EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Physically-based model derived thresholds of sediment disasters for impact-based rainfall forecasts

Srikrishnan Siva Subramanian1, Piyush Srivastava1, Sumit Sen1, and Ali. P. Yunus2
Srikrishnan Siva Subramanian et al.
  • 1Centre of Excellence in Disaster Mitigation and Management, Indian Institute of Technology Roorkee, India (correspondence to:
  • 2Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Mohali, India

Rainfall-induced sediment disasters are catastrophic events that occur compounded during extreme precipitation. Territorial early warning systems (Te-LEWS) are necessary to predict these disasters. The warning information is disseminated based on thresholds derived from the correlation between rainfall magnitude and disaster occurrences. Nations that established successful Te-LEWS have maintained historical rainfall records and corresponding landslide occurrences that result in the precise derivation of early warning thresholds. In contrast, countries newly establishing Te-LEWS face difficulties setting the thresholds due to a lack of precise information on rainfall magnitude and historical landslide occurrences. In India, the India Meteorological Department (IMD) provides impact-based forecasts of rainfall that may induce landslides based on daily, 3-day cumulative and longer antecedent thresholds. However, thresholds correlating landslides with continuous monitoring through hourly/sub-hourly rainfall observations, which are the basis of the nowcast in real time, still need to be developed. Here, we present a framework for predicting landslide occurrences, i.e., shallow landslides, debris slides, and debris flows, using hourly rainfall. Using the framework, we analyse case studies of extreme precipitation-induced landslides in the Himalayas and Western Ghats, India. Through this, catchment-wise early warning thresholds are derived. This study opens avenues to improve the precision of impact-based rainfall forecasts for landslides. 

How to cite: Siva Subramanian, S., Srivastava, P., Sen, S., and Yunus, Ali. P.: Physically-based model derived thresholds of sediment disasters for impact-based rainfall forecasts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8674,, 2023.