Using High-Density LiDAR Data and 2D Streamflow Hydraulic Modeling to Improve Urban Flood Hazard Maps: A HEC-RAS Multi-Scenario Approach

Alin Mihu-Pintilie; Cătălin Ioan Cîmpianu; Cristian Constantin Stoleriu; Martín Núñez Pérez; Larisa Elena Paveluc

doi:10.3390/w11091832

Using 1D HEC-RAS Modeling and LiDAR Data to Improve Flood Hazard Maps Accuracy: A Case Study from Jijia Floodplain (NE Romania)

Water ◽

10.3390/w12061624 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1624 ◽

Cited By ~ 1

Author(s):

Elena Huţanu ◽

Alin Mihu-Pintilie ◽

Andrei Urzica ◽

Larisa Elena Paveluc ◽

Cristian Constantin Stoleriu ◽

...

Keyword(s):

Geographical Information Systems ◽

Large Scale ◽

Flood Hazard ◽

Hydraulic Modeling ◽

Geographical Information ◽

Computer Algorithms ◽

Hazard Maps ◽

Modeling Software ◽

Recurrence Intervals ◽

Flood Hazard Maps

The ability to extract flood hazard settings in highly vulnerable areas like populated floodplains by using new computer algorithms and hydraulic modeling software is an important aspect of any flood mitigation efforts. In this framework, the 1D/2D hydraulic models, which were generated based on a Light Detection and Ranging (LiDAR) derivate Digital Elevation Model (DEM) and processed within Geographical Information Systems (GIS), can improve large-scale flood hazard maps accuracy. In this study, we developed the first flood vulnerability assessment for 1% (100-year) and 0.1% (1000-year) recurrence intervals within the Jijia floodplain (north-eastern Romania), based on 1D HEC-RAS hydraulic modeling and LiDAR derivate DEM with 0.5 m spatial resolution. The results were compared with official flood hazards maps developed for the same recurrence intervals by the hydrologists of National Administration “Romanian Waters” (NARW) based on MIKE SHE modeling software and a DEM with 2 m spatial resolutions. It was revealed that the 1D HEC-RAS provides a more realistic perspective about the possible flood threats within Jijia floodplain and improves the accuracy of the official flood hazard maps obtained according to Flood Directive 2007/60/EC.

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Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps

Water ◽

10.3390/w13111577 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1577

Author(s):

David C. Mason ◽

John Bevington ◽

Sarah L. Dance ◽

Beatriz Revilla-Romero ◽

Richard Smith ◽

...

Keyword(s):

Synthetic Aperture Radar ◽

Real Time ◽

Rural Areas ◽

Urban Areas ◽

Flood Hazard ◽

Synthetic Aperture ◽

Hazard Maps ◽

Urban Flood ◽

Flood Hazard Maps ◽

Aperture Radar

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method.

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Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives

Environmental Modelling & Software ◽

10.1016/j.envsoft.2020.104889 ◽

2021 ◽

Vol 135 ◽

pp. 104889

Author(s):

Pierfranco Costabile ◽

Carmelina Costanzo ◽

Gianluca De Lorenzo ◽

Rosa De Santis ◽

Nadia Penna ◽

...

Keyword(s):

Urban Areas ◽

Laser Scanning ◽

Flood Hazard ◽

Airborne Laser Scanning ◽

Hazard Maps ◽

Airborne Laser ◽

Flood Hazard Maps

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Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

Geosciences ◽

10.3390/geosciences8080275 ◽

2018 ◽

Vol 8 (8) ◽

pp. 275 ◽

Cited By ~ 11

Author(s):

Daniela Rincón ◽

Usman Khan ◽

Costas Armenakis

Keyword(s):

Flood Risk ◽

Flood Hazard ◽

Flood Mitigation ◽

Base Case ◽

Hazard Maps ◽

Economic Vulnerability ◽

The Social ◽

Risk Maps ◽

Multi Criteria Analysis ◽

Flood Hazard Maps

Given the increase in flood events in recent years, accurate flood risk assessment is an important component of flood mitigation in urban areas. This research aims to develop updated and accurate flood risk maps in the Don River Watershed within the Great Toronto Area (GTA). The risk maps use geographical information systems (GIS) and multi-criteria analysis along with the application of Analytical Hierarchy Process methods to define and quantify the optimal selection of weights for the criteria that contribute to flood risk. The flood hazard maps were generated for four scenarios, each with different criteria (S1, S2, S3, and S4). The base case scenario (S1) is the most accurate, since it takes into account the floodplain map developed by the Toronto and Region Conservation Authority. It also considers distance to streams (DS), height above nearest drainage (HAND), slope (S), and the Curve Number (CN). S2 only considers DS, HAND, and CN, whereas S3 considers effective precipitation (EP), DS, HAND, and S. Lastly, S4 considers total precipitation (TP), DS, HAND, S, and CN. In addition to the flood hazard, the social and economic vulnerability was included to determine the total flood vulnerability in the watershed under three scenarios; the first one giving a higher importance to the social vulnerability, the second one giving equal importance to both social and economic vulnerability, and the third one giving more importance to the economic vulnerability. The results for each of the four flood scenarios show that the flood risk generated for S2 is the most similar to the base case (S1), followed by S3 and S4. The inclusion of social and economic vulnerability highlights the impacts of floods that are typically ignored in practice. It will allow watershed managers to make more informed decisions for flood mitigation and protection. The most important outcome of this research is that by only using the digital elevation model, the census data, the streams, land use, and soil type layers, it is possible to obtain a reliable flood risk map (S2) using a simplified method as compared to more complex flood risk methods that use hydraulic and hydrological models to generate flood hazard maps (as was the case for S1).

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Deriving global flood hazard maps of fluvial floods through a physical model cascade

Hydrology and Earth System Sciences ◽

10.5194/hess-16-4143-2012 ◽

2012 ◽

Vol 16 (11) ◽

pp. 4143-4156 ◽

Cited By ~ 120

Author(s):

F. Pappenberger ◽

E. Dutra ◽

F. Wetterhall ◽

H. L. Cloke

Keyword(s):

Land Surface ◽

Large Scale ◽

Flood Hazard ◽

Regional Scale ◽

Surface Model ◽

Return Periods ◽

Hazard Maps ◽

Data Set ◽

Flood Hazard Maps ◽

River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

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An integrated hydrological and hydraulic modelling approach for the flood risk assessment over Po river basin

10.5194/nhess-2019-356 ◽

2019 ◽

Cited By ~ 2

Author(s):

Rita Nogherotto ◽

Adriano Fantini ◽

Francesca Raffaele ◽

Fabio Di Sante ◽

Francesco Dottori ◽

...

Keyword(s):

Ad Hoc ◽

Flood Hazard ◽

Hydraulic Modeling ◽

Return Periods ◽

Hazard Maps ◽

Joint Research ◽

Po River ◽

Essential Information ◽

Flood Prone Areas ◽

Flood Hazard Maps

Abstract. Identification of flood prone areas is instrumental for a large number of applications, ranging from engineering to climate change studies, and provides essential information for planning effective emergency responses. In this work we describe an integrated hydrological and hydraulic modeling approach for the assessment of flood-prone areas in Italy and we present the first results obtained over the Po river (Northern Italy) at a resolution of 90 m. River discharges are obtained through the hydrological model CHyM driven by GRIPHO, a newly-developed high resolution hourly precipitation dataset. Runoff data is then used to obtain Synthetic Design Hydrographs (SDHs) for different return periods along the river network. Flood hydrographs are subsequently processed by a parallelized version of the CA2D hydraulic model to calculate the flow over an ad hoc re-shaped HydroSHEDS digital elevation model which includes information about the channel geometry. Modeled hydrographs and SDHs are compared with those obtained from observed data for a choice of gauging stations, showing an overall good performance of the CHyM model. The flood hazard maps for return periods of 50, 100, 500 are validated by comparison with the official flood hazard maps produced by the River Po Authority (Adbpo) and with the Joint Research Centre's (JRC) pan-European maps. The results show a good agreement with the available official national flood maps for high return periods. For lower return periods the results and less satisfactory but overall the application suggests strong potential of the proposed approach for future applications.

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Using image-based deep learning to identify river defences from elevation data for large-scale flood modelling

10.5194/egusphere-egu2020-8522 ◽

2020 ◽

Author(s):

Catharine Brown ◽

Helen Smith ◽

Simon Waller ◽

Lizzie Weller ◽

David Wood

Keyword(s):

Deep Learning ◽

Large Scale ◽

Flood Hazard ◽

The United States ◽

Hazard Maps ◽

Learning Techniques ◽

Geographical Regions ◽

River Flood ◽

Flood Maps ◽

Flood Hazard Maps

National-scale flood hazard maps are an essential tool for the re/insurance industry to assess property risk and financial impacts of flooding. The creation of worst-case scenario river flood maps, assuming defence failure, and additional separate datasets indicating areas protected by defences enables the industry to best assess risk. However, there is a global shortage of information on defence locations and maintenance. For example, in the United States it is estimated that there are around 160,000 kilometres (100,000 miles) of defence levees, but the location of many of these is not mapped in large-scale defence datasets. We present a new approach to large-scale defence identification using deep learning techniques.In the generation of flood hazard maps, the elevation depicted in the Digital Elevation Model (DEM) used in the hydraulic modelling is fundamental to determining the routing of water flow across the terrain and thus determining where flooding occurs. The full or partial representation of raised river defences in DEMs affects this routing and subsequently causes difficulty when developing both undefended and defended flood maps. To generate undefended river flood maps these raised defences need to be entirely removed, which requires knowledge of their locations. Without comprehensive defence datasets, an alternative method to identify river defences on a large-scale is required.The use of deep learning techniques to recognise objects in images is fast developing. DEMs and other related datasets can be represented in a similar raster format to images. JBA has developed a successful methodology which involves training a U-Net Convolutional Neural Network, originally designed for image segmentation, to identify raised river defences in DEMs. Using this defence dataset, we have been able to generate true river undefended flood maps for a selection of countries including Italy, Germany, Austria and the US. We present details of the methodology developed, the model training and the challenges faced when applying the model to different geographical regions.

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Deriving global flood hazard maps of fluvial floods through a physical model cascade

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-6615-2012 ◽

2012 ◽

Vol 9 (5) ◽

pp. 6615-6647 ◽

Cited By ~ 13

Author(s):

F. Pappenberger ◽

E. Dutra ◽

F. Wetterhall ◽

H. Cloke

Keyword(s):

Land Surface ◽

Large Scale ◽

Flood Hazard ◽

Regional Scale ◽

Surface Model ◽

Return Periods ◽

Hazard Maps ◽

Data Set ◽

Flood Hazard Maps ◽

River Routing

Abstract. Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25 × 25 km grid, which is then reprojected onto a 1 × 1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25 × 25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

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Design Floods Considering the Epistemic Uncertainty

Water ◽

10.3390/w13111601 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1601

Author(s):

Radu Drobot ◽

Aurelian Florentin Draghia ◽

Daniel Ciuiu ◽

Romică Trandafir

Keyword(s):

Flood Hazard ◽

Epistemic Uncertainty ◽

Statistical Distributions ◽

Hazard Maps ◽

Design Flood ◽

Flood Hydrograph ◽

Recorded Data ◽

Different Shapes ◽

Significant Spread ◽

Flood Hazard Maps

The Design Flood (DF) concept is an essential tool in designing hydraulic works, defining reservoir operation programs, and identifying reliable flood hazard maps. The purpose of this paper is to present a methodology for deriving a Design Flood hydrograph considering the epistemic uncertainty. Several appropriately identified statistical distributions allow for the acceptable approximation of the frequent values of maximum discharges or flood volumes, and display a significant spread for their medium/low Probabilities of Exceedance (PE). The referred scattering, as a consequence of epistemic uncertainty, defines an area of uncertainty for both recorded data and extrapolated values. In considering the upper and lower values of the uncertainty intervals as limits for maximum discharges and flood volumes, and by further combining them compatibly, a set of DFs as completely defined hydrographs with different shapes result for each PE. The herein proposed procedure defines both uni-modal and multi-modal DFs. Subsequently, such DFs help water managers in examining and establishing tailored approaches for a variety of input hydrographs, which might be typically generated in river basins.

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