A high-resolution global flood hazard model

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Rapid mapping of global flood precursors and impacts using novel five-day GRACE solutions

Floods affect communities and ecosystems worldwide, emphasizing the importance of identifying their precursors and enhancing resilience to these events. Here, we calculated Antecedent Total Water Storage (ATWS) anomalies from the new 5-day (5D) Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) satellite solutions to enhance the detection of pre-flood and active flood conditions and to map post-flood storage anomalies. The GRACE data were compared with ~ 3300 flood events reported by the Dartmouth Flood Observatory (2002-2021), revealing distinct ATWS precursor signals in 5D solutions, in contrast to the monthly solutions. Specifically, floods caused by saturation-excess runoff-triggered by persistent rainfall, monsoonal patterns, snowmelt, or rain-on-snow events-show detectable ATWS increases 15 to 50 days before and during floods, providing a valuable opportunity to improve flood monitoring. These 5D solutions also facilitate a more rapid mapping of post-flood storage changes to assess flood recovery from tropical cyclones and sub-monthly weather extremes. Our findings show the promising potential of 5D GRACE solutions, which are still in the development phase, for future integration into operational frameworks to enhance flood detection and recovery, facilitating the rapid analysis of storage changes relative to monthly solutions.

Integrating social vulnerability into high-resolution global flood risk mapping

High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying 'hotspots' characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.

Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk

Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%-24%, depending on flood hazard zone (50%-0.2% annual probability). We simulated various "what-if" scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.

Global evidence of rapid urban growth in flood zones since 1985

Disaster losses are increasing and evidence is mounting that climate change is driving up the probability of extreme natural shocks1-3. Yet it has also proved politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local and national authorities4,5. However, locally determined patterns of urbanization and spatial development are key factors to the exposure and vulnerability of people to climatic shocks6. Using high-resolution annual data, this study shows that, since 1985, human settlements around the world-from villages to megacities-have expanded continuously and rapidly into present-day flood zones. In many regions, growth in the most hazardous flood zones is outpacing growth in non-exposed zones by a large margin, particularly in East Asia, where high-hazard settlements have expanded 60% faster than flood-safe settlements. These results provide systematic evidence of a divergence in the exposure of countries to flood hazards. Instead of adapting their exposure, many countries continue to actively amplify their exposure to increasingly frequent climatic shocks.

Can atmospheric reanalysis datasets reproduce flood inundation at regional scales? A systematic analysis with ERA5 over Mahanadi River Basin, India

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95th and 99th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience.

Human alterations of the global floodplains 1992-2019

Floodplains provide critical ecosystem services; however, loss of natural floodplain functions caused by human alterations increase flood risks and lead to massive loss of life and property. Despite recent calls for improved floodplain protection and management, a comprehensive, global-scale assessment quantifying human floodplain alterations does not exist. We developed the first publicly available global dataset that quantifies human alterations in 15 million km2 floodplains along 520 major river basins during the recent 27 years (1992-2019) at 250-m resolution. To maximize the reuse of our dataset and advance the open science of human floodplain alteration, we developed three web-based programming tools supported with tutorials and step-by-step audiovisual instructions. Our data reveal a significant loss of natural floodplains worldwide with 460,000 km2 of new agricultural and 140,000 km2 of new developed areas between 1992 and 2019. This dataset offers critical new insights into how floodplains are being destroyed, which will help decision-makers to reinforce strategies to conserve and restore floodplain functions and habitat.

Mapping global non-floodplain wetlands

Non-floodplain wetlands - those located outside the floodplains - have emerged as integral components to watershed resilience, contributing hydrologic and biogeochemical functions affecting watershed-scale flooding extent, drought magnitude, and water-quality maintenance. However, the absence of a global dataset of non-floodplain wetlands limits their necessary incorporation into water quality and quantity management decisions and affects wetland-focused wildlife habitat conservation outcomes. We addressed this critical need by developing a publicly available "Global NFW" (Non-Floodplain Wetland) dataset, comprised of a global river-floodplain map at 90 m resolution coupled with a global ensemble wetland map incorporating multiple wetland-focused data layers. The floodplain, wetland, and non-floodplain wetland spatial data developed here were successfully validated within 21 large and heterogenous basins across the conterminous United States. We identified nearly 33 million potential non-floodplain wetlands with an estimated global extent of over 16×106 km2. Non-floodplain wetland pixels comprised 53% of globally identified wetland pixels, meaning the majority of the globe's wetlands likely occur external to river floodplains and coastal habitats. The identified global NFWs were typically small (median 0.039 km2), with a global median size ranging from 0.018-0.138 km2. This novel geospatial Global NFW static dataset advances wetland conservation and resource-management goals while providing a foundation for global non-floodplain wetland functional assessments, facilitating non-floodplain wetland inclusion in hydrological, biogeochemical, and biological model development. The data are freely available through the United States Environmental Protection Agency's Environmental Dataset Gateway (https://gaftp.epa.gov/EPADataCommons/ORD/Global_NonFloodplain_Wetlands/, last access: 24 May 2023) and through https://doi.org/10.23719/1528331 (Lane et al., 2023a).

Flood hazard potential reveals global floodplain settlement patterns

Flooding is one of the most common natural hazards, causing disastrous impacts worldwide. Stress-testing the global human-Earth system to understand the sensitivity of floodplains and population exposure to a range of plausible conditions is one strategy to identify where future changes to flooding or exposure might be most critical. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. Here we show that topography and drainage areas correlate with flood sensitivities as well as with societal behaviour. We find clear settlement patterns in which floodplains most sensitive to frequent, low magnitude events, reveal evenly distributed exposure across hazard zones, suggesting that people have adapted to this risk. In contrast, floodplains most sensitive to extreme magnitude events have a tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes given climate change.

Flood impacts on urban road connectivity in southern China

Effective measures to improve road accessibility during storms are required as traffic congestion caused by storm floods increasingly constrains the efficiency of urban commuting. However, flood impacts on urban road connectivity are not yet well assessed due to inaccurate simulation of flood processes in urban areas where high-resolution data for drainage networks and gauged hydrological data are insufficient. Thus, this study assesses flood impacts on road network connectivity in an urban area of southern China through joint modeling of 1-D hydrodynamic processes in drainage networks and 2-D flood inundation processes on roads using MIKE Urban and MIKE 21. High-resolution DEM images of 5 m and a drainage network of 5635 pipelines were used for urban hydrological simulation. Flood depths were gauged for model calibration and validation by recruited volunteers in the context of citizen science. The results show that road network connectivity decreases as rainfall increases. More than 40% of road connectivity is lost in the study area when a 1-in-100-year return period rainfall occurs. The study results can help to inform more adaptive strategies for local flood control. The study methods are also applicable to improving urban hydrological modeling in broader regions.

Assessing protected area vulnerability to climate change in a case study of South African national parks

Climate change is challenging the ability of protected areas (PAs) to meet their objectives. To improve PA planning, we developed a framework for assessing PA vulnerability to climate change based on consideration of potential climate change impacts on species and their habitats and resource use. Furthermore, the capacity of PAs to adapt to these climate threats was determined through assessment of PA management effectiveness, adjacent land use, and financial resilience. Users reach a PA-specific vulnerability score and rank based on scoring of these categories. We applied the framework to South Africa's 19 national parks. Because the 19 parks are managed as a national network, we explored how resources might be best allocated to address climate change. Each park's importance to the network's biodiversity conservation and revenue generation was estimated and used to weight overall vulnerability scores and ranks. Park vulnerability profiles showed distinct combinations of potential impacts of climate change and adaptive capacities; the former had a greater influence on vulnerability. Mapungubwe National Park emerged as the most vulnerable to climate change, despite its relatively high adaptive capacity, largely owing to large projected changes in species and resource use. Table Mountain National Park scored the lowest in overall vulnerability. Climate change vulnerability rankings differed markedly once importance weightings were applied; Kruger National Park was the most vulnerable under both importance scenarios. Climate change vulnerability assessment is fundamental to effective adaptation planning. Our PA assessment tool is the only tool that quantifies PA vulnerability to climate change in a comparative index. It may be used in data-rich and data-poor contexts to prioritize resource allocation across PA networks and can be applied from local to global scales.

A Comprehensive Approach for Floodplain Mapping through Identification of Hazard Using Publicly Available Data Sets over Canada

Quantifying flood inundation and hazards over large regions is paramount for gaining critical information on flood risk over the vulnerable population and environment. Readily available global data and enhancement in computational simulations have made it easier to simulate flooding at a large scale. This study explores the usability of publicly available datasets in flood inundation and hazard mapping, and ensures the flood-related information reaches the end-users efficiently. Runoff from the North American Regional Reanalysis and other relevant inputs are fed to the CaMa-Flood model to generate flooding patterns for 1 in 100 and 1 in 200-year return period events over Canada. The simulated floodplain maps are overlaid on the property footprints of 34 cities (falling within the top 100 populated cities of Canada) to determine the degree of exposure during 1991, 2001 and 2011. Lastly, Flood Map Viewer—a web-based public tool, is developed to disseminate extensive flood-related information. The development of the tool is motivated by the commitment of the Canadian government to contribute $63 M over the next three years for the development of flood maps, especially in high-flood risk areas. The results from the study indicate that around 80 percent of inundated spots belong to high and very-high hazard classes in a 200-year event, which is roughly 4 percent more than observed during the 100-year event. We notice an increase in the properties exposed to flooding during the last three decades, with a signature rise in Toronto, Montreal and Edmonton. The flood-related information derived from the study can be used along with vulnerability and exposure components to quantify flood risk. This will help develop appropriate pathways for resilience building for long-term sustainable benefits.

Locally Relevant High-Resolution Hydrodynamic Modeling of River Floods at the Regional Scale

This paper deals with the simulation of inundated areas for a region of 84,000 km² from estimated flood discharges at a resolution of 2 m. We develop a modeling framework that enables efficient parallel processing of the project region by splitting it into simulation tiles. For each simulation tile, the framework automatically calculates all input data and boundary conditions required for the hydraulic simulation on-the-fly. A novel method is proposed that ensures regionally consistent flood peak probabilities. Instead of simulating individual events, the framework simulates effective hydrographs consistent with the flood quantiles by adjusting streamflow at river nodes. The model accounts for local effects from buildings, culverts, levees, and retention basins. The two-dimensional full shallow water equations are solved by a second-order accurate scheme for all river reaches in Austria with catchment sizes over 10 km², totaling 33,380 km. Using graphics processing units (GPUs), a single NVIDIA Titan RTX simulates a period of 3 days for a tile with 50 million wet cells in less than 3 days. We find good agreement between simulated and measured stage-discharge relationships at gauges. The simulated flood hazard maps also compare well with local high-quality flood maps, achieving critical success index scores of 0.6-0.79.

Flood exposure and poverty in 188 countries

Flooding is among the most prevalent natural hazards, with particularly disastrous impacts in low-income countries. This study presents global estimates of the number of people exposed to high flood risks in interaction with poverty. It finds that 1.81 billion people (23% of world population) are directly exposed to 1-in-100-year floods. Of these, 1.24 billion are located in South and East Asia, where China (395 million) and India (390 million) account for over one-third of global exposure. Low- and middle-income countries are home to 89% of the world’s flood-exposed people. Of the 170 million facing high flood risk and extreme poverty (living on under $1.90 per day), 44% are in Sub-Saharan Africa. Over 780 million of those living on under $5.50 per day face high flood risk. Using state-of-the-art poverty and flood data, our findings highlight the scale and priority regions for flood mitigation measures to support resilient development.

Floods are most devastating for those who can least afford to be hit. Globally, 1.8 billion people face high flood risks; 89% of them live in developing countries; 170 million of them live in extreme poverty making them most vulnerable.

On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions

Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.

Urban growth modelling and social vulnerability assessment for a hazardous Kathmandu Valley

In our rapidly urbanizing world, many hazard-prone regions face significant challenges regarding risk-informed urban development. This study addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH model; and (3) the assessment of social vulnerability, using a composite index tailored for the case-study area. Results show that built-up areas in Kathmandu Valley will increase to 352 km2 by 2050, effectively doubling the equivalent 2018 figure. The most socially vulnerable villages will account for 29% of built-up areas in 2050, 11% more than current levels. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km2 and 49 km2 today to 83 km2 and 108 km2 in 2050. Additionally, built-up areas in liquefaction-susceptible zones will expand by 13 km2 to 47 km2. This study illustrates how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, it emphasizes an urgent need to implement effective policy measures for reducing tomorrow's natural-hazard risks.

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

Poplar's Waterlogging Resistance Modeling and Evaluating: Exploring and Perfecting the Feasibility of Machine Learning Methods in Plant Science

Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar's waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar's waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R²) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.

Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique)

Flood hazard and risk analysis in developing countries is a difficult task due to the absence or scarce availability of flow data and digital elevation models (DEMs) with the necessary quality. Up to eight DEMs (ALOS Palsar, Aster GDEM, Bare Earth DEM, SRTM DEM, Merit DEM, TanDEM-X DEM, NASA DEM, and Copernicus DEM) of different data acquisition, spatial resolution, and data processing were used to reconstruct the January 2015 flood event. The systematic flow rate record from the Mocuba city gauge station as well as international aid organisms and field data were used to define both the return period peak flows in years for different flood frequencies (Tyear) and the January 2015 flooding event peak flow. Both visual and statistical analysis of flow depth values at control point locations give us a measure of the different hydraulic modelling performance. The results related to the Copernicus DEM, both in visual and statistical approach, show a clear improvement over the results of the other free global DEMs. Under the assumption that Copernicus DEM provides the best results, a flood hazard analysis was carried out, its results being in agreement with previous data of the effects of the January 2015 flooding event in the Mocuba District. All these results highlight the step forward that Copernicus DEM represents for flood hazard analysis in developing countries, along with the use of so-called “citizen science” in the form of flooding evidence field data acquisition.

Impact of identical digital elevation model resolution and sources on morphometric parameters of Tena watershed, Ethiopia

Digital elevation models (DEMs) are the primary form of satellite data used to design and analyze the hydrology and hydraulic behavior of watersheds for water resource development. The primary objective of this study is to conduct morphometric parameter analysis using SRTM_30m, ASTER_30m, and ALOS_30m data to determine the impact of identical DEM resolution and DEM sources on the Tena watershed by computing the basic and derived parameters. In this study I used data from two sources for morphometric parameter analysis with ArcGIS software. The results indicate that the DEM sources did not provide similar results for all parameters: ASTER_30m was the maximum output for almost all parameters, followed by SRTM_30m and ALOS_30m. The findings of this study suggest that ASTER_30m is the most suitable data source for flood risk assessment, soil erosion, sediment, streamflow, and other watershed modelling, while ALOS_30m is best suited for peak discharge analysis. All of the used DEM sources were suitable for computing watershed shape parameters. In general, the resolution of DEMs impacts the hydrological and hydraulic study of any watershed, with resulting effects on decision-making for watershed management and development.

Efficient Reservoir Modelling for Flood Regulation in the Ebro River (Spain)

The vast majority of reservoirs, although built for irrigation and water supply purposes, are also used as regulation tools during floods in river basins. Thus, the selection of the most suitable model when facing the simulation of a flood wave in a combination of river reach and reservoir is not direct and frequently some analysis of the proper system of equations and the number of solved flow velocity components is needed. In this work, a stretch of the Ebro River (Spain), which is the biggest river in Spain, is simulated solving the Shallow Water Equations (SWE). The simulation model covers the area of river between the city of Zaragoza and the Mequinenza dam. The domain encompasses 721.92 km2 with 221 km of river bed, of which the last 75 km belong to the Mequinenza reservoir. The results obtained from a one-dimensional (1D) model are validated comparing with those provided by a two-dimensional (2D) model based on the same numerical scheme and with measurements. The 1D modelling loses the detail of the floodplain, but nevertheless the computational consumption is much lower compared to the 2D model with a permissible loss of accuracy. Additionally, the particular nature of this reservoir might turn the 1D model into a more suitable option. An alternative technique is applied in order to model the reservoir globally by means of a volume balance (0D) model, coupled to the 1D model of the river (1D-0D model). The results obtained are similar to those provided by the full 1D model with an improvement on computational time. Finally, an automatic regulation is implemented by means of a Proportional-Integral-Derivative (PID) algorithm and tested in both the full 1D model and the 1D-0D model. The results show that the coupled model behaves correctly even when controlled by the automatic algorithm.

Python program for spatial reduction and reconstruction method in flood inundation modelling

Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method.•The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas.•The representative locations selected following the SRR method have sufficient flood data for developing emulation models.•Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.

Data-driven flood hazard zonation of Italy

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

Satellite imaging reveals increased proportion of population exposed to floods

Flooding affects more people than any other environmental hazard and hinders sustainable development^1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods³. Where and how floods occur and who is exposed are changing as a result of rapid urbanization⁴, flood mitigation infrastructure⁵ and increasing settlements in floodplains⁶. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty^3,7-11. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255-290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58-86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates⁷. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods.

Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

Risky Development: Increasing Exposure to Natural Hazards in the United States

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered.

Methodology for Determining the Nearest Destinations for the Evacuation of People and Equipment from a Disaster Area to a Safe Area

Floods are the most frequent natural disasters in the world. In the system of warning and flood protection of areas at risk of flooding in the event of its occurrence, it seems advisable to initially work out the possibility of evacuating the population, animals, equipment, material values, etc. In this article, a methodology for determining destinations (points of destination) for the evacuation of people and equipment from a predicted flood zone (of a natural disaster) to a safe area is proposed based upon the criterion of the shortest possible distance. In the paper, a scenario is considered that involves the contours of the flood zone boundaries for several variants of the intensity of the probable development of future events (with the aid of geoinformation technologies), and the coordinates of the objects to evacuate are permanent and known in advance. With the known coordinates of the objects and the closest points of the boundary of the predicted flood zone, the shortest distances can be calculated. Based on these calculations, the appropriate destinations for evacuation are determined. The proposed methodology can be used for flood forecasting and flood zone modeling to assess the economic and social risks of their aftereffects and to allow the public, local governments, and other organizations to better understand the potential risks of floods and to identify the measures needed to save lives and avoid damage to and loss of property and equipment. This methodology, in contrast to known approaches, allows the determination of the nearest locations for the evacuation of people and equipment from a flood zone (of a natural disaster) to safe areas, to be determined for several variants, depending on the possible development of future events. The methodology is algorithm-driven and presented in the form of a flowchart and is suitable for use in the appropriate software. The proposed methodology is an introduction to the next stages of research related to the determination of safe places for evacuation of people and their property (equipment) to safe places. This is especially important in case of sudden weather events (flash floods).

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.

A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.

Role of dams in reducing global flood exposure under climate change

Globally, flood risk is projected to increase in the future due to climate change and population growth. Here, we quantify the role of dams in flood mitigation, previously unaccounted for in global flood studies, by simulating the floodplain dynamics and flow regulation by dams. We show that, ignoring flow regulation by dams, the average number of people exposed to flooding below dams amount to 9.1 and 15.3 million per year, by the end of the 21^st century (holding population constant), for the representative concentration pathway (RCP) 2.6 and 6.0, respectively. Accounting for dams reduces the number of people exposed to floods by 20.6 and 12.9% (for RCP2.6 and RCP6.0, respectively). While environmental problems caused by dams warrant further investigations, our results indicate that consideration of dams significantly affect the estimation of future population exposure to flood, emphasizing the need to integrate them in model-based impact analysis of climate change.

Two-Dimensional Flood Inundation Modeling in the Godavari River Basin, India—Insights on Model Output Uncertainty

Most flood inundation models do not come with an uncertainty analysis component chiefly because of the complexity associated with model calibration. Additionally, the fact that the models are both data- and compute-intensive, and since uncertainty results from multiple sources, adds another layer of complexity for model use. In the present study, flood inundation modeling was performed in the Godavari River Basin using the Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D) model. The model simulations were generated for six different scenarios that resulted from combinations of different geometric, hydraulic and hydrologic conditions. Thus, the resulted simulations account for multiple sources of uncertainty. The SRTM-30 m and MERIT-90 m Digital elevation Model (DEM), two sets of Manning’s roughness coefficient (Manning’s n) and observed and estimated boundary conditions, were used to reflect geometric, hydraulic and hydrologic uncertainties, respectively. The HEC-RAS 2D model ran in an unsteady state mode for the abovementioned six scenarios for the selected three flood events that were observed in three different years, i.e., 1986, 2005 and 2015. The water surface elevation (H) was compared in all scenarios as well as with the observed values at selected locations. In addition, ‘H’ values were analyzed for two different structures of the computational model. The average correlation coefficient (r) between the observed and simulated H values is greater than 0.85, and the highest r, i.e., 0.95, was observed for the combination of MERIT-90 m DEM and optimized (obtained via trial and error) Manning’s n. The analysis shows uncertainty in the river geometry information, and the results highlight the varying role of geometric, hydraulic and hydrologic conditions in the water surface elevation estimates. In addition to the role of the abovementioned, the study recommends a systematic model calibration and river junction modeling to understand the hydrodynamics upstream and downstream of the junction.

Predicting Future Urban Flood Risk Using Land Change and Hydraulic Modeling in a River Watershed in the Central Province of Vietnam

Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models to explore future urban flood risk, aiming to reduce it under different vulnerability and exposure scenarios. SPOT-3 and Sentinel-2 images were processed and classified to create land cover maps for 1995 and 2019, and these were used to predict the 2040 land cover using the Land Change Modeler Module of Terrset. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. We have compared flood risk in 1995, 2019, and 2040. Although flood risk increases with urbanization, population density, and the number of hospitals in the flood plain, especially in the coastal region, the area exposed to high and very high risks decreases due to a reduction in poverty rate. This study can provide a theoretical framework supporting climate change related to risk assessment in other metropolitan regions. Methodologically, it underlines the importance of using satellite imagery and the continuity of data in the planning-related decision-making process.

Assessing Property Level Economic Impacts of Climate in the US, New Insights and Evidence from a Comprehensive Flood Risk Assessment Tool

Hurricanes and flood-related events cause more direct economic damage than any other type of natural disaster. In the United States, that damage totals more than USD 1 trillion in damages since 1980. On average, direct flood losses have risen from USD 4 billion annually in the 1980s to roughly USD 17 billion annually from 2010 to 2018. Despite flooding’s tremendous economic impact on US properties and communities, current estimates of expected damages are lacking due to the fact that flood risk in many parts of the US is unidentified, underestimated, or available models associated with high quality assessment tools are proprietary. This study introduces an economic-focused Environmental Impact Assessment (EIA) approach that builds upon an our existing understanding of prior assessment methods by taking advantage of a newly available, climate adjusted, parcel-level flood risk assessment model (First Street Foundation, 2020a and 2020b) in order to quantify property level economic impacts today, and into the climate adjusted future, using the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathways (RCPs) and NASA’s Global Climate Model ensemble (CMIP5). This approach represents a first of its kind—a publicly available high precision flood risk assessment tool at the property level developed completely with open data sources and open methods. The economic impact assessment presented here has been carried out using residential buildings in New Jersey as a testbed; however, the environmental assessment tool on which it is based is a national scale property level flood assessment model at a 3 m resolution. As evidence of the reliability of the EIA tool, the 2020 estimated economic impact (USD 5481 annual expectation) was compared to actual average per claim-year NFIP payouts from flooding and found an average of USD 5540 over the life of the program (difference of less than USD 100). Additionally, the tool finds a 41.4% increase in average economic flood damage through the year 2050 when environmental change is included in the model.

Application of HEC-RAS (2D) for Flood Hazard Maps Generation for Yesil (Ishim) River in Kazakhstan

The use of hydraulic models for carrying out flood simulations is a common practice globally. The current study used HEC-RAS (2D) in order to simulate different flood scenarios on the River Yesil (Ishim). Comparison of different mesh sizes (25, 50 and 75 m) indicated no significant difference in model performance. However, a significant difference was observed in simulation time. In addition, the inclusion of breaklines showed that there was a slight improvement in model performance and a shortening of the simulation time. Sensitivity analysis and the consequent manual calibration of sensitive parameters resulted in a slight improvement (an increase in the model accuracy from 58.4% for uncalibrated to 59.7% for calibrated). Following the simulations inundation maps for 10-, 20- and 100-year flood events were obtained. Hazard classification of the flood extents generated indicated that the settlements of Zhibek Zholy and Arnasay were flooded in all the simulated events. Volgodonovka village experienced flooding when a 100-year flood event was simulated. On the other hand, settlement No. 42 did not experience any flooding in any of the scenarios. The model results also demonstrate that the Counter-Regulator was not overtopped in the event of the 100-year hydrograph.

Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology

Consistent data are seldom available for whole-catchment flood modelling in many developing regions, hence this study aimed to explore an integrated approach for flood modelling and mapping by combining available segmented hydrographic, topographic, floodplain roughness, calibration, and validation datasets using a two-dimensional Caesar-Lisflood hydrodynamic model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Available segments of remotely-sensed and in situ datasets (including hydrological, altimetry, digital elevation model, bathymetry, aerial photo, optical imagery, and radar imagery data) available to different degrees in the Niger-South hydrological area were systematically integrated to draw maximum benefits from all available data. Retrospective modelling, calibration, and validation were undertaken for the whole Niger- South hydrological catchment area of Nigeria, and then these data were segmented into sub-domains for re-validation to understand how data variability and uncertainties impact the accuracy of model outcomes. Furthermore, aerial photos were applied for the first time in the study area for flood model validation and for understanding how different physio-environmental properties influenced the synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria. This study demonstrates how the complementary strengths of open, readily available geospatial datasets and tools can be leveraged to model and map flooding within acceptable levels of uncertainty for flood risk management.

Performances of the New HEC-RAS Version 5 for 2-D Hydrodynamic-Based Rainfall-Runoff Simulations at Basin Scale: Comparison with a State-of-the Art Model

The Hydrologic Engineering Centre-River Analysis System (HEC-RAS), developed by the US Army Corps of Engineers, is one of the most known, analyzed and used model for flood mapping both in the scientific literature and in practice. In the recently released version (release 5.0.7), the HEC-RAS model has been enriched with novel modules, performing fully 2-D computations based on the 2-D fully dynamic equations as well as the 2-D diffusion wave equations; moreover the application of rainfall to each cell of the two-dimensional domain is now possible. Contrarily to the common applications for flood propagation in river reach, this specific module has never been analyzed in the literature. Therefore, the main purpose of this work is to assess the potential and the capabilities of the 2-D HEC-RAS model in rainfall-runoff simulations at the basin scale, comparing the results obtained using both the options (fully dynamic equations and diffusion wave equations) to the simulations obtained by using a 2-D fully dynamic model developed by the authors for research purposes. Both models have been tested in a small basin in Northern Italy to analyze the differences in terms of discharge hydrographs and flooded areas. The application of a criterion for hazard class mapping has shown significant variations between the two models. These results provide practical indications for the water engineering community in the innovative research field related to the use of 2-D SWEs at the basin scale.

Capturing transformation of flood hazard over a large River Basin under changing climate using a top-down approach

Existing flood modeling studies over coastal catchments involving different combinations of model chain setup imparting complex information fails to entail the needs of policy or decision-makers. Thus, a comprehensive framework that pertains to the requirements of practitioners and provides more perspicuous flood hazard information is required. In this paper, a novel approach translating complex flood hazard information in the form of decision priority maps derived using a rational combination of models (physical and statistical) is elucidated at the finest administrative scale. The proposed methodology is illustrated over a highly flood-prone deltaic region in Mahanadi River Basin, India, to characterize impacts of climate change for a 1:100 years return period flood event under future conditions (2026-2055). The modeled flood events are further analyzed to capture the transformation dynamics of flood hazard classes (FHCs) in near-future, for prioritizing areas with greater hazard potential. Interestingly, the results capture a high transformation characteristic from low to high FHCs in agriculture-dominated areas, which are significantly greater than the areas experiencing flood hazard reduction. The results show a significant increase of 12.5% and 27.35% in areas with high FHCs under RCP4.5 and RCP8.5 scenarios, respectively. Moreover, a notable climate change response is indicated under both climate change scenarios, with approximately 22% (RCP4.5) and 25% (RCP8.5) in villages showing a drastic increment in flood hazard magnitude. The results thus highlight the importance of identifying and prioritizing the areas for flood adaptation where a relative change in flood hazard potential is higher due to climate change. Therefore, we conclude that this study can provide an insight into the implication of new approaches for effective communication of flood information by bridging the gaps between scientific communities and decision-makers in appraisal for better flood adaptation measures.

Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

UAV-DEMs for Small-Scale Flood Hazard Mapping

Devastating floods are observed every year globally from upstream mountainous to coastal regions. Increasing flood frequency and impacts affect both major rivers and their tributaries. Nonetheless, at the small-scale, the lack of distributed topographic and hydrologic data determines tributaries to be often missing in inundation modeling and mapping studies. Advances in Unmanned Aerial Vehicle (UAV) technologies and Digital Elevation Models (DEM)-based hydrologic modeling can address this crucial knowledge gap. UAVs provide very high resolution and accurate DEMs with low surveying cost and time, as compared to DEMs obtained by Light Detection and Ranging (LiDAR), satellite, or GPS field campaigns. In this work, we selected a LiDAR DEM as a benchmark for comparing the performances of a UAV and a nation-scale high-resolution DEM (TINITALY) in representing floodplain topography for flood simulations. The different DEMs were processed to provide inputs to a hydrologic-hydraulic modeling chain, including the DEM-based EBA4SUB (Event-Based Approach for Small and Ungauged Basins) hydrologic modeling framework for design hydrograph estimation in ungauged basins; the 2D hydraulic model FLO-2D for flood wave routing and hazard mapping. The results of this research provided quantitative analyses, demonstrating the consistent performances of the UAV-derived DEM in supporting affordable distributed flood extension and depth simulations.

Satellite-based data driven quantification of pluvial floods over Europe under future climatic and socioeconomic changes

Flooding is one of the major threats jeopardizing lives and properties of the people, and its risk is expected to increase remarkably under changing climatic and socioeconomic conditions. Yet, future flood risk has not been well studied due primarily to a limited availability of detailed and consistent data on future vulnerability components and the computationally expensive continental flood modeling. Here we perform a top-down data driven flood risk assessment for 20-, 30-, 50- and 100-year return periods over Europe at the continental, regional and national levels for the late 21st century. To account for the impact of changes in both climatic and socioeconomic conditions on floods, the Shared Socioeconomic Pathways (SSPs) are merged with Representative Concentration Pathways (RCPs), integrating hazard and several social, economic and agricultural exposure-vulnerability proxy indicators. Our results show a ubiquitous drastic increase up to 87% in future flood risks of different return periods over Europe, with eastern and southern regions experiencing the highest risk increase. A fossil-fuel based development in the future would lead to 14-15% higher flood risk compared to a sustainable development, which goes up to 23% in north Europe. The amplified future flood risk is predominantly driven by climate change, although with a large uncertainty, rather than socioeconomic drivers.

A comprehensive approach to understanding flood risk drivers at the municipal level

During the period 1998-2017, floods were responsible for 11% of the loss of life and 23% of the economic loss caused by climate-related and geophysical-related disasters worldwide. An integrated and effective definition of flood risk management strategies therefore still requires synthesized and comprehensive knowledge about the driving forces of flood risk. In this study, 278 Portuguese municipalities are analyzed and classified according to flood hazard, exposure, and vulnerability. After evaluating the three components that describe risk, an index of the flood risk is calculated and a cluster analysis is further performed to understand the role of the risk drivers (hazard, exposure, and vulnerability) in each municipality. The proposed approach therefore provides flood risk indexes on a municipal basis, which are built upon different sources of both cell-by-cell data and an aggregation of municipal-level data that has been statistically validated. Municipalities both in the NW part of the country and along the valleys of major rivers demonstrate a significant superimposition of high levels of exposure and hazard, while vulnerability presents a disperse pattern throughout the country. The results obtained using this approach should contribute to the diversification of flood risk management strategies. This is still lacking in the majority of the national-level flood risk governance processes, namely those strategies that focus on the contingency of daily activities and those aiming at a long-term reduction of the exposure, vulnerability, and hazard components that shape flood disasters.

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models

The present study is carried out in the context of the continuous increase, worldwide, of the number of flash-floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by flash-flood phenomena. Therefore, Flash-Flood Potential Index (FFPI) was defined and calculated across the Bâsca Chiojdului river basin by using one bivariate statistical method (Statistical Index) and its novel ensemble with the following machine learning models: Logistic Regression, Classification and Regression Trees, Multilayer Perceptron, Random Forest and Support Vector Machine and Decision Tree CART. In a first stage, the areas with torrentiality were digitized based on orthophotomaps and field observations. These regions, together with an equal number of non-torrential pixels, were further divided into training surfaces (70%) and validating surfaces (30%). The next step of the analysis consisted of the selection of flash-flood conditioning factors based on the multicollinearity investigation and predictive ability estimation through Information Gain method. Eight factors, from a total of ten flash-floods predictors, were selected in order to be included in the FFPI calculation process. By applying the models represented by Statistical Index and its ensemble with the machine learning algorithms, the weight of each conditioning factor and of each factor class/category in the FFPI equations was established. Once the weight values were derived, the FFPI values across the Bâsca Chiojdului river basin were calculated by overlaying the flash-flood predictors in GIS environment. According to the results obtained, the central part of Bâsca Chiojdului river basin has the highest susceptibility to flash-flood phenomena. Thus, around 30% of the study site has high and very high values of FFPI. The results validation was carried out by applying the Prediction Rate and Success Rate. The methods revealed the fact that the Multilayer Perceptron - Statistical Index (MLP-SI) ensemble has the highest efficiency among the 3 methods.

New insights into US flood vulnerability revealed from flood insurance big data

Improvements in modelling power and input data have vastly improved the precision of physical flood models, but translation into economic outputs requires depth-damage functions that are inadequately verified. In particular, flood damage is widely assumed to increase monotonically with water depth. Here, we assess flood vulnerability in the US using >2 million claims from the National Flood Insurance Program (NFIP). NFIP claims data are messy, but the size of the dataset provides powerful empirical tests of damage patterns and modelling approaches. We show that current depth-damage functions consist of disparate relationships that match poorly with observations. Observed flood losses are not monotonic functions of depth, but instead better follow a beta function, with bimodal distributions for different water depths. Uncertainty in flood losses has been called the main bottleneck in flood risk studies, an obstacle that may be remedied using large-scale empirical flood damage data.

Flood Risk Assessment of Global Watersheds Based on Multiple Machine Learning Models

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

A global multi-hazard risk analysis of road and railway infrastructure assets

Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that ~27% of all global road and railway assets are exposed to at least one hazard and ~7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which ~73% is caused by surface and river flooding. Global EAD are small relative to global GDP (~0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on ~60% of roads exposed to a 1/100 year flood event.

New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling

A new method for sensitivity analysis of water depths is presented based on a two-dimensional hydraulic model as a convenient and cost-effective alternative to Monte Carlo simulations. The method involves perturbation of the probability distribution of input variables. A relative sensitivity index is calculated for each variable, using the Gauss quadrature sampling, thus limiting the number of runs of the hydraulic model. The variable-related highest variation of the expected water depths is considered to be the most influential. The proposed method proved particularly efficient, requiring less information to describe model inputs and fewer model executions to calculate the sensitivity index. It was tested over a 45 km long reach of the Richelieu River, Canada. A 2D hydraulic model was used to solve the shallow water equations (SWE). Three input variables were considered: Flow rate, Manning’s coefficient, and topography of a shoal within the considered reach. Four flow scenarios were simulated with discharge rates of 759, 824, 936, and 1113 m 3 / s . The results show that the predicted water depths were most sensitive to the topography of the shoal, whereas the sensitivity indices of Manning’s coefficient and the flow rate were comparatively lower. These results are important for making better hydraulic models, taking into account the sensitivity analysis.

New estimates of flood exposure in developing countries using high-resolution population data

Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. When intersected with simulated water depths, this results in a significant mis-estimation. Here, we use new highly resolved population information to show that, in reality, humans make more rational decisions about flood risk than current demographic data suggest. In the new data, populations are correctly represented as risk-averse, largely avoiding obvious flood zones. The results also show that existing demographic datasets struggle to represent concentrations of exposure, with the total exposed population being spread over larger areas. In this analysis we use flood hazard data from a ~90 m resolution hydrodynamic inundation model to demonstrate the impact of different population distributions on flood exposure calculations for 18 developing countries spread across Africa, Asia and Latin America. The results suggest that many published large-scale flood exposure estimates may require significant revision.

Disparities of population exposed to flood hazards in the United States

This study integrates publicly available datasets to provide a county-based assessment of socio-economic disparities of population exposure to flood hazards in the United States. Statistical analyses were applied to reveal the national trends and local deviations from the trends. Results show that approximately 21.8 million (6.87% of) U.S. population are exposed to 100-year-flood in 2015, and most of the exposure is near water bodies (e.g. ocean and rivers). Additionally, communities near water bodies are more responsive to potential flood hazards by avoiding residence in flood zones than inland communities. At the national scale, economically disadvantaged population are more likely to reside in flood zones than outside. At the local scale, economically disadvantaged population tend to reside in flood zones in inland areas, while coastal flood zones are more occupied by wealthier and elderly people. These findings point to an alarming situation of inland communities where people are generally less responsive to flood hazards and people in flood zones are in a lower economic condition. Using "hot spot" analysis, local clusters of disadvantaged population groups with high flood exposure were identified. Overall, this study provides important baseline information for policymaking at different levels of administration and pinpoints local areas where diversified and ad hoc strategies are needed to mitigate flood risk in communities with diverse socio-economic conditions. This study provides empirical evidence of socio-economic disparities and environmental injustice associated with flood exposure in the U.S. and offers valuable insights to the underlying factors.