Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis

Public Health Emergency Response and Recovery in Limited Resource Setting: Lesson learned from Hanang District Floods and Landslide in Tanzania

OBJECTIVE

In December 2023, floods and landslides in Hanang District, Northern Tanzania, caused severe casualties, infrastructure damage, and community displacement. We describe the public health emergency response and lessons learnt during this disaster to guide future mitigations.

METHODS

Retrospective data collection during the disaster was made through quantitative (description of casualties) and qualitative (interviews and focus groups) approaches to provide insights into psychosocial support, coordination, and other response pillars. Microsoft Excel (2019) was used for quantitative data analysis, and MAX Qualitative Data Analysis was used to manage qualitative data.

RESULTS

Soft tissue injuries, bruises, and lacerations were the most common (60.43%), with 87.77% of casualties recovering and a notable fatality rate of 12.23%. Mental health and psychosocial support reached over 3300 individuals, offering depression assessments and family reconnections. Establishing a dual-level public health response team and implementing the Incident Management System demonstrated the country's response efficiency.

CONCLUSIONS

The public health emergency response to the 2023 floods and landslides in Hanang District was largely effective. This demonstrated strong coordination, capacity, and resilience of Tanzania health system; however, the fatality rate highlighted a need for further investment to improve future disaster prevention, preparedness, and response.

Socio-hydrological modelling using participatory System Dynamics modelling for enhancing urban flood resilience through Blue-Green Infrastructure

Cities are complex systems characterised by interdependencies among infrastructural, economic, social, ecological, and human elements. Urban surface water flooding poses a significant challenge due to climate change, population growth, and ageing infrastructure, often resulting in substantial economic losses and social disruption. Traditional hydrological modelling approaches for flood risk management, while providing invaluable support in the analysis of hydrological dynamics of floods, lack an understanding of the complex interplay between hydrological and non-hydrological (i.e., social, environmental, economic) aspects in an urban system, hindering effective flood risk management strategies. In this context, socio-hydrological modelling methods offer a complementary perspective to traditional hydrological models by integrating hydrological and social processes, thereby enhancing the understanding of the complex interactions driving flood resilience. The present work proposes a participatory socio-hydrological modelling approach based on System Dynamics (SD) to quantitatively analyse the interactions and feedback between flood risk and different aspects of the urban system. By combining scientific expertise with stakeholder knowledge, the modelling approach aims to provide decision-makers with a comprehensive understanding of flood dynamics and the effectiveness of resilience-building measures. Furthermore, the role of Blue-Green Infrastructure (BGI) in enhancing urban flood resilience, considering its interplay with grey infrastructure and interactions with various sub-systems, is explored. The results reveal i) the contribution of SD quantitative modelling in supporting the analysis of interactions between flood risk reduction measures and different sub-systems thus offering decision-makers actionable insights into the multifaceted nature of flood risk and resilience; ii) the added value provided by the combination of scientific and stakeholder knowledge in tailoring the model to the case study, quantifying socio-hydrological modelling dynamics limitedly explored in the scientific literature and supporting the selection of measures for increasing flood resilience; iii) the ability of BGI to provide not only hydrological benefits (mainly about the reduction of surface runoff) but also multiple social and environmental benefits (i.e., the co-benefits), especially when coupled with well-functioning grey infrastructure. Reference is made to one of the case studies of the CUSSH and CAMELLIA projects, namely Thamesmead (London, United Kingdom), a formerly inhospitable marshland currently undergoing a process of urban regeneration, with an increasing vulnerability to flooding.

Flood Risk and Preventive Choices: A Framework for Studying Human Behaviors

The topic of flood phenomena has always been of considerable importance due to the high risks it entails, both in terms of potential economic and social damage and the jeopardizing of human lives themselves. The spread of climate change is making this topic even more relevant. This work aims to contribute to evaluating the role that human factors can play in responding to critical hydrogeological phenomena. In particular, we introduce an agent-based platform for analyzing social behaviors in these critical situations. In our experiments, we simulate a population that is faced with the risk of a potentially catastrophic event. In this scenario, citizens (modeled through cognitive agents) must assess the risk they face by relying on their sources of information and mutual trust, enabling them to respond effectively. Specifically, our contributions include (1) an analysis of some behavioral profiles of citizens and authorities; (2) the identification of the "dissonance between evaluation and action" effect, wherein an individual may behave differently from what their information sources suggest, despite having full trust in them in situations of particular risk; (3) the possibility of using the social structure as a "social risk absorber", enabling support for a higher level of risk. While the results obtained at this level of abstraction are not exhaustive, they identify phenomena that can occur in real-world scenarios and can be useful in defining general guidelines.

Characterizing the level of urban Flood vulnerability using the social-ecological-technological systems framework, the case of Adama city, Ethiopia

This study characterizes the flood vulnerability of Adama City, Ethiopia, where the city faces high flood vulnerability due to its unplanned urbanization in low-lying floodplain areas surrounding deforested mountains and ridges. The study applied an interlinked Social-Ecological-Technological-Systems (SETS) vulnerability framework using a GIS-based Multi-Criteria Decision-Making and Analytical Hierarchy Process (MCDM-AHP). The framework analyzed exposure, sensitivity, and adaptive capacity to flooding for each of the three SETS domains. The study analyzed 18 variables at the city level within each SETS domain. The result revealed that clusters of flood-vulnerable areas were identified by each SETS domain showing the concentration of flood vulnerability in the study area and the need to consider prompt adaptive mechanisms to severe and recurring flooding. The finding has significant implications for holistic approaches to sustainable cities. Moreover, the reduction of complex urban flood vulnerabilities according to their priority as individual or combined solutions for decision-makers and professionals in early warning and flood management systems is the other contribution of the study.

Urban flooding resilience evaluation with coupled rainfall and flooding models: a small area in Kunming City, China as an example

Climate change and increasing urbanization have contributed greatly to urban flooding, making it a global problem. The resilient city approach provides new ideas for urban flood prevention research, and currently, enhancing urban flood resilience is an effective means for alleviating urban flooding pressure. This study proposes a method to quantify the resilience value of urban flooding based on the `4R' theory of resilience, by coupling the urban rainfall and flooding model to simulate urban flooding, and the simulation results are used for calculating index weights and assessing the spatial distribution of urban flood resilience in the study area. The results indicate that (1) the high level of flood resilience in the study area is positively correlated with the points prone to waterlogging; the more an area is prone to waterlogging, the lower the flood resilience value. (2) The flood resilience index in most areas shows a significant local spatial clustering effect, the number of areas with nonsignificant local spatial clustering accounting for 46% of the total. The urban flood resilience assessment system constructed in this study provides a reference for assessing the urban flood resilience of other cities, thus facilitating the decision-making process of urban planning and disaster mitigation.

The Identification and Analysis of the Centers of Geographical Public Opinions in Flood Disasters Based on Improved Naïve Bayes Network

The increasing frequency of floods and the lack of protective measures have the potential to cause severe damage. Working from the perspective of network public opinion is an effective way to understand flood disasters. However, the existing research tends to focus on a single perspective, such as the characteristics of the text, algorithm optimization, or spatial location recognition, while scholars have paid much less attention to the impact of social-psychological differences in space on network public opinion. This research is based on the following hypothesis: When public opinions break out, the differences of network public opinions in geography will form spatially different centers of geographical public opinions in flood disasters (CGeoPOFDs). These centers represent the cities that receive the most attention from network public opinion. Based on this hypothesis, this study proposes a new way of identifying and analyzing CGeoPOFDs. First, two optimization strategies were applied to enhance a naïve Bayes network: syntactic parsing, which was used to optimize the selection of feature word vectors, and ensemble learning, which enabled multi-classifier fusion optimization. Social media data were classified through the improved algorithm, and then, various methods (hotspot analysis, geographic mapping, and sentiment analysis) were used to identify CGeoPOFDs. Finally, analysis was performed in terms of spatiotemporal, virtual, and real dimensions. In addition, microblog social data and real disaster data were used to arrive at empirical results. According to the study findings, the identified CGeoPOFDs offered traditional characteristics of network public opinion while also featuring unique spatiotemporal characteristics. Over time, CGeoPOFDs demonstrated spatial aggregation and bias diffusion and an overall positive emotional tendency.