Fluvial Flood Research Articles

Future Change in Urban Flooding Using New Convection‐Permitting Climate Projections

AbstractRainfall intensity in the United Kingdom is projected to increase under climate change with significant implications for rainfall‐driven (combined pluvial and fluvial) flooding. In the UK, the current recommended best practice for estimating changes in pluvial flood hazard under climate change involves applying a simple percentage uplift to spatially uniform catchment rainfall, despite the known importance of the spatial and temporal characteristics of rainfall in the generation of pluvial floods. The UKCP Local Convective Permitting Model (CPM) has for the first time provided the capacity to assess changes in flood hazard using hourly, 2.2 km CPM precipitation data that varies in space and time. Here, we use an event set of ∼13,500 precipitation events across the three UKCP Local epochs (1981–2000, 2021–2040, and 2061–2080) to simulate rainfall‐driven flooding using the LISFLOOD‐FP hydrodynamic model at 20 m resolution over a 750 km2 area of Bristol and Bath, UK. We find that both the event set and uplift approaches indicate an increase in flood hazard under near‐term (2021–2040) and future (2061–2080) climate change. However, the event set produces markedly higher estimates of flood hazard when compared to the uplift approach, ranging from 19% to 49% higher depending on the return period. This suggests including the full spatiotemporal rainfall variability and its future change in rainfall‐driven flood modeling is critical for future flood risk assessment.

The impact of floods on plastic pollution

Abstract Non-Technical Summary Plastic harms ecosystem health and human livelihood on land, in rivers, and in the sea. To prevent and reduce plastic pollution, we must know how plastics move through the environment. Extreme events, such as floods, bring large amounts of plastic into rivers around the world. This article summarizes how different flood types (excessive rainfall, high river flow, or floods from the sea) flush or deposit plastic pollution, and how this impacts the environment. Furthermore, this paper also discusses how improved resilience to floods is important to prevent and reduce plastic pollution. Technical Summary Plastic pollution is ubiquitous in the environment and threatens terrestrial, freshwater, and marine ecosystems. Reducing plastic pollution requires a thorough understanding of its sources, sinks, abundance, and impact. The transport and retention dynamics of plastics are however complex, and assumed to be driven by natural factors, anthropogenic factors, and plastic item characteristics. Current literature shows diverging correlations between river discharge, wind speed, rainfall, and plastic transport. However, floods have been consistently demonstrated to impact plastic transport and dispersal. This paper presents a synthesis of the impact of floods on plastic pollution in the environment. For each specific flood type (fluvial, pluvial, coastal, and flash floods), we identified the driving transport mechanisms from the available literature. This paper introduces the plastic-flood nexus concept, which is the negative feedback loop between floods (mobilizing plastics), and plastic pollution (increasing flood risk through blockages). Moreover, the impact of flood-driven plastic transport was assessed, and it was argued that increasing flood resilience also reduces the impact of floods on plastic pollution. This paper provides a perspective on the importance of floods on global plastic pollution. Increasing flood resilience and breaking the plastic-flood nexus are crucial steps toward reducing environmental plastic pollution. Social Media Summary Floods have a large impact on plastic pollution transport, which can be reduced through improved flood resilience

Two-dimensional flooding model using IBER software case Study: Saidia City North east of Morocco

In the last two decades, Morocco has suffered a lot from catastrophic flooding, resulting in enormous damage to materials and loss of human life. The Sadia City (northeast Morocco), in our case of this study, is the most urbanized area with a high concentration of socio-economic activities in the region. Sadia is located downstream of the Moulouya watershed known for its low topography, and high vulnerability to fluvial and torrential flooding. The study aims to simulate the flood hazard index in the city of Saidia and consists of developing a two-dimensional (2D) flooding model using IBER software, and then producing maps of water flow and water velocity based on a 1-meter DTM. Then, we use GIS to map the hazard index of the areas with a high risk of flooding over a return period of 50 years. These hydraulic models were used to elucidate the risk of the Moulouya River overflowing its catchment area and to assist the stakeholders in decision-making about adequate measures to protect human life in Saidia City.

A Novel Cellular Automata Framework for Modeling Depth-Averaged Solute Transport during Pluvial and Fluvial Floods

Currently, for modeling two-dimensional (2D) solute transport during pluvial and fluvial floods, the finite volume (FV) models are widely used because of their strong ability to handle steep concentration and velocity gradients from the flow advection term. However, heavy computational requirements are subsequently introduced which limit the numerical efficiency. To further increase numerical efficiency but keep the required accuracy, this study proposes a novel Solute Transport Modeling based on Cellular Automata framework (STMCA) to simulate solute transport due to the flow advection, turbulent diffusion, and material decay mechanisms in several sets of explicit algebraic equations. Four studied cases involving steep gradients of solute concentration and velocities in steady/unsteady violent flow conditions are used to compare the accuracy of the STMCA approach with a Godunov-type FV solute transport approach with a total variation diminishing (TVD) scheme. Then, the performances of the two approaches on water quality modeling are assessed through the E. Coli transport modeling during pluvial/fluvial floods on a real-scale terrain. The proposed STMCA approach is found to achieve almost the same accuracy as the FV approach. As to the numerical efficiency, the STMCA approach is faster than the FV approach by 289.6–328.6%. Hence, the proposed STMCA approach is proven to be an effective tool for simulating solute transport.

Supercritical fluvial styles and the shifting aridity in the Early Triassic: the example of the Sanga do Cabral Formation, Paraná Basin, Brazil

Abstract Froude-supercritical bedforms and associated sedimentary structures are formed in turbulent flows when the value of the Froude number is Fr > 1. They have been increasingly studied in recent years, and while they were previously considered to be of rare preservation, they have been increasingly identified in modern settings and the rock record. In alluvial systems, these structures are being recognized as characteristic of rivers with high variability of discharge, especially in arid, semiarid, and subhumid tropical and subtropical climates. However, the development of facies models for such rivers remains tentative, particularly for the rock record, and with the exception of Australia, examples in Gondwana are scarce. The Early Triassic Sanga do Cabral Formation represents an arid to semiarid ephemeral fluvial system cropping out in southern Brazil, southwestern Gondwana. This study reinterprets the sedimentary structures in this formation as Froude-supercritical structures and identifies three fluvial styles (FS). FS1 predominantly consists of fine-grained massive sandstone with interruptions of intraclastic conglomerates, and occasionally visible faint lamination and mud-intraclast levels. It is interpreted as deposited by unconfined flows in the distal part of a fluvial system, generating hyperconcentrated flows which resulted in thin beds of fine-grained sandstone with massive structure or planar lamination and incipient antidunes. FS2 was deposited by flash floods occurring repeatedly within a short period during a wet season. This resulted in a fining-upwards succession of intraclastic conglomerates with supercritical-flow structures, through sandstones with supercritical-flow structures, to sigmoidal cross-stratification and ripple marks with diffuse lamination. FS3 was deposited by catastrophic flash floods characterized by high discharge and flow velocity, possibly generated by erratic storms, which poured in single events. These catastrophic flows generated large-scale sandy antidunes and other Froude-supercritical bedforms with mud intraclasts, which deposited sandstone in undulatory laminae, and other supercritical-flow structures. These floods waned extremely rapidly, bypassing the stability field of lower-flow-regime bedforms. Measurements taken from undulatory stratification, interpreted as antidune deposits, allowed the estimation of paleoflow velocity and depth. The largest antidunes had a maximum estimated wavelength of 28.92 m (with a mean of 15.4 m) and maximum estimated height of 1.42 m (with a mean of 0.85 m), resulting in an estimated paleoflow velocity of up to 6.72 ms−1 (with a mean of 4.9 ms−1) and a maximum flow depth of 1.59 m (with a mean of 0.9 m). These parameters are comparable to those observed in modern fluvial floods. This study reinforces the significance of Froude-supercritical structures in enhancing our understanding of fluvial systems characterized by high variability in discharge, allowing a finer interpretation of their discharge patterns. This approach can be applied to better understand the many arid, semiarid, or strongly seasonal environments of the Early Triassic period in Gondwana, and potentially other regions and geological times.

A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling

Urban floods can cause severe loss of economic and social assets, and remote sensing has been an effective tool for flood mapping during disaster response. Due to the complexity of high-density urban structures, high-resolution (HR) optical images can only extract visible floods in open spaces, and floods in shadows and under the canopy are challenging to map. Accurate digital elevation models (DEMs) are essential for inundation estimation towards urban flood mapping, but HR DEMs are often unavailable due to the high acquisition costs. Through DEM upscaling, HR DEMs could be obtained from existing low-resolution (LR) DEMs using deep learning. To this end, a novel multi-sensory HR urban flood mapping framework is proposed in this research. The framework consists of three components: 1) a new DEM upscaling network to infer HR DEMs from existing LR DEMs with a fusion approach, 2) a rapid flood segmentation network to extract visible flood from very-high-resolution (VHR) optical images with limited human labelling, and 3) an accurate Geographical Information System (GIS)-based tool for floodwater extent and depth estimation from the visible flood information along with HR DEMs. The proposed framework was validated on a fluvial flood that occurred in Calgary, Canada, in 2013, where the proposed DEM upscaling network produced an upscaled HR DEM at 2 m resolution from an existing LR DEM at 18 m resolution. In addition, the proposed flood segmentation network has shown accurate visible flood extraction from VHR RGB aerial imagery with over 80% intersection-over-union (IoU) using 10% of human labelling as training samples. Finally, the floodwater extent and floodwater depth estimation using the proposed GIS tool showed significant improvement over conventional flood mapping methods.

Potential pollution risks of historic landfills in England: Further analysis of climate change impacts

AbstractFive years ago, an article in WIREs Water provided the first comprehensive analysis of historic (legacy) landfill sites vulnerable to coastal flooding and erosion at a national scale (England). This update expands upon that article by considering the potential impacts of climate change upon inland historic landfills. Globally, there are hundreds of thousands of landfills that predate modern environmental regulations, and where waste is not isolated from the surrounding environment, but climate change impacts on the pollution risk from historic landfills in freshwater environments has received little attention. Where climate change causes an increase in the frequency and magnitude of fluvial flood events, this will increase leachate generation and the probability of landfill erosion and solid waste release. Where there is increased drought the landfill capping materials may crack, opening up new pollutant pathways, and increasing the risk of solid waste release. Changes to groundwater movement resulting from climate change may open new leachate pathways, and in England alone, thousands of historic landfills are in (groundwater) Source Protection Zones where modern regulations to protect drinking water supplies would not permit their construction. This increased contaminant release from historic landfills in freshwater environments may impact surface and/or groundwater quality and ecological health, increase costs for drinking water monitoring/treatment, or make some abstraction sources unviable. This is especially of concern where receptors are subject to multiple pressures and may cause tipping points to be reached. Further research is warranted into contaminant behavior, receptor vulnerability, historic landfill risk prioritization, and mitigation/remediation methods.This article is categorized under: Engineering Water > Engineering Water Science of Water > Water Quality Science of Water > Water and Environmental Change Water and Life > Stresses and Pressures on Ecosystems

Beyond the local climate change uplift – The importance of changes in spatial structure on future fluvial flood risk in Great Britain

Widespread spatially coherent flood events can cause severe damage and disruption. Climate change has the potential to change the severity and frequency of such events. Despite this, assessment of future fluvial flood risk typically gives little to no consideration to potential changes in the spatial structure of future events. To understand the significance of this gap, climate model simulations are coupled with a national hydrological model to identify event spatially coherent present and future flood events. A statistical Empirical Copula is used to generate a large number of unseen events and linked to a national flood risk simulation model. The research finds that including changes in the spatial structure of flood events materially increases projected changes in risk when compared to conventional approaches based on local uplifts alone; increasing the projected change in Expected Annual Damage across Great Britain by a factor of ~ 1.5. The event-based approach is also shown to provide new insights into the extreme distribution fluvial risk including single event damage, damage seasons, and damage years. The results suggest the 1-in-100-year winter flood may increase from £1.3b to £2.1b, and the 1-in-100 year single event damage may rise from £1.1b today to £1.7b by the 2080s given a 4 °C rise in Global Mean Surface Temperature (assuming current adaptation policies continue and no population growth). Consequently, the findings suggest a much greater emphasis is needed on spatial ‘flood events’ if future risk is to be understood and adaptation responses appropriately framed.Graphical abstract

Fluvial flood adaptation using nature-based solutions: A comprehensive and effective assessment of hydro-meteorological risks

The growing prominence of Nature-based Solutions (NbS) for disaster risk reduction (DRR) has sparked increased interest. This study is motivated by the need to establish a quantifiable and standardized method for assessing the risks mitigated by NbS in engineering applications. The goal is to establish a comprehensive and effective system framework for assessing hydro-meteorological risks related to NbS in engineering applications. The proposed framework considers flood disaster mechanisms, uncertain factors, and ecosystem services, integrating them to comprehensively assess the benefits of NbS. Specifically, 2-D hydraulic analysis and an in-house adaptive Kriging-based reliability analysis are developed and applied to establish flood prevention standards for NbS. Additionally, the InVEST toolkit is utilized to evaluate ecosystem services. To demonstrate the applicability of the framework, the Baoli River Watershed located in Pingtung County of Taiwan is selected as a case study. It is found that NbS can effectively withstand a 25-year return period flood and reduce flooding on agricultural land by 46.03 %. Furthermore, the probability of flooding decreased from 100 % to 27 % for a 20-year return period flood. NbS was found to provide approximately NT$1.20–4.65 million more in total benefit value compared to the engineering governance strategy. The supporting source codes are available at https://github.com/johnthedy/Adaptive-Kriging-Using-PSO-HHs-in-HECRAS3D.git.

A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends

As climate change increases the occurrences of extreme weather events, like flooding threaten humans more often. Hydrodynamic models provide spatially distributed water depths as inundation maps, which are essential for flood protection. Such models are not computationally efficient enough to deliver results before or during an event. To ensure real-time prediction, we developed a feature-informed data-driven forecast system (FFS), which interpreted the forecasting process as an image-to-image translation, to predict the maximum water depth for a fluvial flood event. The FFS combines a convolutional neural network (CNN) and feature-informed dense layers to allow the integration of the distance to the river of each cell to be predicted into the FFS. The aim is to ensure training for the whole study area on a standard computer. A hybrid database with pre-simulated scenarios is used to train, validate, and test the FFS. The FFS delivers predictions within seconds making a real-time application possible. The quality of prediction compared with the results of the pre-simulated physically-based model shows an average root mean square error (RMSE) of 0.052 for thirty-five test events, and of 0.074 and 0.141 for two observed events. Thus, the FFS provides an efficient alternative to hydrodynamic models for flood forecasting.

Flood susceptibility mapping at the country scale using machine learning approaches

AbstractRiver (fluvial), surface water (pluvial) and coastal flooding pose a significant risk to the United Kingdom. Therefore, it is important to assess flood risk particularly as the impacts of flooding are projected to increase due to climate change. Here we present a high resolution combined fluvial and pluvial flood susceptibility map of England. This flood susceptibility model is created by using past flood events and a series of meaningful hydrological parameters to a training machine learning model. We tested the relative performance of different machine learning algorithms, including Classification and Regression Trees, Random Forest and XGBoost and found the XGBoost performed the best, with an area under the receiver operating characteristic ROC Curve (AUC) of 0.93. We also found the model performed well on unseen areas, and we discuss the possibility of extending to regions that has no information on past flood events. Additionally, to aid in understanding what factors may impact flood risk to a particular area, we used Shapley additive explanations which allowed us to investigate the sensitivity of the predicted flood probability to flood factors at a given location.

Abstract A114: Assessing the impact of extreme climate weather events on cancer patients in Puerto Rico: A cross-sectional study

Abstract BACKGROUND: Island communities such as Puerto Rico (PR) are profoundly impacted by climate extremes. Patients with chronic disease, particularly cancer, have unique needs and challenges in the aftermath of extreme weather events. Understanding cancer patients’ barriers, knowledge, risks, and vulnerabilities are essential to develop equitable adaptation strategies. This research aims to investigate the perceptions and experiences of cancer patients associated with extreme weather events over the past 10 years in Puerto Rico (PR). METHODS: We conducted a cross-sectional study via survey questionnaires (April 22, 2023-June 8, 2023) among adults aged ≥21 years from Puerto Rico who are cancer patients/survivors (n=207). A total of 23 questions listed on the survey were used to collect information on variables of interest which included demographic characteristics, information on extreme weather event experiences, and attitude towards climate change. Using the data collected, descriptive statistics were used to describe the study population and multivariable logistic regression models were used to evaluate the associations of interest (IRB approval # 2023-04-101). RESULTS: The average age of individuals recruited is 56.3 years ±13.5 SD, 79.7% are female, 65.2% reported having additional chronic diseases, and 85.0% have more than a high-school education. Regarding extreme weather events, 99% of cancer patients and survivors reported floods, including coastal, fluvial, and urban floods, impacted their communities, 76% reported tropical cyclones impacted their residences and communities, and 72% reported extreme heat impacted both their residences and communities in the last 10 years. Additionally, the most common problems encountered in the aftermath of these extreme weather events were water (88.4%) and electricity service interruption (91.4%), as well as water (88.9%) and electricity service interruption (77.1%). Most participants reported feeling very and extremely worried about their health in the face of climate change (75.0%) and feeling concerned about climate change (85.0%). After adjusting for age, sex, and education, logistic regression models showed that participants with more than one cancer type were more likely to be worried about their health in the face of climate change (OR=2.40,95% CI=1.06-5.35). CONCLUSION: Study findings highlight the burden of extreme weather events and the problems encountered in the aftermath of such events on cancer patients in Puerto Rico. This population expresses concern and worry about climate change and their health, respectively. This information is important for cancer control and emphasizes the need for targeted interventions and management strategies to remove detrimental and avoidable impacts on cancer populations. Citation Format: Jimena Perez, Pablo A. Méndez-Lázaro, Fabiola A. Rivera-Gastón, Ana P. Ortiz. Assessing the impact of extreme climate weather events on cancer patients in Puerto Rico: A cross-sectional study [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A114.

A study on compound flood prediction and inundation simulation under future scenarios in a coastal city

Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities.

Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new methods and algorithms focused on improving fluvial flood prediction and devising robust flood management strategies are essential. This study explores and assesses the potential application of 1D-Convolution Neural Networks (1D-CNN) for spatial prediction of fluvial flood in the Quang Nam province, a high-frequency tropical cyclone area in central Vietnam. To this end, a geospatial database with 4156 fluvial flood locations and 12 flood indicators was considered. The ADAM algorithm and the MSE loss function were used to train the 1D-CNN model, whereas popular performance metrics, such as Accuracy (Acc), Kappa, and AUC, were used to measure the performance. The results indicated remarkable performance by the 1D-CNN model, achieving high prediction accuracy with metrics such as Acc = 90.7%, Kappa = 0.814, and AUC = 0.963. Notably, the proposed 1D-CNN model outperformed benchmark models, including DeepNN, SVM, and LR. This achievement underscores the promise and innovation brought by 1D-CNN in the realm of susceptibility mapping for fluvial floods.

Uncertainty in flood risk assessment of linear structures: Why correlation matters

Estimation of the risk posed by inland fluvial floods to critical linear transportation infrastructures requires quantifying the damage inflicted by flooding on roads and railways. The estimated risk is often quantified in terms of the Expected Annual Damage [EAD] of the given linear structure. The aim of this article is to shed light on the estimation of this number and its uncertainty. The damage function models, which describe the degree of damage given flood intensity, represent a source of uncertainty. In this article we argue that this uncertainty needs to be embedded with a correlation structure in order to be evaluated for aggregated values. To this end, a novel methodology based on the use of Gaussian Processes to model the uncertainty associated with the damage function is introduced. Assuming a spatial correlation structure, parameterized by the so called decorrelation length scale, this framework is applied to estimate the EAD to roads in Portugal. The study shows that the application of an appropriate decorrelation length scale is decisive to the estimated uncertainty of the EAD. The proposed methodology may also be applicable to other types of hazards, if represented by some hazard intensity parameter associated with a damage function.

Assessing the Suitability of the Flood Defense Policy of Republic of Korea for Risk Reduction in Local Rivers

This study examines whether the fluvial flood defense system of Korea is appropriate for risk reduction. Using spatial socioeconomic data and remote sensing, we estimated the potential economic damage that can be caused by the flooding of local streams and rivers along the Nakdong River (the longest river in Korea). For the analysis, a river risk map including return periods (50, 80, 100, and 200 years) and spatial inventories (residential, agricultural, industrial assets, and human lives) was employed to determine flood-prone areas and assess the damage within the inundation areas. A quantitative flood analysis was conducted using an object-based method to estimate the expected annual damage. We then compared the estimated damage for each tributary within the designed return periods and found no correlation. Numerous tributaries with low-defense targets were considered high-risk, while those with high-defense targets were assessed as low-risk. The dataset used in this study covered four damage categories. Among them, flood damage to residential assets appeared to have the highest value, whereas flood damage to industrial assets had the lowest value. The results demonstrate that the Korean government needs to tailor its flood defense policy based on quantitative risk assessments to effectively manage flood risks, especially given the increasing risk of climate change.

Joint impacts of dams and floodplain on the rainfall-induced extreme flood in the Changjiang (Yangtze) River

Fluvial flooding is the most devastating natural hazard in the world, which presents an increased trend in the context of global warming and the active hydrological cycle with large risk for human survival. In 2020, a historically extraordinary flood hit the Changjiang River Basin where located considerable mega dams and reservoirs, including the world’s largest dam, Three Gorges Dam (TGD). The 2020 extreme flood raises public concern about the effectiveness of the impoundment of dams and reservoirs on flooding. Here, we analyzed the observed water discharge and level during the extreme flood in 2020 as well as the simulated rainfall-induced discharge and level data through the GR4J model and level-discharge curve. Results show that the water levels exhibited a rapid response to the increased water discharge, and exceeded the warning level at almost the same time along the middle and lower reach during the 2020 extreme flood. The intensive rainfall caused the basin-wide extreme flood with extra regional rainwater responsible for the largest flood in the middle and lower reach. Dams and reservoirs significantly mitigated the extreme flood by reducing 1.49 m in the flood levels and contributing to 94 % of the level changes. Meanwhile, the TGD impoundment successfully prevented a disastrous flood that could have happened with the flood level being lowered by 2.93 m. However, the considerable recession of the downstream floodplain amplified the flood level by 0.08 m, which was equal to increasing the total flood level by 5 %. Our results highlighted the crucial role of mega dams in flood control and the potential of restored floodplain storage for sustainable flood modulation in the future.

Substantial increase in future fluvial flood risk projected in China’s major urban agglomerations

Urban land will face high fluvial flood risk against the background of climate change and urban expansion. The effect of urban spatial expansion, instead of densification of assets within existing urban cells, on flood risk has rarely been reported. Here, we project the future flood risk of seven urban agglomerations in China, home to over 750 million people. The inundated urban land areas in the future are projected to be 4 to 19 times that at present. Without considering the urban spatial expansion, the inundated urban land areas will be underestimated by 10-50%. Urban land is more likely to be inundated than non-urban land, and the newly-developed urban land will be inundated more easily than the historical urban land. The results demonstrate the urgency of integrating climate change mitigation, reasonable urban land expansion, and increased flood protection levels to minimize the flood risk in urban land.

Hindcasting compound pluvial, fluvial and coastal flooding during Hurricane Harvey (2017) using Delft3D-FM

Hindcasting compound pluvial, fluvial and coastal flooding during Hurricane Harvey (2017) using Delft3D-FM

Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts

This study investigates residential building damage model transferability between coastal and fluvial flood hazard contexts. Despite the frequency of damaging coastal flood events, empirical damage models from fluvial flooding are often applied in quantitative coastal flood risk assessments. This assumes that building damage response is similar from the exposure to different flood sources. Here, we use empirical data from coastal, riverine and riverine-levee breach flooding events to analyse residential building damage. Damage is analysed by applying univariable and multivariable learning models to determine the importance of explanatory variables for relative damage prediction. We observed that the larger explanatory variable range considered in multivariable models led to higher predictive accuracy than univariable models in all flood contexts. Transfer analysis using multivariable models showed that models trained on event-specific damage data had higher predictive accuracy than models learned on all damage data or on data from other events and locations. This finding highlights the need for damage models to replicate local damage factors for reliable application across different flood hazard contexts.