Abstract. Extreme rainfall events have increased community and asset vulnerability to hazards like flash floods, particularly in mountainous regions. This study adopts SMART principles emphasizing inclusiveness and a bottom-up, community-led approach towards the development of an urban flood early warning system (EWS) in the Lesser Himalayas. We demonstrate how low-cost, community-engaged hydrometeorological monitoring captures the nuances of watershed and storm characteristics to improve urban flood early warning in data-scarce mountain regions. A hydrometeorological monitoring network comprising three LiDAR-based water-level sensors and four rain-gauges was deployed across the Bindal watershed, Uttarakhand, India. Observations reveal pronounced spatial variability, as seen in September 2022, with rainfall differences of 187 mm and inter-station correlations ranging from r = 0.82 to 0.20 over distances of 2.74–8.24 km. A southwestward movement of rainfall systems with an approximate 15 min lag was also observed within the watershed. In contrast, secondary datasets (GPM-IMERG and ERA5) failed to capture the magnitude and heterogeneity of rainfall patterns, raising concerns about their reliability for flash flood studies in mountainous areas. While developing an operational EWS is beyond the scope of this study, the findings provide foundational hydrometeorological insights and practical evidence to inform the implementation of SMART, community-centered urban flood EWS in Himalayan regions.
Dixit et al. (Tue,) studied this question.