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Abstract:Droughts tend to evolve slowly and affect large areas simultaneously, which suggests that improved understanding of spatial coherence of drought would enable better mitigation of drought impacts through enhanced monitoring and forecasting strategies. This study employs an up-to-date dataset of over 500 river flow time series from 11 European countries, along with a gridded precipitation dataset, to examine the spatial coherence of drought in Europe using regional indicators of precipitation and streamflow deficit. The drought indicators were generated for 24 homogeneous regions and, for selected regions, historical drought characteristics were corroborated with previous work. The spatial coherence of drought characteristics was then examined at a European scale. Historical droughts generally have distinctive signatures in their spatio-temporal development, so there was limited scope for using the evolution of historical events to inform forecasting. Rather, relationships were explored in time series of drought indicators between regions. Correlations were generally low, but multivariate analyses revealed broad continental-scale patterns, which appear to be related to large-scale atmospheric circulation indices (in particular, the North Atlantic Oscillation and the East Atlantic-West Russia pattern). A novel methodology for forecasting was developed (and demonstrated with reference to the United Kingdom), which predicts 'drought from drought'-i.e. uses spatial coherence of drought to facilitate early warning of drought in a target region, from drought which is developing elsewhere in Europe. Whilst the skill of the methodology is relatively modest at present, this approach presents a potential new avenue for forecasting, which offers significant advantages in that it allows prediction for all seasons, and also shows some potential for forecasting the termination of drought conditions.

To date, national‐ and regional‐scale flood risk assessments have provided valuable information about the annual expected consequences of flooding, but not the exposure to widespread concurrent flooding that could have damaging consequences for people and the economy. We present a new method for flood risk assessment that accommodates the risk of widespread flooding. It is based on a statistical conditional exceedance model, which is fitted to gauged data and describes the joint probability of extreme river flows or sea levels at multiple locations. The method can be applied together with data from models for flood defence systems and economic damages to calculate a risk profile describing the probability distribution of economic losses or other consequences aggregated over a region. The method has the potential to augment national or regional risk assessments of expected annual damage with new information about the likelihoods, extent and impacts of events that could contribute to the risk.

Flooding is a natural phenomenon that regularly causes financial and human devastation around the world. In many countries the risk of flooding is managed by society through a combination of governmental agencies and the insurance industry. For both these types of organisation an estimate of the largest, or most widespread, events that can be expected to occur is useful. Such estimates can be used to help in preparing or co‐ordinating flood mitigation activities and by the insurance and re‐insurance industries to assess financial risk. In this paper we develop a method to simulate a set of synthetic flood events that can be used to estimate the probability of widespread floods. We demonstrate this method using data from a set of UK river flow gauges. The model used in this simulation process is based on the conditional exceedance model of Heffernan and Tawn, extended to incorporate features typically found in the data for extreme river floods. We also present an improved estimation method for the model parameters and demonstrate its advantages through the results of a simulation study. The benefits of the method over previous models used are that it provides a theoretical basis for extrapolation and is flexible enough to account for varying strengths of extremal dependence that are observed in flood data. Copyright © 2012 John Wiley & Sons, Ltd.

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