Improve Flood Resilience Research Articles

Dynamic Impact-Based Heavy Rainfall Warning with Multi-classification Machine Learning Approaches

The majority of flood assessment and warning systems primarily focus on the occurrence of floods caused by river overflow, taking into account factors such as intense precipitation. Improving flood resilience, on the other hand, requires a deeper understanding of how these factors affect each other and how specific local conditions can have an impact. This study offers impartial tools for estimating the severity of the effects brought on by heavy rainfall to facilitate the prompt communication of effective measures, such as the evacuation of livestock and human settlements and the provision of medical assistance. These tools take into account the cascading effects of various causative factors contributing to heavy rainfall. This article aims to assess the various factors that contribute to the impacts of heavy rainfall, including the timestamp (indicating soil saturation and moisture levels), river gauges (determining water congestion in canal systems), average aerial precipitation (indicating runoff), and the rainfall itself, taking into account both in situ and ex-situ impacts. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. This analysis is conducted by leveraging historical observed datasets. The results demonstrate the feasibility of mitigating the issue of excessive and insufficient flood warnings resulting from the cumulative effects of intense precipitation. By implementing a categorization system that divides the affected areas into various portions, or districts, according to the main factors contributing to flooding, namely rainfall, river discharge, and runoff, The suggested model presents novel insights into the sequential consequences of intense precipitation in the regularly inundated regions of North Bihar, India. Innovative tools can serve as valuable resources for flood forecasters and catastrophe managers to comprehend the extent of flooding and the consequential effects of intense precipitation.

How do lessons learned from past flood experiences influence household-level flood resilience: A case study of Jiangnan village in Southern China

The concept of flood resilience has gained increasing attention due to climate change. Although it is argued that learning is a crucial mechanism for improving flood resilience, few empirical studies have explored how lessons learned from past flood experiences influence flood resilience. It is also unknown whether there are any barriers preventing people from implementing the lessons learned and eroding the development of flood resilience. To address these gaps, this paper conducts a case study of Jiangnan village, a frequently flooded rural village in southern China. Jiangnan village was seriously flooded in 2022; however, it is surprising that only a few people living in the village experienced flood losses. Forty-four in-depth interviews with local villagers were conducted between 2022 and 2023, revealing that extensive local knowledge learned from past flood experiences helped reduce flood losses during the 2022 flood. We further concluded that the lessons learned significantly contributed to flood resilience by improving villager agility during flooding and promoting a holistic approach to flood risk reduction. Moreover, several barriers preventing people from implementing the lessons learned were identified, including a lack of accurate and timely flood risk communication, personal barriers, and changes in flood regimes. This study provides insights for flood risk management stakeholders that help them understand the value and limitations of local knowledge. We also advocate for flood management to reduce reliance on flood control and integrate a learning-based approach, enabling people to experiment with alternative practices during floods and then learn from their subsequent feedback.

Coastal urban flood risk management: Challenges and opportunities − A systematic review

Generational mechanisms and spatio-temporal evolution patterns of coastal urban flood risk involve complex interactions between climate change, sea level rise and human-induced factors, necessitating integrated adaptive flood management strategies to mitigate evolving vulnerabilities. This systematic review offers a thorough assessment of the challenges and strategic opportunities for sustainable adaptation in managing flood risk in coastal urban areas. It integrates emerging innovative technologies and financial solutions to identify promising approaches to implement mitigation strategies and improve coastal urban flood resilience. Enhancing governance and policy frameworks is crucial for the successful implementation of coastal urban flood risk management (CUFRM) plans. An innovative participatory planning framework is developed to promote flood management practices which are socially inclusive and equitable. Funding for green infrastructure and nature-based solutions and the strategic use of public-private partnerships are effective methods for advancing sustainable flood risk management (FRM). The advancements in emerging technologies, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), social media and digital twin technologies, provide dynamic and collaborative platforms for simulating flood scenarios and have potential to significantly improve CUFRM practices. In the end, a cross-country comparison of current practices in Australia, China, the Netherlands, the UK and the USA reveals a diverse range of approaches and valuable insights derived from regional experiences. The review provides a comprehensive analysis for researchers, policymakers and practitioners aiming to improve flood resilience in coastal metropolitan regions by learning from effective UFRM approaches that enhance governance structures, infrastructure resilience and funding mechanisms.

Dual level attention based lightweight vision transformer for streambed land use change classification using remote sensing

Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature’s flood buffers for economic advancement.

Content Analysis of Flood Relief Efforts: Examining Coping and Recovery Themes in Resilience Narrative Analysis

Climate change and pollution have increased frequency of floods. According to statistics, floods are becoming more frequent and causing significant damage, especially to the victims. Subsequently, victims must develop greater resilience. The aim of this study is to determine the focus on flood victims dealing with flood disasters. As part of the inclusion criteria, six relevant government documents were reviewed, all related to flood resilience. Thematic analysis was conducted using ATLAS.ti software identifies five main themes such as resilience behavior, community sustainability, disaster management, strategic planning, and global security. Therefore, the government should develop innovative strategies, utilize the latest technologies, and promote international collaboration to improve flood resilience. Encouraging community sustainability can help flood victims develop resilience behaviors, which is important for creating a society that can withstand natural disasters.

Interagency deployment of a shared low‐cost flood monitoring system to improve flood resilience across Southeast Texas: A case study

AbstractIn 2021, eight counties in Southeast Texas formed a non‐tax authority flood control partnership to provide a forum for communication, create a regional database and improve flood modeling, early warning, and flood mitigation decisions. This partnership is able to successfully deploy and manage an early warning flood system network at relatively low‐cost by: (i) assigning the university as technical expert and general network administrator so that no additional agency workforce is required to operate and maintain the system, (ii) the flood monitoring system is a simple system and (iii) the data was able to be displayed in real‐time using an existing web‐based dashboard owned and operated by another agency within the partnership. The low‐cost sensors are lightweight and easily mountable which allowed them to be rapidly deployed within a short period of time (73 sensor sites installed within 7‐month window) over a 6000 square mile area. The data is shared with the public, and local, state, and federal agencies to monitor drainage infrastructure, and provide the community with flash flood and roadway closure alerts.

Small Island City Flood Risk Assessment: The Case of Kingston, Jamaica

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country’s socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience.

Scenario-based performance assessment of green-grey-blue infrastructure for flood-resilient spatial solution: A case study of Pazhou, Guangzhou, greater Bay area

Flood resilience has aroused significant interest in coastal areas dealing with a growing frequency of severe rainstorms caused by climate change and urbanisation. At the core of flood resilience is the development of a resilient green-grey-blue infrastructure system that can resist, absorb, and recover from floods in a timely manner. Current flood resilience research, however, is limited to evaluating single infrastructure systems, failing to examine the dynamic process or find ideal spatial infrastructure designs for decision-makers. This research proposes a scenario-based assessment framework for integrated green-grey-blue infrastructure systems to improve flood resilience during urban design decision-making. Rainfall-runoff, drainage networks, and river system models are interlinked to provide quantitative simulation evaluations of water quantity and urban impact in various spatial organisations of infrastructure design. A dynamic, multi-criteria decision-making process is used to reveal the importance of five temporal indicators and rank design alternatives. In Guangzhou, China, the efficiency of this architecture is demonstrated on Pazhou Island, a typical river network area. Given the limited water and green space available, the results demonstrate that submerged areas exert a greater influence during peak rainfall, and blue infrastructure storage becomes an essential factor following rainfall. Furthermore, from a spatial perspective, the looped network of green-blue infrastructure enhances flood resilience, and downstream waterway connections and green space-aligned waterways boost the water storage capacity of green-grey-blue infrastructure. This paradigm can improve flood resilience in the Greater Bay Area in the future, especially in response to heavy rainstorms and river floods.

Flood cascading on critical infrastructure with climate change: A spatial analysis of the extreme weather event in Xinxiang, China

Floods caused by extreme weather events and climate change have increased in occurrence and severity all over the world, resulting in devastation and disruption of activities. Researchers and policy practitioners have increasingly paid attention to the role of critical infrastructure (CI) in disaster risk reduction, flood resilience and climate change adaptation in terms of its backbone functions in maintaining societal services in hazard attacks. The analysed city in this study, Xinxiang (Henan province, China), was affected by an extreme flood event that occurred on 17–23 July 2021, which caused great socio-economic losses. However, few studies have focused on medium-sized cities and the flood cascading effects on CI during this event. Therefore, this study explores the damages caused by this flooding event with links to CI, such as health services, energy supply stations, shelters and transport facilities (HEST infrastructure). To achieve this, the study first combines RGB (red, green blue) composition and supervised classification for flood detection to monitor and map flood inundation areas. Second, it manages a multiscenario simulation and evaluates the cascading effects on HEST infrastructure. Diverse open-source data are employed, including Sentinel-1 synthetic aperture radar (SAR) data and Landsat-8 OIL data, point-of-interest (POI) and OpenStreetMap (OSM) data. The study reveals that this extreme flood event has profoundly affected croplands and villagers. Due to the revisiting period of Sentinel-1 SAR data, four scenarios are simulated to portray the retreated but ‘omitted’ floodwater: Scenario 0 is the flood inundation area on 27 July, and Scenarios 1, 2 and 3 are built based on this information with a buffer of 50, 100 and 150 m outwards, respectively. In the four scenarios, as the inundation areas expand, the affected HEST infrastructure becomes more clustered at the centre of the core study area, indicating that those located in the urban centre are more susceptible to flooding. Furthermore, the affected transport facilities assemble in the north and east of the core study area, implying that transport facilities located in the north and east of the core study area are more susceptible to flooding. The verification of the flood inundation maps and affected HEST infrastructure in the scenario simulation support the series method adopted in this study. The findings of this study can be used by flood managers, urban planners and other decision makers to better understand extreme historic weather events in China, improve flood resilience and decrease the negative impacts of such events on HEST infrastructure.

A System Dynamics Model of Urban Rainstorm and Flood Resilience to Achieve the Sustainable Development Goals

The combined effects of climate change and accelerated urbanization have increased the risk of urban rainstorm and flood disasters, which seriously affects the achievement of the Sustainable Development Goals (SDGs) in cities. Improving urban flood resilience is one of main ways to deal with flooding. This study established an urban rainstorm and flood resilience model to sort out the behavioral mechanisms of multiple subjects (government, flood control department, residents and media) and the internal action mechanisms from different dimensions (economic, social, natural, infrastructure and information) of urban rainstorm and flood resilience using system dynamics approach, and to investigate the urban rainstorm and flood resilience in Xi'an city. The results show that the damage losses rate reduced by 44.44%, 10.8%, 9.48% and 3.37% by improving flood resilience, resident engagement, government attention degree and information construction level, respectively. The degree of improvement of urban rainstorm and flood resilience varied for six development scenarios when predict flood resilience. The development pattern can be divided into the beginning phase, preparation phase and development phase, and three improvement strategies are proposed based on the development pattern of flood resilience. This study helps to provide refined strategies for urban rainstorm and flood resilience assessment construction.

Improving flood resilience through optimal reservoir operation

Reservoir operation is an effective measure for flood disaster reduction. Previous studies focused on minimizing flood risk to reduce potential flood damage, however, failure and recovery processes are not considered. To address this shortcoming, this study introduces the concept of resilience to measure the ability of flood control systems to maintain or return to the original states after an extreme flood. The quantitative expression of resilience, resilience metric, is proposed based on the system performance and defined as the summation of system functionality loss throughout the entire flood process. Based on the proposed resilience metric, a multi-objective optimization model with flood risk represented by maximum reservoir water level and downstream peak flow, and flood resilience as objectives, is established. Nierji Reservoir, located in Northeast China is taken as a case study. Results show that the proposed method can improve resilience without increasing flood risks (i.e., maximum reservoir water level and downstream peak flow) compared with the traditional model. Pareto optimates of the optimization model show that there exist tradeoffs between risk and resilience, improving resilience inevitably reducing risk. The resilience improvement is large when the flood risk of the reservoir or downstream is medium and is limited when the flood risk is high. Moreover, the resilience improvement is higher for a smaller flood. The improvement is achieved through pre-releasing at the early stage of the flood and releasing slowly at the recession stage of the flood. Six typical schemes, selected referring to the existing scheme are comprehensively compared, and an operation rule for flood control with the highest resilience but without reducing risks is recommended. This study provides a new way for flood control management.

Quantifying and improving flood resilience of urban drainage systems based on socio-ecological criteria

In this paper, a new framework is developed for evaluating the resilience of urban drainage systems (UDSs) under floods by proposing and quantifying some technical and socio-ecological (SE) criteria. The proposed criteria are used to quantify the seven principles of building resilience in socio-ecological systems. The criteria mainly focus on preserving diversity and multiplicity in a UDS, managing variables that gradually change over time (slow variables), improving structural and functional connectivity, maintaining system adaptability, encouraging learning, broadening participation, and promoting polycentric governance systems. For evaluating the efficiency of the proposed framework, it is applied to a real-world case study of improving resilience of the UDS in the eastern part of Tehran metropolitan area. Three scenarios for flood management are proposed based on the Low Impact Development (LID) practices which are simulated using the Storm Water Management Model (SWMM). The Entropy method is used to consider the uncertainty in the relative importance of different criteria in estimating the flood resilience. The estimated values for the proposed criteria regarding the current drainage system in the study area show its undesirable condition in many sub-catchments. The results also show that using around 2.3 km2 of LID practices in this urban watershed can significantly improve the resilience in many sub-catchments (nearly, 30%) and reduce the total volume of the overflow (about 50%). The results also show that using the flood management scenarios, improving connectivity is the most influential factor that enhances the general resilience of the system.

Urban Flood Resilience Evaluation Based on GIS and Multi-Source Data: A Case Study of Changchun City

With extreme rainfall events and rapid urbanization, urban flood disaster events are increasing dramatically. As a key flood control city in China, Changchun City suffers casualties and economic losses every year due to floods. The improvement of flood resilience has become an important means for cities to resist flood risks. Therefore, this paper constructs an assessment model of urban flood resilience from four aspects: infrastructure, environment, society and economy. Then, it quantifies infrastructure and environmental vulnerability based on GIS, and uses TOPSIS to quantify social and economic recoverability. Finally, based on k-means clustering of infrastructure and environmental vulnerability and social and economic recoverability, the flood resilience of Changchun City was evaluated. The results show that different factors have different effects on flood resilience, and cities with low infrastructure and environmental vulnerability and high socioeconomic recoverability are more resilient in the face of floods. In addition, cities in the same cluster have the same flood resilience characteristics. The proposed framework can be extended to other regions of China or different countries by simply modifying the indicator system according to different regions, providing experience for regional flood mitigation and improving flood resilience.

An integrated indicator-based approach for constructing an urban flood vulnerability index as an urban decision-making tool using the PCA and AHP techniques: A case study of Alexandria, Egypt

Several cities are exceptionally vulnerable to flood impacts due to increasing urbanization, population growth, and climate change. Quantifying flood vulnerability is useful for identifying the system's weakness, monitoring its evolution, and supporting targeted flood risk adaptation policies. One of the vital aims of assessing urban flood vulnerability is to create an understandable link between flood vulnerability conceptual theories and the daily decision-making process through an easily accessible tool. Although several studies have described the development of an integrated flood vulnerability index (FVI) combining physical, social, and economic dimensions in urban areas, this index has not been assessed in developing countries. Therefore, this study focuses on an integrated indicator-based approach to develop an urban FVI based on exposure, susceptibility, and resilience to urban flooding at the neighbourhood scale. To evaluate the flood vulnerability of the population, we used the Improvement of Vulnerability Assessment in Europe (MOVE) framework. Accordingly, the vulnerability indices cover exposure, susceptibility and resilience aspects. The index is applied to Alexandria, one of the most important coastal cities of Egypt, which is highly vulnerable due to its dense population, low adaptive capacity, flat topography, and exposure to various water-related disasters, such as cyclones, storm surges, bank erosion, sea-level rise, tidal floods, and frequent urban floods. In this study, we use inductive principal component analysis (PCA) to develop a composite indicator for the FVI and to evaluate the vulnerability of 101 census administrative units (sheyakhahs) in Alexandria. We apply the Kaiser–Meyer–Olkin (KMO) test and Bartlett's test of sphericity to assess sample adequacy and perform data standardization for all indicators. Furthermore, the analytic hierarchy process (AHP) is adopted for simplicity and comparison with the PCA results to assess their robustness. We clustered 58 and 13 flood vulnerability-related indicators into three major dimensions, i.e., physical, social, and economic, through PCA and the AHP, respectively. Official collected data are analysed using combined methods using advanced statistical analysis (SPSS) software and a geographic information system (GIS). The findings highlight the variability in flood vulnerability across highly urbanized and suburban areas. Based on the PCA, 38 indicators were defined as the most comprehensive flood vulnerability assessment (FVA) used in Egyptian cities. Additionally, due to reliability of the approach to indicator selection and the weighting process, the chosen 13 indicators for the AHP analysis yielded similar results. This research provides spatial planners and decision-makers with an integrated, comprehensive, and unified urban FVI to assess vulnerability and, thus, improve flood resilience in Egypt and countries in similar situations.

Flood-resilient governance in Okanagan valley of British Columbia: current practices and future directives

The governance of flood resilience comprises an array of policy instruments that can be applied to reduce damages and risks to the communities. Policy instruments available at the local level tend to have the greatest impact on communities’ ability to absorb shock and resume normal operation. This study is the first attempt to analyze policy instruments and regulations guiding the progression of flood resilience measures in the Okanagan Valley, British Columbia. It aims to offer recommendations specific to the local context and identify the best practices to improve flood resilience of the communities. Therefore, the study adopted a multistage methodology including literature review, stakeholder consultations with the organizations (consisting of municipalities, regional districts, and First Nations), and quantitative analysis using present/absent criteria to measure prevalence of given indicators and five flood resilience classes. The results suggest that the flood resilience policies and strategies of 22 organizations in the Okanagan valley are predominantly focused on flood preparation and mitigation, whereas policies related to during and post-flood management are still in infancy and do not offer satisfactory support. Notably, some organizations are proactive in recognizing looming flood risks, thus enacting essential policy measures to strengthen resilience. These results further highlight that these organizations have focused on a variety of measures to improve resilience, such as spatial planning, building construction setbacks, enhancing natural environment, protecting riparian areas, and stormwater management. As well, current flood-resilient practices by the Prairies (Calgary) paved the way for how both regions can learn from each other. The study sheds light on broader takeaways of what the findings of this study mean for other world regions. The study results are of great importance to the organizations across the Okanagan Valley as they lay the groundwork for resource mobilization to achieve flood resilience and sustainability.

Investigating flood resilience perceptions and supporting collective decision-making through fuzzy cognitive mapping.

Improving flood resilience of communities requires a holistic understanding of risks and resilience options as well as the preferences and priorities of different stakeholders. Innovations in risk and resilience assessment have helped communities to identify gaps in their flood risk management strategy but selecting and implementing resilience solutions remains a big challenge for many decision-makers. In addition to traditional appraisals and cost-benefit assessments this also calls for a participatory process in which various stakeholders are encouraged to adopt a system-level approach in identifying interventions that can maximise a range of benefits and co-benefits. In this study, we investigate how a combination of modelling and measurement methods can help decision-makers with their flood resilience strategies. We apply a participatory system thinking approach combining Fuzzy Cognitive Mapping (FCM) with a flood resilience measurement framework called Flood Resilience Measurement for Communities (FRMC). We first investigate stakeholders' biases on flood resilience interventions, and then lead them through a system thinking exercise using FCM and FRMC to elicit mental models representing important aspects of flood resilience and their interrelation. These are then aggregated, representing the collective perceptions and knowledge of stakeholders, and used to identify the most beneficial resilience actions in terms of direct and indirect impacts on flood resilience. We apply this approach to the case of Lowestoft, a coastal town in England exposed to significant flood risk. Developed in close collaboration with the local authorities, the ambition is to support decision-making on flood resilience interventions. We find that this combination of methods enables system-level thinking and inclusive decision-making about flood resilience which can ultimately encourage transformative decisions on prioritization of actions and investments.

Urban Flood Resilience Assessment Based on VIKOR-GRA: A Case Study in Chongqing, China

Flooding has been considered as one of the major challenges of urban hazards resilience assessment. Improving urban flood risk resilience has become a critical issue for city management. However, urban flood assessment models on the resilience perspective are rare. Meanwhile, in the perspective of resilience, the existing studies using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) assessment method for disaster assessment lack consideration of evaluation scenarios located in the midpoint of positive and negative ideal solutions. To address these issues, this study establishes an urban flood disaster resilience assessment model, selects indicators according to the TOSE (Technical, Organizational, Social and Economic) framework, ranks cities through the VIKOR-GRA (VlseKriterijumska Optimizacija I Kompromisno Resenje, Grey Relational Analysis) method, and proposes measures to improve urban disaster resilience through the resilience assessment. Chongqing is taken as an example in this study to verify the feasibility and effectiveness of the proposed model, and practical management opinions are presented to improve flood resilience in these cities. Through the analysis of actual cases, it can be seen that the model has good applicability and can better reflect the actual situation. The knowledge of flood resilience assessment methods is of particular value to policy makers, researchers and industry professionals in assessing potential risks and recognizing the importance of flood prevention mechanisms.

Evaluation of Urban Flood Resilience Enhancement Strategies—A Case Study in Jingdezhen City under 20-Year Return Period Precipitation Scenario

Various flood resilience enhancement measures have been proposed to deal with the growing problem of urban flooding. However, there is a lack of evaluation about the applicability of these measures at a community scale. This paper investigates the effects of two types of flood resilience enhancement measures: engineering measures and adaptive measures, in order to explore their effectiveness in different flood-prone communities. A community-scale oriented flood resilience assessment method is used to assess the impact of different types of measures. A case study is applied in three communities that suffer from waterlogging problems in Jingdezhen city, China. Results show that there are spatial differences of flood resilience in three flood-prone communities. Future scenarios present a poorer performance in flood resilience compared to current scenarios due to the effects of urbanization and human activities. Engineering measures are suitable for the old communities with high-density residential areas when sitting alongside the river, for example the communities of Fuliang and Zhushan. On the other hand, adaptive measures exhibit more efficiency in improving flood resilience in all communities, especially effective for the new city town Changjiang where engineering measures are nearly saturated. The findings can help local governments develop appropriate flood resilience enhancement strategies for different types of communities.

Assessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study

Urban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.

Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data

Abstract Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.