LiDAR-Derived Flood-Inundation Maps for Real-Time Flood-Mapping Applications, Tar River Basin, North Carolina

2007 ◽  
Author(s):  
Jerad D. Bales ◽  
Chad R. Wagner ◽  
Kirsten C. Tighe ◽  
Silvia Terziotti
Keyword(s):  
North Carolina ◽  
Real Time ◽  
River Basin ◽  
Flood Mapping ◽  
Flood Inundation ◽  
Inundation Maps

Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)

2021 ◽  
pp. 1-26
Author(s):  
Bikash Ranjan Parida ◽  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Amit Kumar
Keyword(s):  
River Basin ◽  
Flood Inundation ◽  
Inundation Maps

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

2021 ◽  
Author(s):  
Goutam Konapala ◽  
Sujay Kumar
Keyword(s):  
Flood Mapping ◽  
Attractive Alternative ◽  
Flood Inundation ◽  
Positive Role ◽  
Inundation Maps ◽  
Flood Inundation Mapping ◽  
Sar Imagery ◽  
Operational Errors ◽  
Water Indices ◽  
Sentinel 2

<p>Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. But MS sensors may not penetrate cloud cover, whereas SAR is plagued by operational errors such as noise-like speckle challenging their viability to global flood mapping applications. An attractive alternative is to effectively combine MS data and SAR, i.e., two aspects that can be considered complementary with respect to flood mapping tasks. Therefore, in this study, we explore the diverse bands of Sentinel 2 (S2) derived water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to access their capability in generating accurate flood inundation maps. For this purpose, a fully connected deep convolutional neural network known as U-Net is applied to combinations of S1 and S2 bands to 446 (training: 313, validating: 44, testing: 89) hand labeled flood inundation extents derived from Sen1Floods11 dataset spanning across 11 flood events. The trained U-net was able to achieve a median F1 score of 0.74 when using DEM and S1 bands as input in comparison to 0.63 when using only S1 bands highlighting the active positive role of DEM in mapping floods. Among the, S2 bands, HSV (Hue, Saturation, Value) transformation of Sentinel 2 data has achieved a median F1 score of 0.94 outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Also, when combined with Sentinel 1 SAR imagery too, HSV achieves a median F1 score 0.95 outperforming all the well-established water indices in detecting floods in majority of test images.</p>

scholarly journals Framework for Offline Flood Inundation Forecasts for Two-Dimensional Hydrodynamic Models

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 346 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Warning System ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Online Maps ◽  
The City ◽  
Selection Of

The paper presents a new methodology for hydrodynamic-based flood forecast that focuses on scenario generation and database queries to select appropriate flood inundation maps in real-time. In operational flood forecasting, only discharges are forecasted at specific gauges using hydrological models. Hydrodynamic models, which are required to produce inundation maps, are computationally expensive, hence not feasible for real-time inundation forecasting. In this study, we have used a substantial number of pre-calculated inundation maps that are stored in a database and a methodology to extract the most likely maps in real-time. The method uses real-time discharge forecast at upstream gauge as an input and compares it with the pre-recorded scenarios. The results show satisfactory agreements between offline inundation maps that are retrieved from a pre-recorded database and online maps, which are hindcasted using historical events. Furthermore, this allows an efficient early warning system, thanks to the fast run-time of the proposed offline selection of inundation maps. The framework is validated in the city of Kulmbach in Germany.

Flood Inundation Mapping of the Hitachi Region in the Kuji River Basin, Japan, During the October 11–13, 2019 Extreme Rain Event

2020 ◽  
Vol 15 (6) ◽  
pp. 712-725 ◽  
Author(s):  
Shakti P. C. ◽  
◽  
Kohin Hirano ◽  
Satoshi Iizuka
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Rapid Development ◽  
Mitigation Strategies ◽  
Flood Mapping ◽  
Rain Event ◽  
Flood Inundation ◽  
Advantages And Disadvantages ◽  
Inundation Mapping ◽  
Flood Inundation Mapping

The frequency of severe flood events has been increasing recently in Japan. One of the latest events occurred in October 2019 and caused extensive damage in several river basins, especially in the central and northern regions of the country. In this study, we selected the Hitachi region (Hitachi-Omiya and Hitachi-Ota) within the Kuji River Basin which underwent considerable flooding due to the failure of embankments at two locations in the region. Maximum-possible flood inundation maps were generated using survey-based data and hydrological modeling for the Hitachi region. These maps incorporated the flood scenarios (embankment failures). All the generated products were compared with the reference flood mapping, i.e., Sentinel-1 data and Geospatial Information Authority of Japan (GSI) data for that region. It was observed that generated flood inundation mapping product based on the survey-data yielded results similar to those obtained with GSI data for the Hitachi region. Although each flood mapping product has advantages and disadvantages, they can be a good reference for the proper management and mitigation of flood disaster in the future. The rapid development of flood inundation mapping products that consider varying flood scenarios is an important part of flood mitigation strategies.

scholarly journals Real-Time Flood Mapping on Client-Side Web Systems Using HAND Model

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 65
Author(s):  
Anson Hu ◽  
Ibrahim Demir
Keyword(s):  
Flood Mapping ◽  
Flood Inundation ◽  
Inundation Maps ◽  
Hand Model ◽  
Digital Elevation ◽  
Insurance Rate ◽  
Elevation Model ◽  
Client Side ◽  
High Degree ◽  
The City

The height above nearest drainage (HAND) model is frequently used to calculate properties of the soil and predict flood inundation extents. HAND is extremely useful due to its lack of reliance on prior data, as only the digital elevation model (DEM) is needed. It is close to optimal, running in linear or linearithmic time in the number of cells depending on the values of the heights. It can predict watersheds and flood extent to a high degree of accuracy. We applied a client-side HAND model on the web to determine extent of flood inundation in several flood prone areas in Iowa, including the city of Cedar Rapids and Ames. We demonstrated that the HAND model was able to achieve inundation maps comparable to advanced hydrodynamic models (i.e., Federal Emergency Management Agency approved flood insurance rate maps) in Iowa, and would be helpful in the absence of detailed hydrological data. The HAND model is applicable in situations where a combination of accuracy and short runtime are needed, for example, in interactive flood mapping and supporting mitigation decisions, where users can add features to the landscape and see the predicted inundation.

scholarly journals Dynamic Flood Inundation Forecast for the City of Kulmbach Using Offline Two-Dimensional Hydrodynamic Models

10.29007/c4gq ◽  
2018 ◽  
Author(s):  
Punit Bhola ◽  
Jorge Leandro ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
Markus Disse
Keyword(s):  
Real Time ◽  
Dynamic Models ◽  
Water Levels ◽  
Flood Inundation ◽  
Hydrological Models ◽  
Hydrodynamic Models ◽  
Database Queries ◽  
Inundation Maps ◽  
Inundation Map ◽  
The City

The paper presents a new methodology for hydrodynamic-based flood forecast focusing on sce- nario generation and database queries to select the appropriate flood inundation map in real-time. In operational flood forecasting, discharges are forecast at specific gauges using hydrological models. The water levels are obtained from a rating curve designed for each respective gauge. Particularly for higher discharges when the flow over-spills the side banks, these curves are highly uncertain. Hy- drodynamic models are then required to produce realistic inundation maps and water levels. Hydro- dynamic models are computationally expensive and therefore not feasible for real-time forecasting. Alternatively, pre-calculated inundation maps can be stored in a database which contains a substantial number of scenarios, and used for extracting the most likely map in real-time. This study investigates the application of offline inundation forecast in the city Kulmbach in Germany.

Exploring stage-based flood frequency analysis for flood inundation mapping

2021 ◽  
Author(s):  
Shobhit Singh ◽  
Somil Swarnkar ◽  
Rajiv Sinha
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Flood Inundation ◽  
Return Periods ◽  
Theoretical Probability ◽  
Inundation Maps

<p>Floods are one of the worst natural hazards around the globe and around 40% of all losses worldwide due to natural hazard have been caused by floods since 1980s. In India, more than 40 million hectares of area are affected by floods annually which makes it one of the worst affected country in the world. In particular, the Ganga river basin in northern India which hosts nearly half a billion people, is one of the worst floods affected regions in the country. The Ghaghra river is one of the highest discharge-carrying tributaries of the Ganga river, which originates from High Himalaya. Despite severally affected by floods each year, flood frequencies of the Ghaghra river are poorly understood, making it one of the least studied river basins in the Ganga basin. It is important to note that, like several other rivers in India, the Ghaghra also has several hydrological stations where only stage data is available, and therefore traditional flood frequency analysis using discharge data becomes difficult. In this work, we have performed flood frequency analysis using both stage and discharge dataset at three different gauge stations in the Ghaghra river basin to compare the results using statistical methods. The L-moment analysis is applied to assess the probability distribution for the flood frequency analysis. Further, we have used the TanDEM-x 90m digital elevation model (DEM) to map the flood inundation regions. Our results suggest the Weibull is statistically significant distribution for the discharge dataset. However, stage above danger level (SADL) follows General Pareto (GP3) and Generalized Extreme Value (GEV) distributions. The quantile-quantile plot analysis suggests that the SADL probability distributions (GP3 and GEV) are closely following the theoretical probability distributions. However, the discharge distribution (Weibull) is showing a relatively weak corelation with the theoretical probability distribution. We further used the probability distribution to assess the SADL frequencies at 5-, 10-, 20-, 50- and 100-year return periods. The magnitudes of SADL at different return periods were then used to map the water inundation areas around different gauging stations. These inundation maps were cross-validated with the globally available flooding extent maps provided by Dartmouth flood observatory. Overall, this work exhibits a simple and novel technique to generate inundation maps around the gauging locations without using any sophisticated hydraulics models.</p>

scholarly journals Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1283 ◽  
Author(s):  
Li-Chiu Chang ◽  
Mohd Amin ◽  
Shun-Nien Yang ◽  
Fi-John Chang
Keyword(s):  
Real Time ◽  
Warning System ◽  
Google Earth ◽  
Flood Inundation ◽  
Self Organizing Map ◽  
Storm Events ◽  
Inundation Maps ◽  
Forecast Models ◽  
Som Network ◽  
Precautionary Measures

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

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