Yong et al. (2026) Revealing the influence of topography and vegetation on hydrological processes using a stepwise modelling approach in cold alpine basins of the Mongolian Plateau

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-03-27
• Authors: Leilei Yong, Yahui Wang, Batsuren Dorjsuren, Z. Duan, Hongkai Gao
• DOI: 10.5194/hess-30-1585-2026

Research Groups

• Key Laboratory of Geographic Information Science (Ministry of Education of China), School of Geographical Sciences, East China Normal University, Shanghai 200241, China
• Department of Environment and Forest Engineering, National University of Mongolia, Ulaanbaatar 210646, Mongolia
• Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62 Lund, Sweden

Short Summary

This study employed a stepwise modelling approach to systematically assess the influence of topography and vegetation on hydrological processes in cold alpine basins of the Mongolian Plateau, revealing that distributed and landscape-based models significantly improve runoff and snow water equivalent simulations, with high elevations driving sustained snowmelt runoff and low elevations generating rapid rainfall-driven runoff.

Objective

• How can runoff be effectively simulated in data-scarce, cold mountainous regions using a top-down modelling approach?
• How can the contribution of snowmelt runoff to streamflow be quantified using a landscape-based hydrological model?
• How do topography and vegetation influence runoff generation processes?

Study Configuration

• Spatial Scale: Two cold alpine basins on the western Mongolian Plateau: Bogd Uliastai river basin (1610 km², elevations 1753 to 3972 m) and Zavkhan Guulin river basin (12258 km², elevations 1785 to 3980 m).
• Temporal Scale:
  • Bogd Uliastai: Warm-up 2007, Calibration 2008–2011, Validation 2012–2015.
  • Zavkhan Guulin: Warm-up 2000, Calibration 2001–2010, Validation 2011–2020.
  • Arctic Snow Water Equivalent (SWE) Grid Dataset: 2003–2016.
  • Normalized Difference Vegetation Index (NDVI) data: 2013–2020.

Methodology and Data

• Models used:
  • FLEX L: Lumped conceptual hydrological model.
  • FLEX L-S: Lumped model with a snow module.
  • FLEX D: Semi-distributed model with elevation bands, same structure as FLEX L-S.
  • FLEX T: Landscape-driven semi-distributed model, integrating landscape types (bare soil/rock, forest, grassland, riparian area) and elevation bands into Hydrological Response Units (HRUs).
  • MOSCEM-UA (Multi-objective Shuffled Complex Evolution Metropolis Algorithm) for parameter optimization and uncertainty assessment.
• Data sources:
  • Hydrometeorological data: Daily precipitation, runoff, and temperature from the Information and Research Institute of Meteorology, Hydrology, and Environment (IRIMHE).
  • Snow Water Equivalent (SWE): Arctic Snow Water Equivalent Grid Dataset (10 km spatial resolution) from the National Tibetan Plateau/Third Pole Environment Data Center.
  • Topographic data: Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM) (90 m spatial resolution) from CGIAR Consortium for Spatial Information (CGIAR-CSI).
  • Land cover data: Sentinel-2 10 m Land Use/Land Cover from ESRI's official platform.
  • NDVI data: Derived from Landsat 8 Operational Land Imager (OLI) Level-2 surface reflectance products (30 m spatial resolution) from the United States Geological Survey (USGS) EarthExplorer.
  • Forcing data distribution: Precipitation increase rate of 4.2% per 100 m and temperature lapse rate of 0.6 °C per 100 m, derived from a reference catchment.

Main Results

• The stepwise modelling approach progressively improved model performance: FLEX L-S (KGE: 0.65/0.65) outperformed FLEX L (KGE: 0.53/0.52); FLEX D (KGE: 0.77/0.68) outperformed FLEX L-S; and FLEX T (KGE: 0.78/0.68) performed comparably to FLEX D for runoff simulation.
• FLEX D and FLEX T significantly improved Snow Water Equivalent (SWE) simulation, achieving KGE values of 0.61 and 0.63, respectively, in the Bogd Uliastai basin, compared to 0.37 for FLEX L-S. In the Zavkhan Guulin basin, FLEX D (KGE: 0.55) and FLEX T (KGE: 0.56) showed slight improvements over FLEX L-S (KGE: 0.50).
• Annual snowmelt runoff accounted for 23.6% ± 0.7% of streamflow in the Bogd Uliastai river basin and 15.9% ± 1.3% in the Zavkhan Guulin river basin.
• Seasonally, snowmelt runoff dominated spring flows, contributing 70.1% ± 1.7% and 76.9% ± 3.2% of streamflow in the Bogd Uliastai and Zavkhan Guulin basins, respectively.
• Both the snowfall-to-precipitation ratio and the snowmelt runoff-to-streamflow ratio increased with elevation. High elevation areas (above 2900 m in Bogd Uliastai, 2825 m in Zavkhan Guulin) contributed approximately 30% of total basin runoff, characterized by delayed and gradual snowmelt release. Lower elevations generated rapid, rainfall-driven runoff.
• Different landscape units contributed unequally to streamflow: grasslands (82.8% and 85.4% of area) contributed 80.0% and 82.0% of total runoff; forested areas (4.7% of area in Bogd Uliastai) contributed 2.9%; riparian areas (10.3% and 13.0% of area) contributed 12.8% and 14.7%; and bare soil/rock areas (2.2% and 1.6% of area) contributed 4.3% and 3.3%.

Contributions

• Developed and validated a stepwise modelling framework (FLEX L, FLEX L-S, FLEX D, FLEX T) that systematically incorporates snow processes, topographic distribution, and landscape-based parameterization to improve hydrological simulations in data-scarce, cold alpine basins.
• Quantified the contribution of snowmelt runoff to streamflow using a process-based tracking method, demonstrating its superiority over traditional indirect approaches which tend to overestimate snowmelt contributions.
• Provided novel insights into the elevation-dependent and landscape-specific runoff generation mechanisms in the cold alpine basins of the Mongolian Plateau, highlighting the distinct roles of high-elevation snowmelt and low-elevation rainfall.
• Demonstrated the effectiveness of the FLEX T model in enhancing physical interpretability and realism in cryospheric environments, offering a flexible platform for diagnosing hydrological processes under varying landscape complexities.

Funding

• National Key Research and Development Program of China (grant no. 2024YFE0113200)
• National Natural Science Foundation of China (grant no. 42471040)
• Crafoord Foundation, Sweden (grant nos. 20210552 and 20240857)
• IAHS HELPING Working Group on “Development and application of river basin simulators”

Citation

@article{Yong2026Revealing,
  author = {Yong, Leilei and Wang, Yahui and Dorjsuren, Batsuren and Duan, Z. and Gao, Hongkai},
  title = {Revealing the influence of topography and vegetation on hydrological processes using a stepwise modelling approach in cold alpine basins of the Mongolian Plateau},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-1585-2026},
  url = {https://doi.org/10.5194/hess-30-1585-2026}
}