University of Tehran Journal of Environmental Studies 1025-8620 51 3 2025 12 16 Application of the Ordinary Least Squares (OLS) Regression Model to Assess the Impact of Land Use Change on Water Yield Ecosystem Service (Case Study: Lavasanat District, Tehran Province) Application of the Ordinary Least Squares (OLS) Regression Model to Assess the Impact of Land Use Change on Water Yield Ecosystem Service (Case Study: Lavasanat District, Tehran Province) 273 292 105251 10.22059/jes.2025.387056.1008561 FA Marzie Motesharei Department of Environmental Planning, Management, and HSE, Graduate Faculty of Environment, University of Tehran, Tehran, Iran. Bahram Malekmohammadi Department of Disaster Engineering, Education and Environmental Systems, Graduate Faculty of Environment, University of Tehran, Tehran, Iran. Majid Ramezanimehrian Research and Development Department of Humanities, Faculty of Samt Research Institute, Tehran, Iran. Journal Article 2025 01 16 Objective: In this study, the ordinary least squares (OLS) regression model was applied to estimate the impacts of land use and land cover (LULC) changes from 1999 to 2023 derived from Landsat satellite imagery on water productivity as a key ecosystem service essential for environmental sustainability. Focusing on the Lavasanat district in Tehran province, which has undergone rapid urbanization and severe land use/cover changes, this study determined the extent to which water production performance (runoff) responded to land use/cover changes, thereby providing significant information on the environmental consequences of land use/cover changes under increasing human pressures. Method: This study used Landsat satellite imagery to assess the trends in land use/cover (LULC) changes over time at a spatial resolution of 30 m. Water yield modeling was performed using the annual water yield index of the InVEST software. The model inputs included land use and cover maps from three different time series along with data on precipitation, potential evapotranspiration, soil depth, water availability for plants, and local biophysical tables. The results from the InVEST model were analyzed using an ordinary least squares (OLS) regression model to estimate the impact of land use/cover changes on water yield. This method allows for a detailed examination of the relationship between land use/cover changes and their impacts on the water yield. Results: The results showed that between 1999 and 2023, the area of green spaces, including agricultural lands, gardens, and pastures, decreased by 161.21 km², or 31 percent. Consequently, the annual water production (runoff) increased from 105 to 130 million cubic meters. In addition, the area of the minimum error zone decreased from 445.3 to 23.5 km², indicating a decrease in the reliability of the model. Such findings indicated the high variability and complexity of hydrological interactions and indicated the severe effects of overdevelopment and increasing land use/cover pressures. Conclusions: The results of this study showed that the water production capacity (runoff) of the ecosystem was vulnerable to changes in land use and land cover. These changes increased runoff, reduced water permeability in the soil, and disrupted the hydrological balance of the region. Therefore, it is necessary to use integrated modeling approaches and simultaneously pay attention to climate change, socio-economic factors, and land use/cover planning. Regional planning should also emphasize preservation and restoration of green spaces and smart management of urban development, which is an inevitable necessity to maintain ecosystem resilience and water resource sustainability. Objective: In this study, the ordinary least squares (OLS) regression model was applied to estimate the impacts of land use and land cover (LULC) changes from 1999 to 2023 derived from Landsat satellite imagery on water productivity as a key ecosystem service essential for environmental sustainability. Focusing on the Lavasanat district in Tehran province, which has undergone rapid urbanization and severe land use/cover changes, this study determined the extent to which water production performance (runoff) responded to land use/cover changes, thereby providing significant information on the environmental consequences of land use/cover changes under increasing human pressures. Method: This study used Landsat satellite imagery to assess the trends in land use/cover (LULC) changes over time at a spatial resolution of 30 m. Water yield modeling was performed using the annual water yield index of the InVEST software. The model inputs included land use and cover maps from three different time series along with data on precipitation, potential evapotranspiration, soil depth, water availability for plants, and local biophysical tables. The results from the InVEST model were analyzed using an ordinary least squares (OLS) regression model to estimate the impact of land use/cover changes on water yield. This method allows for a detailed examination of the relationship between land use/cover changes and their impacts on the water yield. Results: The results showed that between 1999 and 2023, the area of green spaces, including agricultural lands, gardens, and pastures, decreased by 161.21 km², or 31 percent. Consequently, the annual water production (runoff) increased from 105 to 130 million cubic meters. In addition, the area of the minimum error zone decreased from 445.3 to 23.5 km², indicating a decrease in the reliability of the model. Such findings indicated the high variability and complexity of hydrological interactions and indicated the severe effects of overdevelopment and increasing land use/cover pressures. Conclusions: The results of this study showed that the water production capacity (runoff) of the ecosystem was vulnerable to changes in land use and land cover. These changes increased runoff, reduced water permeability in the soil, and disrupted the hydrological balance of the region. Therefore, it is necessary to use integrated modeling approaches and simultaneously pay attention to climate change, socio-economic factors, and land use/cover planning. Regional planning should also emphasize preservation and restoration of green spaces and smart management of urban development, which is an inevitable necessity to maintain ecosystem resilience and water resource sustainability.

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