Urban Inundation Research Articles

Short-term Prediction of Urban Inundation based on Radar-predicted Rainfall through Real-time SWMM Parameter Calibration

In recent times, the rapid pace of climate change has been causing urban inundation in areas where drainage of inland water is difficult. In response to this concern, studies that use SWMM are being actively conducted to predict urban inundation. However, the parameters applied to SWMM use historical values, resulting in suboptimal accuracy in predictions. Therefore, this study employed the genetic algorithm, among various optimization techniques, to bolster the accuracy of rainfall-runoff analysis of the SWMM model. This was achieved by linking ASOS data and real-time ground-observation rainfall to real-time water gauge data. Consequently, the water level could now be predicted by applying radar predicted rainfall at 10-minute intervals to the completed SWMM module. The required time for prediction of urban inundation was about 2 minutes. This timeframe proved highly practical when the radar rainfall data was entered into the SWMM module at 10-minute intervals. The water level predictions, when applied to the four rainfall scenarios, also aligned within a reasonable range.

A Case Study on Simulation of Urban Inundation by Inland Flooding and River Flooding

Urban flooding can occur due to a combination of factors such as insufficient drainage capacity in stormwater systems, inland flooding from elevated river levels, and river flooding. This study proposes a method for analyzing urban inundation resulting from these complex phenomena using XP-SWMM. This is essentially a model capable of calculating the flow of drainage systems in urban watersheds and analyzing surface inundation. Moreover, it can accommodate irregular channel cross-sections and bridges, enabling river flooding modeling. However, it has limitations in terms of replicating the actual flooding patterns of rivers due to its representation of rivers through one-dimensional links and nodes. This study implemented virtual control structures to reflect the flooding effects caused by river flooding. This approach was applied to the Dongcheon watershed in Busan, where two instances of river and inland flooding coincided in July 2020. The results indicated the appropriateness of the approach.

Urban inundation forecasting based on hydraulic models coupling MIKE Flood and MIKE Urban: A case study of Tam Ky City, Vietnam

AbstractUrban inundation has become a growing concern for many coastal cities around the world. Tam Ky City, located downstream of Ban Thach and Tam Ky rivers in central Vietnam, is no exception. According to annual statistics, the city frequently experiences heavy rainfall from tropical storms and monsoons, making inundation a recurring issue. In response to this challenge, this study developed a flood forecasting model specifically tailored for Tam Ky City, based on forecast rainfall and tidal levels. A key strength of the flood forecasting model lies in its integration of rainfall‐runoff processes, effectively connecting the river basin and the city. To ensure the model's accuracy and reliability, the parameters of the flood forecasting model were meticulously calibrated and verified for floods occurred on rivers as well as in urban areas. An experimental flood forecast was performed during Typhoon Nuru on September 28, 2022, and the forecast results of inundation locations and depths in the city well reflected the actual observations.

Developing synthetic sewer pipe system for data-scarce domains in application for urban flood modeling

Abstract In urban flood modeling, the accuracy of surface and subsurface flow calculations greatly depends on the parameterization of the drainage system. Incorporating the influence of the sewer pipe system is, therefore, integral to accurately simulating urban inundation during short-duration rainfall events. However, obtaining comprehensive data on sewer systems in developing countries is currently challenging. To mitigate this situation, we propose a method for developing a synthetic sewer network to supplement the representation of the sewer system in urban flood models, particularly in data-scarce domains. The model implements the concept of shallow water equation for surface flow and a 1D slot model for pipe flows with an interaction equation between them. We compare case studies with and without discharge interaction between the surface and hypothetical sewer system for the 2020 flood event in an urban subcatchment within the Marikina Basin, Philippines. Results show that the synthetic sewer pipe integration can capture the urban flood propagation more appropriately. Information such as flood depth and pipe flow discharge can aid in identifying flood-prone areas where sewer system parameters may require modifications. The proposed method can be used as an alternative to performing high-resolution urban flood simulations with limited availability of sewer network data.

Coupled sink and flow accumulation analyses with single flow direction and multiple flow direction algorithms

Abstract A static flood analysis (SFA) toolset is implemented with the purpose of performing simplified, event-based flood inundation modeling. The simulation process is divided into two steps: a topographic sink analysis of the terrain followed by flow accumulation (FA) of runoff volumes. Both procedures are coupled to account for sink storage effects. The sink analysis procedure determines the morphology of present sinks that are utilized during FA to calculate the sink storage by solving a mass balance, whereby inflows are captured in each sink according to their capacity and overflows are routed further downstream. Each sink is enriched with a set of hydrological attributes such as the total inflow, overflow, and flood depth. The flood depth is then utilized to determine the flood extent and flood depths of each sink. Two main options are made available for the FA procedure: either a single flow direction (SFD) or a novel formulation of the multiple flow direction (MFD) algorithm. Both methods were compared in terms of their accuracy with results from the TELEMAC-2D finite-volume solver for an urban inundation model. Both methods showed good agreement when compared with the validation results, with the MFD method performing marginally better than the SFD method.

Urban flood risk assessment characterizing the relationship among hazard, exposure, and vulnerability.

Risk assessment is an effective means to alleviate urban flood disasters and has attracted the attention of many studies. However, most previous studies about urban flood risk assessment often focused more on urban inundation area and depth, less on the inter-relationship of the components of risk. In this study, an urban flood risk assessment approach that characterizes the relationship among the three components of risk "hazard-exposure-vulnerability" (H-E-V) is developed. Firstly, eleven flood risk indicators are selected based on the flood simulation results of urban flood model and statistical data to establish the urban flood risk assessment index system. Then, the combination of analytic hierarchy process (AHP) and entropy weight method is employed to determine the weight of each indicator and the comprehensive urban flood risk is assessed. Most importantly, the coupling coordination degree model (CCDM) is used to reveal the relationship among H-E-V. After applying this method to Haikou city, China, the results show that the comprehensive effect and the coupling coordination degrees among H-E-V have a multidimensional impact on urban flood risk. For example, some sub-catchments, although at high risk of flooding, may experience a potential waste of resources. Urban flood assessment can be made more detailed and three-dimensional by comparing hazard, exposure, and vulnerability horizontally. Understanding and grasping the internal relationships among these three risk components can help implement flood prevention measures, optimize the allocation of flood prevention resources, and effectively reduce urban flood risks.

Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction

Abstract In the context of global climate change and the continuous development of urban areas, rainfall-inundation modeling is a common approach that provides critical support for the protection and early warning of urban waterlogging protection. The present study conducts a data-driven model for hourly urban rainfall-inundation depth prediction, which is based on a gated recurrent unit (GRU) neural network and uses the simulated annealing (SA) algorithm for the hyperparameter optimization of GRU, namely the SA-GRU model. To verify the performance of the proposed model, backpropagation, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) neural networks are set as benchmarks. Results show that the SA-GRU has high accuracy in the case of short-term inundation prediction, with the Nash–Sutcliffe efficiency from 0.999 to 0.596 for the 1-h-ahead to 8-h-ahead predictions. And further research reveals that the SA-GRU integrates the significant optimization of SA, with an average 20% reduction of the root mean square error within the first eight prediction periods, and the efficient training speed of GRU, with 23.7% faster than LSTM and 44.2% faster than BiLSTM. In conclusion, the SA-GRU excels in urban inundation prediction, demonstrating its value in flood management and decision-making.

Derivation of short-term design rainfall intensity from daily rainfall data for urban drainage design using empirical equations in robe town, Ethiopia

Flooding is a significant impact that regularly affects the majority of cities/towns in developing countries due to inadequate drainage systems that were designed without considering hydrological-hydraulic efficiency caused by design rainfall intensity. Design rainfall intensity is essential to urban stormwater management systems; however, obtaining long-term, short-term design rainfall in developing countries such as Ethiopia is challenging. As a result, this study is conducted to derive short-term design rainfall intensity from daily rainfall data for Robe town using three empirical equations for urban drainage design: Indian Meteorological Method (IMD), Modified Indian Meteorological Method (MIMD), and Ethiopian Road Authority Method (ERAM). The results show that the estimated design rainfall by IMD is greater than other methods; the relationship between IMD, MIMD, ERAM, and existing ranges from 0.57 to 1.001, 1.015 to 1.218, and 0.890 to 1.027, respectively. According to the findings of this study, the design rainfall intensity of all empirical equations is not the same in all return periods and durations. Furthermore, the peak stormwater that caused flooding will vary due to variations in rainfall intensity. Thus, this study strongly suggested that the experts consider each empirical equation and select the accurate design rainfall to design adequate stormwater drainage systems for Robe Town. To manage unexpected urban inundation caused by urban development, short-duration design rainfall and other sub-catchment characteristics will need to be considered.

Potential Performance Assessment of Coastal Urban Sewer Network according to Sea Level Rise

The rise in sea levels due to climate change causes a decrease in the discharge capacity of sewer pipes and an increase in the risk of urban inundation due to the encroaching of outfall to the coast. Therefore, it is crucial to quantitatively evaluate and predict the increasing degree of risk to properly respond to the risk of inundation that is aggravated by the effects of rising sea levels. In this study, two research areas were selected in consideration of the regional characteristics of the coastal area and the sea level rise scenarios. In the selected research areas, the performance of the sewer network was quantified through a reliability technique. Accordingly, the change in the Disaster Prevention Performance Target Rainfall according to the sea level rise was analyzed and reevaluated. The results showed that the target rainfall for disaster prevention performance by rainfall duration in Wando was 35.6% to 41.7% and in Sacheon, it was 36.7% to 67.5% under the influence of the approximate highest high water. This study presented a plan to adjust the target rainfall for disaster prevention performance, which should be raised in consideration of the sea level rise to meet the current target rainfall for disaster prevention performance. In response to the flood risk of coastal cities increasing due to rising sea levels, the results of this study are expected to serve as basic data to establish disaster reduction strategies for coastal cities.

The characteristics and evolution process of presummer intense regional rainfall over the Pearl River Delta metropolitan region

AbstractThe presummer intense regional rainfall events (RREs) over the Pearl River Delta (PRD) metropolitan region have been defined using rainy stations, maximum instantaneous regional rainfall coefficient (RRCt) and maximum rainfall intensity based on the rain gauge data from 2008 to 2017. Most of intense RREs with the maximum rainfall intensity about 20–60 mm·h−1 and the lifetime shorter than 24 h have the primary rainfall peak from noon to afternoon. Based on the rainfall pattern at 3 h prior to the events start, the intense RREs can be classified into two groups that one is southeastward propagation mode originating from the northwestern mountains of PRD and another is coastal mode from South China Sea (SCS). During the premonsoon‐onset period, the first type of RREs is the main mode causing the heavy rain in the cities of the inland region owing to the anomalous cold air from north confronting the wet and warm air from south with the increased southwesterly winds resulting in the strengthening frontal zone and moisture convergence. After the summer monsoon onset, the main rainfall pattern changes to the coastal mode having the significant impact on the coastal cities. The obvious anomalous cyclonic circulation related with the increased moisture convergence in the coastal regions providing the favourable conditions of the convection initiation and development. The study of the characteristics and evolution process of preceding intense RREs can improve the forecasting skills and reduce the risk of urban inundation.

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.

Method for a new risk assessment of urban inundation: G-DEMATEL–AHP

Urban flooding disasters caused by frequent heavy rainfall threaten the safety of residents and socioeconomic development. Thus, risk evaluation of urban inundation is a vital component of sustainable urban development. Analytical hierarchical process (AHP) and decision-making trial and evaluation laboratory (DEMATEL) are common methods applied to flood risk evaluation. The former focuses on the relationship among different layers, while the latter reflects the relationship of factors in one layer. Moreover, the ambiguity in the decision-making process can be reduced by the uncertainty system. Therefore, the proposed method integrates grey theory and utilises the characteristics of DEMATEL to establish the AHP comparison matrix, particularly when the scores given by experts’ questionnaires are scattered. The method was applied to the risk assessment of urban inundation in Zhengzhou and verified by its flooding disaster sites that occurred during the 20 July storm in 2021 [1].•Developed approach merges grey theory, DEMATEL, and AHP methods;•Application of G-DEMATEL-AHP method in risk assessment of urban inundation;•The proposed method can increase the reliability of risk assessment results.

Development of an Urban Flood Forecast Model Using Lumped Pipe Networks

Typical urban flood simulation is conducted using physical models such as the 1D storm water analysis model and 2D inundation analysis model. Although 2D inundation analysis can predict flow velocity, inundation depth, and inundation area throughout an inundated urban area, it is difficult for it to produce a near real-time urban flood forecast for a metropolitan area such as Seoul. In this study, a physical urban flood forecast model was developed using lumped pipe networks to produce a near real-time urban inundation forecast. The dense pipe networks within a drainage basin were simplified as a single conceptual lumped pipe that has drainage and storage functions, and new pipe networks were constructed using lumped pipe networks. The model was applied to the August 2018 storm events in Seoul and showed a prediction accuracy of 0.71. The results demonstrated that the model can obviate the limitations of the near real-time operation of existing physical flood forecasting models to yield useful information for urban flood response, though showing room for improvement.

Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models

Abstract. Urbanization and climate change are critical challenges in the 21st century. Flooding by extreme weather events and human activities can lead to catastrophic impacts in fast-urbanizing areas. However, high uncertainty in climate change and future urban growth limit the ability of cities to adapt to flood risk. This study presents a multi-scenario risk assessment method that couples a future land use simulation (FLUS) model and floodplain inundation model (LISFLOOD-FP) to simulate and evaluate the impacts of future urban growth scenarios with flooding under climate change (two representative concentration pathways (RCP2.6 and RCP8.5)). By taking the coastal city of Shanghai as an example, we then quantify the role of urban planning policies in future urban development to compare urban development under multiple policy scenarios (business as usual, growth as planned, growth as eco-constraints). Geospatial databases related to anthropogenic flood protection facilities, land subsidence and storm surge are developed and used as inputs to the LISFLOOD-FP model to estimate flood risk under various urbanization and climate change scenarios. The results show that urban growth under the three scenario models manifests significant differences in expansion trajectories, influenced by key factors such as infrastructure development and policy constraints. Comparing the urban inundation results for the RCP2.6 and RCP8.5 scenarios, the urban inundation area under the growth-as-eco-constraints scenario is less than that under the business-as-usual scenario but more than that under the growth-as-planned scenario. We also find that urbanization tends to expand more towards flood-prone areas under the restriction of ecological environment protection. The increasing flood risk information determined by model simulations helps us to understand the spatial distribution of future flood-prone urban areas and promote the re-formulation of urban planning in high-risk locations.

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting.

Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.

Application of SIND model for the Prediction of Flooding in Coastal Cities

Flooding damage is increasing due to abnormal climates. In particular, in the case of coastal cities, it is necessary to simultaneously consider inundation caused by marine disasters such as storm surge and tsunami as well as inundation due to urbanization. In order to respond to disasters in coastal cities, flood prediction must be preceded. In Korea, the intensity of disasters has been classified by frequency, and flood hazard maps have been produced accordingly. The map is generally derived through numerical simulation, which is based on a scenario, so there is a lot of uncertainty, and it is difficult to predict a disaster for which a scenario has not been established. Because the target range of the coast is wide, the calculation speed of numerical simulation is slow, and a considerable amount of time is required for inundation prediction. It is practically difficult to predict a disaster such as torrential rain in a short period of time. Therefore, in this paper, by using the SIND model, a scientific interpolation model proposed by Kim et al. (2018), the ability to predict the inundation of coastal cities was reviewed and the method of using the model was presented. The SIND model is a short-term prediction model of urban inundation for a desired scenario within the range by using a pre-established inundation forecast map, and can be used for short-term inundation prediction such as torrential rain. To examine the applicability, the accuracy of the flood hazard map derived from the SIND model for coastal cities was analyzed. As a result, it was confirmed that the shape similarity suggested by Kim et al. (2019) was about 0.7 or higher, and it was judged to be appropriate in terms of shape similarity. If the shape similarity technique used for model validation is improved to suit the urban flooding characteristics, the use of the SIND model is expected to increase.

A Coupled Hydrodynamic and Rainfall-tracking Model for Source-to-impact Analysis in Urban Inundation

A Coupled Hydrodynamic and Rainfall-tracking Model for Source-to-impact Analysis in Urban Inundation

A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale

AbstractUrban flooding from extreme precipitation and storm surge is a growing threat to cities, and detailed forecasts of urban inundation are needed for emergency response. We present a mechanistic framework to simulate flood inundation over metropolitan‐wide areas at fine resolution (3 m). A dual‐grid shallow‐water model is used to overcome computational bottlenecks, and an application to Hurricane Harvey focused on pluvial flooding provides a multi‐dimensional assessment of predictive skill. A hindcast model is shown to simulate peak stage across 41 stream gages with a mean absolute error (MAE) of 0.63 m, and hourly stage levels over a 5‐day period with a median MAE and Nash‐Sutcliffe Efficiency (NSE) of 0.74 m and 0.55, respectively. Peak flood level across 228 high water marks (HWMs) were captured with an MAE of 0.69 m. A forecast model forced by Quantitative Precipitation Forecast data is shown to be only marginally less accurate than the hindcast model. Peak stage is simulated with an MAE of 0.86 m, hourly stage is captured with a median MAE and NSE of 0.90 m and 0.41, respectively, and HWMs are captured with an MAE of 0.77 m. The forecast system also achieves hit rates of 90% and 73% predicting distress calls and FEMA damage claims, respectively, based on simulated flood depth. These results demonstrate the potential to operationally forecast pluvial flood inundation in the U.S. with the timeliness and accuracy needed for early warning, and we also highlight future research needs.

Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis

• Build a novel forecast model (PCA-SOM-NARX) by statistical & machine learning tools. • 2-dimensional SOBEK simulates the full-scale flooding processes of rainfall patterns. • PCA extracts principal components to present spatial distributions of inundation. • SOM clusters inundation data to form a spatial–temporal topological feature map. • PCA-SOM-NARX makes reliable and accurate multi-step-ahead regional flood nowcasts. The frequency and severity of floods have noticeably increased worldwide in the last decades due to climate change and urbanization. This study aims to build an urban flood warning system for reducing the impact of flood disasters. A great number of storm-induced rainfall data were collected in Taipei, Taiwan, and the corresponding 2-D inundation maps were simulated for illustrating urban rainfall-flood inundation processes. We proposed a novel urban flood forecast methodology framed by machine learning and statistical techniques to mine the spatial–temporal features between rainfall patterns and inundation maps for making multi-step-ahead regional flood inundation forecasts. The proposed methodology (PCA-SOM-NARX) integrated the advantages of Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Nonlinear Autoregressive with Exogenous Inputs (NARX). PCA was used to extract principal components representing the different spatial distributions of urban inundation. SOM was used to cluster high dimensional inundation datasets to form a two-dimensional topological feature map. NARX was used to establish multi-step-ahead flood forecast models for the next hour at a 10-minute scale. The results show that the PCA-SOM-NARX approach not only produced more stable and accurate multi-step-ahead forecasts on flood inundation depth but was also more indicative of the spatial distribution of inundation caused by torrential rain events, compared to the SOM-NARX approach (the benchmark). The results demonstrate the proposed methodology can adequately grasp the inundation status associated with different rainfall distributions to reliably and accurately forecast regional flood inundation depths, which can help decision makers respond to flooding earlier and mitigate flood disasters.

Application of Head Loss Coefficient for Surcharge Straight Path Manhole to Improve the Accuracy of Urban Inundation Analysis

Currently, adopted runoff analysis models focus on the characteristic factors of watersheds and neglect the analysis of the flow in conduits. Additionally, the usually employed XP-SWMM modeling package generally underestimates the flood area because it considers manholes as nodes and does not consider local head losses according to the shape and size of the nodes. Therefore, it is a necessity to consider the loss coefficient in surcharge manholes to improve inundation and runoff analysis methods. This study aims at improving the accuracy of discharge analysis before analyzing the storage and runoff reduction effects of storage facilities. Hydraulic experiments were conducted according to the changes in discharge and manhole shapes. We show that the flood area increases as the overflow discharge at manhole increases due to the application of the head loss coefficient. We demonstrate a concordance rate ≥95% between results and observed flood area when accurate input data (from the parameters of the target watershed) and the head loss coefficient (from hydraulic experiments) are applied. Therefore, we demonstrate that the result of our 2D inundation analysis, considering the head loss coefficient in surcharge manhole, can be used as basic data for accurately identifying urban flood risk areas.