environment
Type of resources
Available actions
Topics
Keywords
Contact for the resource
Provided by
Years
Formats
Representation types
Update frequencies
Scale
Resolution
-
These data are derived from a dust leaching experiment, an in-lake mesocosm experiment and from sediment cores obtained from lakes in the Kangerlussuaq area of West Greenland. The dust leaching experiment was set up in 2017 and the data show which elements and ions were leached from dust into different types of waters. The in-lake mesocosm experiment applied dust over a two week period in July 2018 resulting in chemical and algal pigment data. Data on chlorophyll and carotenoid pigments are presented from sediment cores sampled from six lakes 2017 and sectioned into 0.5-1cm intervals. Full details about this dataset can be found at https://doi.org/10.5285/9115bc7a-adb6-4a3c-8506-32d0b39bcf6f
-
Data comprise results of social surveys carried out in China during 2016 – 2018 to environmental scientists and the local stakeholders (farmers and village to county level officials) to understand their knowledge learning dynamics and preference. Surveys were conducted in the rural villages in Puding County, Guizhou Province, Changwu County, Shaanxi Province, and Yujiang County, Jiangxi Province. Full details about this dataset can be found at https://doi.org/10.5285/e674e08c-fbf5-411b-940c-7e31014f0e76
-
These spatial layers contain risk factors and overall risk scores, representing relative risk of Phytophthora infection (Phytophthora ramorum and P. kernoviae), for heathland fragments across Scotland. Risk factors include climate suitability, proximity to road and river networks and suitability of habitat for key hosts of Phytophthora and were broadly concurrent with the period between 2007 and 2013. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forestry Commission, the Food and Environment Research Agency (FERA) and the Centre for Ecology & Hydrology (CEH). Full details about this dataset can be found at https://doi.org/10.5285/8f09b7e6-6daa-4823-b338-4edad8de1461
-
[THIS DATASET HAS BEEN WITHDRAWN]. The leaf phenology product presented here shows the amplitude of annual cycles observed in MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) 16-day time-series of 2000 to 2013 for Meso- and South America. The values given represent a conservative measure of the amplitude after the annual cycle was identified and tested for significance by means of the Fourier Transform. The amplitude was derived for four sets of vegtation indices (VI) time-series based on the MODIS VI products (500m MOD13A1; 1000m MOD13A2). The amplitude value can be interpreted as the degree in which the life cycles of individual leaves of plants observed within a pixel are synchronised. In other words, given the local variation in environment and climate and the diversity of species leaf life cycle strategies, an image pixel will represent vegetation communities behaving between two extremes: * well synchronized, where the leaf bud burst and senescence of the individual plants within the pixel occurs near simultaneously, yielding a high amplitude value. Often this matches with an area of low species diversity (e.g. arable land) or with areas where the growth of all plants is controlled by the same driver (e.g. precipitation). * poorly synchronized, where the leaf bud burst and senescence of individual plants within a pixel occurs at different times of the year, yielding a low amplitude value. Often this matches with an area of high species diversity and/or where several drivers could be controlling growth. Full details about this dataset can be found at https://doi.org/10.5285/36795e9d-2380-465c-947b-3c9ae26f92d0
-
[This dataset is embargoed until June 1, 2023]. This dataset consists of a single orthophoto mosaic image of Irontongue Hill on Swineshaw Moor. The area of interest includes seven erosion plots (approximately 5 x 5 m) which were set up on 26/07/2018 to capture the state of the burnt moorland surface and monitor subsequent erosion and vegetation recovery. The area of interest is approximately 0.45 km2. Full details about this dataset can be found at https://doi.org/10.5285/aff5210d-27e9-4655-badb-4d16c3adeb17
-
[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains daily micro-meteorological data from the experimental plots at the Climoor field site in Clocaenog forest, NE Wales. It runs from 15/7/1999 until 31/12/2013, and contains air temperature, soil temperature at 2 depths (5 and 20cm) as well as soil moisture. The dataset has been quality checked, and incorrect or missing values removed, data has not been infilled. Full details about this dataset can be found at https://doi.org/10.5285/4260dc49-764f-4ba5-b215-dab7eaaf35c8
-
[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains hourly micro-meteorological data from the experimental plots at the Climoor field site in Clocaenog forest, NE Wales. It runs from 11/9/2008 until 31/12/2013, and contains air temperature, soil temperature at two depths (5cm and 20cm) as well as soil moisture. Climoor is a climate change experiment which investigates the possible impact of increased temperatures and repeated summer drought on an Atlantic upland moorland. The experiment uses automatic roof technology to warm experimental plots by 0.5 - 1 degC and reproduces drought conditions in other experimental plots (July to September annually). In 2014, the Climoor experiment was the second longest running climate change experiment in the UK and data from the experiment has been used in several modelling exercises. The site was originally established under a EU consortium project - called CLIMOOR - where replica manipulation experiments were built in six European countries. As well as our site in North-East Wales (United Kingdom), there are identical sites in Denmark, the Netherlands, Sardinia (Italy) and Hungary. There was also a site in Catalonia (Spain). Full details about this dataset can be found at https://doi.org/10.5285/124ae988-41d3-4555-b704-5acc85633a05
-
The dataset contains model output from the CityCAT hydrodynamic model showing maximum water depths in Jakarta, Indonesia, during the January/February 2007 flood. The hourly rainfall and hourly lateral inflow boundary conditions from rivers used to obtain the flooding depths are also included. Full details about this dataset can be found at https://doi.org/10.5285/8e58f0bb-3ff1-41e8-b8f4-380983ec68bc
-
The dataset contains parameter values that maximize revised Kling Gupta Efficiency (KGE’) between modelled and observed daily mean river flows when running one of 24 different hydrological models with one of 21 different climatic input datasets in one of 33 different catchments across the Citarum basin or 5 catchments across the Ciliwung basin, both in Java island, Indonesia. This dataset was created as part of a study on the advantages and disadvantages of using existing hydrological models, primarily developed for temperate and cold climates, in a tropical volcanic region. The hydrological models were based on those created for MARRMoT v1.2 (10.5194/gmd-12-2463-2019), recoded as sequential models in the R programming language. This work was supported by the Natural Environment Research Council (Grants NE/S00310X/1 and NE/S002790/1). Full details about this dataset can be found at https://doi.org/10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d
-
The data consists of estimated hourly average inflow discharge (m3s-1) and water temperature (°C) of the inflow of the inner basin of Elterwater (lat: 54.428, long: -3.034) from January 2012 to December 2019. The work was supported by the Natural Environment Research Council (Grant NE/ L002604/1). Full details about this dataset can be found at https://doi.org/10.5285/2883aaf1-6148-49cb-904a-d271a028c716