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Applied flood risk analyses, especially in urban areas, very often pose the question how detailed the analysis needs to be
in order to give a realistic figure of the expected risk. The methods used in research and practical applications range from
very basic approaches with numerous simplifying assumptions up to very sophisticated, data and calculation time demanding
applications both on the hazard and on the vulnerability part of the risk. In order to shed some light on the question of
required model complexity in flood risk analyses and outputs sufficiently fulfilling the task at hand, a number of combinations
of models of different complexity both on the hazard and on the vulnerability side were tested in a case study. The different
models can be organized in a model matrix of different complexity levels: On the hazard side, the approaches/models selected
were (A) linear interpolation of gauge water levels and intersection with a digital elevation model (DEM), (B) a mixed 1D/2D
hydraulic model with simplifying assumptions (LISFLOOD-FP) and (C) a Saint-Venant 2D zero-inertia hyperbolic hydraulic model
considering the built environment and infrastructure. On the vulnerability side, the models used for the estimation of direct
damage to residential buildings are in order of increasing complexity: (I) meso-scale stage-damage functions applied to CORINE
land cover data, (II) the rule-based meso-scale model FLEMOps+ using census data on the municipal building stock and CORINE
land cover data and (III) a rule-based micro-scale model applied to a detailed building inventory. Besides the inundation
depths, the latter two models consider different building types and qualities as well as the level of private precaution and
contamination of the floodwater. The models were applied in a municipality in east Germany, Eilenburg. It suffered extraordinary
damage during the flood of August 2002, which was well documented as were the inundation extent and depths. These data provide
an almost unique data set for the validation of flood risk analyses. The analysis shows that the combination of the 1D/2D
model and the meso-scale damage model FLEMOps+ performed best and provide the best compromise between data requirements, simulation
effort, and an acceptable accuracy of the results. The more detailed approaches suffered from complex model set-up, high data
requirements, and long computation times. 相似文献
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In urban drainage systems, knowledge of short-duration rainfall events can be considered as one of the most critical elements when their hydrological behaviour wants to be investigated. The temporal resolution of rainfall data usually available for practical applications is often lower than the data requested for the design procedures or mathematical models application, greatly affecting their reliability. Moreover, when high resolution rain gauges are available in the catchment, the registration period cannot be sufficiently long for obtaining practically usable statistical analyses. The present study proposes a method for estimating the distribution of sub-hourly extreme rainfalls at sites where data for time interval of interest do not exist, but rainfall data for longer duration are available. The proposed method is based on the “scale-invariance” (or “scaling”) theory whose concepts imply that statistical properties of the extreme rainfall processes for different temporal scales are self-related by a scale-changing operator involving only the scale ratio. The methodology is applied to extreme rainfall data from a rain gauge network within the metropolitan area of Palermo (Italy). Following the application, it is shown that the statistical properties of the rainfall series have a simple scaling property over the range of duration 10 min–24 h. A simple parsimonious analytical formulation for the DDF curves, which embodies the scaling behaviour, is presented. 相似文献
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In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain‐averaged (zero‐dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero‐dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n‐dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
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Previously we have detailed an application of the generalized likelihood uncertainty estimation (GLUE) procedure to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. This method was applied to two sites where a single consistent synoptic image of inundation extent was available to test the simulation performance of the method. In this paper, we extend this to examine the predictive performance of the method for a reach of the River Severn, west‐central England. Uniquely for this reach, consistent inundation images of two major floods have been acquired from spaceborne synthetic aperture radars, as well as a high‐resolution digital elevation model derived using laser altimetry. These data thus allow rigorous split sample testing of the previous GLUE application. To achieve this, Monte Carlo analyses of parameter uncertainty within the GLUE framework are conducted for a typical hydraulic model applied to each flood event. The best 10% of parameter sets identified in each analysis are then used to map uncertainty in flood extent predictions using the method previously proposed for both an independent validation data set and a design flood. Finally, methods for combining the likelihood information derived from each Monte Carlo ensemble are examined to determine whether this has the potential to reduce uncertainty in spatially distributed measures of flood risk for a design flood. The results show that for this reach and these events, the method previously established is able to produce sharply defined flood risk maps that compare well with observed inundation extent. More generally, we show that even single, poor‐quality inundation extent images are useful in constraining hydraulic model calibrations and that values of effective friction parameters are broadly stationary between the two events simulated, most probably reflecting their similar hydraulics. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
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An innovative approach in the investigation of complex landscapes for hydraulic modelling applications is the use of terrestrial laser scanner (TLS) that can lead to a high-resolution digital elevation model (DEM). Another notable factor in flood modelling is the selection of the hydrodynamic model (1D, 2D and 1D/2D), especially in complex riverine topographies, that can influence the accuracy of flood inundation area and mapping. This paper uses different types of hydraulic–hydrodynamic modelling approaches and several types of river and riparian area spatial resolution for the implementation of a sensitivity analysis for floodplain mapping and flood inundation modelling process at ungauged watersheds. Four data sets have been used for the construction of the river and riparian areas: processed and unprocessed TLS data, topographic land survey data and typical digitized contours from 1:5000-scale topographic maps. Modelling approaches combinations consist of: one-dimensional hydraulic models (HEC-RAS, MIKE 11), two-dimensional hydraulic models (MIKE 21, MIKE 21 FM) and combinations of coupled hydraulic models (MIKE 11/MIKE 21) within the MIKE FLOOD platform. Historical flood records and estimated flooded area derived from an observed extreme flash-flood event have been used in the validation process using 2 × 2 contingency tables. Flood inundation maps have been generated for each modelling approach and landscape configuration at the lower part of Xerias River reach at Volos, Greece, and compared for assessing the sensitivity of input data and model structure uncertainty. Results provided from contingency table analysis indicate the sensitivity of floodplain modelling on the DEM spatial resolution and the hydraulic modelling approach. 相似文献
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G. Aronica E. Athanassoula M. Bureau A. Bosma R.-J. Dettmar D. Vergani M. Pohlen 《Astrophysics and Space Science》2003,284(2):753-756
We present a near-infrared K
n-band photometric study of edge-on galaxies with a box/ peanut-shaped `bulge'. The morphology of the galaxies is analysed
using unsharp masking and fits to the vertical surface brightness profiles, and the results are compared to N-body simulations
and orbital calculations of barred galaxies. Both theoretical approaches reproduce the main structures observed.
This revised version was published online in August 2006 with corrections to the Cover Date. 相似文献
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Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation 总被引：1，自引：0，他引：1
The goal of the presented research was the derivation of flood hazard maps, using Monte Carlo simulation of flood propagation at an urban site in the UK, specifically an urban area of the city of Glasgow. A hydrodynamic model describing the propagation of flood waves, based on the De Saint Venant equations in two‐dimensional form capable of accounting for the topographic complexity of the area (preferential outflow paths, buildings, manholes, etc.) and for the characteristics of prevailing imperviousness typical of the urban areas, has been used to derive the hydrodynamic characteristics of flood events (i.e. water depths and flow velocities). The knowledge of the water depth distribution and of the current velocities derived from the propagation model along with the knowledge of the topographic characteristics of the urban area from digital map data allowed for the production of hazard maps based on properly defined hazard indexes. These indexes are evaluated in a probabilistic framework to overcome the classical problem of single deterministic prediction of flood extent for the design event and to introduce the concept of the likelihood of flooding at a given point as the sum of data uncertainty, model structural error and parameterization uncertainty. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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When does spatial resolution become spurious in probabilistic flood inundation predictions? 
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下载免费PDF全文 James Thomas Steven Savage Paul Bates Jim Freer Jeffrey Neal Giuseppe Aronica 《水文研究》2016,30(13):2014-2032
Advances in remote sensing have enabled hydraulic models to run at fine scale resolutions, producing precise flood inundation predictions. However, running models at finer resolutions increase their computational expense, reducing the feasibility of running the multiple model realizations required to undertake uncertainty analysis. Furthermore, it is possible that precision gained by running fine scale models is smoothed out when treating models probabilistically. The aim of this paper is to determine the level of spatial complexity that is required when making probabilistic flood inundation predictions. The Imera basin, Sicily is used as a case study to assess how changing the spatial resolution of the hydraulic model LISFLOOD‐FP impacts on the skill of conditional probabilistic flood inundation maps given model parameter and boundary condition uncertainties. We find that model performance deteriorates at resolutions coarser than 50 m. This is predominantly caused by changes in flow pathways at coarser resolutions which lead to non‐stationarity in the optimum model parameters at different spatial resolutions. However, although it is still possible to produce probabilistic flood maps that contain a coherent outline of the flood extent at coarser resolutions, the reliability of these maps deteriorates at resolutions coarser than 100 m. Additionally, although the rejection of non‐behavioural models reduces the uncertainty in probabilistic flood maps the reliability of these maps is also reduced. Models with resolutions finer than 50 m offer little gain in performance yet are more than an order of magnitude computationally expensive which can become infeasible when undertaking probabilistic analysis. Furthermore, we show that using deterministic, high‐resolution flood maps can lead to a spurious precision that would be misleading and not representative of the overall uncertainties that are inherent in making inundation predictions. Copyright © 2015 The Authors Hydrological Processes Published by John Wiley & Sons Ltd. 相似文献
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