Development of large scale inland flood scenarios for disaster response planning based on spatial/temporal conditional probability analysis

Abstract

Extreme event scenarios are useful for civil emergency services to help in developing contingency plans for responding effectively to major flooding incidents. In the UK, the official national risk register includes a scenario for inland flooding (from rivers and other sources), which is described in terms of a probability of occurrence over a five year period of between 1 in 200 and 1 in 20. This scenario was previously based on recent extreme floods, in conjunction with maps produced to aid in development planning on floodplains. At the time it was constructed, it was not feasible to assess scientifically the combined probability of a nationally-significant flood event of this type, therefore the scenario probability assessment was ambiguous. Recent developments in multivariate extreme value statistics now allow the probability of large scale flood events to be assessed with reference to hydrological summary statistics or impact metrics. Building on theory and pilot studies by Heffernan and Tawn [1], Lamb et al. [2] and Keef et al. [3], we describe the development of a set of national-scale scenarios based on a high-dimensional (ca. 1,100 locations) conditional probability analysis of extreme river flows and rainfall. The methodology provides a theoretically justified basis for extrapolation into the joint tail of the distribution of these variables, which is then used to simulate extreme events with associated probabilities. The probabilistic events are compared with current understanding of meteorological scenarios associated with significant, large-scale flooding in the UK, and with historical flooding, in order to identify plausible events that can inform national risk scenarios. Additionally, we combined scenarios of inland and coastal extremes that have been considered by linking the analysis discussed in this paper with methods presented in a companion paper by Wyncoll et al.