Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data

Impact of 2D and 3D factors on urban flooding: Spatial characteristics and interpretable analysis of drivers

Urban flooding poses a serious threat to both the ecological environment and human society. Previous studies identified natural and anthropogenic factors as contributors to urban flooding, but little attention has been paid to the influence of urban horizontal and vertical factors. To address this gap, we conducted a comparative analysis of the patterns in spatial distribution of urban flooding in two megacities in eastern China (Beijing and Guangzhou). We then used Pearson's correlation to investigate the associations between flooding events and multiple influencing factors. Finally, two scenarios were designed to quantify the relative contributions of each driver using the Light Gradient Boosting Machine (LightGBM) and Shapley (SHAP) interpretable models. The results show that: (1) urban flooding points in Guangzhou and Beijing are predominantly clustered in central areas, with mid-rise and high-density buildings presenting the highest flood risk. (2) in the base scenario, Annual precipitation (AP) is the primary influencing factor for urban flooding in both Beijing and Guangzhou. However, in the enhanced scenario, the addition of 2D and 3D (two-dimensional and three-dimensional) metrics shifts the main drivers to factors like Aggregation index (AI), Patch density (PD), and Building density (BD), significantly impacting urban flooding. This study highlights the critical impacts of horizontal and vertical urban structures and layouts, emphasizing the need for comprehensive urban planning and design strategies to effectively mitigate flood risk. It also provides new perspectives on urban flood risk management.

Developing national-scale basic guideline on flood-adaptation strategies under climate change using probabilistic and deterministic factors

As climate adaptation strategies against floods, implementing structural measures in damage-prone areas, supplemented by nonstructural measures (e.g., ecosystem-based disaster risk reduction (Eco-DRR)), is a viable approach. However, under climate change, predicting damage-prone areas is challenging, hindering the development of effective adaptation strategies. The increase in floods under climate change can be broadly attributed to probabilistic, triggerring hazards, and deterministic, inducing vulnerability factors. Therefore, quantification for levels of probabilistic and deterministic factors may establish adaptation strategies such as prioritize areas where structural measures should be implemented. Herein, we establish basic guideline for developing adaptation strategies against floods, considering probabilistic and deterministic factors simultaneously. We investigated all the municipalities in Japan and modeled flood occurrence from 2010 to 2019 based on government statistics, using the rainfall indicator as a probabilistic factors and terrain factor, which considers land use as a deterministic factor to decide appropriate indicators. Thereafter, we quantified the increase and decrease in rainfall indicator as probabilistic factor. Additionally, we used terrain factor, which considers current land use as a deterministic factor. We implemented nonhierarchical clustering using probabilistic and deterministic factors and classified 1795 municipalities in Japan into six clusters. The findings confirm the feasibility of developing specific adaptation strategies based on the clusters, such as strengthening the installation of artificial structures in areas belonging to the cluster in which floods expectedly increase and enhancing measures in clusters that remain unchanged based on flood histories.

The cost of flooding on housing under climate change in the Philippines: Examining projected damage at the local scale

While the Philippines has made significant strides in proactive disaster risk reduction measures, current planning actions are undertaken primarily based on historical flood risk. There are gaps in understanding how the escalating impacts of climate change will alter flood dynamics. This study examines shifting local flood risk patterns in the Municipality of Carigara in Leyte. We quantify probabilistic flood damage on residential structures for early, mid-, and late-term flood scenarios under RCP4.5 and RCP8.5 pathways. By utilising localised housing vulnerability functions, we assess risk trends at a household level, considering concrete, light material, and elevated light material housing typologies. Our results indicate a 3 % decrease in future flood damages to residential structures under RCP 4.5 and a 34 % decrease in damages under RCP 8.5 by 2100 attributable to climate change for 100-year flood events. These shifts highlight the nuances of regional changes in flood damages over the next century. The findings provide insights into how localised climate-risk assessments for municipalities might be established as entry points to inform climate change policies and projects. Through established mechanisms such as Local Disaster Risk Reduction Management Funds (LDRRMF) in the Philippines, we propose methods of climate-informed decision-making for local government units to minimise damage for future climate scenarios.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing "ED of building footprint" and increasing "ED of impervious surface" can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

The shift in the spatiotemporal relationship between supply and demand of ecosystem services and its drivers in China

In China, over 65% of human activities are concentrated in cities, resulting in a conflict between the supply and demand of ecosystem services (ESs). To alleviate this problem, many cities have adopted eco-friendly development modes, however, the effectiveness of these models in reducing ESs supply-demand conflicts has not been comprehensively reviewed, and the human and natural drivers behind these relationship shifts remain unclear. To bridge this gap, this study analyzed the shifts in the relationships between supply and demand of ESs across China from 2010 to 2020 at a city level, as well as identified the human and natural drivers behind them. Firstly, the InVEST models were integrated with socioeconomic data to evaluate the supply and demand distribution for three pivotal ESs: water yield (WY), habitat quality (HQ), and soil retention (SR). Then, a four-quadrant diagram approach was proposed to enhance the analysis of their spatiotemporal relationships. Furthermore, random forest models were employed to examine the drivers of the shifts in these relationships. The results showed that WY and SR services witnessed growth until 2015, and then receded, while HQ saw a modest decline from 2010 to 2020. Spatial synergies in the supply and demand of ESs were primarily observed in the southern cities, with a significant northward extension by 2020. From a temporal perspective, the percentage of cities achieving coordination in WY and SR services increased from 32.6% to 57.3%, respectively, in the 2010-2015 period to 42.4% and 63.3% between 2015 and 2020, meanwhile, HQ service conflicts diminished from 58.7% to 53.5%. The changes in socioeconomic and land use factors contributed to 64.3%, 36.1%, and 33.3% of the shifts in the supply-demand relationship for HQ, WY, and SR services, respectively. Our analysis highlights the potential of human-driven ecological management to enhance the balance of this relationship. It can support the design of city-specific policies that foster a balance between ecological processes and socio-economic development.

Assessment of urban underground spaces inundation during extreme rainfall events

As urbanization progresses and the impacts of climate change become more pronounced, urban flooding has emerged as a critical challenge for resilient cities, particularly concerning urban underground spaces where flooding can lead to significant loss of life and property. Drawing upon a comprehensive review of global research on underground space flood simulation and evacuation, this paper undertakes the modelling of inundation in a substantial underground area during the extraordinary rainfall event on 7 September 2023, in Shenzhen, China. Specifically, it introduces a two-step method to simulate the coupled surface-underground inundation process with high accuracy. The study simulates the inflow processes in three types of underground spaces: parking lots, metro stations, and underpasses. Utilizing the specific force per unit width evaluation, the research examines how varying flood barrier heights influence evacuation time and inundation risk. Subsequently, the paper proposes corresponding evacuation strategies based on the obtained findings. By highlighting the vulnerability of urban underground spaces to flooding, the study underscores the urgent need for further research in this domain.

A novel flood risk management approach based on future climate and land use change scenarios

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Spatial congruency or discrepancy? Exploring the spatiotemporal dynamics of built-up expansion patterns and flood risk

Most coastal cities have been experiencing unprecedented urbanization-induced flood risk, climatic events, and haphazard anthropogenic activities, jeopardizing residents' lives and building environments. Despite mounting flood-related studies, analyzing the correlation between the spatiotemporal dynamics of Built-up Expansion patterns (BE) and flood risk remains unknown and holds divergent perspectives. In this context, the coastal city of Alexandria, Egypt, characterized by multiple urban patterns and experiencing heavy rainfall annually, was selected as a testbed. Our method defined the spatiotemporal rates of BE from 1995 to 2023, quantified flood risk spatially, and finally investigated the correlation between BE and flood risk through spatial and statistical analysis. Our results show the built-up area occupied 30.32 % of the total city area till 2023, and the infilling pattern dominated the BE growth by 45.21 % of the total built-up area, followed by leapfrogging and edge expansion by 33.25 % and 21.55 %, respectively. The unplanned-infilling pattern is predominantly highly correlated with the flood-vulnerable peaks (correlation coefficient (rk) = 0.975, p-value < 0.05) and lowers dramatically towards planned-infilling regions with flood protections. Meanwhile, a spatial mismatch exists between high-risk peaks and leapfrogging and edge expansion (rk = 0.118 and 0.662, respectively, with a p-value < 0.01), indicating that controlling the built-up amount is inadequate for mitigating flood risk. Porosity-based urban configuration and spatial distribution of built-up patches in harmony with nature-based solutions are recommended for shaping flood-resilient and effective urban planning.

Fatal drowning in Indonesia: understanding knowledge gaps through a scoping review

Little is known about unintentional drowning deaths in Indonesia, the world's fourth most populous and largest archipelagic country. This study aimed to describe the epidemiology and risk factors of unintentional drowning in Indonesia and explore existing health promotion and drowning prevention approaches in Indonesia within a socio-ecological health promotion framework. A scoping review, guided by PRISMA-ScR, was conducted to locate peer-reviewed studies and government reports/policy documents published until May 2023, in English or Indonesian language, using MEDLINE (Ovid), CINAHL, Informit, PsycINFO (ProQuest), Scopus, SafetyLit, BioMed Central and Google Scholar, Indonesian journal databases (Sinta, Garuda) and government agencies websites around the terms: drown, swim, flood, hurricane, cyclone, disaster, water rescue and maritime/boat safety. This review identified 32 papers. However, a paucity of information on unintentional drowning rates, risk factors and prevention in Indonesia was noted. The unavailability of a coordinated national drowning data collection system in Indonesia, from which national and subnational subcategory data can be collected, underlines the possibility of under-representation of drowning mortality. The association between various exposures and drowning incidents has not been fully investigated. An over-reliance on individual-focused, behaviour-based, preventive measures was observed. These findings highlight the need for improving drowning surveillance to ensure the availability and reliability of drowning data; and strengthening research to understand the risk factors for drowning and delivery of drowning prevention programs. Further policy development and research focusing on health promotion approaches that reflect a socio-ecological approach to drowning prevention in Indonesia is imperative.

Conceptual and quantitative categorization of wave-induced flooding impacts for pedestrians and assets in urban beaches

Beaches combined with sloping structures are frequently the first element of defense to protect urban areas from the impact of extreme coastal flooding events. However, these structures are rarely designed for null wave overtopping discharges, accepting that waves can pass above the crest and threat exposed elements in hinterland areas, such as pedestrians, urban elements and buildings, and vehicles. To reduce risks, Early Warning Systems (EWSs) can be used to anticipate and minimize the impacts of flooding episodes on those elements. A key aspect of these systems is the definition of non-admissible discharge levels that trigger significant impacts. However, large discrepancies in defining these discharge levels and the associated impacts are found among the existing methods to assess floodings. Due to the lack of standardization, a new conceptual and quantitative four-level (from no-impact to high-impact) categorization of flood warnings (EW-Coast) is proposed. EW-Coast integrates and unifies previous methods and builds on them by incorporating field-based information. Thus, the new categorization successfully predicted the impact level on 70%, 82%, and 85% of the overtopping episodes affecting pedestrians, urban elements and buildings, and vehicles, respectively. This demonstrates its suitability to support EWSs in areas vulnerable to wave-induced flooding.

System comprehensive risk assessment of urban rainstorm-induced flood-water pollution disasters

The urban rainstorm-induced flood-water pollution disaster is a kind of systematic risk, which may induce secondary disasters that can lead to more serious damage, so this paper first adopts the fuzzy comprehensive evaluation method to determine the flood risk by combining with the submergence depth derived from the risk field and other factors data, and then the grid environmental risk evaluation method, which is improved by increasing the induced possibility based on Bayesian theory, is used to evaluate the flood-induced water pollution risk, and the system comprehensive risk of rainstorm-induced flood-water pollution disasters is finally obtained by constructing risk level matrix, which can well depict the coupling superposition effect. Shenzhen City is selected as the study area, and the results showed that the area with high-risk of both flood and water pollution only accounts for about 0.14% of the total area, mainly distributed in the eastern junction of Longgang district and Pingshan district, where the rainstorms occur frequently and the enterprise risk sources are dense. The system comprehensive risk is mostly very low-low and very high-low, accounting for more than 76% of the total area. It is always necessary to pay attention not only to the areas with high risk level of both disasters, but also to the areas with high risk level of one disaster. The method proposed in this study can not only quantitatively reveal the formation of the induced risk, but also provide reference for early warning.

Sea level extremes and compounding marine heatwaves in coastal Indonesia

Low-lying island nations like Indonesia are vulnerable to sea level Height EXtremes (HEXs). When compounded by marine heatwaves, HEXs have larger ecological and societal impact. Here we combine observations with model simulations, to investigate the HEXs and Compound Height-Heat Extremes (CHHEXs) along the Indian Ocean coast of Indonesia in recent decades. We find that anthropogenic sea level rise combined with decadal climate variability causes increased occurrence of HEXs during 2010-2017. Both HEXs and CHHEXs are driven by equatorial westerly and longshore northwesterly wind anomalies. For most HEXs, which occur during December-March, downwelling favorable northwest monsoon winds are enhanced but enhanced vertical mixing limits surface warming. For most CHHEXs, wind anomalies associated with a negative Indian Ocean Dipole (IOD) and co-occurring La Niña weaken the southeasterlies and cooling from coastal upwelling during May-June and November-December. Our findings emphasize the important interplay between anthropogenic warming and climate variability in affecting regional extremes.

Flood impacts on urban road connectivity in southern China

Effective measures to improve road accessibility during storms are required as traffic congestion caused by storm floods increasingly constrains the efficiency of urban commuting. However, flood impacts on urban road connectivity are not yet well assessed due to inaccurate simulation of flood processes in urban areas where high-resolution data for drainage networks and gauged hydrological data are insufficient. Thus, this study assesses flood impacts on road network connectivity in an urban area of southern China through joint modeling of 1-D hydrodynamic processes in drainage networks and 2-D flood inundation processes on roads using MIKE Urban and MIKE 21. High-resolution DEM images of 5 m and a drainage network of 5635 pipelines were used for urban hydrological simulation. Flood depths were gauged for model calibration and validation by recruited volunteers in the context of citizen science. The results show that road network connectivity decreases as rainfall increases. More than 40% of road connectivity is lost in the study area when a 1-in-100-year return period rainfall occurs. The study results can help to inform more adaptive strategies for local flood control. The study methods are also applicable to improving urban hydrological modeling in broader regions.

Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Modelling and quantifying tomorrow's risks from natural hazards

Understanding and modelling future risks from natural hazards is becoming increasingly crucial as the climate changes, human population grows, asset wealth accumulates, and societies become more urbanised and interconnected. This need is recognised by the 2015-2030 Sendai Framework for Disaster Risk Reduction, which emphasises the importance of preparing for the disasters that our world may face tomorrow through strategies/policies that aim to minimise uncontrolled development in hazardous areas. While the vast majority of natural-hazard risk-assessment frameworks have so far focused on static impacts associated with current conditions and/or are influenced by historical context, some authors have sought to provide decision makers with risk-quantification approaches that can be used to cultivate a sustainable future. This Review documents these latter efforts, explicitly examining work that has modelled and quantified the individual components that comprise tomorrow's risk, i.e., future natural hazards affected by climate change, future exposure (e.g., in terms of population, land use, and the built environment), and the evolving physical vulnerabilities of the world's infrastructure. We end with a discussion on the challenges faced by modellers in determining the risks that tomorrow's world may face from natural hazards, and the constraints these place on the decision-making abilities of relevant stakeholders.

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

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

Flood Risk Assessment under Land Use and Climate Change in Wuhan City of the Yangtze River Basin, China

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.

Adaptation of urban drainage networks to climate change: A review

The present work reviews the main challenges regarding adaptation of urban drainage networks to climate change by comparing 32 case studies from 29 articles, published between 2003 and 2020. The aim is to: (i) identify the state-of-the-art scientific approaches of adaptation of urban drainage networks to climate change; (ii) assess whether or not these approaches incorporated monetization of the adaptation practices and the associated costs/benefits; and (iii) define a novel approach (Blueprint) for the future development and assessment of urban drainage network adaptation to climate change and other drivers. First, the motivation is provided that makes urban drainage adaptation a globally relevant issue. Second, the main impacts of climate change on precipitation, flooding and urban drainage systems are discussed. Then, current practices are described. Finally, a blueprint for an integrated urban adaptation framework to climate change and other drivers is proposed. Our research indicated that future quantity and quality of urban runoff is not widely addressed in the scientific literature. The Storm Water Management Model is the most widely used software in modeling adaptation options. Solutions such as plans of maintenance and rehabilitation, public awareness, flood forecasting and warning, mobility measures and insurance measures are not widely reflected in the literature. Uncertainties of climate projections and bias correction methods are still significant, and uncertainties of socio-economic scenarios, hydrologic and hydrodynamic models, and adaptation options are not fully addressed. Finally, environmental cost and benefits associated with the ecosystem services provided by the adaptation options are not fully addressed.

Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future

Accurate runoff forecasting plays a considerable role in the appropriate water resource planning and management. The spatial and temporal evaluation of the flood susceptibility was explored in the Quebec basin, Canada. This study provides a new strategy for runoff modelling as one of the complicated variables by developing new machine learning techniques along with remote sensing. A novel scheme of the Group Method of Data Handling (GMDH) known as the generalized structure of GMDH (GSGGMDH) is developed to overcome this classical approach's limitation. A simple time series based scenario with exogenous variables including precipitation and Normalized Difference Vegetation Index (NDVI) was introduced for runoff forecasting. MODIS data included MOD13Q1 product was employed and a JavaScript code was developed to preprocess collected data in the Google Earth Engine (GEE) environment. Using different seasonal and non-seasonal lags of all input variables, the developed GSGMDH found the most optimum input combination for each station in terms of simplicity and accuracy, simultaneously (average values; SI = 0.554, RMSRE = 1.55, MAE = 5.076). The precipitation values are modelled with the CanEsm2 climate change model. To apply NDVI for runoff forecasting, a simple spatial-temporal GSGMDH based model was developed (average values; SI = 0.27; RMSRE = 8.27, MAE = 0.08). The forecasting results indicated that the months in which the maximum runoff occurred have changed, and these months have increased compared to the historic period. In the historical period, the frequency of maximum runoff was in April and March. Still, for the two forecasting periods (i.e. 2020-2039 and 2040-2059), the months in which the maximum runoff has occurred have changed, and their amount has been reduced and added to other months, especially February and August.

Expression of soybean plant hemoglobin gene family under abiotic stresses

Many abiotic stresses induce the generation of nitric oxide (NO) in plant tissues, where it functions as a signal molecule in stress responses. Plants modulate NO by oxidizing it to NO₃ ^- with plant hemoglobin (GLB), because excess NO is toxic to cells. At least eight genes encoding GLB have been identified in soybean, in three clades: GLB1, GLB2, and GLB3. However, it is still unclear which GLB genes are responsible for NO regulation under abiotic stress in soybean. We exposed soybean roots to flooding, salt, and two NO donors-sodium pentacyanonitrosylferrate (III) dihydrate (SNP) and S-nitroso-N-acetyl-d,l-penicillamine (SNAP)-and analyzed expression of GLB genes. GmGLB1, one of two GLB1 genes of soybean, significantly responded to both SNP and SNAP, and its induction was almost completely repressed by a NO scavenger, 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide. GmGLB1 responded to flooding but not to salt, suggesting that it is responsible for NO regulation under NO-inducing abiotic stresses such as flooding. GmGLB3, one of two GLB3 genes of soybean, did not respond to NO donors at all but did respond to flooding, at a lower level than GmGLB1. These results suggest that flooding induces not only NO but also unknown factor(s) that induce GmGLB3 gene in soybean.

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.

A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.

Proteomic analysis reveals the effects of melatonin on soybean root tips under flooding stress

Flooding constrains soybean growth, while melatonin enhances the ability of plants to tolerate abiotic stresses. To interpret the melatonin-mediated flooding response in soybeans, proteomic analysis was performed in root tips. Retarded growth and severe cell death were observed in flooded soybeans, but these phenotypes were ameliorated by melatonin treatment. A total of 634, 1401, and 1205 proteins were identified under control, flood, and flood plus melatonin conditions, respectively; and these proteins were predominantly associated with metabolism of protein, RNA, and the cell wall. Among these melatonin-induced proteins, eukaryotic aspartyl protease family protein was increased after flood compared with melatonin treatment group, in accordance with its upregulated transcript levels during stress. Eukaryotic translation initiation factor 5A was decreased after flood compared with melatonin. When stress was prolonged, its transcript levels were upregulated by flood, while they were not changed by melatonin. Furthermore, 13-hydroxylupanine O-tigloyltransferase was decreased by flood compared with melatonin; however, its transcription was upregulated by melatonin. In addition, reduced lignification in root tips of flooded soybeans was restored by melatonin. These results suggest that factors related to protein degradation and functional states of RNA play critical roles in promoting the effects of melatonin on soybean plants under flooding. SIGNIFICANCE: Flooding stress threatens soybean growth, while melatonin treatment enhances plant tolerance to stress stimuli. To examine the effects of melatonin on flooded soybeans, morphological analysis was performed. Melatonin promoted soybean growth as judged from greater fresh weight of plant, longer seedling length, and less evident cell death in flooding-stressed soybeans treated with melatonin than those plants exposed to flood alone. Proteomic analysis was conducted to explore the promoting effects of melatonin on soybeans under flooding stress. As a result, metabolism of protein metabolism, RNA regulation, and cell wall was enriched by proteins identified under control, flood, and flood plus melatonin conditions. Among these melatonin-induced proteins, abundance of eukaryotic aspartyl protease family protein, eukaryotic translation initiation factor 5A, and 13-hydroxylupanine O-tigloyltransferase displayed similar change patterns between the control and melatonin compared with flood; and transcript levels of genes encoding these proteins responded to flooding stress and melatonin treatment. In addition, activated cell degradation, expanded intercellular spaces, and reduced lignification in root tips of flooded soybeans were ameliorated by melatonin treatment.

An Entropic Approach to Estimating the Instability Criterion of People in Floodwaters

People are always susceptible to a loss of stability in urban floodwaters that leads to serious casualties. Thus, the safety criterion for the instability of people in floodwaters must be determined. In this study, the hydrodynamic criterion of the instability of people in floodwaters in terms of the incipient velocity and water depth is derived using the probability method based on Shannon entropy theory. The derived model can characterize variations in the incipient velocity of people in floodwaters with respect to the inundating water depth. Furthermore, a comparison with seven experimental datasets available in the literature shows the validity of the proposed entropy-based model considering data scattering. A sensitivity analysis of the derived model to some of the incorporated parameters was performed, and the qualitative results are in accordance with our understanding of the physical mechanism of the instability of people in floodwaters. Taking the physical parameters (height and mass) of Chinese adults and children as a representative example, this study also showed the vulnerability degree of Chinese adults and children subject to floodwaters. These findings could provide a reference for administrators and stakeholders for flood hazard mitigation and flood strategy management. This study shows that an entropy-based method could be a valuable addition to existing deterministic models for characterizing the instability criterion of people in an urban flooding event.

Intra-annual oxygen isotopes in the tree rings record precipitation extremes and water reservoir levels in the Metropolitan Area of São Paulo, Brazil

The impacts of climate change on precipitation and the growing demand for water have increased the water risks worldwide. Water scarcity is one of the main challenges of the 21st century, and the assessment of water risks is only possible from spatially distributed records of historical climate and levels of water reservoirs. One potential method to assess water supply is the reconstruction of oxygen isotopes in rainfall. We here investigated the use of tree-ring stable isotopes in urban trees to assess spatial/temporal variation in precipitation and level of water reservoirs. We analyzed the intra-annual variation of δ¹³C and δ¹⁸O in the tree rings of Tipuana tipu trees from northern and southern Metropolitan Area of São Paulo (MASP), Brazil. While variation in δ¹³C indicates low leaf-level enrichments from evapotranspiration, δ¹⁸O variation clearly reflects precipitation extremes. Tree-ring δ¹⁸O was highest during the 2014 drought, associated with the lowest historical reservoir levels in the city. The δ¹⁸O values from the middle of the tree rings have a strong association with the mid-summer precipitation (r = -0.71), similar to the association between the volume of precipitation and its δ¹⁸O signature (r = -0.76). These consistent results allowed us to test the association between tree-ring δ¹⁸O and water-level of the main reservoirs that supply the MASP. We observed a strong association between intra-annual tree-ring δ¹⁸O and the water-level of reservoirs in the northern and southern MASP (r = -0.94, r = -0.90, respectively). These results point to the potential use of high-resolution tree-ring stable isotopes to put precipitation extremes, and water supply, in a historical perspective assisting public policies related to water risks and climate change. The ability to record precipitation extremes, and previously reported capacity to record air pollution, place Tipuana tipu in a prominent position as a reliable environmental monitor for urban locations.

Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study

Preparing cities for sea-level rise is one of the critical challenges of the twenty-first century. Extreme weather events, natural hazards, and the failure of climate mitigation and adaptation are substantial risks. These risks are especially significant in fast-urbanizing deltas, such as the Pearl River Delta in China, because the conflict between urbanization and flooding caused by climate change will be more significant in the future. This paper elaborates on an approach that employs a future land-use simulation (FLUS) model for scenario-based 100-year coastal flood risk assessment. Storylines of future scenarios from the Intergovernmental Panel on Climate Change (IPCC), called the representative concentration pathways (RCPs) 2.6 and 8.5, are utilized in the present study. The Guangzhou Metropolitan Area (GMA) is used as a case study to explore the probable implications of future land-use changes due to the ongoing urbanization process in the region in relation to projected environmental changes (sea-level rise, storm surge, and land subsidence). The results indicate that there will be a significant increase in flooded urban areas in the future. The simulations show that, as compared to 2015, the built-up area in the GMA will increase by 246.57 km² in 2030 and 513.03 km² in 2050. As compared to 2015, the flooding of built-up areas in 2030 and 2050 will respectively increase by about 31.32 km² and 48.49 km² under the RCP 8.5 scenario. It is also found that, as the main driving factor, urbanization will increase the flooding of built-up areas in Guangzhou in 2030 and 2050 by about 1.9 km² and 5.9 km², respectively, under the RCP 2.6 scenario as compared to 2015. Additionally, due to environmental changes, the flooding of built-up areas in Guangzhou will increase by about 24.2 km² and 26.8 km², respectively, under the RCP 8.5 scenario by 2030 and 2050 as compared to 2015. This increasing flood risk information determined by the simulation provides insight into the spatial distribution of future flood-prone urban areas to facilitate the development and prioritization of flood mitigation measures at the most critical locations in the region.

Future urban development exacerbates coastal exposure in the Mediterranean

Changes in the spatial patterns and rate of urban development will be one of the main determinants of future coastal flood risk. Existing spatial projections of urban extent are, however, often available at coarse spatial resolutions, local geographical scales or for short time horizons, which limits their suitability for broad-scale coastal flood impact assessments. Here, we present a new set of spatially explicit projections of urban extent for ten countries in the Mediterranean, consistent with the Shared Socioeconomic Pathways (SSPs). To model plausible future urban development, we develop an Urban Change Model, which uses input variables such as elevation, population density or road network and an artificial neural network to project urban development on a regional scale. The developed future projections for the five SSPs indicate that accounting for the spatial patterns of urban development can lead to significant differences in the assessment of future coastal urban exposure. The increase in exposure in the Extended Low Elevation Coastal Zone (E-LECZ = area below 20 m of elevation) until 2100 can vary, by up to 104%, depending on the urban development scenario chosen. This finding highlights that accounting for urban development in long-term adaptation planning, e.g. in the form of land-use planning, can be an effective measure for reducing future coastal flood risk on a regional scale.

Coastal Flooding Risk Assessment Using a GIS-Based Spatial Multi-Criteria Decision Analysis Approach

Coastal areas are expected to be at a higher risk of flooding when climate change-induced sea-level rise (SLR) is combined with episodic rises in sea level. Flood susceptibility mapping (FSM), mostly based on statistical and machine learning methods, has been widely employed to mitigate flood risk; however, they neglect exposure and vulnerability assessment as the key components of flood risk. Flood risk assessment is often conducted by quantitative methods (e.g., probabilistic). Such assessment uses analytical and empirical techniques to construct the physical vulnerability curves of elements at risk, but the role of people’s capacity, depending on social vulnerability, remains limited. To address this gap, this study developed a semiquantitative method, based on the spatial multi-criteria decision analysis (SMCDA). The model combines two representative concentration pathway (RCP) scenarios: RCP 2.6 and RCP 8.5, and factors triggering coastal flooding in Bandar Abbas, Iran. It also employs an analytical hierarchy process (AHP) model to weight indicators of hazard, exposure, and social vulnerability components. Under the most extreme flooding scenario, 14.8% of flooded areas were identified as high and very high risk, mostly located in eastern, western, and partly in the middle of the City. The results of this study can be employed by decision-makers to apply appropriate risk reduction strategies in high-risk flooding zones.

Which Aspects of Hydrological Regime in Mid-Latitude Montane Basins Are Affected by Climate Change?

This study analyzed the long-term alterations in runoff regime, seasonality and variability in headwater montane basins in Central Europe in response to the manifestations of climate change. We tested the common hypotheses on climate change effects on surface runoff dynamics in the Central Europe region, assuming that (i) recent climate warming will result in shifts in the seasonality of runoff, (ii) the runoff balance will remain without significant changes and (iii) that higher variability in runoff can be expected. The analyses were done on eight montane catchments in four mid-latitude mountain ranges in Central Europe, based on the uninterrupted time series of daily discharge observations from 1952 to 2018. We used 33 indicators of hydrologic alteration (IHA), 34 indicators of environmental flow components, the baseflow index, the calculation of surplus and deficit volumes and the frequency of peak and low flows. Homogeneity testing using Buishand, Pettitt and SNHT tests was applied to test the response of the hydrological alteration indicators to climate warming. We have proved the significant shifts in runoff seasonality, coinciding with the timing of the air temperature rise, marked by earlier snowmelt, followed by a decline in spring flows and a prolonged period of low flows. There was detected a rise in the baseflow index across the mountain ranges. Unlike the common hypotheses, the expected rise of runoff variability and frequency of peak flows was not demonstrated. However, we have identified a significant change of the flood hydrographs, tending to steeper shape with shorter recessing limbs as a sign of rising inner dynamics of flood events in montane catchments.

Flood Risk Assessment and Regionalization from Past and Future Perspectives at Basin Scale

Flooding is a major natural disaster that has brought tremendous losses to mankind throughout the ages. Even so, floods can be controlled by appropriate measures to minimize loss and damage. Flood risk assessment is an essential analytic step in preventing floods and reducing losses. Identifying previous flood risk and predicting future features are conducive to understanding the changing patterns and laws of flood risk. Taking the Dongjiang River basin as a study case, we assessed and regionalized flood risk in 1990, 2000, and 2010 from the past perspective and explored dynamic expansion during 1990-2010. Then, we projected land-use type, population, and gross domestic product in 2030 and 2050 and finally assessed and regionalized the risk from a future perspective. Results show that areas with very high risk accounted for 14.98-18.08% during 1990-2010; approximately 13.90% areas of the basin transformed from lower-level risk to higher-level risk whereas 9.07% fell from a higher level to a lower level during the period. For the future scenario, areas with very high and high risk in 2030 and 2050 are expected to account for 21.55% and 24.84%, respectively. Generally, our study can better identify changes in flood risk at a spatial scale and reveal the dynamic evolution rule, which provides a synthetical means of flood prevention and reduction, flood insurance, urban planning, and water resource management in the future under global climate change, especially for developing or high-speed urbanization regions.

A Proposed Simultaneous Calculation Method for Flood by River Water, Inland Flood, and Storm Surge at Tidal Rivers of Metropolitan Cities: A Case Study of Katabira River in Japan

All metropolitan cities in Japan are located in low-lying areas that surround ports. Accordingly, significant floods that occur in these cities will trigger the simultaneous occurrence of flooding by river water and inland flooding. However, existing studies have focused on the impact of flooding by river water, inland flooding, and high tide in tidal rivers, and disaster mitigation measures focused on detailed flooding processes in such flooding areas have not been conducted thus far. This study focused on a tidal river, i.e., Katabira River of Yokohama city, one of Japan’s metropolitan cities, to construct a simultaneous occurrence model of flooding by river water and inland flooding, including the impact of a high tide. Numerical analysis was conducted using this model, and the results show that the flooded area significantly changed from 0.004 to 0.149 km2 according to the tide level of the estuary. Moreover, by simultaneously solving the calculation of flooding by river water and inland flooding, we found that there was a difference of 50 min between the occurrences of these floods. Therefore, we found that there is a possibility that, if evacuation is not conducted at the time of occurrence of inland flooding, evacuation during subsequent river-water flooding may not be possible. Based on these results, our proposed method was found to be useful for tidal rivers of metropolitan cities.

Indirect Impact Assessment of Pluvial Flooding in Urban Areas Using a Graph-Based Approach: The Mexico City Case Study

This paper presents the application of a graph-based methodology for the assessment of flood impacts in an urban context. In this methodology, exposed elements are organized as nodes on a graph, which is used to propagate impacts from directly affected nodes to other nodes across graph links. Compared to traditional approaches, the main advantage of the adopted methodology lies in the possibility of identifying and understanding indirect impacts and cascading effects. The application case concerns floods numerically reconstructed in Mexico City in response to rainfall events of increasing return periods. The hazard reconstruction was carried out by using a simplified hydrological/hydraulic model of the urban drainage system, implemented in EPASWMM, the Storm Water Management Model developed by the United States Environmental Protection Agency. The paper shows how the impacts are propagated along different orders of the impact chain for each return period and compares the risk curves between direct and indirect impact. It also highlights the extent to which the reduction in demand of services from consumers and the loss of services from suppliers are respectively contributing to the final indirect impacts. Finally, it illustrates how different impact mitigation measures can be formulated based on systemic information provided by the analysis of graph properties and taking into account indirect impacts.

Coastal infrastructure operativity against flooding - A methodology

The operativity of the transport infrastructures and urban developments protected by coastal structures is conditioned by flooding events and the resulting wave overtopping. This work presents a methodology to assess the operational conditions of infrastructures located in coastal areas based on the combination of advanced statistical techniques, laboratory experiments and state-of-the-art numerical models properly validated. It is applied to a case study in the SW coast of England, the railway seawall at Dawlish, which was subjected to recurrent wave overtopping until its dramatic collapse in February 2014. To quantify the increase in overtopping discharges with wave height and water level, we define an ad hoc variable, the effective overtopping forcing, which explains 98% of the variability of the overtopping discharge. The return periods associated to the operational thresholds for coastal structures protecting people and railways are also obtained. The proposed methodology enables the assessment of the overtopping discharge induced by a given sea state and, thus, check if a coastal infrastructure will be or not operational under any expected marine condition. This innovative methodology can also be used to analyse the flooding event consequences on urban areas protected by coastal infrastructures.

Flood Risk Assessment for the Long-Term Strategic Planning Considering the Placement of Industrial Parks in Slovakia

The intention of the article is to demonstrate how data from historical maps might be applied in the process of flood risk assessment in peri-urban zones located in floodplains and be complementary datasets to the national flood maps. The research took place in two industrial parks near the rivers Žitava and Nitra in the town of Vráble (the oldest industrial park in Slovakia) and the city of Nitra (one of the largest industrial parks in Slovakia, which is still under construction concerning the Jaguar Land Rover facility). The historical maps from the latter half of the 18th and 19th centuries and from the 1950s of the 20th century, as well as the field data on floods gained with the GNSSS receiver in 2010 and the Q100 flood line of the national flood maps (2017), were superposed in geographic information systems. The flood map consists of water flow simulation by a mathematical hydrodynamic model which is valid only for the current watercourse. The comparison of historical datasets with current data indicated various transformations and shifts of the riverbanks over the last 250 years. The results proved that the industrial parks were built up on traditionally and extensively used meadows and pastures through which branched rivers flowed in the past. Recent industrial constructions intensified the use of both territories and led to the modifications of riverbeds and shortening of the watercourse length. Consequently, the river flow energy increased, and floods occurred during torrential events in 2010. If historical maps were respected in the creation of the flood maps, the planned construction of industrial parks in floodplains could be limited or forbidden in the spatial planning documentation. This study confirmed that the flood modelling using the Q100 flood lines does not provide sufficient arguments for investment development groups, and flood maps might be supplied with the data derived from historical maps. The proposed methodology represents a simple, low cost, and effective way of identifying possible flood-prone areas and preventing economic losses and other damages.

Modeling Urban Flood Inundation and Recession Impacted by Manholes

Urban flooding, caused by unusually intense rainfall and failure of storm water drainage, has become more frequent and severe in many cities around the world. Most of the earlier studies focused on overland flooding caused by intense rainfall, with little attention given to floods caused by failures of the drainage system. However, the drainage system contributions to flood vulnerability have increased over time as they aged and became inadequate to handle the design floods. Adaption of the drainages for such vulnerability requires a quantitative assessment of their contribution to flood levels and spatial extent during and after flooding events. Here, we couple the one-dimensional Storm Water Management Model (SWMM) to a new flood inundation and recession model (namely FIRM) to characterize the spatial extent and depth of manhole flooding and recession. The manhole overflow from the SWMM model and a fine-resolution elevation map are applied as inputs in FIRM to delineate the spatial extent and depth of flooding during and aftermath of a storm event. The model is tested for two manhole flooding events in the City of Edmonds in Washington, USA. Our two case studies show reasonable match between the observed and modeled flood spatial extents and highlight the importance of considering manholes in urban flood simulations.

Detailed Quantification of the Reduction Effect of Roof Runoff by Low Impact Development Practices

Low impact development (LID) has been widely applied to mitigate urban rainwater problems since the 1990s. However, the effect of LID practices has seldom been evaluated in detail. In this study, the effect of individual and combined LID practices on the reduction of roof runoff are specifically quantified considering the hydrological relationship between LID at the building scale and the campus scale at Beijing Normal University (BNU). The results show that individual and combined LID practices effectively reduce roof runoff for all types of rainfall and for rainfall with return periods from 0.5 to 50 years at the building scale. Combination scenarios maintain good performance with fewer areas of composed LID. Most values for the effect of combination scenarios are between the effects of composed LID. To achieve the highest cost efficiency, low elevation greenbelts should be the first choice, and green roofs should only be selected when low elevation greenbelts and rain barrels cannot be implemented. At the campus scale, individual and combined LID practices effectively reduce the outflow from and the overflow in the campus and combination scenarios have the best reduction performance. This study provides an important reference for urban water management and LID related decision making.

The Role of Urban Growth in Resilience of Communities Under Flood Risk

Flood risk to urban communities is increasing significantly as a result of the integrated effects of climate change and socioeconomic development. The latter effect is one of the main drivers of rising flood risk has received less attention in comparison to climate change. Economic development and population growth are major causes of urban expansion in flood-prone areas, and a comprehensive understanding of the impact of urban growth on flood risk is an essential ingredient of effective flood risk management. At the same time, planning for community resilience has become a national and worldwide imperative in recent years. Enhancements to community resilience require well-integrated and enormous long-term public and private investments. Accordingly, comprehensive urban growth plans should take rising flood risk into account to ensure future resilient communities through careful collaboration between engineers, geologists, socialists, economists, and urban planners within the framework of life-cycle analysis. This paper highlights the importance of including urban growth in accurate future flood risk assessment and how planning for future urbanization should include measurement science-based strategies in developing policies to achieve more resilient communities.

Application of Reliability Analysis for Risk Ranking in a Levee Reconstruction Project

Levees are embankments designed for passive flood protection. In order to reduce the potential of climate-induced flooding risks, it is necessary to reconstruct or upgrade the existing levees. Flood risk management aims to reduce the probability of floods and their potential adverse effects on the population, economy, and environment. This paper presents the novel application of reliability analysis for risk ranking in the Otok Virje-Brezje levee reconstruction project in the Republic of Croatia. To identify, verify and analyse key risks, a group of 35 experts, who were directly involved in the levee reconstruction project or have extensive experience in similar projects, was selected. An Analytic network process (ANP) was used for group multi criteria decision-making. Quantitative and qualitative approaches to risk analysis were combined. Different experts from the various organisations may have diverse interests and goals. The geometric mean method was chosen to reach group consensus. The resources that will be allocated to the risk responses are proportional to the risk exposures. To analyse the reliability of the group consensus-reaching method a determination of the risk ranking probability matrix is proposed by using the Monte Carlo simulation method. Different decision-making approaches are proposed for cases in which consensus is not reached with satisfactory reliability.

A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions

Identification of flood-risk dynamics is pivotal for refurbishing the existing and future flood-management options. The present study quantifies the marginal and compound contributions of hazard and vulnerability to flood-risk through an innovative concept of Risk-classifier, designed in the form of a 5 × 5 choropleth. The proposed framework is demonstrated at the finest administrative scale of village-level over Jagatsinghpur district in Mahanadi River basin, Odisha (India) for two-time frames: Scenario-I (1970-2011) and Scenario-II (1970-2001). An increase in high and very high hazard and vulnerable villages is noticed in Scenario-I, the majority of them lying in the coastal stretches (S-E region) and adjoining flood plains of Mahanadi River (N-W region). Scenario-I is characterized by the majority of hazard-driven and compound (both hazard and vulnerability) risk villages, while Scenario II is characterized by a majority of vulnerability driven-risk villages. For the vulnerability-driven risk villages, rigorous enforcement of policies and mitigation schemes are recommended, while for hazard-driven risk villages, enhancement of structural measures and flood-plain zoning should be exercised. Such exhaustive flood-risk information may serve as a valuable cartographic product for the civic authorities and stakeholders and help in prioritizing flood mitigation actions for improved environmental planning and management.

Typology of coastal urban vulnerability under rapid urbanization

Coastal areas are urbanizing at unprecedented rates, particularly in low- and middle-income countries. Combinations of long-standing and emerging problems in these urban areas generate vulnerability for human well-being and ecosystems alike. This baseline study provides a spatially explicit global systematization of these problems into typical urban vulnerability profiles for the year 2000 using largely sub-national data. Using 11 indicator datasets for urban expansion, urban population growth, marginalization of poor populations, government effectiveness, exposures and damages to climate-related extreme events, low-lying settlement, and wetlands prevalence, a cluster analysis reveals a global typology of seven clearly distinguishable clusters, or urban profiles of vulnerability. Each profile is characterized by a specific data-value combination of indicators representing mechanisms that generate vulnerability. Using 21 studies for testing the plausibility, we identify seven key profile-based vulnerabilities for urban populations, which are relevant in the context of global urbanization, expansion, and climate change. We show which urban coasts are similar in this regard. Sensitivity and exposure to extreme climate-related events, and government effectiveness, are the most important factors for the huge asymmetries of vulnerability between profiles. Against the background of underlying global trends we propose entry points for profile-based vulnerability reduction. The study provides a baseline for further pattern analysis in the rapidly urbanizing coastal fringe as data availability increases. We propose to explore socio-ecologically similar coastal urban areas as a basis for sharing experience and vulnerability-reducing measures among them.

A nonstructural flood prevention measure for mitigating urban inundation impacts along with river flooding effects

In recent decades, urban developments along rivers have experienced high flooding risks, elevated by increasing urbanization. Due to the expansion of urban areas, flood mitigation strategies must rely on nonstructural flood management policies. This study evaluated the impacts of pluvial floods considering fluvial flooding effects through field surveys and numerical model simulations. Nonstructural flood protection measures are provided by establishing warning water levels based on variant scenario simulations. The results reveal that the aggravated drainage system overflow induced by elevated river water is significant when considering river flooding effects. As a result, current drainage systems have failed to meet the designed flood protection standards, indicating the need to produce potential inundation maps and to establish warning water levels. To prevent the main evacuation route of each settlement from being blocked due to flooding, the proposed warning water levels support timely and effective evacuations. Public community centers and schools in noninundation areas near settlements were identified as possible shelters based on the proposed warning water levels and inundation maps. We conclude that although the riverside areas without dike protection could sustain natural environments and landscapes, they must incorporate nonstructural flood protection measures. At the same time, it must be considered that river flood levels may reduce urban drainage capacity.

Synthesized trade-off analysis of flood control solutions under future deep uncertainty: An application to the central business district of Shanghai

Coastal mega-cities will face increasing flood risk under the current protection standard because of future climate change. Previous studies seldom evaluate the comparative effectiveness of alternative options in reducing flood risk under the uncertainty of future extreme rainfall. Long-term planning to manage flood risk is further challenged by uncertainty in socioeconomic factors and contested stakeholder priorities. In this study, we conducted a knowledge co-creation process together with infrastructure experts, policy makers, and other stakeholders to develop an integrated framework for flexible testing of multiple flood-risk mitigation strategies under the condition of deep uncertainties. We implemented this framework to the reoccurrence scenarios in the 2050s of a record-breaking extreme rainfall event in central Shanghai. Three uncertain factors, including precipitation, urban rain island effect and the decrease of urban drainage capacity caused by land subsidence and sea level rise, are selected to build future extreme inundation scenarios in the case study. The risk-reduction performance and cost-effectiveness of all possible solutions are examined across different scenarios. The results show that drainage capacity decrease caused by sea-level rise and land subsidence will contribute the most to the rise of future inundation risk in central Shanghai. The combination of increased green area, improved drainage system, and the deep tunnel with a runoff absorbing capacity of 30% comes out to be the most favorable and robust solution which can reduce the future inundation risk by 85% (±8%). This research indicates that to conduct a successful synthesized trade-off analysis of alternative flood control solutions under future deep uncertainty is bound to be a knowledge co-creation process of scientists, decision makers, field experts, and other stakeholders.

Blockchain and Building Information Modeling (BIM): Review and Applications in Post-Disaster Recovery

Blockchain Technology (BCT) is a growing digital technology that in recent years has gained widespread traction in various industries in the public and private sectors. BCT is a decentralized ledger that records every transaction made in the network, known as a ‘block’, the body of which is comprised of encrypted data of the entire transaction history. BCT was introduced as the working mechanism that forms the operational basis of Bitcoin, the first digital cryptocurrency to gain mainstream appeal. The introduction of decentralized data exchange technology in any industry would require strengthened security, enforce accountability, and could potentially accelerate a shift in workflow dynamics from current centralized architectures to a decentralized, cooperative chain of command and affect a cultural and societal change by encouraging trust and transparency. BCT aims at creating a system that would offer a robust self-regulating, self-monitoring, and cyber-resilient data transaction operation, assuring the facilitation and protection of a truly efficient data exchange system. In the state of Florida, climate change and unpredicted weather disasters have put pressure on state and local decision-makers to adapt quick and efficient post-disaster recovery systems. Part of the recovery efforts is the reconstruction of buildings and infrastructure. The introduction of new technologies in the Architecture, Engineering, and Construction (AEC) industry can contribute to addressing recovery and rebuilding after the event of a natural disaster. With parallel technological advancement in geospatial data and Geographic Information System (GIS), as well as worsening climatic conditions, concerns can be suitably addressed by employing an integrated system of both Building Information Modeling (BIM) and BCT. While several potential applications of BIM must provide solutions to disaster-related issues, few have seen practical applications in recent years that indicate the potential benefits of such implementations. The feasibility of BIM-based applications still rests on the reliability of connectivity and cyber-security, indicating a strong use case for using BCT in conjunction with BIM for post-disaster recovery. This research depicts a survey of BCT and its applications in the Architecture, Engineering, and Construction (AEC) industries and examines the potential incorporation within the BIM process to address post-disaster rebuilding problems. Moreover, the study investigates the potential application of BCT in improving the framework for automating the building permitting process using Smart Contract (SC) technologies and Hyperledger Fabric (HLF), as well as discussing future research areas. The study proposes a new conceptualized framework resulting from the integration of BCT and BIM processes to improve the efficiency of building permit processes in post-disaster events.

Event-based probabilistic risk assessment of livestock snow disasters in the Qinghai–Tibetan Plateau

Abstract. Understanding risk using quantitative risk assessment offers critical information for risk-informed reduction actions, investing in building resilience, and planning for adaptation. This study develops an event-based probabilistic risk assessment (PRA) model for livestock snow disasters in the Qinghai–Tibetan Plateau (QTP) region and derives risk assessment results based on historical climate conditions (1980–2015) and present-day prevention capacity. In the model, a hazard module was developed to identify and simulate individual snow disaster events based on boosted regression trees. By combining a fitted quantitative vulnerability function and exposure derived from vegetation type and grassland carrying capacity, we estimated risk metrics based on livestock mortality and mortality rate. In our results, high-risk regions include the Nyainqêntanglha Range, Tanggula Range, Bayankhar Mountains and the region between the Kailas Range and the neighbouring Himalayas. In these regions, annual livestock mortality rates were estimated as >2 % and mortality was estimated as >2 sheep unit km−1 at a return period of 20 years. Prefectures identified with extremely high risk include Guoluo in Qinghai Province and Naqu, and Shigatse in the Tibet Autonomous Region. In these prefectures, a snow disaster event with a return period of 20 years or higher can easily claim total losses of more than 500 000 sheep units. Our event-based PRA results provide a quantitative reference for preparedness and insurance solutions in reducing mortality risk. The methodology developed here can be further adapted to future climate change risk analyses and provide important information for planning climate change adaption in the QTP region.

Assessment of urban flood susceptibility using semi-supervised machine learning model

In order to identify flood-prone areas with limited flood inventories, a semi-supervised machine learning model-the weakly labeled support vector machine (WELLSVM)-is used to assess urban flood susceptibility in this study. A spatial database is collected from metropolitan areas in Beijing, including flood inventories from 2004 to 2014 and nine metrological, geographical, and anthropogenic explanatory factors. Urban flood susceptibility is mapped and compared using logistic regression, artificial neural networks, and a support vector machine. Model performances are evaluated using four evaluation indices (accuracy, precision, recall, and F-score) as well as the receiver operating characteristic curve. The results show that WELLSVM can better utilize the spatial information (unlabeled data), and it outperforms all comparison models. The high-quality WELLSVM flood susceptibility map is thus applicable to efficient urban flood management.