At the national scale, this work focused on two overarching questions: i )What happens when we join-up data and modelling nationally? and ii) What would this deliver in terms of national capability? The approach was to bring together, for the first time, national datasets, models, and uncertainty analysis approaches into cloud-computing environments to explore and benchmark the current predictive capability for hydrology nationally. The challenge was to manage and visualise these complex results to synthesise and improve scientific understanding. Key to this was providing a framework to evaluate which model structures emerged as the best predictors of stream flow across a diverse range of catchment characteristics.
This work has provided the first UK-wide benchmarking of hydrological predictions using multiple-model structures and including prediction uncertainty. This allows the quantification and visualisation of the uncertainty within model predictions for both high flow and low flow simulations, and how this varies for different catchments across the UK and at different times of the year.
These results were further developed into a web-based portal to aid visual interpretation of the results, and to show how such capability could be made available at national scale using cloud-computing resources.
The benefits of this work include –
A next step is to dynamically couple hydrology and biogeochemistry across the geoclimatic landscape of the UK to improve predictions of water quantity and quality, for scientific and policy support. This would provide an improved national simulation framework for land use, mitigation measures, and climate change scenarios (that include prediction uncertainties) to be quantified .