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The UK Met Office state-of-the-art, deterministic, convection-permitting, coupled land-atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in-situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw-Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field-scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS-UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS-UK soil moisture observations

Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution.

In this report we document work aimed at improving the quality of the representation of land and rivers in multi-year, coupled atmosphere-land (UM-JULES) simulations over the British Isles. The approach taken was to use standalone (uncoupled) simulations of JULES to investigate the potential to improve the coupled system. The use of alternative soil ancillary information, generated by using a data assimilation framework and observations of soil moisture from the COSMOS-UK network to optimise the constants in a pedotransfer function, was found to result in improved simulations by JULES of river flow in a diverse sample of British rivers (as measured by standard statistics). The revised soil parameters tended to increase the variability of the simulated river on short timescales, and reduce variability on the annual timescale. In catchments with a large influence of slow baseflow the revised parameters tended to give poorer simulations, with too much variability on short timescales. A new representation of groundwater processes was implemented in JULES and applied, for the first time, across Great Britain. This was shown to allow an influence of groundwater on nearsurface hydrology and fluxes over large parts of the country. Modelled river flows were more realistic in many cases, though much of the improvement was due to differences in the representation of runoff generation rather than the introduction of groundwater. A more physically-complete parameterisation of river physics, using the local inertial equation, was applied in a range of catchments, again for the first time. Simulated river flows were improved in most cases. The final section of this report offers a perspective on how terrestrial hydrology and its impacts could be considered in the next generation of UK Climate Projections. The work reported here has significantly improved our capability in several areas of land surface modelling, to the extent that new developments could be tested in nationwide sim