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The COSMOS-UK observation network has been providing field scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high temporal resolution data in near-real time. Each site utilises a cosmic-ray neutron sensor, which counts fast neutrons at the land surface. These measurements are used to derive field scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localised point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at https://doi.org/10.5285/37702a54-b7a4-40ff-b62e-d14b161b69ca (Stanley et al., 2020). [ABSTRACT FROM AUTHOR]

A method of model evaluation is presented which utilises a comparison with a benchmark model. The proposed benchmarking concept is one that can be applied to many hydrological models but, in this instance, is implemented in the context of an in-stream water quality model. The benchmark model is defined in such a way that it is easily implemented within the framework of the test model, i.e. the approach relies on two applications of the same model code rather than the application of two separate model codes. This is illustrated using two case studies from the UK, the Rivers Aire and Ouse, with the objective of simulating a water quality classification, general quality assessment (GQA), which is based on dissolved oxygen, biochemical oxygen demand and ammonium. Comparisons between the benchmark and test models are made based on GQA, as well as a step-wise assessment against the components required in its derivation. The benchmarking process yields a great deal of important information about the performance of the test model and raises issues about a priori definition of the assessment criteria. [ABSTRACT FROM AUTHOR]