Martins et al. (2025) SWAT Model and Drought Indices: A Systematic Review of Progress, Challenges and Opportunities

Identification

• Journal: Water
• Year: 2025
• Date: 2025-12-23
• Authors: Letícia Lopes Martins, Wander Araújo Martins, Maria Eduarda Cruz Ferreira, Jener Fernando Leite de Moraes, Bolfe Edson Luis, Gabriel Constantino Blain
• DOI: 10.3390/w18010041

Research Groups

• Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas, SP, Brazil
• Agronomic Institute of Campinas, Campinas, SP, Brazil
• Post-Graduation Program in Tropical and Subtropical Agriculture, Agronomic Institute of Campinas, Campinas, SP, Brazil
• Institute of Geosciences, State University of Campinas—Unicamp, Campinas, SP, Brazil

Short Summary

This systematic review analyzes the integrated application of the Soil and Water Assessment Tool (SWAT) model and drought indices for drought monitoring and prediction, identifying significant progress but also highlighting critical gaps in advanced statistical methodologies and index harmonization.

Objective

• To identify the key advances and limitations in the integrated application of the SWAT model and drought indices.
• To map the spatial distribution of studies applying this integrated approach, thereby providing a comprehensive overview of current progress and research gaps in drought monitoring through hydrological modeling.

Study Configuration

• Spatial Scale: Global systematic review of studies covering scales from watershed to continental (e.g., river basins in China, United States, Ethiopia, Vietnam, Brazil, Mekong River Basin, contiguous United States).
• Temporal Scale: Systematic review of articles published from 2011 to 2025.

Methodology and Data

• Models used:
  • SWAT (Soil and Water Assessment Tool)
  • Various drought indices (e.g., SPI, SPEI, SSI, SRI, PDSI, SMDI, NJHDI, EDDI, scPDSI, ETDI, aSPI, SSDI, MPDSI, SSWI, J, Ped, K, SBI, ADI, PHDI, MAHDI, SWI, NSSI, ESI, IDI, SWSI, SWYI, PERCI, RFI, WYLDI, SDI, SPAEI, MSDI)
  • (Reviewed studies also integrated models like VIC and CFSv2 for forecasting.)
• Data sources:
  • Bibliographic databases: Scopus and Web of Science (for the review itself).
  • For reviewed studies: Observational ground data, satellite-based products, reanalysis products, Global Climate Models (GCMs), Regional Climate Models (RCMs), and Climate Forecasting System version 2 (CFSv2) meteorological forecasts.

Main Results

• A total of 115 articles published between 2011 and 2025 were included from an initial 803 identified.
• Significant advances were found in using SWAT for drought monitoring and prediction, including the development of indices and forecasting systems.
• The review identified 47 distinct drought indices, highlighting a lack of harmonization and standardization (e.g., identical acronyms used for different indices).
• Research is predominantly concentrated in China (38 publications) and the United States (11 publications).
• Key gaps include the limited use of advanced statistical methodologies (e.g., machine learning, non-stationarity analyses, with only two studies applying machine learning) and insufficient evaluation of suitable probability distributions for regional applications.
• SWAT model calibration in reviewed studies primarily focused on streamflow, often using Nash–Sutcliffe Efficiency (NSE) as the sole performance metric, indicating a lack of multi-criteria evaluation.
• Critical limitations identified across the literature include uncertainties related to the availability and quality of observational data, low resolution of soil datasets, and uncertainties associated with GCM projections.

Contributions

• Provides a comprehensive systematic literature review, following the PRISMA protocol, on the integrated application of the SWAT model and drought indices.
• Maps the current state of advancements, limitations, and geographical distribution of research in this interdisciplinary field.
• Identifies critical research gaps, particularly concerning the adoption of advanced statistical methods (e.g., machine learning, non-stationarity approaches) and the need for harmonization of drought indices.
• Establishes SWAT as a robust tool for supporting drought management strategies while highlighting its substantial untapped potential for future research.
• Proposes a detailed future research agenda, with a specific focus on addressing challenges and opportunities in data-scarce tropical regions like Brazil.

Funding

• São Paulo Research Foundation (FAPESP), Brazil (Process Number #2022/09319-9, #2024/08132-8)
• Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) (Finance Code 001)
• National Council for Scientific and Technological Development (CNPq)/Research Productivity Fellowship (Process Number #302963/2025-1, #304609/2022-6)

Citation

@article{Martins2025SWAT,
  author = {Martins, Letícia Lopes and Martins, Wander Araújo and Ferreira, Maria Eduarda Cruz and Moraes, Jener Fernando Leite de and Luis, Bolfe Edson and Blain, Gabriel Constantino},
  title = {SWAT Model and Drought Indices: A Systematic Review of Progress, Challenges and Opportunities},
  journal = {Water},
  year = {2025},
  doi = {10.3390/w18010041},
  url = {https://doi.org/10.3390/w18010041}
}