Articles | Volume 17, issue 3
Research article
20 Mar 2017
Research article |  | 20 Mar 2017

Community-based early warning systems for flood risk mitigation in Nepal

Paul J. Smith, Sarah Brown, and Sumit Dugar

Abstract. This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2–3 to 7–8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Short summary
Risks from flooding are of global importance. Experience gained in Nepal is presented to demonstrate that empowering the communities impacted by flooding to be active participants in risk mitigation can have significant positive impacts. In part this is achieved through community involvement in the provision of warnings based on observations of river flow upstream. The success of simple, robust methodology for the early provision of such warnings based on predicting future river flows is shown.
Final-revised paper