This Fundamental Climate Data Record (FDCR) ensemble product contains both recalibrated AVHRR/3 MetOp-A Radiance/Brightness Temperature data with associated metrologically traceable uncertainties in the FIDUCEO FCDR format. It also contains files containing an Ensemble dataset consisting of perturbations to the associated FIDUCEO FCDR radiances and brightness temperatures. By applying the 10 perturbations to the baseline FCDR radiances and brightness temperatures a user is able to generate 10 sets of new measurements whose variance capture the associated underlying uncertainty distributions contained in the Easy FCDR itself. The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) project AVHRR FCDR improves on existing AVHRR level-1B: in the infrared the calibration has been improved with a measurement function approach such that the data is of better quality (noise has been reduced, outliers have been filtered) the metrologically traceable uncertainties have been derived together with their associated effects, cross-channel correlations and long-term correlation structures have now been calculated from the processed data and are being understood and used to improve data quality and consistency. For the Ensemble product the sensors have been calibrated against the Advanced Along-Track Scanning Radiometer (AATSR) sensor with additional corrections to calibration parameters which make the data better able to derive sea surface temperature estimates that are consistent with theInternational Comprehensive Ocean-Atmosphere Data Set (ICOADS) drifting buoy network. Because the Ensemble has been tuned for Sea Surface Temperature retrieval it should only be used over ocean scenes.

The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. The BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. Crucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system.

Simulated 15-min discharge time-series (1/10/2015-17/1/2016) for the River Kent at Sedgwick following a Natural Flood Management intervention of ‘Enhanced Hillslope Storage’ plus the baseline simulations are presented. To derive these data, the observed 15-minute discharge River Kent measured at the Environment Agency (EA) Sedgwick gauging station (https://nrfa.ceh.ac.uk/data/station/info/73005) through the 1 Oct 2015 to 17 Jan 2016 period were modelled using the latest version of Lancaster University’s Dynamic TOPMODEL (https://cran.r-project.org/web//packages/dynatop/index.html). The spatially distributed rainfall field used as input to TOPMODEL was derived from a new direction-dependent and topographically controlled interpolation using observed rainfall data for the Cumbrian Mountains (Page et al., 2022. Hydrological Processes 36: e14758, https://doi.org/10.1002/hyp.14758). Lack of perfect understanding of the hydrological processes routing rainfall for stream channels and then along stream channels to the Sedgwick gauge was represented by using a very wide range of model parameters applied randomly within 10,000 simulations. Using the approach detailed in Beven et al. (2022a. Hydrological Processes 36(10): e14703, https://doi.org/10.1002/hyp.14703), the resultant wide range of simulated discharge time-series was reduced by rejecting all but 67 simulations that passed the prescribed criteria. These 67 baseline simulations of observed behaviour through the +3 month period at Sedgwick are presented here. To represent the effect of adding surface storage distributed across this 209 sq km River Kent catchment, the Digital Elevation Model (DEM) used in the baseline simulations according to Hankin et al (2018. Technical report SC150005/R6. Environment Agency, Bristol. 77pp, https://www.gov.uk/flood-and-coastal-erosion-risk-management-research-reports/working-with-natural-processes-to-reduce-flood-risk) to represent bunds placed on hillslopes in rural areas. The bunds are a type of flood mitigation measure known as Natural Flood Management or NFM. These are known formally as ‘Enhanced Hillslope Storage’ or EHS features (Beven et al 2022b. Hydrological Processes 36: e14752, https://doi.org/10.1002/hyp.14752). The TOPMODEL parameter sets producing the 67 ‘acceptable’ baseline simulations were then re-run with the modified DEM. These results are also presented here. Full details about this dataset can be found at https://doi.org/10.5285/af081a90-b014-43f7-9399-c948a8b7672f