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

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1624 ◽  
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.

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

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1832 ◽  
Author(s):  
Alin Mihu-Pintilie ◽  
Cătălin Ioan Cîmpianu ◽  
Cristian Constantin Stoleriu ◽  
Martín Núñez Pérez ◽  
Larisa Elena Paveluc
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Flood Hazard ◽  
Hydraulic Modeling ◽  
Flood Mitigation ◽  
Small Scale ◽  
Lidar Data ◽  
Hazard Maps ◽  
Urban Flood ◽  
Flood Hazard Maps

The ability to extract streamflow hydraulic settings using geoinformatic techniques, especially in high populated territories like urban and peri-urban areas, is an important aspect of any disaster management plan and flood mitigation effort. 1D and 2D hydraulic models, generated based on DEMs with high accuracy (e.g., Light Detection and Ranging (LiDAR)) and processed in geographic information systems (GIS) modeling software (e.g., HEC-RAS), can improve urban flood hazard maps. In this study, we present a small-scale conceptual approach using HEC-RAS multi-scenario methodology based on remote sensing (RS), LiDAR data, and 2D hydraulic modeling for the urban and peri-urban area of Bacău City (Bistriţa River, NE Romania). In order to test the flood mitigation capacity of Bacău 1 reservoir (rB1) and Bacău 2 reservoir (rB2), four 2D streamflow hydraulic scenarios (s1–s4) based on average discharge and calculated discharge (s1–s4) data for rB1 spillway gate (Sw1) and for its hydro-power plant (H-pp) were computed. Compared with the large-scale flood hazard data provided by regional authorities, the 2D HEC-RAS multi-scenario provided a more realistic perspective about the possible flood threats in the study area and has shown to be a valuable asset in the improvement process of the official flood hazard maps.

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

2012 ◽  
Vol 16 (11) ◽  
pp. 4143-4156 ◽  
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.

scholarly journals Flood Mapping Proposal in Small Watersheds: A Case Study of the Rebollos and Miranda Ephemeral Streams (Cartagena, Spain)

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Valentina Betancourt-Suárez ◽  
Estela García-Botella ◽  
Alfredo Ramon-Morte
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Flood Hazard ◽  
Satellite Image ◽  
Temporal Analysis ◽  
Mediterranean Coast ◽  
Geographical Information ◽  
Aerial Photographs ◽  
Hazard Maps ◽  
Flood Hazard Maps

Anthropogenic landscape changes cause significant disturbances to fluvial system dynamics and such is the case of the watersheds studied near the Spanish Mediterranean coast (Cartagena). Economic growth resulted in the addition of external water resources from the Tajo River (1979) as part of the National Water Plan (1933). Irrigation water has caused the water table to rise since 1979. Furthermore, water resources have boosted urban touristic expansion, industrial estates, and road infrastructures. This study presents a diagnosis of the official flood hazard maps by applying remote sensing techniques that enable the identification of (i) areas flooded during recent events; and (ii) the possible effects of anthropogenic actions on fluvial processes affecting flooding (land use and land cover change—LULCC). The flooded areas were identified from a multispectral satellite image taken by a sensor on Sentinel-2. A multi-temporal analysis of aerial photographs (1929, 1956, 1981, 2009, and 2017) showing the fluvial and anthropic environment at a detailed scale (1:25,000) was used to define the fluvial geomorphology and the main anthropic alterations on the Rebollos ephemeral stream. Official inputs from geographical information repositories about land use were also gathered (LULC). The result was compared to the official flood hazard maps (SNCZI) and this revealed floodable areas that had not been previously mapped because official maps rely only on the hydraulic method. Finally, all the recent changes that will have increased the disastrous consequences of flooding have been detected, analyzed, and mapped for the study area.

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

2019 ◽  
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.

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

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

<p>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.</p><p>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.</p><p>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.</p>

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

2012 ◽  
Vol 9 (5) ◽  
pp. 6615-6647 ◽  
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.

scholarly journals Design Floods Considering the Epistemic Uncertainty

Water ◽  
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.

Moving to 3-D flood hazard maps for enhancing risk communication

2019 ◽  
Vol 111 ◽  
pp. 510-522 ◽  
Author(s):  
Francesco Macchione ◽  
Pierfranco Costabile ◽  
Carmelina Costanzo ◽  
Rosa De Santis
Keyword(s):  
Risk Communication ◽  
Flood Hazard ◽  
Hazard Maps ◽  
Flood Hazard Maps

scholarly journals Determination of Flood Hazard Zones Using Geographical Information Systems and Remote Sensing Techniques: A Case Study in Part Yenagoa Metropolis

2019 ◽  
pp. 1-9
Author(s):  
Eteh Desmond ◽  
Francis Emeka Egobueze ◽  
Francis Omonefe
Keyword(s):  
Remote Sensing ◽  
Geographical Information Systems ◽  
Flood Hazard ◽  
Environmental Law ◽  
Remote Sensing Data ◽  
Flood Management ◽  
Geographical Information ◽  
Flood Level ◽  
The Government ◽  
Srtm Data

Flood has been a serious hazard for the past decades in Nigeria at large. The incidence of 2012 and 2018 flood disaster in Yenagoa, Amassoma and other parts of the state have not been recover till date and the government is not consigned about the well been of the people. The major causes of the flood are attributed to increased rainfall and lack of drainages including dredging of rivers and disobeying of environmental law and infrastructure failure. Coastal Towns or communities are one of the most affected areas of flood and their farms and fishing implements were washed away by the floodwater in 2012 and 2018 in Bayelsa State. Flood management is needed for provision of time information so quick response can be done as soon as possible. Using SRTM data to produce digital elevation model and IDW Contour, the 3D model from ground data of Yenagoa metropolis using ArcGIS 10.6 to generate and analyze them. As a result of field survey, flood level calculation was made to classified flood hazard zones for migration, Agricultural Educational, and construction purpose such as land suitability. This was used in ascertaining the extent of the flooded area. The result reveals that an area of over 5.9888882km2 and riverine and coastal area is flooded, affecting more than 15 coastal and riverine communities. The finding also concludes that remote sensing data like SRTM data and Geospatial techniques seems effective in mapping and identifying areas prone to flooding. Therefore Remote sensing and Geospatial database should be established for proper flood mapping and the government should constantly dredge the area from time to time. 

scholarly journals Multivariate clustering of reindeer herding districts in Sweden according to range prerequisites for reindeer husbandry

Rangifer ◽  
2009 ◽  
Vol 27 (2) ◽  
pp. 107-119
Author(s):  
Henrik Lundqvist ◽  
Öje Danell
Keyword(s):  
Geographical Information Systems ◽  
Large Scale ◽  
Global Climate ◽  
Meteorological Data ◽  
Principal Component ◽  
Geographical Information ◽  
Reindeer Herding ◽  
Factors Affecting ◽  
Decision Tools ◽  
Forage Value

The 51 reindeer herding districts in Sweden vary in productivity and prerequisites for reindeer herding. In this study we characterize and group reindeer herding districts based on relevant factors affecting reindeer productivity, i.e. topography, vegetation, forage value, habitat fragmentation and reachability, as well as season lengths, snow fall, ice-crust probability, and insect harassment, totally quantified in 15 variables. The herding districts were grouped into seven main groups and three single outliers through cluster analyses. The largest group, consisting of 14 herding districts, was further divided into four subgroups. The range properties of herding districts and groups of districts were characterized through principal component analyses. By comparisons of the suggested grouping of herding districts with existing administrative divisions, these appeared not to coincide. A new division of herding districts into six administrative sets of districts was suggested in order to improve administrative planning and management of the reindeer herding industry. The results also give possibilities for projections of alterations caused by an upcoming global climate change. Large scale investigations using geographical information systems (GIS) and meteorological data would be helpful for administrative purposes, both nationally and internationally, as science-based decision tools in legislative, economical, ecological and structural assessments. Abstract in Swedish / Sammanfattning: Multivariat gruppering av svenska samebyar baserat på renbetesmarkernas grundförutsettningar Svenska renskötselområdet består av 51 samebyar som varierar i produktivitet och förutsättningar för renskötsel. Vi analyserade variationen mellan samebyar med avseende på 15 variabler som beskriver topografi, vegetation, betesvärde, fragmentering av betesmarker, klimat, skareförekomst och aktivitet av parasiterande insekter och vi föreslår en indelning av samebyar i tio grupper. Den största gruppen, som bestod av 14 samebyar, delades vidare in i 4 undergrupper. Klusteranalyser med 4 olika linkage-varianter användes till att gruppera samebyarna. Principalkomponentsanalys användes för att kartlägga undersökta variabler och de resulterande samebygruppernas karaktär. Samebygrupperna följde inte länsgränser och tre samebyar föll ut som enskilda grupper. Denna undersökning ger underlag för jämförelser mellan samebyar med beaktande av likheter och olikheter i fråga om produktivitet och funktionella särdrag istället för länsgränser och historik. Vi föreslår en ny administrativ indelning i sex områden som skulle kunna fungera som ett alternativt underlag för planering och beslut som rör produktionsaspekter i rennäringen. Resultaten ger också underlag för förutsägelser av förändringar i samebyars produktionsförutsättningar till följd av klimatförändringar.

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