Global-scale river flood vulnerability in the last 50 years

A unified probability distribution of second order difference of global streamflow

Hydrological processes, as part of a natural system, are highly complex and chaotic. By analyzing long time series of streamflow data from ~ 4800 hydrologic stations, it is interesting to find out that probability density functions in second order difference (SOD) of the streamflow data are fat-tailed and bell-shaped curves, and their cumulative distribution functions (CDFs) follow an S-shaped curve (S-curve). We found that t-distribution is a good approximation for S-curve, which uses the degree of freedom (DF) to control the tail thickness. We also found that DF of more than 80% of stations are gathered in the range between 5 and 8. Analysis of the S-curves in seven large river basins indicated that the S-curves can vary with time and space, which is regarded as a good indicator for identifying natural and anthropogenic changes. This study provides a symmetrical, identical and concise probability distribution to describe global streamflow under changing environment.

Evaluating the influence of human activities on flood severity and its spatial heterogeneity across the Pearl River Delta

With climate change and intensified human activities, disasters such as heavy rainfall, flooding, typhoons, and storm surges are becoming more frequent, posing significant threats to lives, property, and economic development. We propose a method combining extreme value theory and probability distribution to examine the flood severity under the effect of strong human activities. By focusing on the Pearl River Delta (PRD), as one of the most populated areas of China, we quantified changes in the severity of extreme water level for different return levels between 1966 and 1990 and 1991-2016 (with strong human activities), associated with the spatial patterns over the PRD. The flood severity decreased near inland areas of the delta but increased in coastal areas. Additionally, in coastal areas, the flood severity of long return levels has increased more significantly than that of short return levels, under the effect of strong anthropogenic activities. This study further examined the spatial heterogeneity in extreme water levels under the influence of human activities over the PRD. This study provides new insights into basin-scale flood responses to human activities and strategies for mitigating flood disasters over the PRD. The proposed method offers a promising approach for evaluating the severity of flooding in regions experiencing hydrological changes similar to those observed in the PRD under the influence of human intervention.

Neglected pathways of heavy metal input into agricultural soil: Water-land migration of heavy metals due to flooding events

Flooding, carrying sediments, inundates farmlands across the world due to extreme adverse weather conditions. The casualties and property damage associated with flooding are important direct impacts. However, there is currently insufficient understanding of the remobilization and distribution of heavy metals (HMs) caused by flooding. Few studies have specifically considered flooding as a pathway for HMs contamination of soil. Herein, a novel methodological framework for revealing the input pathways of HMs in agricultural soils in mining-intensive areas is proposed and applied. Flooding is considered one of the pathways for HMs inputs during source apportionment. The results demonstrated a high degree of overlap between the distribution characteristics of major HMs in agricultural soils and sediments. The degree of soil Cd pollution was significantly positively correlated with the inundation depth in the flooded area. It took 8.4-11.5 times of flood inundation or 98.5-119.9 years of accumulation of atmospheric deposition to reach HMs contamination levels in the soil of the study area. Flooding brought in most of the soil Cd, while atmospheric deposition was the primary input pathway for soil Pb and Zn. Our results identified the role of flood inundation on the input of HMs in mining-intensive areas. These results demonstrated the value of our framework for studying the impact of flooding on HMs in agricultural soils from the perspective of input pathways, providing new insights not only into identifying the sources of soil HMs but also into enhancing understanding of the impact of flooding on soil environments. With the potential increase in the frequency and intensity of flooding inundating farmlands in the future, it is essential to consider flooding as a pathway for HMs inputs in order to comprehensively assess their environmental impact.

Characterization of the Regulatory Network under Waterlogging Stress in Soybean Roots via Transcriptome Analysis

Flooding stress caused by climate change is a serious threat to crop productivity. To enhance our understanding of flooding stress in soybean, we analyzed the transcriptome of the roots of soybean plants after waterlogging treatment for 10 days at the V2 growth stage. Through RNA sequencing analysis, 870 upregulated and 1129 downregulated differentially expressed genes (DEGs) were identified and characterized using Gene Ontology (GO) and MapMan software (version 3.6.0RC1). In the functional classification analysis, "alcohol biosynthetic process" was the most significantly enriched GO term in downregulated DEGs, and phytohormone-related genes such as ABA, cytokinin, and gibberellin were upregulated. Among the transcription factors (TFs) in DEGs, AP2/ERFs were the most abundant. Furthermore, our DEGs encompassed eight soybean orthologs from Arabidopsis and rice, such as 1-aminocyclopropane-1-carboxylate oxidase. Along with a co-functional network consisting of the TF and orthologs, the expression changes of those genes were tested in a waterlogging-resistant cultivar, PI567343. These findings contribute to the identification of candidate genes for waterlogging tolerance in soybean, which can enhance our understanding of waterlogging tolerance.

Adaptation of the Invasive Plant Sphagneticola trilobata to Flooding Stress by Hybridization with Native Relatives

Hybridization is common between invasive and native species and may produce more adaptive hybrids. The hybrid (Sphagneticola × guangdongensis) of Sphagneticola trilobata (an invasive species) and S. calendulacea (a native species) was found in South China. In this study, S. trilobata, S. calendulacea, and Sphagneticola × guangdongensis were used as research materials to explore their adaptability to flooding stress. Under flooding stress, the ethylene content and the expression of key enzyme genes related to ethylene synthesis in Sphagneticola × guangdongensis and S. calendulacea were significantly higher than those in S. trilobata. A large number of adventitious roots and aerenchyma were generated in Sphagneticola × guangdongensis and S. calendulacea. The contents of reactive oxygen species and malondialdehyde in Sphagneticola × guangdongensis and S. calendulacea were lower than those in S. trilobata, and the leaves of S. trilobata were the most severely damaged under flooding stress. The results indicate that hybridization catalyzed the tolerance of Sphagneticola × guangdongensis to flooding stress, and the responses of Sphagneticola × guangdongensis to flooding stress were more similar to that of its native parent. This suggests that hybridization with native relatives is an important way for invasive species to overcome environmental pressure and achieve invasion.

CiAP2/ERF65 and CiAP2/ERF106, a pair of homologous genes in pecan (Carya illinoensis), regulate plant responses during submergence in transgenic Arabidopsis thaliana

When plants are entirely submerged, photosynthesis and respiration are severely restricted, affecting plant growth and potentially even causing plant death. The AP2/ERF superfamily has been widely reported to play a vital role in plant growth, development and resistance to biotic and abiotic stresses. However, no relevant studies exist on flooding stress in pecan. In this investigation, we observed that CiAP2/ERF65 positively modulated the hypoxia response during submergence, whereas CiAP2/ERF106 was sensitive to submergence. The levels of physiological and biochemical indicators, such as POD, CAT and among others, in CiAP2/ERF65-OE lines were significantly higher than those in wild-type Arabidopsis thaliana, indicating that the antioxidant capacity of CiAP2/ERF65-OE lines was enhanced under submergence. The RNA-seq results revealed that the maintenance of the expression levels of the antenna protein gene, different signaling pathways for regulation, as well as the storage and consumption of ATP, might account for the opposite phenotypes of CiAP2/ERF65 and CiAP2/ERF106. Furthermore, the expression of some stress-related genes was altered during submergence and reoxygenation. Overall, these findings enhance our understanding of submergence stress in pecan, providing important candidate genes for the molecular design and breeding of hypoxia resistant in plants.

Societal determinants of flood-induced displacement

What explains human consequences of weather-related disaster? Here, we explore how core socioeconomic, political, and security conditions shape flood-induced displacement worldwide since 2000. In-sample regression analysis shows that extreme displacement levels are more likely in contexts marked by low national income levels, nondemocratic political systems, high local economic activity, and prevalence of armed conflict. The analysis also reveals large residual differences across continents, where flood-induced displacement in the Global South often is much more widespread than direct human exposure measures would suggest. However, these factors have limited influence on our ability to accurately predict flood displacement on new data, pointing to important, hard-to-operationalize heterogeneity in flood impacts across contexts and critical data limitations. Although results are consistent with an interpretation that the sustainable development agenda is beneficial for disaster risk reduction, better data on societal consequences of natural hazards are critically needed to support evidence-based decision-making.

Characterizing the level of urban Flood vulnerability using the social-ecological-technological systems framework, the case of Adama city, Ethiopia

This study characterizes the flood vulnerability of Adama City, Ethiopia, where the city faces high flood vulnerability due to its unplanned urbanization in low-lying floodplain areas surrounding deforested mountains and ridges. The study applied an interlinked Social-Ecological-Technological-Systems (SETS) vulnerability framework using a GIS-based Multi-Criteria Decision-Making and Analytical Hierarchy Process (MCDM-AHP). The framework analyzed exposure, sensitivity, and adaptive capacity to flooding for each of the three SETS domains. The study analyzed 18 variables at the city level within each SETS domain. The result revealed that clusters of flood-vulnerable areas were identified by each SETS domain showing the concentration of flood vulnerability in the study area and the need to consider prompt adaptive mechanisms to severe and recurring flooding. The finding has significant implications for holistic approaches to sustainable cities. Moreover, the reduction of complex urban flood vulnerabilities according to their priority as individual or combined solutions for decision-makers and professionals in early warning and flood management systems is the other contribution of the study.

Human displacements, fatalities, and economic damages linked to remotely observed floods

We present a new open source dataset FLODIS that links estimates of flood-induced human displacements, fatalities, and economic damages to flooded areas observed through remote sensing. The dataset connects displacement data from the Internal Displacement Monitoring Centre (IDMC), as well as data on fatalities and damages from the Emergency Events Database (EM-DAT), with the Global Flood Database (GFD), a satellite-based inventory of historic flood footprints. It thereby provides a spatially explicit estimate of the flood hazard underlying each individual disaster event. FLODIS contains two datasets with event-specific information for 335 human displacement events and 695 mortality/damage events that occurred around the world between 2000 and 2018. Additionally, we provide estimates of affected population, GDP, and critical infrastructure, as well as socio-economic indicators; and we provide geocoding for displacement events ascribed to other types of disasters, such as tropical cyclones, so that they may be linked to corresponding hazard estimates in future work. FLODIS facilitates integrated flood risk analysis, allowing, for example, for detailed assessments of local flood-damage and displacement vulnerability.

GIS-based hydrodynamic modeling for urban flood mitigation in fast-growing regions: a case study of Erbil, Kurdistan Region of Iraq

Floods threaten urban infrastructure, especially in residential neighborhoods and fast-growing regions. Flood hydrodynamic modeling helps identify flood-prone locations and improve mitigation plans' resilience. Urban floods pose special issues due to changing land cover and a lack of raw data. Using a GIS-based modeling interface, input files for the hydrodynamic model were developed. The physical basin's properties were identified using soil map data, Land Use Land Cover (LULC) maps, and a Digital Elevation Model (DEM). So, the HEC-RAS 2-D hydrodynamic model was developed to estimate flood susceptibility and vulnerability in Erbil, Iraq. The case study examines the quality of flood modeling results using different DEM precisions. Faced with the difficulty, this study examines two building representation techniques: Building Block (BB) and Building Resistance (BR). The work presented here reveals that it is possible to apply the BR technique within the HEC-RAS 2-D to create urban flood models for regions that have a lack of data or poor data quality. Indeed, the findings confirmed that the inundated areas or areas where water accumulated in past rainfall events in Erbil are the same as those identified in the numerical simulations. The study's results indicate that the Erbil city is susceptible to flood hazards, especially in areas with low-lying topography and substantial precipitation. The study's conclusions can be utilized to plan and develop flood control structures, since it identified flood-prone areas of the city.

Oxygenation alleviates waterlogging-caused damages to cherry rootstocks

Waterlogging has occurred more frequently in recent years due to climate change, so it is a huge threat to crop yield and quality. Sweet cherry, a fruit tree with a high economic value, is sensitive to waterlogging stress. One of the most effective methods for enhancing the waterlogging tolerance of sweet cherries is to select waterlogging-tolerant rootstocks. However, the waterlogging tolerance of different cherry rootstocks, and the underlying mechanism remains uncharacterized. Thus, we first evaluated the waterlogging resistance of five sweet cherry rootstocks planted in China. The data showed that 'Gisela 12' and 'Colt' were the most waterlogging-sensitive and -tolerant among the five tested varieties, respectively. Oxygenation effectively alleviated the adverse impacts of waterlogging stress on cherry rootstocks. Moreover, we found that the waterlogging group had lower relative water content, Fv/Fm value, net photosynthetic rate, and higher antioxidant enzyme activities, whereas the oxygenated group performed better in all these parameters. RNA-Seq analysis revealed that numerous DEGs were involved in energy production, antioxidant metabolism, hormone metabolism pathways, and stress-related transcription factors. These findings will help provide management strategies to enhance the waterlogging tolerance of cherry rootstocks and thereby achieve higher yield and better quality of cherries.

Exploring the links between flood events and the COVID-19 infection cases in Romania in the new multi-hazard-prone era

The occurrence of flood events amid the COVID-19 pandemic represents a prominent part of the emerging multi-hazard landscape, as floods are one of the most frequent and destructive natural hazards. This spatial and temporal overlap of hydrological and epidemiological hazards translates into compounded negative effects, causing a shift in the hazard management paradigm, in which hazard interaction takes centre stage. This paper calls into question whether the river flood events that occurred during the COVID-19 pandemic in Romania and the way that they were managed had an impact on the infection with the SARS-CoV-2 virus at county scale. To this end, hazard management data concerning the flood events that were severe enough to impose the evacuation of the population were corroborated with COVID-19 confirmed cases data. A definite link between the flood events and the dynamics of COVID-19 cases registered in the selected counties is difficult to identify, but the analysis shows that all flood events were followed by various size increases in the COVID-19 confirmed cases at the end of the incubation time range. The findings are critically interpreted by providing viral load and social-related contexts, allowing a proper understanding of the interactions between concurrent hazards.

Evaluating Indirect Economic Losses from Flooding Using Input-Output Analysis: An Application to China's Jiangxi Province

Quantifying total economic impacts of flood disaster in a timely manner is essential for flood risk management and sustainable economic growth. This study takes the flood disaster in China's Jiangxi province during the flood season in 2020 as an example, and exploits the input-output method to analyze indirect economic impacts caused by the agricultural direct economic loss. Based on regional IO data and MRIO data, a multi-dimensional econometric analysis was undertaken in terms of inter-regional, multi-regional, and structural decomposition of indirect economic losses. Our study reveals that the indirect economic losses caused by the agricultural sector in other sectors in Jiangxi province were 2.08 times the direct economic losses, of which the manufacturing sector suffered the worst, accounting for 70.11% of the total indirect economic losses. In addition, in terms of demand side and supply side indirect losses, the manufacturing and construction industries were found to be more vulnerable than other industries, and the flood disaster caused the largest indirect economic loss in eastern China. Besides, the supply side losses were significantly higher than the demand side losses, highlighting that the agricultural sector has strong spillover effects on the supply side. Moreover, based on the MRIO data of the years 2012 and 2015, dynamic structural decomposition analysis was undertaken, which showed that changes in the distributional structure appear to be influential in the evaluation of indirect economic losses. The findings highlight the spatial and sectoral heterogeneity of indirect economic losses caused by floods, and have significant implications for disaster mitigation and recovery strategies.

Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models

Floods are among the most destructive disasters because they cause immense damage to human life, property (land and buildings), and resources. They also slow down a country's economy. Due to the dynamic and complex nature of floods, it is difficult to predict the areas that are prone to flooding. In this study, an attempt was made to create a suitability map for future urban development based on flood vulnerability maps for the catchment area of Taif, Saudi Arabia. Three models were used for this purpose, including bivariate (FR), multivariate (LR), and machine learning (SVM) were used. Thirteen parameters were used as flood-contributing parameters. The inventory map was constructed using field surveys, historical data, analysis of RADAR (Sentinel-1A), and Google Earth imagery collected between 2013 and 2020. In general, 70% flood locations were randomly selected from the flood inventory map to generate the flood susceptibility model, and the remaining 30% of the flood locations were used for model validation. The flood susceptibility map was classified into five zones: very low, low, moderate, high, and very high. The AUC value used to predict the performance of the models showed that the accuracy reached 89.5, 92.0, and 96.2% for the models FR, LR, and SVM, respectively. Accordingly, the flood susceptibility map produced by the SVM model is accurate and was used to produce a flood vulnerability map with the help of urban and road density maps. Then slope and elevation maps were integrated with the flood vulnerability model to produce the final suitability map, which was classified into three zones: isolated zone, low suitability, and high suitability areas. The results showed that the highly suitable areas are located in the east and northeast of the Taif Basin, where the flood risk is low and very low. The results of this work will improve the land use planning of engineers and authorities and take possible measures to reduce the flood hazards in the area.

Flood Hazard Zoning of Upper Awash River Basin, Ethiopia, Using the Analytical Hierarchy Process (AHP) as Compared to Sensitivity Analysis

Floods and droughts have been two of the most devastating consequences of the climate crisis affecting billions of people in the world. However, unlike the other natural hazards, flooding is manageable through appropriate flood management mechanisms. This study emphasizes on developing a flood hazard zone for the Upper Awash River Basin (UARB), Ethiopia. Six relevant climate, physiographic, and biophysical factors were considered. Then, a flood hazard map was developed employing the analytic hierarchy process (AHP) method and further validated using sensitivity analysis and collected flood marks. The results revealed that drainage density, rainfall, and elevation have higher significance, while land use and soil permeability have a low impact in the process of flood generation. The map showed vulnerable areas at different levels and can serve as a valuable input for the decision makers to consider in the process of implementing emergency plans as well as long-term flood mitigation options.

Classification of flood-generating processes in Africa

River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa.

Unraveling the complexities of urban fluvial flood hydraulics through AI

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.

Impact of extreme weather events on cropland inundation over Indian subcontinent

Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The "Gulab" cyclonic storm formed on the 24^th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26^th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the "Gulab" cyclonic event. The Otsu's thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km²), followed by Telangana (~2040 km²) and Odisha (~1132 km²), and the least in Chhattisgarh (~93.75 km²). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years.

Proteomic and Biochemical Approaches Elucidate the Role of Millimeter-Wave Irradiation in Wheat Growth under Flooding Stress

Flooding impairs wheat growth and considerably affects yield productivity worldwide. On the other hand, irradiation with millimeter waves enhanced the growth of chickpea and soybean under flooding stress. In the current work, millimeter-wave irradiation notably enhanced wheat growth, even under flooding stress. To explore the protective mechanisms of millimeter-wave irradiation on wheat under flooding, quantitative proteomics was performed. According to functional categorization, proteins whose abundances were changed significantly with and without irradiation under flooding stress were correlated to glycolysis, reactive-oxygen species scavenging, cell organization, and hormonal metabolism. Immunoblot analysis confirmed that fructose-bisphosphate aldolase and β tubulin accumulated in root and leaf under flooding; however, even in such condition, their accumulations were recovered to the control level in irradiated wheat. The abundance of ascorbate peroxidase increased in leaf under flooding and recovered to the control level in irradiated wheat. Because the abundance of auxin-related proteins changed with millimeter-wave irradiation, auxin was applied to wheat under flooding, resulting in the application of auxin improving its growth, even in such condition. These results suggest that millimeter-wave irradiation on wheat seeds improves the recovery of plant growth from flooding via the regulation of glycolysis, reactive-oxygen species scavenging, and cell organization. Additionally, millimeter-wave irradiation could promote tolerance against flooding through the regulation of auxin contents in wheat.

One Health Concept against Schistosomiasis: An Overview

Schistosomiasis (bilharziasis) is a parasitic disease caused by Schistosoma spp. that belongs to trematode worms. These worms are known as “blood parasites”. This disease is included in “neglected tropical diseases” and “water-borne diseases”. The main species are Schistosoma (S.) haematobium, S. japonicum, S. mansoni, S. intercalatum, S. mekongi, S. guineensis and S. intercalatum, though there are more than 20 different species. The parasite in the definitive host may affect many organs and systems. The disease may become chronic and lasts 3–8 years and even up to 20–30 years. The definitive host is primarily human; however, in endemic areas animals such as monkeys, cattle, horses, rodents, cats, dogs are reservoirs. According to World Health Organization (WHO), schistosomiasis affects 250 million people, and causes 1.9 million deaths yearly in endemic areas. Moreover, due to global warming, the spread of the disease may increase. The effective way to fight against schistosomiasis is following the “one-health system”. Indeed, to overcome or “eradicate” this disease, we have to strive against different forms at different evolutionary stages of the worm such as, forms in humans, domestic or wild animals, and freshwater snails. If we combine the knowledge of professionals, we may achieve this goal.

Global-Scale Assessment of Economic Losses Caused by Flood-Related Business Interruption

Estimating river flood risk helps us to develop strategies for reducing the economic losses and making a resilient society. Flood-related economic losses can be categorized as direct asset damage, opportunity losses because of business interruption (BI loss), and high-order propagation effects on global trade networks. Biases in meteorological data obtained from climate models hinder the estimation of BI loss because of inaccurate input data including inundation extent and period. In this study, we estimated BI loss and asset damage using a global river and inundation model driven by a recently developed bias-corrected meteorological forcing scheme. The results from the bias-corrected forcing scheme showed an estimated global BI loss and asset damage of USD 26.9 and 130.9 billion (2005 purchase power party, PPP) (1960-2013 average), respectively. Although some regional differences were detected, the estimated BI loss was similar in magnitude to reported historical flood losses. BI loss tended to be greater in river basins with mild slopes such as the Amazon, which has a long inundation period. Future flood risk projection using the same framework under Representative Concentration Pathway 8.5 (RCP8.5) and Shared Socioeconomic Pathway 3 (SSP3) scenarios showed increases in BI loss and asset damage per GDP by 0.32% and 1.78% (2061–2090 average) compared with a past period (1971–2000 average), respectively.

Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data

Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy (77% overall agreement compared with participatory mapping outputs), in comparison with existing global flood datasets (43% overall agreement for the moderate-resolution imaging spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Similarly, increased flood exposure was also observed, however its estimation remains highly uncertain and sensitive to the input dataset used. Population exposure varied greatly depending on the population dataset used, while the greatest farmland and infrastructure exposure was estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans.

Local impacts on road networks and access to critical locations during extreme floods

Floods affected more than 2 billion people worldwide from 1998 to 2017 and their occurrence is expected to increase due to climate warming, population growth and rapid urbanization. Recent approaches for understanding the resilience of transportation networks when facing floods mostly use the framework of percolation but we show here on a realistic high-resolution flood simulation that it is inadequate. Indeed, the giant connected component is not relevant and instead, we propose to partition the road network in terms of accessibility of local towns and define new measures that characterize the impact of the flooding event. Our analysis allows to identify cities that will be pivotal during the flooding by providing to a large number of individuals critical services such as hospitalization services, food supply, etc. This approach is particularly relevant for practical risk management and will help decision makers for allocating resources in space and time.

Cation transporters in cell fate determination and plant adaptive responses to a low-oxygen environment

Soil flooding creates low-oxygen environments in root zones and thus severely affects plant growth and productivity. Plants adapt to low-oxygen environments by a suite of orchestrated metabolic and anatomical alterations. Of these, formation of aerenchyma and development of adventitious roots are considered very critical to enable plant performance in waterlogged soils. Both traits have been firmly associated with stress-induced increases in ethylene levels in root tissues that operate upstream of signalling pathways. Recently, we used a bioinformatic approach to demonstrate that several Ca2+ and K+ -permeable channels from KCO, AKT, and TPC families could also operate in low oxygen sensing in Arabidopsis. Here we argue that low-oxygen-induced changes to cellular ion homeostasis and operation of membrane transporters may be critical for cell fate determination and formation of the lysigenous aerenchyma in plant roots and shaping the root architecture and adventitious root development in grasses. We summarize the existing evidence for a causal link between tissue-specific changes in oxygen concentration, intracellular Ca2+ and K+ homeostasis, and reactive oxygen species levels, and their role in conferring those two major traits enabling plant adaptation to a low-oxygen environment. We conclude that, for efficient operation, plants may rely on several complementary signalling pathway mechanisms that operate in concert and 'fine-tune' each other. A better understanding of this interaction may create additional and previously unexplored opportunities to crop breeders to improve cereal crop yield losses to soil flooding.

Remobilization of pollutants during extreme flood events poses severe risks to human and environmental health

While it is well recognized that the frequency and intensity of flood events are increasing worldwide, the environmental, economic, and societal consequences of remobilization and distribution of pollutants during flood events are not widely recognized. Loss of life, damage to infrastructure, and monetary cleanup costs associated with floods are important direct effects. However, there is a lack of attention towards the indirect effects of pollutants that are remobilized and redistributed during such catastrophic flood events, particularly considering the known toxic effects of substances present in flood-prone areas. The global examination of floods caused by a range of extreme events (e.g., heavy rainfall, tsunamis, extra- and tropical storms) and subsequent distribution of sediment-bound pollutants are needed to improve interdisciplinary investigations. Such examinations will aid in the remediation and management action plans necessary to tackle issues of environmental pollution from flooding. River basin-wide and coastal lowland action plans need to balance the opposing goals of flood retention, catchment conservation, and economical use of water.

Flood resilience in paired US-Mexico border cities: a study of binational risk perceptions

Disastrous floods in the twin cities of Nogales, Arizona, USA, and Nogales, Sonora, Mexico (collectively referred to as Ambos Nogales) occur annually in response to monsoonal summer rains. Flood-related hazards include property damage, impairment to sewage systems, sewage discharge, water contamination, erosion, and loss of life. Flood risk, particularly in Nogales, Sonora, is amplified by informal, "squatter" settlements in the watershed floodplain and associated development and infrastructure. The expected increase in precipitation intensity, resulting from climate change, poses further risk to flooding therein. We explore binational community perceptions of flooding, preferences for watershed management, and potential actions to address flooding and increase socio-ecological resilience in Ambos Nogales using standardized questionnaires and interviews to collect data about people and their preferences. We conducted 25 semi-structured interviews with local subject matter experts and gathered survey responses from community members in Ambos Nogales. Though survey response was limited, expected frequencies were high enough to conduct Chi-squared tests of independence to test for statistically significant relationships between survey variables. Results showed that respondents with previous experience with flooding corresponded with their level of concern about future floods. Additionally, respondents perceived greater flood-related risks from traveling across town and damage to vehicles than from inundation or damages to their homes or neighborhoods. Binationally, women respondents felt less prepared for future floods than men. On both sides of the border, community members and local experts agreed that Ambos Nogales lacks adequate preparation for future floods. To increase preparedness, they recommended flood risk education and awareness campaigns, implementation of green infrastructure, additional stormwater infrastructure (such as drainage systems), enhanced flood early warning systems, and reduction of flood flows through regulations to reduce the expansion of hard surfaces. This study contributes systematic collection of information about flood risk perceptions across an international border, including novel data regarding risks related to climate change and gender-based assessments of flood risk. Our finding of commonalities across both border communities, in perceptions of flood risk and in the types of risk reduction solutions recommended by community members, provides clear directions for flood risk education, outreach, and preparedness, as well as measures to enhance cross-border cooperation.

From the One Health Perspective: Schistosomiasis Japonica and Flooding

Schistosomiasis is a water-borne parasitic disease distributed worldwide, while schistosomiasis japonica localizes in the People’s Republic of China, the Philippines, and a few regions of Indonesia. Although significant achievements have been obtained in these endemic countries, great challenges still exist to reach the elimination of schistosomiasis japonica, as the occurrence of flooding can lead to several adverse consequences on the prevalence of schistosomiasis. This review summarizes the influence of flooding on the transmission of schistosomiasis japonica and interventions responding to the adverse impacts from the One Health perspective in human beings, animals, and the environment. For human and animals, behavioral changes and the damage of water conservancy and sanitary facilities will increase the intensity of water contact. For the environment, the density of Oncomelania snails significantly increases from the third year after flooding, and the snail habitats can be enlarged due to active and passive diffusion. With more water contact of human and other reservoir hosts, and larger snail habitats with higher density of living snails, the transmission risk of schistosomiasis increases under the influence of flooding. With the agenda set for global schistosomiasis elimination, interventions from the One Health perspective are put forward to respond to the impacts of increased flooding. For human beings, conducting health education to increase the consciousness of self-protection, preventive chemotherapy for high-risk populations, supply of safe water, early case finding, timely reporting, and treating cases will protect people from infection and prevent the outbreak of schistosomiasis. For animals, culling susceptible domestic animals, herding livestock in snail-free areas, treating livestock with infection or at high risk of infection, harmless treatment of animal feces to avoid water contamination, and monitoring the infection status of wild animals in flooding areas are important to cut off the transmission chain from the resources. For the environment, early warning of flooding, setting up warning signs and killing cercaria in risk areas during and post flooding, reconstructing damaged water conservancy facilities, developing hygiene and sanitary facilities, conducting snail surveys, using molluscicide, and predicting areas with high risk of schistosomiasis transmission after flooding all contribute to reducing the transmission risk of schistosomiasis. These strategies need the cooperation of the ministry of health, meteorological administration, water resources, agriculture, and forestry to achieve the goal of minimizing the impact of flooding on the transmission of schistosomiasis. In conclusion, flooding is one of the important factors affecting the transmission of schistosomiasis japonica. Multi-sectoral cooperation is needed to effectively prevent and control the adverse impacts of flooding on human beings, animals, and the environment.

Assessment and mitigation of tangible flood damages driven by climate change in a tropical city: Hat Yai Municipality, southern Thailand

Climate change-induced floods in tropical urban areas have presented a serious global challenge because of failed conventional stormwater management practices. This research aims to develop a comprehensive methodological framework for flood damage estimation and mitigation, particularly in a tropical urban city. In this study, interdisciplinary fields were integrated through statistical downscaling, hydrologic-hydraulic modeling, and the development of flood damage curves. Relationships between tangible flood damage and flood-borne outbreak with flood depths were elucidated to predict future damage. Various flood mitigation strategies were evaluated. Herein, Hat Yai Municipality in Southern Thailand was selected as the study area. The flood simulation was conducted for 2010 and the highest flood damage sensitivity was exhibited by non-commercial buildings due to significant commercial stock damage, which was followed by that observed for detached houses. There was a strong linear relationship between the number of patients infected with leptospirosis and flood depth (R² = 0.85). For climate change studies, flood maps for storms with 20-, 50-, and 100-year return periods under the A2/RCP8.5 scenario were generated using hydrological-hydraulic 1D/2D model; these maps were applied with the developed flood damage curves for damage estimation. It was found that reducing flood damage by implementing agroforestry and expanding the main bypass channel provides comparable damage reductions of -25.5% and - 27.5%, respectively, under the worst-case scenario of a 100-year return period in 2040-2059. Therefore, to deal with uncertain climate change situations, the incorporation of structural and non-structural measures is recommended. Such a combination when coupled with an eight-hour flood awareness time can result in a damage reduction of -59.9%. A flood warning system was in high demand by residents in the area; however, damage reduction from this measure alone was not high (approximately -17.0%) when compared to that obtained with other measures; consequently, additional measures were needed.

Proteomic and Biological Analyses Reveal the Effect on Growth under Flooding Stress of Chickpea Irradiated with Millimeter Waves

Chickpea cultivated on marginal lands in arid and semiarid tropics is one of the food legumes, and its growth is reduced by flooding stress. Millimeter-wave irradiation has influences on organisms, and it improves the growth of plants such as soybean. To reveal the dynamic effects of millimeter-wave irradiation on chickpea under flooding, gel- and label-free proteomic analysis was conducted. Millimeter-wave irradiation improved chickpea growth and its tolerance to flooding stress. According to functional categorization, oppositely changed proteins were correlated with photosynthesis, fermentation, and protein degradation. Immunoblot analysis confirmed that RuBisCO activase and large subunits decreased in leaves under flooding; however, they are recovered in irradiated chickpea even if it was in this condition. The activity and accumulation of alcohol dehydrogenase increased in roots under flooding; however, this followed the same pattern. Cell death was significantly increased and decreased by flooding on unirradiated and irradiated chickpeas, respectively. These findings suggest that irradiation with millimeter waves on chickpea seeds improves the recovery of plant growth through regulation of photosynthesis in leaves and fermentation in roots. Furthermore, millimeter-wave irradiation might promote chickpea tolerance under flooding via the regulation of cell death.

Rewilding crops for climate resilience: economic analysis and de novo domestication strategies

To match predicted population growth, annual food production should be doubled by 2050. This is not achievable by current agronomical and breeding practices, due to the impact of climate changes and associated abiotic stresses on agricultural production systems. Here, we analyze the impact of global climate trends on crop productivity and show that the overall loss in crop production from climate-driven abiotic stresses may exceed US$170 billion year-1 and represents a major threat to global food security. We also show that abiotic stress tolerance had been present in wild progenitors of modern crops but was lost during their domestication. We argue for a major shift in our paradigm of crop breeding, focusing on climate resilience, and call for a broader use of wild relatives as a major tool in this process. We argue that, while molecular tools are currently in place to harness the potential of climate-resilient genes present in wild relatives, the complex polygenic nature of tolerance traits remains a major bottleneck in this process. Future research efforts should be focused not only on finding appropriate wild relatives but also on development of efficient cell-based high-throughput phenotyping platforms allowing assessment of the in planta operation of key genes.

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.

Proteomic and Biochemical Analyses of the Mechanism of Tolerance in Mutant Soybean Responding to Flooding Stress

To investigate the mechanism of flooding tolerance of soybean, flooding-tolerant mutants derived from gamma-ray irradiated soybean were crossed with parent cultivar Enrei for removal of other factors besides the genes related to flooding tolerance in primary generated mutant soybean. Although the growth of the wild type was significantly suppressed by flooding compared with the non-flooding condition, that of the mutant lines was better than that of the wild type even if it was treated with flooding. A two-day-old mutant line was subjected to flooding for 2 days and proteins were analyzed using a gel-free/label-free proteomic technique. Oppositely changed proteins in abundance between the wild type and mutant line under flooding stress were associated in endoplasmic reticulum according to gene-ontology categorization. Immunoblot analysis confirmed that calnexin accumulation increased in both the wild type and mutant line; however, calreticulin accumulated in only the mutant line under flooding stress. Furthermore, although glycoproteins in the wild type decreased by flooding compared with the non-flooding condition, those in the mutant line increased even if it was under flooding stress. Alcohol dehydrogenase accumulated in the wild type and mutant line; however, this enzyme activity significantly increased and mildly increased in the wild type and mutant line, respectively, under flooding stress compared with the non-flooding condition. Cell death increased and decreased in the wild type and mutant line, respectively, by flooding stress. These results suggest that the regulation of cell death through the fermentation system and glycoprotein folding might be an important factor for the acquisition of flooding tolerance in mutant soybean.

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.

Applicability of a nationwide flood forecasting system for Typhoon Hagibis 2019

Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.

Climate signals in river flood damages emerge under sound regional disaggregation

Climate change affects precipitation patterns. Here, we investigate whether its signals are already detectable in reported river flood damages. We develop an empirical model to reconstruct observed damages and quantify the contributions of climate and socio-economic drivers to observed trends. We show that, on the level of nine world regions, trends in damages are dominated by increasing exposure and modulated by changes in vulnerability, while climate-induced trends are comparably small and mostly statistically insignificant, with the exception of South & Sub-Saharan Africa and Eastern Asia. However, when disaggregating the world regions into subregions based on river-basins with homogenous historical discharge trends, climate contributions to damages become statistically significant globally, in Asia and Latin America. In most regions, we find monotonous climate-induced damage trends but more years of observations would be needed to distinguish between the impacts of anthropogenic climate forcing and multidecadal oscillations.

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.

Quantifying the inundation impacts of earthquake-induced surface elevation change by hydrological and hydraulic modeling

Current estimates of flood hazards are often based on the assumption that topography is static. When tectonic and/or anthropogenic processes change the land surface elevation, the spatial patterns of floods might also change. Here, we employ the hydrological and hydraulic modeling to simulate floods in the Kujukuri Plain, Japan, in the years 2004 and 2013, when two severe floods occurred. In between the two floods, land surface elevations were changed by the 2011 Tohoku-Oki earthquake. The effects of land surface elevation changes on inundation areas were quantified by changing input topographies. Our results showed that, without taking into account land surface elevation changes, around 10% of inundation areas were underestimated at the time of flood events in the year 2013. The spatial distribution of inundation locations varied with local topographical features, for example, the areas with backmarsh and valley fill deposits were sensitive to the extent of inundation by land surface elevation changes. The sub-watershed near the coastal shoreline having below-zero meter elevation areas showed that the earthquake-induced land surface elevation changes exacerbated an additional 22% inundation area. This study suggests that the inundation areas will increase in catchments suffering severe settlements, which highlights the necessity of taking into account the spatio-temporal changes of land surface elevations on the assessment of flood hazards.

Global exposure to flooding from the new CMIP6 climate model projections

Estimates of future flood risk rely on projections from climate models. The relatively few climate models used to analyze future flood risk cannot easily quantify of their associated uncertainties. In this study, we demonstrated that the projected fluvial flood changes estimated by a new generation of climate models, the collectively known as Coupled Model Intercomparison Project Phase 6 (CMIP6), are similar to those estimated by CMIP5. The spatial patterns of the multi-model median signs of change (+ or -) were also very consistent, implying greater confidence in the projections. The model spread changed little over the course of model development, suggesting irreducibility of the model spread due to internal climate variability, and the consistent projections of models from the same institute suggest the potential to reduce uncertainties caused by model differences. Potential global exposure to flooding is projected to be proportional to the degree of warming, and a greater threat is anticipated as populations increase, demonstrating the need for immediate decisions.

Plant Morphological, Physiological and Anatomical Adaption to Flooding Stress and the Underlying Molecular Mechanisms

Globally, flooding is a major threat causing substantial yield decline of cereal crops, and is expected to be even more serious in many parts of the world due to climatic anomaly in the future. Understanding the mechanisms of plants coping with unanticipated flooding will be crucial for developing new flooding-tolerance crop varieties. Here we describe survival strategies of plants adaptation to flooding stress at the morphological, physiological and anatomical scale systemically, such as the formation of adventitious roots (ARs), aerenchyma and radial O₂ loss (ROL) barriers. Then molecular mechanisms underlying the adaptive strategies are summarized, and more than thirty identified functional genes or proteins associated with flooding-tolerance are searched out and expounded. Moreover, we elaborated the regulatory roles of phytohormones in plant against flooding stress, especially ethylene and its relevant transcription factors from the group VII Ethylene Response Factor (ERF-VII) family. ERF-VIIs of main crops and several reported ERF-VIIs involving plant tolerance to flooding stress were collected and analyzed according to sequence similarity, which can provide references for screening flooding-tolerant genes more precisely. Finally, the potential research directions in the future were summarized and discussed. Through this review, we aim to provide references for the studies of plant acclimation to flooding stress and breeding new flooding-resistant crops in the future.

Exposure to natural hazard events unassociated with policy change for improved disaster risk reduction

Natural hazard events provide opportunities for policy change to enhance disaster risk reduction (DRR), yet it remains unclear whether these events actually fulfill this transformative role around the world. Here, we investigate relationships between the frequency (number of events) and severity (fatalities, economic losses, and affected people) of natural hazards and DRR policy change in 85 countries over eight years. Our results show that frequency and severity factors are generally unassociated with improved DRR policy when controlling for income-levels, differences in starting policy values, and hazard event types. This is a robust result that accounts for event frequency and different hazard severity indicators, four baseline periods estimating hazard impacts, and multiple policy indicators. Although we show that natural hazards are unassociated with improved DRR policy globally, the study unveils variability in policy progress between countries experiencing similar levels of hazard frequency and severity.

Whether disasters spur policy change remains contested. Here, the authors utilize a dataset of 10,976 natural hazard events and multiple disaster risk reduction (DRR) policy indicators across 85 countries over eight years to show that frequency and severity factors are unassociated with improved DRR policy.

Exploring multidecadal changes in climate and reservoir storage for assessing nonstationarity in flood peaks and risks worldwide by an integrated frequency analysis approach

The changing climate and reservoir storage have a far-reaching influence on the nonstationarity in flood peaks worldwide, but the quantification of the relative contribution of each covariate (i.e., climate and reservoir storage) is fundamentally challenging especially under the time-varying mechanisms in statistical properties. This study proposed an integrated flood frequency analysis for assessing the impacts of changing climate and reservoir storage on the nonstationarity in flood peaks and flood risks worldwide. The 32 major river catchments covering more than 60% of hydro-meteorological observation stations and 70% of reservoir storage worldwide constituted the case study. The proposed three-faceted approach was explored systematically through: modeling the nonstationarity in global flood peaks, identifying the contribution of changing climate and reservoir storage to the nonstationarity of flood peaks, and quantifying the change in flood risks under the nonstationary condition. The findings pointed out that global flood trends varied from increasing +19.3%/decade to decreasing -31.6%/decade. Taking the stationary flood frequency analysis as the benchmark, the comparative results revealed that the flood risk in 5 rivers under the nonstationary condition in response to warming climate significantly increased (1% → 5%) over the historical period whereas the flood risk in 7 rivers in response to increasing reservoir storage largely reduced (1% → 0.5%). Despite the spatiotemporal heterogeneity of observations, the changes in flood peaks evaluated here were explicitly in lined with the changing climate and reservoir storage, supporting the demand for considering the nonstationarity of flood peaks and risks in social infrastructure planning and designing as well as water management.

Google Earth Engine for concurrent flood monitoring in the lower basin of Indo-Gangetic-Brahmaputra plains

The present study focused on the recent flood inundation (July 2020) that occurred in the lower Indo-Gangetic-Brahmaputra plains (IGBP) using concurrent C-band Sentinel-1A Synthetic Aperture Radar images in Google Earth Engine. The study exhibited that a substantial proportion of IGBP (40,929 km²) was inundated primarily in Bangladesh (9.09% of the total inundation), Assam (8.99%), and Bihar (6.29%) during June-July 2020. The severe impact of flood inundation was observed in croplands (4.41% of the total cropland), followed by settlements (20.98% of the total settlements) that affected a large population (~ 10,046,262) in IGBP. The prevailing COVID-19 pandemic has debilitated the efforts of mitigation and responses to flooding risks. The study necessitates adopting an integrated, multi-hazard, multi-stakeholder approach with an emphasis on self-reliance of the community for sustenance with local resources and practices.

Advances in Flood Early Warning: Ensemble Forecast, Information Dissemination and Decision-Support Systems

Floods are usually highly destructive, which may cause enormous losses to lives and property. It is, therefore, important and necessary to develop effective flood early warning systems and disseminate the information to the public through various information sources, to prevent or at least mitigate the flood damages. For flood early warning, novel methods can be developed by taking advantage of the state-of-the-art techniques (e.g., ensemble forecast, numerical weather prediction, and service-oriented architecture) and data sources (e.g., social media), and such developments can offer new insights for modeling flood disasters, including facilitating more accurate forecasts, more efficient communication, and more timely evacuation. The present Special Issue aims to collect the latest methodological developments and applications in the field of flood early warning. More specifically, we collected a number of contributions dealing with: (1) an urban flash flood alert tool for megacities; (2) a copula-based bivariate flood risk assessment; and (3) an analytic hierarchy process approach to flash flood impact assessment.

A Framework for Modeling Flood Depth Using a Hybrid of Hydraulics and Machine Learning

Solving river engineering problems typically requires river flow characterization, including the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing equations of the flow in motion (conservation of mass and momentum principles) to predict the flow characteristics. However, solving such equations can be substantially expensive, depending upon their spatial extension. Moreover, modeling two- or three-dimensional river flows with high-resolution topographic data for large-scale regions (national or continental scale) is next to impossible. Such simulations are required for comprehensive river modeling, where a system of connected rivers is to be simulated simultaneously. Machine Learning (ML) approaches have shown promise for different water resources problems, and they have demonstrated an ability to learn from current data to predict new scenarios, which can enhance the understanding of the systems. The aim of this paper is to present an efficient flood simulation framework that can be applied to large-scale simulations. The framework outlines a novel, quick, efficient and versatile model to identify flooded areas and the flood depth, using a hybrid of hydraulic model and ML measures. To accomplish that, a two-dimensional hydraulic model (iRIC), calibrated by measured water surface elevation data, was used to train two ML models to predict river depth over the domain for an arbitrary discharge. The first ML model included a random forest (RF) classification model, which was used to identify wet or dry nodes over the domain. The second was a multilayer perceptron (MLP) model that was developed and trained by the iRIC simulation results, in order to estimate river depth in wet nodes. For the test data the overall accuracy of 98.5 percent was achieved for the RF classification. The regression coefficient for the MLP model for depth was 0.88. The framework outlined in this paper can be used to couple hydraulics and ML models to reduce the computation time, resources and expenses of large-scale, real-time simulations, specifically for two- or three-dimensional hydraulic modeling, where traditional hydraulic models are infeasible or prohibitively expensive.

Physical Vulnerability and Local Responses to Flood Damage in Peri-Urban Areas of Dhaka, Bangladesh

In Eastern Dhaka, perennial flood remains a constant threat to people and livelihoods. Learning from the micro-level experiences of the poor in the peri-urban areas of Dhaka provides insights on the intersections between physical vulnerability, flood response strategies, and adaptive capacity. Through a convergent mixed method, this study examines the physical vulnerability of residential buildings, flood damages, and local physical responses in three neighborhoods of Eastern Dhaka. Results show that the level of damage to buildings is the most important predictor of physical vulnerability to floods. Buildings that are older than 20 years old and built with natural materials are likely to experience high flood damages compared to buildings that are less than 10 years and constructed with durable materials. The study concludes that in addition to socio-economic interventions, a targeted and people-centered flood management regime that pays attention to age, material composition, and structural quality of houses is necessary to build residents’ adaptive capacities and long-term resilience to flooding. This study contributes to the emerging work on grassroots responses to flood vulnerabilities with practical insights for urban planners and disaster management professionals on particular interventions needed to improve the performance of local responses to flood risks and vulnerabilities.

Framework of Spatial Flood Risk Assessment for a Case Study in Quang Binh Province, Vietnam

Vietnam has been extensively affected by floods, suffering heavy losses in human life and property. While the Vietnamese government has focused on structural measures of flood defence such as levees and early warning systems, the country still lacks flood risk assessment methodologies and frameworks at local and national levels. In response to this gap, this study developed a flood risk assessment framework that uses historical flood mark data and a high-resolution digital elevation model to create an inundation map, then combined this map with exposure and vulnerability data to develop a holistic flood risk assessment map. The case study is the October 2010 flood event in Quang Binh province, which caused 74 deaths, 210 injuries, 188,628 flooded properties, 9019 ha of submerged and damaged agricultural land, and widespread damages to canals, levees, and roads. The final flood risk map showed a total inundation area of 64,348 ha, in which 8.3% area of low risk, 16.3% area of medium risk, 12.0% area of high risk, 37.1% area of very high risk, and 26.2% area of extremely high risk. The holistic flood risk assessment map of Quang Binh province is a valuable tool and source for flood preparedness activities at the local scale.

A Conceptual Framework to Understand the Dynamics of Rural–Urban Linkages for Rural Flood Vulnerability

Rural areas are highly vulnerable to floods due to limited social, economic, and physical resources. Understanding rural vulnerability is vital for developing effective disaster risk reduction strategies. Even though rural areas and cities are intrinsically linked, rural vulnerability was assessed without considering its relation to cities. Numerous theoretical frameworks on systemizing and assessing vulnerability were developed with varying level of scope and depth in terms of scale, dimensions, and components. Nevertheless, these frameworks did not explicitly mention the impact of flood or other hazards on the linkages between spatial units i.e., rural and urban. This study aims to understand and conceptualize the rural vulnerability with respect to the dynamics of rural–urban linkages in the case of flood events. To do so, current literature on rural–urban linkages, vulnerability, as well as factors that influence them were critically reviewed. Taking into account the main elements of rural–urban linkages (flow of people, information, finances, goods and services), components of vulnerability (exposure, susceptibility, and capacity), and factors (social, economic, institutional, infrastructural, spatial, and environmental), a unified framework is proposed. The framework underscores that the role of rural–urban linkages is essential to fully understand rural flood vulnerability. Moreover, the framework highlights the role of spatial factors—city size and proximity to the city—as crucial to comprehend rural vulnerability. This framework can be used as a tool for understanding multifaceted rural vulnerability for climate change adaptation and disaster risk reduction considering spatial development perspective. In this context, empirical investigations can be made to validate the proposed framework and policies can be introduced accordingly. Overall, the proposed framework can help recognize concepts and links of vulnerability, rural–urban dependencies, and rural development dynamics.

Social Vulnerability Assessment for Flood Risk Analysis

This paper proposes a methodology for the analysis of social vulnerability to floods based on the integration and weighting of a range of exposure and resistance (coping capacity) indicators. It focuses on the selection and characteristics of each proposed indicator and the integration procedure based on the analytic hierarchy process (AHP) on a large scale. The majority of data used for the calculation of the indicators comes from open public data sources, which allows the replicability of the method in any area where the same data are available. To demonstrate the feasibility of the method, a study case is presented. The flood social vulnerability assessment focuses on the municipality of Ponferrada (Spain), a medium-sized town that has high exposure to floods due to potential breakage of the dam located upstream. A detailed mapping of the social vulnerability index is generated at the urban parcel scale, which shows an affected population of 34,941 inhabitants. The capability of working with such detailed units of analysis for an entire medium-sized town provides a valuable tool to support flood risk planning and management.

Streamflow Intensification Driven by the Atlantic Multidecadal Oscillation (AMO) in the Atrato River Basin, Northwestern Colombia

The impact of the Atlantic Multidecadal Oscillation (AMO) on the variations in the streamflow in the Atrato River Basin (ARB) during the 1965–2016 period was analyzed here by considering the cold (1965–1994) and warm (1995–2015) phases of this oscillation. The mean streamflow increased after 1994 (AMO phase change). This increase is related to the strengthening of the zonal gradients of the sea surface temperature (SST) and sea level pressure (SLP) between the tropical central Pacific and the tropical Atlantic after 1994 (warm AMO phase). These gradients contributed to strengthen the Walker cell related upward movement over northern and northwestern South America, in particular during November-December (ND). Consistently, the frequency (R20 mm) and intensity (SDII) of extreme daily rainfall events increased during the 1995–2015 period. Our results show a connection between the AMO and the increase in the streamflow in the ARB during the last five decades. These results contribute to the studies of resilience and climate adaptation in the region.

Sentinel-2 Satellites Provide Near-Real Time Evaluation of Catastrophic Floods in the West Mediterranean

Flooding is among the most common natural disasters in our planet and one of the main causes of economic and human life loss worldwide. Evidence suggests the increase of floods at European scale with the Mediterranean coast being critically vulnerable to this risk. The devastating event in the West Mediterranean during the second week of September 2019 is a clear case of this risk crystallization, when a record-breaking flood (locally called the “Cold Drop” (Gota Fría)) has swollen into a catastrophe to the southeast of Spain surpassing previous all-time records. By using a straightforward approach with the Sentinel-2 twin satellites from the Copernicus Programme and the ACOLITE atmospheric correction processor, an initial approximation of the delineated flooded zones, including agriculture and urban areas, was accomplished in quasi-real time. The robust and flexible approach requires no ancillary data for rapid implementation. A composite of pre- and post-flood images was obtained to identify change detection and mask water pixels. Sentinel-2 identifies not only impacts on land but also on water ecosystem and its services, providing information on water quality deterioration and concentration of suspended matter in highly sensitive environments. Subsequent water quality deterioration occurred in large portions of Mar Menor, the largest coastal lagoon in the Mediterranean. The present study demonstrates the potentials brought by the free and open-data policy of Sentinel-2, a valuable source of rapid synoptic spatio-temporal information at the local or regional scale to support scientists, managers, stakeholders, and society in general during and after the emergency.