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Critical infrastructure (CI) is exposed to natural hazards that may lead to the devastation of these infrastructures and burden society with the indirect consequences that stem from this. The vulnerability is a key determinant for understanding, assessing and reducing natural hazard-induced risks to these infrastructures. To date, however, essential vulnerability information for CI is distributed across literature instead of being accessible through a centralized dataset. This study, through a systematic literature review, synthesises the state-of-the-art of fragility and vulnerability curves for CI assets of energy, transport, water, waste, telecoms, health and education in context of natural hazards and offers a unique physical vulnerability database. The publicly available centralized database that contains over 1,250 curves can directly be used as input for risk assessment studies that evaluate the expected or potential damages to assets due to flooding, earthquakes, windstorms and landslides. The literature review highlights that vulnerability development has mainly focused on earthquake curves for a wide range of infrastructure types. Windstorms have the second largest share in the database, but are especially limited to energy curves. While all CI systems require more vulnerability research, additional efforts are needed for telecommunication which is largely underrepresented in our database. [ABSTRACT FROM AUTHOR]

Coastal flooding and sea level rise (SLR) will affect farmers in coastal areas, as increasing salinity levels will reduce crop yields, leading to a loss of net annual income for farming communities. In response, farmers can take various actions. In order to assess such a response under SLR, we applied an agent-based model (ABM) to simulate the adaptation and migration decisions of farmers in coastal Mozambique. The ABM is coupled with a salinization module to simulate the relationship between soil salinity and SLR. The decision rules in the model (DYNAMO-M) are based on the economic theory of subjective expected utility. This theory posits that households can maximize their welfare by deciding whether to (a) stay and face losses from salinization and flooding, (b) stay and adapt (switching to salt-tolerant crops and enhancing physical resilience such as elevating houses), or (c) migrate to safer inland areas. The results show that coastal farmers in Mozambique face total losses of up to US$12.5 million per year from salt intrusion and up to US$800 million per year from flooding of buildings (RCP8.5 in the year 2080). Sorghum farmers may experience little damage from salt intrusion, while rice farmers may experience losses of up to US$15,000 per year. We show that medium-sized farmers (1-20 ha) are most at risk. This is because their farm size means that adaptation costs are substantial, while their incomes are too low to cover these costs. The number of households adapting varies between different districts (6%-50%), with salt adaptation being the most common, as costs are lowest. Despite adaptation measures, about 13%-20% of the total 300,000 farmers in coastal flood zones will migrate to safer areas under different settings of adaptive behaviour and different climatic and socioeconomic scenarios. [ABSTRACT FROM AUTHOR]

Analyses of future agricultural drought impacts require a multidisciplinary approach in which both human and environmental dynamics are studied. In this study, we applied the agent-based drought risk model ADOPT to assess the effect of various drought risk reduction interventions on smallholder farmers in the Kenyan drylands. Moreover, the robustness of these (non-)governmental interventions under different climate change scenarios was evaluated. ADOPT simulates water management decisions of smallholder farmers, and evaluates household food insecurity, poverty and emergency aid needs due to drought disasters. Model dynamics were informed by extensive field surveys and interviews from which decision rules were distilled based on bounded rational behaviour theories. Model results suggest that extension services increase the adoption of low-cost, newer drought adaptation measures while credit schemes are useful for cost-effective but expensive measures, and ex-ante cash transfers allow the least wealthy households to adopt low-cost well-known measures. Early warning systems show more effective in climate scenarios with less frequent droughts. Combining all four interventions displays a mutually-reinforcing effect with a sharp increase in the adoption of measures resulting in reduced food insecurity, decreased poverty levels and drastically lower need for emergency aid, even under hotter and drier climate conditions. These nonlinear synergies indicate that a holistic perspective is needed to support smallholder resilience in the Kenyan drylands. [ABSTRACT FROM AUTHOR]

Rapid impact assessments immediately after disasters are crucial to enable rapid and effective mobilization of resources for response and recovery efforts. These assessments are often performed by analysing the three components of risk: hazard, exposure and vulnerability. Vulnerability curves are often constructed using historic insurance data or expert judgments, reducing their applicability for the characteristics of the specific hazard and building stock. Therefore, this paper outlines an approach to the creation of event-specific vulnerability curves, using Bayesian statistics (i.e., the zero-one inflated beta distribution) to update a pre-existing vulnerability curve (i.e., the prior) with observed impact data derived from social media. The approach is applied in a case study of Hurricane Dorian, which hit the Bahamas in September 2019. We analysed footage shot predominantly from unmanned aerial vehicles (UAVs) and other airborne vehicles posted on YouTube in the first 10 days after the disaster. Due to its Bayesian nature, the approach can be used regardless of the amount of data available as it balances the contribution of the prior and the observations. [ABSTRACT FROM AUTHOR]

In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data is becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia, and found that rural flood risk accounts for about 22 % of simulated damages; rural damages are generally neglected in the typical land-use-based damage models particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries. It also enables comparison across different natural hazard types that also use material-based vulnerability, paving the way to the enhancement of multi-risk assessments. [ABSTRACT FROM AUTHOR]

A mega-flood in 1998 caused tremendous losses in China, and triggered large investments in flood risk management (Bryan et al., 2018). However, rapid urbanization and climate change pose new challenges and it is time to rethink whether China is prepared for the next mega-flood. In China's fast growing economy, with rapid urbanization, novel flood risk management approaches are required in addition to reinforcing structural protection, such as levees. These include a risk-based urban planning and a coordinated water governance system with public participation. [ABSTRACT FROM AUTHOR]

The availability of timely and accurate information about ongoing events is important for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, and in particular Twitter, have gained traction as a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, analyses of a tweet's text, combined with an evaluation of its metadata, can help to increase the number of geo-located tweets. This paper describes a new algorithm (TAGGS), that georeferences tweets by using the spatial information of groups of tweets mentioning the same location. This technique results in a roughly twofold increase in the number of geo-located tweets as compared to existing methods. We applied this approach to 35.1 million flood-related tweets in 12 languages, collected over 2.5 years. In the dataset, we found 11.6 million tweets mentioning one or more flood locations, which can be towns (6.9 million), provinces (3.3 million), or countries (2.2 million). Validation demonstrated that TAGGS correctly located about 65-75% of the tweets. As a future application, TAGGS could form the basis for a global event detection and monitoring system. [ABSTRACT FROM AUTHOR]

In a cross-discipline study, we carried out an extensive literature review to increase understanding of vulnerability indicators used in both earthquake- and flood vulnerability assessments. We provide insights into potential improvements in both fields by identifying and comparing quantitative vulnerability indicators. Indicators have been categorised into physical- and social categories, and then, where possible, further subdivided into measurable and comparable indicators. Next, a selection of index- and curve based vulnerability models that use these indicators have been described, comparing several characteristics such as temporal- and spatial aspects. It appears that earthquake vulnerability methods traditionally have a strong focus on object-based physical attributes used in vulnerability curve-based models, while flood vulnerability studies focus more on indicators applied to aggregated land-use classes in curve-based models. Flood risk studies could be improved using approaches from earthquake studies, such as incorporating more detailed physical indicators, developing object-based physical vulnerability curve assessments and incorporating time-of-the-day based building occupation patterns. Likewise, earthquake assessments could learn from flood studies by refining their selection of social vulnerability indicators. Based on the lessons obtained in this study, we recommend future studies for exploring risk assessment methodologies cross-different hazard types. [ABSTRACT FROM AUTHOR]