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11 results for “gridded runoff”
GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
<p>This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.).</p> <p>The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users.</p> <p><strong>Domain boundaries (WGS84 system): </strong><br> - lon_min = -85.5, lon_max = -77.94<br> - lat_min = 40.3, lat_max = 44.26</p> <p><strong>Resolution of model variables provided:</strong><br> - spatial: ~10km x 10km <br> - temporal: hourly </p> <p><strong>Simulation period:</strong><br> - 01 Jan 2011 - 31 Dec 2014 <br> - 01 Jan 2010 - 31 Dec 2010 (warm-up)</p> <p><strong>Meteorological input data:</strong><br> - RDRS-v1; see Mai et al. 2020 (in prep)</p> <p><strong>Variables available:</strong><br> float <strong>PR_0</strong>(time, rlat, rlon) ;<br> PR_0:units = "m" ;<br> PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ;<br> float <strong>AHFL_0</strong>(time, rlat, rlon) ;<br> AHFL_0:units = "mm" ;<br> AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ;<br> float <strong>TRAF_60268832</strong>(time, rlat, rlon) ;<br> TRAF_60268832:units = "mm" ;<br> TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ;<br> float <strong>ALAT_0</strong>(time, rlat, rlon) ;<br> ALAT_0:units = "mm" ;<br> ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ;<br> float <strong>ALAT_0_full</strong>(time, rlat, rlon) ;<br> ALAT_0_full:units = "mm" ;<br> ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ;<br> float <strong>O1_0</strong>(time, rlat, rlon) ;<br> O1_0:units = "mm" ;<br> O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ;<br> float <strong>O1_0_full</strong>(time, rlat, rlon) ;<br> O1_0_full:units = "mm" ;<br> O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ;<br> float <strong>WT_59868832</strong>(time, rlat, rlon) ;<br> WT_59868832:units = "1" ;<br> WT_59868832:long_name = "Fraction of grid cell covered with land" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. </p>
Observation based gridded annual runoff estimates over Victoria, Australia
<p>The dataset provides observation-based interpolated gridded annual runoff estimates over Victoria, Australia during 1982 - 2012. The methodology extended the R package <em>rtop</em> to allow <em>top-kriging</em> with external drift by employing spatial variability of gridded rainfall estimates. This dataset can be useful to estimate runoff at ungauged or poorly gauged catchments in Victoria. The full paper with the methodology is available at https://mssanz.org.au/modsim2021/papers/K11/weligamage.pdf</p> <p> </p>
Gridded reconstruction of monthly runoff for Northwest Russia
<p>Developed reconstructions of monthly runoff for Northwest Russia -- BASE and SOTA -- are the part of the manuscript "The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff" by G. Ayzel, L. Kurochkina, and S. Zhuravlev which was submitted in the Special Issue on “Hydrological Data: Opportunities and Barriers” of the Hydrological Sciences Journal (http://explore.tandfonline.com/cfp/est/hydrological-science-data).</p>
LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks
<p>LSTM-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: LSTM.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1980-2016.</p>
GR4J-REG: regional gridded runoff dataset for northwest Russia based on GR4J model and nearest-neighbor regionalization technique
<p>GR4J-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: GR4J.</p> <p>Regionalization technique: Nearest-neighbor.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1979-2016.</p>
GDRD: gridded daily runoff dataset for the Nadym, Pur, and Taz river basins
<p>Historical variations in daily runoff for large Russian Arctic domain of the Nadym, Pur and Taz basins were calculated. Robustness estimation of proposed dataset for monthly runoff predictions in ungauged basins shows high efficiency on historical observations period. The dataset is open and could be freely distributed.</p>
Gridded runoff reanalysis datasets for Northwest Russia
<p>Developed gridded runoff reanalysis datasets -- GR4J-GRDC and GR4J-GRDC-R5 -- are the part of the manuscript "When barriers are gone: On the importance of making runoff observations openly available to increase the efficiency of regional hydrological models" by G. Ayzel, L. Kurochkina, and S. Zhuravlev which was submitted in the Special Issue on “Hydrological Data: Opportunities and Barriers” of the Hydrological Sciences Journal (http://explore.tandfonline.com/cfp/est/hydrological-science-data).</p>
mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie
<p>This dataset provides gridded model simulations in netcdf format over the Lake Erie using the mHM model (Samaniego, et al., 2010, Kumar et al, 2013), done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E).</p> <p>Model code is available under: <a href="https://git.ufz.de/mhm/mhm">https://git.ufz.de/mhm/mhm</a>, revision number: 8271b54</p> <p>Domain boundaries (WGS84 system): lon_min = -86.0, lon_max = -78.0, lat_min = 40.0, lat_max = 45.0</p> <p>Model variables are simulated at spatial grid of 0.125deg x 0.125deg, daily time step. </p> <p>Simulation period: 01 Jan 2011 - 31 Dec 2014 (with 1year -2010- warm-up)</p> <p>Meteorological input data, see Mai et al. 2020 (in prep)</p> <p>Following model variables are provided for two mHM_parameter.nml realizations (*obj1 and *obj2, as defined in Mai et al. 2020 in prep). </p> <p>double <strong>snowpack</strong>(time, northing, easting) ;<br> snowpack:long_name = "depth of snowpack" ;<br> snowpack:unit = "mm" ;<br> double <strong>SM_Lall</strong>(time, northing, easting) ;<br> SM_Lall:long_name = "average soil moisture over all layers" ;<br> SM_Lall:unit = "mm mm-1" ;<br> double <strong>unsatSTW</strong>(time, northing, easting) ;<br> unsatSTW:long_name = "reservoir of unsaturated zone" ;<br> unsatSTW:unit = "mm" ;<br> double <strong>satSTW</strong>(time, northing, easting) ;<br> satSTW:long_name = "water level in groundwater reservoir" ;<br> satSTW:unit = "mm" ;<br> double <strong>aET</strong>(time, northing, easting) ;<br> aET:long_name = "actual Evapotranspiration" ;<br> aET:unit = "mm d-1" ;<br> double <strong>Q</strong>(time, northing, easting) ;<br> Q:long_name = "total runoff generated by every cell" ;<br> Q:unit = "mm d-1" ;<br> double <strong>QD</strong>(time, northing, easting) ;<br> QD:long_name = "direct runoff generated by every cell (runoffSeal)" ;<br> QD:unit = "mm d-1" ;<br> double <strong>QIf</strong>(time, northing, easting) ;<br> QIf:long_name = "fast interflow generated by every cell (fastRunoff)" ;<br> QIf:unit = "mm d-1" ;<br> double <strong>QIs</strong>(time, northing, easting) ;<br> QIs:long_name = "slow interflow generated by every cell (slowRunoff)" ;<br> QIs:unit = "mm d-1" ;<br> double <strong>QB</strong>(time, northing, easting) ;<br> QB:long_name = "baseflow generated by every cell" ;<br> QB:unit = "mm d-1" ;<br> double <strong>recharge</strong>(time, northing, easting) ;<br> recharge:long_name = "groundwater recharge" ;<br> recharge:unit = "mm d-1" ;</p> <p>===============================================================</p> <p>These data and model runs have been performed under the Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program. This work has received funding from the Initiative and Networking Fund of the Helmholtz Association through the project Advanced Earth System Modelling Capacity (ESM) (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/www.esm-project.net">www.esm-project.net</a>).</p>
Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years
<p>1 Introduction</p> <p>The data archive provides the reconstructed dataset capturing the annual runoff across Europe, partitioned into a grid format and preserved in NetCDFv4 (.nc) format for enhanced geospatial information.</p> <p>1.1 Coordinate system and spatial resolution</p> <p>Each grid cell in the dataset corresponds to a 0.5-degree spatial resolution, using the World Geodetic System 1984 (WGS84) as the standard coordinate frame.</p> <p>1.2 Temporal resolution</p> <p>The data encapsulates a yearly temporal resolution, offering a comprehensive outlook from 1500 to 1999. For example data for 1500 are represented by the layer 01/01/1500.</p> <p>1.3 Units</p> <p>Runoff measurements are quantified in millimeters per year (mm/year), providing hydrological data throughout the noted time frame.</p> <p>1.4 Example</p> <p>library(terra)<br> library(raster)</p> <p>> dt_cc<-rast("HEMMF_ERUN_1500_1999.nc")<br> > dt_cc<br> class : SpatRaster <br> dimensions : 70, 104, 500 (nrow, ncol, nlyr)<br> resolution : 0.5, 0.5 (x, y)<br> extent : -12, 40, 35, 70 (xmin, xmax, ymin, ymax)<br> coord. ref. : lon/lat WGS 84 (EPSG:4326) <br> source : HEMMF_ERUN_1500_1999.nc <br> varname : runoff <br> names : runoff_1, runoff_2, runoff_3, runoff_4, runoff_5, runoff_6, ... <br> unit : mm/year, mm/year, mm/year, mm/year, mm/year, mm/year, ... <br> time (days) : 1500-01-01 to 1999-01-01 </p> <p> </p> <p>1.5 Citation</p> <p>The specific data file, named ’HEMMF ERUN 1500 1998.nc,’ is conveniently structured to facilitate easy handling and interpretation of the information. Please ensure to attribute the correct citation when utilizing this dataset, adhering to the subsequent reference: [Singh et al., 2023] References Ujjwal Singh, Petr Maca, Martin Hanel, Yannis Markonis, Rama Rao Nidamanuri, Sadaf Nasreen, Johanna Ruth Bl¨ocher, Filip Strnad, Jiri Vorel, Lubomir Riha, and Akhilesh Singh Raghubanshi. Hybrid multi-model ensemble learning for reconstructing gridded runoff of europe for 500 years. Information Fusion, 97:101807, 2023. ISSN 1566-2535. doi: https://doi.org/10.1016/j.inffus. 2023.101807. URL https://www.sciencedirect.com/science/article/pii/S1566253523001161#d1e5346.</p>
Gridded runoff and evapotranspiration dataset for seven major river basins of the Tibetan Plateau during 1998-2017
<p>This data set describes the spatiotemporal distribution of runoff and evapotranspiration for the headwater of seven river basins (the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) in the Tibetan Plateau. This was achieved using an observation-constrained distributed cryosphere-hydrology model, known as the WEB-DHM. The time range is 1998-2017 at a monthly scale, the spatial resolution is 5km×5km, and the unit is mm/month. </p><p>For the convenience of users, this data is stored in TIF format, with each combination of different watersheds forming a separate file. Each file contains two variable: either runoff or evapotranspiration. These files can be opened with ArcGIS, Python, R, and other tools.</p>
Gridded runoff and evapotranspiration dataset for seven major river basins of the Tibetan Plateau during 1998-2017
<p>This data set describes the spatiotemporal distribution of runoff and evapotranspiration for the headwater of seven river basins (the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, and Indus) in the Tibetan Plateau. This was achieved using an observation-constrained distributed cryosphere-hydrology model, known as the WEB-DHM. The time range is 1998-2017 at a monthly scale, the spatial resolution is 5km×5km, and the unit is mm/month. </p><p>For the convenience of users, this data is stored in ASCII format, with each combination of different watersheds forming a separate file. Each file contains three parts: two data variables (either runoff or evapotranspiration) and the coordinate (named as latgrid.txt and longrid.txt). These files can be opened with ArcGIS, Python, R, and other tools.</p>
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