Flash floods are becoming more frequent in mountainous regions due to heavier rainstorms. To protect people and property, we are working to better understand local hydrology and improve the efficiency of early warning systems for urban flooding in Lesser Himalayas. By combining community knowledge, low-cost technology, we can enhance understanding of flood dynamics and strengthen preparedness in mountains. This work is a step toward building resilience by bridging science and community insight.

Rainfall intensity–duration (ID) thresholds can aid in the prediction of natural hazards. Large-scale sediment disasters like landslides, debris flows, and flash floods happen frequently in the Himalayas because of their propensity for intense precipitation events. We provide a new framework that combines the Weather Research and Forecasting (WRF) model with a regionally distributed numerical model for debris flows to analyse and predict intense rainfall-induced landslides in the Himalayas.

Floods continue to be a wicked problem that require developing early warning systems with plausible assumptions of risk behaviour, with more targeted conversations with the community at risk. Through this paper we advocate the use of a SMART approach to encourage bottom-up initiatives to develop inclusive and purposeful early warning systems that benefit the community at risk by engaging them at every step of the way along with including other stakeholders at multiple scales of operations.