VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
Bibliographic record
Abstract
VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model Date uploaded: Nov. 13, 2019 Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2) 1. Department of Civil and Environmental Engineering, UCLA 2. Department of Geography, UCLA Author contact info: jschap@g.ucla.edu ===== Overview ===== VICGlobal is an uncalibrated parameter 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. Vegetation parameters uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are computed based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (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 ~140 GB when unzipped. A data descriptor is in preparation for submission to Earth System Science Data (https://earth-system-science-data.net). ===== List of Contents ===== Inputs for VIC-4 or the VIC-5 Classic Driver --Soil parameter file --Vegetation parameter file --Elevation band file --Vegetation library files (one each for the northern and southern hemispheres) Inputs for the VIC-5 Image Driver --Parameter file --Domain file --Matlab codes for subsetting the VICGlobal parameters to a region of interest ===== 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 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.027 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".