Articles | Volume 12, issue 1
https://doi.org/10.5194/essd-12-87-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-87-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Jan 2020
Data description paper |
| 14 Jan 2020
| 14 Jan 2020
The UK Environmental Change Network datasets – integrated and co-located data for long-term environmental research (1993–2015)
UK Centre for Ecology and Hydrology, Lancaster Environment Centre,
Library Avenue, Bailrigg, Lancaster, LA1 4AP, UK
Chris Andrews
UK Centre for Ecology and Hydrology, Bush Estate, Penicuik, Edinburgh, Midlothian, EH26 0QB, UK
Sarah Atkinson
Defence Science and Technology Laboratory, Dstl Porton, Porton Down, Salisbury, Wiltshire, SP4 0JQ, UK
Deborah Beaumont
Rothamsted Research, North Wyke, Okehampton, Devon, EX20 2SB, UK
Sue Benham
Forest Research, CESB, Alice Holt Lodge, Wrecclesham, Farnham, Surrey, GU10 4LH, UK
Vic Bowmaker
Cyfoeth Naturiol Cymru – Natural Resources Wales, Maes-y-Ffynnon,
Penrhosgarnedd, Bangor, Gwynedd, LL57 2DW, UK
Jan Dick
UK Centre for Ecology and Hydrology, Bush Estate, Penicuik, Edinburgh, Midlothian, EH26 0QB, UK
Bev Dodd
UK Centre for Ecology and Hydrology, Lancaster Environment Centre,
Library Avenue, Bailrigg, Lancaster, LA1 4AP, UK
Colm McKenna
Agri-Food and Biosciences Institute, Newforge Lane, Belfast, BT9 5PX, UK
Denise Pallett
UK Centre for Ecology and Hydrology, Maclean Building, Crowmarsh
Gifford, Wallingford, Oxon, OX10 8BB, UK
Rob Rose
UK Centre for Ecology and Hydrology, Lancaster Environment Centre,
Library Avenue, Bailrigg, Lancaster, LA1 4AP, UK
Stefanie M. Schäfer
UK Centre for Ecology and Hydrology, Maclean Building, Crowmarsh
Gifford, Wallingford, Oxon, OX10 8BB, UK
Tony Scott
Rothamsted Research, West Common, Harpenden, Herts, AL5 2JQ, UK
Carol Taylor
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK
Helen Watson
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK
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Guta Wakbulcho Abeshu, Hong-Yi Li, Zhenduo Zhu, Zeli Tan, and L. Ruby Leung
Earth Syst. Sci. Data, 14, 929–942, https://doi.org/10.5194/essd-14-929-2022, https://doi.org/10.5194/essd-14-929-2022, 2022
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Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Hui Tao, Kaishan Song, Ge Liu, Qiang Wang, Zhidan Wen, Pierre-Andre Jacinthe, Xiaofeng Xu, Jia Du, Yingxin Shang, Sijia Li, Zongming Wang, Lili Lyu, Junbin Hou, Xiang Wang, Dong Liu, Kun Shi, Baohua Zhang, and Hongtao Duan
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During 1984–2018, lakes in the Tibetan-Qinghai Plateau had the clearest water (mean 3.32 ± 0.38 m), while those in the northeastern region had the lowest Secchi disk depth (SDD) (mean 0.60 ± 0.09 m). Among the 10 814 lakes with > 10 years of SDD results, 55.4 % and 3.5 % experienced significantly increasing and decreasing trends of SDD, respectively. With the exception of Inner Mongolia–Xinjiang, more than half of lakes in all the other regions exhibited a significant trend of increasing SDD.
Jiao Lu, Guojie Wang, Tiexi Chen, Shijie Li, Daniel Fiifi Tawia Hagan, Giri Kattel, Jian Peng, Tong Jiang, and Buda Su
Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, https://doi.org/10.5194/essd-13-5879-2021, 2021
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Kytt MacManus, Deborah Balk, Hasim Engin, Gordon McGranahan, and Rya Inman
Earth Syst. Sci. Data, 13, 5747–5801, https://doi.org/10.5194/essd-13-5747-2021, https://doi.org/10.5194/essd-13-5747-2021, 2021
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New estimates of population and land area by settlement types within low-elevation coastal zones (LECZs) based on four sources of population data, four sources of settlement data and four sources of elevation data for the years 1990, 2000 and 2015. The paper describes the sensitivity of these estimates and discusses the fitness of use guiding user decisions. Data choices impact the number of people estimated within LECZs, but across all sources the LECZs are predominantly urban and growing.
Yanhua Xie, Holly K. Gibbs, and Tyler J. Lark
Earth Syst. Sci. Data, 13, 5689–5710, https://doi.org/10.5194/essd-13-5689-2021, https://doi.org/10.5194/essd-13-5689-2021, 2021
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We created 30 m resolution annual irrigation maps covering the conterminous US for the period of 1997–2017, together with derivative products and ground reference data. The products have several improvements over other data, including field-level details of change and frequency, an annual time step, a collection of ~ 10 000 ground reference locations for the eastern US, and improved mapping accuracy of over 90 %, especially in the east compared to others of 50 % to 80 %.
Holger Virro, Giuseppe Amatulli, Alexander Kmoch, Longzhu Shen, and Evelyn Uuemaa
Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, https://doi.org/10.5194/essd-13-5483-2021, 2021
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Water quality modeling is essential for understanding and mitigating water quality deterioration in river networks due to agricultural and industrial pollution. Improving the availability and usability of open data is vital to support global water quality modeling efforts. The GRQA extends the spatial and temporal coverage of previously available water quality data and provides a reproducible workflow for combining multi-source water quality datasets.
Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Pengyu Hao, and Peng Gong
Earth Syst. Sci. Data, 13, 5403–5421, https://doi.org/10.5194/essd-13-5403-2021, https://doi.org/10.5194/essd-13-5403-2021, 2021
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In the study, the first 1 km global cropland proportion dataset for 10 000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
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Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Amanda R. Fay, Luke Gregor, Peter Landschützer, Galen A. McKinley, Nicolas Gruber, Marion Gehlen, Yosuke Iida, Goulven G. Laruelle, Christian Rödenbeck, Alizée Roobaert, and Jiye Zeng
Earth Syst. Sci. Data, 13, 4693–4710, https://doi.org/10.5194/essd-13-4693-2021, https://doi.org/10.5194/essd-13-4693-2021, 2021
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The movement of carbon dioxide from the atmosphere to the ocean is estimated using surface ocean carbon (pCO2) measurements and an equation including variables such as temperature and wind speed; the choices of these variables lead to uncertainties. We introduce the SeaFlux ensemble which provides carbon flux maps calculated in a consistent manner, thus reducing uncertainty by using common choices for wind speed and a set definition of "global" coverage.
Samuel J. Tomlinson, Edward J. Carnell, Anthony J. Dore, and Ulrike Dragosits
Earth Syst. Sci. Data, 13, 4677–4692, https://doi.org/10.5194/essd-13-4677-2021, https://doi.org/10.5194/essd-13-4677-2021, 2021
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Nitrogen (N) may impact the environment in many ways, and estimation of its deposition to the terrestrial surface is of interest. N deposition data have not been generated at a high resolution (1 km × 1 km) over a long time series in the UK before now. This study concludes that N deposition has reduced by ~ 40 % from 1990. The impact of these results allows analysis of environmental impacts at a high spatial and temporal resolution, using a consistent methodology and consistent set of input data.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
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The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
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This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021, https://doi.org/10.5194/essd-13-3951-2021, 2021
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Lakes can be effective indicators of climate change, especially over the Qinghai–Tibet Plateau. Here, we provide the most comprehensive lake mapping covering the past 100 years. The new features of this data set are (1) its temporal length, providing the longest period of lake observations from maps, (2) the data set provides a state-of-the-art lake inventory for the Landsat era (from the 1970s to 2020), and (3) it provides the densest lake observations for lakes with areas larger than 1 km2.
Jie Yang and Xin Huang
Earth Syst. Sci. Data, 13, 3907–3925, https://doi.org/10.5194/essd-13-3907-2021, https://doi.org/10.5194/essd-13-3907-2021, 2021
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We produce the 30 m annual China land cover dataset (CLCD), with an accuracy reaching 79.31 %. Trends and patterns of land cover changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and increase in forest (+4.34 %). The CLCD generally reflected the rapid urbanization and a series of ecological projects in China and revealed the anthropogenic implications on LC under the condition of climate change.
Richard Porter-Smith, John McKinlay, Alexander D. Fraser, and Robert A. Massom
Earth Syst. Sci. Data, 13, 3103–3114, https://doi.org/10.5194/essd-13-3103-2021, https://doi.org/10.5194/essd-13-3103-2021, 2021
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This study quantifies the characteristic complexity
signaturesaround the Antarctic outer coastal margin, giving a multiscale estimate of the magnitude and direction of undulation or complexity at each point location along the entire coastline. It has numerous applications for both geophysical and biological studies and will contribute to Antarctic research requiring quantitative information about this important interface.
Gonçalo Vieira, Carla Mora, Pedro Pina, Ricardo Ramalho, and Rui Fernandes
Earth Syst. Sci. Data, 13, 3179–3201, https://doi.org/10.5194/essd-13-3179-2021, https://doi.org/10.5194/essd-13-3179-2021, 2021
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Fogo in Cabo Verde is one of the most active ocean island volcanoes on Earth, posing important hazards to local populations and at a regional level. The last eruption occurred from November 2014 to February 2015. A survey of the Chã das Caldeiras area was conducted using a fixed-wing unmanned aerial vehicle. A point cloud, digital surface model and orthomosaic with 10 and 25 cm resolutions are provided, together with the full aerial survey projects and datasets.
Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler
Earth Syst. Sci. Data, 13, 3013–3033, https://doi.org/10.5194/essd-13-3013-2021, https://doi.org/10.5194/essd-13-3013-2021, 2021
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With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata from more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real-world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.
Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, https://doi.org/10.5194/essd-13-2701-2021, 2021
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Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Ana Maria Roxana Petrescu, Matthew J. McGrath, Robbie M. Andrew, Philippe Peylin, Glen P. Peters, Philippe Ciais, Gregoire Broquet, Francesco N. Tubiello, Christoph Gerbig, Julia Pongratz, Greet Janssens-Maenhout, Giacomo Grassi, Gert-Jan Nabuurs, Pierre Regnier, Ronny Lauerwald, Matthias Kuhnert, Juraj Balkovič, Mart-Jan Schelhaas, Hugo A. C. Denier van der
Gon, Efisio Solazzo, Chunjing Qiu, Roberto Pilli, Igor B. Konovalov, Richard A. Houghton, Dirk Günther, Lucia Perugini, Monica Crippa, Raphael Ganzenmüller, Ingrid T. Luijkx, Pete Smith, Saqr Munassar, Rona L. Thompson, Giulia Conchedda, Guillaume Monteil, Marko Scholze, Ute Karstens, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2363–2406, https://doi.org/10.5194/essd-13-2363-2021, https://doi.org/10.5194/essd-13-2363-2021, 2021
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This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CO2 fossil emissions and CO2 land fluxes in the EU27+UK. The data integrate recent emission inventories with ecosystem data, land carbon models and regional/global inversions for the European domain, aiming at reconciling CO2 estimates with official country-level UNFCCC national GHG inventories in support to policy and facilitating real-time verification procedures.
Lilu Sun and Yunfei Fu
Earth Syst. Sci. Data, 13, 2293–2306, https://doi.org/10.5194/essd-13-2293-2021, https://doi.org/10.5194/essd-13-2293-2021, 2021
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Multi-source dataset use is hampered by use of different spatial and temporal resolutions. We merged Tropical Rainfall Measuring Mission precipitation radar and visible and infrared scanner measurements with ERA5 reanalysis. The statistical results indicate this process has no unacceptable influence on the original data. The merged dataset can help in studying characteristics of and changes in cloud and precipitation systems and provides an opportunity for data analysis and model simulations.
Yongyong Fu, Jinsong Deng, Hongquan Wang, Alexis Comber, Wu Yang, Wenqiang Wu, Shixue You, Yi Lin, and Ke Wang
Earth Syst. Sci. Data, 13, 1829–1842, https://doi.org/10.5194/essd-13-1829-2021, https://doi.org/10.5194/essd-13-1829-2021, 2021
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Marine aquaculture areas in a region up to 30 km from the coast in China were mapped for the first time. It was found to cover a total area of ~1100 km2, of which more than 85 % is marine plant culture areas, with 87 % found in four coastal provinces. The results confirm the applicability and effectiveness of deep learning when applied to GF-1 data at the national scale, identifying the detailed spatial distributions and supporting the sustainable management of coastal resources in China.
Sebastian Weinert, Kristian Bär, and Ingo Sass
Earth Syst. Sci. Data, 13, 1441–1459, https://doi.org/10.5194/essd-13-1441-2021, https://doi.org/10.5194/essd-13-1441-2021, 2021
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Physical rock properties are a key element for resource exploration, the interpretation of results from geophysical methods or the parameterization of physical or geological models. Despite the need for physical rock properties, data are still very scarce and often not available for the area of interest. The database presented aims to provide easy access to physical rock properties measured at 224 locations in Bavaria, Hessen, Rhineland-Palatinate and Thuringia (Germany).
Claire E. Simpson, Christopher D. Arp, Yongwei Sheng, Mark L. Carroll, Benjamin M. Jones, and Laurence C. Smith
Earth Syst. Sci. Data, 13, 1135–1150, https://doi.org/10.5194/essd-13-1135-2021, https://doi.org/10.5194/essd-13-1135-2021, 2021
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Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance, and ecological habitat.
Fei Feng and Kaicun Wang
Earth Syst. Sci. Data, 13, 907–922, https://doi.org/10.5194/essd-13-907-2021, https://doi.org/10.5194/essd-13-907-2021, 2021
Els Knaeps, Sindy Sterckx, Gert Strackx, Johan Mijnendonckx, Mehrdad Moshtaghi, Shungudzemwoyo P. Garaba, and Dieter Meire
Earth Syst. Sci. Data, 13, 713–730, https://doi.org/10.5194/essd-13-713-2021, https://doi.org/10.5194/essd-13-713-2021, 2021
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This paper describes a dataset consisting of 47 hyperspectral-reflectance measurements of plastic litter samples. The plastic litter samples include virgin and real samples. They were measured in dry conditions, and a selection of the samples were also measured in wet conditions and submerged in a water tank. The dataset can be used to better understand the effect of water absorption on the plastics and develop algorithms to detect and characterize marine plastics.
Susannah Rennie, Klaus Goergen, Christoph Wohner, Sander Apweiler, Johannes Peterseil, and John Watkins
Earth Syst. Sci. Data, 13, 631–644, https://doi.org/10.5194/essd-13-631-2021, https://doi.org/10.5194/essd-13-631-2021, 2021
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This paper describes a pan-European climate service data product intended for ecological researchers. Access to regional climate scenario data will save ecologists time, and, for many, it will allow them to work with data resources that they will not previously have used due to a lack of knowledge and skills to access them. Providing easy access to climate scenario data in this way enhances long-term ecological research, for example in general regional climate change or impact assessments.
Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders
Earth Syst. Sci. Data, 13, 357–366, https://doi.org/10.5194/essd-13-357-2021, https://doi.org/10.5194/essd-13-357-2021, 2021
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Flight data have been used widely for research by academic researchers and (supra)national institutions. Example domains range from epidemiology (e.g. examining the spread of COVID-19 via air travel) to economics (e.g. use as proxy for immediate forecasting of the state of a country's economy) and Earth sciences (climatology in particular). Until now, accurate flight data have been available only in small pieces from closed, proprietary sources. This work changes this with a crowdsourced effort.
Jinshi Jian, Rodrigo Vargas, Kristina Anderson-Teixeira, Emma Stell, Valentine Herrmann, Mercedes Horn, Nazar Kholod, Jason Manzon, Rebecca Marchesi, Darlin Paredes, and Ben Bond-Lamberty
Earth Syst. Sci. Data, 13, 255–267, https://doi.org/10.5194/essd-13-255-2021, https://doi.org/10.5194/essd-13-255-2021, 2021
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Field soil-to-atmosphere CO2 flux (soil respiration, Rs) observations were compiled into a global database (SRDB) a decade ago. Here, we restructured and updated the database to the fifth version, SRDB-V5, with data published through 2017 included. SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field Rs measurements, and it provides opportunities for the scientific community to better understand the spatial and temporal variability of Rs.
Robert A. Rohde and Zeke Hausfather
Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, https://doi.org/10.5194/essd-12-3469-2020, 2020
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A global land and ocean temperature record was created by combining the Berkeley Earth monthly land temperature field with a newly interpolated version of the HadSST3 ocean dataset. The resulting dataset covers the period from 1850 to present.
This paper describes the methods used to create that combination and compares the results to other estimates of global temperature and the associated recent climate change, giving similar results.
Igor Savin, Valery Mironov, Konstantin Muzalevskiy, Sergey Fomin, Andrey Karavayskiy, Zdenek Ruzicka, and Yuriy Lukin
Earth Syst. Sci. Data, 12, 3481–3487, https://doi.org/10.5194/essd-12-3481-2020, https://doi.org/10.5194/essd-12-3481-2020, 2020
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This article presents a dielectric database of organic Arctic soils. This database was created based on dielectric measurements of seven samples of organic soils collected in various parts of the Arctic tundra. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
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Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Clara Lázaro, Maria Joana Fernandes, Telmo Vieira, and Eliana Vieira
Earth Syst. Sci. Data, 12, 3205–3228, https://doi.org/10.5194/essd-12-3205-2020, https://doi.org/10.5194/essd-12-3205-2020, 2020
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In satellite altimetry (SA), the wet tropospheric correction (WTC) accounts for the path delay induced mainly by atmospheric water vapour. In coastal regions, the accuracy of the WTC determined by the on-board radiometer deteriorates. The GPD+ methodology, developed by the University of Porto in the remit of ESA-funded projects, computes improved WTCs for SA. Global enhanced products are generated for all past and operational altimetric missions, forming a relevant dataset for coastal altimetry.
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Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
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Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Eaton,
E., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis,
M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
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Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
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McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
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Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
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grain vegetation data: 1993–2012, NERC Environmental Information Data
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Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Eaton,
E., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis,
M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B.,
McElarney, Y., McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft,
M., Murphy, M., Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce,
I., Pozsgai, G., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall,
C., Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M.,
Turner, A., Tyson, K., Watson, H., Whittaker, M., Wilkinson, M., and Wood, C.:
UK Environmental Change Network (ECN) meteorology data: 1991–2015, NERC
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Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) atmospheric nitrogen chemistry data: 1993–2015, NERC Environmental
Information Data Centre, https://doi.org/10.5285/baf51776-c2d0-4e57-9cd3-30cd6336d9cf, 2017b.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) precipitation chemistry data: 1992–2015, NERC Environmental
Information Data Centre, https://doi.org/10.5285/18b7c387-037d-4949-98bc-e8db5ef4264c, 2017c.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) soil solution chemistry data: 1992–2015, NERC Environmental
Information Data Centre, https://doi.org/10.5285/b330d395-68f2-47f1-8d59-3291dc02923b, 2017d.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) stream water chemistry data: 1992–2015, NERC Environmental Information
Data Centre, https://doi.org/10.5285/fd7ca5ef-460a-463c-ad2b-5ad48bb4e22e, 2017e.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C.,
McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A.,
Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R.,
Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P.,
Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K.,
Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network (ECN)
stream water discharge data: 1993–2015, NERC Environmental Information Data
Centre, https://doi.org/10.5285/8b58c86b-0c2a-4d48-b25a-7a0141859004, 2017f.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Riley, A., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C.,
Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner,
A., Tyson, K., Watson, H., Whittaker, M., Woiwod, I., and Wood, C.: UK
Environmental Change Network (ECN) moth data: 1992–2015, NERC Environmental
Information Data Centre, https://doi.org/10.5285/a2a49f47-49b3-46da-a434-bb22e524c5d2, 2017g.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Tilbury, C.,
Turner, A., Tyson, K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental
Change Network (ECN) butterfly data: 1993–2015, NERC Environmental
Information Data Centre, https://doi.org/10.5285/5aeda581-b4f2-4e51-b1a6-890b6b3403a3, 2017h.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C.,
McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A.,
Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R.,
Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P.,
Tait, R., Taylor, C., Taylor, M., Thurlow, M., Tilbury, C., Turner, A.,
Tyson, K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change
Network (ECN) carabid beetle data: 1992–2015, NERC Environmental Information
Data Centre, https://doi.org/10.5285/8385f864-dd41-410f-b248-028f923cb281, 2017i.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C.,
McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A.,
Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R.,
Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P.,
Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K.,
Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network (ECN)
woodland vegetation data: 1993–2014, NERC Environmental Information Data
Centre, https://doi.org/10.5285/94aef007-634e-42db-bc52-9aae86adbd33, 2017j.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McElarney, Y., McKenna, C.,
McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M., Nelson, A.,
Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G., Rose, R.,
Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R., Smith, P.,
Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson, K.,
Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network (ECN)
fine grain vegetation data: 1994–2015, NERC Environmental Information Data
Centre, https://doi.org/10.5285/b98efec8-6de0-4e0c-85dc-fe4cdf01f086, 2017k.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) frog data: 1994–2015, NERC Environmental Information Data Centre,
https://doi.org/10.5285/4d8c7dd9-8248-46ca-b988-c1fc38e51581,
2017l.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, A., Brunt, J., Clawson, S., Common, G., Cooper, R.,
Corbett, S., Critchley, N., Dennis, P., Dick, J., Dodd, B., Dodd, N.,
Donovan, N., Easter, J., Flexen, M., Gardiner, A., Hamilton, D., Hargreaves,
P., Hatton-Ellis, M., Howe, M., Kahl, J., Lane, M., Langan, S., Lloyd, D.,
McElarney, Y., McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft,
M., Murphy, M., Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce,
I., Pozsgai, G., Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall,
C., Smith, R., Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M.,
Turner, A., Tyson, K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental
Change Network (ECN) bird data: 1995–2015, NERC Environmental Information
Data Centre, https://doi.org/10.5285/5886c3ba-1fa5-49c0-8da8-40e69a10d2b5, 2017m.
Rennie, S., Bater, J., Beaumont, D., Benham, S., Britt, C., Brooks, D., Brunt, A., Brunt, J., Corbett, S., Critchley, N., Donovan, N.,
Hargreaves, P., Howe, M., Lane, M., McMillan, S., Morecroft, M., Onley,
D., Overall, R., Pallett, D., Schafer, S., Scott, T., Sherrin, L.,
Shortall, C., Smith, R., Smith, P., Taylor, M., and Tyson, K.: UK
Environmental Change Network (ECN) common breeding birds data 1971–2007,
NERC Environmental Information Data Centre, https://doi.org/10.5285/8582a02c-b28c-45d2-afa1-c1e85fba023d, 2017n.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., Lundy, M., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) bat data: 1993–2015, NERC Environmental Information Data Centre,
https://doi.org/10.5285/2588ee91-6cbd-4888-86fc-81858d1bf085,
2017o.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
Brooks, D., Brunt, J., Common, G., Cooper, R., Corbett, S., Critchley, N.,
Dennis, P., Dick, J., Dodd, B., Dodd, N., Donovan, N., Easter, J., Flexen,
M., Gardiner, A., Hamilton, D., Hargreaves, P., Hatton-Ellis, M., Howe, M.,
Kahl, J., Lane, M., Langan, S., Lloyd, D., McCarney, B., McElarney, Y.,
McKenna, C., McMillan, S., Milne, F., Milne, L., Morecroft, M., Murphy, M.,
Nelson, A., Nicholson, H., Pallett, D., Parry, D., Pearce, I., Pozsgai, G.,
Rose, R., Schafer, S., Scott, T., Sherrin, L., Shortall, C., Smith, R.,
Smith, P., Tait, R., Taylor, C., Taylor, M., Thurlow, M., Turner, A., Tyson,
K., Watson, H., Whittaker, M., and Wood, C.: UK Environmental Change Network
(ECN) rabbit and deer data: 1993–2015, NERC Environmental Information Data
Centre, https://doi.org/10.5285/0be0aed3-f205-4f1f-a65d-84f8cfd8d50f, 2017p.
Rennie, S., Adamson, J., Anderson, R., Andrews, C., Bater, J., Bayfield, N.,
Beaton, K., Beaumont, D., Benham, S., Bowmaker, V., Britt, C., Brooker, R.,
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Short summary
This paper describes the meteorological, biological and biogeochemical datasets of the UK Environmental Change Network, a nationally unique long-term record environmental variability across UK habitats. The co-location of these measurements provides a rare opportunity to directly investigate relationships between environmental variables over significant time scales (1992–2015). This data record also provides the UK contribution to a global system of long-term environmental research networks.
This paper describes the meteorological, biological and biogeochemical datasets of the UK...
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