Two-dimensional Flood Model Research Articles

An Integrated Model for Dam Break Flood Including Reservoir Area, Breach Evolution, and Downstream Flood Propagation

The reasonable and efficient prediction of dam failure events is of great significance to the emergency rescue operations and the reduction in dam failure losses. This work presents a model that is based on the physical mechanism. It is coupled with a multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, which is based on high-precision terrain and land use data. The aim is to enhance the accuracy of dam break flood process simulations. The model uses DEM data as a computational grid and updates it at each time step to reflect breach evolution. Simultaneously, the breach evolution model incorporates an analysis of stress on sediment particles, establishing the initial erosion state and lateral expansion model while accounting for seepage. The determination of the overflow of the breach is resolved through the application of a two-dimensional hydrodynamic model. This approach achieves a robust connection between the upstream reservoir, the dam structure, and the downstream inundation area. The coupled model is utilized to calculate the failure of earth-rock dams and landslide dams, and a sensitivity analysis is conducted. Taum Sauk Dam and Tangjiashan landslide dam were selected to represent earth dam break and barrier lake break, respectively, which are the main types of dam breaks. The obtained results demonstrate strong concurrence with the measured data, the relative errors of the four important parameters of the application case, the peak discharge of the breach, the top width of the final breach, the depth of the breach and the arrival time of the maximum peak discharge are all within ±10%. Although the relative error of the completion time of the final breach is greater than 10%, it is about 30% less than the relative error of the physical model.

Reply to AlQasimi, E.; Mahdi, T.-F. Comment on “Aureli et al. Review of Historical Dam-Break Events and Laboratory Tests on Real Topography for the Validation of Numerical Models. Water 2021, 13, 1968”

This is the reply to the comments by AlQasimi and Mahdi (2022) on the classification attributed to the Lake Ha! Ha! real-field test case by Aureli et al. (2021) in their review of historical dam-break events useful for the validation of dam-break numerical models. While admitting that this test case is affected by the data shortcomings reported by the Discussers, in the authors’ opinion, it should remain included in the group of well-documented test cases due to the large and complete dataset available in digital format. This conclusion is also supported by the fact that the Lake Ha! Ha! case was chosen as a benchmark in the framework of the 2001–2004 IMPACT (Investigation of Extreme Flood Processes and Uncertainty) European project and was then widely used in the literature for the validation of one-dimensional and two-dimensional geomorphic flood models.

Case Study of Urban Flood Inundation—Impact of Temporal Variability in Rainfall Events

This study aimed to calculate and analyze total overflows that accumulate in urban manholes in the target drainage basin of Samsung-dong, Seoul in heavy rainfall events with different temporal distribution characteristics, using the EPA’s Storm Water Management Model (EPA-SWMM model). Inundation behaviors were analyzed using the two-dimensional flood model (FLO-2D). The extreme rainfall events were produced using different exceedance probability Huff distributions for different durations and return periods, such as from 1 to 3 h and 10 years, 50 years, 80 years, 100 years, respectively. The inundation model was validated using the actual flood observations on 21 September 2010 in the Samsung-dong drainage basin. The total overflow amount showed considerable differences according to the different time distribution characteristics, such as the temporal location of the storm peak and the concentration level of the storm. Furthermore, the inundation behaviors were also related to the temporal characteristics of storms. The results illustrated that the consideration of the temporal distribution characteristics of extreme rainfall events is essential for an accurate understanding of the rainfall–runoff response and inundation behavior in urban drainage basins.

MAPPING LAND COVER CHANGE AND MODELLING ITS IMPACTS ON THE INUNDATION RESPONSES OF THE AGUSAN MARSH, MINDANAO, PHILIPPINES

Abstract. Monitoring of environmental changes is one of the most popular applications of satellite remote sensing. In this study, we applied satellite remote sensing to map and monitor changes in land cover of Agusan Marsh, one of the most ecologically significant wetlands, and an important region of biodiversity in the Philippines. Multi-temporal land cover maps of the Agusan Marsh over a 22-year period from 1995 to 2017 were generated through Maximum Likelihood classification of Landsat 5, TM, ETM+ and OLI images. Post-classification change detection of the land-cover maps showed that more than 60% of the marsh is naturally vegetated during the mapping period. Agricultural/cultivated areas were the second dominant land cover and were found to be increasing through the years while wetland vegetation generally showed a downward trend. Two-dimensional (2D) flood modelling using HEC RAS was also performed to estimate how the Agusan Marsh would react to extreme rainfall events in terms of depth and extent of inundations. Simulation results showed that the Agusan Marsh responded differently for each year in terms of inundation depth and extent. These results of the combined satellite remote sensing + 2D modelling approach implemented in this study would be essential to better understand landscape patterns in the Agusan Marsh, including changes and interactions between human activities and natural phenomenon such as flooding for proper marsh management and improved decision-making.

Two-dimensional flood model for risk exposure analysis of land use/land cover in a watershed.

BACKGROUND AND OBJECTIVES: The study involved developing a two-dimensional flood model to analyze the risk exposure of land use/land cover based on the generated flood hazard maps for the six return period scenarios in the Solana watershed.METHODS: The approach consisted of applying hydrologic and hydraulic numerical flood models and the suite of advanced geographic information systems and remote sensing technologies. The process involved utilizing a high-resolution digital elevation model and a set of high-precision instruments such as the real-time kinematic-global position system receiver, digital flow meter, deep gauge, and automatic weather station in collecting the respective data on bathymetry, river discharge, river depth, and rainfall intensity during a particular climatic event, needed for the model development, calibration and validation.FINDINGS: The developed two-dimensional flood model could simulate flood hazard with an 86% accuracy level based on the coefficient of determination statistics. The flood risk exposure analysis revealed that coconut is the most affected, with 31.3% and 37.1% being at risk across the 2-year and 100-year return period scenarios, respectively. Results also showed that rice and pineapple are at risk of flooding damage with the increasing rate of exposure by a magnitude of 42.9 and 9.3 across the 2-year and 100-year flood scenarios, respectively.CONCLUSION: The study highlighted the integration of the findings and recommendations in the localized comprehensive land use plan and implementation to realize the challenge of building a climate change proof and a flood-resilient human settlement in the urbanizing watershed of Solana.

Automatic incorporation of riverbank failures in two-dimensional flood modeling

Riverbanks undergo changes caused not only by river hydraulics, mainly sediment erosion and deposition processes, but also by the possible landslides that eventually change the channel bank profiles. Those failures are an important form of alluvial channel adjustments but are usually difficult to include during morphodynamic modeling. This paper proposes a novel approach combining a 2D depth-averaged hydrodynamic, sediment transport and mobile-bed model, SRH-2D, a limit equilibrium slope-stability model, BISHOP, and a bank failure sediment redistribution submodel, REDISSED, into a fully automatic and continuous dynamic simulation to predict vertical bed and lateral bank changes for a river reach undergoing exceptional flooding. The in-stream vertical fluvial changes predicted with the SRH-2D model will be automatically used to update the riverbank geometry profile by profile and assess their geotechnical stability to rotational slip failures with a developed slope-stability model based on Bishop’s simplified method. A cone-shaped sliding area is defined in case the driving forces exceed the stabilizing forces. All mesh nodes located within the mass wasting zone will be automatically updated, allowing a new bank face form. The failed materials will be redistributed in the transect according to the geometry of the landslides observed at the study site. The Outaouais River at Notre-Dame-Du Nord, Quebec, is used to test the coupling procedure. Up to 100 m of bank retreat was predicted, and more than 20 cross-sections were reshaped. Typical results showing the effectiveness of the developed framework are presented and discussed.

Real-Time Flood Disaster Prediction System by Applying Machine Learning Technique

As rainfall intensity varies irregularly, urban floods can cause extreme damage. Furthermore, they are extremely nonlinear phenomena that are complex to analyze. Therefore, a classification-based real-time flood prediction model for urban areas is constructed in this study, by combining a numerical analysis model based on hydraulic theory with a machine learning model. Flood databases are constructed in advance for different rainfall scenarios using the Environmental Protection Agency-Storm Water Management Model (EPA-SWMM) and a two-dimensional inundation model. The flood depth data for each map grid are divided into five categories based on the average flood depth using the Latin hypercube sampling (LHS) and probabilistic neural network (PNN) classification techniques for higher-precision flood range prediction. A model is constructed to predict the representative cumulative volume if the observed rainfall is entered. For spatial expansion of the flood depth with the predicted representative cumulative volume, a system capable of generating a real-time flood map is constructed by linking the cumulative volume of each grid with the representative cumulative volume using linear and nonlinear regression. When compared with the results of a verified two-dimensional (2D) flood model, the developed-model goodness-of-fit is 85%, with a required run time of 1 min 12 s. Using the developed system, rainfall-induced flooding can potentially be predicted, facilitating disaster risk management and minimizing damage to property and health.

Is local flood hazard assessment in urban areas significantly influenced by the physical complexity of the hydrodynamic inundation model?

Flood hazard in urban areas is usually assessed by the estimations of parameters like flood extent, water depths, flow velocities and other related quantities. These hydrodynamic variables can be computed using flood inundation numerical models, the complexity of which has been analyzed in several papers in the literature. However, the question of how detailed a model should be in order to give reasonable results is still an open issue and no shared and general conclusions have been reached so far. One of the main reasons for this is that model benchmarking is very often carried out with reference to the flooded areas extent only, sometimes analyzing also the simulated water depths values and, quite rarely, evaluating the computed velocities. This is in contrast with the main purpose of the simulation models which are, from a practical point of view, advanced tools for flood hazard and vulnerability assessments. Finally, the lack of sufficient data on inundation extent and depths for the calibration and validation of the numerical models has been recognized, especially in complex situations that often characterize the flood evolution in a given area due to bridge obstructions, interaction with buildings and so on.In this paper, the performances of three different approaches to two-dimensional flood modeling (fully dynamic, diffusive and porosity approaches) have been evaluated with reference not only to the hydrodynamic variables, water depths and velocities, but also focusing the attention on their product and, thus, to the consequences associated to their different estimations on the vulnerability assessment. This analysis has been focused first on a flash flood flow experiment in a simplified urban district and then to the event which occurred in the city of Crotone (Calabria, Italy) in 1996.The results highlight that fully-dynamic modeling should be the unavoidable reference tool when the goal of the urban flood mapping activity is not limited to the evaluation of the flood-prone areas extent but involves also the local estimation of flood hazard/vulnerability.

Comparison of one- and two-dimensional flood modeling in urban environments

Comparison of one- and two-dimensional flood modeling in urban environments

Regionalized Design Rainfall Estimation: an Appraisal of Inundation Mapping for Flood Management Under Data-Scarce Situations

Availability of regionalized design rainfall is crucial for flood modeling, particularly over the regions with sparse raingauge networks. This study proposes a new comprehensive framework for generating regionalized design rainfall time series for data-poor catchments involving non-linear and non-parametric optimization approaches. A large set of parametric and non-parametric families of distribution were considered for multivariate rainfall frequency analysis using at-site station data, while a unique design temporal pattern over the region was derived by quantifying the flood causing potential of design hyetographs. The regionalized design rainfall time series was used as one of the inputs to a two-dimensional (2D) flood model. The accuracy and performance of the derived regionalized design rainfall for flood inundation modeling were evaluated by comparing with those derived from different spatial interpolation methods. There was a high consensus of the former with those of widely used kriging and spline interpolation methods. A severely flood-prone and data-poor (no raingauge available within study area) large coastal catchment lying along the coast of the Bay of Bengal, India, was chosen for a demonstration of the proposed framework. The study showed that the framework can be used for extreme events arising due to floods, even under changing climatic scenarios. It further invokes the necessity for incorporating the proposed framework into various commercially and freely available flood models along with other existing interpolation techniques to support improved flood management.

Going beyond the flood insurance rate map: insights from flood hazard map co-production

Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but they do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies.

A Semi Risk-Based Approach for Managing Urban Drainage Systems under Extreme Rainfall

Conventional design standards for urban drainage systems are not set to deal with extreme rainfall events. As these events are becoming more frequent, there is room for proposing new planning approaches and standards that are flexible enough to cope with a wide range of rainfall events. In this paper, a semi risk-based approach is presented as a simple and practical way for the analysis and management of rainfall flooding at the precinct scale. This approach uses various rainfall events as input parameters for the analysis of the flood hazard and impacts, and categorises the flood risk in different levels, ranging from very low to very high risk. When visualised on a map, the insight into the risk levels across the precinct will enable engineers and spatial planners to identify and prioritise interventions to manage the flood risk. The approach is demonstrated for a sewer district in the city of Rotterdam, the Netherlands, using a one-dimensional (1D)/two-dimensional (2D) flood model. The risk level of this area is classified as being predominantly very low or low, with a couple of locations with high and very high risk. For these locations interventions, such as disconnection and lowering street profiles, have been proposed and analysed with the 1D/2D flood model. The interventions were shown to be effective in reducing the risk levels from very high/high risk to medium/low risk.

Extracting inundation patterns from flood watermarks with remote sensing SfM technique to enhance urban flood simulation: The case of Ayutthaya, Thailand

Flood watermarks stipulate peak water depths from a flood event, indicating a magnitude of inundation that took place. Such information is invaluable for instantiation and validation of urban flood models. However, collecting and processing such data from land surveys can be costly and time-consuming. New remote sensing and data processing technologies offer improved opportunities to address these issues. The present paper deals with the new structure from motion (SfM) technology and its application in extracting flood watermarks. For this purpose, the first of its kind, side-view SfM surveys with two mobile units were utilised. Survey works were carried out in the vicinity of Ayutthaya heritage area (Thailand) and data obtained were used for setting up numerical models and simulations of the 2011 flood event. The work undertaken demonstrates the significant capability of SfM technology for extraction of flood watermarks. With such technology, it was possible to indicate façades, low-level structures, and susceptible openings, which in turn have improved schematizations of two-dimensional (2D) flood models. The resulting model simulations were found to be more accurate (i.e., more close to the measurements of flood watermarks) than those obtained from models with conventional top-view light detection and ranging (LiDAR) data.

BEYOND FLOOD HAZARD MAPS: DETAILED FLOOD CHARACTERIZATION WITH REMOTE SENSING, GIS AND 2D MODELLING

Abstract. Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS) and Geographic Information System (GIS) are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D) flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers) that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.

An Integrated Numerical Hydrodynamic Shallow Flow-Solute Transport Model for Urban Area

The rapidly changing on land profiles in the some urban areas in Malaysia led to the increasing of flood risk. Extensive developments on densely populated area and urbanization worsen the flood scenario. An early warning system is really important and the popular method is by numerically simulating the river and flood flows. There are lots of two-dimensional (2D) flood model predicting the flood level but in some circumstances, still it is difficult to resolve the river reach in a 2D manner. A systematic early warning system requires a precisely prediction of flow depth. Hence a reliable one-dimensional (1D) model that provides accurate description of the flow is essential. Research also aims to resolve some of raised issues such as the fate of pollutant in river reach by developing the integrated hydrodynamic shallow flow-solute transport model. Presented in this paper are results on flow prediction for Sungai Penchala and the convection-diffusion of solute transports simulated by the developed model.

Accuracy and Computational Efficiency of 2D Urban Surface Flood Modelling Based on Cellular Automata

There is an emerging abundance of freely available high resolution (one meter or less) LIDAR data due to the advent of remote sensing, which enables wider applications of detailed flood risk modelling and analysis. Digital terrain surface data often comes in raster form, i.e., a square regular grid, and often requires conversion into a specific computational mesh for two-dimensional (2D) flood modelling that adopts triangular irregular meshes. 2D modelling of flood water movement through urban areas requires resolution of complex flow paths around buildings, which requires both high accuracy and computational efficiency. Water distribution and wastewater systems in the UK contain over 700,000km of water distribution and sewer pipes, which represents a large risk exposure from flooding caused by sewer surcharging or distribution pipe breaks. This makes it important for utilities to understand and predict where clean or dirty water flows will be directed when they leave the system. In order to establish risk assessment many thousands of simulations may be required, calling for the most computational efficient models possible.Cellular Automata (CA) represents a method of running simulations based on a regular square grid, thus saving set-up time of configuring the terrain data into an irregular triangular mesh. It also offers a more uniform memory pattern for very fast modern, highly parallel hardware, such as general purpose graphical processing units (GPGPU). In this paper the performance of the CADDIES [1], a CA platform and associate flood modelling software caFloodPro, using a square regular grid and Von Neumann neighbourhood, is compared to industry standard software using triangular irregular meshes for similar resolutions. A minimum time step is used to control the computational complexity of the algorithm, which then creates a trade-off between the processing speeds of simulations and the accuracy resulting from the limitations used within the local rule to cope with relatively large time steps. This study shows that using CA based methods on regular square grids offers process speed increases in terms of 5-20 times over that of the industry standard software using irregular triangular meshes, while maintaining 98-99% flooding extent accuracy.

A Pseudospectral Collocation Approach for Flood Inundation Modelling with Random Input Fields

In this study, an efficient framework of pseudospectral collocation approach combined with the generalized polynomial chaos (gPC) and Karhunen-Loevè expansion (gPC/KLE) was introduced to examine the flood flow fields within a two-dimensional flood modelling system. In the proposed framework, the heterogeneous random input field (logarithmic Manning’s roughness) was approximated by the normalized KLE and the output field of flood flow depth was represented by the gPC expansion, whose coefficients were obtained with a nodal set construction via Smolyak sparse grid quadrature. In total, 3 scenarios (with different levels of input spatial variability) were designed for gPC/KLE application and the results from Monte Carlo simulations were provided as the benchmark for comparison. This study demonstrated that the gPC/KLE approach could predict the statistics of flood flow depth (i.e., means and standard deviations) with significantly less computational requirement than MC; it also outperformed the probabilistic collocation method (PCM) with KLE (PCM/KLE) in terms of fitting accuracy. This study made the first attempt to apply gPC/KLE to flood inundation field and evaluated the effects of key parameters (like the number of eigenpairs and the order of gPC expansion) on model performances.

Spatial probabilistic multi-criteria decision making for assessment of flood management alternatives

Summary Flood management alternatives are often evaluated on the basis of flood parameters such as depth and velocity. As these parameters are uncertain, so is the evaluation of the alternatives. It is thus important to incorporate the uncertainty of flood parameters into the decision making frameworks. This research develops a spatial probabilistic multi-criteria decision making ( SPMCDM ) framework to demonstrate the impact of the design rainfall uncertainty on evaluation of flood management alternatives. The framework employs a probabilistic rainfall–runoff transformation model, a two-dimensional flood model and a spatial MCDM technique. Thereby, the uncertainty of decision making can be determined alongside the best alternative. A probability-based map is produced to show the discrete probability distribution function (PDF) of selecting each competing alternative. Overall the best at each grid cell is the alternative with the mode parameter of this PDF. This framework is demonstrated on the Swannanoa River watershed in North Carolina, USA and its results are compared to those of deterministic approach. While the deterministic framework fails to provide the uncertainty of selecting an alternative, the SPMCDM framework showed that in overall, selection of flood management alternatives in the watershed is “moderately uncertain”. Moreover, three comparison metrics, F fit measure, κ statistic, and Spearman rank correlation coefficient ( ρ ), are computed to compare the results of these two approaches. An F fit measure of 62.6%, κ statistic of 15.4–45.0%, and spatial mean ρ value of 0.48, imply a significant difference in decision making by incorporating the design rainfall uncertainty through the presented SPMCDM framework. The SPMCDM framework can help decision makers to understand the uncertainty in selection of flood management alternatives.

Two-dimensional hydrodynamic flood modelling for populated valley areas of Russian rivers

Abstract. Results of flood modelling for three cities located in different parts of Russia: (1) Veliky Ustyug at the Northern Dvina river (Europe); (2) Mezhdurechensk at the Tom river (Siberia); and (3) Blagoveschensk at the Amur river (Far East) are presented. The two-dimensional hydrodynamic model of flow in channels and on floodplain STREAM_2D on the basis of the numerical solution of two-dimensional Saint–Venant equations on a hybrid curvilinear quadrangular and rectangular mesh was used for the simulations. Verification of the model through a comparison of simulated inundated areas with outlines of flooded zones from satellite images for known hydrologic situations demonstrate close correspondence (relative errors of 7–12% in terms of the area for peaks of the analysed floods). Analyses of embankment influence of large-scale levees on the water flow demonstrate that, in some cases, water levels could rise by more than 1 m and the patterns of the flooding zones could significantly differ.

Evaluating the Effects of Inundation Duration and Velocity on Selection of Flood Management Alternatives Using Multi-Criteria Decision Making

Impacts of flood management alternatives are mostly assessed by inundation depth. Other inundation parameters such as velocity and duration are rarely taken into consideration. In this paper, a multi-criteria decision making (MCDM) based framework is used to analyze the effects of inundation velocity and duration on evaluation of flood management alternatives. The framework incorporates a two-dimensional (2D) flood model, Flood2D-GPU and a spatial MCDM (SMCDM) method, Spatial Compromise Programming (SCP). Flood2D-GPU is employed to simulate floods and SCP is applied to rank a set of flood management alternatives. Assessment of flood management options is conducted with multiple different weight set scenarios. First, alternatives are ranked without consideration of inundation velocity and duration. Then, the importance of these parameters increases and the alternatives are ordered in each weight set and a GIS map showing the best alternative in each grid cell is generated in each case. Best alternative maps (BAMs) are compared to investigate the impacts of inundation velocity and duration on selection of best strategy using F fit measure and κ analysis. The framework applicability is illustrated on the Swannanoa River watershed located in the state of North Carolina, US. Case study results indicate up to 49.7 % change of BAM by taking into account inundation velocity and duration. The presented approach addresses the change in selection of flood management strategies resulted by considering other inundation parameters rather than inundation depth. This can potentially reduce the uncertainties associated with the decisions made without consideration of inundation velocity and duration.