Find research datasets worth reusing

opencc-by-4.0Sep 2019View details →

Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)

The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).   The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary. Brief description of dataset. <ul> <li> velocities.csv (in m/s) </li> <li> Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU). </li> <li> qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius). </li> <li> Sealevel.csv (in meters) </li> </ul> A wide description of the datasets can be found in Navarro et al (2011). Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)

opencc-by-4.0Jan 2021View details →

Britain Breathing 2016-2019 Air Quality and Meteorological Regional Estimates Dataset

This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK and crown dependencies postcode districts (e.g. 'AB') for the years 2016-2019, inclusive. The paper describing this dataset is available here: <a href="https://www.nature.com/articles/s41597-022-01135-6">https://www.nature.com/articles/s41597-022-01135-6</a> The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring.  As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence. The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4416028#.YABxNnX7RhF">this Zenodo archive</a>.  The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model. The code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>. The postcode data in postcode_district_data.csv are collated from several sources:  <ul> <li><a href="https://www.doogal.co.uk/UKPostcodes.php">https://www.doogal.co.uk/UKPostcodes.php</a> (population figures for the UK (UK Census 2011))</li> <li><a href="https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm">https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm</a> (postcode boundary polygons for UK and crown dependancies)</li> <li><a href="https://www.gov.gg/population">https://www.gov.gg/population</a> (Guernsey (GY) population data for end June 2020) </li> <li><a href="https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx">https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx</a> (Jersey (JE) population data for end 2019) </li> <li><a href="https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf">https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf</a> (Isle of Man (IM) population data for April 2016)</li> </ul> The data-set is presented in CSV format, as six files: <ol> <li>postcode_district_data.csv: location metadata (region_id, geometry, description, population, country)</li> <li>regional_site_counts.csv: a table showing the number of sites for each measurement (columns), for each region_id (rows). region_id's match those in the postcode_district_data.csv file.</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_imputed_data.csv: uses imputed site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_imputed_data.csv: uses imputed site data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> * Air quality site types:  <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>

opencc-by-4.0Jul 2021View details →

Britain Breathing 2020 Air Quality and Meteorological Regional Estimates Dataset

This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for UK postcode districts (e.g. 'AB') for the year 2020. The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring.  As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence. The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4740965#.YPWJf3VKhhF">this Zenodo archive</a>.  The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model. The code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>. The data-set is presented in CSV format, as two files: <ol> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> * Air quality site types:  <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>

opencc-by-4.0Nov 2022View details →

Meteorological data from the experimental period of the submersion test of photovoltaic cables

Meteorological data recorded by the onsite weather station (coordinates: 38°31'50.0"N 8°00'40.3"W) regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. The metereological data is logged with 1minute time resolution for the period from 16/10/2020 to 25/01/2021. The meteorological station is composed by: Kipp and Zonen Solys2 Sun tracker Kipp and Zonen CMP6 Pyranometer (horizontal global solar radiation, data units W/m2) RH and Air temperature sensor (air relative humidity, data units % and ambient air temperature, data units ºC) Rain Gauge (precipitation, data units mm)

opencc-by-4.0Jan 2023View details →

On-Glacier Meteorological Data for Haut Glacier d'Arolla, Switzerland

The compiled dataset is a series of summer meteorological observations on the Swiss Haut Glacier d'Arolla (45.97°N, 7.52°E)  to support the analysis presented in the manuscript:%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  "The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier",  submitted to Geophysical Research Letters.  Thomas E. Shaw1, Pascal Buri1, Michael McCarthy1, Evan S. Miles1, Álvaro Ayala2, Francesca Pellicciotti11 Swiss Federal Institute, WSL, Birmensdorf, Switzerland 2 Centro de Estudios Avanzados en Zonas Áridas, La Serena, Chile%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%The following files are provided: 1) 6 x xlsx files "Arolla_Meteorological_Data_[YEAR].xlsx"     contains within are tabs for:         i) The station locations and elevations (tab "[YEAR]_Info").         ii) All AWS/Tlogger data in hourly format (tab "[YEAR]_Met_Data").         iii) Only the hourly air temperature data for on-glacier sites (tab "[YEAR]_Ta").2) Glacier outlines (.shp) for years 1850 (GLAMOS), 1973 (GLAMOS), 1994 (Carenzo, 2012), 1999 (Carenzo et al., 2012), 2010 (GLAMOS), 2022 (Digitised from PlanetScope imagery). 3) Debris cover area (.shp) derived by applying an NDSI classification of cloud filtered, summer Landsat scenes in Google Earth Engine following the approach of Scherler et al. (2018).For the meteorological data in 1), the following variables are provided: "TA" = near surface air temperature (°C). "RH" - relative humidity (%). "SWIN" - Shortwave incoming radiation (Wm^-2). "SWOUT" - Shortwave outgoing radiation (Wm^-2). "LWIN" - Longwave incoming radiation (Wm^-2). "LWOUT" - Longwave outgoing radiation (Wm^-2). "FF" - Wind speed (m s-1). "DIR" - Wind direction (°). "PP" - precipitation (mm). "DEW" - dew point temperature (°C).The term "OG" refers to an off-glacier station, which are numbered accordingly. If no variable names are given as a header in the "Met_Data" tab, then the data are air temperature values.   Data are filtered for obvious errors and errors are then removed.  Data are not gap-filled as this would affect the analysis presented about patterns in air temperature data.  Data were checked and compiled by Thomas Shaw (WSL) - thomas.shaw@wsl.ch Data were measured by ETH (2001-2010) and WSL as part of the Marie-Curie Project 'TEMPEST' (2021-2022).Details of data collection and analysis can be found in: Strasser et al. (2004) - 2001. Carenzo (2012) - 2001-2010. Shaw et al. (N.D.) 2021-2022. %% CITED WORKCarenzo, M. (2012). Distributed modelling of changes in glacier mass balance and runoff (Issue 20616). ETH Zurich.Scherler, D., Wulf, H., & Gorelick, N. (2018). Global Assessment of Supraglacial Debris-Cover Extents. Geophysical Research Letters, 45(21), 11,798-11,805. https://doi.org/10.1029/2018GL080158Shaw, T. E., Buri, P., McCarthy, M., Miles, E. S., Ayala, Á., & Pellicciotti, F. (2023). The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier. Geophysical Research Letters, 50, 1–12. <a href="https://doi.org/10.1029/2023GL103043">https://doi.org/10.1029/2023GL103043</a>Strasser, U., Corripio, J. G., Pellicciotti, F., Burlando, P., Brock, B. W., & Funk, M. (2004). Spatial and temporal variability of meteorological variables at Haut Glacier d'Arolla (Switzerland) during the ablation season 2001: Measurements and simulations. Journal of Geophysical Research, 109, D03103. https://doi.org/10.1029/2003JD003973  ------------- This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.

opencc-by-4.0Jul 2022View details →

Meteorological variables for Agriculture: a Dataset for the Italian Area (MADIA)

The dataset is the supplementary material for the following journal paper: Parisse B.*, Alilla R., Pepe A.G., De Natale F., MADIA - Meteorological variables for Agriculture: a Dataset for the Italian Area, Data in Brief, 46 (2023), 108843, <a href="http://doi.org/10.1016/j.dib.2022.108843">10.1016/j.dib.2022.108843</a>, (<a href="https://www.sciencedirect.com/science/article/pii/S2352340922010460">https://www.sciencedirect.com/science/article/pii/S2352340922010460</a>) <ol> </ol>   Abstract The MADIA gridded dataset provides the series of the main agro-meteorological variables derived from ERA5 hourly surface data, across the Italian domain for the period 1981-2022, and their respective 1981-2010 and 1991-2020 climate normals, as well as the following statistics on the 30-year dekadal values of each variable: absolute minimum and maximum, 5th, 10th, 50th, 90th, 95th percentiles. Temporal and spatial resolutions are 10-daily and 0.25 degrees respectively. The dataset contains time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, solar radiation, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method. The dataset is provided in both NetCDF and csv format. In addition, discovery and description metadata are provided. In order to facilitate the data reuse for computing statistics at Italian NUTS 2 and 3 levels, a complementary vector file is provided which reports the cell weight in terms of fraction covered of each administrative unit considered. Another vector file is included with the ERA5 cell polygons covering the Italian country for visualizing and mapping csv data.  A daily version of the MADIA dataset (only in csv format) is also available on Zenodo at <a href="http://doi.org/10.5281/zenodo.7621453">https://doi.org/10.5281/zenodo.7621453</a>. Both MADIA datasets will be periodically updated. Attached content A ZIP archive composed by the following folders <ol> <li>nc_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in NetCDF format</li> <li>csv_data: annual time series from 1981 to 2022 and climate normals (1981-2010 and 1991-2020) in csv format</li> <li>metadata: discovery and description metadata </li> <li>shp_data: two complementary vector layers with the NUTS2-3 cover fractions and the ERA5 cell polygons for Italy</li> </ol> Acknowledgments This work was supported by the Italian Ministry of Agricultural, Food and Forestry Policies (AgriDigit-Agromodelli, DM n. 36502 of 20/12/2018)

opencc-by-4.0Jul 2023View details →

Two Large-Scale Meteorological Patterns Are Associated with Short-Duration Dry Spells in the Northeastern United States

Description This dataset contains processed data from the ERA5 dataset for some of the atmospheric fields considered in this study. Original (pre-processed) ERA5 data (Hersbach et al. 2020) is available at <a href="https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset">https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset</a>. For each processed data file (netCDF format), the time steps correspond with the events and numerical order as listed in Table 1 of the main manuscript text. Processed data files are given for some of the 12-day averaged dry periods. Other processed data files are available from the authors upon reasonable request.    Abstract Large-scale meteorological pattern (LSMP) – based analysis is used novelly to understand antecedent conditions and characteristics of short-duration dry spell events over the northeastern United States. Dry spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting twelve days or longer, which correspond to ~10% of dry spell events, are examined. The 500-hPa stream function anomaly fields for the first twelve days of each event are time-averaged and k-means clustering is applied to isolate the dry spell-related LSMPs. The first cluster has a strong, low-pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong, high-pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm track density across the Northeast is dramatically reduced during these dry spell events. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but quite different remote, properties. More (56%) short-duration dry spells occurred during the numerous non-drought months than drought months, however the frequency of dry spells is more than three times greater during drought than non-drought months.

Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2021 - April 2022

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2021 to April 2022 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C];  Salinity @ 6 m [psu]; Salinity @ 20 m [psu],  Salinity @ 36 m [psu].

Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2020 - July 2021

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2020 to July 2021 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C];  Salinity @ 6 m [psu]; Salinity @ 20 m [psu],  Salinity @ 36 m [psu].

Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) May 2017 - June 2018

Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from May 2017 to June 2018 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C];  Salinity @ 6 m [psu]; Salinity @ 20 m [psu],  Salinity @ 36 m [psu]

Hydro-meteorological database for watersheds across the CIS

The presented database is a set of hydrological, meteorological, environmental and geometric values for Russia Federation for the period from 2008 to 2020. Database consist of next items: <ul> <li>Point geometry for hydrological observation stations from Roshydromet network across Russia</li> <li>Geometry of the catchment for correspond observation station point</li> <li>Daily hydrological values <ul> <li>Water level <ul> <li>In relative representation (sm)</li> <li>In meters of Baltic system (m)</li> </ul> </li> <li>Water discharge <ul> <li>as an observed value (qms/s)</li> <li>as a layer (mm/day)</li> </ul> </li> </ul> </li> <li>Daily meteorological values <ul> <li>Maximum and minimum daily temperatures (°C) from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5</a> and <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form">ERA5-Land</a></li> <li>Total precipitation (mm/day) from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5</a>, <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form">ERA5-Land</a>, <a href="https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary">IMERG v06</a>, <a href="https://10.5067/MEASURES/GPCP/DATA305">GPCP v3.2</a> and <a href="https://www.gloh2o.org/mswep/">MSWEP</a></li> <li>Different kind of evaporation (mm/day) corresponded to each variable calculated in <a href="https://www.gleam.eu/">GLEAM</a> model</li> </ul> </li> <li>Set of hydro-environmental characteristics derived from <a href="https://www.hydrosheds.org/hydroatlas">HydroATLAS</a> database</li> </ul> Each variable derived from the grid data was calculated for each watershed, taking into account the intersection weights of the watershed contour geometry and grid cells. Coordinates of hydrological stations were obtained from resource of Federal Agency for Water Resources of Russia Federation—<a href="https://gmvo.skniivh.ru/index.php?id=505">AIS GMVO</a> To calculate the contours of the catchment areas, a script was developed that builds the contours in accordance with the rasters of flow directions from <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/">MERIT Hydro</a>. To assess the quality of the contour construction, the obtained value of the catchment area was compared with the archival value from the corresponded table from AIS GMVO. The average error in determining the area for 2080 catchments is approximately 2% To derive values for different hydro-environmental values from HydroATLAS were developed approach which calculate aggregated values for catchment, leaning on type of variable: qualitative (Land cover classes, Lithological classes etc.) Or quantitive (Air temperature, Snow cover extent etc.). Every quantitive variable were calculated as mode value for intersected sub-basins and target catchment, e.g. most popular attribute from sub-basins will describe whole catchment which are they relating. Quantitative values were calculated as mean value of attribute from each sub-basin. More detail could be found in <a href="http://ceur-ws.org/Vol-2930/paper13.pdf">publication</a>. Files are distributed as follows: Each file has some connection with the unique identifier of the hydrological observation post. Files in netcdf format (hydrological and meteorological series) are named in response to identifier. Every file which describe geometry (point, polygon, static attributes) has and column named gauge_id with same correspondence. <ul> <li>attributes/static_data.csv – results from HydroATLAS aggregation</li> <li>geometry/russia_gauges.gpkg – coordinates of hydrological observation stations <ul> <li> <table> <thead> <tr> <th> </th> <th>gauge_id</th> <th>name_ru</th> <th>name_en</th> <th>geometry</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>49001</td> <td>р. Ковда – пос. Софпорог</td> <td>r.Kovda - pos. Sofporog</td> <td>POINT (31.41892 65.79876)</td> </tr> <tr> <th>1</th> <td>49014</td> <td>р. Корпи-Йоки – пос. Пяозерский</td> <td>r.Korpi-Joki - pos. Pjaozerskij</td> <td>POINT (31.05794 65.77917)</td> </tr> <tr> <th>2</th> <td>49017</td> <td>р. Тумча – пос. Алакуртти</td> <td>r.Tumcha - pos. Alakurtti</td> <td>POINT (30.33082 66.95957)</td> </tr> </tbody> </table> </li> </ul> </li> <li>geometry/russia_ws.gpkg – catchments polygon for each hydrological observation stations              <ul> <li> <table> <thead> <tr> <th> </th> <th>gauge_id</th> <th>name_ru</th> <th>name_en</th> <th>new_area</th> <th>ais_dif</th> <th>geometry</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>9002</td> <td>р. Енисей – г. Кызыл</td> <td>r.Enisej - g.Kyzyl</td> <td>115263.989</td> <td>0.230</td> <td>POLYGON ((96.87792 53.72792, 96.87792 53.72708...</td> </tr> <tr> <th>1</th> <td>9022</td> <td>р. Енисей – пос. Никитино</td> <td>r.Enisej - pos. Nikitino</td> <td>184499.118</td> <td>1.373</td> <td>POLYGON ((96.87792 53.72708, 96.88042 53.72708...</td> </tr> <tr> <th>2</th> <td>9053</td> <td>р. Енисей – пос. Базаиха</td> <td>r.Enisej - pos.Bazaiha</td> <td>302690.417</td> <td>0.897</td> <td>POLYGON ((92.38292 56.11042, 92.38292 56.10958...</td> </tr> </tbody> </table> </li> <li>Column ais_diff is corresponded to % error in area definition</li> </ul> </li> <li>nc_all_q <ul> <li>netcdf files for hydrological observation stations which has no missing values on discharge for 2008-2020 period</li> </ul> </li> <li>nc_all_h <ul> <li>netcdf files for hydrological observation stations which has no missing values on level for 2008-2020 period</li> </ul> </li> <li>nc_all_q_h <ul> <li>netcdf files for hydrological observation stations which has no missing values on discharge and level for 2008-2020 period</li> </ul> </li> <li>nc_concat <ul> <li>data for all available geometry provided in dataset</li> </ul> </li> </ul> More details on processing scripts which were used for development of this database can be found in <a href="https://github.com/dmbrmv/my_dissertation/tree/main/data_builders">folder</a> of GitHub repository where I store results for my PhD dissertation 05.04.2023 – Significant data changes. Removed catchments and related files that have more than ±15% absolute error in calculated area relative to AIS GMVO information. Now these are data for 1886 catchments across the Russia.   17.05.2023 – Significant data changes. Major review of parsing algorithm for AIS GMVO data. Fixed the way of how 0.0xx values were read. Use previous versions with caution. 11.10.2023 – Significant data changes. Added 278 catchments for CIS region from GRDC resource. Calculate meteorological and environmental attributes for each catchment. New folder /nc_all_q_h with no missing observations on discharge and level. Now these are data for 2164 catchments across CIS.

openCC (other)Feb 2022View details →

Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016

Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.

openCC0Feb 2022View details →

Meteorological data for the Manitou Experimental Forest, Colorado, USA, 1936-1997

The Manitou Experimental Forest is an outdoor research laboratory in Colorado, USA, that has been run by the USDA Forest Service’s Rocky Mountain Research Station since 1936. This data publication contains meteorological data collected at the Manitou Experimental Forest from 1936-11-11 to 1997-06-24. Precipitation amount, current temperature, maximum temperature, and minimum temperature were collected at daily to weekly intervals over most of this period. Precipitation type, aboveground wind speed, ground wind speed, and wind direction were collected at daily to weekly intervals over a portion of the period.

openCC (other)Nov 2017View details →

Meteorological Field Measurements in Yahara River Watershed

These data are collected to support the Water Sustainability and Climate of the Yahara River Watershed Project. Meteorological measurements include air temperature, relative humidity, wind speed, gust wind speed, solar radiation, dew point temperature, and rainfall. Weather stations were installed at 3 locations across the Yahara Watershed at the UW Arboretum, the City of Madisons Cherokee Park, and Waunakee Marsh State Wildlife Area. These data are being collected to observe differences in how water and energy are processed under different land cover types and to create calibration datasets for a separate project goal of creating an agro-biophysical model of the entire Yahara River watershed.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Black Butte Meteorological Station (BLBT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Black Butte Meteorological Station (BLBT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetblbt/. These data complement and extend meteorological data recorded by an adjacent station (Met54), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Bronco Well Meteorological Station (BRWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Bronco Well Meteorological Station (BRWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbrwl/. These data complement and extend meteorological data recorded by an adjacent station (Met45), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Burris Well Meteorological Station (BUWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetbuwl/. These data complement and extend meteorological data recorded by an adjacent station (Met50), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Contreras Meteorological Station (CONT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Burris Well Meteorological Station (BUWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcont/.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Cerro Montoso Meteorological Station (CRMT), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Cerro Montoso Meteorological Station (CRMT). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetcrmt/. These data complement and extend meteorological data recorded by an adjacent station (Met42), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.

Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Deep Well Meteorological Station (DPWL), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.

The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Deep Well Meteorological Station (DPWL). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetdpwl/. These data complement and extend meteorological data recorded by an adjacent station (Met40), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.