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<p>Estimation of potential streamflow (PS) is an essential step for the reallocation of water resources to different water demands in a water resources system. However, observational data of PS are generally unavailable in catchments heavily affected by human activities, where the streamflow is influenced by water withdrawals, dam construction, and land use changes. Therefore, in this study, a novel and comprehensive methodological framework is developed for extracting land use maps, estimating the PS, and analyzing its trend in catchments with extensive irrigated agricultural lands. To apply this framework, a process-based river catchment model of atmosphere–water–soil–plant, i.e., SWAT, was developed and calibrated by incorporating climatic and anthropogenic factors, along with the required hydrological, crop, and land use data. PS was then simulated by removing human factors from the model. For this purpose, the Aji Chai catchment, which is one of the crucial sub-basins of the Lake Urmia basin in northwestern Iran, is selected. Results showed that the area of irrigated agricultural lands in the catchment increased by 41% during the period of 1987–2019. In addition, dam construction, inter-basin transfer, land use change, and agricultural expansion were identified as the most significant human factors influencing the streamflow. Hydrological simulations indicated that, due to human factors, the observed outflow is generally lower than PS across most sub-catchments. Over the study period, the average annual outflow at the catchment outlet decreased by 31%, relative to the corresponding PS. Moreover, in most sub-catchments where streamflow showed a significant decreasing trend, the rate of decrease in PS was typically greater than or at least comparable to that of the outflow. However, along the main river of Aji Chai, the cumulative effects of human interventions intensified downstream, resulting in a higher rate of decrease in the outflow compared to PS. This study provides a replicable framework for separating climatic and anthropogenic effects on river flows, which is crucial for sustainable water reallocation and management.</p>

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Modeling potential streamflow in agricultural catchments: excluding human factors through an advanced framework

  • 期刊论文

  • 发表日期：

    2025年12月20日

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  • Hojat Ahmadzadeh,
  • Ahmad Fakheri Fard,
  • Abolfazl Majnooni,
  • Farshad Fathian

摘要

Estimation of potential streamflow (PS) is an essential step for the reallocation of water resources to different water demands in a water resources system. However, observational data of PS are generally unavailable in catchments heavily affected by human activities, where the streamflow is influenced by water withdrawals, dam construction, and land use changes. Therefore, in this study, a novel and comprehensive methodological framework is developed for extracting land use maps, estimating the PS, and analyzing its trend in catchments with extensive irrigated agricultural lands. To apply this framework, a process-based river catchment model of atmosphere–water–soil–plant, i.e., SWAT, was developed and calibrated by incorporating climatic and anthropogenic factors, along with the required hydrological, crop, and land use data. PS was then simulated by removing human factors from the model. For this purpose, the Aji Chai catchment, which is one of the crucial sub-basins of the Lake Urmia basin in northwestern Iran, is selected. Results showed that the area of irrigated agricultural lands in the catchment increased by 41% during the period of 1987–2019. In addition, dam construction, inter-basin transfer, land use change, and agricultural expansion were identified as the most significant human factors influencing the streamflow. Hydrological simulations indicated that, due to human factors, the observed outflow is generally lower than PS across most sub-catchments. Over the study period, the average annual outflow at the catchment outlet decreased by 31%, relative to the corresponding PS. Moreover, in most sub-catchments where streamflow showed a significant decreasing trend, the rate of decrease in PS was typically greater than or at least comparable to that of the outflow. However, along the main river of Aji Chai, the cumulative effects of human interventions intensified downstream, resulting in a higher rate of decrease in the outflow compared to PS. This study provides a replicable framework for separating climatic and anthropogenic effects on river flows, which is crucial for sustainable water reallocation and management.