National scale conceptual modelling of hydrology coupled to groundwater processes to improve predictions of river flows

The UK's rivers, due to the variability of our climate from year to year and associated extreme weather events, are prone to flooding and periods of drought and water scarcity. Making robust predictions of these impacts is critical to developing effective planning and management of our precious water resources both for now and in the future. Predicting river flows, especially for extreme high and low flows, involve dynamically changing complex, interacting and non linear processes of surface, near subsurface and deeper flow pathways. At national scales, such characterisations are now possible using a range of modelling approaches that differ in their mathematical treatment and level of physically based representation of these combined catchment processes. However such larger scale modelling has many challenges in how to characterise each river catchment individually. Therefore it is necessary to ensure the dominant hydrological processes are well represented and that the models provide robust predictions of river flows for the 'right reasons' over a range of hydrological behaviour. In the context of the UK, better representations of groundwater dynamics in hydrological models will be particularly important in south-east England; here major aquifers provide high quality water into public supply for millions of people, in addition to supporting important aquatic ecosystems. Whilst strategies for exploring sources of uncertainty in complex distributed groundwater models have been developed , there has been little research on the appropriate degree of complexity to use when representing groundwater in conceptual hydrological models, though this is recognised as a limitation. New national scale uncertainty analysis modelling framework will be used to explore these interactions between near surface and groundwater flow paths by improving the conceptualisation of how these flow paths interact and are coupled in space and time.