11Logistic regression studies which assess landslide susceptibility are widely available in the literature. 12However, a global review of these studies to synthesise and compare the results does not exist. There 13 are currently no guidelines for selection of covariates to be used in logistic regression analysis and as 14 such, the covariates selected vary widely between studies. An inventory of significant covariates 15 associated with landsliding produced from the full set of such studies globally would be a useful aid to 16 the selection of covariates in future logistic regression studies. Thus, studies using logistic regression 17 for landslide susceptibility estimation published in the literature were collated and a database created 18 of the significant factors affecting the generation of landslides. The database records the paper the 19 data were taken from, the year of publication, the approximate longitude and latitude of the study 20 area, the trigger method (where appropriate), and the most dominant type of landslides occurring in 21 the study area. The significant and non-significant (at the 95% confidence level) covariates were 22 recorded, as well as their coefficient, statistical significance, and unit of measurement. The most 23 common statistically significant covariate used in landslide logistic regression was slope, followed by 24 aspect. The significant covariates related to landsliding varied for earthquake-induced landslides 25 compared to rainfall-induced landslides, and between landslide type. More importantly, the full range 26 of covariates used was identified along with their frequencies of inclusion. The analysis showed that 27 2 there needs to be more clarity and consistency in the methodology for selecting covariates for logistic 28 regression analysis and in the metrics included when presenting the results. Several recommendations 29 for future studies were given. 30 31

Two‐dimensional flood inundation modelling is a widely used tool to aid flood risk management. In urban areas, the model spatial resolution required to represent flows through a typical street network often results in an impractical computational cost at the city scale. This paper presents the calibration and evaluation of a recently developed formulation of the LISFLOOD‐FP model, which is more computationally efficient at these resolutions. Aerial photography was available for model evaluation on 3 days from the 24 to the 31 of July. The new formulation was benchmarked against the original version of the model at 20 and 40 m resolutions, demonstrating equally accurate simulation, given the evaluation data but at a 67 times faster computation time. The July event was then simulated at the 2 m resolution of the available airborne LiDAR DEM. This resulted in more accurate simulation of the floodplain drying dynamics compared with the coarse resolution models, although maximum inundation levels were simulated equally well at all resolutions tested.

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In this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement.