Qualitative Assessment of Social Vulnerability to Flood Hazards in Romania

Assessing social vulnerability and identifying spatial hotspots of flood risk to inform socially just flood management policy

This study presents the first nationwide spatial assessment of flood risk to identify social vulnerability and flood exposure hotspots that support policies aimed at protecting high-risk populations and geographical regions of Canada. The study used a national-scale flood hazard dataset (pluvial, fluvial, and coastal) to estimate a 1-in-100-year flood exposure of all residential properties across 5721 census tracts. Residential flood exposure data were spatially integrated with a census-based multidimensional social vulnerability index (SoVI) that included demographic, racial/ethnic, and socioeconomic indicators influencing vulnerability. Using Bivariate Local Indicators of Spatial Association (BiLISA) cluster maps, the study identified geographic concentration of flood risk hotspots where high vulnerability coincided with high flood exposure. The results revealed considerable spatial variations in tract-level social vulnerability and flood exposure. Flood risk hotspots belonged to 410 census tracts, 21 census metropolitan areas, and eight provinces comprising about 1.7 million of the total population and 51% of half-a-million residential properties in Canada. Results identify populations and the geographic regions near the core and dense urban areas predominantly occupying those hotspots. Recognizing priority locations is critically important for government interventions and risk mitigation initiatives considering socio-physical aspects of vulnerability to flooding. Findings reinforce a better understanding of geographic flood-disadvantaged neighborhoods across Canada, where interventions are required to target preparedness, response, and recovery resources that foster socially just flood management strategies.

Human Settlement Resilience Zoning and Optimizing Strategies for River-Network Cities under Flood Risk Management Objectives: Taking Yueyang City as an Example

The dense river network and large population in the southern region of China are vulnerable to flooding stress, which challenges the construction of human settlements. This paper analyzes the causes of flood risk and the dilemma of human settlement improvement in river-network cities, introduces the principle of resilience to human settlements, and conducts a quantitative study on the resilience of human settlements in river-network cities with the goal of flood risk management. Taking Yueyang city, a river-network city in the Yangtze River Basin, as the empirical research object, we conduct a flood resilience zoning of the human settlements based on the flood risk assessment model and use the GIS spatial overlay method to correct the resilience of the current human living space against the territorial spatial planning of Yueyang city. Ultimately, we propose a strategy for optimizing human settlements under flood risk management. The results show that (1) the highest-risk and high-risk areas of Yueyang city were mainly located in Dongting Lake and its interconnected water system, the southwest local area and the southeastern fringe, while the low-risk and lowest-risk areas were mainly located in the northeast local area and the northwestern fringe, with low flood stress risk. (2) The spatial system of human settlement resilience was constructed based on the flood risk assessment level. Among them, the human settlement flood resilience zoning of Yueyang city was divided into five categories from low to high: human settlement control zone, restriction zone, buffer zone, construction zone and expansion zone; the flood resilience zoning of Yueyang city’s current living space was divided into three categories from low to high: structure adjustment zone, flood restriction zone and development stability zone. (3) The specific control implementation and execution of the human settlements in Yueyang city mainly focus on the interrelationship between the risk of flooding in the watershed and the development of human activities through zoning regulation and collaborative management to optimize the human settlements. The study results can provide positive intervention and guidance for constructing urban and rural territorial spatial prevention planning and improving human living quality in river-network cities in China.

Flash Flood Risk Assessment and Mitigation in Digital-Era Governance Using Unmanned Aerial Vehicle and GIS Spatial Analyses Case Study: Small River Basins

Watercourses act like a magnet for human communities and were always a deciding factor when choosing settlements. The reverse of these services is a potential hazard in the form of flash flooding, for which human society has various management strategies. These strategies prove to be increasingly necessary in the context of increased anthropic pressure on the floodable areas. One of these strategies, Strategic Flood Management (SFM), a continuous cycle of planning, acting, monitoring, reviewing and adapting, seems to have better chances to succeed than other previous strategies, in the context of the Digital-Era Governance (DEG). These derive, among others, from the technological and methodological advantages of DEG. Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAV) stand out among the most revolutionary tools for data acquisition and processing of data in the last decade, both in qualitative and quantitative terms. In this context, this study presents a hybrid risk assessment methodology for buildings in case of floods. The methodology is based on detailed information on the terrestrial surface—digital surface model (DSM) and measurements of the last historical flash flood level (occurred on 20 June 2012)—that enabled post-flood peak discharge estimation. Based on this methodology, two other parameters were calculated together with water height (depth): shear stress and velocity. These calculations enabled the modelling of the hazard and risk map, taking into account the objective value of buildings. The two components were integrated in a portal available for the authorities and inhabitants. Both the methodology and the portal are perfectible, but the value of this material consists of the detailing and replicability potential of the data that can be made available to administration and local community. Conceptually, the following are relevant (a) the framing of the SFM concept in the DEG framework and (b) the possibility to highlight the involvement and contribution of the citizens in mapping the risks and their adaptation to climate changes. The subsequent version of the portal is thus improved by further contributions and the participatory approach of the citizens.

Comparative Multi-Criteria Assessment of Hydrological Vulnerability—Case Study: Drainage Basins in the Northeast Region of Romania

Hydrological vulnerability (HV) is a (changing) underlying condition in all drainage basins, depending on the dynamics of the potentially dangerous hydrological phenomena, the particularities of drainage networks, land use patterns, and processes that shape landforms in extensive periods. The socioeconomic attributes and the hydrotechnical infrastructure add up to the manifestation of this type of vulnerability. In this paper, we assess the HV levels of 81 drainage basins in the NE of Romania for three distinctive periods (1990–1999, 2000–2009, 2010–2018), using a multi-criteria approach. Two classical multi-criteria decision making (MCDM) methods were combined in order to evaluate the HV according to factors that refer to floods and hydrological drought occurrences, hydrotechnical structure coverage, the drainage network, land use, and landforms characteristics. The Analytic Hierarchy Process (AHP) was applied to weigh these factors and the resulting relative importance values were integrated in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), by which the alternatives represented by the catchments were ranked. The attenuation of the HV through time follows an East–West direction, from the lower elevations of the Moldavian Plateau to the heights of the Carpathians. Hydrological droughts are more likely to occur in the Eastern part of the analyzed territory, while the western section displays a certain propensity for floods. The results may be used by local and national authorities in order to improve the hydrological risk mitigation strategies, and to develop more targeted water management projects, properly calibrated to the conditions of the Northeast Development Region in Romania.

Flood Risk Assessment and Its Mapping in Purba Medinipur District, West Bengal, India

Floods are one of the most common quasi-natural hazards in costal districts of West Bengal, India and thousands of people are affected every year. From the destruction of crop lands and buildings to the disruption of balance of the environment and the spreading of disease, floods can devastate entire regions. The risk of flood depends on the flood intensity, frequency, and duration, the vulnerability of the people, etc. The spatiality of flood risk is still insufficient at micro level study for the management of resource disasters. In consequence, the present study on ‘flood risk mapping’ is performed in Purba Medinipur (one of the coastal districts of West Bengal, India) by considering the flood frequency and vulnerability of the people as flood risk components. The frequency of floods from 2002 to 2019 is considered as a variable of assessment and twenty-five key indicators are employed to understand the vulnerability of the people of the region. From the analysis, Moyna emerges as the highest flood risk prone block and Contai-I is the least flood prone block of the district. The results can help to minimize the chances of death, injury, loss, or harm and establish a good disaster management plan against floods.

Shifting landscapes of risk: Quantifying pluvial flood vulnerability beyond the regulated floodplain

Floods are recognized as the costliest type of natural hazard both worldwide and in the United States, with projected increases in frequency and magnitude in the absence of effective adaptation strategies. In the fall of 2018, Hurricane Florence made landfall in southeastern North Carolina, USA, bringing record rainfall and resulting in widespread inundation that impacted many areas outside of the federally designated Special Flood Hazard Area (SFHA). Much of this flooding was from inland pluvial inundation, which is an understudied component of coastal risk and vulnerability assessments primarily due to the scarcity of infrastructure data and historically lower flooding recurrence rates. This has resulted in severe damages in areas that residents and local officials considered at low risk from flooding. Using nearly-coincident high-spatial, high-temporal resolution CubeSat satellite imagery, we quantified the areal extent of post-Hurricane Florence floodwater within and beyond the 100-year floodplain (SFHA) and the proportion of residential structures exposed to flooding within an eight-county study area. We propose a novel approach to estimate flood risk resulting from this singular event (termed an actualized risk index) when compared to a published empirical model of vulnerability. We show that 24.3% of detected floodwater was outside the 100-year floodplain, 43.4% of exposed residential structures are outside the 100-year floodplain, and communities of highest vulnerability are not only along the coast but also inland along the Cape Fear, Northeast Cape Fear, Trenton, and Neuse Rivers. This suggests that the SFHA may not adequately show the spatial distribution of pluvial flood risk in riverine areas, and that misunderstanding of this risk has led to a pattern of development in which houses have a higher than expected risk of flooding. Moreover, this additional flood risk may disproportionately affect lower-income residents of these largely rural areas. These results have important implications in light of recent policy guidance in southeastern USA states that mandate that predictive coastal vulnerability assessments to sea level rise be conducted relative to 100-year SFHA zones.

Using Spatial Pattern Analysis to Explore the Relationship between Vulnerability and Resilience to Natural Hazards

This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocorrelation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resilience. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.

An Empirical Analysis of Association between Socioeconomic Factors and Communities’ Exposure to Natural Hazards

In today’s urban environments with complex design and configurations, heterogeneous spatial clusters of communities with different socioeconomic characteristics may result in disproportionate exposure of some groups of citizens to natural hazards. The objective of this study was to compare the associations between communities’ socioeconomic characteristics and exposure to different types of natural hazards in New York City (NYC) to examine whether commonly accepted indicators of social vulnerability are associated with similar levels of exposure across various natural hazards. First, we collected socioeconomic data (e.g., population, median income, unemployment rate) at a zip code level of granularity provided by the United States Census Bureau. Next, we identified and gathered spatial data for coastal storms, flooding, extreme heat, and pandemic disease in NYC. We then conducted a pairwise Kendall’s τ-b test to compare the associations. The outcomes showed that the significance and direction of the associations depend on the type of natural hazard. Particularly, the results indicated that zip codes with lower socioeconomic factors and greater percentage of minority ethnicities are exposed disproportionately to extreme heat and COVID-19. On the other hand, zip codes with higher percentage of areas prone to flooding have relatively higher socioeconomic factors. Furthermore, the results did not show any statistically significant association between socioeconomic factors and exposure to coastal storm inundations. The outcomes of this study will help decision makers design and implement better optimized and effective emergency preparedness plans by prioritizing their target areas based on socioeconomic factors in order to enhance social justice.

Social Vulnerability to Natural Hazards in Namibia: A District-Based Analysis

Southern Africa is one of the world’s poorest and most vulnerable regions with severe barriers to its sustainable development. We strived to understand here the patterns and drivers of social vulnerability to natural hazards in Namibia, which is the most arid sub-Saharan country with large social inequalities. We used a total of 12 indicators that characterized social, economic and demographic settings of the 14 districts in the country. Further, we evaluated the countrywide pattern of most relevant natural hazards, including wildfires, floods and drought. We identified the main factors driving social vulnerability in the districts, and we evaluated how the socio-economic fitness of populations coincided with the distribution of high-hazard areas. We found that populations with the poorest socio-economic performance were mostly distributed in the country’s northern districts, which are also exposed to the highest frequency and severity of natural hazards, particularly to floods and wildfires. This coincidence of highly sensitive populations with high exposure to hazards renders these populations particularly vulnerable. That the frequency of natural hazards increases with climate change, and implementation of programs enhancing the social resilience is insufficient, underscores the urgency of actions targeted at the priority areas identified herein.

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and socioeconomic statistics. In particular, an index, Land Resource Pressure (LRP), is proposed to evaluate the land carrying capacity, and a case study was carried out in Wuhan. The LRP of Wuhan was calculated on 250 m * 250 m grids, and showed a circularly declining pattern from central to outer areas. We collected its influencing factors in terms of nature resources, economy, transportation and urban construction, and then analyzed its causes via geographically weighted (GW) models. Firstly, pair-wise correlations between LRP and each influencing factor were explored via the GW correlation coefficients. These local estimates provide an important precursor for the following quantitative analysis via the GW regression (GWR) technique. The GWR coefficient estimates interpret the influences on LRP in a localized view. Results show that per capita gross domestic product (PerGDP) showed a higher absolute estimate among all factors, which proves that PerGDP has a relieving effect on LRP, especially in the southwestern areas. Overall, this study provides a technical framework to evaluate land carrying capacity with multi-source data sets and explore its localized influences via GW models, which could provide practical guidance for similar studies in other cities.