Special issue |
Global- and continental-scale risk assessment for natural hazards: methods and practice
Editor(s): P. Ward, H.L. Cloke, J. Daniell, M. J. Duncan, H. Winsemius, and B. MerzMore information
Reducing natural hazard risk is high on the global political agenda. For example, it is at the heart of the Sendai Framework for Disaster Risk Reduction (and its predecessor the Hyogo Framework for Action) and the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts. In response, the last 5 years have seen an explosion in the number of scientific datasets, methods, and models for assessing risk at the global and continental scale. More and more, these datasets, methods, and models are being applied together with stakeholders in the decision-making process. The purpose of the special issue is to (1) provide a high-quality collection of papers showcasing the current state of the art of global- and continental-scale natural hazard risk assessment and application; (2) foster broader exchange of knowledge, datasets, methods, models, and good practice between scientists and practitioners working on different natural hazards and across disciplines globally; and (3) collaboratively identify future research avenues. We invite contributions related to all aspects of natural hazard risk assessment at the continental to global scale, including contributions focusing on single hazards, multiple hazards, or a combination or cascade of hazards. We also encourage contributions examining the use of scientific methods in practice and the appropriate use of continental to global risk assessment data in efforts to reduce risks. Furthermore, we encourage contributions focusing on globally applicable methods, such as novel methods for using globally available datasets and models to force more local models or inform more local risk assessment.
Preprint under review for NHESS(discussion: final response, 10 comments)
Extreme climate events can cause human and economic catastrophe at the global scale. For specific sectors, such as humanitarian aid or insurance, being able to understand how (i.e. with which frequency and intensity) these events can occur simultaneously at different locations or several times in a given amount of time and hit critical assets is all-important to design contingency plans. Here we develop and indicator to study co-occurence in space and time of wet and dry extremes.
Global floodplain mapping has rapidly progressed over the past few years. Different methods have been proposed to identify areas prone to river flooding, resulting in a plethora of available products. Here we assess the potential and limitations of two main paradigms and provide guidance on the use of these global products in assessing flood risk in data-poor regions.
Landslides cause thousands of fatalities and cost billions of dollars of damage worldwide every year, but different inventories of landslide events can have widely diverging completeness. This can lead to spatial biases in our understanding of the impacts. Here we use a globally homogeneous model of landslide hazard and exposure to provide consistent estimates of where landslides are most likely to cause damage to people, roads and other critical infrastructure at 1 km resolution.
Coastal compound flooding (CF), caused by interacting storm surges and high water runoff, is typically studied based on concurring storm surge extremes with either precipitation or river discharge extremes. Globally, these two approaches show similar CF spatial patterns, especially where the CF potential is the highest. Deviations between the two approaches increase with the catchment size. The precipitation-based analysis allows for considering
local-rainfall-driven CF and CF in small rivers.
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.
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.
Revised manuscript under review for NHESS(discussion: final response, 4 comments)
We have developed a statistical-parametric model of tropical cyclones (TCs), to undertake hazard and risk assessments at continental scales. The model enables users to build an understanding of the likelihood and magnitude of TC-related wind speeds across full ocean basins, but at a fine spatial resolution. The model can also be applied to single events, either scenarios or forecast events, to inform detailed impact assessments.
Houses and their contents in Europe are worth trillions of euros, resulting in high losses from natural hazards. Hence, risk assessments need to reliably estimate the size and value of houses, including the value of durable goods kept inside. In this work we show how openly available or open datasets can be used to predict the size of individual residential buildings. Further, we provide standardized monetary values of houses and contents per square metre of floor space for 30 countries.
Isabel Meza, Stefan Siebert, Petra Döll, Jürgen Kusche, Claudia Herbert, Ehsan Eyshi Rezaei, Hamideh Nouri, Helena Gerdener, Eklavyya Popat, Janna Frischen, Gustavo Naumann, Jürgen V. Vogt, Yvonne Walz, Zita Sebesvari, and Michael Hagenlocher
The paper presents, for the first time, a global-scale drought risk assessment for both irrigated and rainfed agricultural systems while considering drought hazard indicators, exposure and expert-weighted vulnerability indicators. We identify global patterns of drought risk and, by disaggregating risk into its underlying components and factors, provide entry points for risk reduction.
When a high river discharge coincides with a high storm surge level, this can exarcebate flood level, depth, and duration, resulting in a so-called compound flood event. These events are not currently included in global flood models. In this research, we analyse the timing and correlation between modelled discharge and storm surge level time series in deltas and estuaries. Our results provide a first indication of regions along the global coastline with a high compound flooding potential.
Assessing tropical cyclone (TC) wind risk is challenging due to a lack of historical TC wind data. This paper presents a novel approach to simulating landfalling TC winds anywhere on Earth. It captures local features such as high winds over coastal hills and lulls over rough terrain. A dataset of over 700 global historical wind footprints has been generated to provide new views of historical events. This dataset can be used to advance our understanding of overland TC wind risk.
Large volcanic eruptions are rare events; however, they may cause significant economic losses. This work explores a specific type of insurance (parametric insurance) applied to such events. Unlike traditional insurance where payouts occur after often lengthy loss assessments, this type of insurance makes automatic and prompt payments on the basis of the eruption attaining threshold values for objective and easily measurable characteristics (height and direction of the eruption column).
Large-scale risk assessments can be improved by a more direct relation between the type of exposed buildings and their flood impact. Compared to the common land-use-based approach, this model reflects heterogeneous structures and defines building-material-based vulnerability classes. This approach is particularly interesting for areas with large variations of building types, such as developing countries and large scales, and enables vulnerability comparison across different natural disasters.
This paper evaluates the effect of surge water level reduction due to land surface characteristics when assessing flood impacts at global scales. Our results show that uncertainties due to not accounting for water attenuation are of similar magnitude to the uncertainties regarding the total amount of sea-level rise expected by 2100, thus highlighting the need for better understanding of the spatial and temporal variation of water levels across floodplains.
Floods affect many communities and cause a large amount of damage worldwide.
Since we choose to live in natural flood plains and are unable to prevent all floods, a system of insurance and reinsurance was set up.
For these institutes to not fail, estimates are required of the frequency of large-scale flood events.
We explore a new method to obtain a large catalogue of synthetic, spatially coherent, large-scale river discharge events, using a recent (gridded) European discharge data set.
We examine sources of epistemic uncertainty in coastal flood risk models. We find that uncertainty from sea level estimations can be higher than that related to greenhouse gas emissions or climate prediction errors. Of comparable importance is information on coastal protection levels and the topography. In the absence of large datasets with sufficient resolution and accuracy, the last two factors are the main bottlenecks in terms of estimating coastal flood risks at large scales.