Flood Simulation Model Research Articles

Comprehensive Assessment of Flood Hazard, Vulnerability, and Flood Risk at the Household Level in a Municipality Area: A Case Study of Nan Province, Thailand

Estimating flood hazard, vulnerability, and flood risk at the household level in the past did not fully consider all relevant parameters. The main objective of this study is to improve this drawback by developing a new comprehensive and systematic methodology considering all relevant parameters and their weighting factors. This new methodology is applied to a case study of flood inundation in a municipal area of Nan City in the Upper Nan River Basin in Thailand. Field and questionnaire surveys were carried out to collect pertinent data for input into the new methodology for estimating flood hazard, vulnerability, and risk. Designed floods for various return periods were predicted using flood simulation models for assessing flood risk. The flood risk maps constructed for the return periods of 10–500 years show a substantial increase in flood risk with the return periods. The results are consistent with past flood damages, which were significant near and along the riverbanks where ground elevation is low, population density is high, and the number of household properties are high. In conclusion, this new comprehensive methodology yielded realistic results and can be used further to assess the effectiveness of various proposed flood mitigation measures.

Integrating 1D and 2D hydrodynamic models for semi-arid river basin flood simulation

Integrating 1D and 2D hydrodynamic models for semi-arid river basin flood simulation

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management.

Identification of the controlling factors for hydrological responses by artificial neural networks

AbstractIdentifying the controlling factors for hydrological responses is of great importance for artificial neural network‐based flood forecasting models, which are often hindered by the lack of physical mechanisms. To explore the first‐order controlling factors of hydrograph patterns, a hybrid neural network was designed to analyse the impacts of potential driving variables with different temporal and spatial resolutions on hydrograph patterns. The Jinhua River Basin in Southeast China was used as an example in this study. Flood events with different hydrograph patterns and six external factors denoting potential controlling factors were individually classified into specific clusters using self‐organizing maps (SOMs). Based on the back‐propagation neural network (BPNN) and leave‐one‐out cross‐validation methods, the controlling factors of different flood patterns were identified by comparing the performances of flood simulation models trained with datasets before and after the potential controlling factor classification. The results showed that (i) the classification of controlling factors indicating various runoff regimes significantly improved the performance of data‐driven models in flood simulation in terms of correlation coefficient, Nash‐Sutcliffe coefficient, and normalized root mean square error; (ii) the spatial distribution of antecedent soil moisture and vegetation conditions as well as the temporal distribution of rainfall dominated different hydrograph patterns; and (iii) the transition of dominant rainfall‐runoff processes could be identified in an individual flood event using the hybrid SOM–BPNN model, indicating the varying influence of potential controlling factors on streamflow. Overall, the hybrid neural network models trained with datasets classified by controlling factors provide a general analytical framework to identify the governing dynamics for different flood patterns and improve the accuracy of flood simulations. Additionally, more attention should be devoted to improving the time to peak error of hydrological models, which cannot be settled by data‐driven models trained with different data‐splitting strategies.

Research and application of the mathematical model for extreme weather event in coastal urban areas

Extreme weather event simulation in coastal urban areas is more complex and difficult due to their special geography and climate characteristics, different kinds of land uses, close-packed buildings, and large amounts of flood control works and drainage systems. Urban Flood Simulation Model (UFSM) was integrated in this study to simulate the extreme weather events in the Pudong flood protection area (Shanghai, China). The model is established based on two-dimensional unsteady flow theory and nonstructural and irregular mesh technique. A method based on one-dimensional unsteady flow theory is proposed to deal with small-scale river and road. The calculation of the pumping stations, the water gates is indicated based on the real scheduling discipline. The extreme weather event scenarios (typhoon+rainstorm+ astronomical high tide + upstream flood) are simulated. The results show that the flood risk is higher in the coastal area and the upstream of the Huangpu Rive, and UFSM is a suitable method for simulating the flood inundation of coastal urban areas.

Experience in MAPATÓN 2021: "ApuMayu" a Tool for the Analysis of Flood Risk Zones in Piura

Abstract—MAPATÓN 2021 was organized by CONIDA (Peruvian Space Agency) as part of the celebrations for the bicentennial of Peru's independence. MAPATÓN was oriented to disaster management. This work aims to transmit the experience of participation in MAPATON and communicate the results achieved. The authors participated in developing flood simulation models and analyzing SENTINEL-1 satellite images to identify areas affected by floods that occurred in 2017, 2019 and 2020. The results obtained, shown in a story map, show the great utility of this type of analysis for risk management and disaster management. Keywords—MAPATON 2021; natural disasters; Peru; disaster management; Sentinel; satellite images

Bi-directional coupling of an open-source unstructured triangular meshes-based integrated hydrodynamic model for heterogeneous feature-based urban flood simulation

Urban pluvial flash floods have become a matter of widespread concern, as they severely impact people’s lives in urban areas. Hydrological and hydraulic models have been widely used for urban flood management and urban planning. Traditionally, to reduce the complexity of urban flood modelling and simulations, simplification or generalization methods have been used; for example, some models focus on the simulation of overland water flow, and some models focus on the simulation of the water flow in sewer systems. However, the water flow of urban floods includes both overland flow and sewer system flow. The overland flow processes are impacted by many different geographical features in what is an extremely spatially heterogeneous environment. Therefore, this article is based on two widely used models (SWMM and ANUGA) that are coupled to develop a bi-directional method of simulating water flow processes in urban areas. The open source overland flow model uses the unstructured triangular as the spatial discretization scheme. The unstructured triangular-based hydraulic model can be better used to capture the spatial heterogeneity of the urban surfaces. So, the unstructured triangular-based model is an essential condition for heterogeneous feature-based urban flood simulation. The experiments indicate that the proposed coupled model in this article can accurately depict surface waterlogged areas and that the heterogeneous feature-based urban flood model can be used to determine different types of urban flow processes.

Analyzing sensitivity of flood susceptible model in a flood plain river basin

Flood is considered one of the most dangerous natural disasters among all-natural disasters. Prediction of flood susceptible areas is a primary task for adopting management plans for minimizing the colossal failure of the flood. The present work intended to identify the flood susceptible areas by using multi Machine Learning (ML) algorithms based on fourteen flood conditioning parameters in Tangon river basin of Indo-Bangladesh. Apart from statistical tests (AUC of ROC, Friedman test, Wilcoxon Signed Rank Test), Index of Flood Vulnerability (IFV) using field data-sets and 2 D flood simulation model using HEC-RAS software were used to validate the flood susceptibility models. The ML models predicted 4.95% to 17.53% area comes under a very high flood susceptible zone. Support vector machine model best suit model compared to all models. Sensitivity analysis of the model clarified that geology, rainfall, elevation, and drainage factors played a dominant role for determining the model.

A New Dam-Break Outflow-Rate Concept and Its Installation to a Hydro-Morphodynamics Simulation Model Based on FDM (An Example on Amagase Dam of Japan)

Dams are constructed to benefit humans; however, dam-break disasters are unpredictable and inevitable leading to economic and human life losses. The sequential catastrophe of a dam break directly depends on its outflow hydrograph and the extent of population centers that are located downstream of an affected dam. The population density of the cities located in the vicinity of dams has increased in recent times and since a dam break hydrograph relies on many uncertainties and complexities in devising a dam-break outflow hydrograph, more researches for the accurate estimation of a dam-break flood propagation, extent and topography change becomes valuable; therefore, in this paper, the authors propose a novel and simplified dam-break outflow rate equation that is applicable for sudden-partial dam breaks. The proposed equation is extensively affected by a dam-break shape. Therefore, the inference of a dam-break shape on a dam-break outflow rate is investigated in the current study by executing hydraulic experiments in a long, dry bed, frictionless and rectangular water channel connected to a finite water tank to acquire a mean break-shape factor. The proposed equation is further validated by regenerating the Malpasset dam-break hydrograph and comparing it to the existing methods and also by installing it on an existing 2D hydro-morphodynamics flood simulation model. Finally, Amagase Dam’s (arch-reaction dam in Japan) break simulation is executed as a case study. The results of the simulations revealed that the greater the height of a dam-break section, the more devastating its flood consequences would be.

Multi-scale flooding hazards evaluation using a nested flood simulation model: case study of Jamuna River, Bangladesh

ABSTRACT We investigated the merits of performing multi-scale flood simulations with nested two-dimensional flood simulation models for flood risk management applications over an extended period with infiltration and evapotranspiration. For nested calculations, (1) inter-district, (2) district, and (3) regional scales were arranged as three levels in the nested model. Because the nested method can be useful for flat land, the Gaibandha District in Bangladesh was chosen for the study area. This area is a typical flood-prone area that is inundated frequently by flood flow from the Jamuna River. First, we performed a year-round flooding simulation for the year 2007 by focusing on a monsoon flood, and validated the results via satellite images. Second, we evaluated the differences of floods in the case of 2.33- and 100-year return periods by using discharge data produced by a flood frequency analysis. We successfully simulated the flooding in each nested level for 2007, and this enabled us to evaluate the flood risks at different scales. In addition, we delineated the flood extent for different return periods and clarified the relationship between the peak water level of the river and the flood extent. The nested method proved to be advantageous because its implementation is not computationally demanding.

When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias

ABSTRACT Identifying situations where a hydrological model yields poor performance is useful for improving its predictive capability. Here we applied an evaluation methodology to diagnose the weaknesses of a parsimonious rainfall-runoff model for flood simulation. The GR5H-I hourly lumped model was evaluated over a large set of 229 French catchments and 2990 flood events. Model bias was calculated considering different streamflow time windows, from calculations using all observations to analyses of individual flood events. We then analysed bias across seasons and against several flood characteristics. Our results show that although GR5H-I had good overall performance, most of the summer floods were underestimated. In summer and autumn, compensations between flood and recession periods were identified. The largest underestimations of flood volumes were identified when high-intensity precipitation events occurred, especially under low soil moisture conditions.

Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Hydrologic models coupled with 2D hydrodynamic model for high-resolution urban flood simulation

Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network

Sensitivity analysis of urban flood model parameters is important for urban flood simulation. Efficient and accurate acquisition of sensitive parameters is the key to real-time model calibration. In order to quickly obtain the sensitive runoff parameters of the urban flood simulation model, this study proposes an artificial neural network-based identification method for sensitive parameters. Artificial neural network (ANN) models were constructed with the binary classification and multi-classification methods, and used environmental indicators that affect the parameter sensitivity of different hydrological response units as the input, with the sensitivity parameters of the Storm water management model (SWMM) being the output. The optimization of the ANN was realized by adjusting the number of nodes in the hidden layer and the maximum number of iterations. An example application was conducted in Zhengzhou, China. The results show that the binary classification ANN quickly identified sensitive parameters, and the prediction accuracy of all parameters exceeded 96%. Convergence can be achieved when the number of nodes in the hidden layer does not exceed twice the number of input nodes, and the maximum number of iterations does not exceed 200. Rapid and accurate identification of the sensitive runoff parameters of the urban flood simulation model was achieved, which reduced the time required for parameter sensitivity analysis.

Scenario Analysis of Sluice Gate Operations for Evaluating Inland Flood Damage

Typhoon Hagibis, which hit Japan directly on October 12, 2019, caused great damage, including the flooding of rivers, across various parts of Japan. The Tama River, which flows north of Kawasaki City, also experienced flooding which exceeded the designed high water level; although it did not cause fluvial flooding, river water flowed into the urban areas through the sewerage system, causing unprecedented inundation damage. This damage was reproduced with the inland flood simulation model. Furthermore, we performed simulations in which the water level, precipitation, and sluice gate operation of the Tama River differed from actual conditions, and compared them with the actual damage. Based on these results, we examined methods for reducing inundation damage, such as improving the operation method of sluice gates, and confirmed their effects.

Artificial neural network and multi-criteria decision-making models for flood simulation in GIS: Mazandaran Province, Iran

Flood is one of the most destructive natural disasters globally and is a concern due to its high vulnerability. In this study for identification of flood susceptible areas, artificial neural network (ANN) and Multi-Attributive Border Approximation Area Comparison (MABAC) combined with Weights of Evidence (WoE) and Analytical Hierarchy Process (AHP) Models were used in Mazandaran province, Iran. MABAC method was used for the first time to evaluate the flood-prone areas in this study, and Attempts have been made for evaluate the performance of this new method by comparing with ANN model. The output of the neural network was discharge values in hydrometric stations. Using Geographic Information System (GIS) with eight effective factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, a flood model developed. Three precision parameters containing $${R}^{2}$$ , RMSE and MAE were applied to show the performance of the ANN model which yielded the values of 0.89, 0.0024 $${m}^{3}/s$$ , and 0.0018 $${m}^{3}/s$$ , respectively for testing data. The verification results indicated satisfactory agreement between the predicted and the real hydrological records. Also, based on flood inventory map and using the area under receiver operating curve, predictive power of the MABAC-WoE-AHP model was evaluated. The AUC value for prediction rate of this model was 86.1% which indicates the very good accuracy in predicting flood-prone areas. Comparison of flood susceptibility maps for ANN and MABAC-WoE-AHP models showed the good agreement between two models, that clarifies the efficiency of the new proposed method for future preventive measures.

Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models

The flood hazard management is one of the major challenges in the floodplain regions worldwide. With the rise in population growth and the spread of infrastructural development, the level of risk has increased over time. Therefore, the prediction of flood susceptible area is a key challenge for the adoption of management plans. Flood susceptibility modeling is technically a common work, but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner. Therefore, the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network (ANN), radial basis function (RBF), random forest (RF) and their ensemble-based flood susceptibility models. The flood susceptible models were constructed based on nine flood conditioning parameters. The flood susceptibility models were validated in a conventional way using the receiver operating curve (ROC). To validate the flood-susceptible models, a two dimensional (2D) hydraulic flood simulation model was developed. Also, the index of flood vulnerability model was developed and applied for validating the flood susceptible models, which was a very unique way to validate the predictive models. Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models. Results showed that 11.95%–12.99% of the entire basin area (10188.4 km2) comes under very high flood-susceptible zones. Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models. The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models. Therefore, the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.

Integrating 1D and 2D hydrodynamic models for semi-arid river basin flood simulation

Integrating 1D and 2D hydrodynamic models for semi-arid river basin flood simulation

Development of 1D–2D Urban Flood Simulation Model Based on Modified Cellular Automata Approach

AbstractIn the context of urban flood management, it is necessary to estimate flood depth and area accurately. Therefore, development of a flood simulation models with acceptable accuracy could be ...

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle (UAV), and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir, were proposed in this paper. The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x- and y-directions. The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix. The point cloud data with a 3D space were extracted from the registered images, and a digital surface map (DSM) was produced using a point cloud classification geometric mapping technique. To amend the serious elevation errors caused by natural or artificial obstacles, a kriging interpolation method was used to reproduce the DSM. A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group (RNG) turbulence model. The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification. The flooded areas estimated through the simulation were approximately 18.3% larger than those found by visual reading. Turbulent flows were mainly observed in obstacles or curved areas of the stream, and the differences in the water depth could be further increased. However, the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading. Therefore, the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas, and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses. Keywords: reservoir failure, flooded area, finite volume method, unmanned aerial vehicle, photogrammetric survey, digital surface model DOI: 10.25165/j.ijabe.20201306.4909 Citation: Jeon J, Choi W. Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV. Int J Agric & Biol Eng, 2020; 13(6): 7–15.

A stress test of urban system flooding upon extreme rainstorms in Hong Kong

Many cities face high flood risks due to intense rainstorms and dense urban environments. Quantifying the degree and duration of flood impact to urban systems under extreme rainstorms is essential for long-term flood risk management and short-term emergency response. This study aims to develop a novel stress testing framework for evaluating urban system flooding upon extreme rainstorms in Hong Kong. The stress testing framework is based on a pipe-surface coupled hydraulic model for detailed flood simulation in urban settings and a dynamic flood impact evaluation method for assessing interruptions to urban transportation and building systems. The proposed stress testing method is applied to assess the flood impacts in Kowloon, the most densely populated area in Hong Kong. Detailed urban settings such as engineering drainage facilities and buildings are incorporated into the flood simulation model, and the model is validated both qualitatively and quantitatively by field observations. Flood hazard scenarios are investigated under design rainstorms of different return periods (20, 100, and 1000 years), and the impacts on transportation and building systems are evaluated with the new process-based impact evaluation method. The results of the case study indicate that underground mass transit railway (MTR) stations and residential buildings will be more affected than highways and other types of buildings in Kowloon: up to 56% of MTR stations and 43% of residential buildings may be affected under the 1000-year rainstorm, with inundation durations of 8 and 7 h for the MTR stations and residential buildings, respectively. To ensure public transportation services and reduce potential exposure of residents during extreme rainstorms, mitigation measures should be taken to reduce the affected percentage and duration of MTR stations and residential buildings. The proposed methods contribute to enhanced societal flood preparedness, warning, and response.