Global predictability of temperature extremes

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations

Building safety in humanitarian programmes that support post-disaster shelter self-recovery: An evidence review

The humanitarian sector is increasingly aware of the role that good quality evidence plays in underpinning effective and accountable practice. This review addresses the need for reliable evidence by evaluating current knowledge about the intersection of two key outcome targets of post‐disaster shelter response ‐ supporting shelter self‐recovery and building back safer. Evidence about post‐disaster shelter programmes that aim to improve hazard resistance whilst supporting shelter self‐recovery has been systematically analysed and evaluated. Technical support, especially training in safer construction techniques, was found to be a key programme feature, but the impact of this and of other programme attributes on building safety was largely not ascertainable. Programme reports lack sufficient detail, especially about the hazard resistance of repaired houses. Accounts of shelter programmes need to include more reliable reporting of key activities and assessment of outcomes, in order to contribute to the growing evidence base in this field

Probabilistic Assessment of Flood Hazard due to Levee Breaches Using Fragility Functions

Flood hazard maps are useful tools for land planning and flood risk management in order to increase the safety of flood-prone areas that can be inundated in the event of levee failure. However, flood hazard assessment is affected by various uncertainties, both aleatory and epistemic. The flood hazard analysis should hence take into account the main sources of uncertainty and quantify the confidence of the results for a given design flood event. To this end, this paper presents a probabilistic method for flood hazard mapping, which considers uncertainty due to breach location and failure time. A reliability analysis of the discretized levee system, performed using the concept of fragility function, enables the preselection of a set of levee sections more susceptible to failure. The probabilities of the breach scenarios (characterized by different breach locations and times) are then calculated using the probability multiplication rule, neglecting multiple breaches. The method is applied to a 96-km levee-protected reach in the central portion of the Po River (Northern Italy) and to an adjacent 1,900-km(2) flood-prone area on the right-hand side of the river, with a focus on the piping breach mechanism. The numerical simulations are performed through a combined 1D-2D hydrodynamic model using widespread free software. The results show that the method is effective for probabilistic inundation and flood hazard mapping. In addition, it has the advantage of requiring a smaller computational effort in comparison with the methods based on a classic Monte Carlo procedure