The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F⁽²⁾ = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding.

Infrastructure systems are inextricably tied to society by providing a variety of vital services. These systems play a fundamental role in reducing the vulnerability of communities and increasing their resilience to natural and human-induced hazards. While various definitions of resilience for infrastructure systems exist, analyzing the resilience of these systems within cross-sectoral and interdisciplinary perspectives remains limited and fragmented in research and practice. With the aim to assist researchers and practitioners in advancing understanding of resilience in designing infrastructure systems, this systematic literature review synthesizes and complements existing knowledge on designing resilient vital infrastructures by identifying (1) key conceptual tensions and challenges, (2) engineering and non-engineering measures, and (3) directions for future research. Here, a conceptual framework is developed in which infrastructures are defined as a conglomeration of interdependent social–ecological–technical systems. In addition, we define resilient infrastructures as systems with ability to (i) anticipate and absorb disturbances, (ii) adapt/transform in response to changes, (iii) recover, and (iv) learn from prior unforeseen events. Our results indicate that conceptual and practical challenges in designing resilient infrastructures continue to exist. Hence these systems are still being built without taking resilience explicitly into account. Our review of measures and recent applications shows that the available measures have not been widely applied in designing resilient infrastructure systems. Key concerns to address are identified as (i) the integration of social, ecological, and technical resilience of infrastructure systems with explicit attention paid to cascading effects and dependencies across these complex systems and (ii) the development of new technologies to identify factors that create different recovery characteristics.

Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.

The attractiveness of beaches to people has led, in many places, to the construction of buildings at the beach–dune interface. Buildings change the local airflow patterns which, in turn, alter the sediment transport pathways and magnitudes. This induces erosion and deposition patterns around the structures. In this study, a numerical model is developed using the open-source computational fluid dynamics solver OpenFOAM. First, the model is used to predict the airflow patterns around a single rectangular building. The model predictions are validated with wind-tunnel data, which show good agreements. Second, a reference beach building is introduced and then the building dimensions are increased in length, width and height, each up to three times the reference building dimension. The impact of each dimensional extent on the near-surface airflow patterns is investigated. The results show that the near-surface airflow patterns are least dependent on the length of the building in the wind direction and they depend most on the width of the building perpendicular to the wind direction. Third, the convergence of the third-order horizontal near-surface velocity field is calculated to interpret the impact of changes in airflow patterns on potential erosion and deposition patterns around the building. The numerical predictions are compared with the observed erosion and sedimentation patterns around scale models in the field. The comparisons show satisfactory agreements between numerical results and field measurements.

Inlet-driven processes are capable of modifying the adjacent shoreline. However, few studies have attempted to understand how these changes affect coastal dunes. The present study aims to understand how shoreline changes induced by shoal attachment affect coastal dunes. A barrier island in the Netherlands is used as a case study. Both bathymetric and topographic annual data were analysed, together with the application of a cellular automata model for dune development. The objective of the model is to explore idealised scenarios of inlet-driven shoreline movements. With the model, ten different scenarios were examined regarding beach width increase and rate of alongshore spreading of the shoal. Field data showed that, for the case study, dune volume and shoal attachments could not be directly linked. Instead, rates of dune volume change differed significantly only due to long-term ebb-tidal delta evolution. Such morphological evolution oriented the beach towards the main wind direction, increasing overall aeolian transport potential. Modelling results showed that shoals significantly increased dune volumes only on three out of ten scenarios. This suggests that beach width increase, and rate of alongshore sediment spreading, determine whether the shoal will influence dune growth. Therefore, within the studied time-scale, local rates of dune growth are only increased if shoals are capable of increasing the beach width significantly and persistently.