Find research datasets worth reusing

opencc-by-nc-sa-4.0Jul 2019View details →

Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection

Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included: <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> Important notes: Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated. These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative. If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>

opencc-by-4.0Aug 2019View details →

Zambezi dataset to "WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus"

This is the dataset used in the HESS publication "<a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the Water-Energy-Food-Climate Nexus</a>" The dataset describes the water-energy-food nexus of the Zambezi River Basin used as input to the <a href="https://github.com/RaphaelPB/WHAT-IF">WHAT-IF model</a>. The file Data_Organization.pdf, summarizes the available data. For more info look at the <a href="https://www.hydrol-earth-syst-sci-discuss.net/hess-2019-167/">publication</a> and/or <a href="https://github.com/RaphaelPB/WHAT-IF">Github</a>.

opencc-by-4.0Aug 2019View details →

Simulations of shallow water wave turbulence

About This dataset curates all the simulations used to reproduce the paper: <blockquote> Shallow water wave turbulence DOI: <a href="https://doi.org/10.1017/jfm.2019.375">10.1017/jfm.2019.375</a> </blockquote> The source code and scripts necessary to generate the manuscript are archived at: <blockquote> <a href="https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water">https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water</a> </blockquote> See the README in the repository above to generate the manuscript Abstract The dynamics of irrotational shallow water wave turbulence forced at large scales and dissipated at small scales is investigated. First, we derive the shallow water analogue of the ‘four-fifths law’ of Kolmogorov turbulence for a third-order structure function involving velocity and displacement increments. Using this relation and assuming that the flow is dominated by shocks, we develop a simple model predicting that the shock amplitude scales as \((\epsilon d)^{1/3}\), where \( \epsilon\) is the mean dissipation rate and \(d\) the mean distance between the shocks, and that the \(p\)th-order displacement and velocity structure functions scale as \((\epsilon d)^{p/3} r/d\), where \(r\) is the separation. Then we carry out a series of forced simulations with resolutions up to 76802, varying the Froude number,\(F_{f} = (\epsilon L_f)^{1/3}/ c \), where \(L_f\) is the forcing length scale and \(c\) is the wave speed. In all simulations a stationary state is reached in which there is a constant spectral energy flux and equipartition between kinetic and potential energy in the constant flux range. The third-order structure function relation is satisfied with a high degree of accuracy. Mean energy is found to scale approximately as \(E \sim \sqrt{\epsilon L_f c}\), and is also dependent on resolution, indicating that shallow water wave turbulence does not fit into the paradigm of a Richardson–Kolmogorov cascade. In all simulations shocks develop, displayed as long thin bands of negative divergence in flow visualizations. The mean distance between the shocks is found to scale as \( d \sim F_f^{1/2} L_f\). Structure functions of second and higher order are found to scale in good agreement with the model. We conclude that in the weak limit, \(F_f \rightarrow 0 \), shocks will become denser and weaker and finally disappear for a finite Reynolds number. On the other hand, for a given \(F_f\), no matter how small, shocks will prevail if the Reynolds number is sufficiently large.

Monthly water maps of the Camargue from 2018

Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 35 Sentinel-2 scenes, from 12/24/2017 to 12/24/2018

Monthly water maps of the Camargue from 2015-2017

Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 12 monthly scenes of Landsat 8 OLI from 02/10/2015 to 12/16/2017.

Monthly water maps of the Camargue from 2001-2003

Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 12 monthly scenes of Landsat 7 ETM+ from 01/26/2001 to 04/06/2003.

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)

Water temperature measurements collected during austral summer 2017/2018 on lakes located in the Schirmacher oasis, East Antarctica.

Lakes’ water temperature are measured on lakes of three types (epiglacial, epishelf and land-locked) located in the Schirmacher oasis, East Antarctica. The temporal hydrological network is equipped by 11 temperature sensors, which are measured both surface and bottom water temperature of lakes. The surface temperature is recorded with the temperature loggers iButton DS1922L/DS1922T (https://www.maximintegrated.com/en/datasheet/index.mvp/id/4088) on 8 lakes. The sensors are deployed within a distance of 1–3 m from a lake’s coast, on a depth of 0.02 m. The lake’s surface temperature is also measured on two lakes with the temperature sensors by HOBO Water Level U20L (https://www.onsetcomp.com/products/data-loggers/u20l-01), which are deployed on the depth of 0.2 m. One HOBO sensor is installed to be attached to a lake’s ground on a depth approximately 0.5 m. The measurements cover the period of over 12–36 days depending on a lake. The data set includes the field campaign’s report of 63 RAE in the Schirmacher oasis (a text, in Russian) as a pdf-file, the metadata for the measurements (name, elevation, lon/lat of the temperature sensors deployed; name of the lakes; period with measurements; comments) as a shp-file, and the tables with water temperature measured for each lakes (as files of CSV format). Also, the deployment of the temperature sensor on the Lake Pomornik is presented in the jpg-file.

Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"

Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.

openapache2.0Oct 2019View details →

Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017

In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE). There are six fields in the HDF5 product provided here: <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication.  Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. Geophysical Research Letters, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a>

opencc-by-4.0Oct 2019View details →

Water Turbidity Masks Doñana 1984/2019

Time Series water turbidity derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, the product and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Teh methodology is described in the paper: Empirical models to estimate water turbidity from reflectance data from TM or ETM+ Landsat sensors in shallow wetlands such as Doñana marshes. See the reference: Bustamante, J. et al. 2009. Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images. Journal of Environmental Management. 90:2219-2225.https://doi.org/10.1016/j.jenvman.2007.08.021.

opencc-by-4.0Nov 2019View details →

Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins

We have simulated water budget and energy budget over Indian subcontinental basins, using three scenarios from the Variable Infiltration Capacity (VIC) model by including irrigation and reservoir practices in it: 1). No irrigation and reservoir (VIC-NATURAL) 2). Free irrigation and no reservoir (VIC-FREE) 3). Reservoir and restricted irrigation (VIC-MANAGED) Here, we have shared results in below folders. Fig1: Annual precipitation (P) and reservoir locations used in study. Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET. Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED. Fig4: Mean monthly observed and simulated reservoir storage. Fig5: P, ET, total runoff (TR) and LST from one grid. Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run. Fig7: Same as Fig6 but for TR. Fig8: Same as Fig6 but for LST. Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run. Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run. More detail is available in "Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins" paper in JGR-Atmosphere. Or contact at harsh.lovekumar.shah@iitgn.ac.in Harsh Shah

MERICS China Podcast: The dangerous waters of the South China Sea, with Helena Legarda

German Defense Minister Boris Pistorius' trip to the Asia-Pacific region in recent days shows that the region is becoming increasingly important for Germany and Europe. The trip took place against the backdrop of growing tensions in the region, particularly in the South China Sea. Beijing’s claims in the waters are far reaching and cover nearly 90 percent of the area. This claim is mostly based on its use in history by Chinese fishermen – and not supported by international law. Other countries bordering the South China Sea, like Brunei, Malaysia, the Philippines, Taiwan and Vietnam, also lay claim to parts of the same area. The waters of the South China Sea are vitally important for international trade and rich in natural resources – oil and gas fields are suspected to lie underground – and fishing grounds. Yet, the failure to find a mode of cooperation has precluded the extraction of resources and put in question the safety of the region for commercial shipping. In this episode of our MERICS China Podcast, MERICS Lead Analyst Helena Legarda and podcast host Johannes Heller-John talk about recent developments in the region. This episode was recorded on July 17, 2024.

Water Body Checklists 2019: Yellow Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Yellow Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Tyrrhenian Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Tyrrhenian Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Tasman Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Tasman Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Sulu Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Sulu Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Strait of Gibraltar Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Strait of Gibraltar using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Timor Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Timor Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: White Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the White Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.