Janzing et al. (2025) Data supplement to Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2025
• Date: 2025-10-13
• Authors: Janzing, Joren, Wanders, Niko, Van Tiel, Marit, Van Jaarsveld, Barry, Karger, Dirk, Brunner, Manuela
• DOI: 10.16904/envidat.705

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study investigates how improved process representation enhances hyper-resolution large-scale hydrological modeling in mountain regions, providing PCR-GLOBWB 2.0 model output for the Alpine region at 30 arcsec resolution for evaluation.

Objective

• To assess the benefits of improved process representation for hyper-resolution large-scale hydrological modeling, specifically in mountain regions.

Study Configuration

• Spatial Scale: Larger Alpine region at 30 arcsec resolution (approximately 925 meters).
• Temporal Scale: Time series data for evaluation stations; specific duration not provided.

Methodology and Data

• Models used: PCRaster Global Water Balance Model (PCR-GLOBWB) 2.0.
• Data sources: Model output from PCR-GLOBWB 2.0, time series from measurement stations for evaluation, and additional maps for figure recreation.

Main Results

The provided data supplement description does not detail the specific findings. However, the manuscript title suggests that hyper-resolution large-scale hydrological modeling demonstrates benefits from improved process representation in mountain regions. The data provided supports the creation of figures and evaluation of the model.

Contributions

Demonstrates the value of improved process representation in hyper-resolution large-scale hydrological modeling, particularly for complex mountain environments. Provides a valuable dataset (PCR-GLOBWB 2.0 output) for the Alpine region at a high spatial resolution (30 arcsec) for further research and evaluation.

Funding

Not mentioned in the provided text.

Citation

@article{Janzing2025Data,
  author = {Janzing, Joren and Wanders, Niko and Van Tiel, Marit and Van Jaarsveld, Barry and Karger, Dirk and Brunner, Manuela},
  title = {Data supplement to Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions},
  journal = {Open MIND},
  year = {2025},
  doi = {10.16904/envidat.705},
  url = {https://doi.org/10.16904/envidat.705}
}