Modeling of a compound flood induced by the levee breach at Qianbujing Creek, Shanghai, during Typhoon Fitow

Levee-breach-induced flooding occurs occasionally but always causes considerable losses. A serious flood event occurred due to the collapse of a 15 m long levee section in Qianbujing Creek, Shanghai, China, during Typhoon Fitow in October 2013. Heavy rainfall associated with the typhoon intensified the flood severity (extent and depth). This study investigates the flood evolution to understand the dynamic nature of flooding and the compound effect using a well-established 2D hydro-inundation model (FloodMap) to reconstruct this typical event. This model coupled urban hydrological processes with flood inundation for high-resolution flood modeling, which has been applied in a number of different environments, and FloodMap is now the mainstream numerical simulation model used for flood scenarios. Our simulation results provide a comprehensive view of the spatial patterns of the flood evolution. The worst-hit areas are predicted to be low-lying settlements and farmland. Temporal evaluations suggest that the most critical time for flooding prevention is in the early 1–3 h after dike failure. In low-elevation areas, temporary drainage measures and flood defenses are equally important. The validation of the model demonstrates the reliability of the approach.

Levee breach-induced compound flood modeling in Qianbujing Creek, Shanghai during Typhoon "Fitow"

Levee breach-induced flooding occurs occasionally but always causes considerable losses. A serious flood event occurred due to the collapse of a 15-m-long levee section in Qianbujing Creek, Shanghai, China, during typhoon "Fitow" in Oct, 2013. Heavy rainfall associated with the typhoon intensified the flood severity (extent and depth). This study investigates the flood evolution to understand the dynamic nature of flooding and the compound effect using a well-established 2D hydro-inundation model (Floodmap) to reconstruct this typical event. Our simulation results provide a comprehensive view of the spatial patterns of the flood evolution. The worst-hit areas are predicted to be low-lying settlement and farmland. Temporal evaluations suggest that the most critical time for flooding prevention is in the early hours after dike failure. In low-elevation areas, temporary drainage measures and flood defenses are equally important. The validation of the model demonstrates the reliability of the approach.

Accounting for DEM Error in Sea Level Rise Assessment within Riverine Regions; Case Study from the Shatt Al-Arab River Region

Global elevation datasets such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) are the best available terrain data in many parts of the world. Consequently, SRTM is widely used for understanding the risk of coastal inundation due to climate change-induced sea level rise. However, SRTM elevations are prone to error, giving rise to uncertainty in the quality of the inundation projections. This study investigated the error propagation model for the Shatt al-Arab River region (SARR) to understand the impact of DEM error on an inundation model in this sensitive, low-lying coastal region. The analysis involved three stages. First, a multiple regression model, parameterized from the Mississippi River delta region, was used to generate an expected DEM error surface for the SARR. This surface was subtracted from the SRTM DEM for the SARR to adjust it. Second, residuals from this model were simulated for the SARR. Modelled residuals were subtracted from the adjusted SRTM to produce 50 DEM realizations capturing potential elevation variation. Third, the DEM realizations were each used in a geospatial “bathtub” inundation model to estimate flooding area in the region given 1 m of sea level rise. Across all realizations, the area predicted to flood covered about 50% of the entire region, while predicted flooding using the raw SRTM covered only about 28%, indicating substantial underprediction of the affected area when error was not accounted for. This study can be an applicable approach within such environments worldwide.

A Novel Hybrid Approach Based on Cellular Automata and a Digital Elevation Model for Rapid Flood Assessment

An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

Levee breach-induced compound flood modeling in Qianbujing Creek, Shanghai during Typhoon “Fitow”

Levee breach-induced flooding occurs occasionally but always causes considerable losses. A serious flood event occurred due to the collapse of a 15-m-long levee section in Qianbujing Creek, Shanghai, China, during typhoon “Fitow” in Oct, 2013. Heavy rainfall associated with the typhoon intensified the flood extent. This study investigates the flood evolution to understand the dynamic nature of flooding and the compound effect using a well-established 2D hydro-inundation model (Floodmap) to reconstruct this typical event. Our simulation results provide a comprehensive view of the spatial patterns of the flood evolution. The worst-hit areas are predicted to be low-lying farmland. Temporal evaluations suggest that the most critical time for flooding prevention is in the early hours after dike failure. In low-elevation areas, temporary drainage measures and flood defenses are equally important. The validation of the model demonstrates the reliability of the approach.

Calibrating and validating an inundation model with and without crowdsourced water depths and velocities

<p>Remote sensing and crowdsourcing data are new sensing methods that have the potential to improve significantly inundation modelling. That is especially true in data-scarce situations, for example when resources for acquiring sufficient traditional data are limited or when field conditions are not favourable. Crowdsourced water depths and velocities have been demonstrated to be useful for improving inundation models, ranging from the calibration of 1D models to data assimilation in 2D models. In this study, we aim to further evaluate how much the amount and type of crowdsourced data influence model calibration and validation, in comparison with data from traditional measurements. Further, we aim to assess the effects of combining both sources. For that, we developed a 2D inundation model of the Sontea-Fortuna area, a part of the Danube Delta in Romania. This is a wetland area, where data was collected during two 4-day field campaigns, using boat navigation together with the involved citizens. Citizens obtained thousands of images and videos that were converted into water depth and velocity data, while technicians collected ADCP data. We calibrated and validated the model using different combinations of data (e.g. all water depth data, half water depth and half water velocity). Results indicated that velocity data by themselves did not yield good calibration results, being better used in conjunction with water depths or by combining them into discharge. We also observed that calibration by crowdsourced water depths is comparable to the use of water depths from traditional measurements.</p>

InundatEd: A Large-scale Flood Risk Modeling System on a Big-data – Discrete Global Grid System Framework

Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (InundatEd) using the height above the nearest drainage-based solution for Manning's equation, implemented in a big-data discrete global grid systems-based architecture with a web-GIS platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to known flood extents. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation model; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.

Modelling Prospective Flood Hazard in a Changing Climate, Benevento Province, Southern Italy

The change of the Earth’s climate and the increasing human action (e.g., increasing impervious areas) are influencing the recurrence and magnitude of flooding events and consequently the exposure of urban and rural communities. Under these conditions, flood hazard analysis needs to account for this change through the adoption of nonstationary approaches. Such methods, showing how flood hazard evolves over time, are able to support a long-term plan of adaptation in hazard changing perspective, reducing expected annual damage in flood prone areas. On this basis, in this paper a reevaluation of flood hazard in the Benevento province of southern Italy, is presented, providing a reduced complexity methodological framework for near future flood hazard prediction under nonstationary conditions. The proposed procedure uses multiple nonstationary probability models and a LiDAR-derived high-resolution inundation model to provide present and future flood scenarios in the form of hazard maps. Such maps are derived using a spatialization routine of stage probability across the inundation model that is able to work at different scales. The analysis indicates that, overall, (i) flood hazard is going to decrease in the next 30 years over the Benevento province and (ii) many areas of the Calore river floodplain are going to be subject to higher return level events. Consequently, many areas would require new guidelines of use as the hazard level decreases. Limitations of the analysis are related to the choice of the probability model and the parameter estimation approach. A further limit is that, currently, this method is not able to account for the presence of mitigation measurements. However, result validation indicates a very high accuracy of the proposed procedure with a matching degree, with a recently observed 225-years flood, estimated in 98%. On this basis, the proposed framework can be considered a very important approach in flood hazard estimation able to predict near future evolution of flood hazard as modulated by the ongoing climate change.

Efficient Urban Inundation Model for Live Flood Forecasting with Cellular Automata and Motion Cost Fields

The mitigation of societal damage from urban floods requires fast hydraulic models for emergency and planning purposes. The simplified mathematical model Cellular Automata is combined with Motion Cost fields, which score the difficulty to traverse an area, to the urban inundation model CAMC. It is implemented with simple matrix and logic operations to achieve high computational efficiency. The development concentrated on an application in dense urban built-up areas with numerous buildings. CAMC is efficient and flexible enough to be used in a “live” urban flood warning system with current weather conditions. A case study is conducted in the German city of Wuppertal with about 12,000 buildings. The water depth estimation of every time step are visualized in a web-interface on the basis of the virtual globe NASA WorldWind. CAMC is compared with the shallow water equations-based model ANUGA. CAMC is approximatively 5 times faster than ANUGA at high spatial resolution and able to maintain numerical stability. The Nash-Sutcliffe coefficient (0.61), Root Mean Square Error (0.39 m) and Index of Agreement (0.65) indicate acceptable agreement for water depth estimation but identify different areas where important deviations occur. The estimation of velocity performs considerably less well (0.34 for Nash-Sutcliffe coefficient, 0.13 ms − 1 for Root Mean Square Error, and 0.39 for Index of Agreement) because CA ignores momentum conservation.