Data and codes accompany a 2023 Water Resources Research article submitted by João Paulo Lyra Fialho Brêda of Wageningen University & Research. The dataset likely contains predictor importance metrics for hydrological fluxes from global models. It is hosted on Papers with Code under an Open Access (green) license.
Use Cases
- Ranking predictor importance for hydrological fluxes based on the described model outputs.
- Benchmarking global hydrological models using the predictor importance metrics.
- Analyzing the sensitivity of water flux predictions to different input variables.
Strengths
- Associated with a peer-reviewed article submitted to Water Resources Research in 2023.
- Includes accompanying code for reproducibility of the analysis.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and dataset size are unknown, which may limit suitability assessment.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Wageningen University & Research
- Collection Method
- Likely generated from simulations of global hydrological and land surface models.
- Geography
- Global