Kumar et al. (2026) A Framework for Selection of a Hydrological Model-SAWT: A Review

Identification

• Journal: Water Resources Management
• Year: 2026
• Date: 2026-03-31
• Authors: G. (G. Vijay) Kumar, G. S. Dwarakish
• DOI: 10.1007/s11269-026-04600-8

Research Groups

• Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, India

Short Summary

This review provides a detailed overview of the Soil and Water Assessment Tool (SWAT) model, including its development, structure, data requirements, and application strategies. It systematically compares SWAT with HEC-HMS, MIKE-SHE, and VIC based on eight key criteria, concluding that SWAT is a versatile and suitable model for watershed-scale hydrological research and management.

Objective

• To provide a detailed overview of the Soil and Water Assessment Tool (SWAT) model, covering its development, core structure, data requirements, calibration and validation strategies, and commonly used performance indicators.
• To compare SWAT with three other prominent hydrological models (HEC-HMS, MIKE-SHE, and VIC) based on eight key criteria to aid in model selection for water resource management.

Study Configuration

• Spatial Scale: The review focuses on hydrological models applicable at various watershed scales, from small catchments to large river basins, with a particular emphasis on SWAT's adaptability across varied watershed scales.
• Temporal Scale: The models discussed are capable of simulating hydrological processes over various temporal resolutions, including continuous streamflow simulation, and long-term climate change impact assessments using projections like CMIP5 and CMIP6.

Methodology and Data

• Models used: Soil and Water Assessment Tool (SWAT, SWAT+), Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), MIKE-SHE, Variable Infiltration Capacity (VIC) model.
• Data sources: This is a review paper, so its primary "data" is existing scientific literature. The models discussed typically utilize various geospatial and temporal datasets, including climate data (e.g., CMIP5, CMIP6 projections), land-use/land-cover data, soil properties, topographic data (e.g., Digital Elevation Models), and observed hydrological data for calibration and validation.

Main Results

• SWAT is identified as one of the most widely used semi-distributed, physically based hydrological models globally, with its latest version, SWAT+, offering improved structural flexibility and enhanced process representation.
• A systematic comparison of SWAT with HEC-HMS, MIKE-SHE, and VIC was conducted based on eight criteria: physical process representation, spatial discretization, open-source accessibility, multi-process simulation capacity, computational efficiency, calibration complexity, water quality integration, and breadth of global applications.
• SWAT demonstrates high adaptability across diverse watershed scales and climatic conditions, with extensive applications in streamflow simulation, sediment and nutrient transport analysis, climate change impact assessment, land-use change evaluation, best management practice assessment, and integrated groundwater-surface water modeling (e.g., SWAT-MODFLOW).
• Despite some limitations, emerging developments like SWAT-machine learning integration present promising opportunities, reinforcing SWAT's suitability and versatility for watershed-scale hydrological research and management.

Contributions

• Provides a comprehensive and structured review of the SWAT model, detailing its evolution, structure, and application aspects.
• Offers a systematic comparative analysis of SWAT against three other widely used hydrological models (HEC-HMS, MIKE-SHE, VIC) using a defined set of criteria, which serves as a valuable framework for model selection.
• Highlights the adaptability and versatility of SWAT across various hydrological applications and geographical regions, including extensive research from the Indian subcontinent.
• Identifies current limitations and future opportunities for SWAT, such as integration with machine learning, contributing to the ongoing development and application of hydrological modeling.

Funding

Not applicable.

Citation

@article{Kumar2026Framework,
  author = {Kumar, G. (G. Vijay) and Dwarakish, G. S.},
  title = {A Framework for Selection of a Hydrological Model-SAWT: A Review},
  journal = {Water Resources Management},
  year = {2026},
  doi = {10.1007/s11269-026-04600-8},
  url = {https://doi.org/10.1007/s11269-026-04600-8}
}