Flooding Research Articles

Hybrid coral reef restoration can be a cost-effective nature-based solution to provide protection to vulnerable coastal populations.

Coral reefs can mitigate flood damages by providing protection to tropical coastal communities whose populations are dense, growing fast, and have predominantly lower-middle income. This study provides the first fine-scale, regionally modeled valuations of how flood risk reductions associated with hybrid coral reef restoration could benefit people, property, and economic activity along Florida and Puerto Rico's 1005 kilometers of reef-lined coasts. Restoration of up to 20% of the regions' coral reefs could provide flood reduction benefits greater than costs. Reef habitats with the greatest benefits are shallow, nearshore, and fronting low-lying, vulnerable communities, which are often where reef impacts and loss are the greatest. Minorities, children, the elderly, and those below the poverty line could receive more than double the hazard risk reduction benefits of the overall population, demonstrating that reef restoration as a nature-based solution can have positive returns on investment economically and socially by providing protection to the most vulnerable people.

Improving the Accuracy of Flood Damage Assessments to Residential Structures via the Use of Experimental Data

ABSTRACTThe current practice of flood loss prediction presents limitations in accurately predicting building flood losses at multiple scales. While whole‐building estimates can more accurately predict high‐level losses (i.e., large groups of buildings), a significant analysis error is revealed with small‐scale (i.e., individual, or small groups of buildings) investigation. This research presents a more robust, data driven, small‐scale, flood damage estimation approach for residential buildings. The approach is based on component‐level, depth–damage curves derived from experimental analysis. Structures with standard residential construction materials typical to the south‐eastern United States were built and incrementally flooded for short durations. The materials were assessed to determine the level of damage inflicted. This experimentally derived damage data were then translated into a set of flood depth–damage functions (DDFs). The DDFs were tailored for analysis at smaller scales and incorporated the ability to apply damage uncertainty in damage analysis. To demonstrate the applicability of the experimentally derived DDFs to damage estimation at smaller scales, the functions are applied to a hypothetical building design typical of the south‐eastern United States.

Aggregating flood damage functions: The peril of Jensen's gap

AbstractFlood risk models provide important information for disaster planning through estimating flood damage to exposed assets, such as houses. At large scales, computational constraints or data coarseness often lead modelers to aggregate asset data using a single statistic (e.g., the mean) prior to applying non‐linear damage functions. This practice of aggregating inputs to nonlinear functions introduces error and is known as Jensen's inequality; however, the impact of this practice on flood risk models has so far not been investigated. With a Germany‐wide approach, we isolate and compute the error resulting from aggregating four typical concave damage functions under 12 scenarios for flood magnitude and aggregation size. In line with Jensen's 1906 proof, all scenarios result in an overestimate, with the most extreme scenario of a 1 km aggregation for the 500‐year flood risk map yielding a country‐wide average bias of 1.19. Further, we show this bias varies across regions, with one region yielding a bias of 1.58 for this scenario. This work applies Jensen's 1906 proof in a new context to demonstrate that all flood damage models with concave functions will introduce a positive bias when aggregating and that this bias can be significant.

Assessing the geographical diversity of climate change risks in Japan by overlaying climatic impacts with exposure and vulnerability indicators.

Understanding multifaceted climate change risks and their interconnections is essential for effective adaptation strategies, which require comprehensive assessments of both climatic impact variations and social-environmental exposures/vulnerabilities. This study examines these interconnections and creates multitier delineations of future climate risks across Japan by overlaying homogeneous impact zones (HIZs) with exposure-vulnerability complexes (EVCs). We delineated eight EVC regions, each exhibiting similar patterns of exposure and vulnerability, via multivariate clustering and similarity search on the basis of future population and land cover/use data. Under the Shared Socioeconomic Pathway 5 scenario, 8.07% of Japan's area was identified as urban, 66.18% as forest, and 16.66% as agricultural land, depending on vulnerability and exposure characteristics. We then spatially intersected these EVCs with six HIZs, which represent common patterns of multisectoral climatic impact variations. This intersection revealed low geographical correspondence between climatic impact variations and exposure-vulnerability factors, indicating high spatial variability in climate risk across Japan. The use of EVCs helps identify areas with anticipated climate change risks. The spatial nexus between HIZs and EVCs underscores the geographical complexity and diversity of climate risks, revealing regions with high-impact variations alongside significant exposures and vulnerabilities. For example, urban EVCs highlight critical issues such as heat-related mortality and flood damage. By mapping these impact variations while focusing on exposure and vulnerability disparities, insights from HIZs and EVCs can inform future climate risk management and effective adaptation strategies at the national and regional levels.

Assessment of Municipal-Level Flood Vulnerability Using Logistic Growth Function-Based Flood Damage Function

This study evaluated the flood vulnerability of 228 municipalities in South Korea by constructing logistic growth function-based flood damage functions, utilizing flood damage and rainfall data from 1998 to 2021. Among the derived parameters, a represents flood damage sensitivity to rainfall increase, b indicates the rainfall threshold for flood occurrence, and c denotes the maximum damage amount. Parameter a (0.0688-0.3203) showed regional variations without distinct spatial patterns, whereas b (76.8-343.6) exhibited higher values in the Seoul Metropolitan Area and major cities, suggesting that developed areas with well-established disaster prevention infrastructure possess higher flood thresholds. The proportion of flood reduction facilities (pumping stations, river improvements, and retention basins) exhibited positive correlations with b, suggesting the effectiveness of these measures in increasing flood damage thresholds. Flood damage was typically initiated at 127 mm of maximum daily rainfall and approached maximum levels at 272 mm. This study provides quantitative evidence to establish localized flood management strategies that contribute to the development of future climate change adaptation policies.

Analysis of Flood Damage Mitigation Effects Based on the Presence of a Rainwater-Retaining Facility Through Flood-Depth Distribution Analysis by Grid

In recent years, localized heavy rainfall due to climate change has caused increased flood damage in urban areas. To address this issue, efforts are being made to mitigate damage through the installation of a rainwater-retaining facility and improved sewer systems. Various methods have been proposed to analyze the flood mitigation effects of these measures, with commonly used approaches including the analysis of overflow volume, peak flood discharge, and flood maps. However, these methods have limited capacity to accurately assess the reduction in flood depth, which directly affects human safety and property. Additionally, the use of flood maps requires visual analysis, which makes it difficult to evaluate the reduction effects accurately. Therefore, this study proposes a new method for flood reduction using grid-based flood-depth distribution analysis. The maximum flood depth for each grid was determined using the XP-SWMM urban runoff model, and the number of grids for each 0.1-meter flood-depth interval was analyzed using a Python program. Consequently, under various rainfall scenarios and boundary conditions, the proposed analysis method enables a more quantitative assessment of flood mitigation effects by comparing the number of reduced flood-depth grids before and after the installation of the rainwater retention facility. This approach also allows for a more precise assessment of the reconstruction of flood-depth grids, which directly affects human safety and property.

Numerical Analysis of Levee Failure Characteristics for Flood Hydrograph Patterns

Recent climate change has significantly affected the magnitude and frequency of rainfall, which critically affects the safety of levees and leads to severe flooding damage when levees fail. This study aimed to analyze the impact of the shape of flood hydrographs on levee failure characteristics using numerical analysis. Three types of flood hydrographs derived from lognormal, beta, and Weibull probability density functions (PDFs) were utilized, with each showing differences in the timing and magnitude of the peak flows. Levee overtopping and failure phenomena were simulated using a three-dimensional numerical analysis model, namely, FLOW-3D. The simulation results indicate that the failure patterns of levees vary, depending on the PDF used, with the lognormal PDF showing significant levee failure, owing to relatively high initial flows. These results suggest that a rapid increase in the flow at the onset is a direct factor that causes levee failure. These numerical analysis results highlight the importance of understanding the characteristics of each probability density function to accurately predict and effectively respond to levee failure risk. Future research should focus on a more precise analysis of levee failure mechanisms as well as the propagation and impacts of flood waves resulting from such failures.

Establishing Flood Risk Criteria for Vehicles based on Flood Damage Assessment

Establishing Flood Risk Criteria for Vehicles based on Flood Damage Assessment

Study on Flood Control Facility Design Linking Retention Pond and Rainwater Pumping Station

The primary causes of flood damage in a basin include the inadequate water flow capacity of pipelines and rivers, along with the inability to drain inland water, owing to rising water levels. Flood response facilities, such as rainwater pumping stations (pump-integrated gate type) that actively drain inland water, and retention ponds designed to manage peak flood volumes, are essential. The focus of this study is the Gyeticheon Basin in Haman-gun, Gyeongsangnam-do. To manage flood risks, the proposal includes rainwater pumping stations for the downstream area and an off-line retention pond for the midstream area. This study develops a modeling method that simultaneously plans the side-weir of the retention pond and the rainwater pumping station. In addition, the analysis compared the flood control capacity in relation to the height and width of the side weir, and the operating water level (OWL.) of the pumping station. The results showed that when the (OWL.) of the rainwater pumping station surpasses the height of the side weir, the initial flood volume overflows the side weir, significantly reducing the flood control effectiveness of the basin. Consequently, if the side weir is designed to be low in the construction of a retention pond for flood control, or if a retention pond features a movable weir that tilts during the early stages of a flood, allowing water to overflow transversely into the pond, the flood control effectiveness can be significantly reduced. These findings are expected to aid in the design and operation of complex flood control facilities in future.

Flood analysis using the HEC-RAS software for Antakya Altınçay Creek

Flood is one of the most important disasters in the world. Floods, common in our country, are the second disaster after the earthquake in terms of loss of life and property caused by natural disasters and the first among climatic disasters. The district of Antakya is located where streamflow has a high flow coefficient. Because of flooding in Altınçay Creek which flows through the center of Antakya into the Orontes River, flood studies need to be carried out and the necessary precautions taken to prevent flooding. In this study, peak discharges for 2, 5, 10, 25, 50, and 100-year return periods of Altınçay Creek passing through residential areas in Antakya were determined by SCS and DSI synthetic methods. Using these estimated peak discharges the water profile along the Altınçay Creek route has been simulated using HEC-RAS software, and the flood risk areas were determined on cross-sections basis. As a result, flood risk was not observed in peak discharge for return periods of 2, 5 and 10 years. However, flood events were observed at 16, 51, and 73 different cross sections in peak discharges for return periods of 25, 50, and 100 years respectively. To reduce flood damage, it was suggested that the cross-sectional areas having flood risk must be increased.

Modelling of Soil Erosion Susceptibility Using the Multi-Influencing Factor Method in the Amizmiz Basin, Morocco

In the Western High Atlas of Morocco, soil loss caused by hydraulic erosion is a serious environmental issue that has led to the destruction of arable land and the feeding of surface water with large solid loads, resulting in major flood damage and the silting of dams. This study aims to identify areas vulnerable to hydraulic erosion in the Amizmiz watershed in the Western High Atlas to facilitate effective management of natural resources. The methodology adopted in this study is based on the multi-influencing factor (MIF) method coupled with remote sensing and geographic information system (GIS) data. The resulting erodibility map shows that areas with low to very low erosion potential represent 13% and 6% of the watershed, respectively, occupying the downstream and eastern parts of the basin, whereas 62% of the basin is classified as medium-risk. The remaining 19% of the watershed comprises high-risk areas, which are generally located in the axial and western zones. The validation value (AUC) obtained from the ROC curve is 0.78, confirming the sufficient predictive capacity of the MIF model for identifying areas vulnerable to hydraulic erosion in the Amimiz watershed.

Simulating flood risk in Tampa Bay using a machine learning driven approach

Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to simulate flood risk and identify dominant FRFs using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different ML models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted. RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk.

The effect of hydrological model structure on spring flow forecasts when assimilating a distributed snow product

Accurate forecasting of spring flow is essential for mitigating flood damage and optimizing hydroelectric power production. In northern countries such as Canada, this flow is mainly driven by snowmelt processes. By integrating snowpack data from diverse sources (in situ, remote sensing, and reanalysis) with modelled snow-related state variables through data assimilation (DA), it is possible to leverage both modeling and observations for more accurate spring flow estimates. Accurate estimates of snow water equivalent (SWE) within a heterogeneous snowpack are crucial for optimizing the advantages of snow DA. Here we assess the potential effect of distributed SNOw Data Assimilation System (SNODAS) SWE data on improving spring flow in two hydrological models having distinct inner structures: HSAMI, a lumped model, and HYDROTEL, a distributed model. DA analyses used an ensemble Kalman filter scheme, which was run over a three-year period. The results were then compared with those from an open-loop experiment. We used Nash–Sutcliffe efficiency (NSE) and bias to assess the results and found that SNODAS SWE DA did not improve 1-day spring flow forecasts for the lumped model. In contrast, HYDROTEL produced more accurate 1-day spring flow estimates for all 3 years, improving NSE of the spring flow forecasts from 0.52 to 0.70, 0.32 to 0.68, and 0.39 to 0.67 for the 2014–2017 period. This study demonstrates that incorporating distributed snow data into a distributed hydrological model can improve spring flow forecasts. Given the availability of SNODAS dataset over most Canadian watersheds, this DA framework is aimed to serve as an asset to enhance operational flood forecasting systems across Canada.

Assessment of flood damage severity, economic growth dynamics and relief fund allocation in the districts of Bihar: A non-parametric approach

Assessment of flood damage severity, economic growth dynamics and relief fund allocation in the districts of Bihar: A non-parametric approach

Attention-based deep learning framework for urban flood damage and risk assessment with improved flood prediction and land use segmentation

Attention-based deep learning framework for urban flood damage and risk assessment with improved flood prediction and land use segmentation

Advancing Forensic Flood Analysis Through Hydraulic Physical Models

Urban density significantly increases the multi-hazard risks caused by extreme weather events. In Latin America and the Caribbean (LAC) countries, flood damages in urban areas are considerable and have shown a consistent annual increase. This challenge has prompted innovative educational responses to address the growing need for understanding and managing urban flood risks effectively. As part of postgraduate education efforts, the International Flood Initiative under the Intergovernmental Hydrological Programme of UNESCO for the LAC region (IFI-LAC IHP-UNESCO) aims to enhance understanding of vulnerability and flood risk estimation in urban settings. The initiative encourages the development of advanced hydro-informatic tools, watershed and river modeling techniques, and scale prototype construction to foster practical learning.One of the educational approaches involves analyzing and reconstructing hydraulic infrastructure failures to provide insights into urban flood dynamics. This paper highlights the design and construction of a surface channel and a floodgate system equipped to enable controlled flash openings, capable of triggering crash waves. These facilities allow the recreation of three notable urban flood events, facilitating forensic hydraulic analysis. Detailed similitude analyses are provided, illustrating how sonic water level sensors can be effectively implemented and contributing to the understanding of flood risks and mitigation strategies in densely populated urban environments. Keywords: flood; forensic analysis; hydraulics; open channel; damage; disaster; flash flood; break wall

Estimation of River Flood Damages Among Farmers and Small Business in Developing Countries: Evidence from the Kali Lamong Indonesia

Flooding is a severe disaster in the Kali Lamong river basin (58 Km2). For this reason, detailed flood damage estimates are needed to design an optimal flood management strategy. This study aims to estimate flood damage in the Kali Lamong River Basin. The data collection method used cluster sampling in 15 villages with 870 respondents. We surveyed farmers’ households and micro businesses from October to December 2022 to obtain information on flood damage, flood characteristics, and socio-economics characteristics based on the 27-30 September 2022 flood event. Based on those data, we used regression analysis to develop flood damage functions to know how flood characteristics are related to the damage. Our study found that the economic flood damage in the Kali Lamong river basin is estimated to be US$ 2,3 million for farmer’s households and US$ 10,3 million for micro businesses. This study shows that household flood damages 5,2% more than micro business.

Basin-scale spatio-temporal development of glacial lakes in the Hindukush-Karakoram-Himalayas

Glacial lakes are expanding exponentially in the cryospheric environment of the Hindukush-Karakoram-Himalayas (HKH). Rapid glacier melting due to an above mean global annual temperature increase in HKH is attributed as the main reason for the expansion of the glacial lakes. The rapid expansion of glacial lakes increases the risk of future Glacial Lake Outburst Floods (GLOFs) events in the HKH.In the present study, glacial lake inventories for the Indus, Ganga and Brahmaputra (IGB) river basins in the HKH were generated for 1990, 2000, 2010 and 2020 using Landsat (TM & OLI) at the sub-basin level to understand the spatio-temporal and regional patterns of glacial lakes dynamics, elevational evolution, and changes in the typology. We mapped 17,641 glacial lakes (area: 1082.57 ± 192.601 km2) in 1990, 18,206 (area: 1120.95 ± 198.49 km2) in 2000, 18,399 (area: 1147.12 ± 201.26 km2) in 2010, and 19,284 (area: 1191.81 ± 209.21 km2) in 2020. Between 1990 and 2020, IGB basins showed an increase of 9.31 % in total number and 10.09 % in total area of glacial lakes. In 2020, the Brahmaputra basin had the maximum total area (area: 763.59 ± 132.14 km2), followed by Indus basin (area: 217.47 ± 43.39 km2) and the Ganga basin (area: 210.74 ± 33.66 km2). However, between 1990 and 2020, glacial lakes in the Ganga basin (n: 22.08 %) had the highest growth rate, followed by the Indus basin (n: 14.73 %) and the Brahmaputra basin (n: 4.41 %). In 2020, 76.11 % of glacial lakes were end-moraine-dammed M(e) lakes, followed by other bedrock-dammed B(o) lakes (16.45 %), supraglacial lakes (2.79 %), lateral moraine-dammed M(l) lakes (2 %), cirque B(c) lakes (1.06 %), other moraine-dammed M(o) lakes (0.38 %), and other glacial (O) lakes (1.18 %). Given the rapid growth of glacial lakes in the region along with their likely flood volumes and damage potential in case of their failures, the present study will be of importance for disaster management authorities, an important input for detection of potentially hazardous glacial lakes and for development of mitigation strategies to minimize the impact of potential future GLOF events.

Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta

In the face of global climate change and rapid urbanization, the Pearl River Delta is confronted with frequent river floods and heavy rainfall, which leads to substantial economic losses and casualties. Enhancing the role of blue-green space in rain-flood resilience is crucial for mitigating such damages in this new era. Firstly, based on an analysis of the current status quo of blue-green space in the Pearl River Delta and the identification of potential areas at risk from rain and floods, this paper elucidates that resilient blue-green space in the Pearl River Delta should be guided by a systematic, bottom-line, and forward-looking orientation while considering spatial characteristics such as multi-scale network connectivity, redundancy and diversity/multi-functionality. Secondly, an optimization route is proposed based on steps of analysis of existing blue-green space, identification of inundated areas prone to rain and flood damage and optimization of blue-green spaces. Strategies for optimizing blue-green space are put forth including enhancing water corridor connectivity, optimizing ecological barriers and corridors, as well as constructing water gates to control hydrological flow direction. Simulation results demonstrate that under similar rain-flood disaster conditions, optimized blue-green space exhibits smaller sizes and lower depths of potential inundated areas compared to the original ones.

Integrating socio-economic variables in urban flood damage assessments: a case study of Bengaluru, India

Integrating socio-economic variables in urban flood damage assessments: a case study of Bengaluru, India