Flood Inundation Mapping Research Articles

RIVER MODELING FOR FLOOD INUNDATION MAP PREDICTIONS USING 2D-HEC-RAS HYDRAULIC MODELING WITH INTEGRATION OF GIS

Floods are natural disasters which if occurred produce damage to property and human life. The study of floods is important for protection of properties and human life risk. This study focuses on flooding characteristics in the flood plain of Tikurwha river catchment using two-dimensional (2D) hydrodynamic modeling. The flood inundation map of the flood prone areas is carried out using HEC-RAS 2D models with incorporation of Arc-GIS tools. Hence, using the magnitude of the flood at 2, 10, 50- and 100-year return period with a respective value of 72, 98.69, 123 and 135 (m3/s). The output of flood hazard inundation maps are carried out in 2D HEC-RAS Mapper in which the flood area at a respective return period’s flood magnitude are 75 ha, 102 ha, 108 ha, and 122 ha respectively. Based on the obtained flood inundation area, the economic impact of the flood at the specified return periods are estimated as 1791.99, 2436.89, 2589.45, and 2914.861 quintals of crop production were damaged due to flooding at respective return periods of 2, 10, 50 and 100 years.

Agricultural flood vulnerability assessment and risk quantification in Iowa

Agricultural lands are often impacted by flooding, which results in economic losses and causes food insecurity across the world. Due to the world's growing population, land-use alteration is frequently practiced meeting global demand. However, land-use changes combined with climate change have resulted in extreme hydrological changes (i.e., flooding and drought) in many areas. The state of Iowa has experienced several flooding events over the last couple of decades (e.g., 1993, 2008, 2014, 2016, 2019). Also, agribusiness is conducted across 85% of the state. In this research, we present a comprehensive assessment for agricultural flood risk in the state of Iowa utilizing most up-to-date flood inundation maps and crop layer raster datasets. The study analyzes the seasonal variation of the statewide agricultural flood risk by focusing on corn, soybean, and alfalfa crops. The results show that over $230 million average annualized losses estimated at statewide considering studied crop types. The crop frequency layers and corn suitability rating datasets are investigated to reveal regions with lower or higher productivity ratings. The study founds nearly half a million acres of cropland is under 2-year return period flood zone. Additionally, a data-driven flood model, Height Above the Nearest Drainage (HAND), is used to analyze performance against the FEMA maps. We found that the HAND flood maps performed with the correlation of 0.93 and 0.94 for 100-year and 500-year flood events regarding to the FEMA maps.

Two-Dimensional Dam Break Modelling of Beris Dam using HEC-RAS

Dam is built for various functions such as water supply, flood and sediment control, electricity generation and irrigation. Floods due to dam breaches create higher peak flows than floods produced by heavy rainfall. When a dam collapses, the stored water may cause devastating catastrophe towards downstream areas by causing fatalities and property damages. The aim of this study is to perform a two-dimensional dam break modelling of Beris Dam due to probable maximum flood (PMF). The HEC-RAS software was used to predict the breach outflow’s hydrograph and produce the flood inundation and velocity map. Dam breach parameters were calculated using Froehlich and MacDonald and Langridge-Monopolis (MDLM) equations. A 4-day simulation conducted showed that a breach was initiated at almost 10.35 am, 34 hours and 35 minutes after the PMF scenario. Peak discharge during the PMF failure is 10,745.12 m3/s at 12.45 pm the next day which is after 2 hours and 10 minutes of dam breach initiation. Felda Lubuk Merbau, Kampung Betong, Jeniang, Padang Serai and Kota Kuala Muda are among the areas affected by the simulated Beris Dam break. Information obtained on flood velocity, depth and arrival time are useful to local authorities in order to respond effectively and immediately in the event of dam break.

Stage-based flood inundation mapping

New methods allow the direct computation of flood inundation maps from lidar data, independently of discharge estimates, hydraulic analysis, or defined cross sections. One method projects the interpolated profile of measured flood levels onto surrounding topography, creating a smooth inundation surface that is entirely based on data and geometrical relationships. A second method computes inundation maps for any simple function that relates the water surface to the elevation of the channel bottom, exploiting their known, sub-parallel character. A final method theoretically combines the elevation of the channel bottom and the upstream catchment area for points along the thalweg, all defined by lidar data. Historical data from stream gauges can be incorporated to generate inundation maps for floods having different return periods. The conceptual simplicity and realism of these maps facilitate data-based planning.

Identification of flood‐prone areas with GeoFlood: Lessons learned from the Tiber River case study

AbstractFlood mapping is a vital component for sustainable land use in flood‐prone areas. Due to the frequency of flood events, local authorities demand effective yet simple methods for the preliminary identification of flood‐prone areas at large scales to subsequently define mitigation strategies. We focus here on the workflow GeoFlood, a parsimonious model which uses only high‐resolution Digital Terrain Models (DTMs) to define the geomorphological and hydrological information necessary for flood inundation mapping, thus allowing for large‐scale simulations at a reasonable computational cost. The purpose of the present study is to investigate the conditions under which GeoFlood is able to correctly reproduce inundation scenarios (with an assigned return period) and their flooding characteristics. Specifically, we analyze its performance over a highly urbanized area, the mid‐lower portion of the Tiber River (Italy). We simulated the 200‐year return period scenario and compared the results to those provided by the local authority. A sensitivity analysis is performed to quantify the influence of the main geometric and hydraulic parameters involved. Results show that GeoFlood produces rapid flood estimation than can be used in support of standard costly methods over large scales, quickly pointing out the critical flood‐prone areas, with consideration of all the uncertainties involved.

Regional Flood Frequency Analysis for Sustainable Water Resources Management of Genale–Dawa River Basin, Ethiopia

Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.

Flood frequency analysis and inundation mapping for lower Narmada basin, India

Abstract Floods are one of the world's most destructive natural disasters, taking more lives and causing more infrastructural damage than any other natural phenomenon. Floods have a significant economic, social, and environmental impact in developing countries like India. As a result, it is essential to address this natural disaster to mitigate its effects. The lower Narmada basin has experienced numerous floods, including severe flooding in 1970, 1973, 1984, 1990, 1994, and 2013. The objective of the present study is to use flood frequency analysis to anticipate peak floods and prepare flood inundation maps for the lower Narmada River reach. The flood frequency analysis was carried out using Gumbel's and Log-Pearson Type III Distribution methods. The hydrodynamic simulation was performed using HEC-RAS v6.0 to prepare flood inundation maps for predicted flood peaks. The result shows that the Log-Pearson Type-III distribution method gives good results for the lower return period while Gumbel's method gives good results for the higher return period. The hydrodynamic model results indicate that as the return period increases, the area of the high-risk zone increases while the area of the low-risk zone remains almost constant. The present study concludes that the existing embankment system on the banks of the Narmada River is not sufficient for significant floods. The developed maps will be helpful to government authorities and individual stakeholders to decide the flood mitigation measures.

Topography based flood susceptibility prediction by using the GIS platform and historical flood data of lower Ajay river basin

Flood inundation mapping of lower Ajay River basin was implemented in the Geographic Information System (GIS) using the historical flood data and statistical frequency analysis by following the Gumbel probability distribution method. The historical data proofs the magnitudes of the flood intensity by various recurrence intervals on the basis of the projected flood height and present topographic character inundation mapping done in the GIS platform. Most importantly, due to the lack of hydrological past gauge height data are used in different gauge stations for projecting flood inundation mapping. Open access SRTM DEM data provides the topographic characteristics of the basin region and by the combined data of flood magnitudes and topographic data, flood inundation-mapping may help in the identification of the hazards prone area. The study area is regularly flooded and due to high population density huge damage takes place. So for the better management planning and risk estimation, hazards mapping is essential. Flood hazard mapping is done on the basis of 2, 10 and 25-year return period. The result depicts that 44% area in the lower part of the basin is flood prone and almost more than 474000 population and almost 59000 child population will be affected during the flood. The model is validated followed by overall accuracy assessment and KAPPA statistics methods and both results above 80% proof the model suitability.

Hybrid Surrogate Model for Timely Prediction of Flash Flood Inundation Maps Caused by Rapid River Overflow

Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime.

Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study

Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study

Flood mitigation data analytics and decision support framework: Iowa Middle Cedar Watershed case study

Flooding is one of the most frequent natural disasters, causing billions of dollars in damage and threatening vulnerable communities worldwide. Although the impact of flooding can never be diminished, minimizing future losses is possible by taking structural or non-structural mitigation actions. Mitigation applications are often costly practices. However, they can be more feasible for long-term planning and protection. On the other hand, selecting a feasible option requires a comprehensive analysis of potential risk and damages and comparing the costs and benefits of different mitigation types. This paper presents a web-based decision support framework called Mitigation and Damage Assessment System (MiDAS) that analyzes flood risk impacts and mitigation strategies at the community and property-level with the goal of informing communities on the consequences of flooding and mitigation alternatives and encouraging them to participate in the community rating system. The framework utilizes regulatory flood inundation maps, damage functions, property information, scenario-based climate projections, and mitigation inputs and guidelines from the Federal Emergency Management Agency (FEMA) and the United States Army Corps of Engineers (USACE). It will help users select the appropriate flood mitigation measures based on various characteristics (e.g., foundation type, occupancy, square footage) and provide cost estimates for implementing measures. The system also provides a decision tree algorithm for analyzing and representing the mitigation decision by reviewing existing guidelines (e.g., FEMA, USACE). We analyzed the community-level mitigation for three major cities in Eastern Iowa (Cedar Falls, Cedar Rapids, and Waterloo) and found certain measures (e.g., wet/dry floodproofing) are cost-effective for community-level mitigation. Implementation of mitigation measures can reduce the property's vulnerability and improve the response to a flooding event.

Subpixel Flood Inundation Mapping Based on Spatial–Spectral Information in Irregular Regions

Sub-pixel flood inundation mapping (SPFIM) could handle the mixed pixels to obtain the spatial distribution of flood inundation at sub-pixel scale. However, the spatial-spectral information of flood inundation used by SPFIM is usually constructed in a specified rectangular local window, and the number of spectral bands utilized by SPFIM is also little. To solve this problem, this paper proposes SPFIM based on spatial-spectral information in irregular regions (SSIIIR). In SSIIR, the extended random walker algorithm is used to calculate the spatial correlation in irregular regions to obtain spatial information, and the normalized model is constructed to calculate the all spectral bands in irregular regions to yield spectral information. With the help of spatial-spectral information in irregular regions, the flood inundation mapping result is improved. The experimental results show that SSIIIR produces the better flood inundation mapping result than the traditional SPFIM methods.

Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe

AbstractFloods are one of the most devastating weather‐related hazards that are affecting millions of people over the world every year. In some poor resource areas such as Mbire District in Zimbabwe, the floods are difficult to anticipate and prepare for. Hence the need for spatial modelling of the past flood events for effective response and management. This study modelled the flood extent and depth based on data from household surveys, transect walks and a digital elevation model (DEM). A sample of 304 households was used, with 70% for calibration and 30% for validation of the flood extent. Twenty‐four flood depth measurements obtained from transect walks were used to validate the modelled flood depths based on a linear regression model. The flood depth of the worst most recent flood (January 2015) at each household was combined with altitude from the DEM using the sum function, and the inverse distance weighting was applied to model the worst flood depth. The flood extent was considered as those areas where flood depth was higher than the DEM. Approximately 24% of the area was covered by floods. The modelled flood extent agreed reasonably well with what was reported during the survey (probability of detection 0.93 and accuracy level about 0.8). Most of the areas in the wards experienced flood depths greater than 2 m, especially along the major rivers. Such areas are dangerous for people, animals and properties such as boreholes, houses, schools and clinics located on the floodplain. These results can be used for planning purposes in preparing and responding to stages of the flood management cycle. However, there is a need for further research to improve the performance and applicability of the methodology applied in this study in other settings.

Unraveling the Temporal Importance of Community-Scale Human Activity Features for Rapid Assessment of Flood Impacts

The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Accordingly, community-scale big data (such as satellite imagery) has been utilized for the early estimation of end flood impacts. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features (such as human mobility, visits to points-of-interest, and social media posts) in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity (e.g., travel, credit card transactions, and online communications) and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts (measured by the flood insurance claims and flood inundations) in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking in both response and recovery stages; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. Insights derived from data analysis reveal the potential for harnessing community-scale data characterizing human activity fluctuations for rapid assessment of flood impacts. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.

Characterization and assessment of flood inundated areas of lower Brahmaputra River Basin using multitemporal Synthetic Aperture Radar data: A case study from NE India

Floods are known to be one of the greatest disasters in documented human history. In the Indian subcontinent, the Ganga‐Brahmaputra plains annually accommodate monsoon‐driven flooding of the Brahmaputra River and its tributaries, causing widespread flood inundation and geomorphic changes. In the present study, we present flood inundation maps of Assam and suggest a possible geomorphic explanation for the frequent flood inundation areas. We have used a novel SAR (Synthetic Aperture Radar)‐based approach for a comprehensive flood inundation mapping of Assam. The Google Earth Engine (GEE), a cloud‐based data computing and analysis platform, is used to access and analyse the Sentinel SAR 1 GRD datasets for 30 events from 2018 to 2020. The Sentinel‐2 imagery, WWF Hydrosheds, 90 m DEM (SRTM), and JRC Global Surface Water Dataset are also used to conduct the flood inundation mapping for 80,236 (1 × 1 km) grids in Assam and morphometric analysis in the lower Brahmaputra River Basin. We also carried out detailed mapping and geomorphic analysis on channel and sediment bar area and channel width in 100 (6.25 km wide) grids on the Brahmaputra River in Assam. Our results suggest that high flood inundation areas are well scattered in Assam, and Lakhimpur, Nagaon, Sibsagar, Sonitpur, and Barpeta are the most affected districts from 2018 to 2020. We also observe that the geomorphology can act as primary control over the frequent flood inundation in highlighted areas in Assam. On a basin scale, we recognize a low correlation between the stream power index, topographic wetness index, plan curvature, slope, and flood inundated areas, mainly due to the coarse resolution of the digital elevation model. In a braided river system like the Brahmaputra River, the channel and bar area and channel width are well correlated during flood events. Finally, the channel sinuosity also controls channel and bar area and width during regular and influential flood events.

Using CYGNSS Data to Map Flood Inundation during the 2021 Extreme Precipitation in Henan Province, China

On 20 July 2021, parts of China’s Henan Province received the highest precipitation levels ever recorded in the region. Floods caused by heavy rainfall resulted in hundreds of casualties and tens of billions of dollars’ worth of property loss. Due to the highly dynamic nature of flood disasters, rapid and timely spatial monitoring is conducive for early disaster prevention, mid-term disaster relief, and post-disaster reconstruction. However, existing remote sensing satellites cannot provide high-resolution flood monitoring results. Seeing as spaceborne global navigation satellite system-reflectometry (GNSS-R) can observe the Earth’s surface with high temporal and spatial resolutions, it is expected to provide a new solution to the problem of flood hazards. Here, using the Cyclone Global Navigation Satellite System (CYGNSS) L1 data, we first counted various signal-to-noise ratios and the corresponding reflectivity to surface features in Henan Province. Subsequently, we analyzed changes in the delay-Doppler map of CYGNSS when the observed area was submerged and not submerged. Finally, we determined the submerged area affected by extreme precipitation using the threshold detection method. The results demonstrated that the flood range retrieved by CYGNSS agreed with that retrieved by the Soil Moisture Active Passive (SMAP) mission and the precipitation data retrieved and measured by the Global Precipitation Measurement mission and meteorological stations. Compared with the SMAP results, those obtained by CYGNSS have a higher spatial resolution and can monitor changes in the areas affected by the floods over a shorter period.

Flood inundation mapping and hazard assessment for mitigation analysis of local adaptation measures in Upper Ping River Basin, Thailand

Flood inundation mapping and hazard assessment for mitigation analysis of local adaptation measures in Upper Ping River Basin, Thailand

The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.

Toward improved river boundary conditioning for simulation of extreme floods

Hydrodynamic modeling is a common approach for flood inundation mapping. These models use conservation of mass and momentum to simulate the river hydrodynamics during flood events. To simulate floods and estimate the spatio-temporal distribution of extreme water levels, the upstream and downstream flows are used as the minimum required boundary conditions (BCs) in a hydrodynamic model. In this study, we show that such approach does not provide a realistic estimate of fluxes at boundaries and so poses uncertainty to estimated flood inundation, especially during extreme floods. To overcome these limitations, we propose a new BC approach that demonstrates the significance of two additional BC components, namely lateral flows and vertical fluxes, applied to the channel, and incorporates hydrologic bias impacts into the hydrodynamic model setup. The basis for applying these BC modifications is to conserve the water mass flux into the river systems during extreme flood events. We utilize two well-known hydrodynamic flood inundation models, LISFLOOD-FP and HEC-RAS 2D, to simulate a flash flood in 2016 as well as an extreme flood during Hurricane Harvey in fully-gauged and semi-gauged river systems, respectively. Results from both case studies confirm that modeling with only upstream and downstream BCs underestimates the flood hydrographs and inundation areas while using the proposed BC approach can significantly improve the modeling performance.

Assessment of Flood Risks in Ifo Local Government Area of Ogun State

The most frequent natural hazards and the most costly in terms of property damage in study area is Flood. The purpose of this work is use Geographic Information Systems (GIS) Hydrological tools to model and map flood event in Ifo Local Government Area of Ogun state, Nigeria using Shuttle Radar Topographic Mission ( SRTM) data and land use maps as input data layers. In this study, flood inundation depth is calculated with GIS in order to understand the temporal, spatial scale and transition. Flood inundation map and 3D views of the hydrological modelling results are provided for a better understanding of the severity of flooding in the location. It is recommended that the processed data from this study be used as input for future research and the development of flood warning systems for the area. It will also be a base study for future study when rainfall data will be available.