Different Types Of Floods Research Articles

Towards development of a roadway flood severity index

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

Advancing Flood Prediction: Leveraging Machine Learning for Accurate Prediction

Abstract: Floods, a force of nature that cannot be escaped, relentlessly strike without discrimination. Every nation, to shield itself from this calamity, strives to make adequate preparations. Countless countries endure the devastation of floods annually, suffering immeasurable losses in various aspects such as finance, environment, and human lives. Regrettably, these losses are irreparable, no matter the efforts made. Amongst the different types of floods, flash floods pose the gravest danger as they possess both the destructive force of a flood and an astonishing velocity. These flash floods occur when intense rainfall surpasses the soil's ability to absorb water. They also occur when water fills dry creeks or streams or when sufficient water accumulates to breach riverbanks, resulting in rapid surges in a short period. Flash floods can materialize within minutes after triggering incidents, leaving little time for public warning and protection. As one facet of artificial intelligence, machine learning greatly contributes to the progression of predictive systems, offering enhanced performance and cost-effective solutions. Flood prediction must be accurate and fast to mitigate its damage. Machine learning algorithms have gained popularity for flood prediction recently because of their capacity to analyse massive volumes of data and find subtle patterns. This article presents an overview of flood prediction technology utilizing machine learning, encompassing the diverse algorithms employed, the benefits and drawbacks of this technology, and the challenges that lie ahead along with potential future developments.

Co-occurrence of pluvial and fluvial floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in Longyan, China

Hydrodynamics and socio-economic impacts of compound floods as the result of co-occurring pluvial and fluvial floods, have not been well studied, which challenges conventional urban flood risk management that treats different types of floods separately. This study generates a high-resolution land surface digital surface model based on images from unmanned aerial vehicles, and incorporates it into a combined flooding model that integrates the 1D river hydrodynamic model, 1D urban drainage hydrodynamic model and 2D overland flow model, in order to simulate the inundation characteristics of extreme floods when local pluvial floods coincide with upstream fluvial floods, and in a further step to quantify the direct economic losses on urban physical systems. As demonstrated by an empirical study of Longyan, China, the combination of pluvial and fluvial floods leads to substantial exacerbation in inundation extent and depth. Consequently, the joining of pluvial and fluvial floods greatly amplifies economic losses to urban physical systems, indicating that ignoring co-occurrence of different types of floods will potentially lead to underestimation of flood risk and insufficient adaptation. Our study emphasizes the need to account for the co-occurrence of multiple types of floods in flood risk assessment and management, to avoid the shortcomings of fragmented responses.

Flood Simulations Using a Sensor Network and Support Vector Machine Model

This study aims to couple the support vector machine (SVM) model with a hydrometeorological wireless sensor network to simulate different types of flood events in a montane basin. The model was tested in the mid-latitude montane basin of Vydra in the Šumava Mountains, Central Europe, featuring complex physiography, high dynamics of hydrometeorological processes, and the occurrence of different types of floods. The basin is equipped with a sensor network operating in headwaters along with the conventional long-term monitoring in the outlet. The model was trained and validated using hydrological observations from 2011 to 2021, and performance was assessed using metrics such as R2, NSE, KGE, and RMSE. The model was run using both hourly and daily timesteps to evaluate the effect of timestep aggregation. Model setup and deployment utilized the KNIME software platform, LibSVM library, and Python packages. Sensitivity analysis was performed to determine the optimal configuration of the SVR model parameters (C, N, and E). Among 125 simulation variants, an optimal parameter configuration was identified that resulted in improved model performance and better fit for peak flows. The sensitivity analysis demonstrated the robustness of the SVR model, as different parameter variations yielded reasonable performances, with NSE values ranging from 0.791 to 0.873 for a complex hydrological year. Simulation results for different flood scenarios showed the reliability of the model in reconstructing different types of floods. The model accurately captured trend fitting, event timing, peaks, and flood volumes without significant errors. Performance was generally higher using a daily timestep, with mean metric values R2 = 0.963 and NSE = 0.880, compared to mean R2 = 0.913 and NSE = 0.820 using an hourly timestep, for all 12 flood scenarios. The very good performance even for complex flood events such as rain-on-snow floods combined with the fast computation makes this a promising approach for applications.

Mapping of QTLs for flood tolerance in rice using recombinant inbred lines of Indra and a new plant genetic resource AC 39416 A

AbstractRice crop is affected by different types of floods at different stages of the crop cycle. Constant efforts of researchers resulted in the development of rice varieties for anaerobic germination, flash floods and stagnant flooding by both conventional and molecular breeding approaches. Detection of QTLs for different types of floods in new genetic source (AC39416A) is needed to combat adverse effects of climate change. Present investigation was carried out to identify QTLs for flood tolerance using recombinant inbred lines derived from Indra and AC39416A. QTL mapping resulted in identification of QTLs, qAG3.1 on chromosome 3 for anaerobic germination and qSF10.1 on chromosome 10 for plant survival % under stagnant flooding. These QTLs explain 59.08 and 13.21% of phenotypic variance respectively. Two candidate genes were identified in qAG3.1 region, LOC_Os03g42130 gibberellin 20 oxidase2 and LOC_Os03g44170 glutathione S-transferase. The underlying mechanism might be the inhibition of gibberellic acid synthesis and thereby protecting seedlings from oxidative stress under anoxia condition. Genomic region of qSF10.1 revealed LOC_Os10g35020 glycosyltransferase and LOC_Os10g35050 aquaporin protein loci, which might be responsible for adaptive mechanism for plant survival % under stagnant flooding. This indicates that the new genetic resource AC39416A has an ability to adopt to different types of flood tolerance in response to environmental stress. Unveiling physiological and molecular mechanisms for flood tolerance in AC39416A using advanced omics studies would help in precise genomic selections for sustained production in flood-prone areas.

Multivariate return period for different types of flooding in city of Monza, Italy

The return period is a probabilistic criterion used to measure and communicate the random occurrence of geophysical events such as floods in risk assessment studies. Since an individual risk may be strongly affected by the degree of dependence amongst all risks, the need for the provision of multivariate design quantiles has gained ground. Consequently, several recent studies have focused on estimation of multi-hazard risk resulted from different hazard types. In this study, multi-hazard return periods are derived for riverine and pluvial floods in city of Monza, Italy, based on different copula dependence structures. It is shown that ignoring statistical dependence among different inter-correlated hazards may cause significant misestimation of risks.

Flood risk index development at the municipal level in Costa Rica: A methodological framework

Floods constitute one of the most damaging natural hazards in the world. Seasonal and extraordinary rainfall recurrently trigger different types of floods in Costa Rica. An integrated and efficient flood risk management requires comprehensive understanding on the flood driving variables. This study analyzes and classifies the 82 Costa Rican municipalities in terms of hazard, exposure, and vulnerability to floods. Then, an index for flood risk is designed to comprehend the risk driver’s role (hazard, exposure, and vulnerability) at a local level. The present method provides a flood risk index on a municipal scale done through a statistical validation of different sources of municipal-level data. Higher flood risk values mostly occur in municipalities located in extensive flatlands, medium to large areas in both the Pacific and Caribbean basins, as well as borderlands and coastal regions. The results can promote flood risk assessment plans in developing countries or regions where baseline information is limited. This approach has been absent in most of the national flood risk policies, specifically the plans analyzing the likelihood of short, mid, and long-term decline of risk conditions influencing flood disasters.

Flood forecasting in urban reservoir using hybrid recurrent neural network

Flood forecasting can provide accurate inferences and early warnings for flood control work during the flood season. Due to the variability of local rainfall and the complexity of geographic conditions, existing prediction methods were unable to accurately predict the flooding process in a particular basin. Additionally, the water level sensor generates a significant amount of noise in the inbound flow data during period measurement. To address these issues, this article proposes a real-time flood forecasting model, which is used to accurately predict flood trends and peak times in the flood period. The model uses a convolution kernel function to smooth out local noise and neighborhood values, minimizing the impact of non-stationary series on the training process while retaining the true evolution of the flood in the original data. In our model, we develop a time series attention mechanism that is used to apply various weights to time series input vectors, such as outflow flow and rainfall from upstream reservoirs, this mechanism also improves the accuracy of short-term series prediction. To obtain additional information about the output of the recurrent neural network layer, we also include a multivariate autoregressive integrated moving average module. This method can add a linear component to the output, allowing the prediction result to adapt to the input period's scale shift. This article develops matching models for interval and full basin floods based on the geographical characteristics of China's urban Reservoir and the river basin, thresholds are established based on the outflow from upstream reservoirs, which enables the flood forecasting system to dynamically adjust model parameters in response to the threshold, it also circumvents the scaling problem inherent in flood time series at various scales. We trained and predicted using 25 different types of floods in Ankang Reservoir from 2010 to 2020. Three on-site real-time forecasts of catastrophic flooding at the Ankang Reservoir were conducted in September 2021 to validate the model's accuracy. The algorithm's efficiency in forecasting flood inflows is demonstrated through comparisons to traditional hydrological models and other machine learning networks, and our model consistently forecasts the peak time and total flood volume with the least amount of error in the comparison algorithm.

Spatiotemporal variation in global floods with different affected areas and the contribution of influencing factors to flood-induced mortality (1985\u20132019)

Floods are great threats to human life and property. Extensive research has investigated the spatiotemporal variation in flood occurrence, while few have studied the heterogeneity in global flood events of different sizes, which may require different coping strategies and risk reduction policies. In this study, we analysed the spatiotemporal patterns of global flood events with different affected areas (classified in three levels) during 1985–2019 and examined the contribution of different influencing factors to flood-induced mortality using Geodetector. The results show that (1) the increase in global flood frequency was mainly caused by Level II and Level III floods, and the average area affected by flood events has been increasing yearly since 1985. (2) In America and Africa, the frequency of Level III floods has increased monotonically. At the same time, the frequency of Level I floods in Europe and Level II floods in Asia has increased significantly. (3) For Europe and Asia, most of the deaths occurred with Level II floods; while for America and Africa, Level III floods caused the most mortality. (4) The top three factors contributing to the spatial heterogeneity in flood-induced mortality were the affected population, GDP per capita and flood duration. The contribution of each factor varied among the different types of floods. Topographic factors (percentage of mountainous area) magnified flood-induced mortality during extreme events with heavy rainfall, especially for Level III floods. The heterogeneity in flood frequency and flood-induced mortality indicates that flood protection measures should be more targeted. In addition, the increase in large-scale floods (Level III) highlights the need for transregional cooperation in flood risk management.

Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments

AbstractLong short‐term memory (LSTM) networks represent one of the most prevalent deep learning (DL) architectures in current hydrological modeling, but they remain black boxes from which process understanding can hardly be obtained. This study aims to demonstrate the potential of interpretive DL in gaining scientific insights using flood prediction across the contiguous United States (CONUS) as a case study. Two interpretation methods were adopted to decipher the machine‐captured patterns and inner workings of LSTM networks. The DL interpretation by the expected gradients method revealed three distinct input‐output relationships learned by LSTM‐based runoff models in 160 individual catchments. These relationships correspond to three flood‐inducing mechanisms—snowmelt, recent rainfall, and historical rainfall—that account for 10.1%, 60.9%, and 29.0% of the 20,908 flow peaks identified from the data set, respectively. Single flooding mechanisms dominate 70.7% of the investigated catchments (11.9% snowmelt‐dominated, 34.4% recent rainfall‐dominated, and 24.4% historical rainfall‐dominated mechanisms), and the remaining 29.3% have mixed mechanisms. The spatial variability in the dominant mechanisms reflects the catchments' geographic and climatic conditions. Moreover, the additive decomposition method unveils how the LSTM network behaves differently in retaining and discarding information when emulating different types of floods. Information from inputs within previous time steps can be partially stored in the memory of LSTM networks to predict snowmelt‐induced and historical rainfall‐induced floods, while for recent rainfall‐induced floods, only recent information is retained. Overall, this study provides a new perspective for understanding hydrological processes and extremes and demonstrates the prospect of artificial intelligence‐assisted scientific discovery in the future.

Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River

The paper presents a hybrid approach for short-term river flood forecasting. It is based on multi-modal data fusion from different sources (weather stations, water height sensors, remote sensing data). To improve the forecasting efficiency, the machine learning methods and the Snowmelt-Runoff physical model are combined in a composite modeling pipeline using automated machine learning techniques. The novelty of the study is based on the application of automated machine learning to identify the individual blocks of a composite pipeline without involving an expert. It makes it possible to adapt the approach to various river basins and different types of floods. Lena River basin was used as a case study since its modeling during spring high water is complicated by the high probability of ice-jam flooding events. Experimental comparison with the existing methods confirms that the proposed approach reduces the error at each analyzed level gauging station. The value of Nash–Sutcliffe model efficiency coefficient for the ten stations chosen for comparison is 0.80. The other approaches based on statistical and physical models could not surpass the threshold of 0.74. Validation for a high-water period also confirms that a composite pipeline designed using automated machine learning is much more efficient than stand-alone models.

Understanding and Assessing Flood Risk Management in Balochestan Iran: With Integrated River Basin Approach, Future Direction

A holistic perspective on changing rainfall-driven flood risk is provided for the late 20th and early 21st centuries. Balochistan is exposed to different types of floods that cause severe economic losses, damage to infrastructures, and loss of lives. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Balochistan. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in this large province. A society well-aware of risks must not only give attention to the prevention of flood risks but must also consider disaster management, i.e. minimizing casualties and flood damages, and enhancing recovery. Balochistan has No solid network of levees along any of the rivers that protect the many low-lying polders from flooding, however the government trying to build few in Sarbaz river from Shirgwaz to Kolani and in Kajo river from Kalat to Dalgan. Well, nature is unpredictable, extreme events may happen, and absolute protection against flooding cannot be offered. It is common practice to perform technical and economic analyses to determine the feasibility of flood protection plans and also institutional and administrative aspects are addressed. But how the people that live in the polders feel about the flood risk and protection plans seldom gets attention.

Understanding and Assessing Flood Risk Management in Balochestan Iran: With Integrated River Basin Approach, Future Direction

A holistic perspective on changing rainfall-driven flood risk is provided for the late 20th and early 21st centuries. Balochistan is exposed to different types of floods that cause severe economic losses, damage to infrastructures, and loss of lives. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Balochistan. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in this large province. A society well-aware of risks must not only give attention to the prevention of flood risks but must also consider disaster management, i.e., minimizing casualties and flood damages, and enhancing recovery. Balochistan has No solid network of levees along any of the rivers that protect the many low-lying polders from flooding, however the government trying to build few in Sarbaz river from Shirgwaz to Kolani and in Kajo river from Kalat to Dalgan. Well, nature is unpredictable, extreme events may happen, and absolute protection against flooding cannot be offered. It is common practice to perform technical and economic analyses to determine the feasibility of flood protection plans and also institutional and administrative aspects are addressed. But how the people that live in the polders feel about the flood risk and protection plans seldom gets attention.

Analysis of Flood Fatalities in the United States, 1959–2019

Flooding is one of the main weather-related disasters that cause numerous fatalities every year across the globe. This study examines flood fatalities reported in the contiguous United States (US) from 1959 to 2019. The last two decades witnessed major flood events, changing the ranking of the top states compared to previous studies, with the exception of Texas, which had significantly higher flood-related fatalities than any other state. The rankings of counties within some states changed as well. The study aims to improve understanding of the situational conditions, demographics, and spatial and temporal characteristics associated with flood fatalities. The analysis reveals that flash flooding is associated with more fatalities than other flood types. In general, males are much more likely to be killed in floods than females. The analysis also suggests that people in the age groups of 10–19, 20–29, and 0–9 are the most vulnerable to flood hazard. Purposely driving or walking into floodwaters accounts for more than 86% of total flood fatalities. Thus, the vast majority of flood fatalities are preventable. The results will help identify the risk factors associated with different types of flooding and the vulnerability of the exposed communities.

Understanding and assessing flood risk in Vietnam: Current status, persisting gaps, and future directions

AbstractVietnam is exposed to different types of floods that cause severe economic losses, damage to infrastructure, and loss of life. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Vietnam. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in the country. The literature review revealed that: (i) 65 % of the reviewed papers did not provide a clear definition of flood risk, (ii) assessments had a tendency to prioritize physical and environmental drivers of risk over social, economic or governance‐related drivers, (iii) future‐oriented assessments tended to focus on hazard and exposure trends, while vulnerability scenarios were often lacking, (iv) large and middle‐sized cities were assessed more frequently than others, (v) only few studies engaged with relevant local stakeholders for the assessment of risk and the development of potential solutions, and (vi) ecosystem‐based adaptation and flood risk insurance solutions were rarely considered. Based on these findings, we point out several directions for future research on flood risk in Vietnam.

Recent Advances of Genetic Resources, Genes and Genetic Approaches for Flooding Tolerance in Rice.

Flooding is one of the most hazardous natural disasters and a major stress constraint to rice production throughout the world, which results in huge economic losses. The frequency and duration of flooding is predicted to increase in near future as a result of global climate change. Breeding of flooding tolerance in rice is a challenging task because of the complexity of the component traits, screening technique, environmental factors and genetic interactions. A great progress has been made during last two decades to find out the flooding tolerance mechanism in rice. An important breakthrough in submergence research was achieved by the identification of major quantitative trait locus (QTL) SUB1 in rice chromosomes that acts as the primary contributor for tolerance. This enabled the use of marker-assisted backcrossing (MABC) to transfer SUB1 QTL into popular varieties which showed yield advantages in flood prone areas. However, SUB1 varieties are not always tolerant to stagnant flooding and flooding during germination stage. So, gene pyramiding approach can be used by combining several important traits to develop new breeding rice lines that confer tolerances to different types of flooding. This review highlights the important germplasm/genetic resources of rice to different types of flooding stress. A brief discussion on the genes and genetic mechanism in rice exhibited to different types of flooding tolerance was discussed for the development of flood tolerant rice variety. Further research on developing multiple stresses tolerant rice can be achieved by combining SUB1 with other tolerance traits/genes for wider adaptation in the rain-fed rice ecosystems.

Assessment of Community Vulnerability to Different Types of Urban Floods: A Case for Lishui City, China

Urban flooding is a severe and pervasive hazard caused by climate change, urbanization, and limitations of municipal drainage systems. Cities face risks from different types of floods, depending on various geographical, environmental, and hydrometeorological conditions. In response to the growing threat of urban flooding, a better understanding of urban flood vulnerability is needed. In this study, a comprehensive method was developed to evaluate the vulnerability of different types of urban floods. First, a coupled urban flood model was built to obtain the extent of influence of various flood scenarios caused by rainfall and river levee overtopping. Second, an assessment framework for urban flood vulnerability based on an indicator method was used to evaluate the vulnerability in different flood hazard scenarios. Finally, the method was applied to Lishui City, China, and the distribution and pattern of urban flood vulnerability were studied. The results highlight the spatial variability of flooding and the vulnerability distributions of different types of urban floods. Compound floods were identified to cause more severe effects in the urban areas.

Improved Framework for Assessing Vulnerability to Different Types of Urban Floods

Vulnerability assessment is an essential tool in mitigating the impact of urban flooding. To date, most flood vulnerability research has focused on one type of flood, such as a pluvial or fluvial flood. However, cities can suffer from urban flooding for several reasons, such as precipitation and river levee overtopping. Therefore, a vulnerability assessment considering different types of floods (pluvial floods, fluvial floods, and compound flooding induced by both rainfall and river overtopping) was conducted in this study. First, a coupled urban flood model, considering both overland and sewer network flow, was developed using the storm water management model (SWMM) and LISFLOOD-FP model to simulate the different types of flood and applied to Lishui, China. Then, the results of the flood modeling were combined with a vulnerability curve to obtain the potential impact of flooding on different land-use classes. The results indicated that different types of floods could have different influence areas and result in various degrees of flood vulnerability for different land-use classes. The results also suggest that urban flood vulnerability can be underestimated due to a lack of consideration of the full flood-induced factors.

A local-scale analysis to understand differences in socioeconomic factors affecting economic loss due to floods among different communities

Investigation of the relationships between socioeconomic factors and water-related disasters, such as floods, is rather complex. The general practice is to undertake a qualitative analysis of the impact of socioeconomic factors on economic loss in disasters, while climate and structural impacts are investigated quantitatively. As such, an integrated approach is timely for understanding socioeconomic influences on economic loss due to floods. This paper is an attempt to understand the influence of different factors on economic loss, in different types of floods, among different economic groups, using a quantitative approach. Data was collected using a questionnaire survey delivered to randomly selected households in Rathnapura, Sri Lanka, in September 2017. Path analysis was used to analyze the influence of socioeconomic status on economic loss due to floods, using an effective sample of 231 households after subdividing the sample into poor and non-poor subcategories, based on socioeconomic condition. The results suggest that flood characteristics and household income level have a direct impact on economic loss in severe floods for both economic groups, with more significant impacts among poor households. Even for minor floods, inundation depth is the most significant factor affecting relative loss, irrespective of the economic group. Further, the absolute loss difference between poor and non-poor households is 48% of the loss experienced by non-poor households in severe floods, and 10% of the loss due to minor floods. These results indicate that severe floods increase the economic gap between the poor and non-poor cohorts. Through this analysis, it is concluded that floods exacerbate the economic gap between poor and non-poor communities, while the factors affecting economic loss due to floods differ among the different economic groups.

The role of floods and droughts on riverine ecosystems under a changing climate

AbstractFloods and droughts are key driving forces shaping aquatic ecosystems. Climate change may alter key attributes of these events and consequently health and distribution of aquatic species. Improved knowledge of biological responses to different types of floods and droughts in rivers should allow the better prediction of the ecological consequences of climate change‐induced flow alterations. This review highlights that in unmodified ecosystems, the intensity and direction of biological impacts of floods and droughts vary, but the overall consequence is an increase in biological diversity and ecosystem health. To predict the impact of climate change, metrics that allow the quantitative linking of physical disturbance attributes to the directions and intensities of biological impacts are needed. The link between habitat change and the character of biological response is provided by the frequency of occurrence of the river wave characteristic—that is the event's predictability. The severity of impacts of floods is largely related to the river wave amplitude (flood magnitude), while the impact of droughts is related to river wavelength (drought duration).