Inland Flooding Research Articles

Population exposure to flooding in Small Island Developing States under climate change

Abstract Estimates of current and future population exposure to both coastal and inland flooding do not exist consistently in all Small Island Developing States (SIDS), despite these being some of the places most at risk to climate change. This has primarily been due to a lack of suitable or complete data. In this paper, we utilise a ∼30 m global hydrodynamic flood model to estimate population exposure to coastal and inland flood hazard in all SIDS under present day, as well as under low, intermediate, and very high emissions climate change scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Our analysis shows that present day population exposure to flooding in SIDS is high (19.5% total population: 100 year flood hazard), varies widely depending on the location (3%–66%), and increases under all three climate scenarios—even if global temperatures remain below 2 °C warming (range in percentage change between present day and SSP1-2.6: −4.5%–44%). We find that levels of flood hazard and population exposure are not strongly linked, and that indirect measures of exposure in common vulnerability or risk indicators do not adequately capture the complex drivers of flood hazard and population exposure in SIDS. The most exposed places under the lowest climate change scenario (SSP1-2.6) continue to be the most exposed under the highest climate change scenario (SSP5-8.5), meaning investment in adaptation in these locations is likely robust to climate scenario uncertainty.

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Proposed Technique for Estimating Rainwater Storage Tank Locations Using Vulnerability Analysis of Critical Flooding Nodes

Even if a pumping station, which is a representative structural flood defense facility, operates optimally, flooding occurs at certain nodes in the drainage area. In other words, even if rainwater pumping stations and rainwater storage tank, which are structural flood defense measures, operate at maximum, it is necessary to review areas where overflow occurs. Therefore, in areas where it is impossible to protect against inland flooding using structural flood defense measures alone, new flood defense measures are required. In this study, based on previous research on rainwater pumping station operation techniques to reduce critical flooding, the Daelim 3 region was divided into 45 basic areas and vulnerability analysis techniques were applied to select the location of rainwater storage tank for effective flood defense. A possible technique is presented in this paper. As a result of the study, sub-region 1 had the highest impact index value, so installing a rainwater storage tank was calculated to be more effective in preventing floods than installing a rainwater storage tank in other sub-regions. The priority-estimation technique for flood defense facilities presented in this study can be effective in establishing plans for flood control structures other than rainwater storage tank.

Storm surge, seawater flooding, and sea-level rise paradoxically drive fresh surface water expansion

Abstract Coastal storms and sea-level rise are expected to increase seawater flooding in low-elevation coastal zones. High sea levels and seawater flooding can drive groundwater table rise via ocean-aquifer connections. These dynamics are often overlooked but can cause groundwater flooding and salinization hazards, increasing freshwater security challenges for coastal communities and driving ecosystem transgressions. Field data and numerical modeling were used to evaluate how heavy rainfall, storm surge, and seawater flooding and infiltration during Hurricane Fiona (September 2022) and projected sea-level rise impact groundwater levels, inland surface waters, and saltwater intrusion on Sable Island National Park Reserve, Canada. During the passage of Hurricane Fiona, precipitation increased groundwater and pond levels before seawater flooded the beach. Seawater flooding and infiltration caused a sharp rise in beach groundwater levels, which in turn caused inland pond levels to rise without coincident direct inputs from precipitation or seawater. Model simulations reveal that seawater infiltration on beaches flooded the subsurface and drove the observed inland groundwater rise and freshwater pond expansion. Simulations of projected sea-level rise show that seawater flooding will only inundate a small area of land along the coast; however, inland groundwater rise and flooding, which is less well-studied, may inundate up to 30 times more land area. Further, groundwater flooding driven by rising sea levels decreases hydraulic gradients and increases saltwater intrusion via freshwater lens contraction. Findings demonstrate that seawater flooding from coastal storms and sea-level rise paradoxically cause concurrent fresh surface water expansion but freshwater lens contraction. This study provides new insights into the spatiotemporal dynamics of island freshwater resources and highlights that unseen and often overlooked groundwater-surface water exchanges are critical to consider when evaluating coastal flooding and groundwater salinization hazards and management strategies for low-elevation coastlines.

Pluvial flood modeling for coastal areas under future climate change – A case study for Prince Edward Island, Canada

It has been increasingly understood that pluvial flooding poses a substantial risk to numerous communities across the globe. This is especially relevant for the Canadian province of Prince Edward Island (PEI), which is susceptible to a compound flood consisting of both inland and coastal flooding. Despite various studies, a comprehensive pluvial flood model still lacks that addresses the complex interplay of compound floods. Therefore, this research aims to bridge the gap in flood mitigation by developing a pluvial flood model for PEI’s coastal communities. The Intensity-Duration-Frequency (IDF) curves under current and future climatic conditions were used to portray rainfall intensity over the study area corresponding to 10-year, 25-year, 50-year, and 100-year return periods. In addition, a hydraulic model (HEC-RAS 2D) was used to drive pluvial flood maps based on two configurations, including detailed flood maps for major municipalities and an island-wide level. The validated results showed consistency in model simulation when compared to observations. The high-resolution flood maps produced by this study can support the development of flood mitigation and adaptation strategies in PEI and other parts of the world.

Leading role of Saharan dust on tropical cyclone rainfall in the Atlantic Basin.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

Quantifying Compound and Nonlinear Effects of Hurricane‐Induced Flooding Using a Dynamically Coupled Hydrological‐Ocean Model

AbstractWe recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.

Global expansion of tropical cyclone precipitation footprint

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

A new framework for quantifying alongshore variability of swash motion using fully convolutional networks

Waves running up and down the beach (‘swash’) at the landward edge of the ocean can cause changes to the beach topology, can erode dunes, and can result in inland flooding. Despite the importance of swash, field observations are difficult to obtain in the thin, bubbly, and potentially sediment laden fluid layers. Here, swash excursions along an Atlantic Ocean beach are estimated with a new framework, V-BeachNet, that uses a fully convolutional network to distinguish between sand and the moving edge of the wave in rapid sequences of images. V-BeachNet is trained with 16 randomly selected and manually segmented images of the swash zone, and is used to estimate swash excursions along 200 m of the shoreline by automatically segmenting four 1-h sequences of images that span a range of incident wave conditions. Data from a scanning lidar system are used to validate the swash estimates along a cross-shore transect within the camera field of view. V-BeachNet estimates of swash spectra, significant wave heights, and wave-driven setup (increases in the mean water level) agree with those estimated from the lidar data.

A stochastic optimization model for patient evacuation from health care facilities during hurricanes

We propose a rigorous modeling and methodological effort that integrates statistical implementation of hydrology models in predicting inland and coastal flood scenarios due to hurricanes and a scenario-based stochastic integer programming model which suggests resource and staging area decisions in the first stage and the evacuation decisions in the second stage. This novel study combines physics-based flood prediction models and stochastic optimization for large-scale multi-facility coordination of hospital and nursing home evacuations before impending hurricanes. The optimization model considers scenario-dependent evacuation demand, transport vehicles with varying capacities, and both critical and non-critical patients. Utilizing Hurricane Harvey of 2017 as a case study and actual healthcare facility locations in southeast Texas, we explore various evacuation policies, demonstrating the impact of routing strategies, staging area decisions, flood thresholds, and receiving facility capacities on evacuation outcomes. One of the findings is that choosing staging area(s) and deploying evacuation vehicles optimally considering the uncertainty of the hurricane’s path at the time of decision making could have significant effect on the total cost of the operation and evacuation time experienced by the evacuees. We also show the non-negligible value of the scenario-based staging and routing solution conservatively calculated in relation to a single scenario solution using the concept of value of stochastic solution.

Measuring community resilience inequality to inland flooding using location aware big data

Understanding community resilience is critical for effective resource allocation and planning. However, classical resilience measures often fall short in capturing the diverse characteristics of each stage: preparation, resistance, recovery, and adaptability. We propose a multi-stage resilience assessment framework integrating morphological indicators to measure community resilience. Applying this framework to evaluate disparities in community resilience after the inland flooding in Changsha, we find that morphological indicators provide a more accurate description of resilience priority and ability compared to speed indicators. The analysis reveals stronger complementarity and lower redundancy among resilience indicators. Moreover, we establish a strong connection between resilience, driving factors, and government actions. Neighborhoods with high flood risk and socioeconomic status encounter the severest impact and highest recovery priority, leading to substantial government engineering measures. Conversely, vulnerable communities facing high flood risk and low socioeconomic status display the lowest resistance and recovery ability, emphasizing the necessity for tailored initiatives like increasing green infrastructure and constructing flood buffer zones. Additionally, low river flood risk communities with high socioeconomic status experience minimal flood impact, underscoring the significance of prioritizing low-lying regions susceptible to surface water flooding. The study informs government decision-making, emphasizing differentiated strategies for enhancing community resilience across regions and stages.

High-resolution, open-source modeling of inland flooding impacts on the North Carolina bulk electric power grid

Although damages to local distribution systems from wind and fallen trees are typically responsible for the largest fraction of electricity outages during hurricanes, outages caused by flooding of electrical substations pose a unique risk. Electrical substations are a key component of electric power systems, and in some areas, the loss of a single substation can cause widespread power outages. Before repairing damaged substations, utilities must first allow floodwaters to recede, potentially leaving some customers without power for weeks following storms. As economic losses from flooding continue to increase in the U.S., there has been increasing attention paid to the potential impacts of flooding on power systems. Yet, this attention has mostly been limited to geospatial risk assessments that identify what assets are in the path of flooding. Here, we present the first major attempt to understand how flooding from hurricanes and other extreme precipitation events affects the dynamic behavior of power networks, including losses of demand and generation, and altered power flows through transmission lines. We use North Carolina, hit by major hurricanes in three of the past seven years, as a test case. Using open-source data of grid infrastructure, we develop a high-resolution direct current optimal power flow model that simulates electricity production and generators and power flows through a network consisting of 662 nodes and 790 lines. We then simulate grid operations during the historical (2018) storm Hurricane Florence. Time series of flooding depth at a discrete set of ‘high water’ mark points from the storm are used to spatially interpolate flooding depth across the footprint area of the storms on an hourly basis. Outages of substations and solar farms due to flooding are translated to location-specific losses of demand and solar power production throughout the network. We perform sensitivity analysis to explore grid impacts as a function of the height of sensitive equipment at substations. Results shed light on the potential for localized impacts from flooding to have wider impacts throughout the grid (including in areas not affected by flooding), with performance tracked in terms of transmission line flows/congestion, generation outputs, and customer outages.

Confirming the safety improvement and evacuation time reduction effects by the method for evacuating inundated areas via the shortest possible route

It is important to take evacuation behavior when a small river overflows or inland flooding occurs and there is a risk of subsequent flooding of a large river. To this end, even if roads are flooded, people should consider taking horizontal evacuation measures if possible and evacuating to places with a low risk of flooding or to evacuation shelters. Although local governments have published hazard maps for flood damage, these are mostly for large rivers and only provide information on the maximum depth of inundation. In addition, no evacuation routes have been described, and evacuees often choose the shortest route to evacuation shelters. However, if flooding significantly affects the shortest route to an evacuation shelter, then that is not the best route. Therefore, it is important to consider multiple evacuation routes that take flooding conditions into account. Thus, this study aims to validate whether method for evacuating inundated areas via the shortest possible route help improve evacuation safety and reduce evacuation time. The validation was conducted by means of a multiagent model evacuation simulation considering the time-series inundation depth of the inundation simulation results. After performing validation under multiple conditions, this study found that evacuating inundated areas via the shortest possible route not only increases evacuation safety but also significantly reduces the evacuation time required. The verification results show that method for evacuating inundated areas through the shortest route can be positioned as a safe and optimal evacuation action under occasionally changing circumstances when evacuation decisions are delayed.

Impact of green infrastructure creation scenarios on the periphery of metropolitan areas on inland flood control effects

Impact of green infrastructure creation scenarios on the periphery of metropolitan areas on inland flood control effects

Physics-informed neural networks as surrogate models of hydrodynamic simulators

In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations have made notable advancements, they remain computationally expensive, underscoring the need for efficient machine learning surrogate models. As a result of sparse empirical observation and expensive data collection, there is a growing need for the models to perform effectively in ‘small-data’ contexts, a characteristic typical of many scientific problems. This research combines the latest developments in surrogate modelling and physics-informed machine learning to propose a novel Physics-Informed Neural Network-based surrogate model for hydrodynamic simulators governed by Shallow Water Equations. The proposed method incorporates physics-based prior information into the neural network structure by encoding the conservation of mass into the model without relying on calculating continuous derivatives in the loss function. The method is demonstrated for a high-resolution inland flood simulation model and a large-scale regional tidal model. The proposed method outperforms the existing state-of-the-art data-driven approaches by up to 25 %. This research demonstrates the benefits and robustness of physics-informed approaches in surrogate modelling for flood and hydroclimatic modelling problems.

Laboratory evaluation of the effectiveness of nature-assisted beach enhancement techniques

Beaches are eroded and accrete under the effect of storms and calm marine conditions, respectively. Normally, beaches reach their narrower state in spring, after the action of winter storms. Accretion processes are slow, and maximum beach recovery doesn't occur until late summer. Sometimes this recovery is not enough to reach the width the beach had the previous year, producing a progressive shoreline retreat and an increased risk of dune erosion and inland flooding during the following winter seasons. The need for wider beaches in early summer for touristic purposes and social support to soft-engineering measures, have increased the interest in Nature-Assisted Beach Enhancement (NABE) techniques. In this study, reduced-scale laboratory experiments on beach ploughing and scraping allowed the comparison of various of these techniques and their effectiveness in controlled conditions for the first time. The beach widening and accretion achieved for five different NABE geometries were analysed and contrasted with natural (control) conditions. Our results show that the best technique is goal-dependent. For dry beach widening, ploughing is recommended as an effective and easy-to-design technique. Scraping the lower intertidal area and placing the sand on an intertidal bar or the beachfront are also effective alternatives if adequately designed. For dune nourishment, the best option is scraping the upper intertidal area and using the borrowed sand for dune regeneration. In general, all the analysed techniques enhance natural beach accretion, in collaboration with natural processes, thus reducing the human action required to achieve the desired objectives from a Building with Nature perspective.

Future sea level rise exacerbates compound floods induced by rainstorm and storm tide during super typhoon events: A case study from Zhuhai, China

Compound floods are becoming a growing threat in coastal cities against a background of global sea level rise (SLR), and may cause increasing impacts on societal safety and economy. How to quantify the impact of SLR and compound effects among various flood causes on compound flood have become important challenges. We propose a modeling framework which integrates atmospheric, storm tide and urban flood (IASTUF) models to characterize the various physical processes related to compound flood. Future SLR projections under various shared socioeconomic and respective concentration pathway emission scenarios are considered. Hengqin Island (Zhuhai City, China) frequently experiences typhoon conditions combined with rainstorm and storm surge events. Its population has increased more than sixfold during the past decade, stimulating urgent demands for assessments of the potential risks associated with future compound floods in the context of potential SLR. A compound flood event in northern Hengqin Island, caused by the super typhoon Mangkhut in 2018, is selected as a case study to verify the proposed modeling framework. Results show that the IASTUF modeling framework can capture well the combined processes of typhoon, rainstorm, storm tide and inland flooding and demonstrates good performance in quantifying compound flood magnitudes. Compared to the current scenario, the node flooding volume (from the drainage system) and the maximum inundation area (with inundation depths >1 m) in 2050 are projected to increase by 20–26 % and 41–85 %, respectively, and these increases rise to 46–84 % and 23–71 times by 2100. The inundation volumes and water depths due to compound events are larger than the sum of those caused by the corresponding single-cause events, indicating that concurrent rainstorm and storm surge induce positive compound effects on flood magnitude. These findings can provide guidance for the management and mitigation of future compound flood hazards driven by super typhoon events.

Human displacements from Tropical Cyclone Idai attributable to climate change

Abstract. Extreme weather events, such as tropical cyclones, often trigger population displacement. The frequency and intensity of tropical cyclones are affected by anthropogenic climate change. However, the effect of historical climate change on displacement risk has so far not been quantified. Here, we show how displacement can be partially attributed to climate change using the example of the 2019 Tropical Cyclone Idai in Mozambique. We estimate the population exposed to high water levels following Idai's landfall using a combination of a 2D hydrodynamical storm surge model and a flood depth estimation algorithm to determine inland flood depths from remote sensing images, factual (climate change) and counterfactual (no climate change) mean sea level, and maximum wind speed conditions. Our main estimates indicate that climate change has increased displacement risk from this event by approximately 12 600–14 900 additional displaced persons, corresponding to about 2.7 % to 3.2 % of the observed displacements. The isolated effect of wind speed intensification is double that of sea level rise. These results are subject to important uncertainties related to both data and modeling assumptions, and we perform multiple sensitivity experiments to assess the range of uncertainty where possible. Besides highlighting the significant effects on humanitarian conditions already imparted by climate change, our study provides a blueprint for event-based displacement attribution.

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

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

Opportunity mapping for nature-based solutions: Mitigating storm surge and land erosion in the Caribbean

The islands of the Caribbean are particularly susceptible to the effects of climate change due to their low-lying coastal areas and location within the Atlantic basin's hurricane belt. The UK Overseas Territory of Anguilla is one such island. The predicted increase in the severity of hurricanes and sea-level rise is highly likely to increase the flood risk of already vulnerable island communities. In this study, flood risk and erosion models are used to prioritise opportunity areas for nature-based restoration and to identify those that would have the greatest impact on coastal and in-land flood risk reduction. Two study sites in Anguilla were selected to highlight this ecologically-based modelling approach; Cove Bay and Pond, a degraded sand dune system and brackish pond, and the East End Pond, an Important Bird and Biodiversity Area that floods following heavy rainfall events. At the coastal site, the restoration of mangroves, sand dunes and coral reefs have the potential to provide flood risk reduction up to 500 m inland and protect homes, infrastructure and tourism developments. For the in-land East End Pond, areas of high erosion risk were predominately identified as bare or disturbed land within 1 km of the pond's basin. Habitat restoration of these areas was identified as having the greatest impact on reducing flood risk. The creation of flood risk, opportunity and impact models are invaluable tools that can be used to inform, advocate and justify the implementation of nature-based solutions to a range of stakeholders from policy-makers to local communties.