Fluvial Flood Research Articles

Enhancing Fluvial Modeling and Flood Prediction Accuracy through the Fusion of 3D Point Clouds, Multispectral, and RGB Data

Enhancing Fluvial Modeling and Flood Prediction Accuracy through the Fusion of 3D Point Clouds, Multispectral, and RGB Data

Simulation of Flood-Control Reservoirs: Comparing Fully 2D and 0D–1D Models

Flood-control reservoirs are often used as a structural measure to mitigate fluvial floods, and numerical models are a fundamental tool for assessing their effectiveness. This work aims to analyze the suitability of fully 2D shallow-water models to simulate these systems by adopting internal boundary conditions to describe hydraulic structures (i.e., dams) and by using a parallelized code to reduce the computational burden. The 2D results are also compared with the more established approach of coupling a 1D model for the river and a 0D model for the reservoir. Two test cases, including an in-stream reservoir and an off-stream basin, both located in Italy, are considered. Results show that the fully 2D model can effectively handle the simulation of a complex flood-control system. Moreover, compared with the 0D–1D model, it captures the velocity field and the filling/emptying process of the reservoir more realistically, especially for off-stream reservoirs. Conversely, when the basin is characterized by very limited flood dynamics, the two approaches provide similar results (maximum levels in the reservoir differ by less than 10 cm, and peak discharges by about 5%). Thanks to parallelization and the inclusion of internal boundary conditions, fully 2D models can be applied not only for local hydrodynamic analyses but also for river-scale studies, including flood-control reservoirs, with reasonable computational effort (i.e., ratios of physical to computational times on the order of 30–100).

Assessment of Climate Change Impact on Flood Hazard Zones

AbstractThere have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil & Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development.

Preface: Advances in pluvial and fluvial flood forecasting and assessment and flood risk management

Preface: Advances in pluvial and fluvial flood forecasting and assessment and flood risk management

A Coupled River–Overland (1D-2D) Model for Fluvial Flooding Assessment with Cellular Automata

To provide accurate and efficient forecasting of fluvial flooding assessment in the river basin, the present study links the well-known CA-based urban inundation modeling (2D-OFM-CA) with a one-dimensional river flow model (1D-RFM) as a coupled 1D-2D river–overland modeling. Rules to delineate the geometric linking between the 1D-RFM and 2D-OFM-CA along embankments are developed. The corresponding exchanged water volume across an embankment is then computed by using the free and submerged weir flow formulas. The applicability of the proposed coupled model on fluvial flooding assessment is then assessed and compared with a well-recognized commercial software (HEC-RAS model) through an idealized fluvial case and an extensively studied real-scale fluvial case in the Severn River Basin. Based on the simulated results concerning the numerical accuracy, the coupled model is found to give similar results in the aspects of the river flow and overland flow modeling in both two study cases, which demonstrates the effectiveness of the linking methodology between the 1D-RFM and 2D-OFM-CA. From the viewpoint of numerical efficiency, the coupled model is 47% and 41% faster than the HEC-RAS model in the two cases, respectively. The above results indicate that the coupled model can reach almost the same accuracy as the HEC-RAS model with an obvious reduction in its computational time. Hence, it is concluded that the coupled model has considerable potential to be an effective alternative for fluvial flooding assessment in the river basin.

Analisis Spasial Bencana Banjir Di Kota Padang Periode Tahun 2020-2024

During the period of 2020-2024, the frequency and intensity of flooding in the City of Padang showed a significant increase, impacting the community. This study aims to: 1) identify the spatial distribution of flood disasters in the City of Padang, 2) classify the types of floods that occurred, and 3) identify the impact of floods on damage and losses. This type of research is quantitative, with the population being all sub-districts in the City of Padang. The sample was taken using total sampling, covering all sub-districts affected by flooding, totaling 11 sub-districts, including Padang Barat, Padang Utara, Padang Timur, Padang Selatan, Nanggalo, Kuranji, Lubuk Begalung, Lubuk Kilangan, Pauh, Koto Tangah, and Bungus Teluk Kabung. The data analysis technique used is spatial analysis. The results of the study show that there were 121 flood events distributed across various sub-districts, with the highest occurrences in Koto Tangah (56 events) and the fewest in Padang Barat (2 events). Floods in the City of Padang are classified into three categories: fluvial floods, tidal floods, and flash floods. Fluvial floods are the most common, while tidal floods occur in Padang Barat, and flash floods occur in Lubuk Begalung. The impact of the floods includes damage and losses; Lubuk Begalung experienced the most significant damage, with 70 houses affected, and the largest losses occurred in Pauh in 2023, amounting to Rp1,786,150,000, while the lowest losses occurred in Koto Tangah in 2022, amounting to Rp18,000,000.

A Data-Driven Multi-Step Flood Inundation Forecast System

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Geospatial approach to pluvial flood-risk and vulnerability assessment in Sunyani Municipality

Historically, and in recent times, efforts have been to understand, predict, analyze, and quantify floods and their impacts in various countries of the globe. Although recent scientific advances have introduced approaches to assessing the risks presented by flooding, little studies have been carried out in the Sunyani Municipality of Ghana for generating a pluvial flood-risk and vulnerability map for risk identification, resilience, emergency preparedness, and urban spatial planning. In this study, five parameters that influence both pluvial and fluvial flooding were assessed to map flood-prone areas within the Sunyani Municipality. These are precipitation, drainage density, LULC, elevation, and slope, which were integrated in GIS. Using an AHP, weights were assigned to each parameter based on its level of influence on flooding. The findings reveal that 21.32 % of the Sunyani Municipality lies within a highly flood-prone area, 39.65 % in a flood-prone area, while 28.06 % and 10.97 % in slightly flood-prone and not flood-prone areas respectively. Built-up areas close to watersheds with lower elevations and larger drainage density are the places that are highly flood-prone. Some towns within the highly flood-prone and flood-prone areas are Abesim, Newtown, Nkwarbeng, Baakoniaba, Kootokrom, and Penkwase. Highly valued infrastructure such as schools, churches, and hospitals have also been found within these highly flood-prone areas. These findings can aid the government and relevant stakeholders in disaster risk management to be better informed, and to effectively plan and prevent flood challenges in the Sunyani Municipality. Moreover, urban spatial planners in the study setting can consider incorporating the flood hazard maps generated from this study into their spatial plans for proactive physical developments.

Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event

Abstract. Flash floods pose a distinct challenge compared to traditional fluvial flooding, with infrastructure-based solutions proving less effective. Effective responses hinge on advanced early warning systems providing actionable information, emphasising the necessity for computational flood forecasting models. However, hydrodynamic models, renowned for accuracy and completeness, face limitations due to computational intensity. This study explores two 2D flood forecasting models, RIM2D and SERGHEI, both with GPU implementations which allow us to maximise the forecast lead time. While RIM2D is less computationally intensive, suitable for operational use, SERGHEI, with higher computational costs, targets large-scale high-performance computing (HPC) systems. The assessment of applicability and trade-offs is carried out on the 2021 Eifel flood event, particularly in the lower Ahr valley. A set of simulations were performed at various resolutions from 1 to 10 m, which reveal similar accuracy among both models at coarser resolutions, yet discrepancies arise at finer resolutions due to the distinct formulations. Both models exhibit a rapid computational cost escalation, but at resolutions equal to or coarser than 5 m, forecasts are remarkably faster than the real-time ideal for operational use, paving the way for their use in early warning systems. However, higher resolutions necessitate multi-GPU and HPC capabilities, underlining the importance of embracing such technology in addressing broader flood domains.

Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai

Abstract. Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.

Physics-informed geostatistical approach (PIGA) for real-time fluvial flood modeling

This study presents the Physics-Informed Geostatistical Approach (PIGA), a novel methodology aimed at improving flood prediction accuracy in riverine systems during hurricane events. PIGA integrates reduced spatial correlations derived from fluvial hydrodynamic models with a reduced geostatistical approach (RGA) based on monitoring data to provide real-time estimations of water surface elevations (WSE) across river channels and reaches. Focusing on the Savannah River basin, an area prone to riverine flooding during hurricane seasons, we developed a 1D-2D HEC-RAS model as the physical model and compared its performance with PIGA for various hurricane events, including Hurricane Matthew, Irma, Dorian, and Idalia. Our findings reveal that PIGA consistently outperforms the HEC-RAS model in predicting WSE, particularly in areas with limited bathymetry data. Statistical analyses demonstrate significant improvements in prediction accuracy achieved by PIGA, emphasizing its potential as a reliable tool for flood prediction in data-deficient regions. Additionally, we explore the transferability of reduced spatial correlations across different hurricane events. Our results indicate that spatial correlations derived from one hurricane event can effectively predict WSE for other events, with Hurricane Irma’s spatial correlations proving to be the most versatile for our study site. However, we also observe variability in spatial correlations among different hurricane events, highlighting the importance of selecting the optimal alternative spatial reference for prediction. Furthermore, we highlight PIGA’s computational efficiency, with simulations completed in significantly less time compared to traditional physical models, rendering its potential to provide real-time prediction of WSE during flooding events.

Assessment of vulnerability to fluvial floods: Case of municipalities of the Gidra River Basin, Slovakia

Assessment of vulnerability to fluvial floods: Case of municipalities of the Gidra River Basin, Slovakia

Quantifying risk contagion of fluvial flood disaster chain

ABSTRACT The fluvial flood disaster (FFD) poses a great threat to human civilisation. The high water level can cause overtopping, piping, and even breach of the levees, which, in turn, further results in urban flood. These secondary disasters form a disaster chain with multipaths that amplify the risk of the fluvial flood. This work develops a stochastic multi-path network method to quantify the risk contagion for the FFD chain. First, the multi-path network structure for the chain was identified, characterised by reflecting the uncertainties of levee failure modes. Second, a model for the evolutionary probabilities of the chain was proposed by combining the directed network technique with the network structure. Finally, the systematic risk of the chain was quantified by considering the consequence of the disasters. A risk contagion factor was proposed to quantify the attenuation or amplification effect of the risk during the evolution of the chain. A case study in Huizhou, China, shows that the FFD chain is characterised by “small probability” but “extremely high risk”. The most likely path is overtopping-urban flooding, indicating the priority of the area is to heighten the levees. This work paves the way for a more entrepreneurial future on disaster prevention and mitigation.

Towards integrated flood management: Vulnerability and flood risk in the Ayeyarwady Delta of Myanmar

Despite the rising global flood risk, the impacts of flooding remain systematically underestimated, leading to significant consequences for particularly vulnerable river deltas. Most studies focus either on single hazards or social vulnerability while overlooking the interconnected dynamics of deltaic social-ecological systems. In response to the first priority of the Sendai Framework, which calls for an understanding of disaster risk in all its dimensions, we apply the Global Delta Risk Index in the Ayeyarwady Delta of Myanmar. We combine 55 indicators of social- and ecosystem vulnerability with 100-year, 500-year, and 1,000-year scenarios of pluvial, fluvial, and coastal flood hazards and exposure at the sub-delta scale. Using townships as units of analysis allows for bridging the gap between global and local case studies, providing insights that are meaningful for risk-informed development of the delta as a whole system. We also examine the distinctive characteristics that define the delta systems that are particularly prone to flooding. Our results reveal patterns and drivers of flood risk and vulnerability that affect at least 65 % of the delta's population and 60 % of its ecosystem, with self-reinforcing dynamics, but also those that contribute to the mutual resilience of both systems. We argue that the principles of integrated flood management should be applied to leverage scarce resources to simultaneously reduce risk, secure livelihoods and preserve ecosystem services.

Interactions between vegetation, sedimentation and flood inundation levels in wetlands

Wetlands produce key ecosystem services to mitigate the impacts of peak flows caused by pluvial or fluvial floods or storm surges. Sediment floods were characterized by a peak flow flowing over a simulated wetland, populated with two natural species. Floods have been drawn as flows of height H, into waters of height h, where H > h. Peak flow along the flume passed through: peak flow adjustment; peak flow; drag-dominated peak flow; and gravity current regimes. For high inundation wetland levels, settling rates of coarse and fine sediment were similar during the peak flow regime. At larger distances, sedimentation decreased monotonically, with higher sedimentation of fine particles. For low inundation levels, the sedimentation rate during the drag-dominated peak flow regime was higher for coarse particles. Vegetation decreased the inundation level needed for enhancing sedimentation. Our study then adds practical knowledge at considering that the synergies between the vegetation and the inundation level may enhance wetland services such as the mitigation of pluvial, fluvial or coastal floodings.

Land suitability analysis for blue-green infrastructure implementation in an urban stormwater management system in Surabaya, Indonesia

ABSTRACT In low-lying areas, pluvial, fluvial, and coastal flooding can combine to form complex flood events. In this context, integrated blue-green infrastructure (BGI) has emerged as a viable approach to environmentally friendly stormwater management. This study concentrated on two primary objectives: i) the development of a geospatial technology-based land suitability map for BGI implementation and ii) the identification of optimal BGI nodes and corridors. Surabaya City in the Delta Brantas region of Indonesia was selected as the location for a pilot study. The land suitability analysis considered five criteria (slope, potential drainage density, land use/land cover, soil types, and proximity to roads), and the analytic hierarchy process (AHP) was employed to assign weights to these criteria before calculating a BGI index using the raster calculator tool in GIS. A BGI index map was then employed to identify potential BGI nodes on public land and priority corridors. The eastern and western regions of Surabaya, which are characterized by agriculture, fishponds, salt ponds, and mangrove forests, were identified as prime candidates for BGI implementation. Areas of public land under the jurisdiction of the city government, such as government offices, police stations, and educational zones, also offered the opportunity for BGI initiatives.

Impacts of Sea‐Level Rise on Coastal Groundwater Table Simulated by an Earth System Model With a Land‐Ocean Coupling Scheme

AbstractSea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs.

Flood risk and flood management in Zarabad and Konarak Districts 2024 (Makoran), Balochestan, Iran

Flooding is the most devastating natural hazard in Iran and the recent flooding has demonstrated its severeness. Floods are common throughout the country. However, their characteristics differ from region to region. Flooding behavior of the major basins and flood management at the national level are investigated in this article. Monsoon rainfalls are the main source of floods in the River Rapch Zarabad Basin, while Mediterranean Waves and Cyclones, which are generated over the Arabian Sea, induce flooding in the Makoran Coastal Area Rapch and Kahir Basin. Fluvial floods in the Zarabad Basin have caused major economic losses. Iran government has spent vast resources on relief operations and flood works since the country came into existence. A number of provincial and federal acts, ordinances, accords, and treaties shape the national flood policy. Institutional setup for flood hazard and crisis management has evolved over the years. Nevertheless, data show no major reduction in the flood-to-damage ratio. The inter-linkage of structural and non-structural measures and their combined efficiency must be analyzed and optimized for more effective flood management. Risk management has been established as a well defined procedure for handling risks due to natural, environmental or man made hazards, of which floods are representative. Risk management has been discussed in many previous papers giving different meanings to the term—a result of the fact that risk management actually takes place on three different levels of actions: the operational level, which is associated with operating an existing system, a project planning level, which is used when a new, or a revision of an existing project is planned, and a project design level, which is embedded into the second level and describes the process of reaching an optimal solution for the project.

A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.