VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
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Bibliographic record
Abstract
## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model Date updated: June 28, 2021 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 The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes. 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). ## Overview 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). 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). 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. 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. A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/). Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631 ## List of contents 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) 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 Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided. ## References * 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 * 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 * 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. * 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
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it