The relationship between Lamb weather types and long-term changes in flood frequency, River Eden, UK

Research has found that both flood magnitude and frequency in the UK may have increased over the last five decades. However, evaluating whether or not this is a systematic trend is difficult because of the lack of longer records. Here we compile and consider an extreme flood record that extends back to 1770. Since 1770, there have been 137 recorded extreme floods. However, over this period, there is not a unidirectional trend of rising extreme flood risk over time. Instead, there are clear flood-rich and flood-poor periods. Three main flood-rich periods were identified: 18731904, 19231933, and 1994 onwards. To provide a first analysis of what is driving these periods, and given the paucity of more sophisticated datasets that extend back to the 18th century, objective Lamb weather types were used. Of the 27 objective Lamb weather types, only 11 could be associated with the extreme floods during the gauged period, and only 5 of these accounted for > 80% of recorded extreme floods The importance of these five weather types over a longer timescale for flood risk in Carlisle was assessed, through calculating the proportion of each hydrological year classified as being associated with these flood-generating weather types. Two periods clearly had more than the average proportions of the year classified as one of the flood causing weather types; 19001940 and 19832007; and these two periods both contained flood-rich hydrological records. Thus, the analysis suggests that systematic organisation of the North Atlantic climate system may be manifest as periods of elevated and reduced flood risk, an observation that has major implications for analyses that assume that climatic drivers of flood risk can be either statistically stationary or are following a simple trend. Copyright (c) 2011 Royal Meteorological Society

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.