Large-scale Flood Research Articles

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation

Flood risk assessment and quantification at the community and property level in the State of Iowa

Flood risk assessment contributes to identifying at-risk communities and supports mitigation decisions to maximize benefits from the investments. Large-scale risk assessments generate invaluable inputs for prioritizing regions for the distribution of limited resources. High-resolution flood maps and accurate parcel information are critical for flood risk analysis to generate reliable outcomes for planning, preparedness, and decision-making applications. Large-scale damage assessment studies in the United States often utilize the National Structure Inventory (NSI) or HAZUS default dataset, which results in inaccurate risk estimates due to the low geospatial accuracy of these datasets. On the other hand, some studies utilize higher resolution datasets, however they are limited to focus on small scales, for example, a city or a Hydrological United Code (HUC)-12 watershed. In this study, we collected extensive detailed flood maps and parcel datasets for many communities in Iowa to carry out a large-scale flood risk assessment. High-resolution flood maps and the most recent parcel information are collected to ensure the accuracy of risk products. The results indicate that the Eastern Iowa communities are prone to a higher risk of direct flood losses. Our model estimates nearly $10 million in average annualized losses, particularly in large communities in the study region. The study highlights that existing risk products based on FEMA's flood risk output underestimate the flood loss, specifically in highly populated urban communities such as Bettendorf, Cedar Falls, Davenport, Dubuque, and Waterloo. Additionally, we propose a flood risk score methodology for two spatial scales (e.g., HUC-12 watershed, property) to prioritize regions and properties for mitigation purposes. Lastly, the watershed-scale study results are shared through a web-based platform to inform the decision-makers and the public.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations

Large-scale floods are one of the major events that impact the national economy and people’s livelihood every year during the flood season. Predicting the factors of flood evolution is a worldwide problem. We use the two-dimensional Saint-Venant equations as an example and for high-performance computing in modelling the flood behavior. Discretization of the two-dimensional Saint-Venant equations with initial and boundary conditions with the finite difference method in the explicit leapfrog scheme is carried out. Afterwards, we employed a large-scale heterogeneous parallel solution on the “SunRising-1” supercomputer system using MPI, OpenMP, Pthread, and OpenCL runtime libraries. On this basis, we applied communication/calculation overlapping and the local memory acceleration to optimize the performance. Finally, various performance tests of the parallel scheme are carried out from different perspectives. We have found this method is efficient and recommend this approach be used in solving systems of partial differential equations similar to the Saint-Venant equations.

Assessing Residents' Flood Preparedness through Adaption of Protective Behaviour in Melaka, Malaysia

Flood is a natural disaster experienced by Malaysians including residents in Melaka. Floods cause huge damage and loss, and can affect the health of the flood victims. However, flood prone residents' awareness and preparation towards floods are still lacking. Despite many efforts done by different organizations to reduce the flood risk, flood hazard remains the main problem in Melaka. Several studies had been conducted to investigate the level of flood preparedness in some regions in Malaysia, but the level of flood preparedness amongst residents in Melaka is still unknown. Therefore, this research is conducted to assess the Melaka residents' flood preparedness using the Adoption of Protective Behaviour (APB) scale. The nine measurements APM scale is utilized and another three measurement items are created and added into the scale which makes it twelve measurement items. The mean for the twelve measurement items ranges between 2.85 to 4.03 while the likelihood percentage ranges between 31.67% and 74.17%. The twelve measurement items are significantly correlated with flood preparedness of the residents in Melaka. The result shows that flood preparedness among the residents in Melaka is still low. Most respondents are not well prepared in facing the flood. As the study was conducted on the states of Melaka only, it is recommended that future study should be carried out in all states in Malaysia especially in the east coast of Malaysia where large scale flooding occurs every year. The findings in this research can assist the authorities and the government to plan on creating public awareness and education for flood disasters. This helps the residents in Melaka to increase their knowledge on flood risk and establish an emergency plan for their families. The study also shows that residents in flood prone area put inadequate efforts in preparing for a flood event.

Urbanizing the Eternal City: How did the ancient Romans transform and adapt to Rome’s riverine landscape?

This case study considers the trajectory of Ancient Rome’s urban development in its riverine environment. Recent geoarchaeological investigations in Rome’s river valley have shed light on the pre-urban setting and landscape changes that occurred alongside urbanization. With these new discoveries and a rich corpus of archaeological and historical evidence, Rome offers one of the longest records available for exploring how rivers impact cities and cities impact rivers.In order to build in their flood-prone lowlands in the mid-1st millennium BCE, the inhabitants of Rome engaged in large-scale landscape modification projects and flood mitigation measures. Urbanization at Rome, however, triggered fresh ecological challenges, including rapid sedimentation and escalating floods. The inhabitants of Rome responded to their volatile river with a range of adaptive strategies. Over the centuries, they continued to mitigate the effects of overbank flooding through the use of strategic urban planning, land reclamation, flood-resistant architecture, and bureaucratic oversight of the riverine landscape. It is noteworthy that, despite having the capabilities, the ancient Romans chose not to enact large-scale engineering projects to prevent floods altogether. The so-called Eternal City, therefore, should be seen as a resilient city—one that persevered in the face of dynamic ecological conditions and recurrent inundations.This article emphasizes the value of historical applications for modern sustainability studies: the past offers both empirical evidence and a vibrant perspective for conceptualizing the complexities of human-environment interactions over many centuries. The story of Rome’s relationship with its river ultimately underscores the eternal co-evolution of society and the environment.

River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies

Abstract. Global-scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large-scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and increasing computing capacities have allowed better performance by RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides the flow directions of all grid cells. River networks may be derived at various spatial resolutions by upscaling high-resolution hydrography data. This paper presents a new global-scale river network at 1/12∘ derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the hierarchical dominant river tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is provided here with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high-resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global-scale simulations with the CTRIP model, one with the current spatial resolution (1/2∘) and the other with the new spatial resolution (1/12∘). It is shown that, overall, CTRIP at 1/12∘ outperforms CTRIP at 1/2∘, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters, freely available for download from Zenodo (https://doi.org/10.5281/zenodo.6482906, Munier and Decharme, 2022), may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.

The role of human-induced climate change in heavy rainfall events such as the one associated with Typhoon Hagibis

Around October 12, 2019, torrential rainfall from Typhoon Hagibis caused large-scale flooding in a large area around the metropole region of Tokyo leading to large-scale destruction including losses of lives, livelihoods, and economic losses of well over $10 bn US dollars. In this paper we use a multi-method probabilistic event attribution framework to assess the role of human-induced climate change in the heavy rainfall event responsible for a large proportion of the damages. Combining different observational datasets and various climate model simulations, we find an increase in the likelihood of such an event to occur of 15–150%. We use this assessment and the calculated fraction of attributable risk (FAR) to further estimate the economic costs attributable to anthropogenic climate change based on the insured economic losses. Our conservative estimate is that ~$4bn of the damages due to the extreme heavy rainfall associated with Typhoon Hagibis are due to human-induced climate change.

Research and Application of Reservoir Flood Control Optimal Operation Based on Improved Genetic Algorithm

This paper took the Foziling Reservoir in the Pi River Basin as an example, used an improved genetic algorithm to optimize the flood control dispatching during the flood process, and compared the results with the traditional genetic algorithm and the dispatching plan in the 2020 large-scale reservoir flood control operation plan. The results showed that, compared with the traditional genetic algorithm, the improved genetic algorithm saved the time for the model to determine the penalty coefficients and made the model application more convenient. At the same time, the design of the original scheduling scheme also has certain limitations. The scheduling results obtained by improving the genetic algorithm could occupy a small flood control capacity as much as possible under the premise of ensuring the safety of the reservoir itself and the downstream area.

Seismic modelling and observations of rainfall

Rainfall is a key driver of geomorphological processes ranging from impacting drops that lead to the small-scale dislodgement of soil particles to large-scale morphogenic floods and rainfall-induced hillslope processes. Although rainfall has been identified in seismic records, the associated power spectral density and its quantitative relation to the underlying physical processes have not yet been studied. Here, we analyze nearly 2 years of combined seismic and optical disdrometer measurements, where the latter enables the drop-based quantification of rainfall physical properties. Our measurements confirm the broadband observation of ground velocity power spectral density due to rainfall, allowing the seismic identification of rainfall at intensities as low as 1 mm/h. Seismic power, P, shows a power-law scaling with rainfall intensity, I, and kinetic energy, E: P∝I2.1 and P∝E1.6. The observed scaling relations are consistent between the three monitored sites although there are absolute differences in seismic power of about 1 order of magnitude, which are likely due to variability in landcover and subsurface seismic properties. With a physical model, we demonstrate that the observed power-law relations are set by an underlying linear relation between seismic power and rainfall impulse power, and that the associated exponent values of I and E are due to the covariance of the raindrop size distribution with the total number of drops. The largest raindrop fractions, whose relative contribution increases with rainfall intensity, dominate the seismic signal where, in our case, 90% of the seismic power is attributed to drops larger than 3 mm. Using our model, we estimate the contributing area of rainfall to seismic observations to be within a radial distance of ∼5–25 m. The spatially integrated nature of the seismic measurements and their sensitivity to large raindrops, which control the disaggregation and the mobilization of soil particles, make seismic records well-suited for the investigation of soil erosion processes. More generally, our work provides a basis for the temporally-resolved seismic quantification of rainfall that drives the dynamics of various hydro-geomorphological processes.

Effects of seasonal hydrological regulation of cascade dams on the functional diversity of zooplankton: Implications for the management of massive reservoirs and dams

The construction of cascade dams has led to a lack of longitudinal connectivity in rivers, fragmentation of habitats, slowing of the spread of biota, and loss of biodiversity. Here, we examined the zooplankton functional diversity in a typical river with cascade dams and compared the differences between the upstream and downstream parts of the river during the dry and wet seasons. The findings were as follows: (1) during the dry season, the nutrient load in the lower reaches of the river increased, and the water quality deteriorated, while the conditions during the wet season did not change significantly; (2) during the dry season, the functional beta diversity of downstream zooplankton caused by cascade dams was significantly (p < 0.05) lower than that upstream, but this phenomenon was not prominent during the wet season; (3) during the dry season, environmental factors and dam numbers significantly affected the functional diversity of zooplankton, and the number of dams directly impacted the formation of the distribution pattern of zooplankton functional traits. These findings highlight the effects of the hydrological regulation of cascade dams in different seasons on the functional diversity of zooplankton and suggest that: 1) water retention times and pollution source inputs should be reduced during the dry season, 2) dedicated biological channels should be established to reduce the harm caused by clustered pollution to zooplankton, and 3) thermal pollution and strong scouring caused by large-scale flood discharge simultaneously should be avoided during the rainy season. Zooplankton functional diversity is sensitive to dam hydrological regulation, and we advise it be included as a critical indicator during ecosystem assessment in the dam management process, along with additional biological indicators are recommended.

A new dataset of river flood hazard maps for Europe and the Mediterranean Basin

Abstract. In recent years, the importance of continental-scale hazard maps for riverine floods has grown. Nowadays, such maps are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as supporting management plans for national and local flood risk. In this paper we present a new set of high-resolution (100 m) hazard maps for river flooding that covers most European countries, as well as all of the river basins entering the Mediterranean and Black Sea in the Caucasus, the Middle East and northern Africa. The new river flood hazard maps represent inundation along 329 000 km of the river network, for six different flood return periods, expanding on the datasets previously available for the region. The input river flow data for the new maps are produced by means of the hydrological model LISFLOOD using new calibration and meteorological data, while inundation simulations are performed with the hydrodynamic model LISFLOOD-FP. In addition, we present here a detailed validation exercise using official hazard maps for Hungary, Italy, Norway, Spain and the UK, which provides a more detailed evaluation of the new dataset compared with previous works in the region. We find that the modelled maps can identify on average two-thirds of reference flood extent, but they also overestimate flood-prone areas with below 1-in-100-year flood probabilities, while for return periods equal to or above 500 years, the maps can correctly identify more than half of flooded areas. Further verification is required in the northern African and eastern Mediterranean regions, in order to better understand the performance of the flood maps in arid areas outside Europe. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with an upstream area below 500 km2 and the limitations in representing river channels and the topography of lowland areas. In addition, the different designs of reference maps (e.g. extent of areas included) affect the correct identification of the areas for the validation, thus penalizing the scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets, combined with reliable data of river channel geometry, may greatly contribute to improving future versions of continental-scale river flood hazard maps. The new high-resolution database of river flood hazard maps is available for download at https://doi.org/10.2905/1D128B6C-A4EE-4858-9E34-6210707F3C81 (Dottori et al., 2020a).

Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin

The Ganga-Brahmaputra basin is highly sensitive to the impacts of climate change and experiences recurrent flooding, which affects large agricultural areas and poses a high risk to the population. The present study is focused on the recent flood disaster in the Ganga-Brahmaputra basin, which mainly affected the regions of Bihar, West Bengal, and Assam in India and neighboring Bangladesh during July, August, and September 2020. Using the Sentinel-1A Synthetic Aperture Radar (SAR) data, the flood extent was derived in the Google Earth Engine (GEE) platform. The composite area under flood inundation for July–September was estimated to be 25,889.1 km2 for Bangladesh, followed by Bihar (20,837 km2), West Bengal (17,307.1 km2), and Assam (13,460.1 km2). The Copernicus Global Land Cover dataset was used to extract the affected agricultural area and flood-affected settlement. Floods have caused adverse impacts on agricultural lands and settlements, affecting 23.68–28.47% and 5.66–9.15% of these areas, respectively. The Gridded Population of the World (GPW) population density and Global Human Settlement Layer (GHSL) population dataset were also employed to evaluate flood impacts, which revealed that 23.29 million of the population was affected by floods in the Ganga-Brahmaputra basin. The highest impacts of floods can be seen from the Bihar state, as people reside in the lower valley and near to the riverbank due to their dependency on river water. Similarly, the highest impact was from Bangladesh because of the high population density as well as the settlement density. The study provided a holistic spatial assessment of flood inundation in the region due to the combined impact of the Ganga-Brahmaputra River basin. The identification of highly flood-prone areas with an estimated impact on cropland and build-up will provide necessary information to decision-makers for flood risk reduction, mitigation activities, and management.

Comparative Assessment of Data Mining Techniques for Flash Flood Prediction

Abstract Data mining techniques have recently drawn considerable attention from the research community for their ability to predict flash flood phenomena. These techniques can bring large-scale flood data into real practice and have become the necessary tools for impact assessment, societal resilience, and disaster control. Although numerous studies have been conducted on data mining techniques and flash flood predictions, domain-specific flash flood prediction models based on existing data mining techniques are still lacking. Notably, this study has focused on the performance of four data mining techniques, namely, logistic regression (LR), artificial neural networks (ANN), k-nearest neighbour (kNN), and support vector machine (SVM) in a comparative assessment as prediction models. The area under the curve (AUC) was utilised to validate these models. The value of AUC was higher than 0.9 for all models. Accordingly, the outcomes outlined in this study can contribute to Halim et al. the current literature by boosting the performance of data mining techniques for predicting flash floods through a comparison of the most recent data mining techniques. Keywords: Artificial neural networks (ANN), Flash flood, k-nearest neighbor (kNN), Logistic regression (LR), Support vector machine (SVM)

The Impacts of Flooding and Business Activity and Employment: A Spatial Perspective on Small Business

Severe flooding events often cause significant damage to an area, including affecting the local economy, disrupting transportation, and damaging infrastructure. While raw statistics offer some understanding of crop and property-related damages, resulting from large-scale floods, we also need to consider the longer-term impacts and recovery within an area and the interaction between adjacent areas during the recovery process. In this paper, we examine the impacts of major and minor flood events on business employment and the number of establishments in different sectors of the economy. While we find that flood events had a negative short-run impact on agricultural services and particularly small establishments, estimations show positive impacts in the service sector. We also identify significant spatial spillovers.

Improving the performance of city-scale hydrodynamic flood modelling through a GIS-based DEM correction method

Flood modelling can provide useful information to support flood risk assessment and management. The accuracy of flood simulation results is highly dependent on the quality of input data. In particular, digital elevation models (DEMs) may directly influence the performance of flood predictions and improper representation of complex urban features including buildings and bridges may lead to incorrect prediction of flooding paths and extents, and consequently miscalculate flood risk. In this work, a geographic information system (GIS)-based correction method is proposed to make modifications in high-resolution DEMs by adding building complexes and removing unphysical representations of bridges for a more realistic description of flood paths in considering the flow connectivity in intensely urbanized areas and with the objective of obtaining more accurate flood simulation results. The proposed DEM correction method is applied to support large-scale urban flood modelling in Fuzhou City, China, using an established hydrodynamic flood model known as High-Performance Integrated hydrodynamic Modelling System (HiPIMS). Comparisons are made to the simulation results with and without the DEM improvements using the proposed correction method. The results demonstrate that correct representation of the artificial structures in the urban DEM can significantly improve the flood simulation results.

A Decision-Making Framework Integrating Fluid and Solid Systems to Assess Resilience of Coastal Communities Experiencing Extreme Storm Events

The precise assessment of flood damage and structural resiliency is of the utmost importance for coastal communities, mitigating risk from repeated extreme storm events. However, most flood resiliency studies have been criticized for lack of accuracy and failing to depict the relationships among the hydrodynamics, structural characteristics, and community preparedness. This work intends to present an inclusive approach for quantifying community-scale flood damage and structural resiliency. Large-scale coastal flooding has been simulated and validated with semi-coupled storm surge and 2D inundation models. The depth and momentum components of flood flow were integrated with a newly developed multidimensional flood-damage assessment model, which includes the traditional depth–damage relationship as well as building height, age, configuration, and construction material to calculate the resiliency of structures as a function of recovery time, community preparedness, and level of flood-induced damage. The study region experienced flood depths ranging from 4.91 m to 8.06 m from various hurricane categories with 28.69%, 45.62%, and 92.13% damage to properties. The flood damage and resiliency results were presented via geospatial analytics at property level (i.e., microscale) and aggregated census block group (i.e., macroscale) levels, indicating the developed framework's universal applicability and scalability.

Monitoring Post-Flood Recovery of Croplands Using the Integrated Sentinel-1/2 Imagery in the Yangtze-Huai River Basin

The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing and cloud computing technology. Yet, the effects of agricultural emergency response to floods have been limitedly evaluated by satellite-based remote sensing, resulting in biased post-flood loss assessments. Addressing this challenge, this study presents a method for monitoring post-flood agricultural recovery using Sentinel-1/2 imagery, tested in three flood-affected main grain production areas, in the middle and lower Yangtze and Huai River, China. Our results indicated that 33~72% of the affected croplands were replanted and avoided total crop failures in summer 2020. Elevation, flood duration, crop rotation scheme, and flooding emergency management affect the post-flood recovery performance. The findings also demonstrate rapid intervention measures adjusted to local conditions could reduce the agricultural failure cost from flood disasters to a great extent. This study provides a new alternative for comprehensive disaster loss assessment in flood-prone agricultural regions, which will be insightful for worldwide flood control and management.

On the role of floodplain storage and hydrodynamic interactions in flood risk estimation

ABSTRACT Hydrodynamic interactions, i.e. the floodplain storage effects caused by inundations upstream on flood wave propagation, inundation areas, and flood damage downstream, are important but often ignored in large-scale flood risk assessments. Although new methods considering these effects sometimes emerge, they are often limited to a small or meso scale. In this study, we investigate the role of hydrodynamic interactions and floodplain storage on flood hazard and risk in the German part of the Rhine basin. To do so, we compare a new continuous 1D routing scheme within a flood risk model chain to the piece-wise routing scheme, which largely neglects floodplain storage. The results show that floodplain storage is significant, lowers water levels and discharges, and reduces risks by over 50%. Therefore, for accurate risk assessments, a system approach must be adopted, and floodplain storage and hydrodynamic interactions must carefully be considered.

Numerical Modeling the Flood and Pollutant Transport Processes in Residential Areas with Different Land Use Types

Large-scale flooding causes widespread disaster, and harmful pollutant concentration in water following flood affects public safety and the environment. In this study, a numerical model for solving the 2D shallow water equations and the solute transport equation is proposed to simulate overland flood and pollutant transport caused by floods. The present model is verified by comparing the predictions with the analytical solutions and simulation results; sufficiently high computational accuracy is achieved. The model is also used to simulate flood inundation and pollution spread in the area of Hun and Taizi Lane (HTL) in China due to river dike breaches; the results show that the coupling model has excellent performance for simulating the flooding process and the temporal and spatial distribution of pollutants in urban or rural areas. We use remote sensing techniques to acquire the land coverage in the area of HTL based on Landsat TM satellites. The impacts of changed land use on mitigation of flooding waves and pollutant spread are investigated; the results indicate that the land cover changes have an obvious influence on the evolution process of flood waves and pollutant transport in the study areas, where the transport of pollutants is very dynamic during flood inundation in HTL area. Furthermore, the motion of pollutants considering anisotropic diffusion is more reasonable than that due to isotropic dispersion in simulating pollutant transport associated with the flood in urban or farmland environments.

Development of the snowmelt prediction model using Artificial Intelligence (AI)

Droughts and abnormal heavy rains have frequently occurred in Japan due to the effects of climate change in recent years, and flexible operations that maximize the functions of dams are required. Especially in cold snowy regions, snowmelt water is stored in a dam to cover water demand from early spring to early summer, but during the snowmelt season, a sudden rise in temperature and heavy rain could cause large-scale floods. Therefore, the highly accurate prediction of dam inflow during the snowmelt season is extremely important from the viewpoint of effective use of water resources and prevention of snowmelt floods. On the other hand, in recent years, research utilizing Artificial Intelligence (AI) has also been promoted in the hydrological field. This study clarified the problems of the conventional physical model (rainfall runoff model) and the prediction model by AI for the inflow of the dam during the snowmelt season in order to support efficient dam management. Then, by constructing a semi-physical model that complements the problems of the physical model and the AI model, we developed a more accurate model for predicting the inflow of snowmelt water during the snowmelt season compared to the single model.