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<p>Dust storms represent one of the major environmental challenges in arid and semi-arid regions. This study proposes a system dynamics (SD) framework to assess how moisture injection and water-level restoration of the Jazmourian lake (southeast Iran) influence regional dust activity. Unlike previous studies that focused mainly on meteorological drivers, this research integrates climatic, hydrological, and vegetation-related feedbacks into a dynamic modeling structure. We compiled high-resolution DEM (10&#xa0;m), field observations, historical documents, meteorological data (2010–2020), MODIS-based NDVI, and satellite-derived soil-moisture datasets to construct a Vensim-based model linking lake level, evaporation, relative humidity, vegetation cover, soil moisture, and a Dust Storm Index (DSI). Vensim was used because it enables transparent representation of nonlinear feedbacks and time-dependent interactions among variables, facilitating the evaluation of dust–moisture–vegetation dynamics. Model parameterization combined empirical hydrological equations, multiple linear regression analysis, and documented functional relationships from the literature. Validation against observed DSI yielded RMSE = 0.4, indicating acceptable predictive performance. Scenario simulations show that maintaining the lake near the 390&#xa0;m elevation (≈7,014 km<sup>2</sup>; ≈104 × 10⁹ m<sup>3</sup>) substantially reduces annual average DSI (≈75%) relative to lower-level or dewatered scenarios, primarily through increased atmospheric humidity, enhanced surface-soil moisture, and improved vegetation cover. The study highlights key model uncertainties—including limited observations and challenges in sustaining high lake levels—and urges future work with broader datasets and stronger constraints. Overall, the research affirms the value of system dynamics modeling for reconstructing complex environmental conditions and guiding water management and dust-storm mitigation in arid regions.</p>

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Simulation of the effect of moisture injection of the Jazmourian Lake on the phenomenon of dust, Southeast of Iran

  • 期刊论文

  • 发表日期：

    2026年5月15日

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  • Mousa Kordavani,
  • Mohammad Hosein Ramesht,
  • Morteza pourzare

摘要

Dust storms represent one of the major environmental challenges in arid and semi-arid regions. This study proposes a system dynamics (SD) framework to assess how moisture injection and water-level restoration of the Jazmourian lake (southeast Iran) influence regional dust activity. Unlike previous studies that focused mainly on meteorological drivers, this research integrates climatic, hydrological, and vegetation-related feedbacks into a dynamic modeling structure. We compiled high-resolution DEM (10 m), field observations, historical documents, meteorological data (2010–2020), MODIS-based NDVI, and satellite-derived soil-moisture datasets to construct a Vensim-based model linking lake level, evaporation, relative humidity, vegetation cover, soil moisture, and a Dust Storm Index (DSI). Vensim was used because it enables transparent representation of nonlinear feedbacks and time-dependent interactions among variables, facilitating the evaluation of dust–moisture–vegetation dynamics. Model parameterization combined empirical hydrological equations, multiple linear regression analysis, and documented functional relationships from the literature. Validation against observed DSI yielded RMSE = 0.4, indicating acceptable predictive performance. Scenario simulations show that maintaining the lake near the 390 m elevation (≈7,014 km2; ≈104 × 10⁹ m3) substantially reduces annual average DSI (≈75%) relative to lower-level or dewatered scenarios, primarily through increased atmospheric humidity, enhanced surface-soil moisture, and improved vegetation cover. The study highlights key model uncertainties—including limited observations and challenges in sustaining high lake levels—and urges future work with broader datasets and stronger constraints. Overall, the research affirms the value of system dynamics modeling for reconstructing complex environmental conditions and guiding water management and dust-storm mitigation in arid regions.