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

This dataset contains information about the background hydrochemistry and nutrient biogeochemistry of water samples collected from networks of interconnected rivers and lakes. Each water sample was analysed for the concentration of multiple fractions of nitrogen and phosphorus, and for the stable oxygen and nitrogen isotope composition of nitrate. Water samples were collected across the period 2017-2018 from multiple river-lake networks in the English Lake District and the Norfolk Broads. This research was supported by the Natural Environment Research Council (Grant NE/N006453/1: Hydroscape – Connectivity x Stressor Interactions) Full details about this dataset can be found at https://doi.org/10.5285/0d6de9b6-1f80-4f78-b65a-503db8ba63cf

The data are dynamic response characteristics (DRCs) produced by modelling the rainfall-runoff behaviour of a series of micro-basins installed by the NERC Q-NFM project largely in Cumbria (UK) and ranging in scale from 0.0071 to 2.7329 sq. km. Specifically, the rainfall to discharge response of these basins has been modelled with the RIV algorithm of the CAPTAIN Toolbox (Taylor et al., 2007 doi.org/10.1016/j.envsoft.2006.03.002). The resultant modelled characteristics of the rainfall-discharge dynamics are presented on an event-by-event basis. Full details about this dataset can be found at https://doi.org/10.5285/ea641367-dc35-4695-97b8-63f7d6fa9105

This dataset comprises of derived annual statistics for measures of rainfall, streamflow, temperature and stream acidity (pH) for a stream, draining a small, approximately 0.6 square kilometres, upland grassland catchment. The stream, Nant Esgair Garn, drains into the Llyn Brianne reservoir, Powys, United Kingdom. The data are for a 31 year period covering 1st April 1982 to 1st April 2012. The streamflow and acidity data are derived from 15 minute resolution observations throughout the calendar year 2013 from associated stream gauging and water quality stations on the Nant Esgair Garn. The monthly rainfall measures presented, were derived from local rain gauges. The monthly temperature measures presented were derived from observations at a weather station near Talgarth, Powys. Routines within the Lancaster University Computer-Aided Program for Time-series Analysis and Identification of Noisy Systems (CAPTAIN) Toolbox for Matlab were used to develop a dynamic model of these data. These models were then used to simulate the 31-year record for which monthly statistics were derived. The statistics were derived to develop greater understanding of the controls on the long-term dynamics of aquatic biodiversity observed by other researchers in this stream. The work was part of the Diversity in Upland River Ecosystem Service Sustainability (DURESS) project, NERC grant NE/J014826/1. Members of staff from the Lancaster Environment Centre, Lancaster University installed, maintained and downloaded the stream gauging and water quality stations and also carried out statistical analysis of the data. Full details about this dataset can be found at https://doi.org/10.5285/00185590-537e-40e4-969c-039f44b4dad9

This dataset comprises of derived annual statistics for measures of rainfall, streamflow, temperature and stream acidity (pH) for a stream, draining a small, approximately 1.2 square kilometres, upland conifer catchment. The stream, Nant Trawsnant, drains into the Llyn Brianne reservoir, Powys, United Kingdom. The data are for a 31 year period covering 1st April 1982 to 1st April 2012. The streamflow and acidity data are derived from 15 minute resolution observations throughout the calendar year 2013 from associated stream gauging and water quality stations on the Nant Trawsnant. The monthly rainfall measures presented, were derived from local rain gauges. The monthly temperature measures presented were derived from observations at a weather station near Talgarth, Powys. Routines within the Lancaster University Computer-Aided Program for Time-series Analysis and Identification of Noisy Systems (CAPTAIN) Toolbox for Matlab were used to develop a dynamic model of these data. These models were then used to simulate the 31-year record for which monthly statistics were derived. The statistics were derived to develop greater understanding of the controls on the long-term dynamics of aquatic biodiversity observed by other researchers in this stream. The work was part of the Diversity in Upland River Ecosystem Service Sustainability (DURESS) project, NERC grant NE/J014826/1. Members of staff from the Lancaster Environment Centre, Lancaster University installed, maintained and downloaded the stream gauging and water quality stations and also carried out statistical analysis of the data. Full details about this dataset can be found at https://doi.org/10.5285/b085a784-0e16-4174-b208-465a8f43c8c8

This dataset includes measurements of stem respiration in 20 plots (250 x 10 m each) in the Brazilian Amazon. Study plots were distributed across a gradient of forest disturbance, including: undisturbed primary forests , logged primary forests, logged-and-burned primary forests, and secondary forests. Data were collected from June 2015 until July 2018. In December 2015, during the El Niño-mediated drought, eight of our study plots were affected by understory fires. Full details about this dataset can be found at https://doi.org/10.5285/4826f7c3-d6f2-47e5-8dda-c084e54720f6

This dataset includes measurements of soil respiration in 20 plots (250 x 10 m each) in the Brazilian Amazon. Study plots were distributed across a gradient of forest disturbance, including: undisturbed primary forests , logged primary forests, logged-and-burned primary forests, and secondary forests. Data were collected from October 2014 until May 2018. In December 2015, during the El Niño-mediated drought, eight of our study plots were affected by understory fires. Full details about this dataset can be found at https://doi.org/10.5285/def51d3d-d653-40ca-8231-a238b0c66975

This dataset includes measurements of litter in 20 plots (250 x 10 m each) in the Brazilian Amazon. Study plots were distributed across a gradient of forest disturbance, including: undisturbed primary forests , logged primary forests, logged-and-burned primary forests, and secondary forests. Data were collected from January 2015 until October 2018. In December 2015, during the El Niño-mediated drought, eight of our study plots were affected by understory fires. Full details about this dataset can be found at https://doi.org/10.5285/d01084b2-c3b1-4187-b2e7-b0827c738855

This dataset includes measurements of stem radial growth in 20 plots (250 x 10 m each) in the Brazilian Amazon. Study plots were distributed across a gradient of forest disturbance, including: undisturbed primary forests , logged primary forests, logged-and-burned primary forests, and secondary forests. Data were collected from December 2014 until October 2018. In December 2015, during the El Niño-mediated drought, eight of our study plots were affected by understory fires. Full details about this dataset can be found at https://doi.org/10.5285/c8bc6982-d4de-4e6f-aadb-08aac877b263

This dataset contains nitrogen data from nitrate, ammonium and nitrite, total nitrogen and carbon data, and elemental composition data from anaerobic digestate and biomass ash from UK bioenergy production. Anaerobic digestate was sampled 8 times from different industrial scale plants across the UK between January 2015 and January 2018 and biomass ash was sampled in January 2015 and June 2016. Anaerobic digestate was sourced from segregated food waste (mainly household waste), pig slurry, maize silage, vegetables waste, sweet corn waste, aerobically treated food waste, food manufacturer waste and other biodegradable sludge from within the UK. Biomass ash, both fly and bottom ash, from virgin and recycled wood was sourced from three sites within the UK and one from Spain. All laboratory analyses were undertaken at Lancaster University using standardised methods. The data were collected as part of the research grant, Developing a suite of novel land conditioners and plant fertilizers from the waste streams of biomass energy generation. The research was funded by NERC, award NE/L014122/1. Full details about this dataset can be found at https://doi.org/10.5285/990c54f6-5c92-4054-8bfa-953533a89149