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676 results for “hydrology”
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
UWSCatCH: Urban Water Supply Catchment Contributions and Hydrological Statistics for large cities of the conterminous United States.
<p>UWSCatCH extends and enhances the Urban Water Blueprint (McDonald et al., 2014) for a selection of 116 cities (population > 150,000) and their associated surface water supply catchments in the conterminous United States. The two major enhancements to the Urban Water Blueprint are: [1] estimates of the relative contributions of each surface water catchment to each city's average water supply (as well as updated estimates of any contributions from groundwater); [2] NHDplusV2 reach codes for each water supply intake stream location and associated average flow estimates (regulated and unregulated) (local upstream USGS gage IDs are also provided). UWSCatCH also features a raster file with spatially distributed (1/24° grid) runoff (average of 1980 - 2012 reanalysis simulation) which is masked to watershed polygons (included as a shapefile) to explore spatial distribution of average runoff generation affecting each city. UWSCatCH is designed for use in the R package "gamut" (https://github.com/IMMM-SFA/gamut), and may be applied in a variety of regional and national scale research studies concerning drinking water supply to major US cities.</p>
Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories
<p>These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).</p>
Summer and winter invertebrate and physicochemical data from the Coweeta Hydrologic Lab
<p>This resource contains data for aquatic invertebrates collected from leaf litterbags, which were deployed in 11 streams at the Coweeta Hydrologic Lab (Macon County, North Carolina, USA) during winter and summer months in 2017-2018. Litterbags consisted of fine-mesh bags (250µm) attached to coarse-mesh bags (5mm), each containing <em>Rhododendron maximum</em> leaf litter. The litterbags were deployed for two-month periods, which were as follows: 19 October - 11 or 18 December, 2017; 15 November - 5 January 2017-2018; 9 May - 5 July 2018; and 5 July - 31 August 2018. We collected invertebrate samples from the >1mm size fraction from 3 coarse-fine litterbag pairs incubated in each of our streams during the aforementioned 2-month periods. Invertebrates were preserved in ethanol, identified, and classified into functional feeding groups based on classifications in Merritt et al. 2019. We identified invertebrates in the "shredder" functional feeding group to genus and all other insects to family (Merritt et al. 2019). We also measured invertebrate lengths in mm and converted these lengths to masses using information from Benke 1999. This resource also contains daily temperature and discharge data, litter breakdown data from the coarse-mesh bags associated with the invertebrate data, and weekly nutrient concentration data from the streams during the study period. Discharge data was provided by the USFS Coweeta Hydrologic Lab and can also be found here: https://www.fs.usda.gov/rds/archive/catalog/RDS-2016-0025-2</p> <p>Discharge data citation:</p> <p>USFS Coweeta Hydrologic Laboratory. 2023. Daily streamflow data for gauged watersheds at Coweeta Hydrologic Laboratory, North Carolina. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0025-2</p>
Parameter fields for the Hydrological Discharge (HD) model at 0.5° and 5 Min. horizontal resolution
<p><strong>HD model parameter files</strong></p> <p>This dataset comprises global parameter data that are necessary to run the Hydrological Discharge (HD) model Vs. 5.1, which has been published on <a href="http://doi.org/10.5281/zenodo.5707587">Zenodo</a>. The HD model calculates the lateral transport of water over the land surface to simulate discharge into the oceans. The HD model parameter dataset comprises parameter fields at 0.5° global resolution and at 5 Min. resolution (global, Europe). Details for both resolutions are provided below. </p> <p><strong>Authors</strong>: Stefan Hagemann, Tobias Stacke <br> <strong>Copyright 2021</strong>: Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon<br> <strong>License</strong>: under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0; https://creativecommons.org/licenses/)</p> <p><br> <strong>HD model parameter file at 5 Min resolution: hdpara_vs5_1.nc</strong></p> <p>River directions and digital elevation data were provided by Bernhard Lehner (pers. comm., 2014) and were derived from the HydroSHEDS (Lehner et al., 2006) database and from the Hydro1K dataset for areas north of 60°N (https://lta.cr.usgs.gov/HYDRO1K).<br> For a number of rivers (most of them north of 60°N), flow directions and model orography were manually corrected based on available GIS data, such as from DIVA (https://www.diva-gis.org/gdata), CCM River and Catchment Database<br> (Vogt et al. 2007), SMHI (Swedish Meteorological and Hydrological Institute), NVE (Norges vassdrags- og energidirektorats), SYKE (Finnish Environment Institute).</p> <p>This corrected dataset is referred to as HDvs5 in the following.<br> The HD model parameters for overland flow, base flow and river flow are generated as described in Hagemann and Dümenil (1998) and Hagemann et al. (2020). However, different to the HD model vs. 4 described in Hagemann et al. (2020), Vs5 utilizes inland water fractions from the ESA CCI Water Bodies Map v4.0 (Lamarche et al. 2017) and wetland fractions from the Global Lakes and Wetlands Database (Lehner and Döll 2004) instead of the previously used lake and wetlands fractions. Changes from Vs. 5.0 to Vs. 5.1 are provided in the file history_data.md that should be previewed below.</p> <p>The HD parameter dataset contains 14 variables which are shortly described in the following table.</p> <ul> <li> FLAG | Land sea mask | -</li> <li> FDIR | Flow direction | - | defined as written below</li> <li> ALF_K | HD model parameter Overland flow k | d-1</li> <li> ALF_N | HD model parameter Overland flow n | -</li> <li> ARF_K | HD model parameter Riverflow k | d-1</li> <li> ARF_N | HD model parameter Riverflow n | -</li> <li> AGF_K | HD model parameter Baseflow flow k | d-1</li> <li> AREA | Grid cell area | m-2 | based on own computation</li> <li> FILNEW | River flow target indices for longitudes | -</li> <li> FIBNEW | River flow target indices for latitudes | -</li> <li> DISTANCE | Distance between gridboxes in flow direction | m</li> <li> RIVERLENGTH | Distance between gridbox and the river mouth (or final sink) | km</li> <li> CAT_AREA | Upstream catchment area of gridbox | km²</li> <li> CAT_ID | Catchment ID of gridbox | -</li> </ul> <p> <em> Flow directions in variable FDIR are defined as on the Num Pad of a PC keyboard: </em> </p> <ul> <li> 7 8 9</li> <li> \ | /</li> <li> \|/</li> <li> 4--5--6</li> <li> /|\</li> <li> / | \</li> <li> 1 2 3</li> </ul> <p> Special directions: 5 = Sink point, i.e. no outflow<br> 0 = River mouth point in the ocean <br> -1 = Ocean point, but no river mouth</p> <p>Forcing data masks file: masks_5min.nc</p> <p>In the offline HD model version, this file is usually only used to obtain the grid information of the forcing data, i.e. of surface runoff and drainage (subsurface runoff). However, it contains four variables that are read in by the model, and that are actually used un coupled applications within the MPI-ESM. Even though these variables are not used in the HD model offline version, it was decided to keep them in order to allow future developments regarding the usage of these data and to keep some consistency with the HD model code implemented in MPI-ESM.</p> <ul> <li> ALAKE | Lake fraction within a grid box | Lamarche et al. 2017</li> <li> GLAC | Glacier fraction within a grid box | Hagemann 2002</li> <li> SLF | Land fraction within a grid box | Lamarche et al. 2017</li> <li> SLM | Land Sea Mask | Lamarche et al. 2017</li> </ul> <p>For simplicity, the data provided at the HD model resolution. Hence, these masks can be used when the forcing data are interpolated to the HD model resolution before they are read during the model run.</p> <p>This tar archive also include a subset of this global dataset for the European domain, hdpara_vs5_0_euro5min.nc.</p> <p> </p> <p><strong>HD model parameter file at 0.5° resolution: hdpara_vs1_11.nc</strong></p> <p>In addition, a global 0.5° HD parameter file hdpara_vs1_11 included. This is an update of the previous version 1.10 that was consistent to the parameter files used in previous offline and coupled applications of the HD model at 0.5° resolution (see, e.g. studies cited in Sect. 2.1 of Hagemann et al., 2020). Compared to the previous version 1.10, Vs. 1.11 now also utilzes the ESA water bodies and GLWD wetlands database such as in the 5 Min vs. 5.1 (see above). In additon, some flow directions have been updated. Except for DISTANCE and RIVERLENGTH, it comprises the same variables as for the 5 Min. version, but flow directions and parameters are generated as described in Hagemann and Dümenil (1998) and Hagemann and Dümenil Gates (2001). Here, the 0.5 degree mask file mask_05.nc comprises those masks that were utilized in the HD parameter generation. Only the land fraction is taken from Hagemann (2002) where the HD land sea mask indicates land.</p> <p><br> <strong>References</strong></p> <ul> <li>Hagemann, S., L. Dümenil (1998) A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 14 (1), 17-31</li> <li>Hagemann, S., L. Dümenil Gates (2001) Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model, J. Geophys. Res. 106, 1503-1510</li> <li>Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models, MPI Report No. 336, Max Planck Institute for Meteorology, Hamburg, Germany</li> <li>Hagemann, S., T. Stacke and H. Ho-Hagemann (2020) High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci., 8:12. doi: 10.3389/feart.2020.00012.</li> <li>Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O. (2017) Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sensing, 9(1), p.36.</li> <li>Lehner, B., P. Döll (2004) Development and validation of a global database of lakes, reservoirs and wetlands.</li> <li>J. Hydrol., 296: 1-22, doi:10.1016/j.jhydrol.2004.03.028.</li> <li>Vogt, J.V. et al. (2007): A pan-European River and Catchment Database. European Commission - JRC, Luxembourg, (EUR 22920 EN) 120 pp.</li> </ul> <p> </p>
Database of geo-hydrological hazards in Apulia (Italy)
<p>Geospatial database containing data on geo-hydrological processes (Landslides, Floods, Sinkholes) and/or related damage, occurred between 2008 and 2019 in the Apulia Region (Italy).</p> <p>We provide a GPKG file containing multiple layers for the different types of geometries (point, line, polygon).<br> Data are extracted from a complex relational database structure described originally here https://doi.org/10.1016/j.jenvman.2017.11.022 but recently updated and improved.<br> For the different damage and phenomena we provide information about type, data and time of occurrence, temporal and spatial accuracy, main predisposing factor, etc..<br> For floods we provides codes and information compliant with the EC Flood Directive.</p> <p>The different phenomena and damages are grouped based on the meteorological event responsible for their occurrence.</p>
PEATCLSM_Trop: Integrating peat-specific land surface hydrology of natural and drained tropical peatlands in the GEOS CLSM framework
<p>The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or *.nc4c) for CLSM, the natural (PEATCLSM<sub>Trop,Nat</sub>), and drained (PEATCLSM<sub>Trop,Drain</sub>) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSM<sub>North,Nat</sub>) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files:<br> • daily_images_*.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack;<br> • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image;<br> • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.</p> <p>The file content is described in the file PEATCLSM_Trop-Simulations.pdf.</p> <p>Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.<br> <br> References:<br> Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of Advances in Modeling Earth Systems</em>. https://doi.org/10.1029/2021MS002784<br> Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems, 11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574<br> De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. <em>Journal of Advances in Modeling Earth Systems, 6</em>(4), 957– 979. https://doi.org/10.1002/2014MS000330<br> Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. <em>Catena, 160</em>, 134–140. https://doi.org/10.1016/j.catena.2017.09.010</p>
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 1: Groundwater measurements
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated groundwater and stream interactions in order to characterize the physical processes that control the water and solute exchange in the river corridor and their variability over time. To do so, we drilled 36 wells in the near-stream domain, and 7 piezometers in the stream channel. We observed the water level and electrical conductivity every 15 min at 22 of the 36 wells with a water level sensor (Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS) over a period of 32 months (July 2018 - March 2021).</p> <p>The dataset includes the following files:</p> <p>> "Raw groundwater measurements.xlsx" <br> This file includes groundwater table elevation measured as depth from the upper limit of the well (time step of 15 minutes, Orpheus Mini, OTT, Kempten, Germany, resolution of 1 mm and accuracy of ±0.05% FS). Every excel file includes also Electrical Conductivity measurements (μS/cm) and Voltage (V) of the instruments. Every sheet in this .xlsx file reports measurement for one sensor in the specific observation-well where it was placed.</p> <p>> "Groundwater elevation data - wells metadata and fixed groundwater table elevation.xlsx"<br> This file includes the raw groundwater table measurements measured via the OTT, information on the well network, elevation and location of the observation wells, location of subsurface layers, and suggested correction of the groundwater table measurements for short periods with missing data.</p> <p>> "Hand-measurements and metadata.xlsx"<br> This file includes the list of in-situ inspections and hand-measurements of the groundwater table conducted over the entire observation period in every well and piezometer of the groundwater-monitoring well network. Every sheet includes details on the instruments measuring the groundwater table, their offset with hand-measured data, information on their re-calibration, and calculation of the groundwater elevation above the reference plane after each hand-measurement.</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
River network and hydro-geomorphological parameters at 1/12° resolution for global hydrological and climate studies
<p>Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data.<br> This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography).<br> The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase.<br> The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.</p>
Rosalia: An experimental research site to study hydrological processes in a forest catchment - data repository
<p>This repository is a supplement to the paper <strong>Fürst, J., et al. (2021). “Rosalia: an experimental research site to study hydrological processes in a forest catchment.” Earth Syst. Sci. Data 13(8): 4019-4034.</strong></p> <p>Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19<sup>th</sup> and early 20<sup>th</sup> century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called “Rosalia” with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the “Rosalia” instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum.</p> <p>This repository contains the datasets collected by a monitoring network of 4 discharge gauging stations, 7 rain-gauges, together with observations of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community.</p>
Intensified atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial from a dynamical downscaling perspective
<p>We provide the datasets run for investigating the atmospheric branch of the hydrological cycle over the Tibetan Plateau during the Last Interglacial (127 ka), based on the mesoscale Weather Research and Forecasting (WRF) model driven by the Community Earth System Model (CESM). We upload summer mean of the model outputs from the WRF over the Tibetan Plateau used in estimating the atmospheric branch of the hydrological cycle.</p>
Compilation of hydrological datasets at the global scale
<p>Hydrological variables (evapotranspiration, precipitation, runoff, and terrestrial water storage) are distributed through several datasets that do not have the same characteristics (spatial and temporal coverage, spatial and temporal resolution). This dataset aims at providing a coherent gathering of all datasets available as of January 2021, covering at least the period from 2003 to 2014, and from 50°S to 50°N.</p> <p>This file contains:</p> <ul> <li>14 datasets for evapotranspiration (ERA5-Land, FLUXCOM, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GLEAM, JRA55, MERRA2, MOD16, and SEBBop)</li> <li>11 datasets for precipitation (CPC, CRU, ERA5-Land, PGF, GPCC, GPCP, GPM, JRA55, MERRA2, MSWEP, and TRMM)</li> <li>11 datasets for runoff (ERA5-Land, GLDAS2.2 CLSM2.5, GLDAS2.1 CLSM2.5, GLDAS2.1 NOAH3.6, GLDAS VIC4.1.2, GLDAS2.0 CLSM2.5, GLDAS2.0 NOAH3.6, GLDAS2.0 VIC4.1.2, GRUN, JRA55, and MERRA2)</li> <li>2 datasets for terrestrial water storage (GRACE CSR mascons, and GRACE JPL mascons)</li> </ul> <p>Each dataset is given as the original version (on a regular grid) and also as a post-treated file averaged for 189 river basins (whose borders are also provided).</p>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
OMS Project for the hydrological modelling of the Posina River
<p>The OMS project contains the simulations, jar files of the components, the inputs and the ouputs used in the thesis " A flexible approach to the estimation of water budgets and its connection to the travel time theory ", Bancheri (2017). The project can be run using the OMS console available within the project.</p>
Atmospheric_river_land_hydrology_western_US_HUC8_datasets
<p>Dataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.</p> <p> </p> <p>To use this dataset, please cite the following two publications:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. <em>Geophysical Research Letters</em>, 45, 11,693–11,701. <a href="https://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. <em>Journal of Geophysical Research: Atmospheres</em>, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Plantain mulch and groundcover soil hydrological experiment in St. Croix, Virgin Islands
<p>.csv files containing data used in paper titled "Soil water regimes in tropical plantain production under organic and living mulches" cnducted in 2021-22. Includes plantain agronomic data, time-series data from soil water content sensors, bulk density and particle size information, and input data to the HYDRUS-1D model for water balance simulations. Column names and units are fully described in metadata.csv.</p>
Coupled Hydrological and Thermal Models of Rockwall Permafrost
<p>This dataset contains forcing data and selected output of coupled thermal and hydrological simulations applied to a high-elevated rockwall site (the Aiguille du Midi, 3842 m asl, Mont Blanc massif, France).</p> <p>All data are provided as .shp and .shx for display, as well as a .dbf file for quick reading. They are made of 5 columns, whose:</p> <ul> <li>« Node » is the Node ID,</li> <li>« X » is the position (in m) on the x axis,</li> <li>« Y » is the position (in m) on the y axis,</li> <li>« xINIT » is the calculated value for the parameter indicated in the file name(head, temperature, etc.)</li> <li>« Time » is the time step at which the value is calculated.</li> </ul> <p>The model output are gathered according to various cases studies of saturation and water flows. The model settings of the various cases studies are outlined in this file but more details about the mathematical approach and modeling settings and strategy are provided in the study to which the dataset belongs and which was submited for the first time to Journal of Geophysical Research: Earth Surface in July 2020.</p> <ul> <li><strong><em>SaFl </em></strong>corresponds to a saturated with forced water flows case study by assuming a constant recharge and discharge.</li> <li><strong><em>SaNF</em></strong> is a saturated case study with no water flows.</li> <li><strong><em>uSFl</em></strong> corresponds to an unsaturated case study with forced water flows in selected fractures only.</li> <li><strong><em>uSLF</em></strong> is unsaturated with limited water flows.</li> </ul> <p>For <strong><em>SaFl</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at various time step after of transient simulations (1550 AD, 2000 AD, 2015 AD, 2030 AD) such as displayed in Figures S2 and S3.</li> <li>Hydraulic heads (m) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 6 and S3.</li> <li>The ice bulk volumetric fraction at various time step after of transient simulations (2000 AD, 2015 AD, 2030 AD) such as in Figure S3.</li> <li>Temperature (°C) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 4 and S3.</li> </ul> <p>For <strong><em>SaNF </em></strong>the following output are provided:</p> <ul> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> <li>Hydraulic heads (m) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 6.</li> </ul> <p>For <strong><em>uSFl </em></strong>the following output are provided:</p> <ul> <li>Hydraulic heads (m) and temperature (°C) at 2100 AD such as in Figure 4 and 6.</li> <li>Saturation in 852 AD, 853 AD and 854 AD such as in Figure 5.</li> </ul> <p>For <strong><em>uSLF</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at 1550 AD and 2015 AD such as in Figure S2</li> <li>Hydraulic heads (m) after initialization (0 AD) and at 1850 AD and 2100 AD) such as in Figure 6.</li> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> </ul> <p>In addition, the temperature and hydrological (hydraulic head changes for the unsaturated cases studies only) forcing data (« Input_data_boundary_conditions.csv ») are provided. This last file contains:</p> <ul> <li><em>A to D</em>: the surface points extracted along the 4-m resolution DEM transect (Horizontal (X) position in m, Vertical (Y) position (elevation in m)) and MARST map (for the period 1961-1990: MARST<sub>init</sub>) as illustrated on Fig. S1, together with the adjusted MARST to run the model initialization simulations.</li> <li><em>F to I</em>: the surface points taken on for forcingthe model at its upper boundary and in between which the forcing data were interpolated.</li> <li><em>L to BH</em>: Data used for transient simulations with the time in year (<em>L</em>), the MARST anomaly applied to the adjusted MARST (<em>M</em>), the time in days (<em>N</em>), the MARST value applied at each surface node from 1 to 23 (<em>O </em>to <em>AK</em>), as well as the changes in head values applied at at each surface node from 1 to 23 (<em>AL </em>to <em>BH</em>) for the concerned simulations.</li> </ul> <p> </p> <p>More information can be made available by contacting Florence Magnin or Jean-Yves Josnin at <a href="mailto:florence.magnin@univ-smb.fr"><em>florence.magnin@univ-smb.fr</em></a><em> </em>or <a href="mailto:jean-yves.josnin@univ-smb.fr"><em>jean-yves.josnin@univ-smb.fr</em></a></p>
HISAR - Hydrologic Indices of South American Rivers
<p>This is a preview of the HISAR dataset (Hydrologic Indices of South American Rivers). </p> <p>The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics.</p> <p>The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area > 1000 km². The ESRI shapefile also has the attributes of drainage area (<em>Upst_Area_</em> in km²), length (<em>Ltr_Km_ in</em> km), <em>UC</em> (corresponding catchment attribute from hydrological modelling), <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.</p> <p>Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases.</p> <p>The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km<sup>2</sup>. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (<em>Upst_Area_</em> in km²), Country were the gauge point are located, <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.</p> <p>For more information about HISAR dataset see the journal article DOI: in preparation.</p>
Hydrological simulations for Costa Rica from 1985 to 2019 using HYPE CR 1.0
<p>This dataset corresponds to the hydrological simulations of <strong>605 catchments</strong> in <strong>Costa Rica</strong> using the<strong> HYPE CR</strong> 1.0 for the period from <strong>1985 to 2019</strong>.</p> <p>The conceptual <a href="https://www.smhi.se/en/research/research-departments/hydrology/hype-our-hydrological-model-1.7994">HYPE</a> (Hydrological Predictions for the Environment) model was used to assess the water resources of tropical Costa Rica at a national scale at daily scale using adjusted global topography, soil, land use, and remotely-sensed climate products to force, calibrate and independently evaluate the model.</p> <p> A bias corrected temperature data from <a href="https://psl.noaa.gov/data/gridded/data.ghcncams.html">NOAA</a> (National Oceanic and Atmospheric Administration) and <a href="https://www.chc.ucsb.edu/data/chirps">CHIRPS</a> (Climate Hazards Group InfraRed Precipitation with Station data) precipitation were used as model forcings. Daily streamflow from 13 gauges for the period 1990-2003 and monthly <a href="https://www.ntsg.umt.edu/project/modis/mod16.php">MODIS</a> (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000-2014 were used to calibrate and evaluate the model.</p> <p><strong>Here, we presented the results of our calibrated HYPE model for Costa Rica (HYPE CR) version 1.0.</strong></p> <p> </p> <p>The files contained in this repository are described below :</p> <ul> <li><strong>CHIRPS_bias_corrected.zip</strong>: contains the daily gridded CHIRPS bias corrected rainfall using a linear bias correction method using ground stations in NetCDF4 files.</li> <li><strong>Catchments_CostaRica.geojson</strong>: simulated catchments' boundaries in GIS format. The columns "subid" correspond to the identifier of the catchments.</li> <li><strong>Streams_CostaRica.geojson</strong>: streams network used to delineate the catchments.</li> <li><strong>AET_daily_mm.csv</strong>: time series of daily actual evapotranspiration in mm.</li> <li><strong>AET_month_mm.csv</strong>: time series of monthly actual evapotranspiration in mm (sum of daily).</li> <li><strong>AET_year_mm.csv</strong>: time series of annual actual evapotranspiration in mm (sum of monthly).</li> <li><strong>Baseflow_daily_mm.csv</strong>: time series of daily baseflow in mm, as the sum of discharge of three soil layers. Represents the contribution of the subcatchment area.</li> <li><strong>Baseflow_month_mm.csv</strong>: time series of monthly baseflow in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Baseflow_year_mm.csv</strong>: time series of annual baseflow in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>Infiltration_daily_mm.csv</strong>: time series of daily infiltration in the upper layer, in mm.</li> <li><strong>Infiltration_month_mm.csv</strong>: time series of monthly infiltration in the upper layer, in mm (sum od daily).</li> <li><strong>Infiltration_year_mm.csv</strong>: time series of annual infiltration in the upper layer, in mm (sum of monthly).</li> <li><strong>PET_daily_mm.csv</strong>: time series of daily potential evapotranspiration in mm.</li> <li><strong>PET_month_mm.csv</strong>: time series of monthly potential evapotranspiration in mm (sum of daily).</li> <li><strong>PET_year_mm.csv</strong>: time series of annual potential evapotranspiration in mm (sum of monthly).</li> <li><strong>Prec_daily_mm.csv</strong>: time series of daily bias corrected precipitation in mm.</li> <li><strong>Prec_month_mm.csv</strong>: time series of monthly bias corrected precipitation in mm (sum of daily).</li> <li><strong>Prec_year_mm.csv</strong>: time series of annual bias corrected precipitation in mm (sum of monthly).</li> <li><strong>Qsim_daily_m3-s.csv</strong>: time series of daily streamflow in m3-s. Represents the contribution of all upstream area.</li> <li><strong>Qsim_month_m3-s.csv</strong>: time series of monthly streamflow in m3-s (average of daily). Represents the contribution of all upstream area.</li> <li><strong>Qsim_year_m3-s.csv</strong>: time series of annual streamflow in m3-s (average of monthly). Represents the contribution of all upstream area.</li> <li><strong>Runoff_daily_mm.csv</strong>: time series of daily runoff in mm. Represents the contribution of the subcatchment area.</li> <li><strong>Runoff_month_mm.csv</strong>: time series of monthly runoff in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Runoff_year_mm.csv</strong>: time series of annual runoff in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>SM_daily_mm.csv</strong>: time series of daily soil moisture in mm. Represents the contributionof three soil layers.</li> <li><strong>SM_month_mm.csv</strong>: time series of monthly soil moisture in mm (average of daily). Represents the contributionof three soil layers.</li> <li><strong>SM_year_mm.csv</strong>: time series of annual soil moisture in mm (average of monthly). Represents the contributionof three soil layers.</li> <li><strong>Streamflow_daily_mm.csv</strong>: time series of daily streamflow in mm. Represents the contribution of the subcatchment area.</li> <li><strong>Streamflow_month_mm.csv</strong>: time series of monthly streamflow in mm (sum of daily). Represents the contribution of the subcatchment area.</li> <li><strong>Streamflow_year_mm.csv</strong>: time series of annual streamflow in mm (sum of monthly). Represents the contribution of the subcatchment area.</li> <li><strong>Tmean_daily_deg.csv</strong>: time series of daily mean air temperature in degrees.</li> <li><strong>Tmean_month_deg.csv</strong>: time series of monthly mean air temperature in degrees.</li> <li><strong>Tmean_year_deg.csv</strong>: time series of yearly mean air temperature in degrees.</li> </ul>
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.