Multiscale prediction of groundwater response to extreme events

点击翻译按钮获取中文摘要

Groundwater dynamics are key to minimising ecological, societal and economic impacts of, and driving resilience to, extreme meteorological events (drought or flooding). The temporal dynamics of groundwater behaviour, driven by local weather and recharge patterns, are well understood. However, there are slower, global climate oscillations which have a significant impact on the terrestrial water cycle, and for which there is almost no understanding of either how they (mediated by the role of the unsaturated zone) affect long-term groundwater storage dynamics nor what the consequences may be for predicting the risk of drought. Limited previous work, including between Cranfield and the British Geological Survey, has identified that there are potentially significant temporal relationships between large-scale ocean-atmosphere teleconnection patterns such as the North Atlantic Oscillation and groundwater levels. Holman et al (2009, 2011) used wavelet techniques to investigate spatio-temporal interactions between large scale ocean-atmosphere teleconnections and groundwater levels in three chalk boreholes with long term records, while Folland et al. (2014) found evidence for potential causal relationships between La Niña episodes and winter rainfall deficits in some major multi-annual groundwater drought episodes in the English lowlands based on two long groundwater level records. This project will seek to expand on this earlier work by combining advanced statistical methods, extensive spatiotemporal (including climatological, meteorological, hydrogeological) datasets and groundwater modelling to further analyse these relationships. This will enable improved prediction and management of groundwater level response to extreme events (particularly droughts), and the knock-on effects on surface water systems and hydroecology. The research will focus on a larger number of boreholes across the UK and more broadly in Europe where long-term groundwater level data are available to understand the differences in sensitivity between different aquifers or climatological areas and hence risk of drought.