Doostmohamadi et al. (2026) Projecting Surface Runoff Variability Under Climate Change in a Water-Stressed Arid Watershed; Using SWAT Model and CMIP6 Scenarios (Case Study: Zardtol Watershed, Semnan, Iran)

Identification

• Journal: Iranian Journal of Science and Technology Transactions of Civil Engineering
• Year: 2026
• Date: 2026-03-09
• Authors: Mahsa Doostmohamadi, Khosrow Hosseini, Candidate
• DOI: 10.1007/s40996-026-02136-w

Research Groups

• Department of Water Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran

Short Summary

This study projects surface runoff variability in the arid Zardtol watershed under climate change using the SWAT model and CMIP6 scenarios, revealing significant future reductions in average monthly runoff and an intensification of hydrological droughts.

Objective

• To investigate the effects of climate change on surface runoff variability in the arid Zardtol watershed, Semnan, Iran, using the SWAT model and CMIP6 scenarios, and to assess future hydrological drought trends.

Study Configuration

• Spatial Scale: Zardtol watershed, Semnan County, Iran, covering an area of 67.52 square kilometers, with an average elevation of 2.468 kilometers above sea level. The main river is approximately 19.15 kilometers in length.
• Temporal Scale:
  • Historical/Base Period: 1990–2014 for climate data; 1993–2012 for SWAT model calibration; 2013–2023 for SWAT model validation.
  • Future Period: 2025–2049 for climate change projections and runoff simulation.

Methodology and Data

• Models used:
  • Hydrological Model: Soil and Water Assessment Tool (SWAT) model (specifically SWAT2012).
  • Downscaling Model: Statistical Downscaling Model (SDSM).
  • Climate Model: CanESM5 (Canadian Earth System Model version 5) from CMIP6.
  • Calibration/Validation Tool: SWAT-CUP Premium with the SWAT Parameter Estimator (SPE) algorithm.
• Data sources:
  • Climate Data: CMIP6 climate data (SSP126, SSP245, SSP585 scenarios) for daily minimum/maximum temperature and precipitation.
  • Topographic Data: Digital Elevation Model (DEM).
  • Soil Data: FAO global soil map.
  • Land Use Data: Land cover maps.
  • Observed Data: Meteorological data from Semnan synoptic station (precipitation, minimum/maximum temperature) and observed runoff data for model calibration and validation.

Main Results

• The SWAT model demonstrated acceptable performance for runoff simulation (calibration: Nash-Sutcliffe Efficiency (NSE) = 0.54, R² = 0.56; validation: NSE = 0.67, R² = 0.71).
• Future precipitation is projected to decrease in most months compared to the historical period, with the largest reduction of -60.60% in August under the SSP245 scenario, though some months show increases (e.g., 58.12% in August under SSP585).
• An overall warming trend is projected, intensifying under higher-emission scenarios (SSP585), with the strongest monthly maximum temperature increases observed in winter months (e.g., 6.64% in December under SSP585).
• Average monthly surface runoff is projected to decrease for most months across all future scenarios compared to the historical period. The greatest monthly average runoff reductions are:
  • SSP126: -71.74% in October and -64.40% in September.
  • SSP245: -83.32% in September and -82.02% in October.
  • SSP585: -76.21% in October and -55.39% in September.
• While higher greenhouse gas emissions (SSP126 to SSP585) generally lead to slightly increased runoff compared to lower emission scenarios, these values remain lower than historical runoff.
• Analysis using the Standardized Streamflow Index (SSI) indicates an intensification of hydrological droughts, particularly during the dry season (May to November), with SSP585 showing the most substantial SSI reduction.
• Vulnerability assessment highlights seasonal susceptibility, with SSP245 showing four months of critical runoff reduction (>50%), SSP126 three critical months, and SSP585 two critical months but also six months with increased runoff.

Contributions

• Integration of CMIP6 model ensembles with a locally calibrated SWAT model for a comprehensive evaluation of runoff variability in a water-stressed arid region.
• Improved uncertainty assessment by quantifying runoff variability within the 95% prediction uncertainty (95PPU).
• Provision of scenario-based interpretations to support adaptive water allocation, drought preparedness, and long-term planning for climate-resilient water resources management in arid regions of Iran.
• Addresses the research gap in arid, data-scarce watersheds using advanced CMIP6 climate projections.

Funding

No funding was received for conducting this study. The authors did not receive support from any organization for the submitted work.

Citation

@article{Doostmohamadi2026Projecting,
  author = {Doostmohamadi, Mahsa and Hosseini, Khosrow and Candidate},
  title = {Projecting Surface Runoff Variability Under Climate Change in a Water-Stressed Arid Watershed; Using SWAT Model and CMIP6 Scenarios (Case Study: Zardtol Watershed, Semnan, Iran)},
  journal = {Iranian Journal of Science and Technology Transactions of Civil Engineering},
  year = {2026},
  doi = {10.1007/s40996-026-02136-w},
  url = {https://doi.org/10.1007/s40996-026-02136-w}
}