Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
39
datasets available to search
ShareScore release 0.9.0
Dataset results
39 results for “hydrometeorology”
Preprocessed EUMETSAT H-SAF h61 Satellite and ECMWF HRES 24h Forecast Precipitation Datasets for Hydrometeorological Applications over Central Europe
<p>This dataset contains preprocessed precipitation data from the ECMWF's high resolution HRES forecast (24h) and EUMETSAT's blended infrared and microwave remotely sensed data for use in hydrological and meteorological research. <br><br><em>* Preprocessing procedure and codes are accessible <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HRES_PP.ipynb?ref_type=heads">here for HRES</a>, and <a href="https://gitlab.jsc.fz-juelich.de/kiste/atmoscorrect/-/blob/master/HSAF_PP.ipynb?ref_type=heads">here for H-SAF</a> datasets.</em></p> <p><strong>HRES Data (HRES_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://confluence.ecmwf.int/display/FUG/Section+2.1.2.4+HRES+-+High+Resolution+Forecasts">ECMWF's high-resolution, deterministic HRES 24-hour precipitation forecast at 12UTC.</a></li> <li><strong>Resolution and domain:</strong> 0.1° × 0.1° grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5)</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Extracted the precipitation variable (tp) out of variables.</li> <li>Converted precipitation units from meters (m) to millimeters (mm).</li> <li>Changed cumulative precipitation to instantaneous.</li> <li>Selected the first 24 hours of forecast data from the available 90-hour forecasts.</li> <li>Merged all processed files into a single NetCDF file.</li> </ol> </li> </ul> <p><strong>H-SAF Data (HSAF_pr.nc):</strong></p> <ul> <li><strong>Source:</strong> <a href="https://hsaf.meteoam.it/Products/Detail?prod=H61B">EUMETSAT's H-SAF h61B</a></li> <li><strong>Resolution and domain:</strong> Original product: ~4.8 km at nadir, ~8km in Europe; preprocessed product: resampled to 0.1° × 0.1° (~10 km) grid in (longmin: -1.1, longmax: 18.4, latmin: 44.1, latmax: 56.5).</li> <li><strong>Preprocessing Steps:</strong> <ol> <li>Trimmed the MSG coverage data to cover the study domain.</li> <li>Calculated the grid correspondance using lat/lon information from MSG grid using a <a href="https://www-cdn.eumetsat.int/files/2020-04/pdf_conf_2018_s1_mueller_p.pdf">reference method</a>.</li> <li>Merged all processed files into a single NetCDF file.</li> <li>Regridded the data to the 0.1° × 0.1° resolution using bilinear function in <a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></li> </ol> </li> </ul> <p><strong>Data Period:</strong> 01/07/2020-25/04/2023</p>
Cloud forests of the Orinoco River Basin (Colombia): Variation in vegetation and soil macrofauna composition along the hydrometeorological gradient
<p>We present vegetation, soil macrofauna, soil, hydrometeorological and topographical data collected from Tropical Montane Cloud Forests in the Orinoco River basin. Specifically, from the municipality of Chámeza, department of Casanare, Colombia. These data sets were used to evaluate how vegetation and soil macrofauna diversity vary along the 1700–2200 m a.s.l. elevation range. Within this elevation range, we have previously described a hydrometeorological gradient largely driven by a fog incidence increase with elevation. Vegetation data were collected for all individuals with a diameter at breast height (DBH) > 5 cm in four vegetation plots (5 x 50 m; total: 0.1 ha) every 100 m in altitude between 1700–2200 m a.s.l. From each plot, we obtained three soil monoliths from the organic layer and three from the mineral horizon, and manually extracted their soil macrofauna, and soil samples for determining pH, organic matter content, and soil texture, among others in a soil laboratory. Topographical data was inferred from Digital Elevation Models. Hydrometeorological data was collected in a previous study, but it was interpolated to the sampling plots. Here we present the interpolated hydrometeorological data.</p>
Cloud forests of the Orinoco River Basin (Colombia): Variation in vegetation and soil macrofauna composition along the hydrometeorological gradient
Open the record for dataset details and reuse information.
Hydrometeorological dataset of West Siberian boreal peatland: a 10-year records from the Mukhrino field station.
<p>Northern peatlands represent one of the largest carbon pools in the biosphere the carbon they store are increasingly vulnerable to perturbations from climate and land-use change. Meteorological observations directly at peatland areas in Siberia are unique and rare, while peatlands characterized by a specific local climate. This paper presents a hydrological and meteorological dataset collected at the Mukhrino peatland, Khanty–Mansi Autonomous Okrug – Yugra, Russia over the period of 08 May 2010 to 31 December 2019. Hydrometeorological data collected from stations located at the small pine-shrub-Sphagnum ridge and Scheuchzeria-Sphagnum hollow at the ridge–hollow complexes of ombrotrophic peatland. Monitored meteorological variables include air temperature, air humidity, atmospheric pressure, wind speed and direction, incoming and reflected photosynthetically active radiation, net radiation, soil heat flux, precipitation (rain), and snow depth. The gap-filling procedure based on the gaussian process regression model with exponential kernel was developed to obtain a continuous time series. For the record from 2010 to 2019, the average mean annual air temperature site was −1.0 ◦C, with a mean monthly temperature of the warmest month (July) recorded as 17.4 ◦C and for the coldest month (January) −21.5 ◦C. The average net radiation was about 35.0 W m<sup>-2</sup>, the soil heat flux was 2.4 and 1.2 W m<sup>-2</sup> for the hollow and the ridge sites, respectively.</p> <p>DATASETS:</p> <p><strong>meteo_MFC_raw.dat</strong> - Raw data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). Note: The time step differs during measured period 01.01.2010 – 15.07.2012: 15 minutes; 15.07.2012 – 20.06.2014: 1 hour; 20.06.2014 – 31.12.2020: 30 minutes.</p> <p><strong>meteo_MFC_qq_1h.dat</strong> – Quality controlled data collected from the automated weather station at the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.</p> <p><strong>meteo_MFC_gapfilled_1h.dat</strong> – Quality controlled and gap-filled hydrometeorological data for the Mukhrino field station (Khanty–Mansi Autonomous Okrug – Yugra, Russia). The time step is 60 minutes. The missing data denoted by “NA”.</p> <p><strong>meteo_MFC_raw.dat parameters:</strong></p> <p>1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).</p> <p>2. ta_H - Air temperature at 2 m, hollow ( oC ).</p> <p>3. ta_R - Air temperature at 2 m, ridge ( oC ).</p> <p>4. rh_H - Relative air humidity at 2 m, hollow ( % ).</p> <p>5. rh_R - Relative air humidity at 2 m, ridge ( % ).</p> <p>6. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).</p> <p>7. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).</p> <p>8. ws_10m - Wind speed at 10 m ( m s-1 ).</p> <p>9. wd_10m - Wind direction at 10 m ( deg ).</p> <p>10. ws_2m - Wind speed at 2 m ( m s-1 ).</p> <p>11. wd_2m - Wind direction at 2 m ( deg ).</p> <p>12. stdwd_10m - Standard deviation of wind direction at 10 m for the period of measurement ( m s-1 ).</p> <p>13. stdwd_2m - Standard deviation of wind direction at 2 m for the period of measurement ( m s-1 ).</p> <p>14. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).</p> <p>15. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).</p> <p>16. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).</p> <p>17. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).</p> <p>18. nr_H - Net radiation balance, hollow ( Uncalibrated ).</p> <p>19. nr_R - Net radiation balance, ridge ( Uncalibrated ).</p> <p>20. shf_H - Soil heat flux, hollow ( Uncalibrated ).</p> <p>21. shf_R1 - Soil heat flux, ridge, site 1 ( Uncalibrated ).</p> <p>22. shf_R2 - Soil heat flux, ridge, site 2 ( Uncalibrated ).</p> <p>23. ts_2cm_R1 - Soil temperature at 2 cm, ridge, site 1 ( oC ).</p> <p>24. ts_5cm_R1 - Soil temperature at 5 cm, ridge, site 1 ( oC ).</p> <p>25. ts_10cm_R1 - Soil temperature at 10 cm, ridge, site 1 ( oC ).</p> <p>26. ts_20cm_R1 - Soil temperature at 20 cm, ridge, site 1 ( oC ).</p> <p>27. ts_50cm_R1 - Soil temperature at 50 cm, ridge, site 1 ( oC ).</p> <p>28. ts_2cm_R2 - Soil temperature at 2 cm, ridge, site 2 ( oC ).</p> <p>29. ts_5cm_R2 - Soil temperature at 5 cm, ridge, site 2 ( oC ).</p> <p>30. ts_10cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).</p> <p>31. ts_20cm_R2 - Soil temperature at 20 cm, ridge, site 2 ( oC ).</p> <p>32. ts_50cm_R2 - Soil temperature at 50 cm, ridge, site 2 ( oC ).</p> <p>33. ts_2cm_H1 - Soil temperature at 2 cm, hollow, site 1 ( oC ).</p> <p>34. ts_5cm_H1 - Soil temperature at 5 cm, hollow, site 1 ( oC ).</p> <p>35. ts_10cm_H1 - Soil temperature at 10 cm, hollow, site 1 ( oC ).</p> <p>36. ts_20cm_H1 - Soil temperature at 20 cm, hollow, site 1 ( oC ).</p> <p>37. ts_50cm_H1 - Soil temperature at 50 cm, hollow, site 1 ( oC ).</p> <p>38. ts_2cm_H2 - Soil temperature at 2 cm, hollow, site 2 ( oC ).</p> <p>39. ts_5cm_H2 - Soil temperature at 5 cm, hollow, site 2 ( oC ).</p> <p>40. ts_10cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).</p> <p>41. ts_20cm_H2 - Soil temperature at 20 cm, hollow, site 2 ( oC ).</p> <p>42. ts_50cm_H2 - Soil temperature at 50 cm, hollow, site 2 ( oC ).</p> <p>43. T_cont - Temperature at data logger ( oC ).</p> <p>44. batt_1 - Battery output voltage at data logger 1 ( V ).</p> <p>45. batt_2 - Battery output voltage at data logger 2 ( V ).</p> <p>46. batt_2 - Battery output voltage at data logger 3 ( V ).</p> <p><strong>meteo_MFC_qq_1h.dat</strong> <strong>and meteo_MFC_gapfilled_1h.dat</strong> <strong>parameters:</strong></p> <p>1. date - Date and time ( DD/MM/YYYY hh:mm:ss ).</p> <p>2. ta_H - Air temperature at 2 m, hollow ( oC ).</p> <p>3. ta_R - Air temperature at 2 m, ridge ( oC ).</p> <p>4. vp_H - Water vapor pressure at 2 m, hollow ( kPa ).</p> <p>5. vp_R - Water vapor pressure at 2 m, ridge ( kPa ).</p> <p>6. ipar_H - Incoming PAR, hollow ( µmol m-2 s-1 ).</p> <p>7. ipar_R - Incoming PAR, ridge ( µmol m-2 s-1 ).</p> <p>8. rpar_H - Reflected PAR, hollow ( µmol m-2 s-1 ).</p> <p>9. rpar_R - Reflected PAR, ridge ( µmol m-2 s-1 ).</p> <p>10. alb_H - Albedo PAR, hollow ( [] ).</p> <p>11. alb_R - Albego PAR, ridge ( [] ).</p> <p>12. nr_H - Net radiation balance, hollow ( W m-2 ).</p> <p>13. nr_R - Net radiation balance, ridge ( W m-2 ).</p> <p>14. shf_H - Soil heat flux, hollow ( W m-2 ).</p> <p>15. shf_R1 - Soil heat flux, ridge, site 1 ( W m-2 ).</p> <p>16. shf_R2 - Soil heat flux, ridge, site 2 ( W m-2 ).</p> <p>17. ws_10m - Wind speed at 10 m ( m s-1 ).</p> <p>18. wd_10m - Wind direction at 10 m ( deg ).</p> <p>19. ws_2m - Wind speed at 2 m ( m s-1 ).</p> <p>20. wd_2m - Wind direction at 2 m ( deg ).</p> <p>21. wU_10m - U component of wind at 10 m ( m s-1 ).</p> <p>22. wV_10m - V component of wind at 10 m ( m s-1 ).</p> <p>23. wU_2m - U component of wind at 2 m ( m s-1 ).</p> <p>24. wV_2m - V component of wind at 2 m ( m s-1 ).</p> <p>25. prs - Atmospheric pressure ( hPa ).</p> <p>26. sdp - Snow depth ( cm ).</p> <p>27. prc - Liquid precipitations ( mm ).</p>
Atmospheric Surface Flux Station #50 measurements (level 2 Processed), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023
<p>Processed (Level 2) measurements and derived variables from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables of the surface energy budget, momentum flux, near-surface meteorology, and soil properties. Measurements of high-resolution 3-dimensional winds were observed at a nominal height of 4.6 m (depending on snow depth). Measurements of upwelling broadband radiation and meteorology observed from a nominal height of 2.9 m. The measurements are included in three netCDF files per day. The "sledmet" files are comprised of 1-min averages of measured and derived variables, including near-surface meteorology, surface skin temperature, snow depth, radiative fluxes, and conductive fluxes. The "sledseb" files are 10-min averages of the same variables as in the 1-min files and also include calculations of turbulent sensible and latent heat fluxes, momentum flux, and associated diagnostics, surface stress, and Monin-Obukhov parameters using both eddy covariance and bulk methodologies, all valid for the 10-min intervals. Both of these file types also contain a "_qc" variable paired with each measurement variable, or family of variables, that is a temporally-matched quality control code: 0 = good data, 1 = caution (data may be suspect), 2 = bad data, and -1 = missing (no data was collected). The "10hz" files include 3-dimensional winds and gas densities of water vapor and carbon dioxide that are quality-controlled, aggregated to a regular 10-Hz temporal grid, and (for winds) rotated into the earth coordinate frame. A detailed documentation of the measurement conditions, the processing steps taken to construct this data set, and other caveats and uncertainties will be provided in an accompanying published data manuscript.</p> <p>Note on update: v2_1 update provides double rotation ("dbl") turbulent fluxes in addition to planar fit ("pf"), as well as corrects a rotation problem with the v2 data that primarily affects the momentum fluxes. v2_1 only includes updates for the sledseb file set: for sledmet and sledwind10hz, continue to refer to v2 data set.</p>
Atmospheric Surface Flux Station #30 measurements (level 2 Processed), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023
<p>Processed (Level 2) measurements and derived variables from the Atmospheric Surface Flux Station #30 (ASFS-30) deployed at the Kettle Ponds Annex site (38°56.3686' N, 106°58.1781' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from September 2021 through July 2023. The ASFS measured variables of the surface energy budget, momentum flux, near-surface meteorology, and soil properties. Measurements of high-resolution 3-dimensional winds were observed at a nominal height of 4.6 m (depending on snow depth). Measurements of upwelling broadband radiation and meteorology observed from a nominal height of 2.9 m. The measurements are included in three netCDF files per day. The "sledmet" files are comprised of 1-min averages of measured and derived variables, including near-surface meteorology, surface skin temperature, snow depth, radiative fluxes, and conductive fluxes. The "sledseb" files are 10-min averages of the same variables as in the 1-min files and also include calculations of turbulent sensible and latent heat fluxes, momentum flux, and associated diagnostics, surface stress, and Monin-Obukhov parameters using both eddy covariance and bulk methodologies, all valid for the 10-min intervals. Both of these file types also contain a "_qc" variable paired with each measurement variable, or family of variables, that is a temporally-matched quality control code: 0 = good data, 1 = caution (data may be suspect), 2 = bad data, and -1 = missing (no data was collected). The "10hz" files include 3-dimensional winds and gas densities of water vapor and carbon dioxide that are quality-controlled, aggregated to a regular 10-Hz temporal grid, and (for winds) rotated into the earth coordinate frame. A detailed documentation of the measurement conditions, the processing steps taken to construct this data set, and other caveats and uncertainties will be provided in an accompanying published data manuscript.</p> <p>Note on update: v2_1 update provides double rotation ("dbl") turbulent fluxes in addition to planar fit ("pf"), as well as corrects a rotation problem with the v2 data that primarily affects the momentum fluxes. v2_1 only includes updates for the sledseb file set: for sledmet and sledwind10hz, continue to refer to v2 data set.</p>
Atmospheric Surface Flux Station #30 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023
<p>Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS-30) deployed at the Kettle Ponds Annex site (38°56.3686' N, 106°58.1781' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from September 2021 through July 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties. These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.</p>
Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023
<p>Raw (Level 1) measurements from the Snow Ice Mass Balance Apparatus (SIMBA) deployed at the Avery Picnic site (~ 38°58.345' N, 106°59.811' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from November 2021 through June 2023. The SIMBA, originally designed for observing the mass balance of sea ice, is comprised of a thermistor chain with 2 cm spacing (Jackson et al., 2013). This system was configured for terrestrial snowpack by the manufacturer, SAMS Enterprise, to the specifications for SPLASH. The chain was installed suspended from a tripod and fixed to a rigid plastic bar near in time to the onset of snowpack in November 2022. The lowest 10 cm of the chain were buried within the soil. The top of the chain reached approximately 180 cm above the soil surface and snow was permitted to accumulate around the chain throughout the winter of 2022-2023. In the files, negative values of the "height" vector are below the soil surface and positive levels are above, which may be either snow or air depending on the snow depth. The system also uses a low-power heating cycle to measure thermistor's temperature response time for aiding in determining material interfaces: see Jackson et al. (2013) for details. </p><p>There are several cautions to be aware of when using these data. The data has been ingested into daily netCDF and metadata (in attributes) have been provided but no quality control has been carried out on this raw version of the data set. From 1 November through 22 December 2022, the sensor obtained profiles every 10 min after which corruption of the configuration file reverted the profiles to every 6 hours (0, 6, 12, and 18 UTC). After 1 January a problem in the firmware caused the system to lose connection to the time-synching GPS network and therefore the clock drifted from January through June 2023 (the maximum potential time stamping error is likely < 81 sec). Finally, from 23 March through 4 April 2023, the depth of the snow at the location of the sensor was deeper than 180 cm and thus measurements in the upper part of the snowpack were not observed then.</p><p>Jackson, K., J. Wilkinson, T. Maksym, D. Meldrum, J. Beckers, C. Haas, and D. Mackenzie (2013) A novel and low-cost sea ice mass balance buoy. Journal of Atmosphere and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/JTECH-D-13-00058.1.</p>
Atmospheric Surface Flux Station #50 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023
<p>Raw (Level 1) measurements from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties. These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.</p>
Daily and sub-daily hydrometeorological and soil data (2013-2023) [COSMOS-UK]
<p>This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2023. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables.</p>
Daily and sub-daily hydrometeorological and soil data (2013-2023) [COSMOS-UK]
<p>This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2023. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables.<br><br>Each site in the network records the following hydrometeorological and soil data at 30-minute resolution: Radiation (short wave, long wave, and net), precipitation, atmospheric pressure, air temperature, wind speed and direction, humidity, soil heat flux, and soil temperature and volumetric water content (VWC), measured by point sensors at various depths.</p>
Supplementary material for: Lala, J., Tilahun, S., and Block, P. (2020). Predicting rainy season onset in the Ethiopian Highlands for agricultural planning. Journal of Hydrometeorology.
<p>Supplementary material for: Lala, J., Tilahun, S., and Block, P. (2020). Predicting rainy season onset in the Ethiopian Highlands for agricultural planning. Journal of Hydrometeorology. This includes onsets and cessations for northwestern Ethiopia, climate signals, and MATLAB scripts for calculating hindcasts</p>
Data from: Cryospheric hydrometeorology observation in the Hulu Catchment (CHOICE), Qilian Mountains, China
Understanding cryospheric hydrology and the effects of cryospheric changes on river runoff is critical for sustainable water management, especially in arid inland river basins, such as those in Northwest China, where water resources mainly come from alpine areas. A cryospheric hydrometeorology observation system (CHOICE) has been established since 2008 in the Hulu Catchment, which is a well instrumented experimental and representative catchment in the upper reaches of the inland Hei River, Qilian Mountains, Northwest China. The CHOICE includes dense meteorological measurements from 2,980 to 4,800 m a.s.l., such as glacier, snow and permafrost hydrology; water and heat balance in the vertical landscape zones, including alpine grassland, meadow, shrub, coniferous forest, marshy meadow and moraine-talus zones. The comprehensive of long-term observations available for the CHOICE provides the basis for model development and application in cryospheric hydrological research. We try to study on cryospheric hydrometeorological process of precipitation, freeze-thaw cycle, energy balance, soil-vegetation-atmosphere-transfer (SVAT), runoff, groundwater reservoir and hydrological resiliency within vertical altitude in CHIOCE. In addition, the CHOICE of data sharing are mainly through website (http://hhsy.casnw.net/) and WestDC database (http://westdc.westgis.ac.cn/). The CHOICE, as implied by as its name, is an open cryospheric hydrology observation and research system.
Data from: Cryospheric hydrometeorology observation in the Hulu Catchment (CHOICE), Qilian Mountains, China
Open the record for dataset details and reuse information.
Daily and sub-daily hydrometeorological and soil data (2013-2023) [COSMOS-UK]
<p>This dataset contains daily and sub-daily hydrometeorological and soil moisture observations from COSMOS-UK (cosmic-ray soil moisture) monitoring network from October 2013 to the end of 2023. These data are from 51 sites across the UK recording a range of hydrometeorological and soil variables.</p>
Central Asian Snow Cover from Hydrometeorological Surveys, Version 1
This data set provides observations of end of month snow depth, snow density, and snow water equivalent from three river basins in Central Asia: Amu Darya, Sir Darya, and Naryn. Temporal coverage varies for each snow point, with the longest station record extending from 1932 through 1990.
The North American Monsoon GPS Hydrometeorological Network 2017: Flux Data
<p>Water, energy and carbon fluxes and acnillary meteorological measurements taken during The North American Monsoon GPS-Hydrometeorological Network 2017. The experiment was carried out during the summer of 2017 in the state of Sonora in northwestern Mexico.</p>
Spetiotemperal-hydrometeorological-data-in-Taiwan
<ul> <li><strong>CEEMD</strong> <ul> <li><a href="https://zenodo.org/api/files/0002c7c4-19a9-400e-bf33-239f6dc46ff6/ceemd.m?versionId=9dc81967-96ef-4965-a4a1-7e805ec069b6">ceemd.m</a>/ <a href="https://zenodo.org/api/files/0002c7c4-19a9-400e-bf33-239f6dc46ff6/extrema.m?versionId=6a08e6d0-4722-4836-afa1-714e8ba04477">extrema.m</a></li> </ul> </li> <li><strong>Daily mean temperature</strong> <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tm.m?versionId=6cbbd819-9ee3-4fe3-bc60-1760f7a25f3a">data_IMF_tm.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/tm.mat?versionId=8c53d669-fdc4-41fb-aafe-3e9811c07298">tm.mat</a></li> </ul> </li> <li><strong>Daily maximum temperature</strong> <ul> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/tmax.mat?versionId=36582dd1-16e1-4533-b723-22a304bee4a7">tmax.mat</a></li> </ul> </li> <li><strong>Heatwave</strong> <ul> <li>CTX90pct <ul> <li>all <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tmax_ctx.m?versionId=78eda985-d21d-4d5d-adc1-549c05c2c1e3">data_IMF_tmax_ctx.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/heatwave_CTX90pct.mat?versionId=ed6c40b7-2371-4bf9-a61a-f7f2dcc43cfe">heatwave_CTX90pct.mat</a></li> </ul> </li> <li>summer <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tmax_ctx_summer.m?versionId=1a036064-461d-4f22-8e4d-aa467adbcac6">data_IMF_tmax_ctx_summer.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/heatwave_CTX90pct_summer.mat?versionId=6eee8eed-a98f-4400-9bf9-b2a918f2e2fd">heatwave_CTX90pct_summer.mat</a></li> </ul> </li> </ul> </li> <li>EHF <ul> <li>all <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tmax_EHF.m?versionId=0380befe-3a35-4d49-81d9-04101db58c63">data_IMF_tmax_EHF.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/heatwave_EHF2.mat?versionId=5127bde1-ab30-4580-8840-f401af1889e3">heatwave_EHF2.mat</a></li> </ul> </li> <li>summer <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tmax_EHF_summer.m?versionId=e8e0b3bd-dd68-43fe-abf5-8cb06783f7b9">data_IMF_tmax_EHF_summer.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/heatwave_EHF_summer.mat?versionId=d1a6fd71-6a6e-4bbd-a5bf-3a985f29abe7">heatwave_EHF_summer.mat</a></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Extreme cold events</strong> <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_tmin.m?versionId=48d587d7-3b15-415c-8b2e-4e0c47e63417">data_IMF_tmin.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/tmin.mat?versionId=fedc9607-578a-4870-8b63-5fd3be367a56">tmin.mat</a></li> </ul> </li> <li><strong>Heavy rain events</strong> <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/data_IMF_pr.m?versionId=7c2aa126-2060-4744-8aae-81e83c24df06">data_IMF_pr.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/pr.mat?versionId=a64187ca-da09-421f-ae32-fa90eb4e928f">pr.mat</a></li> </ul> </li> <li><strong>Long-term heatwaves</strong> <ul> <li>code: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/meemd_future.m?versionId=0a4210b2-84a1-4f0b-9480-b1b7d653c06f">meemd_future.m</a></li> <li>data: <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/futuredata45.mat?versionId=e2b6bcae-1845-4f3d-bbe3-7b88610ff2af">futuredata45.mat</a>/ <a href="https://zenodo.org/api/files/0a980371-71f7-4e36-9794-26cb5a65fdc9/futuredata85.mat?versionId=c34d8226-a04b-432a-9d41-befeaa3b1a06">futuredata85.mat</a> </li> </ul> </li> </ul>
The derived catchment-scale hydrometeorological data for hydrological Ep
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.