Flood Inundation Model Research Articles

Effects of reconstruction of variables on the accuracy and computational performance of upscaling solutions of the shallow water equations

This paper presents a new sub-grid flood inundation model obtained by upscaling the shallow water equations (SWE) to enhance the model efficiency in large-scale problems. The model discretizes study domains using two nested meshes. The equations are solved at the coarse mesh by a second-order accurate in space (i.e. piecewise linear reconstruction of variables) Godunov-type finite volume (FV) method, while the fine mesh is used to incorporate high-resolution topography and roughness into the solution. The accuracy and performance of the model were compared against a first-order version of the model recently proposed by the authors and a second-order conventional FV model using artificial and real-world test problems. Results showed that improved accuracy is delivered by the proposed model, and that at low-resolution meshes, the spatial reconstruction of variables of the numerical scheme plays a major role in the solution's accuracy.

Flooding in the Digital Twin Earth: The Case Study of the Enza River Levee Breach in December 2017

The accurate delineation of flood hazard maps is a key element of flood risk management policy. Flood inundation models are fundamental for reproducing the boundaries of flood-prone areas, but their calibration is limited to the information available on the areas affected by inundation during observed flood events (typically fragmentary photo, video or partial surveys). In recent years, Earth Observation data have supported flood monitoring and emergency response (e.g., the Copernicus Emergency Service) thanks to the proliferation of available satellite sensors, also at high spatial resolution. Under this umbrella, the study investigates a levee breach that occurred in December 2017 along the Enza River, a right tributary of the Po River, that caused the inundation of a large area including Lentigione village. The flood event is simulated with a 2D hydraulic model using satellite images to calibrate the roughness coefficients. The results show that the processing and the timing of the high-resolution satellite imagery is fundamental for a reliable representation of the flooded area.

Improving flood inundation modeling skill: interconnection between model parameters and boundary conditions

Improving flood inundation modeling skill: interconnection between model parameters and boundary conditions

The development of the Raster-based Probability Flood Inundation Model (RProFIM) approach for flood modelling in the upstream Citarum Watershed, West Java, Indonesia

The development of the Raster-based Probability Flood Inundation Model (RProFIM) approach for flood modelling in the upstream Citarum Watershed, West Java, Indonesia

Real-time Flood Inundation Modeling with Microtopography and Vegetation Resistance Parameter Learning

Real-time Flood Inundation Modeling with Microtopography and Vegetation Resistance Parameter Learning

Real-time Flood Inundation Modeling with Microtopography and Vegetation Resistance Parameter Learning

Real-time Flood Inundation Modeling with Microtopography and Vegetation Resistance Parameter Learning

Impact of cross‐sectional orientation in one‐dimensional hydrodynamic modeling on flood inundation mapping

AbstractFlood management activities require development of flood maps that depict the spatial and temporal extent of floods with the help of hydrodynamic models. Two‐dimensional (2‐D) hydrodynamic models are frequently employed for flood inundation modeling and mapping with the help of high‐end computational resources and high‐resolution terrain information such as LiDAR (light detection and ranging) data. However, LiDAR data are either unavailable or not freely accessible in many parts of the world, especially in nations belonging to Global South. Hence, one‐dimensional (1‐D) models are still in practice owing to their lesser computational cost and data requirement. Nonetheless, the successful application of a 1‐D model depends mainly on the representation of the natural river and floodplain geometry, which is the primary input in the form of discrete cross‐sections. The assumed flow directions on floodplains while orienting the cross‐sections in 1‐D models induce some uncertainty in the model simulations. This study aims to evaluate the variability in the model simulations caused by the cross‐sectional orientations. Flood simulations were performed using a 1‐D hydrodynamic model for six different cross‐sectional realizations for three river reaches, having distinct morphological and topographical characteristics, and were compared with the simulations of a 2‐D hydrodynamic model and available reference inundation maps. The study suggests that the simulations of flood inundation extent and maximum flow depth variation are influenced by the cross‐sectional orientation on flood plains in river reaches characterized by broad flood plains with complex local topographical variations. In contrast, for reaches with relatively less wide and complex terrains, 1‐D models can generate robust simulations of flood inundation extent and spatial variation in maximum flood depth (R2 and NSE greater than 0.89) for high flood events.

Snow drought and monsoon floods—hydrological extremes in the Cedar Valley watershed during water year 2021, southwestern Utah

Water year 2021 was a year of extremes in the Cedar Valley watershed of southern Utah, with snow drought resulting in extremely low snowmelt runoff of Coal Creek and intense monsoon rainfall resulting in several floods in different parts of the valley. Winter snow accumulation was depressed throughout southern Utah, perhaps due to La Nina conditions affecting winter storm trajectories. Coal Creek, the principal stream providing surface water to Cedar Valley, typically receives most of its annual flow from snowmelt runoff, but in 2021 had a peak snowmelt discharge 15% to 25% of that recorded in the previous two years and the third lowest snowmelt runoff on record. Following this extremely low snowmelt runoff period, more than 10 floods of Coal Creek occurred following monsoon storms in July and August that exceeded the 2021 snowmelt peak. Additionally, several thunderstorms produced rainfall rates in exceedance of the 100-year event and induced flooding within Cedar City and the town of Enoch. Flood inundation modeling using HEC-RAS and high-resolution topographic data showed good agreement with field and pubic-survey data on the high-water stage during the Enoch flooding, and demonstrated that the flooding was likely exacerbated by the low topography and limited drainage potential in flooded areas. Whereas the monsoon storms improved soil moisture and helped alleviate drought conditions, they also resulted in urban flooding and did little to replenish the regional water supply.

Sensitivity Analysis of Modelled Flood Inundation Extents over Hawkesbury–Nepean Catchment

Rainfall runoff and topography are among the major factors controlling the accuracy of modelled riverine inundation extents. We have evaluated the sensitivity of both these variables on a novel 1-D conceptual flood inundation model employing Height Above Nearest Drainage (HAND) thresholds within sub-catchment units called Reach Contributing Area (RCA). We examined the March 2021 flood extent over the Hawkesbury–Nepean Valley (HNV) with 0.05′ gridded runoff derived from the Australian Water Resources Assessment (AWRA) modelling framework. HAND thresholds were enforced within each RCA using rating curve relationships generated by a modelled river geometry dataset obtained from Jet Propulsion Laboratory (JPL) and by modelling Manning’s roughness coefficient as a function of channel slope. We found that the step-like topographic nature of HNV significantly influences the back-water effect within the floodplain. At the same time, the improved accuracy of the GeoFabric Digital Elevation Model (DEM) outperforms SRTM DEM-derived flood output. The precision of HAND thresholds does not add significant value to the analysis. With enhanced access to river bathymetry and an ensemble point-based runoff modelling approach, we can generate an ensemble runoff-based probabilistic extent of inundation.

A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling

Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling.

Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities

With increasing urban flood risk due to urbanisation and climate change, flood hazard prediction is ever more crucial for flood risk management and emergency response. However, when sufficient high-quality data are lacking, a standard flood inundation modelling approach has significant uncertainties. Therefore, this study develops a bottom-up approach for urban flood hazard mapping at multiple levels (grid-kilometre-district), built upon the integration of grid-based flood modelling with data acquisition from open sources. This reduces the adverse effects of data scarcity and quality on hazard modelling. In the paper, we first set out an integrated approach for gridded inundation mapping in an urban basin using a hydrodynamic model supported by crowd-sourced social media data. Then, applying the model to flooding in Chengdu in August 2020, we articulate how input data bias and parameter uncertainty both affect urban inundation modelling. The results show that the choice of terrain data and the quantification of urban drainage flows significantly influence the modelled urban inundation extent, depth and duration. This indicates the potential for large variation when using urban inundation mapping merely relying on a single-scale flood hydrodynamic modelling even when supported by crowd-sourced data. The multilevel hazard mapping approach developed here presents multi-layered and comprehensive inundation mapping; thus the effects of data bias or availability are reduced and the coarser hazard mapping shows less sensitivity to the data input quality and model uncertainty, indicating relatively higher reliability at this higher spatial scale. The grid-kilometre-district three level approach provides more reliable flood hazard mapping, which can support rainstorm-induced flood management in data scarce cities.

Flash Flood Risk Assessment Due to a Possible Dam Break in Urban Arid Environment, the New Um Al-Khair Dam Case Study, Jeddah, Saudi Arabia

Recent years have seen an increase in floods with severe damage due to the intensity and frequency of rains. One of the periodic hydrological problems affecting Jeddah city, the second-biggest city in Saudi Arabia, is unexpected flash flooding. In dam breaks, water that has been retained is released uncontrollably. This study is related to a flood simulation methodology after a possible break of the New Um Al-Khair Dam, a dam built in 2012 outside residential areas, to replace the Old Um Al-Khair Dam built inside a residential area, which broke in January 2011. In fact, we simulated the impact on flood wave propagation in the study area through the use of GIS techniques coupled with hydrological/hydraulic modeling tools and the development of a flood inundation model. Planning a good emergency response in the future is possible by analyzing a supposed disaster. Based on the likelihood that there will be a flood and the corresponding inundation depth, a flood risk matrix is created as a quantitative tool to estimate flood damage, which is crucial to decision-makers. Negligible, low, moderate, high, and very high-risk categories are assigned according to that flood risk matrix. The results indicated a low to very high risk for 5 years, 50 years and 100 years return periods and a negligible to very high risk for a 200 years return period. To estimate the extent of damage, a quantitative summary of the results has been outlined graphically in order to visualize the scope of the inundation areas.

An archaeological example of Aboriginal management of a hydro-ecological system in the Murray River valley, South Australia

Context A rare archaeological example of Aboriginal water management in a Murray River floodplain upstream of Renmark is described and contextualised. Aims Historical accounts, archaeological descriptions and hydrological modelling are used to propose a likely function of the site. Methods The site was documented through archaeological field recording, a digital elevation model (DEM) developed through a real-time kinematic (RTK) survey and flood inundation modelling (FIM), and is interpreted through a review of archival and documentary sources. Key results The site represents one of only a few extant physical examples of Aboriginal water management in the Riverland and greater Murray–Darling Basin. Conclusions Our assessment indicates that Aboriginal people had excavated an artifical channel within the Calperum floodplain in order to manipulate natural water connectivity, most likely as part of a fish trap or weir structure. Implications The site builds an appreciation of the complexity of Aboriginal land practices and supports the impression gained though historical accounts of a curated and managed riverscape.

A rapid flood inundation model for urban flood analyses

An efficient inundation model is required for flood early warning systems in urban areas. A 2D flood model uses a governing shallow water equation, and this model is computationally expensive despite benefiting from parallel computing techniques. As an alternative to conventional flood models, cellular automata (CA) and DEM-based models (DBMs) have been studied. CA flood models simulate floods efficiently. However, a small time step is required to ensure model stability when the grid size decreases due to its diffusive characteristics. Conversely, DBM models produce results quickly, but they only show the maximum flood extent. Additionally, pre- and postprocessing are required, which take considerable time. This study proposes a hybrid inundation model that combines the two alternative approaches, and it efficiently produces a high- resolution flood map without complex pre- and postprocessing. The hybrid model is also integrated with a 1D drainage module, and thus, the model reliably simulates urban area floods.•The rapid flood inundation model integrates CA module to simulate temporal distribution of floods and DEM module to provide details of floods.•A 1D Saint Venant equation is also solved in the rapid flood inundation model to simulate the drainage sytems in urban areas.•Two-way coupling between 2D-surface and 1D-drainag models are considered in the rapid flood inundation model.

LiDAR DTM Application for Flood Inundation Modeling and Visualization (Case Study: Batu City, East Java)

LiDAR DTM has been widely used for simulating flood inundation from river overflows. This flood inundation simulation needs to be done as an alternative step in the flood control system and flood disaster mitigation to determine the estimated area affected by the flood. Flood simulation is a form of unsteady flow that is simulated with the HEC-RAS program that allows the user to perform one-dimensional steady flow, one and two-dimensional unsteady flow calculations, sediment transport/mobile bed computations, and water temperature/water quality modeling, and visualizes flooded areas and flood-affected areas using a geographic information system approach. Hydrological data is obtained from daily rainfall processing at the time of station rain. Hydrograph analysis was performed using the SCS CN model on the HEC-HMS software to estimate the flow rate and peak discharge. Watershed area, curve number, Impervious value, Initial ion, and time lag are parameters that affect hydrograph analysis. From the results of the hydrograph analysis, the peak discharge of the model is 119.992 m2/s. Meanwhile, hydraulic analysis using HEC-RAS resulted in an inundation of 364 m2 which inundated three villages, namely Bulukerto, Bumiaji and Sidomulyo villages. The depth of the inundated area ranged from 0-15 m with an average depth of 4.534 m. The Sidomulyo area affected by the flood inundation scenario is 4,924 meters with an area of 0,02 ha. Bumiaji village was flooded with an area of 0,007 ha with a scenario of inundation depth of 4,049 m. Bulukerto village which was affected by the flood has an area of 20,972,832 m2 with a scenario of inundation depth reaching 4,343 m. Then some land cover such as residential land that is inundated by this flood simulation is 0,002 ha. Buildings from residential areas that were flooded reached 209 buildings. Then the area of agricultural land affected is 0,02 ha. In addition, there are also green open spaces affected by this simulation with an inundation area of 0,003 ha.

Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths

AbstractFlood inundation emulation models based on deep neural networks have been developed to overcome the computational burden of two‐dimensional (2D) hydrodynamic models. Challenges remain for flat and complex floodplains where many anabranches form during flood events. In this study, we propose a new approach to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. A U‐Net‐based spatial reduction and reconstruction method (USRR) is used to find representative locations on the floodplain with complex flow paths. The water depths at these locations are simulated using one‐dimensional convolutional neural network (1D‐CNN) models, which are well‐suited to handling multivariate timeseries inputs. The flood surface is then reconstructed using the USRR method and the simulated flood depths at the representative locations. The combined 1D‐CNN and USRR method is compared with a previously developed approach based on the long short‐term memory recurrent neural network (LSTM) models and a 2D linear interpolation‐based SRR method. Compared to the LSTM model, the 1D‐CNN model is not only more accurate, but also takes less time to develop. Although both surface reconstruction methods take <1 s to produce an inundation map for a specific point in time, the USRR method is more accurate than the SRR method, leading to an increase of 5.6% in the proportion of correctly detected inundation area. The combination of 1D‐CNN and USRR can detect over 95% of the inundated area simulated using a 2D hydrodynamic model but is 98 times faster.

Flood Inundation Modelling in Greater Colombo Region Using HEC-RAS 2D

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.Cover DescriptionCover images show a few views of the Maduru Oya reservoir and construction of right bank canal. The Institution of Engineers, Sri Lanka, acknowledges Eng. L.D. Amila Sanjeewa of the Department of Irrigation for providing images and related information of the project.

Special Issue on SATREPS Area-BCM Part 2

This special issue includes works related to the ongoing project “Regional Resilience Enhancement through Establishment of Area-BCM at Industry Complexes in Thailand.” This special issue can be considered evidence that the research and development of this project follows and supports the sustainable development goals (SDGs). Let’s imagine we are taking a tour of Area-Business Continuity Management (Area-BCM) and SDGs together. As a starter, the baseline concept of our Area-BCM is discussed (Kodaka et al.) followed by the papers focusing on the specific SDGs. Toward SDG Goal 11 Sustainable Cities and Communities We can start the tour with this goal, as it is the project’s primary focus. In order to have sustainable cities and communities, the concept of Area-BCM can be considered. Related to it, there is a study that reviews and suggests the critical success factors (Meechang and Watanabe). Moreover, we can see the possibility of extending the concept of business continuity to other service sectors, such as tourism during COVID-19 (Yudha et al.). Toward SDG Goal 8 Decent Work and Economic Growth Let’s move to the next goal. There is a study exploring the well-being of working people and its likelihood among the local people in the community (Duangkaew et al.). People can be considered human resources in the Area-BCM. Accordingly, learning how people fared during a disaster allows us to design the suitable Area-BCM strategies for organizations. With sustainable employment aspects, inundation model for industrial park-scale is proposed for decision making regarding business continuity (Kakinuma et al.). And a practical model for shared risk assessment within multi-stakeholder is discussed for decision as a community (Ono). Toward SDG Goal 13 Climate Action Regarding the third goal, it is necessary to know the outcome of hazard analysis for us to understand the risks. Thus, an approach to flood hazard mapping using the Rainfall-Runoff-Inundation model is presented (Sriariyawat et al.). Through various techniques, flood inundation models are developed. Furthermore, detailed flood inundation model with different return period is proposed for more flexible and feasible decision making (P. C. et al.). There is still on a way to go. Nevertheless, we hope that these current findings will guide the future research and development of Area-BCM.

Integration of Hydrological and Flood Inundation Models for Assessing Flood Events in the Lower Prek Thnot River Basin under Climate Change

Integration of Hydrological and Flood Inundation Models for Assessing Flood Events in the Lower Prek Thnot River Basin under Climate Change

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.