Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

HighlightsLMD was used to investigate 3-D molecular heterogeneity in ovarian cancer tissue Diverse molecular profiles were identified from 3-D spatially separated samples Molecular heterogeneity impacts HGSOC prognostic sub-type assignment Proteomic heterogeneity analysis web portal deployed at www. lmdomics.org

In a warming climate, the water budget of the land is subject to varying forces such as increasing evaporative demand, mainly through the increased temperature, and changes to the precipitation, which might go up or down. Using a verified, physically based model with 55 years of observation-based meteorological forcing, an analysis of the water budget demonstrates that Great Britain is getting warmer and wetter. Increases in precipitation (2.96.0 ± 2.03 mm yr–1 yr–1) and air temperature (0.20 ± 0.13 K decade–1) are driving increases in runoff (2.18 ± 1.84 mm yr–1 yr–1) and evapotranspiration (0.87 ± 0.55 mm yr–1 yr–1), with no significant trend in the soil moisture. The change in evapotranspiration is roughly constant across the regions, whereas runoff varies greatly between regions: the biggest change is seen in Scotland (4.56 ± 2.82 mm yr–1 yr–1), where precipitation increases were also the greatest (5.4 ± 3.0 mm yr–1 yr–1), and the smallest trend (0.33 ± 1.50 mm yr–1 yr–1, not statistically significant) is seen in the English Lowlands (East Anglia and Midlands), where the increase in rainfall is not statistically significant (1.07 ± 1.76 mm yr–1 yr–1). Relative to its contribution to the evapotranspiration budget, the increase in interception is higher than the other components. This is due to the fact that it correlates strongly with precipitation, which is seeing a greater increase than the potential evapotranspiration. This leads to a higher increase in actual evapotranspiration than the potential evapotranspiration, and a negligible increase in soil moisture or groundwater store.

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.