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<p>This study presents the development and implementation of a Rockfall Early Warning System (REWS) for an active rockfall-prone slope at Manikaran, Himachal Pradesh, India. The system integrates ground-based sensors, including tilt meters, crack meters, and a rain gauge connected to a remote server at Panjab University, Chandigarh, enabling real-time monitoring, automated data transmission, and visualization. Detailed geomorphological and structural investigations identified potentially unstable rock masses, including the Left Rock Mass (LRM), Right Rock Mass (RRM), and several critical blocks upslope of Manikaran. Continuous crack monitoring revealed seasonal cyclic deformation characterized by fracture opening during winter and closure during monsoon periods, together with permanent displacements of ~ 2&#xa0;mm, indicating ongoing rock mass deformation. Several blocks instrumented with tilt meters exhibited cyclic deformation patterns, potentially reflecting the combined influence of hydrological loading and seasonal environmental forcing. Observed deformation patterns suggest that seasonal environmental forcing, including thermal effects and hydrological influences, contributes to slope deformation; however, the relative contributions of rainfall and temperature could not be explicitly separated during the monitoring period. A Python-based workflow integrating real-time data acquisition, visualization, anomaly detection, and automated implementation of the Prospective Failure Time Forecasting (PFTF) framework was developed to support monitoring-driven forecasting capability. However, as no onset of acceleration or rockfall event occurred during the monitoring period, the current REWS is represented as a demonstration framework rather than a fully validated operational forecasting system. Operational for continuous monitoring since November 2023, the developed framework provides a basis for long-term monitoring, understanding climate-slope interactions, and improving rockfall early warning methodologies in Himalayan terrain.</p>

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Multi-sensor monitoring of an active rockfall site in Manikaran, Himachal Pradesh, India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Raj Kiran Dhiman,
  • Mahesh Thakur,
  • Mohd Zaid Habib,
  • Sukhvir Singh,
  • Naveen Aggarwal

摘要

This study presents the development and implementation of a Rockfall Early Warning System (REWS) for an active rockfall-prone slope at Manikaran, Himachal Pradesh, India. The system integrates ground-based sensors, including tilt meters, crack meters, and a rain gauge connected to a remote server at Panjab University, Chandigarh, enabling real-time monitoring, automated data transmission, and visualization. Detailed geomorphological and structural investigations identified potentially unstable rock masses, including the Left Rock Mass (LRM), Right Rock Mass (RRM), and several critical blocks upslope of Manikaran. Continuous crack monitoring revealed seasonal cyclic deformation characterized by fracture opening during winter and closure during monsoon periods, together with permanent displacements of ~ 2 mm, indicating ongoing rock mass deformation. Several blocks instrumented with tilt meters exhibited cyclic deformation patterns, potentially reflecting the combined influence of hydrological loading and seasonal environmental forcing. Observed deformation patterns suggest that seasonal environmental forcing, including thermal effects and hydrological influences, contributes to slope deformation; however, the relative contributions of rainfall and temperature could not be explicitly separated during the monitoring period. A Python-based workflow integrating real-time data acquisition, visualization, anomaly detection, and automated implementation of the Prospective Failure Time Forecasting (PFTF) framework was developed to support monitoring-driven forecasting capability. However, as no onset of acceleration or rockfall event occurred during the monitoring period, the current REWS is represented as a demonstration framework rather than a fully validated operational forecasting system. Operational for continuous monitoring since November 2023, the developed framework provides a basis for long-term monitoring, understanding climate-slope interactions, and improving rockfall early warning methodologies in Himalayan terrain.