We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
This article reports the results of a field survey carried out in the disaster area of the December 2018 Sunda Strait tsunami, Indonesia. It provides data covering run-up heights, inundations, tsunami directions, and sediment characteristics. The data can be used for the validation of hydrodynamic models, and they contribute to a better understanding of the Sunda Strait tsunami caused by the Anak Krakatau volcano. In addition, they are important for spatial planning and mitigation efforts.
A method for the evaluation of a model that maps the susceptibility of a territory to surface runoff is presented. It is based on proxy data of localized impacts related to runoff. It accounts for the hazard level, the vulnerability of the study area and possible mitigation actions taken to reduce the risk. The evaluation is made on a 80 km railway line in Normandy (north of France), where a comprehensive database of runoff-related impacts on the railway has been gathered over the 20th century.
For effective risk management, flood risk should be properly assessed. Traditionally, risk is assessed by making the assumption of invariant flow or loss probabilities (the chance that a given discharge or loss is exceeded) within the river catchment during a single flood event. However, in reality, flooding is more severe in some regions than others. This study indicates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
To consider potential future urban developments in pluvial flood risk assessment, we develop empirical relationships for imperviousness and flood damage based on an analysis of existing urban characteristics. Results suggest that (1) data resolutions must be carefully selected, (2) there are lower limits for the spatial scale at which predictions can be generated, and (3) depth-dependent damage estimates are challenging to reproduce empirically and can be vulnerable to simulation artifacts.
The paper focuses on psychological impacts of river floods and flash floods on affected individuals. Since the connection between psychological characteristics and protection motivation is not yet fully understood, potential coherences are investigated with regard to both flood types. As a main result, the frequency of remembering an event seems to be positively connected to a greater willingness to protect oneself, especially if affected by a weaker flood event.
Timothy Tiggeloven, Hans de Moel, Hessel C. Winsemius, Dirk Eilander, Gilles Erkens, Eskedar Gebremedhin, Andres Diaz Loaiza, Samantha Kuzma, Tianyi Luo, Charles Iceland, Arno Bouwman, Jolien van Huijstee, Willem Ligtvoet, and Philip J. Ward
We present a framework to evaluate the benefits and costs of coastal adaptation through dikes to reduce future flood risk. If no adaptation takes place, we find that global coastal flood risk increases 150-fold by 2080, with sea-level rise contributing the most. Moreover, 15 countries account for 90 % of this increase; that adaptation shows high potential to cost-effectively reduce flood risk. The results will be integrated into the Aqueduct Global Flood Analyzer web tool.
The objective of this article is to propose a spatial decision support tool based on geovisualization techniques and a resilience assessment method for flood risk management. The methodology proposed integrates decision-making by identifying characteristics of urban resilience to facilitate its understanding with a visual tool. Results demonstrate a way to operationalize the concept of resilience at a local scale, integrating local stakeholders into a participative process.
Philip J. Ward, Veit Blauhut, Nadia Bloemendaal, James E. Daniell, Marleen C. de Ruiter, Melanie J. Duncan, Robert Emberson, Susanna F. Jenkins, Dalia Kirschbaum, Michael Kunz, Susanna Mohr, Sanne Muis, Graeme A. Riddell, Andreas Schäfer, Thomas Stanley, Ted I. E. Veldkamp, and Hessel C. Winsemius
We review the scientific literature on natural hazard risk assessments at the global scale. In doing so, we examine similarities and differences between the approaches taken across the different hazards and identify potential ways in which different hazard communities can learn from each other. Finally, we discuss opportunities for learning from methods and approaches being developed and applied to assess natural hazard risks at more continental or regional scales.
Wildfires result in billions of dollars of losses each year. Most wildfire predictions have a 10 d lead-time. This study introduces a framework for a 1-month lead-time prediction of wildfires based on vapor pressure deficit and surface soil moisture in the US. The results show that the model can successfully predict burned area with relatively small margins of error. This is especially important for operational wildfire management such as national resource allocation.
Sea ice disasters seriously threaten the safety of oil platforms in the Bohai Sea. Therefore, it is necessary to carry out risk assessments of sea ice disasters on oil platforms in the Bohai Sea. The analysis results showed that efficient sea ice prevention strategies could largely mitigate the sea-ice-induced vibration-related risks to jacket platforms. The sea ice risk assessment method can be applied in the design, operation, and management of other engineering structures.
This paper compares fire weather indices calculated from the NASA MERRA-2 reanlaysis to those calculated from a global network of weather stations, finding that, globally, biases in reanalysis fire weather are influenced firstly by temperature and relative humidity and, in certain regions, by precipitation biases. Fire weather forecasts using short-term NASA GEOS-5 weather forecasts are skillful 2 d ahead of time. This skill decreases more quickly with longer lead times at high latitudes.
This invited perspective paper addresses how machine learning may change flood risk and impact assessments. It goes through different modelling components and provides an analysis of how current assessments are done without machine learning, current applications of machine learning and potential future improvements. It is based on a 2-week-long intensive collaboration among experts from around the world during the Understanding Risk Field lab on urban flooding in June 2019.
Recovering from major earthquakes is a challenge due to a destablized environment. Over 11 years, we monitored a region hit by the Wenchuan earthquake, finding the loss caused by postseismic hazards was more than that caused by the earthquake. The main reason was a rush in reconstruction without proper hazard and risk assessment. It was concluded that postseismic recovery should consider not only spatial but also temporal dynamics of hazards as well as possible interaction among hazards.
Shraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, and Inbal Becker-Reshef
The region of southern Africa is prone to climate-driven food insecurity events, as demonstrated by the major drought event in 2015–2016. This study demonstrates that recently developed NASA Hydrological Forecasting and Analysis System-based root-zone soil moisture monitoring and forecasting products are well correlated with interannual regional crop yield, can identify below-normal crop yield events and provide skillful crop yield forecasts, and hence support early warning of food insecurity.