Flood Risk Evaluation Research Articles

A novel urban flood risk assessment framework based on refined numerical simulation technology

Due to urbanization and climate change, urban areas are becoming more susceptible to flooding, posing significant risks to human life and property. Numerical simulation techniques can provide valuable support in mitigating urban flood risks. The objective of this study is to establish a novel framework for flood risk assessment based on refined urban flood simulations. To achieve refined urban flood simulations, the Torricelli’s law is incorporated into a two-dimensional fluid dynamics model, and a numerical analysis model is developed to estimate the inundation within buildings and accurately simulate the entire inundation process. The model was used to reproduce a real flood event in Omihachiman City, Japan, with the Nash-Sutcliffe Efficiency (NSE) values of 0.6488 and 0.7182, demonstrating the credibility of the model. Based on the simulation results, the hydrological causes of the flood event were analyzed, and flood prevention measures were proposed. After implementing these measures, the maximum water depths at the study area were reduced to 0.14 m, 0.62 m, and 0.12 m, respectively, proving the effectiveness of the flood prevention measures. To address the issue of inundation depth not fully reflecting flood risk, the instability of human bodies in floodwaters is conducted to evaluate flood risk for adults and children in the target area. In response to increasingly frequent extreme rainfall events, the extreme precipitation events of three different return periods (10-year, 50-year, and 100-year) are used to assess the flood risk and the effectiveness of flood prevention measures. Based on the assessment results, the flood prevention measures not only reduced the flood risk levels but also decreased the flood risk area, further confirming their effectiveness.

Enhanced Flood Risk Evaluation in Ferozepur District, Punjab through GIS and Analytic Hierarchy Process

Enhanced Flood Risk Evaluation in Ferozepur District, Punjab through GIS and Analytic Hierarchy Process

Introducing a climate, demographics, and infrastructure multi-module workflow for projected flood risk mapping in the greater Pamba River Basin, Kerala, India

Enhanced understanding of extreme events is essential for comprehensive risk assessment promoting informed decision-making and early warning to ensure the resilience against challenges imposed by changing climate. This study proposes a novel multi-module integration framework for the comprehensive evaluation of future flood risk in the Greater Pamba River basin, Kerala, India. The integrative modelling framework is developed by assembling a Cellular Automata-Markov Chain (CAMC) based and Use/Land Cover (LULC) projections, Reliability Ensemble Averaging (REA) of Global Climate Model (GCM) rainfall projections, Bayesian approach-based population projection, and spatial analysis of projected infrastructure and Digital Elevation Models (DEMs). By seamlessly interweaving the associated datasets, we illustrate the intricacies of flood vulnerability, exposure, and frequency across multiple return periods (2, 5, 10, and 100, 200 and 500 year) leading to the creation of diagnostic and prognostic high-resolution flood risk maps. The resultant flood analytics and analyses shown that around 36 % of the villages/municipalities and between 70 and 80 % of the critical facilities like schools, hospitals, and flood relief camps are falling under high-risk zones, for the 100-year return period flood. This finding is in line with Kerala's devastating flood episode in 2018, which exhibited characteristics of a nearly 100-year return period flood. In light of a changing climate, population growth, and shifting landscapes, these approaches provide a roadmap for more informed decision-making and adaptive flood risk management measures.

Flood Monitoring and Mapping in the Ghatal Region of West Bengal, India

Abstract The Ghatal block of Paschim Medinipur district in West Bengal, India experiences frequent flooding due to the overflow of the rivers Dwarakeswar, Rupnarayan, Shilabati, and their tributaries. Although the Ghatal master plan was prepared and the foundation stone was placed in 1982 to reduce the flood vulnerability in this region, the plan is still not being implemented. To determine the flood vulnerability in the Ghatal block, very few research investigations have been carried out. The previous studies just applied a successful scientific methodology to evaluate flood risk; they did not engage in participatory research with local community perception. Hence, a thorough investigation is needed to manage floods in a way that increases community resilience. Using a comparative review and the geographic information system (GIS), the study seeks to identify the flood-prone locations in the Ghatal block, which is essential for flood management in any region. The article uses a multi-criteria analysis to pinpoint probable flood hazard regions. Seven parameters are used in the study to carry out the multi-criteria analytical hierarchy procedure. Based on their relative importance for the event, the criteria’s weights are determined using the Analytical Hierarchical Process (AHP) method, and a Suitability Index is prepared. The index indicates that the severe problem of river flooding in the Ghatal block of Paschim Medinipur needs to be looked after on an urgent basis and with path-breaking solutions, especially for the Ghatal municipality and the various gram panchayats such as Irpala, Mansuka-I, Monoharpur-I, Ajobnagar-I, Mohonpur, Dewanchak-I and the North-Eastern part of Sultanpur and the Southern part of Dewanchak-II.

Flood risk assessment in arid and semi-arid regions using Multi-criteria approaches and remote sensing in a data-scarce region

Flooding is a natural disaster that poses a threat to both people and the environment, necessitating proactive assessment and mitigation strategies to protect vulnerable communities and ecosystems. These measures are necessary to reduce the risk of flooding and moderate the impact of rainfall. In this study, an Analytical Hierarchy Process (AHP) was used to evaluate flood risk in a data-limited region by integrating Remote Sensing (RS) and Geographic Information System (GIS) methods. The study identified several key flood risk indicators, including topographic wetness index, elevation, slope, land cover, precipitation, distance to river, distance to road, and NDVI. The flood risk map had a score range of 8.71–30.99 %, with higher scores indicating a greater susceptibility to flooding. These scores were then used to classify the flood risk into five categories: very low, low, moderate, high, and very high. The percentages of regions falling into each category were 8.71 %, 23.52 %, 30.99 %, 22.68 %, and 14.09 % respectively. The area under the Curve (AUC) approach was used to validate the flood risk map, which showed a high degree of accuracy (0.86). The results of this study provide valuable insights for monitoring, and forecasting the probability of floods in the Dosso Region.

Assessing flood risks in the Taquari-Antas Basin (Southeast Brazil) during the September 2023 extreme rainfall surge

This analysis delves into precipitation dynamics in the Bacia Taquari Antas region, with a focus on September 2023. Employing a multi-scale approach encompassing monthly, daily, and subdaily analyses, the study unveils a consistent precipitation distribution throughout the year. September 2023’s anomaly, the second-highest in the dataset, prompts investigation into potential climatic variability. Notably, the daily analysis highlights September 4th, 2023, as significant, emphasizing the importance of historical context in evaluating weather event severity. Subdaily scrutiny of September 4th reveals intense, localized precipitation, raising concerns about hydrological impacts such as flash floods. Positive trends in Rx5day (maximum consecutive 5-day precipitation amount) and R25 (number of days in a year when precipitation exceeds 25 mm) indices indicate an increase in heavy precipitation events, aligning with broader climate change concerns. Shifting focus to flood extent and impact assessment in the Taquari-Antas Basin, a simulation model depicts the temporal evolution of the flood, reaching its peak on September 4th. Examination of affected areas, rainfall volumes, and impacts on census sectors, cities, and buildings furnishes critical data for disaster management. This study contributes to localized precipitation comprehension and broader issues of climate trends, flood risk evaluation, and urban vulnerability, providing a basis for informed decision-making and resilient planning strategies.

Escalating rainstorm-induced flood risks in the Yellow River Basin, China

The warming climate-induced intensification of hydrological cycle is amplifying extreme precipitation and increasing flood risk at regional and global scales. The evaluation of flood risk, which depends on assessment indicators, weights, as well as data quality, is the first step toward mitigation flood disasters. In this study, we accepted ten risk assessment indicators concerning hazard of disaster-causing factors, sensitivity of hazard-forming environments, and vulnerability of disaster-bearing bodies. We used a combined weighting method based on the analytic hierarchy process and entropy weight (AHP-EW) technique to evaluate rainstorm-induced flood risks across the Yellow River Basin (YRB) from 2000 to 2018. We observed flood hazards are intensifying across the YRB. Specifically, areas with medium flood hazards expanded from the lower to the middle and upper YRB. The sensitivity to floods exhibited a spatial pattern of increasing from southeast to northwest (lower to upper YRB). The increase in vegetation coverage in the middle and upper reaches of the YRB reduces the sensitivity to flood disasters. Flood vulnerability shows an increasing trend, with higher vulnerability mainly observed in the middle and lower YRB. The overall flood risk in the YRB shows an increasing trend, with a 9-fold increase in flood risk from 2000 to 2018. Medium to high flood risk and vulnerability can mainly be identified in the middle and lower YRB, where population and gross domestic product are concentrated. The intensifying rainstorm-induced flood risks over urban areas in these regions should arouse public concern.

Flood hazard analysis in Mumbai using geospatial and multi-criteria decision-making techniques

ABSTRACT Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences. This study seeks to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP). In this study, land use/land cover, average annual rainfall, elevation, drainage density, normalized difference vegetation index, distance from rivers as well as distance from roads are identified and considered. For this reason, the expert survey utilizes the AHP weights so as to find out the significance of these factors towards flooding danger. Investigations show a flood risk index and a corresponding map for Mumbai, where all areas are divided into risk zones very low to very high. Very high risks are particularly situated along rivers. Such details offer critical knowledge to policymakers who will undertake informed emergency preparedness measures designed to shield Mumbai's citizens and assets. Therefore, this research is considered as one of the modern techniques for flood risk assessment, which can be applied in other flood-affected areas worldwide.

Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method

The frequent occurrence of floods and waterlogging has significantly impacted coastal cities. Effective mapping of flood risk can enhance the precision of disaster risk reduction strategies. However, there is room for improvement in fields such as flood inundation data, evaluation objectivity, and classification refinement. This study, using Xiamen as a case study, advanced flood inundation data accuracy by generating a frequency-based flood inundation map from multi-year remote sensing imagery. This study integrated the EWM-TOPSIS model with a neural network model to evaluate the flood risk. The EWM-TOPSIS model was employed to assess the flood vulnerability and flood exposure, while the multi-class neural network model to simulate the flood hazard. This combined approach reduced subjectivity in the flood risk assessment of Xiamen and achieved a finer level of risk classification compared to traditional binary neural networks. The results indicated that the flood vulnerable areas of Xiamen are concentrated along waterways distant from roads, with high flood exposure in Tong'an and Xiang'an districts. The flood-prone areas in Xiamen are primarily located along the coastal areas characterized by extensive impermeable surfaces. High flood risk areas are mainly distributed in Tong'an and Xiang'an subdistricts, particularly in Xiangping and Xindian. The method developed offers support for accurate flood risk identification and decision-making in cities with limited data resources.

Decision trees in cost–benefit analysis for flood risk management plans

ABSTRACT According to the European Directive 2007/60/CE, flood risk evaluation should include a cost–benefit analysis (CBA) on a long-term time horizon to evaluate the impact of mitigation measures. The standard CBA assumes to know in advance the events observed in the time horizon and a priori compares all mitigation measures by an economic metric. No change is supposed to be made to these measures throughout the time horizon. This modus operandi is not appropriate in the domain of flood risk management because several conditions are uncertain when the CBA is made (e.g., urban policies). This article faces these challenges by the integration of cost–benefit analysis and decision trees, to prescribe mitigation measures under uncertainty on the budget for mitigation actions because their funding can be modified after the conclusion of the CBA. The former integration is discussed in the real case of the lowland valley of the Coghinas River (Sardinia, Italy), for which the classical CBA compared five mitigation measures of infrastructural works. The integration into the decision tree also allows evaluation of mitigation measures with changes in infrastructural works and a lamination action. The outcomes advise decreasing the maximum storage level and increase the peak lamination.

A comprehensive review of methodologies and implications for evaluating flood susceptibility

Floods, ever since the dawn of human civilization, have been seen as causes of massive disasters. Since its onset, the influence of human activity has increased, making this calamity more severe. Vulnerability to flooding is complex and multifaceted and it is a matter of critical importance. Since the scale of damage changes with location and with time, determining how susceptible a community is to flooding is of paramount importance. The research is an effort to summarize the methods developed to evaluate flood risk. By examining the authors and terms most commonly cited in the works of others, actionable tactics were uncovered. High-resolution data and a multidimensional approach to vulnerability assessment can enhance current procedures and strategies for estimating flood risk. This research recommends merging hydrodynamic models, geospatial methodologies for flood assessment to present a more complete view of flood vulnerability. It is potentially helpful in identifying methodological inadequacies for measuring flood risk at various geographical scales. The study analyzes several vulnerability components. This research has the potential to fill critical knowledge gaps in our understanding of how to measure exposure to flood risk across a variety of geographical scales.

Flood Risk Assessment for an Irrigation Project in Odissa, India

Objectives: Flood risk assessment is a fundamental aspect of disaster management, particularly in regions heavily reliant on irrigation infrastructure for agriculture. This study employs advanced hydrological and hydraulic modeling techniques to evaluate flood risk for the Lower Suktel region in Odisha, India. Methods: The methodology integrates Intensity-Duration-Frequency (IDF) curves, Isopluvial maps, and the Hydrologic Engineering Center-River Analysis System (HEC-RAS) to comprehensively analyze flood risk and its implications. Findings: IDF curves further reveal that the design rainfall intensity for a one-hour duration with a 100-year return period is 152 mm/h, aiding in characterizing rainfall intensity for specific return periods. Model simulation identifies the pump house's susceptibility to flooding, with maximum flood depths ranging from 0 to 2 meters. These findings underscore the significance of employing advanced modeling techniques and Isopluvial maps for precise flood risk assessment. Novelty: The novelty of this paper lies in its pioneering effort to introduce a comprehensive flood risk assessment in an area where it has not been previously conducted. The integration of advanced modeling techniques and spatial analysis tools contributes to the novelty of the research, making it a valuable and innovative contribution to the field of flood risk management. Understanding extreme rainfall events, hydraulic behavior, and potential flood depths is imperative for developing effective flood mitigation strategies. Keywords: Flood risk assessment, Irrigation infrastructure, IDF curves, Hydrological modeling, Hydraulic modeling, GIS

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

ASSESSMENT OF URBAN FLOOD RISKS OF THE CITIES USING ENTROPY-VIKOR METHODS IN TÜRKİYE

In recent years, there is growing interest for evaluation of urban flood risks of cities over the past decade due to rapid urbanization and climate change. The optimal flood risk assessment is strategically achieved not only with classical risk modelling approaches but also with holistic and comprehensive framework. This paper focuses on a detailed flood assessment providing risk database for policymakers and urban planners to decide the flood prone areas in Turkey. In this context, the Entropy based VIKOR (VIseKriterijumska Optimizacija Kompromisno Resenje) was provided to evaluate a range of flood risk criteria named number of floods, population density and number of buildings, flood protection area which are under the concept of risk dimension including “hazard, exposure and vulnerability” aspects. Computational results demonstrate that the provinces of Şanlıurfa, Ordu, Zonguldak and Van are assigned with higher urban risk values, respectively and the ranking of the cities was presented with different q values. The findings should support practitioners and researchers for land use planning and risk reduction works as the detailed flood risk evaluation was presented in terms of the flood management.

An Internet of Things and AI-Powered Framework for Long-Term Flood Risk Evaluation

An Internet of Things and AI-Powered Framework for Long-Term Flood Risk Evaluation

ASSESSING THE IMPACTS OF FLOOD ON THE COMMUNITIES IN DUTSIN-MA LOCAL GOVERNMENT AREA, KATSINA STATE, NIGERIA

Floods is one of the most disturbing environmental hazards in Dutsin Ma Local Government Area of Katsina State, Nigeria. The resulting effect is on property loss, economy and the environment. On this basis, this research was carried out to evaluate flood risk areas in Dutsin-ma local government area of Katsina State. a questionnaire survey gathered information regarding flood in the research area. geospatial approaches were used to obtain Satellite imagery of 2020 from United States Geological Survey (USGS) Google Earth platform. Multi Criteria Evaluation approach was used to develop flood risk-vulnerability map for the Dutsin-Ma. Findings from this study shows that 19% of the respondents believed that relocating people to higher grounds is the best way to mitigate the impact of flood, about 22.8% of the respondents believed that construction and maintenance of new drainages is a good option. Result for vulnerability assessment shows that Makera South, Makera North, Garhi East, Garhi West, Sakarya, Kitibawa, Gandi and Makwanta are identified as very high-risk areas. Other high-risk areas include Gidan Gamawa, Garin Mallam, Koranwaya, Wawaye, Zaki, Dage, Taka, Korafawa, Unguwan Nassarawa, Gago, Mawashi and Karki. The study recommended that Distribution of adequate relief materials and public enlightenment would assist to reduce the impact of in the study area. Result of this study can be beneficial to relevant professional bodies and agencies such as town planners, land use land cover planners, emergency, and contingency planners and the general public for control and management of flood.

Flood Risk Evaluation Using AHP-Based Model and GIS Technique: A Case Study of Ethiope East and West Local Government Areas, Nigeria

Flood Risk Evaluation Using AHP-Based Model and GIS Technique: A Case Study of Ethiope East and West Local Government Areas, Nigeria

Probabilistic mapping of life loss due to dam-break flooding

AbstractAssessment of flood damage caused by dam failures is typically performed deterministically on the basis of a single preselected scenario, neglecting uncertainties in dam-break parameters, exposure information, and vulnerability model. This paper proposes a probabilistic flood damage model for the estimation of life loss due to dam-break flooding with the aim of overcoming this limitation and performing a more comprehensive and informative evaluation of flood risk. The significant novelty lies in the fact that the model combines uncertainties associated with all three components of risk: hazard, exposure, and vulnerability. Uncertainty in flood hazard is introduced by considering a set of dam-break scenarios, each characterized by different breach widths and reservoir levels. Each scenario is linked to a probability, which is assumed conditional on the dam-break event. Uncertainty in exposure is accounted for using dasymetric maps of the population at risk for two socio-economic states (representing business and non-business hours of a typical week), along with associated likelihood. Vulnerability to flooding is described through a well-established empirical hazard-loss function relating the fatality rate of the population at risk to the flood hazard, the flood severity understanding, and the warning time; a confidence band provides quantitative information about the associated uncertainty. The probabilistic damage model was applied to the case study of the hypothetical collapse of Mignano concrete gravity dam (northern Italy). The main outcome is represented by probabilistic flood damage maps, which show the spatial distribution of selected percentiles of a loss-of-life risk index coupled with the corresponding uncertainty bounds.

Evaluation of future flood probability in agricultural reservoir watersheds using an integrated flood simulation system

In agricultural reservoir watersheds, the presence of various agricultural hydraulic structures combined with the influence of climate change necessitates effective flood risk analysis. This study aims to develop a flood risk evaluation technique for agricultural basins by incorporating the low temporal resolution of climate change scenarios. The approach utilizes flood probability as a key component and incorporates the Multiplicative Random Cascade model as a probability-based rainfall time decomposition model to capture the inherent uncertainty in climate change data. Reliability analysis with the limit state equation is employed for probability-based risk evaluation. Given that agricultural watersheds typically consist of agricultural reservoirs and farmland, the study develops a hydraulic and hydrological linkage model to comprehensively calculate flood probability, simulating from the agricultural reservoir in the upper side to the farmland located at the bottom edge of the watershed. The flood risk assessment is applied to the Yedang Reservoir watershed in Korea, using the Shared Socio-economic Pathways climate change scenario. Results indicate that flood probability is the highest in river levees during future precipitation events, converging to nearly 100% under 24-hour duration rainfall, irrespective of the rain frequency. Despite the hydraulic structures at the farmland being designed for a lower frequency of rain events, the average probability of flooding in the farmland have reached 50%. The agricultural reservoir exhibits a low risk of flooding even under climate change conditions. The proposed method not only enables the establishment of flood countermeasures but also facilitates the evaluation and prediction of future flood risk for each hydraulic structure located in rural areas affected by climate change.

The testing of a multivariate probabilistic framework for reservoir safety evaluation and flood risks assessment in Slovakia: A study on the Parná and Belá Rivers

Abstract Intense floods represent a challenge to risk management. While they are multivariate in their nature, they are often studied in practice from univariate perspectives. Classical frequency analyses, which establish a relation between the peak flow or volume and the frequency of exceedance, may lead to improper risk estimations and mitigations. Therefore, it is necessary to study floods as multivariate stochastic events having mutually correlated characteristics, such as peak flood flow, corresponding volume and duration. The joint distribution properties of these characteristics play an important role in the assessment of flood risk and reservoir safety evaluation. In addition, the study of flood hydrographs is useful because of the inherent dependencies among their practice-relevant characteristics present on-site and in the regional records. This study aims to provide risk analysts with a consistent multivariate probabilistic framework using a copula-based approach. The framework respects and describes the dependence structures among the flood peaks, volumes, and durations of observed and synthetic control flood hydrographs. The seasonality of flood generation is respected by separate analyses of floods in the summer and winter seasons. A control flood hydrograph is understood as a theoretical/synthetic discharge hydrograph, which is determined by the flood peak with the chosen probability of exceedance, the corresponding volume, and the time duration with the corresponding probability. The framework comprises five steps: 1. Separation of the observed hydrographs, 2. Analysis of the flood characteristics and their dependence, 3. Modelling the marginal distributions, 4. A copula-based approach for modelling joint distributions of the flood peaks, volumes and durations, 5. Construction of synthetic flood hydrographs. The flood risk assessment and reservoir safety evaluation are described by hydrograph analyses and the conditional joint probabilities of the exceedance of the flood volume and duration conditioned on flood peak. The proposed multivariate probabilistic framework was tested and demonstrated based on data from two contrasting catchments in Slovakia. Based on the findings, the study affirms that the trivariate copula-based approach is a practical option for assessing flood risks and for reservoir safety.