CLIM-SEG: A generalizable segmentation model for heat and flood risk mapping

Flood can be assessed through flood vulnerability, risk, and susceptibility analysis using remote sensing, geographic information system, and hydrological modelling. In this chapter, different stages, complexities, and processes of flood vulnerability, risk, and susceptibility assessment were discussed. The study reveals that flood vulnerability should be assessed based on four aspects: physical, social, economic, and environmental. Flood risk should be assessed by three stages: risk analysis, disaster relief, and preparedness, whereas flood susceptibility assessment involves three processes. Overall, it was found that the responsible factors vary as per the local conditions, which need to be carefully analyzed and selected. Furthermore, the role of remote sensing and geographic information system in flood risk management were found important especially in flood risk mapping and in the selection of responsible flooding factors.

Frequently occurring flood disasters caused by extreme climate and urbanization processes have become the most common natural hazard and pose a great threat to human society. Therefore, urban flood risk assessment is of great significance for disaster mitigation and prevention. In this paper, the analytic hierarchy process (AHP) was applied to quantify the spatiotemporal variations in flood risk in Wuhan during 2000–2018. A comprehensive flood risk assessment index system was constructed from the hazard, sensitivity, and vulnerability components with seven indices. The results showed that the central urban area, especially the area in the west bank of the Yangtze river, had high risk due to its high flood sensitivity that was determined by land use type and high vulnerability with dense population and per unit GDP. Specifically, the Jianghan, Qiaokou, Jiangan, and Wuchang districts had the highest flood risk, more than 60% of whose area was in medium or above-medium risk regions. During 2000–2018, the flood risk overall showed an increasing trend, with Hongshan district increasing the most, and the year of 2010 was identified as a turning point for rapid risk increase. In addition, the comparison between the risk maps and actual historical inundation point records showed good agreement, indicating that the assessment framework and method proposed in this study can be useful to assist flood mitigation and management, and relevant policy recommendations were proposed based on the assessment results.

A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis

Despite an increasing number of people exposed to flood risks in Europe, flood risk perception remains low and effective flood risk management policies are rarely implemented. It becomes increasingly important to understand how local governments can design effective flood risk management policies to address flood risks. In this article, we study whether high flood exposure and flood risk perception correlate with the demand for a specific design of flood risk management policies. We take the ideal case of Switzerland and analyze flood risk management portfolios in 18 flood-prone municipalities along the Aare River. We introduce a novel combination of risk analysis and public policy data: we analyze correlations between recorded flood exposure data and survey data on flood risk perception and policy preferences for selected flood risk management measures. Our results indicate that local governments with high flood risk perception tend to prefer non-structural measures, such as spatial planning and ecological river restoration, to infrastructure measures. In contrast, flood exposure is neither linked to flood risk perception nor to policy preferences. We conclude that flood risk perception is key: it can decisively affect local governments’ preferences to implement specific diversified policy portfolios including more preventive or integrated flood risk management measures. These findings imply that local governments in flood-prone areas should invest in raising their population’s awareness capacity of flood risks and keep it high during periods without flooding.

This research aimed to evaluate flood hazards and risk areas in the Gidabo Watershed using remote sensing (RS), Geographic Information Systems (GIS), and analytical hierarchy process (AHP). Six main factors were considered to identify flooding hazard zones: drainage density (DD), soil, elevation, rainfall, slope, and land use land cover (LULC). Population density, flood hazard zone, and LULC were considered for mapping the flood risk zone in the Gidabo watershed. A weighted overlay analysis tool has been utilized to integrate the thematic layers to identify both flood hazard and flood risk zones. The findings indicated that about 41.6% (337 km2) of the watershed falls within the high and very high flooding hazard zones. Conversely, 31.11% (252 km2) of the watershed is categorized under very-low and low flooding hazards. Moreover, the study identified five flood risk zones in the area viz; very high, high, moderate, low, and very low. The result of the flood risk map revealed that 199.5 km2 (24.5%) of the watershed has a higher and very higher risk of flooding. These zones were validated using the receiver operating characteristic (ROC) curve, showing a correlation coefficient of 0.943. These results emphasize the need to implement prediction of floods, early warning systems, and effective management practices on a regular and sustainable basis.

Flood risk assessment is an important tool for disaster warning and prevention. In this study, an integrated approach based on a D-number-improved analytic hierarchy process (D-AHP) and a self-organizing map (SOM) clustering algorithm are proposed for urban flooding risk assessment. The urban flood inundation model and geographic information system (GIS) technology were used to quantify the assessment indices of urban flood risk. The D-AHP approach was adopted to determine the weights of the indices, which effectively makes up for the shortcomings of the AHP in dealing with uncertain evaluation information (such as fuzzy and incomplete information). In addition, the SOM clustering algorithm was applied to determine the flood risk level. It is a data-driven approach that avoids the subjective determination of a flood risk classification threshold. The proposed approach for flood risk assessment was implemented in Zhengzhou, China. The flood risk was classified into five levels: highest risk, higher risk, medium risk, lower risk, and the lowest risk. The proportion of the highest risk areas was 9.86%; such areas were mainly distributed in the central and eastern parts of the Jinshui District, the eastern part of the Huiji District, and the northeastern part of the Guancheng District, where there were low terrain and serious waterlogging. The higher risk areas accounted for 24.26% of the study area, and were mainly distributed in the western and southern parts of the Jinshui District, the southern part of the Huiji District, the middle and eastern parts of the Zhongyuan District, the northeastern part of the Erqi District, and the northwestern part of the Guancheng District, which consisted of economically developed areas of dense population and buildings, matching well with historical flooding events. To verify the effectiveness of the proposed approach, traditional approaches for risk assessment were compared. The comparison indicated that the proposed approach is more reasonable and accurate than the traditional approaches. This study showed the potential of a novel approach to flood risk assessment. The results can provide a reference for urban flood management and disaster reduction in the study area.

PurposeFloods have been identified as the most frequent and threatening disaster in Sri Lanka amidst an increasing trend of natural and man-made disasters in the world. Subject experts state that disaster risk management should be based on the results of risk assessments, but flood risk management in Sri Lanka is seemingly not based on community-level flood risk assessments. Accordingly, the purpose of this paper is to introduce a community-level flood risk assessment method to the local context of Sri Lanka.Design/methodology/approachThe sample (n = 425) for the study was selected using the stratified random sampling method, and the Deduru Oya basin was selected as the study area. The risk assessment model introduced by Bollin et al. (2003) was used for the current study, but with some modifications. Accordingly, 16 variables were selected for the risk assessment. Descriptive data analysis methods were used in the study.FindingsCommunity-level flood risk assessment method was introduced. Variable index, flood risk index and flood risk map were developed for the study area. The Grama Niladari Divisions (GNDs) were grouped into five categories from very high risk to very low risk. The GNDs named Wirakumandaluwa, Thimbilla, Deduru Oya, Bangadeniya and Elivitiya were ranked as the most flood-risk GNDs, respectively.Originality/valueThis paper produces a flood risk assessment method for the local context. Flood risk in the study area was assessed based on people’s perceptions. Accordingly, the flood risk index and flood risk map for the study area were developed based on the empirical data. GNDs were ranked based on the flood risk index.

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m2 in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.

Frequent flooding in the central part of the Hindu Kush region, Nepal, results in substantial damage to lives and property, particularly in the plains. This study explores the catchment-scale flood hazard and vulnerability, generating flood risks under climate change scenario. Flood model was developed using HEC-RAS 2D rain-on-grid approach and CMIP6 projected climate data for SSP245 and SSP585 scenarios was used. Flood hazard, vulnerability and risk maps were generated delineating potential inundation scenarios for both 50-year, and 100-year return periods, under present and future climate scenarios. Hazard maps revealed a probable increase in flooding under future climate scenarios. Vulnerability assessments also demonstrated an anticipated escalation in future. Cropland dominates the land-use type in the study area, with agriculture vulnerability increasing by 22.14% (SSP245) and 29.322% (SSP585). The number of inundated buildings increases from 2967 in the existing scenario to 4601 (SSP245) and 5274 (SSP585). Similarly, road vulnerability areas are increased by 28.06% (SSP245) and 37.86% (SSP585). The impact of flooding is higher in SSP585 scenario as compared to SSP245 scenario. The catchment level outcomes have diverse applications, particularly in decision-making for flood management, integrating risk maps for effective strategies, and providing valuable insights into shape climate change adaptation policies. Keywords: Catchment scale, Climate change, Flood vulnerability, Flood risk, HEC-RAS 2D.

As the most productive ecosystems in the world, wetlands play a vital role in carbon cycling, climate change mitigation, socio-economic development, and natural disaster protection. The Yangtze River Basin (YRB) contains 40% of the national wetlands and the most frequent floods in China. In recent decades, the abundant wetland resources in the YRB have experienced substantial changes due to the climate change and human activities, significantly affecting the flood risk. Due to the lack of a long-term time series wetland dataset with comprehensive categories for the YRB, the effects of wetland variations on flood risk, as well as improved assessments of past and future flood risk incorporating wetland dynamics, remain underexplored. Moreover, wetland-related flood risk mitigation efforts in the YRB are less widely adopted. It is crucial to address these gaps for enhancing the sustainable wetland management and flood risk mitigation in the YRB. This thesis aims to achieve three primary objectives: 1) to establish a long-term time series wetland classification dataset with the comprehensive categories in the YRB and analyze driving forces of their variations; 2) to investigate the long-term wetland effects on the flood risk in the YRB based on an improved GIS-based multi-index flood risk assessment model incorporating wetlands input; and 3) to predict the future flood risk with wetland effects in the middle-lower YRB under climate change and socio-economic scenarios. The Long-term Wetland Classification Dataset for YRB (LTWCD_YRB) with nine wetland categories from 1984 to 2021 was created by using the Random Forest machine learning classifier on the Google Earth Engine platform with 30m resolution Landsat images. The LTWCD_YRB revealed that: 1) the total wetland area of the YRB in 2021 was larger than that in 1984, with a constant increase in human-made wetlands and fluctuating natural wetlands; 2) aquaculture ponds expanded the most by 4,987 km2, while inland marshes in the source region exhibited the most fluctuations; and 3) seasonal changes in wetlands were prominent in the Poyang Lake Basin, Dongting and Honghu Lake Basin, and YRB source region; and 4) human activities were found to be more dominant than natural driving forces in affecting wetlands. The LTWCD_YRB offers a consistent agreement of wetland area variations with the other satellite-based wetland datasets in the YRB. To investigate the long-term effects of wetland variations on flood risks in the YRB, this thesis developed an improved GIS-based multi-index flood risk assessment model incorporating the wetland input obtained from the LTWCD_YRB. The findings indicated that: 1) wetlands in the Taihu Lake Basin, Wanjiang Plain, Poyang Lake Basin, and Dongting and Honghu Lake Basin could mitigate flood risks, while wetlands in the Sichuan Basin have aggravated but limited impacts on flood risks; and 2) Precipitation in the Taihu Lake Basin and Poyang Lake Basin, runoff and vegetation cover in the Wanjiang Plain, GDP in the Taihu Lake Basin, population density in the Taihu lake Basin, Dongting and Honghu Lake Basin, and the Sichuan Basin are dominant flood risk indicators under wetland effects. The wetland-related suggestions to mitigate flood risks including maximizing stormwater storage capacity of wetlands and increasing vegetation coverage in urbanized and precipitated regions. The flood risk prediction of the middle-lower YRB applied the improved flood risk model to assess the flood risk from 2021 to 2100 under the Shared Socioeconomic Pathways (SSP) 2 - Representative Concentration Pathways (RCP) 4.5 and SSP5-RCP8.5 scenarios. The results indicated that: 1) the high and very high flood risk areas will totally cover 38% and 40% of the total study area under the SSP2-4.5 and SSP5-8.5 scenarios by 2100, respectively; 2) the overall flood risk of the MLYRB was predicted to become severer by 2100 under both scenarios; and 3) there would be a prominent northward expansion of the high and very high flood risk areas in Jiangxi, Hunan and the southern part of the Taihu Lake Basin in Jiangsu. In summary, this thesis provides the data support for the long-term wetland variations in the YRB, develops an improved flood risk model to investigate the long-term wetland effects on the flood risk and predicts the future flood risk incorporating wetland dynamics in the YRB. The efforts of thesis contribute to the sustainable wetland conservation and flood risk mitigation in the YRB, aligning with the United Nations Sustainable Development Goals.

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

Flooding is a major natural hazard that poses a significant threat to lives, infrastructure, and economies, especially in regions with high rainfall and extreme weather events like India during the monsoon season. Accurate flood prediction is essential for timely intervention, efficient resource allocation, and reducing the impact of floods. Machine learning (ML) and deep learning (DL) techniques provide promising new approaches by analyzing large-scale data to enhance prediction accuracy and flood risk assessments. Key factors such as rainfall, temperature, river discharge, elevation, soil type, and population density are examined for their impact on flood risks. This paper reviews the strengths and weaknesses of various predictive models, with a focus on their data needs, computational complexity, and applicability in real-world flood prediction scenarios. The research emphasizes the importance of data preprocessing, feature extraction, and model comparisons to demonstrate how these models can improve predictive accuracy and facilitate proactive disaster management in flood-prone areas. By exploring the potential of ML and DL models, this paper aims to help researchers, policymakers, and practitioners better utilize these tools for adaptive flood risk management strategies, ultimately enhancing resilience to future flood events.

Flood maps are commonly used for flood risk communication. Misinterpretation of these maps can lead to over-heightened flood risk perception, with adverse risks for real estate markets. We, therefore, aim to identify an appropriate approach for framing these maps to mitigate such risks. To do this, we carry out five randomised control trials (RCT) in the UK and Pakistan, with each RCT asking a different group of 40 residents in each country to specify their willingness-to-pay (WTP) for coastal residential properties. In the first RCT, we provide no flood risk map. In each subsequent RCT, we provide a flood risk map framed differently. Our flood risk map frames include those with technical language, colour variations, and more accessible risk information. Our findings indicate that real estate demand uncertainty increases in response to flood risk maps, regardless of the framing approach used. We find that there is less WTP for properties in any zones with flood risk information, regardless of whether the risk is very low. This finding indicates that risk aversion takes precedence in real estate decisions under flood risk maps, leading to property devaluation in all flood risk zone categories. We discuss the wider implications of these findings. • Flood risk maps over-heighten flood risk perception. • Public access to these maps increases residential real estate demand uncertainty. • Flood risk maps have implications for property devaluation. • Risk-averse behaviours outweigh framing of these maps.

Pluvial floods are frequent phenomena in Romania occurring during torrential rainfall, causing significant damage. Pluvial floods occur due to extreme rainfall when the natural infiltration of excess water into the ground is artificially reduced and when runoff into urban areas exceeds the capacity of existing drainage systems. In urban areas, pluvial flooding is more frequent due to extended impermeable areas, buildings, and undersized drainage systems. Tulcea municipality is characterized by periodic rainfall flooding events with consequences on properties and communities, infrastructure, and on the economic activities. In the second cycle of implementation of the Floods Directive 2007/60/EC, 17 Areas of Potential Significant Flood Risk (APFSR) pluvial source areas were identified, covering 19 municipalities and towns, for which pluvial hydraulic models were carried out. Tulcea municipality was one of the cities designated as having areas of potential significant pluvial flood risk. Hydraulic modelling results have been used for the construction of pluvial flood hazard and risk maps, these being the first pluvial maps reported by Romania to the European Commission. Based on these maps, critical drainage areas have been identified at the level of Tulcea Municipality and some potential flood risk reduction measures have been recommended.