Pluvial Flooding Research Articles

A novel and efficient method for real-time simulating spatial and temporal evolution of coastal urban pluvial flood without drainage network

With increasing urban pluvial flood risks, proposing a real-time simulation method is essential. However, accurate simulation of spatiotemporal flood evolution is often impeded by incomplete or missing drainage data. This study proposes a hybrid method where a machine learning module is applied to generate point waterlogging depth for immediate calibration of equivalent infiltration and flood maps in the equivalent drainage module to address this issue. The accuracy and efficiency of hybrid method in flood real-time simulation under missing drainage data are highlighted by comparing with two hydrodynamic models. The outcomes evince that the waterlogging simulation deviation of the hybrid method is less than 0.1 m during design storms, while the computational efficiency can ideally reach up to 5 times of the traditional 1D/2D coupled hydrodynamic model. Overall, the hybrid method offers a promising solution for early warning and mitigation of urban pluvial floods, especially for cities lacking drainage data.

Road criticality assessment to improve commutes during floods

Flooding undermines the functionality of road links, leading to commute disruption, such as increased commuting time and immobility. Therefore, identifying the critical roads under different flood scenarios is of great significance for transportation planning to minimize flood risk. Previous studies on the road criticality under pluvial floods have focused on mostly structural characteristics of the roads, lacking the quantification of the flood impact on traffic. We employed an integrated framework coupling pluvial flood modeling with commute simulation to assess the criticality of flooded road links under three scenarios (10, 30, and 50 year floods). The output of the flood modeling was used to reconstruct the road network. The routes of each individual commuter under a business as usual (BAU) scenario and three flood scenarios were obtained from the commute simulation. As the flood return period increased, the distribution of traffic flow became more uneven. The road criticality assessment revealed that greater flood impact on roads was closely related to the proximity of water bodies, low-lying topography, and greater commute demands of the region and provided evidence to prioritize locations for interventions. This study helps understand the resilience of transportation systems during floods, thereby enabling practitioners to design effective strategic plans in terms of emergency response, risk management, and urban planning.

Pluvial Flood Risk Assessment in Urban Areas: A Case Study for the Archaeological Site of the Roman Agora, Athens

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

Small Island City Flood Risk Assessment: The Case of Kingston, Jamaica

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country’s socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience.

Multi-hazard susceptibility model based on high spatial resolution data-a case study of Sali settlement (Dugi otok, Croatia).

The world has been facing an increase in various natural hazards. The coastal regions are recognized as one of the most vulnerable due to high population pressure and climate change intensity. Mediterranean countries have one of the most burnable ecosystems in the world, one of the most exposed to pluvial floods, and have the highest erosion rates within the EU. Therefore, the aim of this study was to develop the first multihazard susceptibility model in Croatia for the Sali settlement (island of Dugi otok). The creation of a multi-hazard susceptibility model (MHSM) combined the application of geospatial technology (GST) with a local perception survey. The methodology consisted of two main steps: (1) creating individual hazard susceptibility models (soil erosion, wildfires, pluvial floods), and (2) overall hazard susceptibility modeling. Multicriterial GIS analyses and the Analytical Hierarchy Process were used to create individual hazard models. Criteria used (32) to create models are derived from very-high-resolution (VHR) models. Two versions of MHSM are created: 1) all criteria with equal weighting coefficients and 2) weight coefficients determined based on public perception. According to MHSM 1, most of the research (58%) area is moderately susceptible to multiple hazards. Highly and very highly susceptible areas are 27% of the drainage basin and are mostly located near roads and houses. MHSM 2 reveals similar results to MHSM 1. The public perceives that the research area is the most susceptible to wildfires. The wildfire ignition risk is ranked as moderate (3.00) with a standard deviation of 1.16. Pluvial flood risk is ranked low (2.78), with a standard deviation of 1.15. The risk of soil is most inferior (2.24) with a standard deviation of 0.91. The the most significant difference between public perception and the GIS-MCDA model of hazard susceptibility is related to soil erosion. However, the accuracy of the soil erosion model was confirmed by ROC curves based on recent traces of soil erosion in the research area. The proposed methodological framework of multi-hazard susceptibility modeling can be applied, with minor modifications, to other Mediterranean countries.

The effects of multilayer blue-green roof on the runoff water quality

In the context of climate changes, characterized by an increase of short but intense rainfall events and rise of the average temperature, the fast population growth and consequent urbanization require the implementation of innovative solutions to mitigate pluvial floods and, at the same time, reduce the water demand. Among the different nature-based solutions, multilayer blue-green roofs have been widely recognized for their high capacity of reducing runoff generation from rooftops, and their additional storage layer enables to collect water, which could be reused for different purposes. However, the quality of the collected water in a multilayer blue-green roof and the influence that the additional storage layer has on it have not been analysed yet. Following this knowledge gap, we investigated the potential benefits of a multilayer blue-green roof installed in Cagliari, with respect to a traditional roof. The outflow triggered by artificial irrigation and natural rainfall events was analysed, both from a quantitative and qualitative perspective. Results confirm the high contribution of multilayer blue-green roofs in mitigating runoff generation, which is however influenced by antecedent soil moisture and water level conditions. The outflow from the multilayer blue-green roof presents lower suspended solids and heavy metals concentrations than from a traditional roof. On the other hand, Carbon Oxigen Demand (COD) concentrations in the multilayer blue-green roof outflow exceed the limits defined by the Italian regulations (125 mg/l) for water discharge or reuse, partially due to the high residence time in the storage layer. Specific treatments could be planned to reuse the collected water for urban purposes.

Matrix scenario-based urban flooding damage prediction via convolutional neural network

This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess of convolutional neural networks to advance the field of hazard assessment in urban pluvial flooding scenarios. The tool uniquely accounts for the high heterogeneity of urban space and the potential impact of complex climate scenarios, which are often underestimated by traditional data-reliant methods. Employing Shenzhen as a case study, the model showcased superior accuracy, resilience, and interpretability, illuminating potential flood hazards. The performance analysis shows that the model can accurately predict the vast majority of urban flood depths, but has errors in extreme flood predictions (depths greater than 35 cm). Findings underscore escalating flood impacts under enhanced scenario loads, with western and central Shenzhen—regions rife with construction—highlighted as particularly vulnerable. Under the most severe matrix scenario (Scenario 25), economic losses are estimated to be about $25,484 million. These commercial and residential hotspots are anticipated to suffer maximum economic loss, with these two areas accounting for 39.6% and 25.1% of the total losses, necessitating reinforced mitigation efforts, especially during extreme rainfall events and high soil saturation levels. In addition, the flooding control strategies should prioritize the reduction of flood inundation areas and integrate functionally oriented land use characteristics in their development. By aiding in the precise identification of flood-prone areas, this research expedites the development of efficient evacuation plans, bolsters urban sustainability, and augments climate resilience, ultimately mitigating flood-induced economic tolls.

Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors

Pluvial floods are destructive natural disasters in cities. With high computational efficiency, machine learning models are increasingly used for flood susceptibility mapping. However, limited flooded or nonflooded samples constrain models’ predictive capability and introduce uncertainty in feature engineering. This study introduces a semi-supervised graph-structured model, Graph Attention Network (GAT), to address data scarcity and enable the use of only basic flood conditioning factors as inputs. GAT uses nodes and edges to represent spatial units and their relative spatial relationships. Based on its graph structure and attention mechanism, GAT automatically extracts high-order features from inputs of labeled and unlabeled units for modeling. In the metropolitan area of Dalian, China, GAT outperformed other models in flooded-nonflooded sample classification and exhibited a rational flood susceptibility distribution pattern, with only four basic flood conditioning factors and less than 1.2% of units for training. GAT can be an effective tool for practical urban flood management.

Comparative life cycle assessment and life cycle cost analysis of centralized and decentralized urban drainage systems: A case study in Zhujiang New Town, Guangzhou, China

Rapid urbanization and global climate change are increasing the vulnerability of cities to urban flooding, thereby posing significant challenges to the design and implementation of urban drainage systems (UDS). Traditional centralized UDS have been found ineffective in mitigating the uncertainty of future flood risks. Consequently, the need to explore resilient alternatives, such as decentralized UDS, has gained importance. Of particular interest is the coupled approach of grey infrastructure (GREI) and low-impact development (LID), which merges the drainage capabilities of GREI with the versatility and sustainability of distributed green infrastructure. However, there are limited studies assessing the environmental and economic impacts of both centralized and decentralized LID-GREI UDS using integrated Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods. This study, using Zhujiang New Town, Guangzhou, China, as a case study, found that decentralized UDS outperformed centralized UDS from both environmental and economic perspectives, mainly due to smaller pipeline scale and lower maintenance frequency. Furthermore, though the LID-GREI UDS showcased more substantial environmental impacts and higher economic costs compared to GREI UDS, the hydrological benefits obtained from LID operation can counteract part of these environmental impacts. In a unique perspective, this study considers the payback periods for centralized and decentralized LID-GREI UDS, estimating them to be 70 and 55 years respectively, when taking economic implications and hydrologic benefits into an overall consideration. Overall, the findings underscore that the LID-GREI UDS, particularly the decentralized LID-GREI, presents a more sustainable and cost-effective strategy for mitigating pluvial flooding compared to GREI UDS. This study provides comprehensive insights into effective and cost-efficient strategies for alleviating urban flooding vulnerabilities through UDS design and implementation.

From flood paths to floodways, an efficient method to map, identify and evaluate suitable floodways: A case study from Trondheim, Norway

Due to climate change and rapid urbanisation, many Norwegian cities and urban areas suffer from pluvial flooding caused by intense rainfall exceeding the capacity of the stormwater management system. This results in increased runoff rates, volumes and peak flows in the drainage network. In response to these challenges, the authors explore the potential of utilising the urban surface's ability to transport floodwater as an integral component of the stormwater infrastructure. When the capacity of the stormwater drainage system is exceeded, the overland flow paths transporting floodwater are considered a part of the stormwater management system, as floodways. The study proposes a spatial GIS method to map existing drainage lines and identify existing surface areas that function as floodways, combined with an automated process to identify which drainage lines could be implemented as stormwater management measures. Critical points are introduced to assess the floodways' potential hazards, combined with a classification method to evaluate and sort floodways. A case study from Trondheim, Norway, was used to demonstrate how drainage lines can be identified as floodways using the proposed method. The case study is also used to illustrate how a GIS-based analysis can be extended from identifying to evaluating floodways and whether GIS is sufficient for floodway evaluation. The method enables urban planners and municipalities to identify which areas of the urban surface already function as floodways during extreme events, and to prioritise measures to secure such areas and increase the city's flood resilience. The results highlight the need to assess existing areas that function as floodways, and to implement and design needed areas as floodways. GIS-based methods combined with an evaluation scheme can be an adequate tool to map and evaluate floodways in urban areas. When using GIS-based methods, however, the corresponding hazard potential, and also the uncertainty of the floodway's spatial placement, should be considered.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Discharge coefficients to be used in inlet hydraulics

Urban pluvial floods can be produced or exacerbated by an insufficient density of inlets or by their poor hydraulic efficiency. Therefore, proper consideration of the hydraulic performance of inlets is essential to guarantee the correct functioning of urban drainage systems during heavy storm events. Recent advances in computational analysis in the field of hydrodynamics modelling allow the use of the well-known concept of dual drainage for design and planning purposes by simulating flow transfers between the surface layer (streets) and underground layers (sewers) through a proper hydraulic characterisation of inlet efficiency. Powerful commercial software packages allow simulations of flow transfer and use different approaches and formulas. Many of these approaches include the possibility of treating a sewer inlet as an orifice. In this context, this paper presents a methodology to obtain orifice discharge coefficients for three inlets previously tested at the hydraulics laboratory of the Technical University of Catalonia. Discharge coefficients of 0.18–0.58 were obtained for Froude numbers of 1.12–4.40, quite far from the usual recommended values. The proposed procedure can also be applied to non-tested grates.

A novel approach to optimize flow capacity of the polder canal networks through community detection-based topology modification

Polder, a typical landscape in coastal areas, was facing threats from both environmental degradation and pluvial flood, as a result of the decreasing flow capacity in its canal networks which were continually paved during urban development. Filling or connecting canals was the major strategy adopted in such system that modifying its topological structure and was proved to be correlated with its overall flow capacity (r2 = 0.5488). Thus, we proposed a planning method to improve the flow capacity of polder canal systems which will be helpful in strategic water management. With the hypothesis of equivalent expected flow in loop canal networks, an optimum method based on the community-detection algorithm was formulated to partition the canals into groups and make connection plans. Further, a case study was carried out in Wusong Polder (53.5 km2), with 32.8 km canals out of 148 km (133 canals) being low-velocity canals (average velocity < 0.005 m/s, LVC). The current system was clustered into 21 to 22 groups depending on different starting nodes. The best performing plan reduced the total LVC length to 26.3 km by connecting 10 canals (6.09 km). The robustness of flow capacity improvements from the optimized plan and its contribution to flood control was validated.

A novel semantic segmentation approach based on U-Net, WU-Net, and U-Net++ deep learning for predicting areas sensitive to pluvial flood at tropical area

ABSTRACT Floods remain one of the most devastating weather-induced disasters worldwide, resulting in numerous fatalities each year and severely impacting socio-economic development and the environment. Therefore, the ability to predict flood-prone areas in advance is crucial for effective risk management. The objective of this research is to assess and compare three convolutional neural networks, U-Net, WU-Net, and U-Net++, for spatial prediction of pluvial flood with a case study at a tropical area in the north of Vietnam. They are relative new convolution algorithms developed based on U-shaped architectures. For this task, a geospatial database with 796 historical flood locations and 12 flood indicators was prepared. For training the models, the binary cross-entropy was employed as the loss function, while the Adaptive moment estimation (ADAM) algorithm was used for the optimization of the model parameters, whereas, F1-score and classification accuracy (Acc) were used to assess the performance of the models. The results unequivocally highlight the high performance of the three models, achieving an impressive accuracy rate of 96.01%. The flood susceptibility maps derived from this research possess considerable utility for local authorities, providing valuable insights and information to enhance decision-making processes and facilitate the implementation of effective risk management strategies.

Hazards of a flooding event in the city of Gdansk and possible forms of preventing the phenomenon – case study

ABSTRACT The main objective is to examine the urban flood hazard in the city of Gdansk and to determine the possibilities of preventing this phenomenon. Hydrological and hydraulic modeling was used for the case study analysis of urban flood in Strzyża basin, applying the HEC-HMS and HEC-RAS systems. The result of modeling with the assumption of torrential rainfall with a duration of t = 1 h (from 35 to 58 mm) is the probability of pluvial flood occurrence divided into low probability (p = 10%), high probability (p = 1%) and catastrophic probability (p = 0.2%). The normal flow of the Strzyża stream at the mouth is 0.175 m3·s−1, while the modeling results indicate an increase in the flow depending on the assumed flood flow probability from about 41.8 m3·s−1 to 54.7 m3·s−1. In addition, the article presents the basic elements of the policy of the city of Gdańsk in the field of reducing the hazards of urban flooding.

Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area's contexts being investigated.

EXTREME RAINFALL-RUNOFF MODELING DURING REMNANTS OF IDA IN NEW YORK

While the effects of wind and pressure on coastal waters can generate large storm surges that lead to coastal flooding, storm surge represents only one driver of flooding. In reality, flooding is often driven by the combined action of several geophysical drivers (Couasnon et al., 2020; Zscheischler et al., 2018). Formally compound flooding is defined as the combined action of meteorological forces, tidal forces,, fluvial, and pluvial discharge (Zscheischler et al., 2018). Recent events such as the extreme rainfall associated with the remnants of Hurricane Ida in the Northeast United States have demonstrated the need to model the rainfall-runoff impact (i.e., pluvial flooding) and its downstream impact (i.e., fluvial flooding). In extreme rainfall cases in which large quantities of precipitation fall in short periods, urban environments often flood, and wastewater collection systems often exceed their design limit. In severe scenarios, surcharged sewer systems can significantly exacerbate urban flooding. While several existing flood risk methods consider storm surge in them approach, it remains active research topic concerning how and when rainfall-runoff processes should be incorporated in flooding models to assess site-specific flood risk. Often an approach is pursued that involves loosely coupling multiple numerical models each modeling a particular flood driver; however, this approach can become cumbersome. Instead in this work we pursue a different approach in which compound flooding can be resolved in a single numerical model (herein termed a monolithic approach).

MULTI-HAZARD RISK ASSESSMENT FOR TONGATAPU – A COASTAL INUNDATION LENS

A multi-hazard disaster risk assessment was undertaken for Tongatapu through the Asian Development Bank (ADB) on behalf of the Government of Tonga. This was to inform investments, urban planning and improvements to natural hazard resilience across the island (ADB, 2021). This paper presents the methodology and findings of the coastal inundation and sea level rise components and how these relate to other hazards assessed and the overarching risk assessment. Due to its low elevation and flat topography, Tongatapu, and the Tongan capital of Nukualofa, are vulnerable to pluvial (surface) flooding associated with heavy rainfall and coastal flooding from extreme sea levels and cyclone induced storm surge. Similarly, Tongatapu is vulnerable to damaging winds associated with tropical cyclones, earthquakes particularly associated with the nearby Kermadec Tonga Trench, and tsunami inundation. The study assessed these hazards and considered the influence of climate change on intensity of rainfall and sea levels.

Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series

The defense against urban pluvial flooding relies on the prediction of rainfall frequency, intensity, and long-term trends. The influence of the choice of the complete time series or the wet-day series on the rain analyses remains unclear, which affects the adaptive strategies for the old industrial cities such as Changchun in Northeastern China, with the outdated combined sewer systems. Based on the data from the two separate weather stations, four types of distributions were compared for analyzing the complete daily precipitation series, and their fitting accuracy was found in decreasing order of Pearson III, Pareto–Burr–Feller distribution (PBF), generalized extreme value (GEV), and Weibull. The Pearson III and the PBF probability distribution functions established based on the complete time series were found to be at least 458% and 227%, respectively, more accurate in fitting with the consecutive observations than those built from the wet-day-only series, which did not take account of the probability of the dry periods between the rain events. The rain depths of the return periods determined from the wet-day-only series might be over-predicted by at least 76% if the complete daily series were regarded as being more closely representative of the real condition. A clear threshold of 137 days was found in this study to divide the persistent or autocorrelated time series from the antipersistent or independent time series based on the climacogram analysis, which provided a practical way for independence determination. Due to the significant difference in the rain analyses established from the two time series, this work argued that the complete daily series better represented the real condition and, therefore, should be used for the frequency analysis for flood planning and infrastructure designs.