Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2)

Abstract

Abstract. Land surface models (LSMs) represent terrestrial hydrology in weather and climate modelling operational systems and research studies. We aim to improve hydrological performance in the Joint UK Land Environment Simulator (JULES) LSM that is used for distributed hydrological modelling within the new land–atmosphere–ocean coupled prediction system UKC2 (UK regional Coupled environmental prediction system 2). Using river flow observations from gauge stations, we study the capability of JULES to simulate river flow at 1 km2 spatial resolution within 13 catchments in Great Britain that exhibit a variety of climatic and topographic characteristics. Tests designed to identify where the model results are sensitive to the scheme and parameters chosen for runoff production indicate that different catchments require different parameters and even different runoff schemes for optimal results. We introduce a new parameterisation of topographic variation that produces the best daily river flow results (in terms of Nash–Sutcliffe efficiency and mean bias) for all 13 catchments. The new parameterisation introduces a dependency on terrain slope, constraining surface runoff production to wet soil conditions over flatter regions, whereas over steeper regions the model produces surface runoff for every rainfall event regardless of the soil wetness state. This new parameterisation improves the model performance across Great Britain. As an example, in the Thames catchment, which has extensive areas of flat terrain, the Nash–Sutcliffe efficiency exceeds 0.8 using the new parameterisation. We use cross-spectral analysis to evaluate the amplitude and phase of the modelled versus observed river flows over timescales of 2 days to 10 years. This demonstrates that the model performance is modified by changing the parameterisation by different amounts over annual, weekly-to-monthly and multi-day timescales in different catchments, providing insights into model deficiencies on particular timescales, but it reinforces the newly developed parameterisation.