Flood Simulation Research Articles

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee

The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models. Even though forecast stations are expanding across the United States, the coverage is usually limited to major rivers and urban areas. Most rural and sub-urban areas, including recreational areas such as the Window Cliffs State Natural Area, do not have such forecast stations and as such, are prone to the dire effects of unforeseen flooding. In this study, four machine learning model architectures were developed based on the long short-term memory, random forest, and support vector regression techniques to forecast water depths at the Window Cliffs State Natural Area, located within the Cane Creek watershed in Putnam County, Tennessee. Historic upstream and downstream water levels and absolute pressure were used to forecast the future water levels downstream of the Cane Creek watershed. The models were tested with lead times of 3, 4, 5, and 6 h, revealing that the model performances reduced with an increase in lead time. Even though the models yielded low errors of 0.063–0.368 ft MAE, there was an apparent delay in predicting the peak water depths. However, including rainfall data in the forecast showed a promising improvement in the models’ performance. Tests conducted on the Cumberland River in Tennessee showed a promising improvement in model performance when trained with larger data.

High efficiency integrated urban flood inundation simulation based on the urban hydrologic unit

As the impact of flooding intensifies globally in metropolitan areas, there is an urgent need for high-resolution flood forecasting models to facilitate preparedness and mitigate flood impacts. In response, we have proposed a high efficiency algorithm (HEA) designed to simulate urban flood inundation, leveraging the urban hydrologic unit (UHU) concept from an urban hydrological cycle perspective. The HEA significantly reduces the computational demands associated with high-resolution modeling by using a hydrologic unit derived from the digital elevation model (DEM). This approach effectively shifts the overwhelming burden of computational load to the hydrodynamic algorithm (HDA), which is grounded in the Shallow Water Equations (SWEs), thus enabling efficient flood inundation simulation crucial for time-critical decision making during extreme events. Our research also incorporated the HEA and HDA methods into a self-developed, distributed-framework basin modeling system, named the Taihu Basin Model (TBM). Both the HEA and HDA models were calibrated and validated with observed data on pipe level, flow rate, and inundation depth from our Shuangqiaobang experimental fields. Comparative analysis, focusing on simulated urban flood inundation distribution characteristics, simulation accuracy and timeliness, demonstrated the superior effieicney of the HEA method by three orders of magnitude. The consistency between observed data and the simulated results of both HEA and HDA models underscores the accuracy and reliablility of these models. The HEA model’s simulated urban flood inundation distribution appears more patchy and localized compared to the HDA model, due to the generalization of urban UHUs in the TBM.

Numerical simulation and characteristic analysis of flood intrusion into streets and buildings

In recent years, the study of urban floods has gained significant importance due to the impacts of climate change and rapid urbanization. A crucial task in accurately reproducing real-world urban flood events lies in enhancing the modeling capability to incorporate the entry of floodwaters into buildings through openings. This study presents a validated two-dimensional (2D) model based on the Shallow Water Equations (SWEs) to extend the numerical simulations beyond the analysis of water entering a single building to a cluster of buildings. The results confirm the feasibility of incorporating floodwater entering buildings processes in future urban flood simulations, thereby enhancing the realism of the outcomes. Moreover, this research delves into the characteristics of water depth and flow patterns not only on the streets but also inside the buildings. The investigation reveals that building openings play a vital role in the transportation and redistribution of floodwaters within the street network. Furthermore, variations in the rates of water level rise and recession inside buildings are observed, highlighting the influence of opening configurations and building locations. Additionally, a dynamic assessment of pedestrian risk throughout the flood duration is conducted, providing valuable insights and recommendations for disaster prevention and mitigation strategies during urban flood events. It serves as a valuable resource for urban planners and policymakers involved in the development of resilient cities in the face of increasing urban flood challenges.

A modified Xin'anjiang model and its application for considering the regulatory and storage effects of small-scale water storage structures

To improve flood prediction accuracy in the basin, this paper constructs a modified Xin'anjiang (M-XAJ) model to account for the regulatory and storage effects of small-scale water storage structures on surface runoff, based on the original Xin'anjiang (XAJ) model. In the M-XAJ model, the regulatory and storage effects of small water storage are calculated by utilizing an aggregated reservoir using multi-bottom sills graded weir flow pattern (MBSP). The discharge processes of the aggregated reservoir is combined with the processes of runoff production and convergence over the natural area to form the overall discharge processes at the outlet section. Using the two subbasins of the Xianghongdian Reservoir as case studies, 13 flood events were selected to validate the predictive capabilities of the M-XAJ model. The results indicate that (1) the qualification rate for flood peak (Qp) and the qualification rate flood volume (Qv) substantially increased for the two sub-basins during the training and verification periods. Additionally, the mean relative error of peak lag time (MEtp) was considerably reduced. Specifically, the mean Nash efficiency coefficient (MNS) for floods at the Qingshan section improved by 0.18 and 0.24 respectively. (2) the M-XAJ model improved prediction accuracy for the first flood event following an extended drought period without rainfall, as well as for the initial flood preceding a sequence of flooding events. However, the prediction accuracy for continuous flooding events was not noticeably enhanced by the M-XAJ model. (3) the M-XAJ model demonstrated improved simulation capabilities for low- and moderate-magnitude flood events; however, there were no noticeable enhancements for high-magnitude flooding simulations.

Retracted: Flood Simulation Analysis of the Biliu River Basin Based on the MIKE Model

Retracted: Flood Simulation Analysis of the Biliu River Basin Based on the MIKE Model

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

Enhanced oil recovery potential analysis through simulation of a bio-based surfactant using CFD

Bio-based surfactant flooding is encouraged as an EORmethod, as it is obtained from natural sources and is economically attractive. In the present study, the EOR efficiency of a natural surfactant synthesized from rapseed oil has been simulated using ANSYS FLUENT. The process of oil recovery from a core by injection of surfactant has been simulated by incorporatingcore, brine, surfactant, and crude oil properties into the numerical model. Mass transfer of surfactant between the aqueous phase and crude oil has been modeled using species transport without a chemical reaction model. Species transport model has been coupled with the multiphase VOF model, where the flow of each phase is determined as per their respective viscous resistance. Experimental data of inletpressureand oil production has been used for history matchingand later used to visualize the oil flow through the porous media, concludingthat simulation of surfactant flooding can be achieved by species transport model.

A two-dimensional hydrodynamic urban flood model based on equivalent drainage of manholes

Abstract Numerical simulations of urban flood events are of great significance in flood control and disaster reduction. An important part of these numerical investigations concerns drainage, which is crucial to the accuracy of the simulation results. To overcome the difficulty of obtaining underground pipe network data and improve the traditional equivalent drainage simulation method, an equivalent drainage model based on manholes is proposed, to simulate urban flooding based on a two-dimensional hydrodynamic model. The new model is applied to a classic case and the simulation results are compared with those from the MIKE URBAN model to verify the simulation accuracy of the proposed formulation. The submerged areas given by the two models are compared under different rainfall conditions, with an average relative error of 6%. The differences in water depths at various nodes are statistically analyzed, and the average Nash–Sutcliffe efficiency and average root mean square error are found to be 96% and 0.03 m, respectively. The results of this research provide effective urban flood simulation in areas lacking pipe network data and have important significance for promoting the practical application of refined numerical simulations of urban flooding.

Providing solutions for data scarcity in urban flood modeling through sensitivity analysis and DEM modifications

Abstract Developing countries face significant challenges in accessing sufficient and reliable hydro-meteorological data, hindering the implementation of effective disaster management strategies. This research proposes solutions for limitations on performing flood simulations through parameter sensitivity analysis and digital elevation model (DEM) modifications. The methodology provides alternatives to account for insufficient rainfall, roughness coefficient, infiltration data in simulating large-scale rainfall-runoff, and high-resolution DEMs incorporating road and building networks for urban flood modeling. By applying the method to an extreme flood event in the Marikina Basin, Philippines, a combination of ground-based and remotely retrieved rainfall data, roughness (n = 0.3861–0.5005), and infiltration parameters (Δθ = 0.326–0.505 and ψ = 0.4547–1.565) set at the maximum range were found to replicate the increase in the upstream water level. Simulations were able to accurately capture the flood propagation along the natural and artificial barriers in the urban area compared to untreated digital terrain and surface model (DTM and DSM) data, with root-mean-square error range improvements from 0–7.13 (DTM) and 0.29–4.20 (DSM) to 0–0.63 (modified DEM). The proposed methodology significantly improved the accuracy of the simulations, which is crucial for proposing adequate flood action plans, despite the lack of high-resolution data available for under-developed nations.

The Flood Simulation of the Modified Muskingum Model with a Variable Exponent Based on the Artificial Rabbit Optimization Algorithm

In order to further improve the accuracy of flood routing, this article uses the Variable Exponential Nonlinear Muskingum Model (VEP-NMM), combined with the Artificial Rabbit Optimization (ARO) algorithm for parameter calibration, to construct the ARO-VEP-NMM flood routing model. Taking Wilson’s (1974) flood as an example, the model calculation results were compared and analyzed with the Muskingum model constructed with seven optimization algorithms. At the same time, six measured floods in the Zishui Basin were selected for model applicability testing. The results show that the ARO algorithm exhibits stronger robustness and search ability compared with other optimization algorithms and can better solve the parameter optimization problem of the Muskingum model. The use of the ARO-VEP-NMM model for flood routing accurately reflects the movement patterns of floods. The Nash coefficient of the Wilson section reached 0.9983, and the average Nash coefficient during the flood validation period in the Zishui Basin was 0.9, further verifying the adaptability and feasibility of the ARO-VEP-NMM model in flood routing. The research results can provide certain references and a theoretical basis for improving the accuracy of flood forecasting.

Fast-Processing DEM-Based Urban and Rural Inundation Scenarios from Point-Source Flood Volumes

Flooding has always been a huge threat to human society. Global climate change coupled with unsustainable regional planning and urban development may cause more frequent inundations and, consequently, higher societal and economic losses. In order to characterize floods and reduce flood risk, flood simulation tools have been developed and widely applied. Hydrodynamic models for inundation simulation are generally sophisticated, yet they normally require massive setup and computational costs. In contrast, simplified conceptual models may be more easily applied and efficient. Based on the Hierarchical Filling-and-Spilling or Puddle-to-Puddle Dynamic Filling-and-Spilling Algorithms (i.e., HFSAs), Safer_RAIN has been developed as a fast-processing DEM-based model for modelling pluvial flooding over large areas. This study assesses Safer_RAIN applicability outside the context for which it was originally developed by looking at two different inundation problems with point-source flooding volumes: (1) rural inundation modelling associated with levee breaching/overtopping; (2) urban flooding caused by drainage systems outflow volumes.

Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization

An urban hydrological model (UHM) forced by radar-observed rainfalls was developed for producing rainstorm-related urban inundation maps for obtaining flash flood forecasts. The parameter sets of the land use and land cover (LULC) and urban drainage capacity derive from satellite multispectral images and high spatial resolution GIS datasets, relating to the urban hydrology and hydraulic properties. The hydrodynamics model was based on the simplified shallow water equation (SWE) neglecting the convective acceleration and pressure terms in the St. Venant equation. The intense convective rainstorm that induced heavy flash floods on 21 Jul. 2012 and the large-scale stratiform precipitation that occurred from 19 Jul. to 21 Jul. 2016 in Beijing were deliberately selected to perform flood-simulation and model-validation case studies. The simulation of severe flooding scenarios in the 2012 event was fairly reproduced and verified using media reports. In the case studies, the sensitivity tests verified that the flood intensity (1) would be enhanced slightly on pure impervious surfaces, (2) would be reduced significantly on pure pervious surfaces, and (3) would increase by 30%–60% without a drainage system. No false alarm could be perceived in the simulation of the 2016 event in a prolonging stratiform precipitation. The case studies confirmed the perspectives of using the hydrological model fed by QPEs/QPFs for flash flood forecasting and concluded that the hourly precipitation surpassing 30 mm/h would be indicative in impending flash floods in Beijing metropolis.

Flood simulation with the RiverCure approach: the open dataset of the 2016 Águeda flood event

Abstract. Floods are among the most common natural disasters responsible for severe damages and human losses. Numerically produced data, managed by user-friendly tools for geographically referenced data, have been adopted to increase preparedness and reduce vulnerabilities. This paper describes the locally sensed and numerically produced data that characterize a flood event that occurred in February 2016 in the Portuguese Águeda River, referred to as Agueda.2016Flood for short. The data were managed through the RiverCure portal, a collaborative web platform connected to a validated shallow-water model featuring modelled dynamic bed geometries and sediment transport. The dataset provides a synthesis of topo-bathymetric, hydrometric and numerically produced data from a calibrated hydrodynamic model. Due to the lack of measured hydrometric data near the city, the numerically produced data are crucial for a complete description of the flood event. The Agueda.2016Flood dataset constitutes a complete validation test for flood forecasting models and a tool to better mitigate floods in this river and in similar rivers. Thus, Agueda.2016Flood is a relevant dataset for Águeda River stakeholders as well as for the community of flood modellers, as it provides a well-documented validation event for forecasting tools. The dataset can be accessed at https://doi.org/10.4211/hs.937927473a3a4e66a07a2e2fdd9d581e (Ricardo et al., 2024).

Web-based visualization and rendering of aerial LiDAR point cloud for urban flood simulation

Purpose In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management. Design/methodology/approach This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed. Findings The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making. Originality/value The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Reconstructing glacial lake outburst floods in the Poiqu River basin, central Himalaya

The Poiqu River basin in central Himalaya is a transboundary basin between China and Nepal that has experienced rapid glacier ice loss and glacial lake growth, some of which have drained in catastrophic glacial lake outburst floods (GLOFs). Here, we focus on the outbursts of two glacial lakes in this basin, Gongbatongsha Co in 2016, and Poiqu No.1 in 2002, and integrate multiple data sources and approaches to improve our understanding of past GLOFs and project the magnitude of potential future GLOFs. We identify their potential triggers and assess downstream damage by combining remote sensing observations and field surveys. We find that after the outbursts, Gongbatongsha Co shrank by 0.14 ± 0.003 km2 (∼91 %) in 2016 and almost disappeared, and Poiqu No.1 shrank by 0.05 ± 0.001 km2 (∼80 %) in 2002 but then expanded by 0.10 ± 0.002 km2 (∼670 %) by 2022. Our study identifies ice avalanches from the parent glaciers as the dominant trigger of the two GLOF events, and clarifies that the 2002 GLOF event stemmed from Poiqu No.1 instead of the reported Jialong Co. We also reconstructed the flow depths, velocities, and inundation areas using the hydrodynamic models HEC-RAS and FLO-2D. For Gongbatongsha Co, the models suggest that the discharge peaked at ∼550 m3s−1 about 15 min after the outburst, and the flood reached the settlement of Zhangmu with a peak flow of ∼1600 m3s−1 about 8.5 h later. The peak discharge during the GLOF from Poiqu No.1 was ∼2700 m3s−1. The flood reached the town of Nyalam 1.3 h later, with a peak discharge of ∼1100 m3s−1. The simulation of a future GLOF from Poiqu No.1 shows significant potential impacts to Nyalam town and other downstream communities due to the high flow depth. A large GLOF scenario for Poiqu No.1 would result in a peak discharge of more than 2500 m3s−1, with the flood wave reaching Nyalam in less than 40 min. We show that very high-resolution digital elevation models such as obtained from uncrewed aerial vehicles perform better in flood simulation. Our approach can be applied to potentially dangerous glacial lakes in other high mountain areas.

Study on the response analysis of LID hydrological process to rainfall pattern based on framework for dynamic simulation of urban floods

An in-depth analysis of the urban flood disaster level in response to different rainfall characteristics and Low Impact Development (LID) measures is of significant importance for addressing unfavorable management conditions and implementing effective flood control measures. This study proposes a dynamic urban flood simulation framework based on the Storm Water Management Model (SWMM) and Geographic Information System (GIS) spatial analysis, incorporating an active inundation seed search algorithm. The framework is calibrated and validated using nine historical urban flood events. Subsequently, the impact of rainfall patterns on urban inundation under LID measures is analyzed based on the dynamic urban flood simulation framework. The results show that the urban flood simulation framework exhibits good applicability, with Nash-Sutcliffe Efficiency (NSE) values of 0.825 and 0.763 during the calibration and validation periods, respectively. The extent of inundation shows little variation for rainfall events with a return period greater than 20 years, and the location of flooding is minimally affected by rainfall patterns. LID measures have a decreasing effect on urban inundation control as the return period of rainfall increases, and there are variations in hydrological responses to different rainfall patterns under the same return period. For single-peak rainfall events with the same return period, the control rates of inundation volume, flow, and infiltration decrease as the rainfall peak coefficient increases, indicating a weakening effect of LID measures on flood control with increasing rainfall peak coefficient. Under the same return period conditions, LID measures exhibit the best runoff control effect for uniform rainfall, while their effectiveness is lower for double-peak rainfall events and single-peak rainfall events with an r = 0.75 coefficient. The findings of this study provide a theoretical basis for urban flood warning and management of Low Impact Development measures.

Methodological approach for mapping the flood physical vulnerability index with geographical open-source data: an example in a small-middle city (Ponferrada, Spain)

To increase the resilience of communities against floods, it is necessary to develop methodologies to estimate the vulnerability. The concept of vulnerability is multidimensional, but most flood vulnerability studies have focused only on the social approach. Nevertheless, in recent years, following seismic analysis, the physical point of view has increased its relevance. Therefore, the present study proposes a methodology to map the flood physical vulnerability and applies it using an index at urban parcel scale for a medium-sized town (Ponferrada, Spain). This index is based on multiple indicators fed by geographical open-source data, once they have been normalized and combined with different weights extracted from an Analytic Hierarchic Process. The results show a raster map of the physical vulnerability index that facilitates future emergency and flood risk management to diminish potential damages. A total of 22.7% of the urban parcels in the studied town present an index value higher than 0.4, which is considered highly vulnerable. The location of these urban parcels would have passed unnoticed without the use of open governmental datasets, when an average value would have been calculated for the overall municipality. Moreover, the building percentage covered by water was the most influential indicator in the study area, where the simulated flood was generated by an alleged dam break. The study exceeds the spatial constraints of collecting this type of data by direct interviews with inhabitants and allows for working with larger areas, identifying the physical buildings and infrastructure differences among the urban parcels.

Urban Flood Mitigation by Implementing LIDs (Case Study: Bendung Watershed in Palembang City)

Urban areas continue to be affected by flooding, necessitating more sustainable and effective adaptation strategies and mitigation initiatives. This study investigates the potential flood reduction capability achieved through implementing various green infrastructures known as low-impact development (LID). The Bendung watershed, in the center of Palembang City, with a total area of 18.37 km2, is used as the study area to evaluate the performance of LID infrastructure in reducing flood parameters, including total runoff volume, peak runoff discharge, runoff coefficient, and flooding area. Five types of LID infrastructure were simulated, namely bio-retention cells, rain gardens, permeable pavements, rain barrels, and recharge wells. The flood simulations were performed using four design storms with 2-, 5-, 10-, and 25-year return periods. Hydrologic and hydraulic modeling and simulations were carried out using PCSWMM Professional 2D, and the results were integrated with ArcMap to map the flood inundation. The results of this study demonstrate that with only 9.81 percent of the area occupied by LIDs, a flood reduction of more than 30% can be achieved. In addition, implementing LIDs can help restore the watershed’s hydrological condition to its natural state, as indicated by the decrease in the runoff coefficient. Thus, implementing LIDs in a sustainable urban drainage system must be widely promoted in many urban areas, especially in developed countries like Indonesia. This study can be used as a reference for the local government and authorities to create policies and regulations to establish sustainable flood mitigation measures in Palembang City.

The Effect of Sabo Works Design and River Improvement on the Magila River with Consideration on Morphological Changes Influenced by Debris Flow Events

The morphological changes of a river due to debris flow are essential considerations in understanding the dynamics of a river. The Magila River, located in the Sigi Regency, Central Sulawesi Province, is a small river with an area of approximately 125 hectares, but the average riverbed slope exceeds 10%. This river faced water damage issues, such as debris floods on December 12, 2019, and overflowing floods on October 11, 2022. Based on the ongoing mitigation (Sabo Works and River Improvement), an analysis of flow modeling through HEC-RAS software with non-Newtonian parameters is also being conducted. Simulations are performed on both the existing river scenario and the design river scenario. The flood and sediment transport simulations indicate that the capacity of the existing river is insufficient to accommodate the planned 100-year return period flood discharge, leading to overflow in several river sections with a maximum inundation height of 42.80 centimeters. The river also tends to experience riverbed degradation, triggering landslide events. In contrast, in the design river, overflow in river sections is reduced, although some areas still experience inundation with a maximum height of 18 centimeters. The river morphology in this scenario is more stable, with a balanced level of degradation and aggradation. The conclusion of this research indicates that a series of ongoing mitigation efforts needs to be expanded to minimize the risk of floods but is already optimal in maintaining river morphology stability. The research findings are expected to be beneficial in maximizing the management of the Magila River.