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676 results for “hydrologic”
Data and R-Scripts for: Value of crowd-based water level class observations for hydrological model calibration
<p>This dataset corresponds to the study<br> "Value of crowd-based water level class observations for hydrological model calibration"<br> submitted to Water Resources Research in August 2019.</p> <p>Please use the R-Scripts in ascending numbers and adapt the paths to where you stored the files.<br> The helpfunctions.R will be used by some of the scripts and you might<br> want to adapt a path in line 356 for it to be used correctly with the scripts 8a and 8b.</p> <p>The parameter ranges used for the HBV calibration can be found in the "Parameters and parameter ranges.pdf"</p> <p>If you do not wish to calibrate the model, and just perform some statistics<br> start with script 7 and use the<br> - CrossValidation_stats_all.txt in the LUT Tables folder which contains<br> all model performances.<br> - CrossValidation_stats_WP1.txt contains also results of the upper benchmark<br> (only those labelled with no error and hourly).<br> - RandomParamPerformance_Validation.txt contains the results of the random parameters<br> (lower benchmark).<br> - The folders Validation Results and Calibration Results contain the files in HBV-format after the model<br> calibration and validatin were completed. The results of the Calibration and Validation files are also summarized<br> in the aforementioned txt-files within script 6 -HBV CrossValidation.R.<br> Please be aware that for the study only the catchments Murg, Guerbe, Mentue, and Verzasca were used!</p> <p><br> If you run into trouble using the data please contact simon.etter[at]outlook.com.</p> <p>Co-authors are:<br> Prof. Dr. Jan Seibert - jan.seibert[at]geo.uzh.ch<br> Dr. Ilja (H.J.) van Meerveld - ilja.vanmeerveld[at]geo.uzh.ch<br> Barbara Strobl - barbara.strobl[at]geo.uzh.ch</p>
Data to reproduce the results presented in Lake et al. 2024. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2024.131930. ("High-frequency spatial sediment source fingerprinting using in situ absorbance data")
<p>This repository contains data on the used absorbance data, measured at the field site, as described in Lake et al., 2024 (<span>h</span><span>t</span><span>t</span><span>p</span><span>s</span><span>:</span><span>/</span><span>/</span><span>d</span><span>o</span><span>i</span><span>.</span><span>o</span><span>r</span><span>g</span><span>/</span><span>1</span><span>0</span><span>.</span><span>1</span><span>0</span><span>1</span><span>6</span><span>/</span><span>j</span><span>.</span><span>j</span><span>h</span><span>y</span><span>d</span><span>r</span><span>o</span><span>l</span><span>.</span><span>2</span><span>0</span><span>2</span><span>4</span><span>.</span><span>1</span><span>3</span><span>1</span><span>9</span><span>3</span><span>0).</span> Furthermore, data on the turbidity, used calibration curves and R code to prepare the input data for the MixSIAR model are included in the data repository.</p>
Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA
<p>Data and code necessary to replicate the findings from the manuscript titled "Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA".</p> <p><span>Abstract:</span><strong><span> </span></strong><span>Understanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to: (1) assess community resource use among streams fed by different hydrologic sources; (2) explore how variability in resource use relates to feeding strategies; and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice-fed streams were largely supported by the gold alga <em>Hydrurus</em>. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold-water species under climate change by maintaining a longer growing season for this high-quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggests high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.</span></p>
Data for: Antarctic wide subglacial hydrology modeling
Open the record for dataset details and reuse information.
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
Timeseries of simulated glacier meltwater runoff for primary hydrological regions in Svalbard
<p>Timeseries of annual cumulative glacier meltwater runoff for 14 primary hydrological regions of Svalbard, as well as one subregion, for the period September 2003 to September 2013. Regional glacier meltwater runoff are extracted from climatic mass balance simulations for all glaciers in Svalbard published by Aas et al., 2016, "The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model", doi:10.5194/tc-10-1089-2016) and used in a manuscript by Dunse et al., 2021, "Regional-scale phytoplankton dynamics and their association with glacier meltwater runoff in Svalbard", submitted to the EGU journal Biogeosciences in July 2021</p>
Dataset for: Hydrological processes in East African tropical montane headwater catchments influenced by land use
<p>This dataset contains high-resolution (10-minute interval) data for nitrate (concentration and load), precipitation and discharge at four measurement stations (NF, SHA, TTP, OUT) in the South West Mau, Kenya, as well as trace element data and stable isotopes of water for rainfall and stream water samples. The data was used to investigate the influence of land use on hydrological processes through time series analysis (<a href="http://doi.org/10.1002/2017WR021592">Jacobs et al. 2018a</a>), end member mixing analysis and estimation of the young water fraction (<a href="http://doi.org/10.5194/hess-22-4981-2018">Jacobs et al. 2018b</a>).</p>
Fig. 3 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 3. Trophic position estimates of fishes collected at Nicola Lake, Taim Hydrological System. # symbols denotes different individuals of the same species.
Fig. 2 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 2. Plot of δ15N and δ13C values for plants (), mollusks () and fishes () collected at Nicola Lake, Taim Hydrological System. Sources of carbon assimilated by consumers are indicated by the relative positions of taxa on the x-axis; trophic level is indicated by relative position on the y-axis. The dashed line distinguishes between producers and consumers. # symbols denotes different individuals of the same species.
Fig. 1 in Scientific note Preliminary examination of food web structure of Nicola Lake (Taim Hydrological System, south Brazil) using dual C and N stable isotope analyses
Fig. 1. Patos-Mirim Lagoon complex (ca. 14,000 Km2) (A) in southern Brazil showing the Taim Hydrological System (B) with the ecological reserve's limits (320.4 Km2) and Nicola Lake (2.45 Km2) (C).
Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model
<p>Database corresponding to the research work submitted to Water Resources Research :<br> "Capturing the Little Washita watershed water balance with a physically-based two-hydrologic-variable model"<br> Fanny Picourlat (fanny.picourlat@lsce.ipsl.fr), Emmanuel Mouche, Claude Mugler</p> <p> </p> <p>"Geomorphic_Analysis" directory ------------------------------------------------------------------------------</p> <p>Little Washita geomophic analysis results. Analysis conducted on the 100 m resolution DEM (from USGS datadase, accessible at https://www.usgs.gov/core-science-systems/national-geospatial-program/small-scale-data) using a flow paths modeling algorithm developped by Maquin (2016).</p> <p> - Hillslopes_Length.csv : List of hillslopes lengths [m]<br> <br> - Hillslopes_MeanSlopes.csv : List of hillslopes mean slopes [%]<br> <br> <br> "3DREF_model_19981999" directory ------------------------------------------------------------------------------<br> Three-dimensional reference model files : exemple of the 1998-1999 water year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.</p> <p> - parameters_summary.pdf : parameters summary table.<br> <br> - "Outputs" directory ---------------------------------------------<br> Output files that form paper's figures.<br> <br> - LWo.hydrograph.Hydrographe_USGS1_07327550.dat : Streamflow at USGS1<br> - LWo.water_balance.dat : Water balance<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used for plotting depth to water table.<br> - LWo.olf.dat : Surface domain variables for all nodes for all output times. File used for plotting evapotranspiration at the closest node from the Ameriflux station.<br> <br> <br> "Hillslope_Model" directory -----------------------------------------------------------------------------------<br> Equivalent hillslope model files for the 20-year simulation.</p> <p> - LWo.grok : Data file prepared for the pre-processor, which is then run to generate the input data files for HydroGeoSphere.<br> <br> - "Outputs" directory ----------------------------------------------<br> <br> - LWo.water_balance.dat : Water balance. File used for plotting hillslope discharge and evapotranspiration.<br> - LWo.observation_well_flow.Well_1.dat : Outputs at nodes located at x=100m. File used for plotting depth to water table.<br> - LWo.pm.dat : Subsurface domain variables for all nodes for all output times. File used to extract the "post-processed" files described below.</p> <p> - "Post_processed" directory -------------------------------<br> Data extracted from the output file LWo.pm.dat.</p> <p> - TAB_xzy_sat.csv : Saturation for all nodes for all output times. Used for defining the seepage face extension Xs(t).<br> - x(t).csv : x coordinate (for all output times) of the intersection point between roots ending limit and water table. Used for defining the water table slope tan(i(t)).</p> <p><br> "Params" directory --------------------------------------------------------------------------------------------<br> Parameters files for both 3DREF model (1998-1999 simulation) and hillslope model (20-year simulation).<br> <br> - "Topography" directory --------------------------------------------<br> - maillage_hgs_corr3_riv.2dm : Horizontal 3D mesh file<br> - fichier_altitude_grok_corr3_riv_b.txt : 3D surface nodes elevation [m]<br> <br> - "Props" directory -------------------------------------------------<br> - LW.mprops : Subsurface material parameters<br> - LW.oprops : Surface domain parameters<br> - LW_LAI.etprops : Vegetation parameters<br> <br> - "Zones_vg" directory ----------------------------------------------<br> - zone_1_ele.txt : Bare soil 3D elements IDs<br> - zone_2_ele.txt : Deciduous forest 3D elements IDs<br> - zone_3_ele.txt : Evergreen forest 3D elements IDs<br> - zone_4_ele.txt : Shrubs 3D elements IDs<br> - zone_5_ele.txt : Grassland 3D elements IDs<br> - zone_6_ele.txt : Pasture 3D elements IDs<br> - zone_7_ele.txt : Crops 3D elements IDs<br> <br> - "Forcings" directory ----------------------------------------------<br> - NARR_day_9899.csv : Daily rainfall [m/s] on 1998-1999<br> - NARR_day_etp_9899.csv : Daily Potential Evapotranspiration [m/s] on 1998-1999<br> - NARR_day_9313.csv : Daily rainfall [m/s] on 1993-2013<br> - NARR_day_etp_9313.csv : Daily Potential Evapotranspiration [m/s] on 1993-2013<br> <br> - "Output_times" directory ------------------------------------------<br> - output_times_365d.csv : Output times [s] for 365 days<br> - output_times_20y.csv : Output times [s] for 20 years<br> <br> - "Rivers" directory ------------------------------------------------<br> - no_rivers_ele.txt : No river 3D elements IDs<br> - rivers_ele.txt : River 3D elements IDs<br> <br> - "LAI" directory ---------------------------------------------------<br> LAI files (1st column : time [s], 2nd column : LAI [-])<br> - LAI_2_hydro.csv : Deciduous forest LAI over one water year<br> - LAI_4_5_6_hydro.csv : Shrubs, grassland and pasture LAI over one water year<br> - LAI_winterwheat_OAlaoui.csv : Crops LAI over one water year<br> - LAI_eq_20y.csv : Equivalent LAI (for hillslope model) over 20 years<br> <br> "Analytical_Model" directory -----------------------------------------------------------------------------------</p> <p> - Analytical_model.py : Analytical model code for the 20-year simulation of the equivalent hillslope water balance.</p> <p> </p>
Identification and Regionalization of Streamflow Routing Parameters for the HLM Hydrological Model in Iowa
<p>The tables contained the metrics and the peak flows estimated using HLM under three different parameterizations. </p>
High-resolution large weighing lysimeter measurements with meteorological and soil-hydrological variables from a Mediterranean Savanna
<p>Raw and processed lysimeter weighing and flux data at the instrumental site ES-LMa of six large high-precision weighing lysimeters in a Mediterranean Savanna ecosystem for the period from 2019-06-01 to 2020-05-31. Additionally, meteorological and radiometric data are provided. Additionally, code for the lysimeter processing is provided.</p> <p>Reproducible workflow of the article <strong>Paulus et al. 2022: Resolving seasonal and diel dynamics of non-rainfall water inputs in a Mediterranean ecosystem using lysimeters. HESS, https://doi.org/10.5194/hess-2021-519</strong></p> <p>Variables, units and detailed description are found in the README.html file.</p>
The computation results of coupled hydrological and hydrodynamic modelling application for the Nemunas River watershed – Curonian Lagoon – South-Eastern Baltic Sea continuum
<p>The datasets provided here were used to analyse the cumulative impacts of climate change in a Nemunas River watershed – Curonian Lagoon – South‑Eastern Baltic Sea continuum by applying a state-of-the-art coupled modelling system, which consists of hydrological and hydrodynamic models.</p> <p>Meteorological data used for running the models were acquired from CORDEX (Coordinated Regional Downscaling Experiment) scenarios for Europe from the Rossby Centre high-resolution regional atmospheric climate model (RCA4), which consisted of four sets of simulations (downscaling) driven by four global climate models:</p> <table> <tbody> <tr> <th>Abbreviation in datasets</th> <th>Model</th> <th><strong>Institution</strong></th> </tr> </tbody> <tbody> <tr> <td>ICHEC</td> <td>EC-Earth</td> <td>Irish Centre for High-End Computing</td> </tr> <tr> <td>IPSL</td> <td>IPSL-CM 5A-MR</td> <td>The Institut Pierre-Simon Laplace</td> </tr> <tr> <td>MOHC</td> <td>HadGEM2-ES</td> <td>Met Office Hadley Centre</td> </tr> <tr> <td>MPI</td> <td>MPI-ESM-LR</td> <td>Max Planck Institute for Meteorology</td> </tr> </tbody> </table> <p> </p> <p>Climate change scenarios and periods:</p> <ul> <li>Historical/reference (1970-2005);</li> <li>RCP4.5 (2005-2100);</li> <li>RCP8.5 (2005-2100).</li> </ul> <p>The datasets consist of time series for the parameters of:</p> <ul> <li><strong>Ice thickness</strong> - average ice thickness in the Curonian Lagoon;</li> <li><strong>Meteorological data</strong> - bias-corrected temperature and precipitation data for the marine and terrestrial areas;</li> <li><strong>Nemunas River discharge</strong> - simulated average daily values for the discharge and water temperature;</li> <li><strong>Salinity</strong> - selected points in the south-eastern Baltic Sea and one point next to Juodkrantė (in the Curonian Lagoon);</li> <li><strong>Water fluxes</strong> - through four predefined cross-sections in the Curonian Lagoon;</li> <li><strong>Water level</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea;</li> <li><strong>Water residence time</strong> - in the total Curonian Lagoon area, as well as its northern and southern parts;</li> <li><strong>Water temperature</strong> - in 10 preselected points in the Curonian Lagoon and South-eastern Baltic Sea.</li> </ul> <p>Some of the datasets (zip files) have additional information (coordinates, data column explanations, units, etc.) in READ_ME.txt files.</p>
Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"
<p>Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.</p>
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Catchment and hydrological model instance matrices
<p>This dataset contains catchment and hydrological model matrices for 2116 non-U.S. catchments from the CARVAN dataset and 1 million GR4J model instances. GR4J model instance parameters are also provided. Code to use the data is available at https://github.com/stsfk/indexing_catchment_model. The dataset is created in the paper "The Profiling and pairing catchments and hydrological models with latent factor model".</p>
Hydrological signatures for Caravan using TOSSH
<p>This dataset contains hydrological signatures for Caravan (a global large-sample catchment dataset), together with catchment attributes from Caravan (https://www.nature.com/articles/s41597-023-01975-w). The signatures are calculated using TOSSH (https://github.com/TOSSHtoolbox/TOSSH). See https://github.com/SebastianGnann/TOSSH_signatures_Caravan for details.</p>
Dataset used for "Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia"
<p><strong>General Info</strong></p> <p>This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (<a href="https://doi.org/10.5281/zenodo.7864689">https://doi.org/10.5281/zenodo.7864689</a>) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “<em>Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” </em>that is currently under review in the Cryosphere Discussions, <a href="https://tc.copernicus.org/preprints/tc-2022-256/">https://tc.copernicus.org/preprints/tc-2022-256/</a>.</p> <p><strong>Contact</strong></p> <p>René Wijngaard (<a href="mailto:r.r.wijngaard.uu@gmail.com">r.r.wijngaard.uu@gmail.com</a> / <a href="mailto:r.r.wijngaard@uu.nl">r.r.wijngaard@uu.nl</a>) </p> <p><strong>Raw Data</strong></p> <p>Raw monthly and daily unstructured HMA VR model output are available on request. </p> <p><strong>Dataset Contents</strong></p> <pre><code>NE30.tar HMA_VR7a.tar HMA_VR7b.tar </code></pre> <p>These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA. </p> <pre><code>SMB_HMA_VR7a.tar SMB_HMA_VR7b.tar</code></pre> <p>These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.</p>
The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.
<p><strong>GHDC</strong> = Global Hydrological Data Cloud</p> <p><strong>SHUD</strong> = Simulator of Hydrologic Unstructured Domains</p> <p> </p> <p>Files in the package:</p> <p> </p> <ul> <li> <p>Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA.</p> </li> <li> <p>Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia</p> </li> <li> <p>heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China</p> </li> <li> <p>threeBasin - The R analysis code for the three basin simulations, including loading data and visualization.</p> </li> </ul> <p> </p> <p>The file structure in each basin folder:</p> <table> <thead> <tr> <th>FOLDER</th> <th>FILE OR SUBFOLDER(BOLD)</th> <th>DESCRIPTION</th> </tr> </thead> <tbody> <tr> <td>ETV</td> <td>-</td> <td>General ETV data.</td> </tr> <tr> <td> </td> <td>dem.tif</td> <td>DEM subset of ASTER Global DEM.</td> </tr> <tr> <td> </td> <td>Soil.csv</td> <td>Soil texture of soil layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>Geol.csv</td> <td>Soil texture of geology layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Geol.csv</td> <td>Soil texture, organic matter and bulk density of geology layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Soil.csv</td> <td>Soil texture, organic matter and bulk density of soil layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.tif</td> <td>HWSD data subset.</td> </tr> <tr> <td> </td> <td>buff.shp, .dfb, .prj, .shx</td> <td>Shapefile of buffered polygon from user-provided watershed.</td> </tr> <tr> <td> </td> <td>outlets.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>stm_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>wbd_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td><strong>TSD</strong></td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td><strong>GCS</strong></td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td><strong>PCS</strong></td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Geol</td> <td>The geology classification of the research area</td> </tr> <tr> <td>Modeling</td> <td>-</td> <td> </td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td><strong>input</strong></td> <td>Model input folder.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Spatial data for model deployment in GCS.</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Spatial data for model deployment in PCS.</td> </tr> <tr> <td> </td> <td>deployConfig.txt</td> <td>The configuration file for model deployment script.</td> </tr> <tr> <td>StaticFiles</td> <td>-</td> <td>Static files, include the model, citation, description and executable model file.</td> </tr> <tr> <td> </td> <td><strong>SHUD_model</strong></td> <td>Source code of SHUD model</td> </tr> <tr> <td> </td> <td>Citation.bib</td> <td>Citation of Data and models.</td> </tr> <tr> <td> </td> <td>ReadMe_cn.html</td> <td>The readme file in Chinese.</td> </tr> <tr> <td> </td> <td>ReadMe_en.html</td> <td>The readme file in English.</td> </tr> <tr> <td> </td> <td>shud.exe</td> <td>The executable files of SHUD model, for Windows platform only.</td> </tr> <tr> <td>UserData</td> <td>-</td> <td>The files uploaded by user</td> </tr> </tbody> </table>
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