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Twenty soil cores were collected from a field site in Lincolnshire in March 2011, three weeks after planting and Nitrogen fertiliser addition. Soil cores of 150-180 millimetre (mm) depth, containing approximately 1.6 kilogram soil (dry weight) were extracted in Polyvinyl chloride (PVC) pipes (height 215 mm depth 102 mm) and stored at 4 degrees centigrade for 30 days. A four-treatment factorial experiment was designed using soils un-amended or amended with biochar and un-wetted or wetted with deionised water (5 replicates per treatment). Soil in all the cores was mixed to 7 centimetre (cm) depth. To half of the cores, biochar (less than 2 mm) was mixed into the soil at a rate of 3 percent soil dry weight (approximately 22 tons per hectare (t ha-1)). After allowing for any potential Carbon dioxide (CO2) flush from newly-mixed soil to equilibrate for seven days, the cores were placed at 16 degrees centigrade in the dark. Un-wetted soil cores were maintained at 23 percent Gravimetric moisture content (GMC), whilst the GMC of 'wetted' soil cores was increased to 28 percent GMC at the time zero (t0) of four wetting events on day 17, 46, 67 and 116. These water addition rates were based on mean and maximum monthly soil GMC measured in the field between 2009-2010. Data from an investigation of the effects of biochar application to soil on greenhouse gas emissions using soil from a bioenergy crop (Miscanthus X. giganteus). Data include physical (bulk density) and chemical analyses of the soil (total carbon (C) and nitrogen (N), extractable ammonium and nitrate), and greenhouse gas (GHG) emissions (carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)) during incubations. Data were collected during two incubation experiments investigating the effects of temperature, soil moisture and soil aeration on biochar induced suppression of GHG emissions. Biochar is a carbon rich substances which is being advocated as a climate mitigation tool to increase carbon sequestration and reduce nitrous oxide emissions.

The dataset comprises of physical and biogeochemical measurements of belowground (root) biomass from across four Scottish saltmarshes. Sites were chosen to represent contrasting habitats types in Scotland, in particular sediment types, vegetation and sea level history. The data provide a quantitative measure of belowground (root) biomass, organic carbon content and belowground (root) carbon. Samples were collected using a wide gauge gouge corer. The samples were processed to determine belowground (root) biomass, the organic carbon was quantified through elemental analysis and these two data sets were combined to calculate the belowground (root) carbon content. The data were collected to help create a detailed picture of saltmarsh carbon storage in surficial soils across Scotland. The work was carried out under the NERC programme - Carbon Storage in Intertidal Environment (C-SIDE), NERC grant reference NE/R010846/1 Soil cores were taken at each sampling location using a wide diameter gouge corer. The location of the sample was recorded using GPS. Prior to analysis the samples were stored at 4 degrees Celsius at the University of St Andrews. Belowground (root) biomass, organic carbon content and belowground (root) carbon data was produced using standard analytical procedures (detailed in the supporting documentation). All laboratory equipment were calibrated in accordance with the laboratory practises at the University of St Andrews. Results were recorded on to lab sheets and transferred into an Excel file. Results were exported as comma separated value (.csv) files for ingestion into the EIDC.

Global transition towards renewable energy production has increased the demand for new and more flexible hydropower operations. Before management and stakeholders can make informed choices on potential mitigations, it is essential to understand how the hydropower reservoir ecosystems respond to water level regulation (WLR) impacts that are likely modified by the reservoirs' abiotic and biotic characteristics. Yet, most reservoir studies have been case-specific, which hampers large-scale planning, evaluation and mitigation actions across various reservoir ecosystems. Here, we investigated how the effect of the magnitude, frequency and duration of WLR on fish populations varies along environmental gradients. We used biomass, density, size, condition and maturation of brown trout (Salmo trutta L.) in Norwegian hydropower reservoirs as a measure of ecosystem response, and tested for interacting effects of WLR and lake morphometry, climatic conditions and fish community structure. Our results showed that environmental drivers modified the responses of brown trout populations to different WLR patterns. Specifically, brown trout biomass and density increased with WLR magnitude particularly in large and complex-shaped reservoirs, but the positive relationships were only evident in reservoirs with no other fish species. Moreover, increasing WLR frequency was associated with increased brown trout density but decreased condition of individuals within the populations. WLR duration had no significant impacts on brown trout, and the mean weight and maturation length of brown trout showed no significant response to any WLR metrics. Our study demonstrates that local environmental characteristics and the biotic community strongly modify the hydropower-induced WLR impacts on reservoir fishes and ecosystems, and that there are no one-size-fits-all solutions to mitigate environmental impacts. This knowledge is vital for sustainable planning, management and mitigation of hydropower operations that need to meet the increasing worldwide demand for both renewable energy and ecosystem services delivered by freshwaters. Data of environmental characteristics and brown trout populations in 102 Norwegian hydropower reservoirsThe data contains field-collected data of brown trout populations in 102 Norwegian reservoirs with variable environmental characteristics. The brown trout data (i.e. response variables) include estimates of: "Biomass" (grams of fish per 100m2 net per night); "Density" (number of fish per 100m2 net per night); "Mean weight" (mean wet mass in grams); "Mean condition" (mean Fulton's condition factor); and "Mean maturity length" (mean total length of mature females in millimeters). All abbreviations for different variables (columns) are explained in the paper. Many reservoirs ("Lake") have various names, some including Norwegian letters (æ, ø & å). Hence, we recommend to use coordinate data (EPSG:4326; "decimalLongitude" and "decimalLatitude") and Norwegian national lake ID numbers ("Lake_nr"; managed by the Norwegian Water Resources and Energy Directorate; www.nve.no) to locate the reservoirs. The variables "Year", "Month" and "Day" refer to times when survey fishing was conducted. Lake morphometry data ("A"=surface area, "SD"=shoreline development) is obtained from NVE database. The lake climatic and catchment data ("T"=mean July air temperature, "NDVI"= Normalized Difference Vegetation Index, and "SL"=terrain slope) is obtained and measured as described by Finstad et al. (2014; DOI: 10.1111/ele.12201). Other abbreviations include: "FC"=presence of other fish species (1=absent, 2=present); "GS"=gillnet series (1=Nordic, 2=Jensen); and "ST"=brown trout stocking (0=no stocking, 1=stocking). The water level regulation (WLR) metrics include: ): "WLR_magnitude"= maximum regulation amplitude; "WLR_frequency"=relative proportion of weeks with a sudden rise or drop in water level; and "WLR_duration"=the relative proportion of weeks with exceptionally low water levels.Data-in_doi.org-10.1016-j.scitotenv.2017.10.268.xlsx

A virtual lab (MOOC) for students chemical, civil or mechanical engineering. Students are introduced with a fluidised bed reactor (multiphase flow). This expansion column represents a liquid-solid fluidisation process applied in drinking water treatment processes. The knowledge you will gain will help you develop and improve your competence profile of a highly qualified chemical engineer. Students are informed with short lectures (films) and a manual (document). Several assignments must be completed based on recorded laboratory experiments.

Sediment was collected using a shovel before being sieved (0.8 cm for fine, and 1.6 cm for medium and coarse) and homogenised prior to storage. The sediment was stored airtight in the cold and dark. Sediment of varying organic matter content from two geological origins (chalk and sandstone) was incubated at five temperatures (5, 9, 15, 21 and 26°C). Resorufin production was measured using a GGUN-FL30 on-line fluorometer, dissolved oxygen was measured using a Pyro-science Firesting fixed needle-type probe, and carbon dioxide and methane concentrations were measured using an Agilent 7890A Gas Chromatograph - Flame Ionisation Detector. The carbon dioxide and methane concentrations were converted to production per hour by calculating the difference in concentration between zero and five hours and normalising the production by the length of the incubation period. The data was then converted to emissions per volume of dry sediment using the Bunsen coefficient and the volume of sediment in each jar, resulting in units of milligrams of carbon per square metre per hour. Greenhouse gas concentrations were corrected for any machine drift using results from a standard gas mixture ran periodically during gas analysis. The resorufin concentration was converted to production per hour by calculating the difference in concentration between zero and five hours and normalising the production by the length of the incubation period. The production was then normalised by the concentration of resazurin added to the jar, resulting in units of nanograms of resorufin per microgram of resazurin per hour. Data were entered into an Excel spreadsheet and exported as a comma separated value file (.csv) for ingestion into the EIDC. The dataset contains carbon dioxide and methane emissions, as well as resorufin production (as a proxy for microbial metabolic activity) and dissolved oxygen concentrations, resulting from laboratory incubation experiments of streambed sediments. The sediments were collected from the upper 10 centimetres of the streambed in the River Tern and the River Lambourn in September 2015, with three samples collected from each river. These samples were collected from three areas: silt-dominated sediment underneath vegetation (fine), sand-dominated sediment from unvegetated zones (medium) and gravel-dominated sediment from unvegetated zones (coarse). The sediment was used in laboratory incubation experiments to determine the effect of temperature, organic matter content, substrate type and geological origin on streambed microbial metabolic activity, and carbon dioxide and methane production. The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD (NERC award number 1602135). The work was also part funded through the Seventh Framework Programme (EU grant number 607150).

1. Predicted changes in the frequency and intensity of extreme rainfall events in the UK have the potential to disrupt terrestrial ecosystem function. However, responses of different trophic levels to these changes in rainfall patterns, and the underlying mechanisms, are not well characterised. 2. This study aimed to investigate how changes in both the quantity and frequency of rainfall events will affect the outcome of interactions between plants, insect herbivores (above- and below- ground) and natural enemies. 3. Hordeum vulgare L. plants were grown in controlled conditions and in the field, and subjected to three precipitation scenarios: ambient (based on a local 10 year average rainfall); continuous drought (40% reduction compared to ambient); drought/ deluge (40% reduction compared to ambient at a reduced frequency). The effects of these watering regimes and wireworm (Agriotes species) root herbivory on the performance of the plants, aphid herbivores above-ground (Sitobion avenae, Metapolophium dirhodum and Rhopalosiphum padi), and natural enemies of aphids including ladybirds (Harmonia axyridis) were assessed from measurements of plant growth, insect abundance and mass, and assays of feeding behaviour. 4. Continuous drought decreased plant biomass, whereas reducing the frequency of watering events did not affect plant biomass but did alter plant chemical composition. In controlled conditions, continuous drought ameliorated the negative impact of wireworms on plant biomass. 5. Compared to the ambient treatment, aphid mass was increased by 15% when feeding on plants subjected to drought/ deluge; and ladybirds were 66% heavier when feeding on these aphids but this did not affect ladybird prey choice. In field conditions, wireworms feeding below-ground reduced the number of shoot-feeding aphids under ambient and continuous drought conditions but not under drought/ deluge. 6. Predicted changes in both the frequency and intensity of precipitation events under climate change have the potential to limit plant growth, but reduce wireworm herbivory, while simultaneously promoting above-ground aphid numbers and mass, with these effects transferring to the third trophic level. Understanding the effect of future changes in precipitation on species interactions is critical for determining their potential impact on ecosystem functioning and constructing accurate predictions under global change scenarios. Controlled environment and field experimental dataData file containing all data reported in the paper including plant, soil and insect data from controlled environment and field experiments. First spreadsheet in the data file contains a key to explain all abbreviations used throughout the file.Experimental data.xlsx

Projections of Sea Level Rise (SLR) under RCP 4.5 and RCP 8.5 (AR5) along the Mekong Coast, Published1 by the Ministry of Natural Resources and Environment (MONRE), Hanoi, Vietnam. Projections of Mekong River discharge during the dry season under RCP 4.5 and RCP 8.5 at Kratie, Cambodia. The data contains the cumulative, minimum and maximum dry season (January-1st to April-30th) discharge from 5 different climate models. PCR-GLOBWB2 was run at 5 arc-min spatial resolution and forced with the data based on output from five ISIMIP CMIP5 global climate models (HadGEM2-ES, GFDL-ESM2, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M). 1. Ministry of Natural Resources and Environment (MONRE), V. Climate change and sea level rise scenarios for Vietnam, Ministry of Natural Resources and Environment. (2016). 2. Sutanudjaja, E. H. et al. PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geosci. Model Dev. 11, 2429–2453 (2018). {"references": ["Sutanudjaja et al. (2018)", "Ministry of Natural Resources and Environment (2016)"]}

This dataset underpins the study "Synergies and conflicts of energy development and water security in Africa". The study provides insights into energy supply and demand, power generation, investments and total system costs, water consumption and withdrawal as well as carbon dioxide emissions for the African continent. We developed a model to evaluate energy supply and water requirements to cover the energy needs of the African continent during the period 2015-2065. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS. The objective function is to minimise total energy system costs, rather than, for example, co-optimise the energy and water sectors. Other energy resources were also included in the model except for adding the water analysis, and the dataset was updated based on the latest available information. The OSeMOSYS model developed to conduct the study “Energy projections for African countries”, itself extended from the Electricity Model Base for Africa (TEMBA), was further extended, included exports for all fuels, water loss due to evaporation in hydropower plants and more scenarios examined. Furthermore, the latest available data on the energy system of Africa was also updated. The TEMBA model produces aggregate energy, and detailed power system results in each country in the African continent. The power sector results are also reported with power pool aggregation. The OSeMOSYS model and input data used to produce these results can be found at KTH-dESA/jrc_temba: TEMBA 2.1 (Version v2.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4889373 (Authors: Ioannis Pappis, Vignesh Sridharan, Will Usher, & Mark Howells. (2021). The initial study was funded by the Joint Research Centre of the European Commission (contract number C936531 - JRC/PTT/2018/C.7/0038/NC).