elevation and velocity simulations to model formulation and grid resolution are analyzed. The differences in depth and velocity estimates between the diffusive and inertial approximations are within 10% of the simulated value but inertial effects persist at the wetting front in steep catchments. Both models portray a similar scale dependency between 50
cm and 5
m resolution which reiterates previous findings that errors in coarse scale topographic data sets are significantly larger than differences between numerical approximations. In particular, these results confirm the need to distinctly represent the camber and curbs of roads in the numerical grid when simulating surface water flooding events. Furthermore, although water depth estimates at grid scales coarser than 1
m appear robust, velocity estimates at these scales seem to be inconsistent compared to the 50
cm benchmark. The inertial formulation is shown to reduce computational cost by up to three orders of magnitude at high resolutions thus making simulations at this scale viable in practice compared to diffusive models. For the first time, this paper highlights the utility of high resolution terrestrial LiDAR data to inform small-scale flood risk management studies.
Continental–global-scale flood hazard models simulate design floods, i.e. theoretical flood events of a given probability. Since they output phenomena unobservable in reality, large-scale models are ...typically compared to more localised engineering models to evidence their accuracy. However, both types of model may share the same biases and so not validly
illustrate their predictive skill. Here, we adapt an existing continental-scale design flood framework of the contiguous US to simulate historical flood events. A total of 35 discrete events are modelled and compared to observations of flood extent, water level, and inundated buildings. Model performance was highly variable, depending on the flood event chosen and validation data used. While all events were accurately replicated in terms of flood extent, some modelled water levels deviated substantially from those measured in the field. Despite this, the model generally replicated the observed flood events in the context of terrain data vertical accuracy, extreme discharge measurement uncertainties, and observational field data errors. This analysis highlights the continually improving fidelity of large-scale flood hazard models, yet also evidences the need for considerable advances in the accuracy of routinely collected field and high-river flow data in order to interrogate flood inundation models more comprehensively.
Rethinking flood hazard at the global scale Schumann, Guy J.‐P.; Stampoulis, Dimitrios; Smith, Andrew M. ...
Geophysical research letters,
16 October 2016, Letnik:
43, Številka:
19
Journal Article
Recenzirano
Odprti dostop
Flooding is governed by the amount and timing of water spilling out of channels and moving across adjacent land, often with little warning. At global scales, flood hazard is typically inferred from ...streamflow, precipitation or from satellite images, yielding a largely incomplete picture. Thus, at present, the floodplain inundation variables, which define hazard, cannot be accurately predicted nor can they be measured at large scales. Here we present, for the first time, a complete continuous long‐term simulation of floodplain water depths at continental scale. Simulations of floodplain inundation were performed with a hydrodynamic model based on gauged streamflow for the Australian continent from 1973 to 2012. We found the magnitude and timing of floodplain storage to differ significantly from streamflow in terms of their distribution. Furthermore, floodplain volume gave a much sharper discrimination of high hazard and low hazard periods than discharge. These discrepancies have implications for characterizing flood hazard at the global scale from precipitation and streamflow records alone, suggesting that simulations and observations of inundation are also needed.
Key Points
First continental‐wide long‐term event‐continuous 2‐D flood inundation computations
At‐a‐station discharge records do not directly translate to flood hazard
There is a need to rethink flood hazard assessment globally
► Urban DEMs with spatial resolutions of 10cm and 1m are constructed using terrestrial LIDAR data. ► Inundation is simulated using a rapid flood spreading algorithm and a simplified 2D shallow water ...model. ► Hydraulic connectivity over the DEM is shown to be sensitive to small scale features captured by terrestrial LIDAR. ► The simplified shallow water model is more robust to changes in DEM resolution. ► The flood spreading algorithm provides a significant advantage in computational efficiency.
The advent of airborne LIDAR sparked a renewed research drive in two-dimensional hydraulic modelling at the turn of the millennium due to its ability to rapidly generate accurate DEMs over wide areas. Terrestrial LIDAR applies the same principle but uses a mobile ground-based platform, allowing rapid collection of terrain data in urban areas at decimetric scale. Here we apply two computationally efficient hydraulic models to DEMs of a small urban test site in Alcester, UK, derived from terrestrial and airborne LIDAR data at 10cm and 1m scales. The first model, LISFLOOD-FP, is a 2D raster-based model employing a simplified formulation of the de St. Venant equations, whilst the second model, ISIS-FAST, employs a proprietary rapid flood spreading algorithm. The response of the models to changes in DEM resolution and data source are analysed and compared across two event scales. For the first time we show that a flood wave propagating across an urban domain responds to small scale topographic features, such as street kerbs and road surface camber, which are not represented in airborne data but which are resolved by the terrestrial laser scanner. Importantly these features are preserved even if the 10cm terrestrial data are degraded to the 1m scale of the airborne DEM. The results indicate that inclusion of these features improves the representation of hydraulic connectivity over the DEM, and hence flood risk estimation. LISFLOOD-FP is shown to be more robust to changes in DEM resolution than ISIS-FAST due to the momentum conservation inherent to the simplified shallow water formulation, but the reduced computational requirements of ISIS-FAST at 10cm scale allow it to be used for ensemble simulations. The extra detail inherent in terrestrial laser scanning data is advantageous where accurate representation of surface features is required, with potential benefit to high asset-value flood risk analysis and future studies into coupled surface/sewer and pluvial urban inundation models.
In the United States, the Federal Emergency Management Agency (FEMA) delineates 100-year flood zones to define risks, regulate flood insurance premiums, and inform flood management. Evidence ...indicates that FEMA flood maps are incomplete, calling much of our current knowledge of U.S. flood hazard inequities into question. We use a state-of-the-art flood hazard model and census tract-level dasymetrically mapped sociodemographic data to examine flood risk inequities in the Greater Houston area, where increasingly frequent and damaging flood events are occurring. We innovate by analyzing federally overlooked 100-year flood risks (100-year flood zones delineated by the flood hazard model that are outside of FEMA 100-year flood zones). Results indicate that nearly 1 million Greater Houston residents live in federally overlooked 100-year flood zones. Black and Asian neighborhoods experience disproportionate risk in federally overlooked pluvial and fluvial flood zones, and Hispanic neighborhoods experience disproportionate risk in all federally overlooked zones (coastal, pluvial, and fluvial). High flood risk and the relative lack of protective resources in federally overlooked 100-year flood zones doubly jeopardizes racial and ethnic minority communities. Our findings and recent flood disasters suggest that future flood impacts in Greater Houston will be catastrophic and unjust unless FEMA revises their risk mapping and management approach to promote long-term public safety and social equity.
Typical flood models do not take into consideration the spatial structure of flood events, which can lead to errors in the estimation of flood risk at regional to continental scales. Large‐scale ...stochastic flood models can simulate synthetic flood events with a realistic spatial structure, although this method is limited by the availability of gauge data. Simulated discharge from global hydrological models has been successfully used to drive stochastic modeling in data‐rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally: synthetic flood events in different regions of the world (Australia, South Africa, South America, Malaysia, Thailand, and Europe) are simulated using both gauged and modeled discharge. By comparing them, we analyze how a model‐based approach can simulate spatial dependency in large‐scale flood modeling. The results show a promising performance of the model‐based approach, with errors comparable to those obtained over data‐rich sites: a model‐based approach simulates the joint occurrence of relative flow exceedances at two given locations similarly to when a gauge‐based statistical model is used. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data‐scarce regions and loss exceedance curves where exposure data are available.
Key Points
Large scale stochastic flood models can simulate synthetic flood events with a realistic spatial structure even in data‐poor regions
Using a model‐based multivariate extreme model provides more robust dependence estimates than empirical distance decay functions
Modeled flow can be used in data poor‐regions to characterize dependence in large‐scale stochastic flood models and estimate flood risk
Large‐scale flood modelling approaches designed for regional to continental scales usually rely on relatively simple assumptions to represent the potentially highly complex river bathymetry at the ...watershed scale based on digital elevation models (DEMs) with a resolution in the range of 25–30 m. Here, high‐resolution (1 m) LiDAR DEMs are employed to present a novel large‐scale methodology using a more realistic estimation of bathymetry based on hydrogeomorphological GIS tools to extract water surface slope. The large‐scale 1D/2D flood model LISFLOOD‐FP is applied to validate the simulated flood levels using detailed water level data in four different watersheds in Quebec (Canada), including continuous profiles over extensive distances measured with the HydroBall technology. A GIS‐automated procedure allows to obtain the average width required to run LISFLOOD‐FP. The GIS‐automated procedure to estimate bathymetry from LiDAR water surface data uses a hydraulic inverse problem based on discharge at the time of acquisition of LiDAR data. A tiling approach, allowing several small independent hydraulic simulations to cover an entire watershed, greatly improves processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM. Results show significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. The main advantage of the proposed approach is to avoid the need to collect expensive bathymetry data to efficiently and accurately simulate flood levels over extensive areas.
A novel approach based on LiDAR data shows significant improvements to large‐scale flood modelling at the watershed scale with standard deviation in the range of 0.30 m and an average fit of around 90%. Novelties include a GIS‐automated procedure to estimate bathymetry from LiDAR water surface data using hydraulic inverse problem combined with a tiling approach to greatly improve processing time to simulate large watersheds with a 10‐m resampled LiDAR DEM.
Flood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic ...fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide.
Key Points
Global hydrological models can be used to drive a large‐scale stochastic flood inundation model in Southeast Asia
A reanalysis‐based stochastic flood model generates realistic flood events
The computed flood exposure exceedance curve for Southeast Asia compares well to the EM‐DAT database
Global flood exposure from different sized rivers Bernhofen, Mark V; Trigg, Mark A; Sleigh, P. Andrew ...
Natural hazards and earth system sciences,
09/2021, Letnik:
21, Številka:
9
Journal Article
Recenzirano
Odprti dostop
There is now a wealth of data to calculate global flood exposure. Available datasets differ in detail and representation of both global population distribution and global flood hazard. Previous ...studies of global flood risk have used datasets interchangeably without addressing the impacts using different datasets could have on exposure estimates. By calculating flood exposure to different sized rivers using a model-independent geomorphological river flood susceptibility map (RFSM), we show that limits placed on the size of river represented in global flood models result in global flood exposure estimates that differ by more than a factor of 2. The choice of population dataset is found to be equally important and can have enormous impacts on national flood exposure estimates. Up-to-date, high-resolution population data are vital for accurately representing exposure to smaller rivers and will be key in improving the global flood risk picture. Our results inform the appropriate application of these datasets and where further development and research are needed.
Toward Global Stochastic River Flood Modeling Wing, Oliver E. J.; Quinn, Niall; Bates, Paul D. ...
Water resources research,
August 2020, 2020-08-00, 20200801, Letnik:
56, Številka:
8
Journal Article
Recenzirano
Odprti dostop
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 events
Relative flow exceedances simulated by the fusion of global hydrological with statistical models reproduce gauge‐driven flood event sets
At the continental scale, key characteristics of a flood risk model are indistinguishable when driven with observed versus modeled flow data
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