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Climatology, meteorology, atmosphere

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  • 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

  • Averaged outputs from the WRF (Weather Research and Forecasting) model for the Rio Santa and Vilcanota, Urubamba and Vilcabamba catchments in Peru. Averaging was applied over the entire model period from 1980 to 2018. Data includes: - Averaged precipitation and air temperature records and the related standard deviation at a 4km resolution (annually and for each season) for each catchment. Monthly averaged and monthly totals of air temperature and precipitation (averaged over each catchment). - WRF model input elevation for each catchment. - WRF total precipitation and maximum/minimum air temperature at the location of five on-glacier weather stations (Artesonraju Glacier, Shallap Glacier, Cuchillacocha Glacier, Quisoquipina Glacier and Quelccaya Ice Cap) at a daily resolution from 1980 to 2018. Full details about this dataset can be found at https://doi.org/10.5285/7dbb2d72-7032-4cfa-bc9b-aa02bebe8df5

  • The model-generated dataset includes simulated daily dry matter accumulation of above-ground organs (leaves, stems and grains) of winter wheat and maize, soil water content in different soil layers and organic matter stocks in the topsoil and subsoil layers, and final crop dry matter from 1983 to 2004 (wheat) or 2015 (maize). A prediction of the variables under various future climatic scenarios is also included. The SPACSYS model was applied to a historic experimental site on the Loess Plateau in China. Observed crop yields of winter wheat from 1993 to 2004 and maize from 1983 to 2015 were used to validate the model. The validated model was run again under different climate scenarios from 2015 to 2049 to predict daily dry matter accumulation of above-ground organs including leaves, stems and grains, daily soil water content in different layers and soil organic carbon stocks in the topsoil and subsoil layers. Full details about this dataset can be found at https://doi.org/10.5285/03e74f94-88a5-4f09-b9ea-1447dd3e2b85

  • 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

  • This dataset contains information about meteorological conditions and ammonia concentration and deposition rates resulting from an experimental setup. An NH3 enhancement experiment along with a full suite of multi-height meteorological measurements was established in a tropical forest in central Sri Lanka. Under suitable wind conditions measured at the meteorological tower, NH3 is released towards two monitoring transects. Along the downwind monitoring transects, NH3 concentrations in the air are measured using monthly passive samplers. Deposition rates are modelled using a bi-directional resistance model based on measured NH3 concentrations in the air, micrometeorology and plant physiology. Additionally, NH3 concentrations were measured at high temporal resolution at a fixed downwind distance from the source to achieve the target enhancement concentrations. The work was supported by UKRI GCRF South Asian Nitrogen Hub (Grant NE/S009019/1). Full details about this dataset can be found at https://doi.org/10.5285/998c2b2b-7470-42b0-81e7-409b91752377

  • This dataset contains fire emissions from Equatorial Asia for the years 2004, 2006, 2009, 2012, 2014 and 2015. The data is based on the Fire Inventory from National Center for Atmospheric Research with the addition of emissions from Indonesian peat fires, which contribute substantially to fire emissions in the region. The files for each year contain daily information on the area burned and emissions of several species, including CO, CO2 and PM2.5. Data is given for individual fires at 1km resolution. Fire emissions are provided for each year both for fires as measured, and under a scenario where degraded peatland in the region has been partially restored, reducing fire emissions. Full details about this dataset can be found at https://doi.org/10.5285/fdae44ed-8b22-4935-b889-b4b271138385

  • This data represents twenty-four modelled rainfall depth estimates by GridASCII files across the state of Kerala, India, for four durations (1, 6, 24 and 192 hours) and six return periods (2, 5, 10, 25, 50 and 100 years). The estimates were produced using a similar procedure to the Flood Estimation Handbook statistical method for flood frequency estimation: separately for each duration, the estimated median annual maximum (AMAX) rainfall was used as a standardizing “index” value and the estimated L-moments of the AMAX series were used to fit a generalized logistic distribution “growth curve”. The data are in units of mm at a spatial resolution of 0.12 degrees. Full details about this dataset can be found at https://doi.org/10.5285/4a08e6f1-e508-4bb6-b571-b3145dd1588e

  • Hourly precipitation (mm) recorded at distributed points around Kampala between April 2019 and March 2020. Only timestamps where data were available from all sensors have been included. There are 8094 records in total and no missing values. Timestamps are recorded as “YYYY-MM-DD hh:mm:ss”. The geographic coordinates of the sensors are provided in GeoJSON format. The column names in the CSV file correspond to the “id” field in the GeoJSON file. Full details about this dataset can be found at https://doi.org/10.5285/3df031ad-34ec-4abc-8528-f8f20bad12b8

  • Data are presented for daily rainfall, stream discharge and hydraulic conductivity of soils from catchments located in the Upper Nilgiris Reserve Forest in the state of Tamil Nadu. The catchments are dominated by four land cover types, shola, grassland, pine and wattle. The data were collected between May 2014 and December 2016. Tipping bucket wired rain gauges were used to measure rainfall. Stream discharge was measured from stilling wells and capacitance probe-based water level recorders. A mini-disk infiltrometer was used to measure the hydraulic conductivity of soils. Dry season data has not been included in this dataset as its focus is on extreme rain events. The data were collected as part of a series of eco-hydrology projects that explored the impact of land cover on rain-runoff response, carbon sequestration and nutrient and sediment discharge. The dataset presented here was collected by a team of three to five researchers and field assistants who were engaged in the installation of the data loggers and their regular operation and maintenance. Four research agencies have partnered across multiple projects to sustain the data collection efforts that started in June 2013 and continue (June 2020). These are the Foundation for Ecological Research, Advocacy and Learning - Pondicherry, the Ashoka Trust for Research in Ecology and the Environment - Bangalore, the Lancaster Environmental Centre, Lancaster University - UK, and the National Centre for Biological Sciences - Bangalore. Funding was provided by Ministry of Earth Sciences Government of India from the Changing Water Cycle programme (Grant Ref: MoES/NERC/16/02/10 PC-II) and the Hydrologic footprint of Invasive Alien Species project (MOES/PAMC/H&C/85/2016-PC-II). Additional funding was provided by UKRI Natural Environment Research Council grant NE/I022450/1 (Western Ghats-Capacity within the NERC Changing Water Cycle programme) and WWF-India as part of the Noyyal-Bhavani program.This research took place inside protected areas in the Nilgiri Division for which permissions and support were provided continually by the Tamil Nadu Forest Department, particularly the office of the District Forest Officer, Udhagamandalam. Full details about this dataset can be found at https://doi.org/10.5285/9257a999-2844-4be1-80d1-fd29e2ccf9ef

  • The data resource consists of half hourly time series of heat (latent and sensible) and trace gas (carbon dioxide and methane) fluxes obtained by eddy-covariance, gas concentrations and ancillary meteorological data (e.g. air temperature, relative humidity, pressure, photosynthetically active radiation, total incoming radiation, wind speed and direction). The data were collected at Guma Lagoon (18°57'53.01"S; 22°22'16.20"E), in the perennially flooded area of the Okavango Delta, Botswana, for the purpose of quantifying greenhouse gas fluxes over a Cyperus papyrus stand. The measurement period was 01/01/2018 to 31/12/2020. The instrumentation was installed the UK Centre for Ecology and Hydrology; monthly maintenance and data collection visits were effected by the Okavango Research Institute, University of Botswana. The research was funded through NERC grant reference NE/N015746/2 - The Global Methane Budget. Full details about this dataset can be found at https://doi.org/10.5285/d366ed40-af8c-42be-86f2-bb90b11a659e