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[This dataset is embargoed until February 22, 2022]. Half-hourly data from eight eddy covariance towers deployed in the Sevilleta Refuge (New Mexico, USA). The main sensors deployed were sonic anemometer, relative humidity sensor and carbon dioxide concentration sensor . They were deployed and maintained by Fabio Boschetti and Andrew Cunliffe (University of Exeter). The data were collected to test the new design of eddy covariance towers and investigate the spatial variability of fluxes. Data were collected from 2018-11-01 to 2019-11-01. The data contains very few small gaps due to maintenance. Half-hourly data were gap-filled using code published on GitHub. The research was funded through NERC grant reference NE/R00062X/1 - "Do dryland ecosystems control variability and recent trends in the land CO2 sink?" Full details about this dataset can be found at https://doi.org/10.5285/e96466c3-5b67-41b0-9252-8f8f393807d7
5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al . There are seven accumulation periods: 1, 3, 6, 9, 12, 18, 24 months and for each period SPI is calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. This version supersedes previous versions (version 2 and 3) of the same dataset due to minor errors in the data files. NOTE: the difference between this dataset with the previously published dataset "Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]" (Tanguy et al., 2015; https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0) , apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets.  McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. Full details about this dataset can be found at https://doi.org/10.5285/233090b2-1d14-4eb9-9f9c-3923ea2350ff
The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Wind or near surface wind speed at 10m is the near surface wind speed at 10m in m/s-1 at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.
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). Full details about this dataset can be found at https://doi.org/10.5285/3a0a5132-797c-4ed5-98b9-1c17eaa2f2b7
This dataset represents the hydro-meteorological monitoring activities undertaken in Ouagadougou, Burkina Faso, during 2016-2018, as part of the DFID funded AMMA-2050 (African Monsoon Multidisciplinary Analysis) project (amma2050.org). The data comprises time series of rainfall, water level and river flow recorded at locations across Ouagadougou city for the purposes of building an understanding of hydrological function and hydrological model development. In-situ data were collected using tipping bucket raingauges and pressure level sensors, with spot gauging of river flows used to develop rating curves used to derive flow from level measurements in channels. The network was designed and set-up by the UK Centre for Ecology and Hydrology (UKCEH) and maintained by the Burkina Faso Institut International d’Ingenierie de l’Eau et de l’Environment (2iE) and processing was undertaken by UKCEH. Full details about this dataset can be found at https://doi.org/10.5285/30ae0230-e352-4a82-901d-ac1d42449044
The dataset contains model output from an agricultural land use model at kilometre scale resolution over Great Britain (GB) for four different climate and policy scenarios. Specifically, arable area is modelled for with or without a climate tipping point (standard (medium emissions scenario SRES-A1B) climate change vs Atlantic Meridional Overturning Circulation (AMOC) collapse) and with or without widespread irrigation use for farmers from 2000 to 2089. Full details about this dataset can be found at https://doi.org/10.5285/e1c1dbcf-2f37-429b-af19-a730f98600f6
The meteorological data describes the air and soil temperatures, net radiation balance, down-welling photosynthetically active radiation, wind speed, wind direction and the vapour pressure deficit. Data collection was carried out at Cartmel Sands marsh from the 31st of May 2013 till the 26th of January 2015. The Cartmel Sands site is in Morecambe, North West England, and the meteorological tower was situated in the middle of the marsh. This data was collected as part of Coastal Biodiversity and Ecosystem Service Sustainability (CBESS): NE/J015644/1. The project was funded with support from the Biodiversity and Ecosystem Service Sustainability (BESS) programme. BESS is a six-year programme (2011-2017) funded by the UK Natural Environment Research Council (NERC) and the Biotechnology and Biological Sciences Research Council (BBSRC) as part of the UK's Living with Environmental Change (LWEC) programme. Full details about this dataset can be found at https://doi.org/10.5285/b1e2fb9c-8c34-490a-b6ae-2fdf6b460726
Hydrological and meteorological data were collected for three plots (each 50 x 50 m in size) near Andasibe village in the Corridor Ankeniheny-Zahamena (CAZ) in eastern Madagascar. The plots differ in terms of land cover: semi-mature forest, reforested tree fallow (i.e., young secondary forest), and degraded grassland. The plots are located within 2.5 km from each other. See the supporting documentation for detailed information on the plots. Data collection continued for one year (October 2014-September 2015) at each plot and included micrometeorological data (rainfall, temperature, relative humidity, wind speed), soil moisture and overland flow, and for the two forested plots also throughfall, stemflow and sapflow. Full details about this dataset can be found at https://doi.org/10.5285/5d080fef-613a-4f24-a613-b249ccdd12bf
This dataset provides daily estimates of the Snow Water Equivalent (SWE) using data from 46 COSMOS-UK sites across the United Kingdom. One set of estimates is derived from the cosmic ray neutron sensor and provides an estimate of the average SWE within the sensor’s large (>100m) footprint. Other SWE estimates are based on either a snowmelt model, or, for certain sites, either a snow depth sensor or a buried 'SnowFox' neutron sensor. Additionally, daily neutron counts, the albedo, and a collection of figures for each snow event are provided. Full details about this dataset can be found at https://doi.org/10.5285/e1fa6897-0f09-4472-adab-5d0d7bbc2548
The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Snowf or snowfall is the snowfall rate based on the GPCC bias corrected, undercatch corrected measured in kg/m2/s at 3 hourly resolution averaged over the next 3 hours and at 0.5 x 0.5 degrees spatial resolution. Please note that there is also a WFD Snowf CRU bias corrected dataset, but as the GPCC dataset is the preferred dataset only this snowfall dataset is available from the EIDC. These snowfall datasets contain snowfall data only and need to be combined with the respective WFD rainfall datasets to obtain precipitation data.