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<p>Understanding the changes of surface water (SW) in rapidly urbanizing regions is crucial for sustainable urban planning and environmental management. This study investigates the spatiotemporal variation of SW in Gazipur City Corporation (GCC), Bangladesh, and explores the influence of land use/land cover (LULC) change and topography on SW distribution. Landsat-based surface reflectance imagery from 2004 and 2023—pre- and post-establishment of GCC—was used to assess seasonal variations and long-term LULC changes. Classification of LULC types (impervious, vegetation, bare, and waterbody) was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE), supported by spectral indices and ground control points (GCPs). Seasonal changes in SW were assessed using the optimum threshold value of the Modified Normalized Difference Water Index (MNDWI) obtained by Receiver Operating Characteristic (ROC) curve analysis, while the rate of change in LULC was calculated for 500 spatial sub-regions. To explore spatially varying relationships between surface water loss and potential drivers, a Geographically Weighted Regression (GWR) model was applied using Python. Results show a significant reduction in both permanent and seasonal SW over the study period, primarily in areas undergoing rapid urbanization. GWR outcomes indicate strong and statistically significant negative associations between SW change and impervious, bare land, and vegetation change rates, whereas elevation exhibited no significant influence (<i>p</i> = 0.88). The optimal GWR bandwidth encompassed most sub-regions, suggesting that while SW change exhibits strong spatial clustering, the influence of LULC drivers is largely spatially stationary across GCC. Residual spatial autocorrelation analysis further confirmed that the GWR model effectively reduced spatial dependence, although minor localized effects remained. Overall, the study highlights the importance of integrating remote sensing, spatial statistics, and localized regression approaches to better understand urban hydrological responses. The findings provide actionable insights for surface water conservation, flood management, and sustainable land-use planning in rapidly urbanizing cities of Bangladesh and similar developing regions.</p>

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Spatiotemporal changes of surface water and its drivers using geographically weighted regression: A case study of Gazipur City corporation, Bangladesh

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • S. M. Nazmul Haque,
  • A. S. M. Shanawaz Uddin,
  • Wakil Ahmed

摘要

Understanding the changes of surface water (SW) in rapidly urbanizing regions is crucial for sustainable urban planning and environmental management. This study investigates the spatiotemporal variation of SW in Gazipur City Corporation (GCC), Bangladesh, and explores the influence of land use/land cover (LULC) change and topography on SW distribution. Landsat-based surface reflectance imagery from 2004 and 2023—pre- and post-establishment of GCC—was used to assess seasonal variations and long-term LULC changes. Classification of LULC types (impervious, vegetation, bare, and waterbody) was performed using the Random Forest (RF) algorithm in Google Earth Engine (GEE), supported by spectral indices and ground control points (GCPs). Seasonal changes in SW were assessed using the optimum threshold value of the Modified Normalized Difference Water Index (MNDWI) obtained by Receiver Operating Characteristic (ROC) curve analysis, while the rate of change in LULC was calculated for 500 spatial sub-regions. To explore spatially varying relationships between surface water loss and potential drivers, a Geographically Weighted Regression (GWR) model was applied using Python. Results show a significant reduction in both permanent and seasonal SW over the study period, primarily in areas undergoing rapid urbanization. GWR outcomes indicate strong and statistically significant negative associations between SW change and impervious, bare land, and vegetation change rates, whereas elevation exhibited no significant influence (p = 0.88). The optimal GWR bandwidth encompassed most sub-regions, suggesting that while SW change exhibits strong spatial clustering, the influence of LULC drivers is largely spatially stationary across GCC. Residual spatial autocorrelation analysis further confirmed that the GWR model effectively reduced spatial dependence, although minor localized effects remained. Overall, the study highlights the importance of integrating remote sensing, spatial statistics, and localized regression approaches to better understand urban hydrological responses. The findings provide actionable insights for surface water conservation, flood management, and sustainable land-use planning in rapidly urbanizing cities of Bangladesh and similar developing regions.