Flood Inundation Mapping Research Articles

Extreme Coastal Flood Inundation Mapping Based on Sentinel 1 Using Google Earth Engine

Coastal flooding frequently occurs along the Northern Coast of Java due to the continuous land subsidence and sea level rise. On May 23rd, 2022, an extreme tidal flood happened and impacted a wide area around the central part of the Northern Coast of Java, with reports indicating that the inundated area was larger than that of typical flood events. Although several researchers have conducted local inundation mapping in various districts, comprehensive flood mapping of this event over a larger area has not yet been undertaken. Such study would benefit for hydraulic flood models. Therefore, this study aimed to map the coastal flood extent around the central part of the Northern Coast of Java using Sentinel-1 imagery data processed through the Google Earth Engine (GEE). The delineated inundation at several points was then compared with field observations from a previous study. The analysis suggests that Sentinel-1 is generally capable of estimating flood extent, although additional measures are necessary for mapping in densely populated urban areas. The findings indicate that the area inundated by the May 2022 flood was at least 5,562 hectares larger compared to the flooding in April 2022.

Development of an Operational Satellite-Based Flood Monitoring Model for Tanzania

Timely information during water related disasters is of utmost importance for flood preparedness and risk reduction. Real time observation and monitoring of flooded areas is an expensive and time-consuming exercise. Satellite remote sensing is a quick and affordable approach that can be used for concurrent floods detection at different scales. This is important as it facilitates timely information for emergency response to disaster management departments, even in scarcely instrumented catchments. This study presents a novel approach for flood tracking using satellite technology to map flood affected areas. An open-source water detection algorithm is developed that employs readily available satellite images and the Google Earth Engine (GEE) platform. Dar es Salaam and Singida regions in Tanzania were used as the case study for validation of the proposed approach. Use is made of Sentinel-1 satellite images and GEE coding. The after-flood tracking GEE code was validated with the physical flood extent markers and after-event flood extent survey points of the regions provided by the Ministry of Water (MoW). The findings reveal that the approach supports mapping flood extent areas by giving promising results after the satisfaction from validated data. Relevant parameters were then coded in order to develop the flood map of Tanzania. The findings of this study demonstrate the usefulness of open-source GEE in rapid flood inundation mapping.

Morphometry and Topographic Wetness Index Analysis for flood inundation mapping in Mata Allo watershed (South Sulawesi, Indonesia)

Morphometry and Topographic Wetness Index Analysis for flood inundation mapping in Mata Allo watershed (South Sulawesi, Indonesia)

Anticipating ‘The Big One’: Developing decision support with emergency managers to mitigate flood impacts

Flooding from excessive rainfall remains a major weather hazard with respect to lives and property. The US National Weather Service Weather Prediction Center and National Water Center work continuously and collaboratively to produce accurate and timely forecasts of potential flooding disasters for the public. This paper explores their ongoing efforts to mitigate flood impacts with enhanced impact-based decision support services, via the Excessive Rainfall Outlook (ERO) and Flood Inundation Mapping (FIM) services. Future directions for precipitation and flood inundation forecasting are also discussed. Through the ERO and FIM services, the National Weather Service hopes to strengthen collaboration with partners in the weather industry, emergency management and water resources management to develop weather, water and climate readiness in communities around the nation.

Flood hazard assessment in a mountainous river basin in Thessaly, Greece, based on 1D/2D numerical simulation

• Flood inundation and hazard maps, under steady and unsteady flow conditions based on GIS and 1D/2D flow model usage, were generated. • Proposed methodology was based on a comparison between different transform methods. • Flood hazard maps creation is necessary for the positioning and planning of appropriate flood protection projects. • Comparison between 1D/2D numerical results. Floods are amongst the greatest natural hazards in the world. In the last decade, many flood events have been observed and recorded in Greece, which have had a direct effect on river ecosystem consistency. Flood hazard assessment is a critical parameter for flood hazard areas identification and for taking relevant mitigation measures to prevent the hazard. Therefore, the study and evaluation of streams’ hydraulic behaviour is necessary for flood management. The aim of this research work is to present a substantial methodology for flood hazard management based on GIS and hydrological/hydraulic numerical models. Flood inundation and flood hazard maps in a stream area under steady and unsteady flow conditions, using one–dimensional and two–dimensional flow models were generated, as an extension of the European Directive 2007/60/EC. For unsteady flow simulations, a hydrograph was used, based on the available rainfall data of the flood occurrence period. The simulation under steady flow conditions was performed for return periods of 100 and 1000 years. Numerical results of one–dimensional and two–dimensional numerical models were adequately compared with available field measurements providing the reliability of the current methodology.

Probabilistic Forecasts of Flood Inundation Maps Using Surrogate Models

The use of data-driven surrogate models to produce deterministic flood inundation maps in a timely manner has been investigated and proposed as an additional component for flood early warning systems. This study explores the potential of such surrogate models to forecast multiple inundation maps in order to generate probabilistic outputs and assesses the impact of including quantitative precipitation forecasts (QPFs) in the set of predictors. The use of a k-fold approach for training an ensemble of flood inundation surrogate models that replicate the behavior of a physics-based hydraulic model is proposed. The models are used to forecast the inundation maps resulting from three out-of-the-dataset intense rainfall events both using and not using QPFs as a predictor, and the outputs are compared against the maps produced by a physics-based hydrodynamic model. The results show that the k-fold ensemble approach has the potential to capture the uncertainties related to the process of surrogating a hydrodynamic model. Results also indicate that the inclusion of the QPFs has the potential to increase the sharpness, with the tread-off also increasing the bias of the forecasts issued for lead times longer than 2 h.

Differentiated Spatial-Temporal Flood Vulnerability and Risk Assessment in Lowland Plains in Eastern Uganda

This study was conducted to map flood inundation areas along the Manafwa River, Eastern Uganda using HECRAS integrated with the SWAT model. The study mainly sought to evaluate the predictive capacity of SWAT by comparisons with streamflow observations and to derive, using HECRAS, the flood inundation maps. Changes in Land-use/cover showed by decrease in forest areas and wetlands, and conversions into farmlands and built-up areas from 1995 to 2017 have resulted in increased annual surface runoff, sediment yield, and water yield. Flood frequency analysis for 100-, 50-, 10-, and 5-year return periods estimated peak flows of 794, 738, 638, and 510 m3/s, respectively, and total inundated areas of 129, 111, 101, and 94 km2, respectively. Hazard classification of flood extent indicated that built-up areas and commercial farmlands are highly vulnerable, subsistence farmlands are moderately to highly vulnerable, and bushland, grassland, tropical high forest, woodland, and wetland areas are very low to moderately vulnerable to flooding. Results demonstrated the usefulness of combined modeling systems in predicting the extent of flood inundation, and the developed flood risk maps will enable the policy makers to mainstream flood hazard assessment in the planning and development process for mitigating flood hazards.

Comparative analysis of performance and mechanisms of flood inundation map generation using Height Above Nearest Drainage

The National Water Center (NWC) implemented Height Above Nearest Drainage (HAND) for nationwide flood mapping in the continental United States. Although having a large coverage and high accuracy, the implementation (NWCH) relies heavily on the NHDPlus dataset which limits its potential to handle user defined datasets. Comparison of the NWCH model accuracy and computational performance against the original HAND is missing in the literature. This study evaluated the flood maps generated using NWCH and a web-based implementation of the original HAND (WBH). An in-depth sensitivity analysis was conducted for WBH. Results suggest that WBH can generate comparable inundation extent with few inputs in regions where the water depths from the synthetic and catchment rating curves are consistent. Multi-depth approaches help resolve underestimations of WBH. This study demonstrated the original HAND's efficacy in flood mapping and its potential for applications for fast predictions with acceptable accuracy with limited computational resources.

Automated Mapping of Flood Inundation Areas using Cloud Based Processing Tools of Google Earth Engine for Efficient Disaster Management – A Case Study of Kerala Floods

Availability of reliable information on extent and impact of flood becomes critical for faster and efficient disaster management planning. Access to near realtime satellite data from optical and microwave sensors on Google Earth Engine (GEE) platform helps in rapid flood inundation mapping. Also, integration of Optical and Synthetic Aperture Radar (SAR) data from Sentinel series of satellites helps in detection of landuse/landcover features like Built-up, Agriculture Lands and Water bodies under flooding. The GEE platform provides extensive library tools for simultaneous pre-processing of SAR images and cloud free optical images from multiple satellites. In this research, Differential Thresholding and Differential Smoothening algorithms developed in GEE have been applied on Pre and Post SAR images for automatic identification of flood inundation areas. The inundated areas thus delineated were validated with inundation extents provided by the National Database for Emergency Management (NDEM). The methodology of automation developed in this study is applied to severe floods occurred in Kerala, India during August 2018. The sensitivity of polarization on mapping accuracy is estimated using Vertical- Horizontal (VH) and Vertical-Vertical (VV) modes available with Sentinel-1 data. The result indicates that VV mode of polarization provides better accuracy of 96% than VH mode. The coding is implemented in GEE environment with automation to provide extent of flood inundation for efficient management and mitigation planning during flood disasters.

Floodplain Mapping Using Hydraulic Simulation and Geographic Information System

Objectives- A flood is a strangely high stage in a river generally, the level at which the river overflows its banks inundates the adjacent area. Flood occurs due to intense rainfall, dense population, industrialization, illegal settlement along riverbanks, bank erosion, high tide, and urbanization, and it causes harmful damage to human beings and properties. It can be taken care of by adopting flood risk measures. One of the measures is the development of flood inundation maps. Hence in the current study, flood inundation maps are developed with the integration of GIS and HEC-RAS along the Bhīma River reach in Maharashtra, India. Methods: In this study, using the series of annual peak discharges, flood flows were estimated for different recurrence intervals. Inundated areas marked along the Bhīma River represent peak flows of 25 years and 100 years return periods using HEC-RAS, ArcGIS handling of spatial data, and HEC-GEORAS for the interfacing between HEC-RAS and ArcGIS. Simulation for 2, 5, 10, 25, 50, and 100-year return periods is done, and inundation maps have been developed for the 100-years reoccurrence interval. Findings:The analysis on the left and right banks has been done using water surface elevations generated from the HEC-RAS model. The significant findings revealed that the left bank representing the east side of the river is more vulnerable to water spills than the right bank representing the west side of the river. The flooded areas along the Bhīma River are 32.4 km2 and 43.82 km2 for 25-year and 100-year reoccurrence intervals, respectively. Generally, this study revealed that urban and agricultural areas downstream of river reach are more susceptible to flooding. Novelty: The developed inundation map will help the local authority in effective decision-making and disaster management in flooding situations, especially on the left bank side of Bhīma River. Obtained water surface elevations and flood inundation depths can help to decide the necessity of structural measures that can reduce the deteriorating effects of flooding Keywords: Inundation map; flood frequency; DEM; Sensitivity analysis; Return Period; HEC-RAS

Long-Term Flooding Maps Forecasting System Using Series Machine Learning and Numerical Weather Prediction System

Accurate real-time forecasts of inundation depth and area during typhoon flooding is crucial to disaster emergency response. The development of an inundation forecasting model has been recognized as essential to manage disaster risk. In the past, most researchers used multiple single-point forecasts to obtain surface flooding depth forecasts with spatial interpolation. In this study, a forecasting model (QPF-RIF) integrating a hydrodynamic model (SOBEK), support vector machine–multi-step forecast (SVM-MSF), and a self-organizing map (SOM) were proposed. The task of this model was divided into four parts: hydrodynamic simulation, point forecasting, inundation database clustering, and spatial expansion. First, the SOBEK model was used in simulating inundation hydrodynamics to construct the flooding maps database. Second, the SVM-MSF yields water level (inundation volume) forecasted with a 1 to 72 h lead time. Third, the SOM clustered the previous flooding maps database into several groups representing different flooding characteristics. Finally, a spatial expansion module produced inundation maps based on forecasting information from forecasting flood volume and flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we presented an application to the Yilan River basin in Taiwan. Our forecasting results indicated that the proposed model yields accurate flood inundation maps (less than 1 cm error) for a 1 h lead time. For long-term forecasting (46 h to 72 h ahead), the model controlled the error of the forecast results within 7 cm. In the testing events, the model forecasted an average of 83% of the flooding area in the long term. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

Flood mapping and damage assessment due to the super cyclone Yaas using Google Earth Engine in Purba Medinipur, West Bengal, India

This study maps flood inundation and estimates the damage caused by super cyclone Yaas in Purba Medinipur, India. We used Google Earth Engine (GEE) to create a flood inundation map of the research area using pre and post-cyclone Sentinel-1 SAR data. Using ESRI 2020 land cover data, flood damage was analysed. The flood affected 5% (239.69 km2) of the land of Purba Medinipur. The northern and southern regions were affected the most. 95% and 3% of the total flooded area are comprised of agricultural and vegetation, respectively. Kolaghat (24 km2) and Nandigram-II (1 km2) sustained the greatest damage to both agriculture and vegetation. The areas below 18m were impacted by flooding, with the worst damage occurring below 5m. The GEE platform was cost-effective, efficient, and faster at calculating with enhanced precision. The outcomes of this study will aid in the management of cyclone-induced hazards. We advocate planting native and salt-tolerant crops to reduce flood damage.

Flood inundation modelling by a machine learning classifier

ABSTRACT The present study proposes and evaluates a machine-learning classifier to simulate the flood inundation area in which adaptive neuro fuzzy inference system was applied to classify the simulated domain into flooded and non-flooded areas. Particle swarm optimization was utilized in the training process of the data-driven model. Moreover, the outputs of simulating floods by the two-dimensional numerical hydraulic model were used in the training and testing process. However, aerial images of observed floods could be used as well. Based on the results in the case study, the proposed data-driven classifier is able to reduce the computational complexities of the flood inundation modelling including runtime and CPU usage. The proposed model is highly reliable and robust for generating maximum flood inundation map in the major floods. The results indicated that the rate of incorrect assessment is less than 7% in all tests. It is recommendable to apply the proposed method in the future flood engineering projects in which numerous simulations of the maximum flooded area are required. The developed model considerably reduces the computational costs in the projects.

Resilient landscape pattern for reducing coastal flood susceptibility

Evaluating flood susceptibility, identifying flood-prone areas, and planning reasonable landscape patterns are important measures in promoting sustainable urban development and flood mitigation. To this end, this study evaluated the flood susceptibility using a neural network model depending on a flood inundation map created from satellite data from 2010 to 2020, and explanatory factors for flood inundation selected by Geodetector and regularized random forest. Subsequently, the landscape pattern of the coastal city was quantified based on the land cover, and key landscape pattern metrics for flood susceptibility were selected at patch and class levels using statistical approaches. Eventually, urban spatial planning strategies for flood management were proposed based on the ecological significance of key metrics. Taking Xiamen as a case study, the flood susceptibility map showed that flood-prone areas in Xiamen are mainly distributed along river banks and coastlines. Key landscape pattern metrics for flood susceptibility selected by statistical approaches showed that patch-level metrics account for more explanatory power than class-level metrics, and the classes of the landscape would affect the role of patch-level metrics. Overall, the division index of the forest, the connectance index of water, the number of core areas and the fractal dimension index of urban, and the Euclidean nearest-neighbor distance of urban and water are significantly positively related to flood susceptibility, while the core area and the proximity index of urban, the similarity index, the core area index, and the edge contrast index of the forest, and the contiguity index of forest, grass, farmland, and shrub negatively related with flood susceptibility. Based on these findings, intensive urban planning and integrative Nature-based Solutions networks should be considered as strategies for enhancing coastal flood resilience.

Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos

The number and intensity of flood events have been on the rise in many regions of the world. In some parts of the U.S., for example, almost all residential properties, transportation networks, and major infrastructure (e.g., hospitals, airports, power stations) are at risk of failure caused by floods. The vulnerability to flooding, particularly in coastal areas and among marginalized populations is expected to increase as the climate continues to change, thus necessitating more effective flood management practices that consider various data modalities and innovative approaches to monitor and communicate flood risk. Research points to the importance of reliable information about the movement of floodwater as a critical decision-making parameter in flood evacuation and emergency response. Existing flood mapping systems, however, rely on sparsely installed flood gauges that lack sufficient spatial granularity for precise characterization of flood risk in populated urban areas. In this paper, we introduce a floodwater depth estimation methodology that augments flood gauge data with user-contributed photos of flooded streets to reliably estimate the depth of floodwater and provide ad-hoc, risk-informed route optimization. The performance of the developed technique is evaluated in Houston, Texas, that experienced urban floods during the 2017 Hurricane Harvey. A subset of 20 user-contributed flood photos in combination with gauge readings taken at the same time is used to create a flood inundation map of the experiment area. Results show that augmenting flood gauge data with crowdsourced photos of flooded streets leads to shorter travel time and distance while avoiding flood-inundated areas.

Evaluation of Land Use Type and Effects of Runoff in Flood Inundation Maps: The Case of Malatya Province

Dünya genelinde kentleşme artmaktadır ve buna bağlı olarak kentsel nüfus da artmaktadır. Hızlı kentsel nüfus artışının sonucu olarak yetersiz drenaj sistemleri nedeniyle taşkın yağışı durumunda, binaların bodrum katlarını su basması, ulaşım yollarının kapanması gibi can ve mal kaybına neden olabilecek hasarlar meydana gelmektedir. Bu çalışmada, Malatya ili için arazi kullanım türü ve sızma durumlarına bağlı üç farklı senaryo kapsamında taşkın yayılım haritaları oluşturulmuştur. Bu senaryolar, modelde; arazi kullanım türünün olmadığı, arazi kullanım türünün olduğu ve arazi kullanım türü ile yüzeysel akış değerlerinin birlikte olduğu durumuna dayanmaktadır. Çalışmanın, hidrolojik veya hidrodinamik modellerde arazi kullanımı ve yüzeysel akış verilerinin kullanımı hakkında detaylar sunması amaçlanmıştır. Bu kapsamda, InfoWorks ICM yazılımı kullanılarak oluşturulan modeller, model doğruluğunu arttırması için sayısal yükseklik modeli, bina konum verileri, arazi kullanım türü ve gelecekte meydana gelebilecek yağış yükseklikleri verileriyle desteklenmiştir. Sonuçlar, arazi kullanım türü ile yüzeysel akış değerlerinin birlikte kullanıldığı modele dayalı senaryo da diğer iki senaryoya göre havza genelinde daha az akış kollarının oluştuğunu göstermiştir.

Environmental impact assessment of dam-break floods considering multiple influencing factors.

Dam-break floods cause substantial damage to the environment, and evaluating the negative impacts of dam-break floods on the environment (EI) is an important part of flood risk management. EI has been evaluated using various methods with different indices. However, the evaluation results of EI are typically one-sided or inaccurate because of diverse indices and complex influencing factors. A new method was proposed herein to calculate EI with an index system, including geomorphic changes (GC), water pollution (WP), plant biomass loss (PB), and biodiversity loss (BL). Eight factors that influence EI were sorted out, namely, erosion or deposition depth, non-point source (NPS) and point source (PS) pollution, plant biomass, species richness, plant height, and flood depth, velocity, and duration. After combining the proposed damage functions to calculate the influence of flood depth, velocity, and duration, and plant height on the environment, methods to calculate GC, WP, PB, BL, and EI were proposed. A dam-break flood scenario for Luhun Reservoir was used to verify the method. The results showed that (1) the trend in EI was similar to that in geomorphic changes in the inundated area and seriously affected by PS in local areas, (2) the average EI of woodland was the highest, while that of towns was the slowest, and (3) GC and WP contributed 93.7% of EI in the entire inundated area. This study summarized the complex impacts of dam-break floods on the environment from four aspects and proposed a method to quantify the overall impact of dam-break floods on the environment. The evaluation model could evaluate the impact of floods on the environment accurately, presenting the results on a flood inundation map. This provides a scientific basis for evaluating flood consequences and managing flood risk.

Empowering local leaders in flood inundation mapping in Bagelen, Purworejo, Central Java.

This aricle discusses the reliability of flood inundation information that is obtained from participatory mapping. The commonly applied method to map flood inundation requires both direct and interpretive measurement data based on remote sensing images. Such assessments have limited availability of data; as a result, participatory mapping has become the solution. A number of studies have conducted participatory mapping to obtain flood hazard information in areas with limited sources of data, however, there has been little discussion about its reliability. This research conducted participatory flood inundation mapping by involving local leaders as respondents. The mental map drawn by the local leaders was digitised to obtain a shapefile format map. The information obtained from the semistructured interview was then included in the geographic information system (GIS) data as attributes. The obtained information was compared with the field data to determine its quality. A literature study was then conducted to discuss how the participatory mapping could support managing a disaster. Information obtained through participatory mapping can be effectively applied to disaster management because of its precise location information, lower cost and less time-consuming nature. The reliability of the information has weak accuracy of quantitative data; however, it has advantages in terms of qualitative data, especially in the detailed descriptions of flood information. In the future, participatory mapping should rely on integrating the perspectives of cross-disciplinary researchers, a comprehensive study of multidisciplinary knowledge and level of understanding of the stakeholders.

Deep learning methods for flood mapping: a review of existing applications and future research directions

Abstract. Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state of the art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate, as they leverage inductive biases to better process the spatial characteristics of the flooding events. Models based on fully connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist in real-time flood warning during an emergency and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies. Furthermore, all reviewed models and their outputs are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Physics-based deep learning can be used to preserve the underlying physical equations resulting in more reliable speed-up alternatives for numerical models. Similarly, probabilistic models can be built by resorting to deep Gaussian processes or Bayesian neural networks.

Flood inundation mapping under climate change scenarios in the Boyo watershed of Southern Ethiopia

Abstract This research aims to map flood inundated areas under changing climate in the Boyo watershed of Southern Ethiopia. A semi-distributed physically based Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center-River Analysis System (HEC-RAS) were used to simulate the flood events and maps, respectively, for climate scenarios. The bias-corrected data of four climate models were used for the baseline (1976–2005), mid-term (2041–2070) and long-term (2071–2100) cycles under RCP4.5 and RCP8.5 scenarios. The 50- and 100-year return period flood events were generated from the baseline and future period streamflow data. The HEC-RAS model was used to simulate the inundation areas and depths from the flood events. The result exhibited that the average annual rainfall and maximum and minimum temperatures of the catchment will increase in the future with an increase in annual runoff. The severity of annual floods would increase in the future under RCP4.5 and RCP8.5 scenarios. Approximately, 193 ha of the study may be flooded with flood events having a return period of 100 years under the RCP8.5 scenario in the long-term period, which is an extreme case. The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.