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This file includes data from the National Grid, UK for electricity supply and demand which was modified according to the research methodology laid out in the paper here (https://doi.org/10.1016/j.rser.2020.110018). Also, all the data needed for reproducing figures presented in the journal article are also included in the data file.

Data used to obtain the results of the research paper entitled: "Heatwaves, elevated temperatures, and a pesticide cause interactive effects on multi-trophic levels of a freshwater ecosystem", published in the journal "Environmental Pollution". The data derives from an indoor (micro-) cosm experiment in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the effects of two different temperature scenarios (i.e., elevated temperature and reoccurring heatwaves) in combination with the fungicide carbendazim.

This dataset contains the underlying data for the study:Van Oort, P. A. J., B. G. H. Timmermans, H. Meinke, and M. K. Van Ittersum. "Key weather extremes affecting potato production in The Netherlands." European Journal of Agronomy 37, no. 1 (2012): 11-22.http://dx.doi.org/10.1016/j.eja.2011.09.002The possible impact of climate change on frequency and severity of weather extremes is hotly debated among climate scientists. Weather extremes can have a significant impact on agricultural production, but their effect is often unclear; this due to interaction with other factors that affect yield and due to lack of precise definitions of relevant weather extremes. We show that an empirical analysis of historical yields can help to identifying such rare, high impact climate events.A reconstructed time series of ware potato production in Flevoland (The Netherlands) over the last 60 years (1951–2010) enabled us to identify the two main yield affecting weather extremes. In around 10% of the years yield anomalies were larger than −20%. We found that these anomalies could be explained from two weather extremes (and no other), namely a wet start of the growing season and wet end of the growing season. We derived quantitative, meteorological definitions of these extremes. Climate change scenarios for 2050 show either no change or increased frequency of the two extremes. We demonstrate there is large uncertainty about past and future frequencies of the extremes, caused by a lack of sufficiently long historical weather records and uncertainties in climate change projections on precipitation. The approach to identify weather extremes presented here is generally applicable and shows the importance of long term crop and weather observations for investigating key climatic risks to production.

Live woody vegetation is the largest reservoir of biomass carbon with its restoration considered one of the most effective natural climate solutions. However, carbon fluxes associated with terrestrial ecosystems still remain the largest source of uncertainty of the global carbon balance. Here, we develop spatially explicit estimates of global carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show live biomass has removed 4.9-5.5 PgC yr-1 from the atmosphere in this century, offsetting 4.6±0.1 PgC yr-1 of gross emissions from land-use and environmental disturbances and adding substantially (0.23-0.88 PgC yr-1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

Amazonian floodplain forests are particularly vulnerable to wildfires that spread during extreme droughts, causing large scale forest dieback. After a second wildfire, these forests persist trapped with low tree cover and empty seed banks, yet the mechanisms that could explain this arrested succession remain unknown. Here we use a four-year field experiment to test whether tree recruitment failures in burnt floodplain forests are caused by environmental filtering, limiting early seedling emergence and establishment. We sowed seeds and planted seedlings of six floodplain trees with contrasting life strategies, and tested the roles of environmental filters by comparing tree seedling performances under different habitats (i.e. unburned forest, forest edge with burnt site, forest burnt once and forest burnt twice), and by manipulating soil root mats and herbaceous cover. Our results show that seedling emergence was around 15 % across all habitats. In general, seedlings performed best in burnt forests. Seedling growth was highest in forests burnt once, possibly because of high nutrient availability after fire. In forests burnt twice, tree seedlings grew relatively less, as nutrients become limiting due to flood erosion; yet, seedlings survived longer, possibly because of lower competition with sparse, naturally recruiting trees. We found similar patterns for seedlings that emerged in the field from sowed seeds. Synthesis: Our experimental evidence suggests that environmental filtering related to soil nutrient limitations may slow down forest recovery after repeated wildfires. Yet, our findings showing that floodplain trees are able to germinate from seeds and establish successfully in twice burnt forests suggest that seed limitation may be the reason why forest recovery fails persistently. A corollary to the problem is that repeatedly burnt forests seem to be trapped by a self-reinforcing feedback, in which low tree cover reduces seed dispersal and consequently seed availability, keeping tree cover low. Overall, our findings indicate that active restoration initiatives based on seeding native tree species may help accelerating the recovery of degraded floodplain forests after repeated wildfires.

Description: This meta-data on development and management of community managed small-scale irrigation schemes in Ethiopia is presented in a spreadsheet as a table after reviewing 83 published articles. The dataset contains research focus, data collection and analysis method, spatial scale, irrigation typology, irrigation water sources, source of energy for water conveyance from the source, main research results, scheme management systems, identified problems and provided solutions from each research article when available. The spatial scale was categorized as national, regional, district, basin, watershed or scheme, based on the spatial coverage of the research done. The irrigation typology was categorized as large-scale, medium-scale and small-scale based on the Ethiopian classification. Surface water sources such as river, lake, reservoir and flash flood and ground water sources of wells and springs or a combination of more than one source were mentioned as the water sources for irrigation schemes. The sources of energy for water abstraction were categorized as gravity, pump or a combination of both. Year of irrigation scheme commencement, available irrigation scheme infrastructure, number of beneficiaries (households), and stakeholders in scheme management were also extracted when available in the literature. Spatial coverage: Data was collected from community managed small-scale irrigation schemes located in different parts of Ethiopia. Location of some irrigation schemes (in UTM) is indicated in the dataset spreadsheet.

Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms. van der Meij et al_Biogeosciences2017_dataThe experimental set-up, treatments, data collection and data analyses are thoroughly described in the Biogeoscience manuscript ‘Remote sensing of plant trait responses to field-based plant-soil feedback using UAV-based optical sensors’ doi:10.5194/bg-2016-452. Therefore we refer to the manuscript for detailed information an here we provide a brief summary to enable readers to follow what the data entail. The data were collected from a 2-year field experiment with plant rotations in a full factorial design. The plant treatments we focused on are legacy effects of the plant treatments (listed below) to the following oat crop. In this oat crop we quantified several plant traits both in situ and via remote sensing by use of UAV and hyperspectral and EGB sensors. The experiment was set-up in five randomized field blocks. We used part of the in situ collected data to parameterize the hyperspectral data based models and we validated these models with the other half of the field plots. Plant treatments Fa= fallow Lp= Lolium perenne Rs= Raphanus sativus Tr= Trifolium repens Vs= Vicia sativa Lp+Tr= 50:50 species mixture (relative to the monoculture seed densities) of the species Lp and Tr Rs+Vs= 50:50 species mixture (relative to the monoculture seed densities) of the species Rs and Vs