Technical Evolution of Flood Maps Through Spanish Experience in the European Framework

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed.

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Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

Remote Sensing ◽

10.3390/rs13142786 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2786

Author(s):

Roya Narimani ◽

Changhyun Jun ◽

Saqib Shahzad ◽

Jeill Oh ◽

Kyoohong Park

Keyword(s):

High Risk ◽

Hybrid Method ◽

Satellite Images ◽

Controlling Factors ◽

Susceptibility Mapping ◽

Low Risk ◽

Flood Susceptibility ◽

Flood Susceptibility Mapping ◽

Flood Maps

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

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A two-stage analogue model for real-time urban flood forecasting

10.5194/egusphere-egu21-15645 ◽

2021 ◽

Author(s):

Chris Onof ◽

Yuting Chen ◽

Li-Pen Wang ◽

Amy Jones ◽

Susana Ochoa Rodriguez

Keyword(s):

Real Time ◽

Flood Forecasting ◽

Image Feature ◽

Two Stage ◽

Analogue Model ◽

Flood Prediction ◽

Urban Flood ◽

Radar Images ◽

Proposed Model ◽

Flood Maps

In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.The model has two consecutive stages:<ul><li>(1) Rainfall nowcasting: 0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the first layer, the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the second layer, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection.</li> <li>(2) Flood prediction: we predict flood extent using the high-resolution rainfall forecast from Stage 1, along with a database of pre-run flood maps at 1x1 km2 solution from 157 catalogued historical flood events. A deterministic flood prediction is obtained by using the averaged response from the twelve flood maps associated to the twelve ensemble rainfall nowcasts, where for each gridded area the median value is adopted (assuming flood maps are equiprobabilistic). A probabilistic flood prediction is obtained by generating a quantile-based flood map. Note that the flood maps were generated through rolling ball-based mapping of the flood volumes predicted at each node of the InfoWorks ICM sewer model of the pilot area.</li> </ul>The Minworth catchment in the UK (~400 km2) was used to demonstrate the proposed model. Cross&#8209;assessment was undertaken for each of 157 flooding events by leaving one event out from training in each iteration and using it for evaluation. With a focus on the spatial replication of flood/non-flood patterns, the predicted flood maps were converted to binary (flood/non-flood) maps. Quantitative assessment was undertaken by means of a contingency table. An average accuracy rate (i.e. proportion of correct predictions, out of all test events) of 71.4% was achieved, with individual accuracy rates ranging from 57.1% to 78.6%). Further testing is needed to confirm initial findings and flood mapping refinement will be pursued.The proposed model is fast, easy and relatively inexpensive to operate, making it suitable for direct use by local authorities who often lack the expertise on and/or capabilities for flood modelling and forecasting.References: Panziera et al. 2011. NORA&#8211;Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society. 137, 2106-2123.

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Potential and Limitations of Open Satellite Data for Flood Mapping

Remote Sensing ◽

10.3390/rs10111673 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1673 ◽

Cited By ~ 27

Author(s):

Davide Notti ◽

Daniele Giordan ◽

Fabiana Caló ◽

Antonio Pepe ◽

Francesco Zucca ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Flood Hazard ◽

Flood Mapping ◽

Ancillary Data ◽

Po River ◽

Ground Truth Data ◽

Hazard Management ◽

Flooded Areas ◽

Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps.

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Flood Risk Assessment and Quantification at the Community and Property Level in the State of Iowa

10.31223/x5tc92 ◽

2021 ◽

Author(s):

Enes Yildirim ◽

Ibrahim Demir

Keyword(s):

Risk Assessment ◽

High Resolution ◽

Flood Risk ◽

Large Scale ◽

Spatial Scales ◽

The United States ◽

Flood Risk Assessment ◽

Study Region ◽

Study Results ◽

Flood Maps

Flood risk assessment contributes to identifying at-risk communities and supports mitigation decisions to maximize benefits from the investments. Large-scale risk assessments generate invaluable inputs for prioritizing regions for the distribution of limited resources. High-resolution flood maps and accurate parcel information are critical for flood risk analysis to generate reliable outcomes for planning, preparedness, and decision-making applications. Large-scale damage assessment studies in the United States often utilize the National Structure Inventory (NSI) or HAZUS default dataset, which results in inaccurate risk estimates due to the low geospatial accuracy of these datasets. On the other hand, some studies utilize higher resolution datasets, however they are limited to focus on small scales, for example, a city or a Hydrological United Code (HUC)-12 watershed. In this study, we collected extensive detailed flood maps and parcel datasets for many communities in Iowa to carry out a large-scale flood risk assessment. High-resolution flood maps and the most recent parcel information are collected to ensure the accuracy of risk products. The results indicate that the Eastern Iowa communities are prone to a higher risk of direct flood losses. Our model estimates nearly $10 million in average annualized losses, particularly in large communities in the study region. The study highlights that existing risk products based on FEMA's flood risk output underestimate the flood loss, specifically in highly populated urban communities such as Bettendorf, Cedar Falls, Davenport, Dubuque, and Waterloo. Additionally, we propose a flood risk score methodology for two spatial scales (e.g., HUC-12 watershed, property) to prioritize regions and properties for mitigation purposes. Lastly, the watershed-scale study results are shared through a web-based platform to inform the decision-makers and the public.

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Improving Hydrologic Modeling Using Cloud-Free MODIS Flood Maps

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0021.1 ◽

2019 ◽

Vol 20 (11) ◽

pp. 2203-2214 ◽

Cited By ~ 1

Author(s):

Hoang Tran ◽

Phu Nguyen ◽

Mohammed Ombadi ◽

Kuolin Hsu ◽

Soroosh Sorooshian ◽

...

Keyword(s):

Optical Sensors ◽

Large Scale ◽

Dimensional Space ◽

Three Dimensional ◽

Skill Score ◽

Hydrologic Model ◽

Probability Of Detection ◽

Landsat 8 ◽

Inundation Maps ◽

Flood Maps

Abstract Flood mapping from satellites provides large-scale observations of flood events, but cloud obstruction in satellite optical sensors limits its practical usability. In this study, we implemented the Variational Interpolation (VI) algorithm to remove clouds from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Snow-Covered Area (SCA) products. The VI algorithm estimated states of cloud-hindered pixels by constructing three-dimensional space–time surfaces based on assumptions of snow persistence. The resulting cloud-free flood maps, while maintaining the temporal resolution of the original MODIS product, showed an improvement of nearly 70% in average probability of detection (POD) (from 0.29 to 0.49) when validated with flood maps derived from Landsat-8 imagery. The second part of this study utilized the cloud-free flood maps for calibration of a hydrologic model to improve simulation of flood inundation maps. The results demonstrated the utility of the cloud-free maps, as simulated inundation maps had average POD, false alarm ratio (FAR), and Hanssen–Kuipers (HK) skill score of 0.87, 0.49, and 0.84, respectively, compared to POD, FAR, and HK of 0.70, 0.61, and 0.67 when original maps were used for calibration.

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Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study

Water ◽

10.3390/w11122454 ◽

2019 ◽

Vol 11 (12) ◽

pp. 2454 ◽

Cited By ~ 7

Author(s):

Francisco Carreño Conde ◽

María De Mata Muñoz

Keyword(s):

Risk Mitigation ◽

Aerial Photographs ◽

Flood Mapping ◽

Blue Color ◽

Economic Activities ◽

Nature Protection ◽

Horizontal Polarization ◽

Sar Images ◽

Radar Images ◽

Flood Maps

Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering 7 × 7 window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.

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Flood Risk Assessment for the Long-Term Strategic Planning Considering the Placement of Industrial Parks in Slovakia

Sustainability ◽

10.3390/su12104144 ◽

2020 ◽

Vol 12 (10) ◽

pp. 4144 ◽

Cited By ~ 1

Author(s):

Henrich Grežo ◽

Matej Močko ◽

Martin Izsóff ◽

Gréta Vrbičanová ◽

František Petrovič ◽

...

Keyword(s):

Risk Assessment ◽

Flood Risk ◽

River Flow ◽

Flow Simulation ◽

Current Data ◽

Economic Losses ◽

Historical Maps ◽

Flood Risk Assessment ◽

Industrial Parks ◽

Flood Maps

The intention of the article is to demonstrate how data from historical maps might be applied in the process of flood risk assessment in peri-urban zones located in floodplains and be complementary datasets to the national flood maps. The research took place in two industrial parks near the rivers Žitava and Nitra in the town of Vráble (the oldest industrial park in Slovakia) and the city of Nitra (one of the largest industrial parks in Slovakia, which is still under construction concerning the Jaguar Land Rover facility). The historical maps from the latter half of the 18th and 19th centuries and from the 1950s of the 20th century, as well as the field data on floods gained with the GNSSS receiver in 2010 and the Q100 flood line of the national flood maps (2017), were superposed in geographic information systems. The flood map consists of water flow simulation by a mathematical hydrodynamic model which is valid only for the current watercourse. The comparison of historical datasets with current data indicated various transformations and shifts of the riverbanks over the last 250 years. The results proved that the industrial parks were built up on traditionally and extensively used meadows and pastures through which branched rivers flowed in the past. Recent industrial constructions intensified the use of both territories and led to the modifications of riverbeds and shortening of the watercourse length. Consequently, the river flow energy increased, and floods occurred during torrential events in 2010. If historical maps were respected in the creation of the flood maps, the planned construction of industrial parks in floodplains could be limited or forbidden in the spatial planning documentation. This study confirmed that the flood modelling using the Q100 flood lines does not provide sufficient arguments for investment development groups, and flood maps might be supplied with the data derived from historical maps. The proposed methodology represents a simple, low cost, and effective way of identifying possible flood-prone areas and preventing economic losses and other damages.

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