Cho et al. (2026) Basin-scale evaluation of the Noah-MP land surface model for runoff and snow generation in the Missouri River Basin: insights and recommendations for parameterization scheme selection

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-10-19
• Authors: Eunsaem Cho, Eunsaem Cho, Eunsang Cho, Eunsang Cho, Carrie M. Vuyovich, Bailing Li, Jennifer M. Jacobs
• DOI: 10.5194/hess-30-6359-2026

Research Groups

• Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
• Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
• Ingram School of Engineering, Texas State University, San Marcos, TX, USA
• Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
• Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA

Short Summary

This study evaluated the Noah-Multiparameterization (Noah-MP) land surface model's default and four alternative parameterization schemes for runoff and snow generation in the Missouri River Basin. The findings provide recommendations for scheme selection based on basin characteristics, demonstrating improved hydrological prediction in diverse hydroclimatic regions.

Objective

• To quantify how Noah-MP parameterization schemes influence runoff simulation in the Missouri River Basin.
• To evaluate how basin characteristics (climate class, land cover, and snow regime) influence the effectiveness of alternative parameterization schemes in runoff simulation.
• To assess how alternative schemes improve snow water equivalent (SWE) and snowmelt-driven runoff representation relative to default model settings.

Study Configuration

• Spatial Scale: Missouri River Basin, covering 50 Hydro-Climate Data Networks (HCDNs) subbasins. Model simulations were performed at a 0.125° (approximately 12 km) spatial resolution.
• Temporal Scale: Model spin-up from 1 January 1980 to 31 December 2013. Evaluation period from 2014 to 2023, with daily values aggregated to monthly for runoff analysis.

Methodology and Data

• Models used:
  • Noah-Multiparameterization (Noah-MP) land surface model (version 4.0.1) within the NASA Land Information System (LIS) framework.
  • Four alternative parameterization schemes evaluated:
    • Runoff and Groundwater (RUN): TOPMODEL with equilibrium water table.
    • Surface Exchange Coefficient for Heat (SFC): Chen97 scheme.
    • Frozen Soil Permeability (INF): Koren99 scheme.
    • Snow/Soil Temperature Time Scheme (STC): Full implicit scheme.
• Data sources:
  • Meteorological Forcing: North American Land Data Assimilation System Phase 2 (NLDAS-2) meteorological data (hourly, 0.125°).
  • Runoff Evaluation: U.S. Geological Survey (USGS) streamflow observations from 50 HCDN gauges.
  • Snow Water Equivalent (SWE) Evaluation: University of Arizona (UA) gridded SWE dataset (daily, 4 km).
  • Evapotranspiration (ET) Reference: Global Land Evaporation Amsterdam Model (GLEAM) v4.2a evapotranspiration dataset.
  • Basin Characteristics: Modified IGBP MODIS vegetation dataset (land cover), Köppen–Geiger climate classification map (1 km), Sturm snow classification map (1 km), Digital Elevation Model (DEM).

Main Results

• Alternative parameterization schemes significantly improved runoff simulation compared to the default Noah-MP configuration. In representative subbasins, Kling–Gupta Efficiency (KGE) improved by 0.19 (RUN), 0.38 (SFC), 0.44 (INF), and 0.14 (STC).
• The RUN experiment enhanced groundwater dynamics and improved peak runoff simulation, showing widespread improvements across 58% of subbasins.
• The SFC experiment reduced evapotranspiration bias, leading to increased total runoff and improved performance in lower subbasin regions (64% of subbasins).
• The INF experiment improved rapid surface runoff responses by reducing infiltration capacity under frozen soil conditions, particularly in northern grassland areas (64% of subbasins).
• The STC experiment produced the highest average annual maximum SWE (48.9 mm compared to 33.4 mm for default) and effectively reduced SWE bias in snow-dominated subbasins. However, improvements in SWE did not universally translate into better runoff simulation, depending on the pre-existing runoff bias in the default scheme.
• Recommendations for optimal parameterization schemes vary by basin characteristics: RUN and SFC are broadly effective across multiple climate and land cover classes, while INF shows targeted improvements in Cold Semi-Arid climates, Vegetation Mosaic, and Prairie Snow. The default scheme remains effective in Subarctic climates and high-elevation snow regimes (Evergreen Needleleaf Forest, Montane Forest, Boreal Forest, Tundra).

Contributions

• Provided a systematic basin-scale evaluation of Noah-MP's default and four alternative parameterization schemes for runoff and snow generation in a large, diverse river basin.
• Quantified the impact of specific parameterization choices on key hydrological processes and identified their effectiveness based on detailed basin characteristics (climate, land cover, snow regime).
• Offered practical recommendations for selecting optimal Noah-MP parameterization schemes, addressing a critical need for improving hydrological prediction and water management.
• Highlighted that improvements in snow process representation do not always directly translate to overall runoff improvements, emphasizing the complex interactions within land surface models and the importance of application-specific scheme selection.

Funding

• This research was supported by the National Aeronautics and Space Administration (grant no. 80NSSC24K1278).

Citation

@article{Cho2026Basinscale,
  author = {Cho, Eunsaem and Cho, Eunsaem and Cho, Eunsang and Cho, Eunsang and Vuyovich, Carrie M. and Li, Bailing and Jacobs, Jennifer M.},
  title = {Basin-scale evaluation of the Noah-MP land surface model for runoff and snow generation in the Missouri River Basin: insights and recommendations for parameterization scheme selection},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-6359-2026},
  url = {https://doi.org/10.5194/hess-30-6359-2026}
}