Ahmadzadeh et al. (2025) Modeling potential streamflow in agricultural catchments: excluding human factors through an advanced framework

Identification

• Journal: Acta Geophysica
• Year: 2025
• Date: 2025-12-20
• Authors: Hojat Ahmadzadeh, Ahmad Fakheri Fard, Abolfazl Majnooni-Heris, Farshad Fathian
• DOI: 10.1007/s11600-025-01772-6

Research Groups

• Department of Water Engineering, University of Tabriz, Tabriz, Iran
• Department of Water Science & Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

Short Summary

This study developed a novel framework using the SWAT model to estimate potential streamflow (PS) by explicitly excluding human factors in agricultural catchments, revealing a significant reduction in actual streamflow due to anthropogenic activities in the Aji Chai catchment and providing a replicable method for sustainable water management.

Objective

• To develop and validate the SWAT model for the Aji Chai catchment.
• To extract land use maps for the study period.
• To estimate the potential streamflow (PS) time series by eliminating human factors affecting streamflows.
• To analyze the trends of PS and actual streamflow for the study period 1987–2019.

Study Configuration

• Spatial Scale: Aji Chai catchment, the largest sub-basin of the Lake Urmia basin in northwestern Iran, covering an area of over 12,600 square kilometers.
• Temporal Scale: 1987–2019 (33 years).

Methodology and Data

• Models used:
  • Soil and Water Assessment Tool (SWAT) for hydrological and crop process simulation.
  • SWAT-CUP (Calibration and Uncertainty Programs) with SUFI-2 algorithm for model calibration and validation.
  • Mann–Kendall test for trend analysis of streamflow time series.
• Data sources:
  • Climatic Data: Daily time series of precipitation, air temperature, relative humidity, wind speed, evaporation, sunshine duration, and solar radiation from 37 rain gauge, 5 synoptic, and 7 climatological stations (Iran Meteorological Organization).
  • Hydrological Data: Daily streamflow observations from 20 hydrometric stations (Iran Water Resources Management Company).
  • Topographic Data: Digital Elevation Model (DEM) with 30 meter resolution (earthexplorer.usgs.gov).
  • Land Use Data: Land use maps for 1987, 1993, 2000, 2007, 2015, and 2019, extracted from Landsat satellite images (Landsat 8 OLI and Landsat 5 TM) with 30 meter resolution using decision tree classification.
  • Soil Data: Soil map from the "Study and Present Solutions to Implement the Decree of 40% Reduction of Agricultural Water Consumption in Aji Chai Catchment" project (Fakheri Fard et al. 2019).
  • Agricultural Management Data: Planting and harvesting dates, irrigation schedules, fertilizer usage, and cropping patterns (Agricultural Jihad Organization of East Azerbaijan Province, Iran). Agricultural water use (surface and groundwater) (Regional Water Company of East Azerbaijan Province, Iran).
  • Anthropogenic Factors Data: Dam locations and useful volumes (0.3 to 280 million cubic meters), inter-basin water transfer volume (approximately 86 million cubic meters annually from Zarrineh Rood basin).

Main Results

• The area of irrigated agricultural lands in the Aji Chai catchment increased by 41% (from 72,005 hectares to 101,210 hectares) between 1987 and 2019.
• Dam construction, inter-basin water transfer, land use change, and agricultural expansion were identified as the most significant human factors influencing streamflow.
• The SWAT model demonstrated good performance in simulating streamflow (correlation coefficients (R) > 0.70 and Nash–Sutcliffe (NS) values ranging from 0.35 to 0.76 for most hydrometric stations) and actual evapotranspiration (R² = 0.95, RMSE = 20 millimeters).
• The average annual potential streamflow (PS) at the Aji Chai outlet was 12.2 cubic meters per second, while the average annual observed outflow was 8.4 cubic meters per second, indicating an average annual reduction of 120 million cubic meters due to human factors.
• Human factors caused a 31% decrease in the average annual outflow at the catchment outlet relative to the corresponding PS during the study period.
• In most sub-catchments with significant decreasing trends, the rate of decrease in PS was typically greater than or at least comparable to that of the outflow.
• Along the main Aji Chai river, the cumulative effects of human interventions intensified downstream, resulting in a higher rate of decrease in the outflow compared to PS.

Contributions

• Developed a novel and comprehensive methodological framework to estimate potential streamflow (PS) by explicitly isolating and removing the effects of human factors (land use changes, dam construction, inter-basin transfers, water withdrawals) from hydrological simulations in agricultural catchments.
• Provided a replicable framework for separating climatic and anthropogenic effects on river flows, which is crucial for sustainable water reallocation and management, particularly in data-scarce or heavily impacted regions.
• Quantified the significant impact of irrigation expansion and other human activities on streamflow reduction in the Aji Chai catchment, offering new evidence for the decisive role of anthropogenic factors in semi-arid basins.
• Demonstrated the robust capability of the SWAT model, including its plant growth (EPIC) component, to simulate complex atmosphere–water–soil–plant interactions and anthropogenic drivers in heterogeneous agricultural landscapes.
• Offered valuable insights for water resource managers and decision-makers for planning and managing water resources in irrigation-dominated basins by providing a clear distinction between climatically driven and anthropogenically altered streamflow trends.

Funding

• Research grant of the University of Tabriz (Grant number 2895).

Citation

@article{Ahmadzadeh2025Modeling,
  author = {Ahmadzadeh, Hojat and Fard, Ahmad Fakheri and Majnooni-Heris, Abolfazl and Fathian, Farshad},
  title = {Modeling potential streamflow in agricultural catchments: excluding human factors through an advanced framework},
  journal = {Acta Geophysica},
  year = {2025},
  doi = {10.1007/s11600-025-01772-6},
  url = {https://doi.org/10.1007/s11600-025-01772-6}
}