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695 results for “Model output”
Hydrodynamic Model Output Used to Evaluate Chinook Salmon Movements and Distribution in the South Delta
This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin Delta. Work was funded by State Water Contractors (SWC) and completed by Anchor QEA; FlowWest, LLC; and University of Washington under a SWC 2023 Science Plan grant (study name Evaluation of the Influence of State Water Project and Central Valley Project on Chinook Salmon Movements and Distribution in the South Delta), contracted by SWC. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze Chinook Salmon responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with hydrodynamic model output variables provided at mostly the same locations for each period simulated. The years 2011 through 2016 were simulated previously for a prior project and model output provided through the Environmental Data Initiative (edi.1124.1). Files for these years were recreated from the prior simulations for this project to add an output location. Additional locations were added to the 2010 and 2017 simulations for the 2010 and 2017 hydrophone arrays, and thus 2010 and 2017 include additional model output, relative to 2011 through 2016. The model simulation for each year spanned the full period of Chinook Salmon detections in the telemetry data collected during that year.
Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output
A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. Pr
Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output
<p>Model output used in the publication:</p> <p>M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. <em>Proc. Natl. Acad. Sci. </em>(2021) https://doi.org/10.1073/pnas.207541118</p> <p>See README.md for a description of the data.</p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Seafloor organic carbon flux output from the NEMO-MEDUSA model
<p>This output was produced by a simulation using a coupled ocean physics and marine biogeochemistry model. The physical ocean submodel was the Nucleus for European Modeling of the Ocean (NEMO) physical ocean model (Madec, 2014), run here in a global 1/12-degree resolution configuration (ORCA0083). The marine biogeochemistry submodel was the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification (MEDUSA-2), an intermediate-complexity plankton ecosystem model (Yool et al., 2013). The horizontal resolution of this configuration of NEMO has non-uniform grid cells ranging 2 to 9 km in size (mean 7.5 km), with 75 vertical depth levels (31 levels between the surface and 200 m depth). Sea-ice is represented in the model by the Louvian‐la‐Neuve Ice Model (LIM2) (Fichefet, & Maqueda, M. a. M., 1997; Goosse & Fichefet, 1999). The configuration was forced at the air-sea interface with version 5.2 of the DRAKKAR forcing set (DFS) (Brodeau et al., 2010). DFS 5.2 is based on ERA40 reanalysis data, comprising of 6‐hourly means for wind, humidity, and atmospheric temperature, daily means for radiative fluxes (both longwave and shortwave), and monthly means for precipitation. A monthly climatology was used for river runoff, taken from the CORE2 reanalysis (Brodeau et al., 2010; Timmermann et al., 2005). The resulting model hindcast was created using this forcing set for the period 1958–2015, with marine biogeochemistry initialised in 1990.</p> <p>This archive includes the flux of organic carbon reaching the seafloor and the area of the grid cells for the global domain. In MEDUSA, the seafloor flux is the sum of slow- and fast-sinking detrital particles that reach the base of the water column and enter the benthic submodel of MEDUSA. In general, away from shallow water regions (< 200 m), this flux is dominated by fast-sinking material produced by ecological processes associated with the large components of MEDUSA.</p> <p>The specific subset of output used was drawn from the decadal period 2006-2015, and was regridded from the non-uniform ORCA0083 grid to a regular 1/12-degree grid. Output processing was undertaken by A. Yool (axy@noc.ac.uk; National Oceanography Centre, Southampton UK).</p> <p>In addition to the netCDF files, text file dumps of their contents are included to assist with interpretation.</p> <p>References:</p> <p>Brodeau, L., Barnier, B., Treguier, A.‐M., Penduff, T., & Gulev, S. (2010). An ERA40‐based atmospheric forcing for global ocean circulation models. Ocean Modelling, 31, 88–104.</p> <p>Fichefet, T., & Maqueda, M. a. M. (1997). Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. Journal of Geophysical Research, Oceans, 102, 12,609–12,646.</p> <p>Goosse, H., & Fichefet, T. (1999). Importance of ice‐ocean interactions for the global ocean circulation: A model study. Journal of Geophysical Research, Oceans, 104, 23,337–23,355.</p> <p>Kelly, S., Popova, E., Aksenov, Y., Marsh, R., & Yool, A. (2018). Lagrangian modeling of Arctic Ocean circulation pathways: Impact of advection on spread of pollutants. J. Geophys. Res. Oceans, 123, 2882‐2902, doi: 10.1002/2017JC013460.</p> <p>Madec, G. (2014). "NEMO Ocean engine" (draft edition r5171) "NEMO Ocean engine" (draft edition r5171). Note du Pôle de modélisation, Institut Pierre‐Simon Laplace (IPSL), France, 27, 1288–1619.</p> <p>Timmermann, R., Goosse, H., Madec, G., Fichefet, T., Ethe, C., & Dulière, V. (2005). On the representation of high latitude processes in the ORCA‐LIM global coupled sea ice–ocean model. Ocean Modelling, 8, 175–201.</p> <p>Yool, A., Popova, E.E. and Anderson, T.R. (2013). MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies. Geoscientific Model Development 6, 1767-1811, doi: 10.5194/gmd-6-1767-2013.</p>
Southern African Power Pool GridPath Model Output Data - Chowdhury et al 2022 Joule
<p>This data repository holds GridPath model output data for the paper Chowdhury, A.K., Deshmukh, R., Wu, G., Uppal, A., Mileva, A., Curry, T., Armstrong, L., Galelli, S., and Kudakwashe, N. (2022) “Enabling a low-carbon electricity system for Southern Africa”, Joule. See Readme for more details. </p>
Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)
<p>The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper: </p> <p>Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)</p> <p>Details of the model simulation:<br> - COSMO-CLM version 5.0_clm6<br> - Community Land Model version 4.5<br> - Horizontal resolution: 0.25°x0.25°<br> - Vertical resolution: 40 levels<br> - Time period: 1987-2016 (excluding 4 years of spin-up)<br> - Driving model: ERA-Interim<br> </p> <p>The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br> - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br> - ALB: Surface albedo [-] (only for austral summer months)<br> - ALHFL_S: Surface latent heat flux [W m-2]<br> - ALWD_S: Downward longwave radiation at the surface [W m-2]<br> - ALWU_S: Upward longwave radiation at the surface [W m-2]<br> - ASHFL_S: Surface sensible heat flux [W m-2]<br> - ASOB_S: Surface net downward shortwave radiation [W m-2]<br> - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br> - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br> - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br> - ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br> - P: Pressure at 40 vertical levels [Pa]<br> - QV: Specific humidity at 40 vertical levels [kg kg-1]<br> - RH2M: Relative humidity at 2 meter [%]<br> - SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month)<br> - T2M: Temperature at 2 meter [K]<br> - T: Temperature at 40 vertical levels [K]<br> - WS10M: Wind speed at 10 meter [m s-1]<br> - WS: Wind speed at 40 vertical levels [m s-1]</p>
Model output: Crescentic sandbar behaviour along a curved coast
<p>This data set contains the model output used in the journal article Rutten et al. 'Simulating crescentic sandbar behaviour along a curved coast'. </p> <p>ORGANIZATION:<br> The data set consists of 50 netcdf files, corresponding to the 50 model runs. Bed level change was simulated under time-variant and time-invariant wave conditions over a period of 20 days. The reference runs are named RunRef1.nc (time-invariant wave forcing at the offshore boundary) and RunRef2.nc (time-varying wave forcing at the offshore boundary). Runs 1-39 are variations on the forcing, whereas Runs 40-48 are variations on the bathymetry (see Table 1 in the journal publication for the boundary condition in each run). </p> <p>Each file contains the grid, time vector and bed elevation. Also, the x- and y-component of the current and the sediment transport are provided. Output is given every 24 hours over a length of 20 days.</p> <p>longshore Longshore distance [m]<br> cross-shore Cross-shore distance [m]<br> time Time [days]<br> ZF Bed elevation [m]<br> UCX x-component current [m/s]<br> UCY y-component current [m/s]<br> QTX x-component sediment transport [m3/m/s]<br> QTY y-component sediment transport [m3/m/s]</p> <p> </p> <p> </p> <p> </p>
Great Lakes WRF-FVCOM model ensemble outputs: Summer 2018 daily LST and T2m
<p>Postprocessed model data for the paper: "Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model"</p> <p>Perturbed Physics Ensemble outputs from a coupled lake-atmosphere-land Great Lakes regional model: <br>- Time period: May, June, July of 2018 <br>- Computational domain: Great Lakes region as contained within <a href="../api/records/10806629/draft/files/wrf_grid.nc/content" target="_blank" rel="noopener noreferrer">wrf_grid.nc</a> (atmosphere-land) and <a href="../api/records/10806629/draft/files/fvcom_grid.nc/content" target="_blank" rel="noopener noreferrer">fvcom_grid.nc</a> (lake).<br>- Quantities of interest: lake surface temperature and 2-m near-surface air temperature<br>- Training set: "<a href="../api/records/10806629/draft/files/wfv_global_daily_temperature_training_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_training_set.pkl</a>" [18 members]. Associated with "<a href="../api/records/10806629/draft/files/perturbation_matrix_9variables_korobov18.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_korobov18.nc</a>" input model configuration matrix.<br>- Test set: "<a href="https://zenodo.org/api/records/13863491/draft/files/wfv_global_daily_temperature_test_set.pkl/content" target="_blank" rel="noopener noreferrer">wfv_global_daily_temperature_test_set.pkl</a>" [9 members]. Associated with "<a href="https://zenodo.org/api/records/13863491/draft/files/perturbation_matrix_9variables_latin_hypercube9.nc/content" target="_blank" rel="noopener noreferrer">perturbation_matrix_9variables_latin_hypercube9.nc</a>" input model configuration matrix.</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/4-degree</p> <p>- REGRID versions are regridded from ORCA025 grid to a regular 0.25-degree grid</p> <p>- Simulation performed as part of the ROAM project (UK Ocean Acidification Research Programme)</p> <p>- CMIP5 Historical and RCP 8.5 extension (1975-2099 inclusive)</p> <p>- Simulation described in Yool et al., JGR, 2015</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in April 2021</p> <p>- Seafloor fields of physical and biogeochemical properties for the periods 2016-2025 and 2090-2099</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- The output has been regridded to a regular 1/4-degree grid</p>
Seafloor output from the MEDUSA model
<p>- Output from the MEDUSA model (Yool et al., GMD, 2013)</p> <p>- NEMO resolution 1/4-degree</p> <p>- REGRID versions are regridded from ORCA025 grid to a regular 0.25-degree grid</p> <p>- Simulation performed as part of the ROAM project (UK Ocean Acidification Research Programme)</p> <p>- CMIP5 Historical and RCP 8.5 extension (1975-2099 inclusive)</p> <p>- Simulation described in Yool et al., JGR, 2015</p> <p>- Subset of output prepared for Mission Atlantic project by A. Yool in April 2021</p> <p>- Seafloor fields of physical and biogeochemical properties for the periods 2016-2025 and 2090-2099</p> <p>- Note that this is test output produced for a specific purpose</p> <p>- v1.3 corrects a problem in the regridding at v1.2</p>
Model output used in the manuscript "Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency"
<p>This *.zip file contains the model output from seasonal variability experiments using the NPZD-DOP GEOMAR biogeochemical model (<a href="https://doi.org/10.1016/j.pocean.2010.05.002" target="_blank" rel="noopener">Kriest et al., 2010</a>) coupled with the MITgcm 2.8deg ocean circulation via the transport matrix method (<a href="https://doi.org/10.1016/j.ocemod.2004.04.002" target="_blank" rel="noopener">Khatiwala et al., 2005</a>; <a href="https://doi.org/10.1029/2007GB002923" target="_blank" rel="noopener">Khatiwala, 2007</a>; <a href="https://doi.org/10.5281/zenodo.1246300" target="_blank" rel="noopener">Khatiwala, 2018</a>).</p> <p>These model outputs are presented and discussed in the Preprint "<em>Seasonality in carbon flux attenuation explains spatial variability in transfer efficiency</em>", published by Geophysical Research Letters (<a href="https://doi.org/10.1029/2023GL107050" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original model. For this matter, we also refer you to <a href="https://doi.org/10.1029/2021GB007101" target="_blank" rel="noopener">de Melo Viríssimo et al. (2022)</a>.</p> <p>All files uploaded were generated from simulations run by the authors, except: the grid file, the salinity field, and the temperature field, which came with the model; and the density fields, who were computed from the MITgcm 2.8deg transport matrix by Dr Rafaelle Bernadello, using a TEOS-10 Matlab routine (<a href="http://www.teos-10.org/">http://www.teos-10.org/</a>).</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (<a href="https://doi.org/10.1034/j.1600-0870.2001.00241.x" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the article "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>", published by Chaos (<a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al., 2024</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://dx.doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a>.</p> <p>All files uploaded were generated from simulations run by the authors.</p> <p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p> <p><strong>Note:</strong> This version (v1.1) is the same version as v1.0 but with the correct README file.</p>
Air mass back-trajectory modeling output along an urban-rural transect in central Ohio, 2021
This data package contains modeled air parcel back-trajectories generated using the Stochastic Time-Inverted Lagrangian Transport model (STILT) via the R interface. The purpose of the study was to characterize the geochemical and isotopic signatures of dust in relation to different land uses, and to connect the geochemistry of deposited dust to air mass trajectories. Back-trajectories are three-dimensional paths of air parcels from a receptor site (the dust collection site) backwards in time and space for the duration of the tracking interval, calculated iteratively using wind fields from high-resolution gridded meteorological data. To calculate a probability of potential pathways, rather than a single back-trajectory, STILT introduces small random perturbations into the wind fields during each time step. For four sites along an urban-rural transect in central Ohio for June-July 2021, we generated weekly footprints of potential sources for the dust deposited at each site. These back-trajectories can be paired with geochemical data to establish a connection between land use and anthropogenic dust composition. This dataset is complete and will not be updated.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.
The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
Example Cloud-Resolving Model Output (Dec 2013 run)
<p>This dataset contains a selection of hourly, domain-mean quantities from the large-ensemble of realistic cloud-resolving model experiments described here: https://acp.copernicus.org/articles/20/6291/2020/</p>
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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)
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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.