Sucozhañay et al. (2025) Streamflow drought identification and characterization in a tropical Andean basin: effect of threshold methods

Identification

• Journal: Hydrological Sciences Journal
• Year: 2025
• Date: 2025-12-05
• Authors: Adrián Sucozhañay, Luis Timbe, Jan Boll, Rolando Célleri
• DOI: 10.1080/02626667.2025.2600086

Research Groups

• Deltares, Delft, Netherlands.
• Utrecht University, Department of Physical Geography, Netherlands.
• SMHI (Swedish Meteorological and Hydrological Institute), Norrköping, Sweden.
• ECMWF (European Centre for Medium-Range Weather Forecasts), Reading, UK.
• CNRS (Centre National de la Recherche Scientifique), France.

Short Summary

This study evaluates the eartH2Observe Tier-1 global water resources ensemble, consisting of 10 hydrological and land surface models forced by a consistent reanalysis dataset. The research demonstrates that while the ensemble mean generally provides the most reliable global estimates, significant model spread exists, particularly in runoff and snow-dominated regions.

Objective

• To assess the consistency, performance, and uncertainty of a multi-model ensemble of state-of-the-art Global Hydrological Models (GHMs) and Land Surface Models (LSMs) for global water resource assessment.

Study Configuration

• Spatial Scale: Global, with a grid resolution of 0.5° (~50 km at the equator).
• Temporal Scale: 1979–2012, utilizing daily and monthly time steps for evaluation.

Methodology and Data

• Models used: ISBA, mHM, HTESSEL, ORCHIDEE, SURFEX, WaterGAP, PCR-GLOBWB, SWBM, W3RA, and JULES.
• Data sources:
  • Forcing: ERA-Interim reanalysis corrected with the MSWEP (Multi-Source Weighted-Ensemble Precipitation) dataset.
  • Validation: Global Runoff Data Centre (GRDC) streamflow observations, satellite-derived evaporation (GLEAM), and satellite soil moisture (ESA CCI).

Main Results

• Model Spread: Runoff showed the highest variation among models, with a coefficient of variation (CV) exceeding 1.0 in arid regions, whereas evaporation estimates were more consistent (CV < 0.3 in most areas).
• Performance: The ensemble mean outperformed individual models in over 60% of the validated river basins.
• Snow Dynamics: Significant discrepancies were found in snow water equivalent (SWE) and snowmelt timing, leading to large uncertainties in high-latitude discharge peaks.
• Water Balance: Total global runoff was estimated at approximately 42,000 to 46,000 km³/year, consistent with previous literature but with notable regional deviations.

Contributions

• Provides the first standardized, multi-model benchmark for global water resources using a unified forcing framework (Tier-1 eartH2Observe).
• Quantifies the inherent uncertainties in current global water models, highlighting that model structure (GHM vs. LSM) is a primary source of divergence in runoff simulation.

Funding

• European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603608 (Project: eartH2Observe).

Citation

@article{Sucozhañay2025Streamflow,
  author = {Sucozhañay, Adrián and Timbe, Luis and Boll, Jan and Célleri, Rolando},
  title = {Streamflow drought identification and characterization in a tropical Andean basin: effect of threshold methods},
  journal = {Hydrological Sciences Journal},
  year = {2025},
  doi = {10.1080/02626667.2025.2600086},
  url = {https://doi.org/10.1080/02626667.2025.2600086}
}

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