Disruptions to transportation networks by natural hazard events cause direct losses (e.g. by physical damage) and indirect socio-economic losses via travel delays and decreased transportation efficiency. The severity and spatial distribution of these losses varies according to user travel demands and which links, nodes or infrastructure assets are physically disrupted. Increasing transport network resilience, for example by targeted mitigation strategies, requires the identification of the critical network segments which if disrupted would incur undesirable or unacceptable socio-economic impacts. Here, these impacts are assessed on a national road transportation network by coupling hazard data with a transport network model. This process is illustrated using a case study of landslide hazards on the road network of Scotland. A set of possible landslide-prone road segments is generated using landslide susceptibility data. The results indicate that at least 152 road segments are susceptible to landslides, which could cause indirect economic losses exceeding £35 k for each day of closure. In addition, previous estimates for historic landslide events might be significant underestimates. For example, the estimated losses for the 2007 A83 'Rest and Be Thankful' landslide are £80 k day À1 , totalling £1.2 million over a 15 day closure, and are ∼60% greater than previous estimates. The spatial distribution of impact to road users is communicated in terms of 'extended hazard impact footprints' . These footprints reveal previously unknown exposed communities and unanticipated spatial patterns of severe disruption. Beyond cost-benefit analyses for landslide mitigation efforts, the approach implemented is applicable to other natural hazards (e.g. flooding), combinations of hazards, or even other network disruption events. perturbation to network operation (i.e. reduced access, travel delay and costlier routes). Consequently landslide hazard impact and population exposure is distributed far beyond the hazard's physical location. Critical network segments are those characterised by a high consequence of failure generally irrespective of likelihood [4,5]. A poignant example is the estimated £3.0 billion regional economic loss incurred in the South West, UK [6] by damage to 40 m of railway line during storm water levels exceeding previous maxima in 100 year historical records [7]. The identification of critical network segments is integrated within network management guidelines [8,9]. However, these operational assessments are limited to road segment

Translational landslides and debris flows are often initiated during intense or prolonged rainfall. Empirical thresholds aim to classify the rain conditions that are commonly associated with landslide occurrence and therefore improve understating of these hazards and predictive ability. Objective techniques that are used to determine these thresholds are likely to be affected by the length of the rain record used, yet this is not routinely considered. Moreover, remotely sensed spatially continuous rainfall observations are under-exploited. This study compares and evaluates the effect of rain record length on two objective threshold selection techniques in a national assessment of Scotland using weather radar data. Thresholds selected by 'threat score' are sensitive to rain record length whereas, in a first application to landslides, 'optimal point' (OP) thresholds prove relatively consistent. OP thresholds increase landslide detection and may therefore be applicable in early-warning systems. Thresholds combining 1-and 12-day antecedence variables best distinguish landslide initiation conditions and indicate that Scottish landslides may be initiated by lower rain accumulation and intensities than previously thought.